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10.1371/journal.pgen.1005781 | Extensive Recombination of a Yeast Diploid Hybrid through Meiotic Reversion | In somatic cells, recombination between the homologous chromosomes followed by equational segregation leads to loss of heterozygosity events (LOH), allowing the expression of recessive alleles and the production of novel allele combinations that are potentially beneficial upon Darwinian selection. However, inter-homolog recombination in somatic cells is rare, thus reducing potential genetic variation. Here, we explored the property of S. cerevisiae to enter the meiotic developmental program, induce meiotic Spo11-dependent double-strand breaks genome-wide and return to mitotic growth, a process known as Return To Growth (RTG). Whole genome sequencing of 36 RTG strains derived from the hybrid S288c/SK1 diploid strain demonstrates that the RTGs are bona fide diploids with mosaic recombined genome, derived from either parental origin. Individual RTG genome-wide genotypes are comprised of 5 to 87 homozygous regions due to the loss of heterozygous (LOH) events of various lengths, varying between a few nucleotides up to several hundred kilobases. Furthermore, we show that reiteration of the RTG process shows incremental increases of homozygosity. Phenotype/genotype analysis of the RTG strains for the auxotrophic and arsenate resistance traits validates the potential of this procedure of genome diversification to rapidly map complex traits loci (QTLs) in diploid strains without undergoing sexual reproduction.
| The genetic diversity of eukaryotes relies on the diversification of the parental information, mostly occurring by recombination during gamete formation. Homologous chromosomes also recombine in somatic cells, though much less frequently. Here, we sequenced the genome of S. cerevisiae hybrid diploid cells that enter the processes of meiosis and Return To mitotic Growth (RTG). Remarkably, the RTG cells contain recombined diploid genomes derived from both parental origins. Each RTG cell is diversely recombined both in terms of the frequency and location, with important implications in genome evolution of the species. The generation of a diversely recombined diploid cell population has useful downstream genetic applications.
| Genetic diversity relies on diversification of the parental genome information. Besides spontaneous and environmentally induced de novo mutations, sexual reproduction is the prominent source of genetic diversity: it reshuffles the genetic information among individuals from a given species, creating the new combinations of alleles upon which the Darwinian selection will potentially act. Thus, the genetic diversity of a given population depends on the random mating of the gametes, the capacity of meiosis to promote homologous recombination between the polymorphic parental chromosomes as well as to ensure the random segregation of the chromosomes in the gametes.
The meiotic developmental program involves the segregation of the homologous pairs of sister chromatids to opposite poles at the first meiotic division (Meiosis-I or reductional division), followed by the segregation of the sister chromatids at the second meiotic division (Meiosis-II or equational division), which is followed by the differentiation of gametes, or spores in yeast (Fig 1)[1]. Another hallmark of meiosis is the high level of inter-homologs recombination during the prophase-I of meiosis. Meiotic recombination is not evenly distributed along the chromosomes but inter-homolog recombination occurs at least once per chromosome [2]. This is initiated by the formation of programmed Spo11-dependent DNA double-strand breaks (DSBs). Afterwards, inter-homolog repair of these DSBs results in the formation of crossovers (CO) and non-crossover (NCO) recombinant products [3]. The relative outcome of CO and NCO events is genetically controlled, depending on the processing of the recombination intermediates and multiple regulatory pathways [4]. Importantly, the crossovers that physically link each pair of homologs ensure the proper reductional segregation at Meiosis-I [5] which ultimately leads to the halving of the genome content and the formation of viable haploid gametes, or spores. Defects in meiotic recombination can arrest the progression of meiosis and are a source of genomic abnormalities and therefore sterility. Notably, the frequent spontaneous formation of disomic chromosome 21 gametes in the male or female gametogenesis is the cause of Down syndrome in humans [6].
In sharp contrast, in all eukaryotes, recombination between the homologous chromosomes is rare in somatic cells [7,8]. Accidental DSBs are preferentially repaired by Non-Homologous End-Joining, a mutagenic process, or repaired in the G2 phase of the cell cycle by homologous recombination between the identical sister chromatids, being promoted by the existence of sister chromatid cohesion that favors recombination between the sisters at the expense of homologs [9,10]. Thus, the rarity of inter-homolog mitotic recombination contributes to the clonal perpetuation of the parental allelic combinations.
Here, to isolate diploid recombinants in yeast, we used the singular, yet remarkable property of Saccharomyces cerevisiae diploid cells to exit from the prophase-I of meiosis and be able to re-enter into mitosis, a puzzling process termed “Return to Growth (RTG) [11–14]. As illustrated in Fig 1, budding yeast diploid MATa/MATα cells are induced to enter meiosis by nutritional starvation [1]; then, the cells enter into S phase and the chromosomes replicate. Next, ~160–200 Spo11-dependent DSBs occur per cell [15] and are efficiently repaired by homologous recombination before the MI reductional division occurs. Remarkably, the highly differentiated and coordinated progression of the DNA intermediates and changes in chromosomal structures through the prophase-I of meiosis is reversible by the addition of rich medium, up to the irreversible commitment point that precedes the reductional division step, thus after the time of DSB formation. This remarkable transition from the meiotic prophase-I to mitosis is under the regulatory control of the Swe1 kinase, which modulates the Cdk1 activity [16]. This kinase permits an unusual progression of the mitotic cell-cycle events, allowing the induction of bud formation in the absence of re-replication [16,17]. Thus, upon RTG, the diploid cell (hereafter called the mother cell) that entered meiosis and experienced DSB formation with or without repair will exit with or without a recombined 4C genome that will segregate equationally, leading to the random segregation of two non-sister chromatids in the mother cell while the other two chromatids will segregate in the daughter cell. Using a single locus intragenic heteroallelic assay, several authors [11,13] observed that the RTG cells were much more frequently recombined than vegetative cells, strongly supporting the conclusion that these recombination events were initiated in meiosis. More recently, physical analyses of the HIS4-LEU2 hotspot showed that in wild type cells the meiotic Spo11-DSBs are rapidly repaired upon RTG (within 2 h) [17,18] and that the Joint Molecule intermediates (JMs) that accumulate in the prophase-arrested ndt80Δ mutant are well repaired upon RTG, but in contrast to meiosis, preferentially give rise to NCO recombinant molecules rather than CO recombinant molecules [17]. Mutant analyses showed that RTG recombination was dependent on the Rad51 strand exchange protein but not Dmc1 [18] and that most JMs are repaired by the Sgs1 pathway that produces only NCOs, while a fraction of JMs are repaired by the Mus81/Mms4 pathway producing both NCOs and COs[17]. But so far however, very little is known about the outcome of the RTG process on the architecture of yeast hybrid genomes.
Here, we report the whole genome sequencing of 36 RTG strains derived from the S288c/SK1 S. cerevisiae hybrid. We found that the RTG strains are bona fide diploids, diversely recombined both in terms of frequency and location. Furthermore, as a proof of principle, we performed a genotype/phenotype analysis of the RTG strains for three Mendelian and one complex traits. This validates the potential of this previously unappreciated procedure of genome diversification to rapidly map quantitative traits loci (QTLs) in diploid strains, without the necessity to undergo sexual reproduction.
To examine the genome-wide recombination profile of RTG cells, we constructed a yeast diploid hybrid (AND1702) by mating two S. cerevisiae haploid strains from different genetic backgrounds, S288c and SK1 (S1 Table). A similar but differently marked S288c/SK1 hybrid was previously used for meiotic tetrad analyses [19]. Overall, the diploid contains >62,000 single nucleotide polymorphisms (SNPs), distributed along the 16 homologous chromosomes (S1A Fig) resulting in a genome wide divergence of ~0.7% [20,21]. The average inter-SNP distance is 191bp (S1B Fig). Few long regions are devoid of polymorphic SNPs (11 regions ≥10kb). Therefore, this hybrid strain is ideal to achieve high-resolution genotyping and therefore to map recombination events. The strain also carries several auxotrophic markers, appropriate for screening the RTG cells (see below).
The S288c/SK1 hybrid strain sporulates efficiently (88% of asci after 48 h in the sporulation medium), like the SK1 strain and more than the S288c diploid (S2A Fig). However, it produces tetrads with reduced spore viability (71%) relative to both diploid parents (S2B Fig). The distribution of viable spores per tetrad is reported in S2C Fig. The hybrid produces 4 viable spore tetrads (43%) but also a significant fraction of 3, 2, 1 and 0 viable-spores tetrads are observed. Several factors may reduce spore viability [22]. Most likely, an incompatibility between the S288c and SK1 alleles may impair germination and/or growth capacity. In some instances, residual growth observed as micro-colonies are seen under the microscope. An alternative, non-exclusive, hypothesis is the occurrence of Meiosis-I or Meiosis-II chromosome mis-segregations, leading to unviable spores with aneuploid genomes.
In order to isolate the RTG cells, we used two complementary methods illustrated in Fig 1. The first method, “isolation by prototroph selection”, corresponds to the traditional RTG plating assay [11,13] based on the selection of intragenic recombinants, in this case arginine prototrophs (Materials and Methods). The limitation of this selective approach is that upon RTG recombination, only one of the arg4 alleles is converted to ARG4 and therefore only the mother or the bud (daughter cell) that inherits the wild type recombinant allele is recovered after RTG. To overcome this limitation, we devised an alternative single cell micromanipulation method to isolate mother cells, that derived from meiosis upon return to growth, from their first daughter cell that arises upon bud formation (Fig 1, Materials and Methods). This micromanipulation method offers two advantages over the prototroph selection. First, it eliminates cells committed to complete meiosis, as they do not form a bud upon transfer to rich medium (they ultimately form tetrads). Second, since re-replication does not occur before budding [16,17], all four chromatids present in the “returned” meiotic cell are recovered in the pair of mother-daughter cells, similar to the recovery of the four products of meiosis in a tetrad. Thus, in RTG pairs, any anomalies in chromosome segregation and marker segregation, including the gene conversion events, can be identified as in four-spores tetrad analyses.
To isolate recombinant RTG cells, the meiotically induced diploid cells should be retrieved at prophase-I of meiosis, after DSB formation and before their commitment to complete meiosis. To determine this time window in the hybrid background, relatively to the SK1 and S288c backgrounds, we monitored and compared several landmark parameters of the meiotic progression in time course experiments. First, we monitored the progression of the cell population during sporulation by DAPI staining of the cell nuclei in the three backgrounds. We observed that the kinetics of meiotic progression of the hybrid resembles more the one of SK1 than of S288c, and that in the hybrid, ~50% completed the Meiosis-I divisions at t = 8 h (S3A Fig). Second, we performed a physical analysis of meiotic DSBs and recombination products formation at the ARG4/DED81 hotspot of recombination located on chromosome VIII (S3B–S3D Fig). DSB formation at the DSB1 and DSB2 sites [23] is first detected at t = 3 h, similar to SK1 (S3C and S3D Fig). Next, the meiotic recombinant products (R1 and R2) are first observed at t = 5 h and accumulate until 8 h (S3C and S3D Fig). We conclude that, in the cell population, the initiation and completion of meiotic recombination in the hybrid background occur between 4 and 8 hours. Third, we directly examined the occurrence of recombinants in the hybrid by performing a time course RTG experiment. After induction of sporulation for various times in liquid medium, we plated the RTG cells on–Arg plates to select arginine prototroph colonies resulting from intragenic recombination between the arg4-RV and arg4-Bgl heteroalleles [24]. The production of arginine prototrophs arise between t = 4–8 h (S3E Fig), that are enhanced by at least three orders of magnitude, as compared to the basal mitotic level of Arg+ colonies observed at t = 0 h. Finally, in the hybrid, since crossovers formed between the heterozygous recessive alleles at HIS4-LEU2, HIS3 and MET15 loci and their respective centromeres induce loss of heterozygosity (LOH) in RTG (see below), we examined the appearance of auxotrophic colonies in the RTG time course (Materials and Methods). After induction of sporulation for various times, we plated the RTG cells on rich glucose medium and then replica plated the colonies on media lacking histidine, leucine or methionine. Auxotrophic colonies start to appear after 4 h of incubation in the sporulation media and their frequency greatly increases up to 8 h (S3F Fig). Based on these data, the 4–8 hour time window was used for the RTG experiments.
A potential drawback in analyzing a small number of cells from the meiotic time course could be that the cells are not at the expected meiotic stage, due to the relative asynchrony of the meiotic progression. Here, within the 4-8h time window, we observed that an increasing fraction of cells isolated by micromanipulation progressed to give a tetrad (S3G Fig), indicating that those cells had passed the commitment point to irreversibly complete meiosis and sporulation, in a proportion consistent with the kinetics of meiotic progression (S3A Fig). Also, as expected [11–14] [16,17], the vast majority (>94%) of cells that budded gave rise to two viable cells, forming mother and daughter colonies. In the remaining cases, only the mother or daughter cell was viable. Several hypothesis can explain this asymmetric lethality; For example, a technical consequence of separating the mother and daughter cells upon micromanipulation, genetic incompatibilities resulting from loss(es) of heterozygosity in one of the cell or any defect in the process of RTG, independent of the budding process. The rarity of these cases prevented us to further analyze them. In addition, we observed that a significant number of the isolated cells (61% ± 18%, mean ± SD) did not bud nor sporulate (S3G Fig). To eliminate the hypothesis that this lethality is due to the RTG process per se, we examined the viability of unbudded cells isolated at earlier time points of meiosis as well as the viability of the vegetative hybrid and SK1 parent cells grown in rich YPD medium and in the pre-sporulation SPS medium. Again, in all cases, a similar proportion of unbudded cells placed on YPD medium by micromanipulation did not grow, indicating that this cell lethality is not meiosis- nor strain-specific (S3G Fig). Other studies have also reported this observation that, after micromanipulation, a high proportion of cells do not divide, especially when cells are isolated from non-logarithmic vegetative culture [25] or from meiotic cultures [26], compared to when cells were isolated from logarithmic vegetative cultures. We do not know the cause of this cell lethality, but in all conditions, the cells that remained on the inoculum area seemed to undergo normal mitotic divisions, suggesting an effect of the micromanipulation. Altogether, we conclude that the meiotic cells that bud after RTG are in most instances viable and as shown below, properly segregate their chromosomes, giving rise to viable euploid cells.
We analyzed 36 RTG strains subjected to high throughput whole genome sequencing (Materials and Methods). Six strains (RTG1-S to RTG6-S) were isolated by Arg+ selection (method 1) and 30 strains (RTG7-M/-D to RTG21-M/-D) were isolated by mother-daughter dissection (method 2). Their phenotypes were determined with respect to mating and growth on the Arg, His, Leu and Met depleted media and confirmed by the sequencing data. The genetic marker genotypes are shown in S2 Table. Next, the genotype at SNP positions and recombination profiles were extracted using a dedicated bioinformatic pipeline (S4 Fig) to determine: (i) the chromosome copy number based on coverage depth, (ii) the genotype at SNP positions, requiring the determination of thresholds to call for homozygosity or heterozygosity (S5 Fig), (iii) the extent of LOH, and (iv), the frequency, nature and location of the recombination events in individual strains, using the CrossOver algorithm from the ReCombine program, created for the analysis of tetrad data [27,28].
First, we examined the sequence coverage among the individual chromosomes (S6 Fig and S3 Table). Remarkably, all genomes are euploid, indicating that chromosome segregation in the RTG process was accurate. Nevertheless, two strains (RTG4-S and RTG17-D) displayed a coverage depth variation along two different chromosomes (chromosomes V and XVI for RTG4-S and chromosomes III and V for RTG17-D), revealing in both cases a large duplication and a deletion of over 100 kb (S6 and S7A Figs). The duplication/deletion breakpoints, characterized using the Control-FREEC software [29], are located near the Ty elements of the SK1 chromosomes [30] that are absent in the S288c chromosomes (indicated on S7B Fig for RTG4-S). Molecular validation by Pulsed Field Gel Electrophoresis and Southern blot analysis for the RTG4-S (S7C Fig), suggests that these chromosomal-terminal Gross Chromosomal Rearrangements result from Break Induced Replication initiated between Ty elements located on different chromosomes (S7D Fig).
The genotype at all SNP positions of the six RTG diploids isolated by selection is shown in Fig 2. In each strain, the vast majority (on average 86.3%) of the SNP positions remained heterozygous as in the parental strain. However, a substantial fraction (on average 13.7%) of SNP positions are homozygous for either parental origin (Fig 2, S4 Table), demonstrating that the RTG strains are recombined. Remarkably, the total amount of polymorphisms exhibiting LOH varies from 15.2 to 27.8% between the RTG strains, demonstrating that the RTG process generates a high degree of genetic diversity.
Next, we analyzed the segregation at all SNP positions in the 15 pairs of mother-daughter RTG strains. Since the RTG strains remained diploid, the genotyping of RTG pairs provides tetrad-like information concerning the segregation pattern of the four chromatids derived from of a single meiotic cell that underwent RTG. On average, we observed that for 98.6% of the SNP positions, the genetic information segregated 2:2 in mother and daughter RTG pairs. Among them, 89.2% carry a heterozygous genotype in both mother-daughter cells, as the parent diploid. Conversely, 10.8% of the SNP positions segregating as 2:2 carry a homozygous genotype with opposite parental alleles in the mother and daughter cells (S8 Fig and S4 Table). This is exemplified in Fig 3 in which the genotype of the mother strain (RTG11-M) contains 16.3% of homozygous SNP positions, with 10.3% from S288c and 6% from SK1, while the genotype of the daughter strain (RTG11-D), contains 16.2% of homozygous SNP positions, but with the reverse percentage of parental alleles: 5.7% S288c and 10.3% SK1. The homozygous SNP positions exhibiting a 2:2 segregation pattern, grouped as tracts with reciprocal genotypes, correspond to LOH events resulting from reciprocal exchanges between non-sister chromatids. Thus, the meiotic cell that exits from meiosis (i.e. the mother cell) inherits two non-sister chromatids and the bud (i.e. the daughter cell) inherits the other two non-sister chromatids, as expected from a successful re-entry into mitosis in the absence of DNA replication. The non-sister chromatids are often but not always recombined. As observed for the selected RTG strains (Method 1, see Fig 1), the absolute frequency of acquired homozygosity is very different from one RTG pair to another. In this dataset of 15 RTG pairs, we observed 136 reciprocal LOH tracts (rLOH) (S5 and S6 Tables), with a wide variation, from 1 to 34 tracts per RTG pair. On average, the rLOH tracts are large (141 kb), and in some cases, they cover most of the chromosome arm.
Most of the remaining SNP positions (1.4%) exhibit a 3:1 segregation pattern of the genetic information, and carry a heterozygous genotype in one cell and a homozygous genotype in the other cell. The homozygous SNP positions exhibiting a 3:1 segregation pattern are grouped into tracts with non-reciprocal genotype, corresponding to gene conversion. We found 913 non-reciprocal LOH tracts (nrLOH) (S5 and S7 Tables), from 5 to 139 events per RTG pair, which on average are small (2.3 kb) compared to rLOH. Finally, we also identified a low number of SNP positions that exhibited a 4:0 segregation pattern (0.06%), distributed in 38 tracts (1 to 5 per RTG pair). These events can arise from multi-chromatid gene conversion events or from mitotic recombination events that occurred prior to meiotic induction.
The number of regions exhibiting LOH ranged from 5 to 87 per RTG strain, with sizes varying between 5 bp to 0.7 Mbp (Fig 4A). Overall, among the 36 RTGs (6 single and 15 mother-daughter pairs), 90% of the SNP positions were involved in at least one LOH event (Fig 4B). On average, 12.2% of the parental hybrid genome shows LOH, ranging from 0.3% (RTG13-M/RTG13-D pair) to 26.4% (RTG7-M/RTG7-D pair). The ratio of parental alleles also varies from one RTG to another (Fig 4C). Among the 10% of SNP positions that failed to exhibit LOH in all RTG strains, the vast majority are located around the 16 centromeres [15,31–34]. Thus, the centromere-linked SNP positions always remain heterozygous after the equational segregation. This is likely attributable to the depletion of meiotic DSB formation and recombination in the vicinity of the centromeres [15,31–34]. To be noted, the maintenance of heterozygosity in the centromere region and in numerous locations along the chromosome arms eliminates the possibility that the LOH event resulted from iso-chromosomal non-disjunctions or reductional division.
The acquisition of the genome-wide recombination profile of the RTG-M and–D pairs provides unprecedented information on the nature of the recombination events (gene conversions and/or crossovers). Due to the occurrence of a single equational division that occurs when the cells exit from the prophase-I of meiosis resulting in RTG diploid cells, the method to detect the gene conversion and crossovers by genotyping is different than in the four haploid spores derived from a meiotic tetrad [2,19,28,33,35–39]. The expected outcome of a single meiotic DSB repair by gene conversion and/or a crossover in a RTG pair is illustrated in Fig 5. DSB repair event by gene conversion is detected by a 3:1 segregation pattern of the SNP positions in the pair of RTG strains and is manifested by a non-reciprocal LOH (nrLOH). Differently, a crossover is detected by the occurrence of reciprocal tracts of LOH (rLOH) in the RTG pair, where the SNP positions segregation pattern is 2:2. The bioinformatics pipeline developed to detect gene conversions and/or crossovers events in diploid strains is shown in S9 Fig.
To estimate the number of crossovers per RTG, we analyzed the SNP positions segregation pattern in the 15 mother and daughter RTG pairs. The tracts of homozygous SNP positions that define the rLOH regions are comprised of two subclasses illustrated in Figs 3B and 5: (i) the terminal rLOH (trLOH), in which one end likely corresponds to the crossover site and the homozygosity extends to the end of the chromosomal arm (formally to the ultimate SNP position), and (ii) interstitial rLOH (irLOH), where both ends of the homozygous tract are flanked by heterozygous tracts, thus reflecting the occurrence of two consecutive crossovers on the same chromosomal arm. The double crossover can involve 2, 3 or 4 chromatids, which is not distinguishable in diploid genotyping. Altogether, we observed 70 trLOH and 66 irLOH (S6 Table). Assuming that each trLOH reflects the occurrence of one crossover and each irLOH reflects two crossovers, we detected a total of 202 COs in total, ranging from 1 to 54 COs per mother-daughter pair.
Concerning the frequencies of gene conversion events (GC), we found that 1.4% of the SNP positions exhibited a 3:1 segregation pattern, leading to non-reciprocal tracts of LOH (nrLOH) as illustrated for the RTG11-M and -D strains in Fig 3. Altogether, among the 15 RTG mother-daughter pairs, we identified a total of 913 nrLOH tracts (mean length of 2.3 kb), varying from 5 to 139 events per pair (S8 Fig and S7 Table). Once again, the nrLOH tracts can be interstitial or terminal. Not surprisingly, the vast majority of the nrLOH is interstitial (847/913 = 93%), and corresponds to gene conversions, the canonical product of meiotic DSB repair by homologous recombination. We observed that 164 interstitial nrLOH were located at the boundary of rLOH events, reflecting crossovers associated with gene conversions, while the remaining 683 are independent of rLOH events, reflecting NCOs. The terminal nrLOH events (66/913) may be true terminal nrLOH events or may be interstitial if they ends in the non-genotyped repeated sub-telomeric regions of the chromosomes. These terminal nrLOH events may result from Break-Induced replication (termed terminal NCO or terminal gene conversion [2,40,41]). Thus, among the 15 RTG pairs, we detected a total of 951 recombination events: 202 COs, including 164 COs associated with GC (81%) and 38 COs not associated with a GC (19%), and 749 NCOs (GC not associated with a detectable CO).
Due to the random segregation of the non-sister chromatids during the equational RTG division, additional COs may remain undetected upon SNP positions genotyping. As illustrated in Fig 5, upon equational segregation, a single CO leads to rLOH distal to the CO site in only half of the cases in mitotically growing cells, and therefore remains undetected in half of the cases [10,42], while a GC leads to nrLOH regardless of the chromatid segregation. Consistently, all NCOs will be detected as independent nrLOH, while, according to the chromatid segregation, half of the GC associated with a CO (81% of observed COs) will be detected as such (nrLOH at a boundary of a rLOH, i.e a GC associated with a CO), and half will be detected as an independent nrLOH (NCO, or GC not associated with a detectable CO). However, as illustrated in S10 Fig, the probability of CO detection is dependent on the number of CO per chromosome arm; It gradually increases from ½ to ⅔ when more COs occur on the same chromosomal arm. Hence, assuming a random chromatid segregation pattern, and depending on the distribution of CO per chromosome arm, we expect that between ½ and ⅓ of the COs should remain undetected because they do not manifest as a rLOH event. As well, the number of COs will also affect their distribution leading to interstitial or terminal LOH; as the number of COs increases, the probability of interstitial rLOH increases compared to that of terminal rLOH (S10 Fig). Taking into account these parameters, we estimate that the real number of CO in all 15 pairs ranges between 404 (202÷½) and 303 (202÷⅔). Since approximately 81% of the observed COs are associated with a GC, the corrected number of CO associated with a GC might range between 327 (404x0.81) and 245 (303x0.81), and therefore the number of NCO ranges between 586 (913–327) and 668 (913–245). Altogether, this leads to an excess of NCOs over COs of 1.45-fold (327/404) to 2.21 fold (245/303), a ratio opposite to the outcome of uninterrupted meiosis (see Discussion).
To confirm the existence of these masked COs, we induced the sporulation of four RTG pairs (RTG7M-D, RTG8M-D, RTG9M-D, RTG10M-D) showing various extent of recombination frequencies (S8 Fig) and sequenced all four spores arising from one tetrad each. As an example, the genotype of the RTG10-M and RTG10-D pair is illustrated in S11A and S11B Fig and the corresponding tetrads in S11C and S11D Fig. The SNP positions exhibited an expected Mendelian segregation pattern: the homozygous SNP positions of the RTG parental strain segregate 4:0 in the corresponding tetrad (99.69%), and the heterozygous SNP positions exhibit a 2:2 –or, occasionally, a 3:1 –segregation pattern (99.72%), validating our bioinformatics pipeline of diploid cell genotyping. As anticipated, we identified several masked crossovers present in the parent RTG that are revealed in the RTG tetrad by the presence of 2 pairs of reciprocal recombinant molecules among the four spores of the tetrad (S11C and S11D Fig). Altogether, this revealed 37 masked COs (ranging from 0 to 16 per RTG strain) that did not lead to rLOH (S11E Fig), in addition to the 77 COs leading to rLOH, which corresponds to the observed detection frequency of 77/(37+77) = 67.5%, not significantly different from the expected ratio of 60.5% calculated from the distribution of CO per chromosome arm detected in those 4 RTG pairs (p-value = 0.22, Fisher exact test, S8 Table). Two other observations should be mentioned. First, we observed that 86% (32/37) of the masked COs are associated with an adjacent GC. Association with GC is not statistically different between the detected (81%) and masked COs (86%) (p-value = 0.75, Fisher exact test). Second, we observed that in each RTG pair, the number of masked COs is quantitatively related to the number of COs readily identified; namely, in RTG7M-D, RTG8M-D, RTG9M-D, RTG10M-D respectively, we detected 54, 8, 3 and 12 COs by LOH analysis and, we identified 27, 2, 1 and 7 masked COs upon tetrad sequencing. Altogether, these results suggest that the detected and masked COs do not mechanistically differ but simply reflect the way the sister chromatids mitotically segregate upon RTG.
In the absence of recombination between the centromere and the mating type locus on chromosome III, the RTG strains remain heterozygous MATa/MATα, making it possible to repeat the RTG process. Indeed, by phenotypic analysis of the mating behavior of the RTG strains, coupled with bioinformatic analyses, we found that 32/36 RTG strains were MATa/MATα, while the others were homozygous at the MAT locus, as MATa/MATa (RTG11-M and RTG15- M) or MATα/MATα (RTG11-D and RTG15-D) (S2 Table). Consistently, the MAT heterozygous (MATa/MATα) RTG strains sporulated while the MAT homozygous strains did not. The rarity of exchanges between the centromere and the MAT locus on chromosome III is consistent with the unusually low frequency of meiotic DSB formation in this ~100kb region [15,43].
To examine the genome dynamics of the RTG strains throughout successive passages of RTG, we conducted a RTG pedigree analysis starting from the RTG8-M strain and induced two additional rounds of RTG events, using the micromanipulation method and determined the cell genotype by WGS. The genotype of the 10 RTG strains generated in this lineage, which all remained diploid, is shown in Fig 6 and S9 Table. As expected, the LOH regions acquired at passage n were present in passage n+1 but additional LOH events appeared, indicating that these recombinant RTG strains retained the capacity to recombine and faithfully perform the RTG process. Remarkably, as shown in one example in the lineage RTG8-M>RTG8-MD>RTG8-MDD, the genome homozygosity levels increased from 9.7% of the SNP positions (36 LOH tracts, including 6 reciprocal ones) in passage 1 to 32.2% of the SNP positions (81 LOH tracts, including 17 reciprocal ones) in passage 2, finally reaching 46% of the SNP positions (117 LOH tracts, including 21 reciprocal ones) in passage 3.
In conclusion, the reiteration of the RTG protocol perpetuates the newly acquired LOH regions in a clonal manner, increases the degree of homozygosity and expands haplotype combinations in an incremental manner from one passage to the other. Overall, extensive mosaic genomes of either parental origin are generated, a feature that raises the question of the potential roles of the RTG process in yeast genome evolution.
The genomic diversity of the recombinant RTG yeast cells has the potential to translate into phenotypic variations. The SK1 parent is prototrophic for leucine and methionine, but auxotrophic for histidine while the S288c strain is auxotrophic for all three traits. By complementation, the hybrid is prototrophic for all three amino acids. We examined these phenotypes among the 36 RTG strains in comparison with the parental strains by scoring their growth on histidine, leucine and methionine depleted media (Materials and Methods). As expected, according to the segregation of the HIS3, HIS4, LEU2 and MET15 alleles, the RTG strains exhibited growth or no growth on the appropriate media (13 His-/23 His+, 3 Leu- /33 Leu+ and 7 Met-/29 Met+) (S10 Table).
Additionally, to assay complex multi-factorial traits [44,45], we examined the phenotype of the RTG strains with respect to arsenite resistance using the spot dilution assay (Materials and Methods) (S10 Table). We observed that the SK1 parent is highly sensitive to 1.5mM NaAsO2 while the S288c parent is resistant. The hybrid strain shows an intermediate resistance between S288c and SK1. Remarkably, the 36 RTG strains exhibit variation in the strength resistance to arsenite, which we scored in five phenotypic categories (Fig 7 and S10 Table). Certain RTG strains (RTG2-S and RTG9-D) resemble the parental haploid strains while others exhibit increased resistance as compared to the parents (RTG9-M).
To map the causal locus, for each auxotrophic trait, the genotype/phenotype relationship was assayed at each SNP position by linkage analysis (Materials and Methods). For each trait, a single significant linkage interval, overlapping in each case the known causal locus (a region of ~10kb overlapping LEU2, a region of 265kb overlapping MET15 and a region of 219 kb overlapping HIS3, respectively), was mapped (Fig 7B–7D). Surprisingly, for the digenic histidine auxotrophy phenotype, a genetic linkage at the HIS4 locus was not observed. Examination of the individual RTG genotype/phenotype relationship led us to observed that although RTG20-D is homozygous for his4Δ::LEU2 allele, and therefore histidine auxotroph, it carries heterozygous genotype at SNP position in the vicinity of HIS4 locus, suggesting that RTG20-D histidine auxotrophy resulted from a NCO event involving the HIS4 locus. Thus, we investigated if exclusion of the RTG20-D strain from the analysis improved the mapping. It did not, rather suggesting that the small size of our population does not give enough power to map this second locus. With respect to the segregation of a polygenic trait as arsenite resistance, the statistical association between genotype and phenotype allowed to map a significant QTL with an interval size of 106kb, which span the subtelomere of chromosome XVI (Fig 7E). Consistently, this region includes the well characterized ARR (ARsenicals Resistance) gene cluster, a major QTL known to control arsenate resistance [45,46]. The ARR cluster of genes is polymorphic in various strain backgrounds, herein present in the S288c strain background but absent in SK1 [45]. Altogether, these phenotyping and genotyping results provide a proof of concept that quantitative trait mapping in diploid strains can be performed using RTG strains, even with a small set of sequenced samples.
Various aspects of budding yeast “Return To Growth” have been previously studied [11–14,16–18] but the singularity of this process remains to be better understood both at the physiological and molecular levels. Here, we used whole genome sequencing and single cell micromanipulation to comprehensively examine the genome-wide recombination dynamics of RTG hybrid cells. Physiologically, the cells induced to enter meiosis upon carbon and nitrogen starvation, replicate their genome and experience meiotic DSB formation in the prophase-I of meiosis. But when withdrawn before the MI reductional division, the cells rapidly disassemble the synaptonemal complex [18], repair the DSBs [17,18], degrade most meiotic transcripts [47], return to a G1 pattern of gene expression [47], switch to a modified mitotic cell cycle to bud without re-replication [16,17] and finally equationally segregate the (recombined or not) sister chromatids [16,17] into two separated diploid descendants. Precise single cell micromanipulation of the mother and first bud/daughter cells led us to analyze the genotype of the four chromatids that exit from the prophase-I of a single meiosis, before the meiotic cell could become irreversibly engaged to complete meiosis and form haploid spores. We found that each meiotically induced S288c/SK1 S. cerevisiae diploid cell generates two genetically distinct RTG diploids with mirrored recombinant genotypes. Remarkably, recombination is extensive; the number and the position of the recombination events that give rise to LOH regions are highly variable from one RTG cell to another, while additional “masked” crossing overs events lead to heterozygous yet recombinant genotypes that further increase the haplotype diversity of the individual RTG cells. Thus, at a population level, the process of RTG is able to achieve a massive genetic diversification of diploid yeast cells that is rare in mitotically growing cells and is usually limited to a single chromosomal arm per cell [40,41].
Four observations demonstrate that RTG recombination is initiated by the numerous Spo11 DSBs that form in the meiotic mother cell: (i) the absence of RTG recombination in the spo11 mutant [48], (ii) the multiplicity of the recombination events involving several chromosomes ([13], this work), (iii) the multiplicity of exchanges on the same chromosomes leading to a large variety of mosaic haplotypes (this work) and (iv) the frequent recovery of inter-homolog recombination ([13], this work), a hallmark of meiotic recombination that creates the genetic diversity of the gametes. However, how the unrepaired meiotic DSBs and/or the meiotically engaged recombination intermediates are repaired during RTG is still poorly understood. Limited mutant analyses have been reported. Concerning the early steps of DSB processing and strand invasion, the study of Zenvirth et al. [18] showed that the rad50S mutant cells, which accumulate unresected DSBs with covalent attachment of Spo11, sharply lose viability upon RTG, supporting the conclusion that these unprocessed DSBs are not repaired in RTG. Similarly, the rad51 cells, which accumulate resected DSBs, also lose viability in RTG and give very few recombinants [18]. In contrast, the dmc1 cells, which accumulate hyper-resected DSBs, do not lose viability in RTG and exhibit only slightly reduced recombination levels, up to 30–50% of wild type levels [18]. The fate of the subsequent intermediates, further engaged in the recombination process but not yet resolved, has been examined using the benefit of the ndt80Δ mutation, that allows the accumulation of Joint Molecules (JMs) [17,49]. Clearly, these cells retain viability after RTG and efficiently repair the DSBs but their resolution yields reduced CO and increased NCO formation. The study of Dayani et al. [17] revealed the prominent role of the Sgs1-dependent pathway during RTG that processes the JMs by dissolution and produces only NCOs, and the limited formation of COs depends on the Mus81/Mms4 structure selective endonucleases that resolve the JMs into NCOs and COs in an unbiased way. Many other DSB repair intermediates are known to form during meiotic DSB repair [50] but how they are processed during RTG remains to be studied.
Here, in a wild type strain, the genotyping of 15 pairs of RTGs and the sequencing of their sporulation products allowed us to comprehensively examine the genome wide frequencies and the nature of the RTG recombination events, namely gene conversions and crossovers. Altogether, after correction for undetectable events, among the 15 pairs of RTG-M and RTG–D, we estimated between 327 and 245 COs associated with an adjacent GC, between 77 and 58 COs without adjacent GC, and between 586 and 668 NCOs. In contrast, the rate of mitotic recombination events per cell is much lower, in the order of 10−6 per division [41,51–53]. This is also in contrast with the outcome of meiosis characterized in four spore tetrads. In the same hybrid background, Martini et al. [19], characterized 7 tetrads and observed a total of 189 NCO and 511 COs, corresponding to a NCO/CO ratio of 0.37. This is the opposite of the 1.45–2.21 fold excess of NCO versus CO observed in the present RTG cells. This genome wide deficit of COs observed in our RTGs compared to meiotic tetrads is consistent with the genetic and physical analyses at the HIS4-LEU2 recombination hotspot during RTG compared to meiosis [17]. Moreover, in seven tetrads of S288c/SK1 hybrid, approximately 100 recombination events are detected per tetrad, with limited variation (1.3 fold, from 86 to 116) from one tetrad to another [19]. In contrast, as seen in the RTG pairs, the total number of events varies 30-fold (5 in RTG13-M/-D to 151 in RTG7-M/-D) and affect both the absolute number of NCOs and COs.
Finally, to examine whether the cell-to-cell variation is stochastic or coordinated in the individual RTG cells, we compared the observed number of NCOs and COs for the 15 individual RTG pairs. Clearly, the number of NCOs and COs per cell is correlated (correlation coefficient: R2 = 0.69) (S12A Fig). Several non-exclusive hypotheses can explain the variation in recombination frequencies and the nature of the outcome per RTG cell. First, it may originate from the asynchronous formation of the ~160–200 DSBs per meiotic cell that occurs in S. cerevisiae and evolutionary distant yeast strains [54]. We still do not know whether all DSBs are simultaneously formed during the prophase-I of the individual cells but, if not, when nutrient are added to the population of meiotic cells, it would not be surprising that cells with few or numerous DSBs end up with few or numerous recombination events, respectively. So, perhaps, the RTG procedure is able to capture the asynchrony of DSB formation in wild type cells. We can also hypothesize that the RTG regimen disrupts the initial control of DSB formation or the feedback control dependent on Tel1 [55]. However, there are evidences that DSBs do not form once the cells are transferred to rich medium [17,18].
Alternatively, we also envisage that at the time of RTG induction, cells carry broken chromatids that are at different stages of repair and thus can yield distinct outcomes, controlled on a stage-specific or site-specific basis. Likely, a key decisive parameter is the extent to which the DSBs are engaged in the alternative recombination pathways, in particular whether or not they are irreversibly engaged to use the homolog or the sister chromatid as a repair template. Since we retrieved mother/daughter cells at three time points, t = 4, 5 and 8 h, we examined whether the number of recombination events per RTG cell were correlated with the time of withdrawal from sporulation medium. As reported in S12B Fig, we found no correlation; some “early” and “late” RTG cells contain low or high number of recombination events. It should be stressed that although the SK1 strain is best chosen for its sporulation efficiency and synchrony for meiotic recombination studies, the synchrony of the S288c/SK1 hybrid is still not optimized sufficiently in order to conclude on the above time-related questions at the single cell level. In conclusion, although further studies will be required to address the mechanisms and genetics factors involved in RTG recombination, all previous and present data clearly reveal a wider flexibility in the final recombination outcome in individual RTG cells compared to what is observed in 4-spore meiotic products. Likely, the metabolic context of the RTG cells transiently mixes meiotic and mitotic features, once also referred to as “Meiototic” recombination [56].
Although the capacity of S. cerevisiae cells to perform Return To Growth after meiotic induction was discovered more than half a century ago, perhaps the usefulness of generating recombinant diploids has not been fully perceived so far. Here, we tested whether RTG cells from a polymorphic hybrid diploid (S288c/SK1) could be used to produce phenotypic variation and to map the causal trait loci. In budding yeasts, several strategies have been developed to characterize complex traits and map QTLs [57–64]. A common strategy is to take advantage of one or several naturally polymorphic strains, build hybrids and analyze a large population of meiotic progeny from single or multigenerational crosses. However, a recurrent problem is that hybrid strains often exhibit low sporulation efficiencies and poor spore viability, to various degrees. The reduced spore viability may be attributed to sequence divergence, and/or structural variations, that affect meiotic recombination and chromosome segregation, as well as to genetic incompatibilities resulting from genetic mixing. The unprecedented advantage of using recombinant RTG strains is to immediately produce diploid strains and thus bypass the problem of generating a viable haploid progeny. As a proof of concept, we phenotypically screened 36 RTGs for Mendelian auxotrophic phenotypes and arsenite resistance complex trait. In this last case, we observed a large quantitative variation of variant phenotypes (Fig 7) and unambiguously mapped the major QTL with a small and unselected sample of RTG strains. Beyond, the efficiency and technical simplicity of the RTG process suggest that the construction of large libraries of RTG recombinant will be feasible and therefore opens the possibility to explore highly complex traits. One limitation of using natural meiotic recombination is that it is not evenly distributed along the chromosomes but the use of targeted meiotic recombination [5] for finer mapping can be envisaged. The other tremendous advantage of using RTG strains for complex trait studies is that it should be applicable to sterile but meiotic DSB proficient strains for which classical genetic studies assisted by sexual reproduction is impossible. Finally, it should be emphasized that this is based on the natural property of S. cerevisiae cells simply responding to nutrient variations, and therefore, it is most likely that some other yeast species are also able to enter meiosis and return to mitotic growth.
In conclusion, the process of RTG induces a potentially underappreciated diversification of the cell genotype and phenotype, with valuable application for trait studies. We anticipate that the non-GMO RTG process can also be useful for numerous biotechnological applications, using model yeasts as well as more genetically complex yeasts, isolated from natural habitats or domesticated for industrial purposes. Another open question is whether this simple but powerful mechanism of genome diversification, which provides an alternative to meiosis, is occurring in the wild. The RTG protocol is somewhat reminiscent of the fluctuating environments that yeasts experience during the lifecycle in wild settings [65]. If so, we can envision that the RTG process plays an important role in shaping yeast genome evolution and potentially occurs in other unicellular eukaryotes.
All S. cerevisiae strains used in this study are S288c and/or SK1 derivatives. Their genotypes are reported in S1 Table. The haploid S288c (FY1338) and SK1 (DAO20-1) parental strains were kindly provided by G. Simchen (The Hebrew University of Jerusalem). The arg4-Bgl and arg4-RV markers were introduced at the ARG4 locus in both S288c and SK1 backgrounds by two-step gene-replacement using the PstI digested pMY232 or pNPS308 plasmids, respectively [66]. The replacement of ARG4 with the mutant alleles was verified by PCR and Southern blots; Genomic DNA was digested with EcoRV or BglII digestions, and probed with a DED81 fragment. The ORT7235 (S288c, arg4-Bgl) and ORT7237 (SK1, arg4-RV) transformants were crossed to obtain the AND1702 hybrid diploid. The isogenic diploid strains AND1747 and AND1769 were obtained by mating-type switching upon introduction of the HO-containing replicative plasmid pGAL-HO in the ORT7235 and ORT7237 strains, galactose induction and mating [67]. All strains were cultivated on standard media [68].
The prototrophy/auxotrophy phenotypes of all strains was assayed by standard replica plating on SC-His, SC-Leu and SC-Met media. Growth was scored as 0 (no growth) or 1 (growth). The arsenite resistance phenotype was assayed by drop test as previously described [64] by plating serially diluted cells on YPD (control) and on YPD + NaAsO2 1.5mM. Growth in presence of NaAsO2 was scored from 0 (poor growth) to 4 (strong growth).
Diploid strains were streaked from -80°C stock onto YPD, and single colonies were patched onto YPGlycerol medium to confirm that they were competent for mitochondrial respiratory function. Sporulation was performed as previously described [69]. In brief, cells from a 5 ml saturated YPD culture were diluted into 100 ml SPS at a density of 105 cells/ml and grown to 2–4.107 cells/ml at 30°C with shaking at 250 rpm. The cells were washed and diluted into 200 ml sporulation medium (1% Potassium Acetate and required amino-acid supplements) in a 2 l flask and shaken at 250 rpm at 30°C to induce sporulation.
Meiotic progression was monitored by fixing 500 μl of cells in 1.25 ml ethanol and staining the nuclei with 0.5 μg/ml 4’,6-diamidino-2-phenylindole (DAPI) for 30 minutes. Fluorescence microscopy was then used to determine the fraction of bi-nucleate (post-MI) and tetra-nucleate (post-MII) cells. After 48 h in sporulation medium, the sporulation efficiency was determined by phase contrast microscopy as the percentage of tetrads in the culture. Spore viability was measured by dissection of four-spore tetrads. The kinetics of recombination was monitored physically and genetically using the arg4-Bgl and arg4-RV heteroalleles [24]. For the physical assay, meiotic chromosomal DNA was extracted, digested with EcoRV and BglII, and analyzed by Southern blot as described [70], using the EcoRV–BglII (1,016 bp) ARG4 internal fragment as probe. The production of Arg+ cells was monitored by the RTG plating assay (see below).
To isolate RTG cells, samples of the sporulation culture were harvested at various time points from 0 to 24 h after transfer into the sporulation media, and RTG cells isolated using two complementary methods illustrated in Fig 1. The first method called “isolation by prototroph selection”, corresponds to the traditional RTG plating assay [11,13] based on the selection of intragenic recombinants after transfer of heteroallelic auxotrophic cells (here arg4-RV and arg4-Bgl heteroalleles) from a sporulation time course to the selective medium (SC-arginine). The meiotic cells were taken at different times, washed and diluted in H2O, and plated onto SC-Arg and YPD plates (~104 and ~102 cell/plate, respectively). The plates were incubated 3 days at 30°C. For each time point, the frequency of heteroallelic recombination at the ARG4 locus was determined by the ratio of Arg+ colonies on SC-Arg/colonies on YPD plates. Since the entry into sporulation and the synchrony in the cell population is not absolute, this mode of selection does not distinguish between: (i) fast sporulating cells that passed the commitment point to sporulation and produced recombinant spores, (ii) a recombinant RTG cell that entered the meiotic prophase-I and returned to growth before commitment or, (iii) a mitotic recombination in a cell that did not enter sporulation. Recombinant RTG cells and recombinant spores can be differentiated because RTG cells result from an equational chromosome segregation and therefore are diploid, while spores that completed meiosis are haploid. In the absence of recombination between the MAT locus and the centromere (chr. III), mitotic and RTG cells remain heterozygous for MAT and therefore can be screened as non-maters while haploid spores are either a-mater or α-mater. Thus, the Arg+ colonies were screened for non-mater phenotype (indicative of heterozygosity at the MAT locus and therefore diploidy). To ensure that the likely-RTG colonies were the product of meiotic but not mitotic recombination, the Arg+ colonies were also screened for histidine, leucine or methionine auxotrophy on SC-His, SC-Leu and SC-Met plates, respectively.
In the second method, called “isolation by mother-daughter micromanipulation”, cells harvested at various time points in sporulation were washed in H2O and unbudded cells (40–80 per time point) were individually deposited onto YPD plates using a dissecting microscope (Singer MSM system). The plates were incubated at 30°C and regularly observed until bud formation was complete. Then, the mother and daughter cells were separated when a second bud was visible on the mother cell, i.e. between 4h to 7h after deposition of the meiotic cells on the YPD plate. At that stage, the mother cell is rounder, bigger and re-buds first, while the daughter cell is more elongated, smaller and not yet budded, as previously described [16,17]. Then, the mother and daughter cells were incubated 3 days at 30°C to form colonies, and phenotypically analyzed for mating and auxotrophic phenotypes (in this situation the mating type serves as a recombination marker).
Genomic DNA was prepared from single-colony culture as described [71] and sequenced on the NGS platform of the Institut Curie, using the V4 and 5500 SOLiD (Life Technologies) or HiSeq2500™ (Illumina) instruments following the manufacturer’s standard protocols. Libraries were constructed for paired-end sequencing (50x35 bp, 75x35 bp or 100x100 bp) or for mate-pair sequencing (50x50 bp). Sequencing data were aligned onto the SGD reference genome (R64 from 2011-02-03 on SGD website, or SacCer3 on UCSC genome browser), using Lifescope (v2.5) (Life Technologies) local alignment algorithms for SOLiD data and BWA (v0.6.2) [72] for HiSeq data (with options “aln -n 0.04 -l 22 -k 1 -t 12 -R 10”).
PCR duplicates were filtered-out from mapped sequencing reads using MarkDuplicates tool from Picard [http://broadinstitute.github.io/picard/]. The number of read per genomic position was determined using genomeCoverageBed tool from BEDTools [73], and averaged per 10kb window to detect copy number variation along and between chromosomes. The coordinates of copy number variations were determined using the Control-FREEC software [29].
SNP calling was made on the mapped sequencing reads from FY1338 and DAO20-1, using the software implemented in the BioScope (v1.3) framework, with, in addition to default parameters, "High" stringency criterion (i.e. calls should be detected on both DNA strands). We obtained 115 calls for FY1338 and 65,134 calls for SK1. The common calls found in both parental strains were filtered out (53 each), as they represent SNPs of the reference SGD strain that do not discriminate the S288c and SK1 strain backgrounds. Then, heterozygous calls and calls with a score higher than 5.10−7 were removed (32 for FY1338 and 1,180 for DAO20-1), giving a list of 63,901 polymorphic positions differentiating DAO20-1 from SGD reference genome. This list of polymorphisms was further filtered based on the experimental genotyping results (see below for method) from the sequencing of the hybrid diploid (AND1702) and of two haploid parents of each background (FY1338 and ORT7235 for S288c, DAO20-1 and ORT7237 for SK1). 62,218 SNP positions having the expected genotype (heterozygous, homozygous S288c, or homozygous SK1 respectively) were retained. Among the eliminated SNP positions, 12 SNP positions in the ARG4 region had the genotype “homozygous S288c” in ORT7237 and in AND1702 (due to the introduction of the arg4-RV allele in ORT7237), and the 77 mitochondrial SNP positions were homozygous in the hybrid AND1702 strain, which exclusively inherited the mitochondrial DNA from the S288c parental strain.
All RTG strains were genotyped for the robust 62,218 polymorphic SNP positions defined above. The reads covering the polymorphic positions were selected using intersectBed tool from BEDTools [73]. The position and the identity of the polymorphism(s) covered by each read were computed. The base at the designated position was extracted and compared to the base found in SGD reference genome, and in the list of SK1 polymorphisms. The number of reads carrying the S288c allele or the SK1 allele was recorded. A genotype was attributed only if coverage was greater than 5X and if at least 2/3 of the reads display the parental alleles. Genotyping criteria to determine thresholds (described in S5 Fig) were set up based on the distribution of the allelic frequencies observed in 5 control sequencings (FY1338, ORT7235, DAO20-1, ORT7237 and AND1702 strains). A given position was genotyped “S288c” if >95% of the reads exhibited the S288c allele; It was genotyped “SK1” if >75% of the reads exhibited SK1 allele; It was genotyped “heterozygous” if 25–95% of the reads displayed S288c and 5–75% of the reads displayed SK1 allele. These non-symmetrical thresholds, biased/shifted toward S288c, are justified by the alignment against the SGD (S288c) reference genome. Altogether, with these thresholds, >99.5% of the SNP positions had the expected genotype in each of the 5 control sequencings. In diploid samples, a small bias in S288c/SK1 read ratio would transform a heterozygous position into a homozygous call. Therefore, to increase the confidence in genotyping call in diploid strains, only the genotype switched that affect at least 3 adjacent SNP positions were retained. When tetrads from the RTGs were performed, 99.69% of the SNP positions homozygous in the RTG segregated 4:0 in the corresponding tetrad, confirming the robustness of this threshold.
Preliminarily, the LOH analysis was solely based on the SNP positions genotype. Consecutive SNP positions with the same homozygous genotype were grouped into LOH tracts. In the RTG4-S and RTG17-D strains, the SNP positions involved in Copy Number Variation (CNV) were excluded from LOH analysis. Then, in addition to the SNP positions genotyping, the LOH analysis was deepen in the pairs of mother-daughter RTG strains, using custom scripts, to include the segregation information. Only the SNP positions robustly genotyped in both the mother and daughter strains were retained. A SNP position heterozygous in both strains, or homozygous with an opposite genotype in each strain, exhibits a 2:2 segregation pattern. Alternatively, a SNP position that is heterozygous in one strain and homozygous in the other exhibits a 3:1 segregation pattern. The few SNP positions that displayed a 4:0 segregation pattern were excluded from the LOH analysis as they likely result from a pre-meiotic gene conversion event. To assemble the LOH regions and determine their coordinates, in a first step, the SNP positions with a 3:1 segregation pattern were set aside and the SNP positions with a 2:2 segregation pattern were grouped in tracts of the same genotype. The ones of homozygous genotype (SK1 in one strain, S288c in the other one) correspond to reciprocal LOH (rLOH). In a second step, all SNP positions were analyzed together, and grouped in tracts of same genotype/segregation pattern. The tracts made of 3:1 SNP positions were defined as non-reciprocal LOH (nrLOH).
We analyzed the recombination events in the RTG strains based on the position of LOH regions. For the single RTG strains isolated by Arg+ selection, the genotype switches define the positions of the recombination events, without distinction between CO and NCO. In contrast, in the mother-daughter RTG pairs, we could identify COs (at the boundaries of reciprocal LOH regions), GC associated with CO (non reciprocal LOH region in-between a heterozygous region and a reciprocal LOH region), and NCO (non reciprocal LOH region inside a heterozygous region or inside a reciprocal LOH region). To validate the recombination events in the RTG pairs, we adapted the CrossOver (v6.3) algorithm from ReCombine (v2.1) [27], a suite of programs initially dedicated to the analysis of tetrad data (4 haploid genotypes). To adapt the format of the dataset, the genotype of each diploid was split into two haplotypes (or chromatids) using the following criteria: at homozygous positions, the two chromatids have the same genotype, while at heterozygous positions, systematically the first chromatid is S288c and the second SK1. Thus, we obtain a tetrad-like dataset where 2 chromatids were deduced from the genotype of the mother RTG strain and the 2 others from the daughter strain. The output of CrossOver program was manually corrected (as some events were attributed to no chromatid). The output data were run into the groupEvents program, kindly provided by J. Fung lab (UCSF), to merge closely spaced events as single ones and refine the classification of the recombination events [28]. Complex events were manually verified and reclassified when necessary.
However, due to the random distribution of the chromatids in the mother and daughter cells, depending on the number of CO on the same chromosome arm, only between ½ and ⅔ of the crossovers are detected. When the two recombinant chromatids resulting from a CO segregate away from each other, the resulting cells both exhibit a LOH. When the two recombinant chromatids resulting from a CO co-segregate in the same cell, the other cell inherits of the two parental chromosomes, thus both cells remain heterozygous, and the CO remains undetected. To verify the existence of these potentially “masked” COs, we sporulated 8 RTG strains (4 RTG pairs) and sequenced one 4-spore tetrad for each. The haplotyping of the two RTG chromosomes by haploidization led to the detection of “masked” CO: CO involving two chromatids which segregate in the same RTG cell do not induce LOH distal to the CO site, but lead to four recombinant chromatid upon sporulation. Thus, these tetrads were analyzed with CrossOver and groupEvents programs [27,28], and the recombination events resulting from the sporulation were manually separated from the recombination resulting from the RTG to identified the masked COs.
The trait linkage analysis was performed as described [74] using the R/qtl package [75]. For each trait separately, the QTLs were identified using the LOD scores (log10 of the ratio of the likelihood of the experimental hypothesis to the likelihood of the null hypothesis). The linkage was significant when the LOD score was greater than the 5% tail of the LOD scores obtained by 1000 permutations of the phenotype values.
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10.1371/journal.pntd.0006058 | Mycolactone displays anti-inflammatory effects on the nervous system | Mycolactone is a macrolide produced by the skin pathogen Mycobacterium ulcerans, with cytotoxic, analgesic and immunomodulatory properties. The latter were recently shown to result from mycolactone blocking the Sec61-dependent production of pro-inflammatory mediators by immune cells. Here we investigated whether mycolactone similarly affects the inflammatory responses of the nervous cell subsets involved in pain perception, transmission and maintenance. We also investigated the effects of mycolactone on the neuroinflammation that is associated with chronic pain in vivo.
Sensory neurons, Schwann cells and microglia were isolated from mice for ex vivo assessment of mycolactone cytotoxicity and immunomodulatory activity by measuring the production of proalgesic cytokines and chemokines. In all cell types studied, prolonged (>48h) exposure to mycolactone induced significant cell death at concentrations >10 ng/ml. Within the first 24h treatment, nanomolar concentrations of mycolactone efficiently suppressed the cell production of pro-inflammatory mediators, without affecting their viability. Notably, mycolactone also prevented the pro-inflammatory polarization of cortical microglia. Since these cells critically contribute to neuroinflammation, we next tested if mycolactone impacts this pathogenic process in vivo. We used a rat model of neuropathic pain induced by chronic constriction of the sciatic nerve. Here, mycolactone was injected daily for 3 days in the spinal canal, to ensure its proper delivery to spinal cord. While this treatment failed to prevent injury-induced neuroinflammation, it decreased significantly the local production of inflammatory cytokines without inducing detectable cytotoxicity.
The present study provides in vitro and in vivo evidence that mycolactone suppresses the inflammatory responses of sensory neurons, Schwann cells and microglia, without affecting the cell viability. Together with previous studies using peripheral blood leukocytes, our work implies that mycolactone-mediated analgesia may, at least partially, be explained by its anti-inflammatory properties.
| Mycolactone is a complex macrolidic polyketide produced by the skin pathogen Mycobacterium ulcerans, with cytotoxic, analgesic and anti-inflammatory properties. Peripheral nerve destruction and activation of type 2 angiotensin II receptors on sensory neurons have been proposed to mediate bacteria-induced hypoesthesia in infected skin. In addition, mycolactone was recently shown to block the co-translational translocation of secretory proteins into the endoplasmic reticulum in host cells, leading to defective inflammatory responses. Here we examined if this last mechanism may also contribute to inhibit neuro-inflammation and particularly in the context of neuropathic pain. Using a representative panel of primary cells from the central and peripheral nervous systems, we found that indeed, mycolactone potently inhibits the production of pro-inflammatory mediators at non-cytotoxic concentrations. Notably, mycolactone prevented the polarization and pro-inflammatory functions of cortical microglia, which are critical inducers of neuroinflammation. Consistent with our in vitro findings, mycolactone had potent anti-inflammatory effects on the spinal cord of rats injected in the spinal canal, with no apparent side effects. Our data show that mycolactone suppresses inflammatory responses in the nervous system similarly as in the immune system, suggesting that mycolactone-mediated analgesia may, at least partially, be explained by its anti-inflammatory properties.
| Mycolactone is a polyketide-derived macrolide produced by the skin pathogen Mycobacterium ulcerans, the causative agent of Buruli ulcer disease (BU) [1–3]. In addition to inducing local skin ulceration and analgesia, mycolactone diffuses in infected hosts to affect systemic inflammatory immune responses [4]. Why the massive tissue necrosis in BU lesions does not cause acute inflammation and pain has been the subject of an intense research, aiming to better treat BU and discover new means to control inflammation. Early reports favored the hypothesis that BU-associated analgesia was primarily due to nerve destruction. Indeed, the histopathological examination of BU biopsies showed local axonal damages, with loss of myelin in 24% of the patients [5]. Consistently, mouse footpad infection with M. ulcerans induced nerve fiber degeneration in advanced ulcers [6], and injection of purified mycolactone in mouse footpads triggered neurological damages associated with hyposensitivity [7]. However, it was later shown that infection with M. ulcerans, or injection of lower doses of mycolactone, can induce local hypoesthesia without nerve destruction [8]. Mycolactone was proposed to activate type 2 angiotensin II receptors (AT2R) expressed by neurons, leading to cell hyperpolarization and defective pain transmission [8, 9]. This mechanism nevertheless raises controversy as AT2R blockade, instead of activation, was previously reported to promote analgesia [10].
Recently, we and others have identified the Sec61 translocon as the host receptor mediating the anti-inflammatory and cytotoxic effects of mycolactone [11–13]. Sec61 is the channel translocating signal peptide-bearing nascent polypeptides into the endoplasmic reticulum (ER), for introduction into the secretory pathway. Mycolactone binding to the pore-forming subunit of the translocon (Sec61α) was shown to cause the proteasomal degradation of all newly synthesized Sec61 clients blocked in translocation [12]. In immune cells, the functional consequences of mycolactone-mediated Sec61 blockade were migration defects, impaired cytokine production and defective responsiveness to cytokine stimulation [4, 11, 14]. Immune cells play a critical role in pain development, through the release of inflammatory mediators sensitizing nociceptive neurons and through the infiltration of the central nervous system (CNS). Mycolactone-mediated Sec61 blockade in immune cells may thus contribute to analgesia by suppressing inflammation. In support of this hypothesis, systemically-delivered mycolactone protected mice against chemical-induced skin inflammation and acute inflammatory pain [4].
Chronic pain, and in particular neuropathic pain, is a rising health problem for which current treatments are poorly efficient thus requiring new therapeutic strategies. It may result from injury- or disease-induced damages in the nervous system, triggering spontaneous pain, hyperalgesia and allodynia due to central sensitization [15]. Nerve damage causes an inflammatory reaction at the lesion site, leading to disruption of the blood nerve barrier and infiltration of macrophages into the nerve [16, 17]. Peripheral Schwann cells (SCs) and satellite cells; together with central glial cells (microglia, astrocytes and oligodendrocytes) are other key players of pain development (reviewed in [18]). In response to nerve injury, activated SCs release inflammatory mediators such as TNF-α and IL-1β that sensitize nociceptors and play a role in the disruption of the blood nerve barrier [19]. At the central level, microglia initiate neuropathic pain by switching into a pain-related state upon increased pre-synaptic activity (reviewed in [20]). Cytokine production and cytokine receptor signaling being dramatically affected by mycolactone-mediated Sec61 blockade, we postulated that mycolactone may efficiently prevent neuro-inflammation. To address this hypothesis, we examined the cytotoxic and immunomodulatory effects of mycolactone in key cellular components of the central and peripheral nervous systems. We next studied the effects of mycolactone on the neuroinflammation that is associated with chronic pain in vivo.
The ethic committee of the University of Paris Descartes approved all rat experiments under approval number #00354.02, in accordance with French and International laws and policies for use of animals in neuroscience research (European Communities Council Directive No. 87,848, October 1987, Ministère de l’Agriculture et de la Forêt, Service Vétérinaire de la Santé et de la Protection Animale) and with guidelines from the committee for research and ethical issues of the International Association for the Study of Pain [21]. Since we only used mice as a source of primary cells, the described experiments did not require approval from the French Ministry of Higher Education and Research. They were performed in compliance with the European Communities Council Directive of 22 September 2010 on the approximation of laws, regulations, and administrative provisions of the Member States regarding the protection of animals used for scientific purposes.
Mycolactone was purified from M. ulcerans 1615 (ATCC 35840), a strain isolated from a Malaysian patient, which produces a mixture of mycolactones A/B and C [22]. Mycolactone was purified as previously described [1], then quantified by spectrophotometry (λmax = 362 nm; log ε = 4.29) [23]. Stock solutions (1mg/ml and 500 μg/ml) were prepared in DMSO, then diluted respectively at least 1000x in culture medium for cellular assays or 50x in saline solution (sodium chloride 0.9%) before injection in rats (100 ng mycolactone in 10 μl). In all cases, controls exposed to the same volume of vehicle were included.
Adult male Sprague-Dawley rats (200–250 g) were purchased from Laboratoires Janvier (Le Genest-Saint-Isle, France) and housed in a temperature-controlled environment (22 ± 1°C) with a 12/12- hours light–dark cycle. Food and water were available ad libitum. 6–10 weeks-old female mice (C57BL/6JRj) were housed and bred under SPF conditions with food and water ad libitum.
Mixed primary mouse glial cultures were prepared from the cortical region of the brain of 0–2-day-old wild type mice (C57BL/6JRj). In brief, after careful dissection from diencephalic structures, the meninges were removed and the cortex chopped and digested by papain (30U/ cortex, SIGMA P3125-1G) in Hank’s balanced salt solution (HBSS) containing 0.5 mM EDTA, 1.5 mM CaCl2, 0.2 mg/ml L-Cystein and 0.1 μg/ml DNase I (Sigma D5025) for 20 min at 37°C. Cells were mechanically dissociated and plated on poly-D-lysine (PDL) coated plastic T75 flask, in DMEM supplemented with 10% SVF, 1% penicillin/ streptomycin and 1% amphotericin B for astrocytes/ microglia co-cultures. Microglia were detached after 11 to 12 days of maturation by addition of 2.4 mM lidocaine 15 min at 37°C as described [24] then seeded in PDL coated plates. With this method, we obtained a 97% pure population of microglia as evidenced by CD45/CD11b FACS labeling. Astrocytes were detached with trypsin and seeded in PDL coated plates. Primary cortical neuronal cultures were prepared from 0-2-day old C57BL/6JRj mice. Cortex were chopped and digested for 40 min at 37°C in papain solution as described for glial cultures. Cells were washed twice with BME containing 10% DVF, 0.3% glucose, 1 mM Na-Pyruvate, 0.1% Mito serum extender (Corning 355006), 10 mM Hepes, 1% penicillin/ streptomycin. Cells were mechanically dissociated and seeded in poly-L-lysine coated plastic T75 flask. Medium was changed after 4 hours for Neurobasal supplemented with B-27, 2 mM glutamine and 1% penicillin/ streptomycin. Neurons were used after 9 to 11 days. For primary dorsal root ganglion (DRG) cultures, bilateral DRG from all levels were harvested from 8 weeks old female C57BL/6JRj mice as described in [25] with minor modifications. Collected DRGs were mildly digested by Papain (30U) in HBSS 1x in presence of 0.5 mM EDTA, 1.5 mM CaCL2 and 0.2 mg/ml L-Cystein during 20 min at 37°C then by collagenase 5 mg/ml in HBSS, 20 min at 37°C. DRGs were then triturated in complete DMEM. Supernatant containing cells was collected and seeded at the density of 5 x 104 cells per coverslip coated with laminin. DRG cells were then grown in complete DMEM in presence of 200 ng/ml NGF during 48 h before being used. Mouse Schwann cells (SCs) were purchased from Sciencell (M1700) and cultures in Schwann cell medium (Sciencell, 1701) on poly-L-Lysine-coated culture vessels following the instructions of the supplier.
For MTT viability assays, isolated cortical microglia, astrocytes and neurons were seeded at a density of 4 x 104 cells, 3 x 104 and 3 x 104 cells per well respectively, into a 96 wells plates coated with PDL and treated with vehicle (DMSO), or mycolactone for 16 to 72 h. At the end of treatment, MTT (500 μg/ml) was added into each well for 4 hours before being replaced by DMSO in order to solubilize formazan crystals produced. OD was measured at 570 nm. Viability of cells was expressed relatively to absorbance. TUNEL staining was performed using an in situ cell death detection kit (Roche, Mannheim, Germany) to label DNA strand breaks and identify apoptotic cells in cultured DRG cells and histological sections of rat spinal cord. Cultured DRG cells were fixed with 4% paraformaldehyde in PBS for 1 h at room temperature then permeabilized with 0.1% Triton X-100 for 2 min (4°C). They were labeled 1 hour in PBS 1% BSA with a rabbit anti-β-III tubulin antibody (ThermoFisher Scientific, MA1-118) then 45 min with anti-rabbit Cy3 (Jackson ImmunoResearch, 111-165-144) before being submitted to TUNEL labeling following the manufacturer’s indications and mounted in Prolong Gold antifade reagent with Dapi (Molecular Probes, P36931). Nuclei, β-III tubulin and TUNEL positive cells were counted manually on images acquired on Olympus microscope (BX53, Olympus) using FiJI and Icy softwares [26, 27]. TUNEL labeling on fixed rat spinal cord slices was performed following the manufacturer’s indications and mounted in Prolong Gold antifade reagent with Dapi. Positives controls were spinal cord slices incubated 10 min with 3000 U/ml DNase I (Sigma) in 50 mM Tris-HCl pH 7.5, and 1 mg/ml BSA prior to TUNEL staining. Quantification of TUNEL positive cells was performed with the Spot Detection plugin of Icy software [27].
DRG cells were seeded at a density of 5 x 104 cells on coverslips previously inserted in the wells of 24 well-plates. Cells were exposed to 5 and up to 20 ng/ml mycolactone for 30 min prior to a 16 h stimulation with 1μg/ml LPS (Ultra pure Escherichia coli LPS, InvivoGen), in the presence of mycolactone. The release of CCL2, IL-6 and TNF-α into culture supernatant was assessed by ELISA using respectively the mouse CCL2 Uncoated ELISA kit (ThermoFisher scientific, 88-7391-22) and the mouse IL-6 and TNF-α ELISA MAX kits (Biolegend, 431301 and 430904). For IL-6 ELISA assay on SCs, SCs were seeded at 2 x 104 in 24 well plates coated with poly-L-Lysin. Twenty-four hours later, SCs were treated for 30 min with increasing doses of mycolactone, then stimulated by 1 μg/m LPS + 20 ng/ml IFN-γ (R&D Systems, 485-MI) for 16 h, in presence of mycolactone. Production of IL-6 and TNF-α by microglia was assessed in the culture supernatant of cells seeded at a density of 2.5 x 105 cells per well in 12 well-plates, treated overnight with mycolactone before being stimulated for 8 h with 100 ng/ml LPS, in the presence of mycolactone.
The impact of mycolactone on TLR4 and IFNγR surface expression was measured in primary microglia exposed to increasing doses of mycolactone for 16 h. Microglia were detached with Accutase solution (Sigma) and blocked 20 min with FcR blocking reagent mouse (Mitenyi Biotec, 130-092-575) before being incubated 30 min with either a PE anti-mouse TLR4 (CD284)/MD2 complex antibody (117605, Biolegend) or a PE anti-mouse CD119 antibody (12-1191-82, ThermoFisher) or with the corresponding isotype in PBS 2% SVF at 4°C. SCs exposed to mycolactone for 16 h were detached with Accutase before being labeled with the PE anti-mouse TLR4 (CD284)/MD2 complex antibody. NOS-2 expression was monitored in microglia exposed to increasing doses of mycolactone, 30 min prior to LPS/ IFN-γ stimulation during 16 h (respectively 1 μg/ml and 20 ng/ml). Microglia were fixed (BD Lyse/ Fix buffer, BD Biosciences) and permeabilized (BD Perm/ Wash, BD Biosciences), then incubated for 30 min with anti-NOS2 (Santa Cruz Biotechnology, M-19), then for 20 min with the anti-goat DyLight 649 (Rockland, 605-443-002). Intracellular NOS2 staining was analyzed with the BD Accuri C6 flow cytometer (BD).
The rats were anesthetized using 3% isoflurane in O2 at 3 L/min for induction, then maintained with 1.5% isoflurane in O2 at 3 L/min. Their right sciatic nerve (ScN) was exposed at the mid-thigh region on the posterior aspect of the biceps femoris and 4 ligatures (5–0 chromic catgut) with 1 mm spacing were loosely tied around the nerve, proximally to the sciatic trifurcation without any disruption of epineurial circulation. Finally, the skin was sewed up using 4–0 VICRYL sutures. Sham-injured animals were subjected to the same procedure as above, but the ScN was only exposed without ligature. 2 days post-surgery, animals were injected daily during 3 days with either 100 ng of mycolactone diluted in 10 μl of saline solution (sodium chloride 0.9%) or equivalent volume of vehicle (DMSO) through intrathecal route [28]. Rats were sacrificed by pentobarbital surdosage (120 mg/kg) 5 days after surgery. DRG (L4-L6) and dorsal spinal cord were collected on chronic constriction injury (CCI) side and were immediately frozen in liquid nitrogen and stored at -80°C until use. Samples were homogenized in Abcam cell lysis buffer (ab152163) added with proteases inhibitors (Sigma P2714) and 1 μl/ml DTT during 45 min on ice. After centrifugation (13,000 x g for 10 min) protein concentration were determined with the DC Protein Assay kit (Bio-Rad). For histopathology and TUNEL analysis, rats were injected daily during 3 days with either 100 ng of mycolactone diluted in 10 μl of physiological solution or equivalent volume of vehicle (DMSO) through intrathecal route. After the last injection, rats were sacrificed via transcardial perfusion with 100 mL of 0.1% (w/v) sodium nitrite (Honeywell Specialty Chemicals) in saline followed by 500 mL 4% (w/v) paraformaldehyde (PAF, Sigma) in 1X phosphate buffered saline (PBS, Sigma), pH 7.4, after deep anesthesia (sodium pentobarbital, 50 mg/kg, i.p.). The lower spinal cord and corresponding DRG were dissected out and post-fixed by overnight incubation in the fixative solution at 4°C. Spinal cord and DRG were cryoprotected by incubation overnight in 20% sucrose in PBS at 4°C, then sliced into 20 μm-thick sections with a cryostat. Sections were processed for TUNEL or immunostaining.
GM-CSF, IL-1β, IL-6, TIMP-1, IFN-γ, IL-2 and TNF-α were measured with a magnetic Luminex screening assay (LXSARM-9, R&D systems) according to the manufacturer’s instructions. In brief, 50 μl of lysate prepared from DRGs or spinal cord isolated from rat or standard was incubated with antibody-linked beads for 2 h with shaking, then incubated 1 h with biotinylated secondary antibodies before being incubated 30 min with streptavidin-phycoerythrin. Acquisitions were done on the MAGPIX system (Luminex). At least 100 events were acquired for each analyte.
Data of the animal studies were analyzed with the Mann-Whitney rank test to compare each treated group with its vehicle control. Prism software (5.0d; La Jolla, CA) was used for graphical representation and statistical treatments. Values of P ≤ 0.05 were considered significant.
The toxicity of mycolactone varies extensively with the cell type, the most susceptible cells reported to date being macrophages and fibroblasts [4]. Toxicity of mycolactone on such adherent cells was shown to proceed through progressive cell retraction and detachment, culminating in cell death after 48–72 h of treatment with >10 ng/ml mycolactone. To gain an integrated view of mycolactone toxicity on all cellular mediators of pain, we studied its effects on key cellular components of the peripheral and central nervous systems. Dorsal root ganglion (DRG) were collected from adult mice, dissociated and the resulting mix of sensory neurons, glia and fibroblasts was subjected to in vitro treatment with mycolactone. We used a combination of TUNEL assay and β-III tubulin labelling to monitor mycolactone toxicity on the sensory neuron sub-population. We detected an increased incidence of TUNEL+ DRG neurons after 24 h of exposure with mycolactone concentrations superior to 250 ng/ml (Fig 1A). Importantly, this effect was not associated with a decreased number of neurons (Fig 1B and S1A Fig), suggesting that mycolactone-induced cytotoxicity had not reached the stage of cell detachment. However, after 48 h of treatment with >7.5 ng/ml mycolactone, almost all DRG neurons had died (Fig 1B and S1A Fig).
SCs, the glia of the PNS, are increasingly recognized as active mediators of chronic pain syndromes [29]. In mouse sciatic nerve-derived primary SCs, mycolactone did not cause cytotoxicity until 48 h of treatment, irrespective of its concentration (Fig 1C). After 48 h, a dose-dependent effect on cytoxicity was detected. The concentration of mycolactone leading to 50% killing of the whole cell population (IC50), as measured by the MTT reduction assay, was close to 1 ng/ml.
At the central level, microglia and astrocytes contribute to the sensitization of the spinal cord during neuropathic pain. They also elicit the neuro-inflammation that is associated with neurodegenerative diseases. Neurons, astrocytes and microglia were isolated from the cortical region of the brain of mouse neonates, for assessment of mycolactone toxicity with the MTT assay. Astrocytes and neurons showed equivalent responses to mycolactone-induced toxicity, with decreased cell viability manifesting only after 72 h of treatment (IC50 of 2.5 and 9.5 ng/ml respectively, Fig 1D and 1E). In microglia, a sharp decrease in cell viability was observed after 48 h of mycolactone exposure (IC50 close to 10 ng/ml) (Fig 1F). Notably mycolactone did not display cytotoxicity in any of these cell types during the first 24 h of treatment (S1B and S1C Fig).
Upon nerve injury or in chronic pain conditions, nociceptive neurons release pro-inflammatory mediators in the spinal cord, leading to microglia activation [30]. We have shown previously that secreted proteins, including cytokines and chemokines, are amongst the Sec61 clients most potently inhibited by mycolactone [4, 11, 31, 32]. We thus examined if mycolactone altered the inflammatory responses of peripheral sensory neurons, using short treatments (<24 h) that do not affect their viability. Since TLR4 is mostly expressed by neurons in DRG cell suspensions [33], we used the TLR4 agonist LPS to selectively induce neuronal production of pro-inflammatory cytokines and chemokines. A 16 h stimulation of DRG cultures with 1μg/ml LPS induced significant release of CCL-2, IL-6 and to a lower extent TNF-α. The LPS-stimulated production of these molecules was efficiently blocked by a 30 min pre-treatment with 5 ng/ml mycolactone (Fig 2A–2C).
SCs also express TLR4, and it was demonstrated that TLR4 activation of SCs leads to the production of IL-1β, TNF-α and NO [34, 35]. Rat SCs were also reported to produce IL-6 in response to external stimuli [36]. Using primary SCs isolated from mouse sciatic nerve, we assessed the production of TNF-α, IL-1β, IL-6 and NOS-2 in SCs stimulated with LPS and IFN-γ for 16 h. While IL-6 was significantly upregulated (Fig 2D), this stimulation failed to induce TNF-α, IL-1β and NOS-2 in our system. Pre-treating SCs with mycolactone for 30 min dose-dependently suppressed the stimulation-induced production of IL-6 (Fig 2D). Mycolactone-driven suppression of IL-6 production correlated with a dose-dependent decrease in surface TLR4 expression by SCs exposed to >2.5 ng/ml mycolactone (Fig 2E), indicating that mycolactone also prevents the capacity of SCs to respond to TLR4 stimulation.
Activated microglia also produce pro-nociceptive mediators including cytokines (TNF-α, IL-6), chemokines (CCL-2) and NO [37–39], contributing to neuron sensitization. A 16 h pre-treatment with mycolactone dose-dependently downregulated the production of IL-6 and TNF-α by primary mouse cortical microglia stimulated with LPS (Fig 3A and 3B). Mycolactone also suppressed the production of NOS-2 and NO production in cells stimulated with LPS/IFN-γ for 16 h (Fig 3C and S2A Fig).
To see if mycolactone interferes with the polarization of microglia towards the M1 pro- or M2 anti-inflammatory phenotypes, microglia were incubated with either LPS/IFN-γ or IL-4 for 24 h, in the presence or absence of mycolactone. The induction of NOS-2 and Arginase-1, as markers of the M1 and M2 phenotypes respectively, was monitored in each polarizing conditions (S2B and S2C Fig). As expected, LPS/IFN-γ and IL-4 stimulations resulted in increased incidence of NOS-2 and Arginase-1 expressing cells, respectively. Notably, mycolactone suppressed the induction of both markers, indicating that it inhibits the process of microglia polarization either way. As previously showed in macrophages [11], we observed that 16 h of exposure to >2,5 ng/ml mycolactone abrogated the surface expression of IFN-γ receptor (IFNGR) on microglia (Fig 3D). It also reduced their TLR4 surface expression, although to a more limited extent (Fig 3D), suggesting that defective polarization of microglia towards the M1-like phenotype may result from mycolactone-induced downregulation of IFNGR and TLR4.
Our observation that mycolactone suppresses the production of pro-inflammatory mediators by microglia suggested that it may limit the development of neuro-inflammation that is associated with neuropathic pain in vivo. This hypothesis was tested in a preclinical model of peripheral nerve injury-evoked neuropathic pain induced by chronic constriction injury (CCI) of the sciatic nerve (ScN) in the rat. ScN-CCI has been shown previously to trigger inflammation in DRG and spinal cord, with maximal effect at 3 days post-injury [40]. Mycolactone (100 ng in 10 μl of physiologic solution) was delivered intrathecally, 3 consecutive days post-CCI (Fig 4A). As control, we delivered the same treatment to rats not subjected to ScN-CCI (Sham). Twenty-four hours after the last injection of mycolactone, or vehicle as control, animals were sacrificed. Ipsilateral DRGs from lumbar regions 4 to 6 (L4-L6) and the ipsilateral dorsal horn of the spinal cord were collected to assess protein expression of GM-CSF, IL-1β, IL-6, TIMP-1, IFN-γ, IL-2 and TNF-α, using a multiplex approach (Fig 4B–4G). We observed a significant increase of IL-1β and TIMP-1 in the ipsilateral dorsal horn of the spinal cord (SpC) of ScN-CCI rats, compared to Sham controls, reflecting the inflammation induced by CCI (Fig 4B and 4C). A significant increase in IL-6 production (Fig 4D), and a trend for increased IFN-γ and IL-1β was also observed in the DRGs of ScN-CCI treated rats, compared to controls (S3A and S3B Fig). Mycolactone treatment did not prevent the ScN-CCI-induced elevation of IL-1β and TIMP-1 in spinal cord (Fig 4B and 4C). Although a trend towards decrease was observed, the DRG content in IL-6, IFN-γ and IL-1β (Fig 4D and S3A and S3B Fig), and spinal cord content in IL-2, IL-6, TNF-α and GM-CSF β (Fig 4F and 4G and S3C and S3D Fig) was not significantly lowered by mycolactone treatment. Yet, mycolactone decreased markedly the basal level of IFN-γ, IL-2 and IL-6 (and a tendency toward a decrease was observed for TNF-α) in the spinal cord (Fig 4E–4G and S3C Fig) but not in DRGs. Notably, TUNEL labeling did not reveal elevated cell death in the spinal cord (0.1% ± 0.2 and 0.3% ± 0.2 TUNEL positive cells in vehicle and mycolactone conditions respectively, Fig 4H) nor in DRGs (0.1% and 0.3% ± 0.1 TUNEL positive cells in vehicle and mycolactone conditions respectively, S4 Fig) in mycolactone-injected animals. Moreover, the distribution and shape of neurons and microglia in the spinal cord were unchanged (S5 Fig). In conclusion, intrathecally-delivered mycolactone failed to prevent ScN-CCI-induced neuroinflammation, in the conditions tested. However, it had significant anti-inflammatory activity on the spinal cord, in the absence of cytotoxicity.
The principal aim of this work was to determine if mycolactone displays anti-inflammatory effects on the nervous system, similarly to the immune system. Indeed, previous studies have shown that mycolactone-mediated Sec61 blockade has immediate inhibitory effects on the production of secreted mediators of inflammation [3]. These are explained by the direct blockade of Sec61-dependent secretory protein translocation into the ER by mycolactone [11–13]. Bieri et al. reported recently that mycolactone causes BIM-dependent cell apoptosis, through inhibition of mTOR [41]. Meanwhile, we were able to demonstrate that mycolactone-induced cell death fully depends on its interaction with Sec61 [42]. How Sec61 blockade leads to mTOR inhibition remains unclear, and may vary with the cell type. Here, we took a case-by-case approach to investigate both the anti-inflammatory and cytotoxic effects of mycolactone on the major cellular components of the peripheral and central nervous systems, using primary cells as models. Overall, all tested cells lost viability upon sustained exposure to 1–10 ng/ml mycolactone. The kinetics of cytotoxicity nevertheless varied extensively across cell types, the most susceptible cells (DRG neurons, SCs and microglia) succumbing to mycolactone-induced toxicity after 48 h of treatment. Our conclusions differ from those of Song et al. [9], who recently reported limited toxicity in primary DRG neurons exposed to 70 μM mycolactone (52.5 μg/ml) for 72 h. They are instead in line with those of Anand et al. [43], who observed significant neurite retraction and killing of DRG neurons exposed to low doses (7.5 ng/ml) of mycolactone for 48 h. Although primary SCs (our study) appear less susceptible than the SC line SW10 to mycolactone toxicity [44], we found that SC viability decreased upon sustained exposure to mycolactone. This provides an explanation for the degeneration of myelin-forming SCs in the lesions of mice experimentally infected with M. ulcerans [6] or injected with mycolactone [7], and support the concept that mycolactone-mediated nerve destruction is, at least partially, responsible for local hypoesthesia [43].
Following this in vitro work, we were interested to characterize mycolactone impact on the inflammatory condition classically developing at the spinal cord and DRG following nerve injury. The peripheral terminals of primary sensory neurons express several receptors that can be activated by cytokines, chemokines, growth factors or lipids which are released by residents or recruited immune cells upon injury or infection, modulating their sensitivity and activity (for review [30, 45]. These neuroimmune interactions also occur within DRGs, where immune cells interact with the soma of nociceptive neurons which control long-term sensitization through protein synthesis. At the central level, the cross-talk between neurons and glia (microglia, oligodendrocytes and astrocytes) contribute to drive neuropathic pain by inducing synaptic remodeling [18]. Using short-term treatments that were neutral on cell viability (Fig 1), we showed that mycolactone efficiently prevents the production of inflammatory mediators by DRG neurons, SCs and microglia. Moreover, mycolactone prevented microglia polarization and pro-inflammatory functions. Release of pro-inflammatory mediators like TNF-α by microglia is believed to modulate neuronal plasticity and promote nociceptive transmission during neuropathic pain [46]. Whether bacterially-produced mycolactone gains access to these cell compartments during the course of BU disease is unknown. The current view of mycolactone distribution in infected organisms is that it is locally highly concentrated in the tissues surrounding the site of infection, yet able to access peripheral blood cells, lymphoid organs and liver. Mycolactone was not detected in the brain of experimentally infected mice in a previous study, however the quantifying method used was not sensitive enough to detect nanomolar concentrations, which we found potently anti-inflammatory in cellular assays. If mycolactone can cross the blood brain barrier or travel from the peripheral to the central nervous system, our results thus suggest that it could efficiently suppress the inflammatory functions of nervous cells.
To gain a first insight into the therapeutic potential of mycolactone as an anti-neuroinflammatory agent, we used an intrathecal paradigm. This is indeed a clinically-used approach for drug delivery in patients with chronic pain that allowed us to control the amount of mycolactone passing through the blood brain barrier. Strikingly, the serial delivery of 3 daily injections of 100 ng mycolactone in rat spinal cord displayed potent anti-inflammatory effects without inducing detectable cytotoxicity, arguing in favor of its translational potential. This treatment nevertheless failed to significantly decrease ScN-CCI-induced neuroinflammation. One possible explanation is that the timing and dose conditions of mycolactone treatment might not be optimal. Yet, the apparent safety of the tested regimen suggests that the mycolactone dosage could be increased to enhance anti-inflammatory effects, without inducing local tissue damage. We can note that the anti-inflammatory effect of mycolactone in DRGs was not observed in basal condition and was not significant in CCI condition, which could indicate that this mode of administration is not optimal to reach DRGs. In conclusion, we show in the present work that mycolactone displays anti-inflammatory effects on the nervous system, likely proceeding through Sec61 blockade [11, 12]. These new data provide researchers with an experimental basis for further evaluation of mycolactone as a treatment of neuroinflammatory disorders and chronic pain.
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10.1371/journal.pntd.0000758 | Asymmetric Wolbachia Segregation during Early Brugia malayi Embryogenesis Determines Its Distribution in Adult Host Tissues | Wolbachia are required for filarial nematode survival and fertility and contribute to the immune responses associated with human filarial diseases. Here we developed whole-mount immunofluorescence techniques to characterize Wolbachia somatic and germline transmission patterns and tissue distribution in Brugia malayi, a nematode responsible for lymphatic filariasis. In the initial embryonic divisions, Wolbachia segregate asymmetrically such that they occupy only a small subset of cells in the developing embryo, facilitating their concentration in the adult hypodermal chords and female germline. Wolbachia are not found in male reproductive tissues and the absence of Wolbachia from embryonic germline precursors in half of the embryos indicates Wolbachia loss from the male germline may occur in early embryogenesis. Wolbachia rely on fusion of hypodermal cells to populate adult chords. Finally, we detect Wolbachia in the secretory canal lumen suggesting living worms may release bacteria and/or their products into their host.
| Filarial diseases affect over 150 million people in tropical countries. They are caused by parasitic nematodes like Brugia malayi that rely on their endosymbiont Wolbachia for their survival and fertility. These bacteria are a recognized drug target in the search for treatments killing adult worms. To understand the transmission of Wolbachia from the embryonic to adult stages, we developed new techniques to track these bacteria at the cellular and tissue levels. These techniques include immunofluorescence in whole mount adult tissues and embryos. We found that Wolbachia segregate asymetrically in specific cells, in a lineage-specific manner during early Brugia embryogenesis, and rely on cell fusion to subsequently populate the adult hypodermal chords. From the chords, the Wolbachia can be secreted in the secretory-excretory canal, suggesting that in addition to dead worms releasing the bacteria in the human body, living worms may also secrete Wolbachia, whose role in stimulating the immune system in filarial pathologies is now well established.
| Filarial nematodes are the causative agents of human filariasis, affecting over 150 million individuals. The most pathogenic diseases, lymphatic filariasis and onchocerciasis, (river blindness) comprise a major cause of global morbidity in the tropics, with over 1 billion people at risk of these arthropod-transmitted infections [1], [2]. Three filarial nematode species are responsible for lymphatic filariasis: Wuchereria bancrofti, Brugia malayi and Brugia timori, causing pathologies that include hydrocoele and lymphoedema (elephantiasis). Onchocerciasis is caused by Onchocerca volvulus, leading to skin disease, “onchocercoma nodules” and visual impairment, including blindness. These parasitic nematodes rely on alpha-proteobacterial Wolbachia endosymbionts for development, viability and fertility (for reviews see, [3], [4]). This obligate dependence was first discovered using anti-Rickettsial tetracycline antibiotics, in in vitro and in vivo model systems. Treatments deplete Wolbachia, resulting in embryonic arrest and a decrease in microfilarial (larval) production [4]. Human trials with doxycycline or rifampicin provide evidence for long-term sterilization and macrofilaricidal (adulticidal) effects against both lymphatic filariasis and onchocerciasis [5]–[8].
Wolbachia play a significant role in the pathogenesis of filarial disease [9]–[14]. Wolbachia activate inflammatory immune responses, including antibody responses and induction of corneal keratitis in the case of O. volvulus infection, and are implicated in the inflammation response leading to blindness, induced by release of Wolbachia antigens from degenerating microfilariae [3]. In lymphatic filariasis, the major pathologies are attributable to death and destruction of adult worms within the lymphatic vessels and activation of innate inflammation; effects which are lost following antibiotic depletion of bacteria and absent from soluble extracts derived from filarial species naturally lacking Wolbachia such as Acanthocheilonema viteae [4].
To better understand the endosymbiont interaction with the parasitic nematode, it is of primary importance to characterize Wolbachia localization at the host tissue and cellular levels. The histology of the parasitic nematode B. malayi was established more than half a century ago by differential contrast microscopy (DIC) on whole mount adult specimens [15]. Subsequent DIC and electron microscopy studies carried out on cross sections revealed the presence of intracellular bacteria [16]–[20] which were only “re-discovered” and identified as Wolbachia decades later by phylogenetic analysis and genomic studies [21], [22]. Wolbachia are present primarily in the lateral hypodermal chords of both adult males and females and in the ovaries, oocytes and embryonic stages within the uteri of females. The absence of Wolbachia in the male reproductive system indicates that the bacterium is vertically transmitted through the cytoplasm of the egg and not through the sperm [19], [23].
Although Wolbachia are observed in all stages of the host life-cycle, there are significant variations in bacterial growth kinetics in host development [24], . Bacterial numbers remain constant in microfilariae (mf) and the mosquito-borne larval stages (L2 and L3), but the Wolbachia multiply rapidly beginning within the first week of infection of the mammalian host.
Features of the symbiotic relationship left unresolved include the localization and segregation patterns of Wolbachia during embryogenesis, which are essential to understanding the specific localization in adult somatic tissue and the germline. To address this issue, we developed fixation, immunofluorescent staining and imaging protocols to characterize Wolbachia in whole-mount B. malayi embryos and adult specimens at the tissue, cellular and sub-cellular levels. These studies demonstrate that Wolbachia localize to the posterior of the egg upon fertilization and segregate asymmetrically during early embryogenesis, in a lineage-specific manner, resulting in only a small fraction of the cells in the developing embryo containing the endobacteria. Specifically, Wolbachia concentrate in the C blastomere hypodermal descendants, and in the P blastomere germline precursors. The asymmetric and lineage-specific segregation of Wolbachia during the initial stages of embryogenesis resembles that of some Caenorhabditis elegans polarity and lineage-specific determinants, and suggests that Wolbachia may interact with the counterparts of these determinants in B. malayi.
This transmission pattern readily explains the tissue specific pattern of Wolbachia localization in the adult hypodermal lateral chords and female germline. The absence of the bacteria from the embryonic germ line precursors in nearly half of the embryos suggest Wolbachia loss from the male germline may occur during embryogenesis. We find that Wolbachia rely on fusion of hypodermal cells to populate young adult chords. We also detected Wolbachia in the lumen of the secretory-excretory canals embedded in the hypodermal lateral chords, suggesting that in addition to dead or degenerating parasites, live adult worms may also release bacteria and/or their products through this route into the host tissues.
Living B. malayi adult male and female worms were supplied by TRS Laboratories (Athens Georgia). The worms were raised in jirds and the procedures described below were performed approximately 1 to 3 days after their removal.
To prepare worms for whole mount immuno-fluorescence analysis, they were soaked in M9 buffer (see Buffers in Supplementary Experimental Procedures) for 30 seconds to allow them to uncoil and immediately placed in liquid nitrogen. M9 was then removed and replaced with (PBS+ Paraformaldehyde (PFA) 4% final- (Electron Microscopy Sciences)) 1/3+2/3 Heptane on a rotator for 30 minutes at room temperature. If required, worms were cut or open with a blade to expose the different tissues to antibodies prior to fixation. All fixation and immunostaining steps gave better results in eppendorf tubes in rotation compared to whole mount animals on slides.
For propidium iodide (PI) (Molecular Probes) DNA staining, worms were incubated overnight at room temperature in PBS + RNAse A (15 mg/mL, Sigma), in rotating tubes followed by PI incubation (1.0 mg/mL solution) for 20 minutes in PBS (1∶50) and a 5 minute wash. For DAPI DNA staining alone, fixed worms were simply pulled out of the tube with a curved needle, placed on a glass slide with thin needles in a line of PBS. The PBS was then aspirated and worms mounted into Vectashield with DAPI (Vector Laboratories) and left at 4 degrees overnight: DAPI penetrates all tissues and stains Wolbachia very well.
To prepare embryos for whole-mount immuno-fluorescent analysis, females were cut into sections with a blade on a glass slide. The sections were collected and the slide rinsed with PBS and collected in an 0.5mL eppendorf tube. PFA and heptane were added as described above. The tube was vortexed for one minute at this step. After fixation for 10 to 20 minutes, embryos were immersed in (1/4 water, 1/4 KOH 10M, 1/2 NaClO 15%) for 30 seconds to facilitate removal of the eggshell (optional), centrifuged, and rinsed in PBST. Prior to each change of solution or rinse, samples were centrifuged for 1 min at 4,200 rpm. This procedure yielded hundreds of embryos per female, and allowed staining of at least half of them. As an alternative procedure we used the freeze crack techniques that work with C. elegans embryos but these gave unsatisfactory results, likely due to the smaller size of the Brugia embryos. For protocols used to determine the identity of specific embryonic blastomeres and conditions for primary and secondary antibody incubations see Methods S1.
For live fluorescent analysis of Wolbachia and host nuclei, adult worms were incubated in RPMI medium with 1/10,000 Syto11 (Invitrogen) or vital Hoechst for 30 minutes, and observed as for C. elegans on an 2% agarose pad with Sodium Azide 25mM between slide and coverslip (http://www.wormbook.org/chapters/www_intromethodscellbiology/intromethodscellbiology.html). To observe the Secretory-Excretory canal, we added 50µL of Resorufin (Sigma) at 10 µg/mL, 1/10,000 Syto11 for approximately 30 minutes to an hour, and washed the worms in RPMI for 15 minutes. Worms were mounted in PBS and anesthetized with Sodium Azide.
Confocal microscope images were captured on an inverted photoscope (DMIRB; Leitz) equipped with a laser confocal imaging system (TCS SP2; Leica) using an HCX PL APO 1.4 NA 63 oil objective (Leica) at room temperature. Images in epifluorescence were captured on a Leica DMI 6000B microscope and a Zeiss Axioscope 2 plus microscope.
The fertilization and embryogenesis of B. malayi resemble that of other secernentean nematodes. The sperm entry activates the oocyte to complete meiosis I and II and defines the posterior pole of the egg [26], [27]. All examined species of secernentean nematodes undergo asymmetric cleavage leading to early separation of soma and germ line, and establishment of five somatic cell lineages [28]–. This is followed by a developmental phase during which organ identity is specified. Subsequent morphogenetic events such as ventral closure and an elongation phase due to contraction of circumferential actin bundles in the hypodermis lead to newly hatched larvae appearing very similar among nematodes species. It has been shown that despite this high similarity in the anatomy of the first stage larvae (most often the species variation being acquired during larval life), variations can exist from the first asymmetric divisions [31], [32].
Although the cell lineage of B. malayi (a Rhabditia Spirurida of clade III of the Secernentea) has not been established, parallels with the completely defined lineage of C. elegans (a Rhabditia Rhabditida of clade V of the Secernentea) are likely [32], [33]. In secernentean nematodes, the first division cleaves the zygote asymmetrically into somatic cell AB and a smaller P1 germ line precursor cell (Fig. 1A). Most of the embryonic ectodermal cells (hypodermal and neuronal cells) are derived from the anterior AB blastomere. The posterior P1 blastomere, after three rounds of division, primarily gives rise to the somatic gonad, pharynx, ectodermal and mesodermal derivatives (MS), gut (E), posterior hypodermal derivatives (C), body wall muscles (D) and P4 blastomeres. During gastrulation, the posterior P4 cell follows the gut precursors inward and divides to produce the two germline precursors Z2 and Z3. Based on the similarity between the C. elegans lineage and embryonic maps, putative germ line precursor cells can also be localized (the counterparts of the C. elegans Z2 and Z3) during the process of elongation (i.e. when the embryonic tail reaches half the length of the worm body).
In contrast to C. elegans embryonic development, B. malayi embryos grow and increase their volume in the uterus (Fig. 1B, C). The length of the one-cell egg increases from about 16 µm to 38 µm for an egg containing a mature worm-shaped embryo (Fig. 1D). Thus, unlike C. elegans, the B. malayi eggshell grows and suggests that uptake of nutrients through the eggshell occurs while the embryo is still in the uterus. These observations may reflect fundamental metabolic differences during embryonic development between the parasitic B. malayi and free living C. elegans.
We used propidium iodide (PI) to stain the host chromatin and the bacterial DNA, and used an anti-WSP (wBm Surface Protein) specific to Wolbachia to perform a fluorescent analysis of the Wolbachia segregation in the Brugia embryo. The anti-WSP revealed that the punctate staining obtained with PI corresponds to the Wolbachia DNA and does not stain mitochondrial DNA (i.e. Fig. 2). During fertilization, Wolbachia appear distributed throughout the oocyte completing meiosis, although more concentrated in the vicinity of the maternal chromatin in the anterior pole (Fig. 2A, B). This may reflect an interaction with the microtubule spindle as observed at earlier stages (i.e. Fig. S1). As early as the pronuclei migration stage, Wolbachia dramatically relocalize towards the posterior pole of the egg (P0, Fig. 2 C to E, n>50).
We then followed Wolbachia segregation patterns in the two rounds of division following pronuclear fusion to create a diploid P0 zygotic nucleus. P0 division produces anterior -identified by the localization of polar bodies at the anterior surface (Fig. 1A and Fig. 2F)- and posterior localized, AB and P1 blastomeres respectively. Wolbachia always asymmetrically localize in P1 (Fig. 2E, F, n>50). P1 divides to produce EMS and P2 daughter blastomeres (Fig. 1A). Wolbachia preferentially segregate to the posteriorly localized P2 blastomere. P2 divides to produce a dorsal C blastomere (Fig. 2JI) and a posterior P3 blastomere (Fig. 2JII). Most of the Wolbachia segregate to the C blastomere and a minority segregate to the P3 blastomere.
Although during the first zygotic division, the majority of Wolbachia preferentially localize in the P1 blastomere, a few localize to the AB blastomere. Division of the AB blastomere produces daughter blastomeres ABa and ABp (Fig. 2G). Wolbachia titer in these descendants is variable but always lower than in the direct descendants of the P1 lineage (Fig. 2H, I).
In the 12-cell embryo (Fig. 2J) P2 has divided to give dorsally C (Fig. 2JI) and the posterior P3 (Fig. 2JII). Most of the Wolbachia are in C, followed by P3. The titer in the AB descendants, MS or E, although variable, is always lower than that in C and P3. In the next rounds of divisions, C divides asymmetrically to give muscle cells and hypodermal cells (Fig. 1A) [33]. However without specific lineage markers, following the descendants of specific blastomeres is not possible after the 12-cell stage. Fortunately morphogenesis, as revealed by phalloidin-based actin staining, in the early B. malayi embryo is strikingly similar to that of C. elegans (Fig. 2KI to KIV) [34]. At this stage, the cellular proliferation is over, and circumferential actin bundles in hypodermal cells contract, transforming the round-shaped embryo into a worm. In both B. malayi and C. elegans, the hypodermis is composed of intercalated dorsal cells, lateral and ventral cells organized like the C. elegans hyp7, seam and P cells (Fig. 2KI). Under the dorsal and ventral hypodermal cells run the muscle quadrants from the anterior to the posterior (Fig. 2KII,III). Deeper in the embryo, the embryonic neuroblasts, pharyngeal and gut cells can also be localized (Fig. 2KIII, IV). Assuming that the lineage of B. malayi is very similar to the established lineage of C. elegans, we could verify the vertical transmission of Wolbachia in the embryonic blastomeres. Our observations of early stages showed that the Wolbachia were diluted out in the AB descendants, and in the MS and E blastomeres (Figs. 1A, 2G to JII). During morphogenesis, we constantly found the Wolbachia enriched in the dorsal posterior hypodermis, and absent from nearly all anterior cells including neuroblast and pharyngeal cells, as well as muscle and gut cells (Fig. 2KI to KIV, n>30). This also implies that other asymmetrical segregations of Wolbachia occur in the C lineage to exclude them from the C-derived muscle cells and concentrate them in the hypodermis.
Because the germline precursor P4 has already divided into Z2 and Z3 during morphogenesis and because these cells are often difficult to identify (Fig. 3A to C) we used the anti-histone H3K4me2. It has been demonstrated that in C. elegans as well as in Drosophila melanogaster, a subset of nucleosome modifications (dimethylation on lysine 4 of histone H3 and acetylation on lysine 8 of histone H4) are absent from germline precursors but present in all the other blastomeres. In C. elegans embryos, H3K4me2 marks all the blastomeres, including P4, until it divides symmetrically into Z2 and Z3 [35]. Lysine 4 dimethylation on histone H3 is involved in transcription regulation and its absence reveals transcriptional quiescence [36]. In B. malayi however, we found embryos containing only one H3meK4-negative cell, suggesting that the putative P4 blastomere reorganizes its chromatin architecture to enter transcriptional quiescence prior to division (Fig. 3D,E). We found half Wolbachia-infected and half non-infected putative P4 blastomeres or putative Z2/Z3 germline cells (Fig. 3D to L). It is likely the embryos with uninfected blastomeres are males and those with infected blastomeres are females.
We also found the average number of Wolbachia did not differ from early to mid embryogenesis (70+/−12 (n = 10)) and is in general agreement with the average number detected in microfilariae using qPCR [24]. Although this may suggest that asymmetric relocalization prior to division is the major cause for specific enrichment of a given blastomere, it does not rule out a possible stimulation/repression of bacterial replication due to asymmetrically localized cues. In fact, we noticed that in P2, prior to division, Wolbachia appeared as doublets in the enriched antero/dorsal pole while as individual units in the posterior/ventral pole. This was supported by a WSP staining surrounding the doublets, suggesting active replication (Fig. S2A,B).
B. malayi adults, like any secernentean nematodes, have a simple and conserved anatomy. Non segmented, these worms have body walls organized in four longitudinal rows of hypodermal chords secreting the cuticle, and separated by four muscle quadrants. Lateral chords contain the excretory-secretory canal, while dorsal and ventral chords surround the nerves. They lack circulatory and respiratory systems. A nerve ring located around the pharynx constitutes the central nervous system (Fig. S3D, E). The triradiate pharynx is connected to the gut. Females have two gonads starting in the posterior and ending in the anterior vulva, while the male has one gonad starting in the anterior and ending in the posterior cloaca.
To determine Wolbachia distribution in adult tissues, we stained whole-mount fixed adults either with DAPI or live specimens with the vital dye Syto11 ([37], cf. Fig. 4 and Fig. S4). Detailed measurements of body and tissues features have already been reported [15].
The two female distal gonad arms located in the posterior (several millimeters separate the two ovaries distal ends) coil along an anterior-posterior axis (Fig. 4F). The ovaries lead anteriorly to the two uteri that are also coiled around one another (i.e. Fig. 4B), and filled with sperm that has migrated in their distal parts (Fig. 4A, E). The amount of sperm is variable between females and may reflect the time of observation after copulation. Oogenesis begins at the distal region of the ovaries and as the oocytes mature they are pushed proximally and are fertilized in the distal part of the uteri, where developing embryos are present in the proximal regions (Fig. 4A, C, D; Fig. S5). Thousands of microfilariae are released in the lymph of the host through the ovejector that ends the vulva where the uteri meet, in the anterior part of the female, specifically at the level of the posterior pharynx (Fig. 4A, B, C; Fig. S3A to C). The male gonad consists of a testis posterior to the pharynx of the nematode, connected to the sperm duct which in turn leads to a widened seminal vesicle where mature sperm is stored (Fig. 4A; Fig. S4). The gonad ends in the cloaca where two specialized spicules are used for mating (Fig. S4).
The intestine is a thin empty tube connected at the anterior to the pharynx, and at the posterior to the ventral rectum close to the posterior tip in both male and female worms (Fig. 4A, F; Fig. S6). Gonads and intestine fill the pseudocoel contained within the body wall.
Lateral chords are prominent in Brugia. They are formed through fusion of hypodermal cells producing a syncytial chord surrounding the secretory-excretory canal and in between muscle quadrants [32]. The lateral chords project a thin layer of cytoplasm over the muscles to connect the dorsal and ventral chords. These dorsal and ventral chords, containing the dorsal and ventral nerves, are very thin and difficult to observe by differential contrast microscopy but can be revealed by staining the surrounding muscles (Fig. S3F to H). Uteri appear closely apposed or embedded in the lateral chords (Fig. S7). The body wall possesses a slight periodicity, and hypodermal chords and muscle quadrants turn several times around the central axis of the worm between the two tips. The male posterior end is coiled three to four times in the region encompassing the spicules, probably to ensure a better grip during mating (Fig. S4).
In both male and female worms (n>30), Wolbachia concentrate around the two rows of hypodermal nuclei in lateral chords. While most worms displayed two infected lateral chords (Fig. 5A to D), in about 40% only one of the two chords was infected (the left or the right chord). We also observed worms with half of a chord infected (Fig. 5E, F), sometimes in a mosaic pattern (Fig. 5I, J), possibly reflecting the earlier mosaicism in Wolbachia segregation during embryonic development. In the chords, the Wolbachia like the nuclei are located in the basal part, while the circumferentially oriented actin bundles are in the apical compartment (Fig. 5K to L′). In addition, no adult was found with both lateral chords completely lacking Wolbachia, suggesting that Wolbachia localization in these chords may be essential for worm survival. Conversely, the Wolbachia are completely absent from the intestinal cells, the somatic gonad (gonadal contractile sheath cells and epithelium, Fig. S6) and the muscle quadrants (Fig. 5). It is hard to clearly draw a conclusion on the presence of Wolbachia in the nervous system, due to the low quality of the nerve ring chromatin staining with PI but the bacteria appear either absent or at very low titer in this organ (Sup. Fig. 3D, E).
We stained the secretory-excretory canals with phalloidin in non-fixed worms, to avoid the overwhelming signal coming from actin-rich tissues (i.e. muscles). Despite variable results, we have been able to locate the secretory-excretory pore close to the mouth (Fig. 6A). By increasing the phalloidin signal in fixed animals, we could reveal the lumen of the canal (Fig. 6B to G, between arrowheads). We found propidium iodide spots present in the lumen of the canal in variable amounts, similar to those in infected parts of chords (Fig. 6B to D), but also in the lumen of the canal in non-infected parts of chords (i.e. Fig. 6E to G). We confirmed these observations on worms kept alive, by visualizing the secretory-excretory canal with the fluorescent marker resorufin. This marker is a substrate of P-glycoproteins and multidrug resistance associated proteins, localized in the apex of polarized cells, involved in excretory processes (cf. [38]). Resorufin concentrates in the chords and neighboring tissues before its excretion via the canal. We combined it with the DNA vital dye syto11 (Fig. 6H to M). Altogether these data suggest that adult Brugia may secrete/excrete low numbers of Wolbachia into their host.
In the female and male germinal zones, oogoniae or spermatogoniae are partially surrounded by an actin rich membrane connected to the actin-rich central rachis at the distal part of the ovary (Sup. Fig. 5A, C, D). These germ cell nuclei initially organized in a syncitium, then cellularize and detach from the central rachis while migrating proximally towards the uterus or the sperm duct (Fig. S5G to J). All female germ cells are infected and contain an average of 35+/−6.8 Wolbachia (n = 13; Fig. S5A to C) in the most distal part of the ovary, before complete cellularization. This suggests a high replication rate of the bacteria in the mitotic region of the ovary (Fig. S5A to C, G, H). More mature oogoniae are located more proximally in the ovary and contain slightly more bacteria (49+/−10, n = 8). Mature oocytes are fertilized when encountering sperm in the distal uterus to give zygotes.
Surprisingly, analogous studies in the male germline revealed no bacteria at any stage of spermatogenesis (n = 3 males). Although it is difficult to distinguish bacteria from mature sperm chromatin, cytological observations at earlier stages of spermatogenesis left no doubt on the absence of Wolbachia in the male germline (i.e. Fig. S5D to F, I, J).
Characterization of the transmission mechanisms, distribution pattern and titer of Wolbachia in the germline and soma is of primary importance for understanding the biology of the interaction between Wolbachia and its host B. malayi. We found in B. malayi a high Wolbachia/ host nuclei ratio in early embryogenesis and in the adult lateral chords. Wolbachia were also concentrated in the female germline but absent from the male germline (summarized in Fig. 7A). To understand the origin of this distribution pattern, we examined Wolbachia segregation during early embryogenesis. Despite variability in the Wolbachia titer among embryos of the same stage, the bacteria were present in all embryonic blastomeres until about the 6-cell stage, but greatly enriched in the posterior pole, following fertilization. Our data indicate that Wolbachia are present in the most posterior blastomeres, P2 and EMS at the 4-cell stage, followed by C and P3 in all embryos at the 12-cell stage, that is to say both male and female embryos. Presence in C is the main source of transmission to the hypodermis, while maintenance in P3 and subsequently P4 ensures transmission to the germline (Fig. 7B).
During the mitotic proliferation of oogoniae, association of Wolbachia with the mitotic spindle is likely used to ensure an even segregation in the female germline. Interactions of Wolbachia with the host microtubules has been well documented in arthropods (i.e. [39]). During fertilization, the primary enrichment could be due to a passive mechanism involving the deep cytoplasmic flow oriented towards the posterior [40]. It is also well established that in C. elegans the sperm entry induces partitioning of the evolutionary conserved cell polarizing factors PARs [40], [41]. Brugia PAR orthologs and their downstream effectors may be responsible for keeping the Wolbachia in the posterior. Asymmetric segregation has been described as a common feature of Wolbachia localization in arthropods, such as in Drosophila germline and somatic cells, in wasp species and mosquito germlines [42], [43]. Whether the mechanisms used to enrich the posterior of the embryos, to subsequently invade the germ cell precursors, is due to convergent evolution or to common developmental pathways remains to be determined. The latter may provide new targets in anti-Wolbachia based therapies in filariasis. An evolutionarily conserved mechanism for posterior localization maybe supported by the mode of invasion of the chords. Instead of invading the AB lineage, the main source of hypodermal blastomeres, Wolbachia utilize the posterior C blastomere. It is tempting to speculate that during the evolution of the Wolbachia-nematode interaction, the bacteria followed conserved posterior determinants to ensure transmission to the germline, and subsequently acquired an affinity for the C blastomere and its ectodermal derivatives.
In secernentean nematodes, fixed lineages contribute to different types of tissues. This raises the intriguing question of the mechanisms underlying the transmission of the bacteria to the proper differentiated blastomeres. The segregation pattern of Wolbachia in the early embryo could result from sensing regulatory networks patterning the embryo, to asymmetrically segregate and proliferate. From the 2 to 12-cell stages, their transmission pattern is very similar to the expression pattern of the C. elegans homeodomain protein PAL-1, required for the C-lineage expression [44]. In a second phase, C-derived ectodermal derivatives could trigger Wolbachia proliferation [45]. Likewise, germline-specific factors are likely to play a role in segregation in the P germline lineage. It is significant however that some embryos appeared devoid of Wolbachia in the P4 blastomere and the Z2/Z3 germline cells, implying a possible loss of Wolbachia after establishment of the P3 blastomere and before establishment of Z2/Z3. What happens to these Wolbachia remains unclear, they may all segregate into the P4 sister, the D blastomere, to be diluted out without replicating for instance, as observed in the descendants of AB, E or MS. We hypothesize that embryos with and without Wolbachia in the P4 blastomere are female and male embryos respectively, since we find a ratio identical to the equal sex ratio described in larvae of the closely related Brugia pahangi [46]. Our data do not allow us to rule out possible mechanisms of transcellular invasion from neighboring tissues to non-infected germ cells at later stages, or later loss of Wolbachia in the male germline when Wolbachia are initially observed in the germline precursors. In Brugia, sex determination if of XX/XY type, and males possess a heterogametic pair of chromosomes [47]. Wolbachia may sense the gender of the embryo prior to the establishment of the P4 blastomere. Such an early sensing of the embryo's gender may involve interactions with a X- chromosome dosage compensation machinery [48]. In C. elegans for instance, this protein complex is active as early as the 30-cell stage, before formation of the P4 blastomere [49], [50].
Based on lessons from C. elegans genetics and cell biology on embryonic cell fate establishment, immunofluorescence and RNAi techniques on relevant Brugia orthologs should help us to understand the molecular mechanisms of Wolbachia transmission.
We observed the highest bacteria titer in the adult lateral hypodermal chords. Some worms however possess partially infected chords, or one chord lacking Wolbachia. This observation may explain the wide range in Wolbachia load between individual worms as measured by qPCR [24]. Observations at the embryonic level revealed few infected hypodermal cells, mainly posterior-dorsal, in which the bacteria multiplied (i.e. Fig.2K). A common feature of secernentean nematodes is that during development, hypodermal cells fuse creating a syncytium in the adult [51]. Since we did not find nuclei in the adult ventral and dorsal chords, it is likely that all the hypodermal dorsal and ventral nuclei migrate laterally in Brugia. Thus Wolbachia may spread through fusion of infected with uninfected hypodermal cells. The developmental timing of hypodermal fusion is unknown. However since we observed Wolbachia invasion of lateral chords in young adults (Fig. 5I, J), hypodermal fusion in Brugia is likely to have occurred during larval or young adult stages. This would predict a dramatic increase in Wolbachia titer per host nuclei during larval stages and early adult, and is supported by quantitative PCR data [25]. Hence, the selective pressure for somatic invasion must be less important than in the germline, since vertical transmission from a single hypodermal cell of a chord is theoretically sufficient to ensure a successful colonization. We have performed our cytological studies in young adults while B. malayi can live for many years. This could explain the presence of these partially non-infected chords. It would be interesting to determine whether aged adults contain fully infected lateral chords.
The nematode hypodermal chords have been shown to play a fundamental function in the metabolism of stored carbohydrate and protein synthesis, as well as the uptake of nutrients via the transcuticular route [51], [52]. Wolbachia may participate in these lateral chord functions. Moreover it has been recently demonstrated that part of the stress response induced in Wolbachia-depleted B. malayi by tetracycline is an upregulation of amino acids synthesis and protein translation, suggesting an initial compensation for the lack of Wolbachia [53]. Depletion of Wolbachia with antibiotics has been shown to reduce the production of microfilariae and to affect embryogenesis [53]–[55]. This last study also shows that tetracycline treatments result in Wolbachia degeneration in the germline and embryos prior to Wolbachia loss in the lateral chords. Defects in embryogenesis may still be due to a perturbed metabolism starting at the level of the hypodermal chords rather than a direct effect on the few Wolbachia present in embryonic hypodermal and germ line cells. Support for this idea comes from the fact that Wolbachia are present exclusively in the female germ line and not in the male germ line. Thus while Wolbachia are transmitted vertically through the female germline, they may not be necessary for germline development. Selective pressure in the germline may be greatly reduced in endosymbionts such as Wolbachia that are involved in metabolic mutualism. In contrast, Wolbachia are parasitic in many arthropod species and accordingly have a profound influence on host germline function [43]. Second, we observed an increase in embryo size during development suggesting nutrient uptake from the uterus. Third, both in live specimens and in whole mount fixed adults a tight association between lateral chords and the uterus was observed, arguing for a role of the chords in supplying the production demands of microfilariae (i.e. Fig. 5H and Fig. S7).
It has been established that Wolbachia release in the human body, presumably from degenerating worms, has a crucial impact on the development of river blindness and lymphatic filariasis, by activating the host immune response [3], [12], [13], [14]. We detected variable amounts of Wolbachia in the secretory-excretory canals, present in the chords, even in non-infected regions of the chords. This suggests that in addition to degenerating worms, live adults may release Wolbachia, through the excretory pore. PCR analysis of short term in vitro culture supernatant was unable to detect Wolbachia DNA, although 90 Wolbachia proteins were detected in ES products [56]. Furthermore, immunohistochemistry of O. volvulus does not detect the abundant release of Wolbachia into the surrounding tissues [57]. Nevertheless, low numbers of Wolbachia and/or their products may be released via the excretory/secretory canal as previously hypothesized [58], and act as an additional source of immunostimulatory components that contribute to the known innate and adaptive immune responses typical of filarial infections [59]–[61].
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10.1371/journal.pcbi.1005805 | General principles of binding between cell surface receptors and multi-specific ligands: A computational study | The interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion, and environmental responsiveness by representing the initial steps of cell signaling pathways. These interactions can be spatial-temporally regulated when different extracellular ligands are tethered. The detailed mechanisms of this spatial-temporal regulation, including the competition between distinct ligands with overlapping binding sites and the conformational flexibility in multi-specific ligand assemblies have not been quantitatively evaluated. We present a new coarse-grained model to realistically simulate the binding process between multi-specific ligands and membrane receptors on cell surfaces. The model simplifies each receptor and each binding site in a multi-specific ligand as a rigid body. Different numbers or types of ligands are spatially organized together in the simulation. These designs were used to test the relation between the overall binding of a multi-specific ligand and the affinity of its cognate binding site. When a variety of ligands are exposed to cells expressing different densities of surface receptors, we demonstrated that ligands with reduced affinities have higher specificity to distinguish cells based on the relative concentrations of their receptors. Finally, modification of intramolecular flexibility was shown to play a role in optimizing the binding between receptors and ligands. In summary, our studies bring new insights to the general principles of ligand-receptor interactions. Future applications of our method will pave the way for new strategies to generate next-generation biologics.
| In order to adapt to surrounding environments, multiple signaling pathways have been evolved in cells. The first step of these pathways is to detect external stimuli, which is conducted by the dynamic interactions between cell surface receptors and extracellular ligands. As a result, recognition of extracellular ligands by cell surface receptors is an indispensable component of many physiological or pathological activities. In both natural selection and drug design, the presence of multiple binding sites in extracellular ligand complexes (so-called multi-specific ligands) is a common strategy to target different receptors on surface of the same cell. Such spatial organization of ligand binding sites can elaborately modulate the downstream signaling pathways. However, our understanding to the interactions between multi-specific ligands and membrane receptors is largely limited by the fact that these interactions are difficult to quantify and they have only been successfully measured in a very small number of cases in vivo. Using a simple computational model, we can realistically simulate the binding process between specially designed multi-specific ligands and membrane receptors on cell surfaces. This study therefore provides a useful pathway to unravel basic mechanisms of ligand-receptor interactions and design principles for new drug candidates.
| Integral membrane proteins are the sensors of extracellular signals, including cell-cell and cell-substrate interactions, as well as environmental queues. Their interactions with extracellular ligands initiate most of the intracellular signaling pathways [1, 2], while the dysregulation of these receptor-initiated signaling pathways leads to various diseases, such as cancers [3], and greater than 60% of current drugs are designed to target specific cell surface receptors [4, 5]. In many cases, the extracellular ligands are spatially organized into multivalent/multicomponent assemblies. These assemblies, called multi-specific ligands, contain multiple receptor binding sites and are able to target different cell surface receptors simultaneously. For instance, multiple low affinity interactions involving influenza virus hemagglutinin trimers are required for effective recognition of cell surface glycoproteins on bronchial epithelial cells [6, 7]. Another example is the presence of multiple receptor-binding sites in all classes of antibodies (e.g., bivalent, tetravalent and decavalent in IgG, IgA and IgM isotypes, respectively). The overall apparent binding affinity is enhanced due to the synergy between the multiple binding interactions within an immune complex [8], which is commonly referred to as 'avidity' [9]. Although the general properties and biochemical consequences of binding avidity are well appreciated [10], the detailed mechanisms and underlying energetic contributions remain unclear. The importance of several regulatory factors such as the competition between different binding sites and the conformational flexibility in a complex has not been quantitatively evaluated. Moreover, one of the most promising strategies in drug design is the development of synthetic chimeric ligands [11], in which multiple natural ligands are artificially fused to target their cognate receptors on the surfaces of specific cell types. These multi-specific targeting reagents can improve the efficiency and selectivity of drug-based therapies; therefore, enhanced understanding of the basic principles underlying the interactions between multi-specific ligands and their receptors is critical for continued development of new therapeutic strategies.
Computational approaches allow for a wide range of variables to be systematically examined and a variety of different methods have been recently developed to study the interactions between ligands and cell surface receptors. The chemical kinetics of receptor-ligand binding was first described by simple mathematical models [12, 13], and have been improved by consideration of the spatial confinement of membrane receptors [14, 15]. Reaction rates between receptors and ligands were modulated to explore the impact on binding of the reduction in the dimensionality of receptors confined to a two-dimensional bilayer. However, information such as spatial heterogeneity and molecular details were not be captured. In contrast, atom-based molecular dynamic simulations [16] were used to provide full structural descriptions of both ligands and receptors [17–20]. The primary limitation of these atomistic simulations is the large computational overhead, which prohibits these approaches from being applied to multivalent molecular complexes and slower biological processes (i.e., microsecond time scales or longer) [21]. Other hybrid models have been introduced to bridge the gap between mathematical modeling and atomic simulations [22–25], which depend on coarse-grained representations of molecules [26–30], or reduced degrees of freedom in their movements, as captured by lattice-based simplifications [31–33]. For instance, Miguez and colleagues applied Langevin dynamics for ligand-receptor interaction [34], in which ligands and receptors were represented as simplified spherical particles. However, the theoretically “scaled units” used for the simulation parameters in this study are difficult to directly correlated with biological binding properties in a quantitative manner. Moreover, for all the methods described above, the principles of binding avidity between cell surface receptors and multivalent ligands has not been systematically evaluated.
Here we present a computational model to investigate the general mechanism of interactions between membrane receptors and their ligands. The spatial organization of multimeric or multi-domain receptors/ligands is explicitly incorporated into the model. Molecules possessing multiple binding sites are referred as multi-specific receptors/ligands in the following text. In particular, each binding site in a multi-specific receptor/ligand is represented in our coarse grain model as a rigid body, with the binding site explicitly defined on its surface. The overall modeling system contains a large number of individual receptors and ligands, and their diffusion and binding kinetics are simulated by a kinetic Monte-Carlo algorithm. All parameters in the simulation, such as diffusion constants and binding rates, are constrained within biologically relevant ranges. By varying the number of binding sites and the affinity of each binding site, our simulations demonstrate that the overall binding is cooperatively strengthened when multiple binding sites are spatially tethered. Interestingly, this positive coupling effect is reduced in the regime of strong individual binding affinities. Furthermore, by varying the concentrations of receptors on cell surfaces, we illustrate that the cell specificity of ligand binding is highly sensitive to the binding affinity. Finally, by altering the conformational fluctuations within a multi-specific receptor/ligand, we show that molecular flexibility plays an important role in modulating the binding between receptors and ligands. Taken together, our computational model provides insights into both basic mechanisms of ligand-receptor interactions and design principles for new drug candidates. These considerations are especially relevant given the extensive commercial interest in development multi-specific biologics for the treatment of a wide range of clinical indications.
We recently developed a rigid-body (RB) based model to simulate molecular binding in cellular environments [35]. This model has now been enhanced to study the binding interactions between cell surface receptors and soluble ligands. Specifically, the plasma membrane is represented by the bottom surface of a three-dimensional simulation box, the receptor is represented by a rigid body (i.e., cylinder) on the plasma membrane (Fig 1a) and the space above the plasma membrane represents the extracellular region. In the three-dimensional extracellular region, each ligand monomer is simplified as a spherical rigid body with a given radius. To delineate the binding interface, a functional site is defined on the surface of each ligand, as well as the top of each receptor (Fig 1a). Binding between two molecules is triggered by two criteria: 1) the distance between functional sites of two molecules is below a predefined distance cutoff; and 2) the relative orientations of the two engaging molecules fall within specific ranges. In contrast to ligands that can randomly diffuse in bulk solvent with three translational and three rotational degrees of freedom, the diffusion of receptors on the plasma membrane surface are confined. This confinement allows each receptor to rotate only about the axis normal to the plasma membrane, and restricts diffusion to two-dimensional translational movements in the plane of plasma membrane.
To test the relation between the binding avidity of a multi-specific ligand and the affinity of its individual binding sites to receptors, three scenarios were implemented using the above rigid-body model. In the first scenario, receptors A (red) and receptors C (yellow) are placed on cell surface, while ligands B (green) and ligands D (blue) are separately placed in the 3D extracellular region as monomers (Fig 1b). In the second scenario, a ligand B is tethered together with a ligand D in the extracellular region. It is referred as a multi-specific ligand BD in the following text. The multi-specific ligand BD is represented by two tethered rigid bodies with a binding site of B and a binding site of D on each of their surfaces (Fig 1c). Finally, in the third scenario, a higher-order assembly is represented, which contains two ligands B and two ligands D. This assembly is referred to as the multi-specific ligand B2D2 in the following text. The multi-specific ligand B2D2 is represented by four tethered rigid bodies with two binding sites for B and two binding sites for D on each of their surfaces (Fig 1d). Each multi-specific ligand in the second and third scenarios is simulated as a soluble entity in the extracellular region. Additionally, to capture the contributions of conformational flexibility, binding sites in a multi-specific ligand are allowed to undergo small translational and rotational fluctuations around their mean positions and orientations.
Given the concentration of each molecular species and the type of simulation scenario, the dynamics of the modeling system is simulated by a kinetic Monte-Carlo algorithm, starting from an initial random configuration. In each simulation time step, molecules are first selected at random to model stochastic diffusion; diffusion of membrane-bound receptors are confined to the plasma membrane, while extracellular ligands are free to diffusion throughout the volume of the simulation box. The acceptance ratio of diffusion movements for each molecule is determined by its diffusion coefficient, which is different for soluble ligands and membrane confined receptors. A 2D periodic boundary condition is applied for membrane receptors. In the extracellular region, periodic boundary conditions are imposed along X and Y directions, while in the Z direction, free ligands are not allowed to move below the plasma membrane at the bottom of the simulation volume. Any ligand moving beyond the top of the simulation box is reflected back. Binding is triggered if both distance and orientation criteria between any receptor and ligand are satisfied. The probability to trigger the association is determined by the association rate kon. In contrast, the dissociation between a ligand-receptor pair is described by a probability that is calculated by association rate and binding affinity: Poff=koffΔt=C0kone−ΔG0Δt, in which koff is the dissociation rate, Δt is the simulation time step, C0 is the standard unit of concentration and ΔG0 is the binding affinity. After a ligand binds to a receptor, the ligand-receptor pair moves as a single unit on plasma membrane. If the ligand contains multiple binding sites, the entire assembly binds together and diffuses with the receptor, such that the remainders of the vacant binding sites in the assembly are accessible for binding by other plasma membrane restricted receptors. The above diffusion-reaction process is iterated until the system reaches equilibrium in both Cartesian and compositional spaces.
The basic simulation parameters, including time step and binding criteria, were adopted from our previous study [35]. Other crucial parameters were chosen from ranges typical for proteins. Each subunit or domain in a multi-specific ligand is represented by a spherical rigid body with radius of 5 nm. For a receptor, the radius of the cylinder is also 5nm, while the height is 10nm. The translation diffusion constant of a soluble ligand monomer is taken as 100μm2/s and the rotational coefficient as 5° per ns [36], while the translation diffusion constant of a multi-specific ligand is 50μm2/s and the rotational coefficient is 1° per ns. The diffusion of membrane receptors restricted to the plasma surface is much slower, with a translational constant of 10 μm2/s and rotational coefficient of 1° per ns. The on-rate for protein association was calibrated to 108M-1s-1, a relatively high value, to accelerate the simulation. This value is in the typical range of diffusion-limited rate constants, in which association is guided by complementary electrostatic surfaces at binding interfaces [37]. Finally, a wide range of binding affinities, from 5RT to 13RT, was tested, corresponding to dissociation constants between millimolar (mM) and micromolar (μM). Binding of many membrane proteins such as the T-cell receptor (TCR) and T cell co-modulatory molecules are within this range [38]. It is worth mentioning that, although our simulations did not correspond to any specific biological systems due to the lack of sufficient experimental data, it is possible that we can detect some parameters and integrate them into our simulations in the future. For instance, the diffusion constants of membrane receptors on cell surfaces control the kinetics of ligand binding. They can be measured by Total Internal Reflection Fluorescence (TIRF) microscopy by tracking the trajectory of each receptor [39]. Moreover, binding is also affected by the concentrations of ligands and receptors which can be approximately determined by experiments such as flow cytometry [40].
We first investigated how the spatial organization of a multi-specific ligand affects binding between its individual binding sites and their receptors when their affinities are in different ranges. For the spatial organization of a multi-specific ligand, three different simulation scenarios, described in the methods, are used. In all cases, the binding affinities between receptors and ligand binding sites were varied. In order to exclude other factors that can influence binding, such as receptor concentrations in the plasma membrane (i.e., cell surface density), the same size of simulation box and the same number of ligand binding sites were assigned in all three scenarios. Consequently, 100 receptors A and 100 receptors C were placed on a 100nm×100nm cell surface (plasma membrane). In the first scenario, 100 monomer ligands B and 100 monomer ligands D were placed in a 100nm×100nm×50nm cubic box above the cell surface. In the second scenario, 100 tethered ligands BD were placed in the box. In the third scenario, 50 assemblies of B2D2 were placed in the box. Therefore, the total number of binding sites, and B and D ligand modules are the same in all there scenarios.
The binding affinity between receptor C and ligand D was fixed at -9kT in all three scenarios, while different affinities between receptor A and ligand B were examined. Fig 2a and 2b show the simulation results of the first scenario. In Fig 2a, more interactions between receptor A and ligand B were observed when their binding affinities were stronger. In contrast, the number of interactions between receptor C and ligand D were very close in all simulations, consistent with the invariant binding constant. The results of the second and third scenarios are plotted in Fig 2c to 2f. Similar to Fig 2a, 2c and 2e show more interactions between A and B are formed as the binding affinity increases. However, the numbers of interactions in the second and third scenarios are much higher than the monomer scenario due to the increase of binding avidity. Furthermore, distinct from Fig 2b, 2d and 2f shows that although the affinities between C and D in all simulations are the same, they form very different numbers of interactions. These results indicate that the interaction between receptor C and ligand D can be affected by the interaction between receptor A and ligand B, when ligands B and D are tethered. Overall, our simulations indicated that avidity can enhance binding/occupancy and cause coupling effects between different binding sites.
To systematically test the effect of avidity and coupling between different binding sites, we simultaneously changed both binding affinities between receptors A and ligands B (AB), and between receptors C and ligands D (CD). The overall results are illustrated in Fig 3a to 3f as two-dimensional contour plots for all three scenarios. The AB and CD binding affinities are indexed along x axis and y axis, respectively. As shown in Fig 3a, when B and D are unlinked, the numbers of AB interactions do not change with CD binding affinity. Similarly, in Fig 3b, the numbers of CD interaction do not change with AB affinity. Therefore, as expected, the binding of ligands B and D with their respective receptors are independent in the first scenario. In contrast, the diagonal distributions of contours in the second scenario (Fig 3c and 3d) suggest that the AB interaction and the CD interaction are correlated with each other. Secondly, comparing with Fig 3a and 3b, the overall contours are shifted to red with the only exception in the high affinity regions. These results demonstrate that if two types of binding sites are tethered in a multi-specific ligand, the binding to their corresponding receptors will be mutually affected (i.e., coupled). The overall binding will be positively enhanced when their individual affinities are not too strong. Moreover, comparing Fig 3e and 3f with Fig 3c and 3d, when ligands B and D are spatially organized as B2D2 in the third scenario, the regions containing largest number of receptor-ligand interactions (the red regions) in their simulated contours are further enlarged. Therefore, the binding between receptors and multi-specific ligands is further strengthened when the avidity of the ligands is increased from BD to B2D2. It is notable that if both AB and CD affinities are strong (the upper right corners in Fig 3), the binding of a multi-specific ligand BD OR B2D2 with its receptors A and C will be weakened relative to its binding as a monomeric B or D. Possible mechanisms underlying this behavior are considered in the discussions. Finally, the overall binding, the number of both AB and CD interactions, are plotted in S1 Fig as two-dimensional contour plots for all three scenarios under all combinations of AB and CD affinities. The figure shows that there are optimal combinations of AB and CD affinities in the second and third scenarios. The optimal combinations of affinity maximize the total interactions, while these combinations only exist for multi-specific ligands in which binding sites are spatially coupled.
The concentrations of receptors and ligands were fixed in the last section, with the surface density of receptor A equal to that of receptor C. In practice, however, the cell surface expression levels of various proteins vary dramatically. Similarly, the expression levels of a given protein can vary considerably in different cell types. These variations have great functional significance. For example, mutations leading to the overexpression of epidermal growth factor receptor (EGFR) are present in a number of cancer cells and are thought to contribute to the malignant phenotype [41]. Therefore, in this section we examined the consequence of altering the relative concentrations of the two receptors on the plasma membrane. We hypothesize that different concentrations of one receptor type may affect the binding of the other receptor type when their ligands spatially coexist in a single tethered assembly. Specifically, we examined a situation in which the total number of receptor A (100) was fixed in simulations, while the total number of receptor C was varied from 0 to 100. Ligand B and D were tethered as describing in the second scenario. The affinities of both AB and CD interactions were fixed at -7kT and -9kT, respectively. The simulation results, presented in Fig 4a, show that higher surface densities of receptors C lead to more interactions between receptor A and its ligand, although the binding rate and affinity were the same as those used in the original simulations. When no receptor C is present, the number of AB interactions is equivalent to those formed in the first scenario, in which ligand B and D are separated as monomers. In contrast, the presence of more receptors C increases the AB interactions. We speculate that higher surface density of receptors C provides more surface-bound ligand-receptor complexes due to the interaction between receptors C and ligands D. Because ligand B and D are tethered together, the vacant binding sites of ligands B in these surface-bound complexes provide higher local concentration and better orientation to receptors A. These results demonstrate that expression levels of membrane receptors play an important role in regulating the interactions with their multivalent ligands.
In addition to the above simulations, we further changed the affinity between receptor A and ligand B, while maintaining the affinity between receptor C and ligand D. The same simulations were carried out in which ligand B and D are tethered together under different surface densities of receptors C. In short, the concentration of receptor A was fixed, but its affinity with ligand B changed. In contrast, the concentration of receptor C changed, while its affinity with ligand D was fixed. Fig 4b shows how AB interactions change along with receptor C concentrations under different affinities between receptor A and ligand B. The “X index” of the figure is the number of receptors C on cell surfaces. The relative increment of AB interactions, as receptor C changes from 0 to a given concentration, is recorded in the Y axis. The relative increment of AB interactions is calculated as (NABC−NAB0)/NAB0, in which NABC is the number of AB interactions under a given concentration of receptor C, while NAB0 is the number of AB interactions without receptor C on cell surfaces. It offers a quantitative way to measure the relative increase of AB interactions towards cells that express higher levels of receptor C than normal. Therefore, the relative increment of AB interactions defines the specificity that ligand B recognizes the cells overexpressing receptor C. As a result, in addition to the positive correlation between receptor C concentration and increment of AB interactions, which has already been illustrated in Fig 4a and 4b further shows that the lower binding affinity between receptor A and ligand B enhances the relative increment of AB interactions, given the higher surface concentrations of receptor C. The figure thus indicates that, although relatively small numbers of interactions are formed between A and B when their binding affinity is low, these interaction are more sensitive to the change on concentration of receptor C. In another word, when a variety of ligands are exposed to cells with overexpressing surface receptors, our simulations suggest that the ligands with reduced affinity have higher specificity to distinguish these cells relative to the ligands with higher affinity. This is consistent with a previous study using Langevin dynamic simulation [34]. Of particular relevance is a recent experimental report using a chimera containing epidermal growth factor (EGF) as a cell targeting element and interferon-α-2a (IFNα-2a) as an activity element to initiate signal transduction [42]. This study demonstrated that mutations in the chimera that reduced the affinity between IFNα-2a and IFNα receptor 2 (IFNAR2) can bind to cells expressing EGFRs, while the same mutants of IFNα-2a monomers cannot. Moreover, the chimera afforded higher selectivity to cells expressing larger number of EGFRs relative to cells expressing fewer EGFRs. This EGFR-dependent effect is more evident when the affinity between IFNα-2a and IFNAR2 in the chimera was reduced. These experimental observations are quantitatively captured by our computational simulations. Consequently, the negative correlation between binding affinity and cell specificity suggested by our studies brings new insights to the rational design of macromolecular compounds as ligands to stimulate important cellular functions.
The overall binding properties of a tethered multi-specific ligand can be affected by variables/degrees of freedom other than stoichiometry and affinities. For instance, the precise spatial arrangement and overall architecture of tethered ligand assembly can have significant impact on the overall binding behavior. These topological constraints are naturally embodied in our rigid body modeling approach, and in principle, all possible combinations of spatial arrangement can be enumerated with a given number of binding sites and ligand types. To simplify the analysis, only representative models were considered. Specifically, four different topologies were examined for the multi-specific ligand assembly B2D2, as shown in the bottom row of Fig 5. In the first two models, binding sites of all four ligands are oriented in the same direction (downwards), but the relative packing arrangement between ligand B and D is different. In the remaining two models, two groups of binding sites are organized in an anti-parallel fashion (two upwards and two downwards). In the third model, the same types of ligands are in different orientations, while in the fourth model, the same types of ligands are in the same orientations. The binding of all four types of complexes were simulated. The average numbers of interactions between ligands and receptors are plotted as striped bars in Fig 5 for each topology, while the deviations from the average number of interactions are plotted as black bars. The first two models in the figure show similar averages and deviations. In contrast, the last two models show much lower number of interactions. When all binding sites are in the same direction, they can simultaneously engage multiple receptors. Notably, the fourth model has a higher deviation than the third model, although the average numbers of interactions are very similar, suggesting that the anisotropic arrangement of binding sites leads to higher fluctuations in binding. The asymmetry in ligand complexes cause they can only bind to one type of receptors at the same time. This releases the coupling effect between two types of receptors, which further results in the instability and higher fluctuations in binding. Considering that the total binding sites of a multi-specific ligand are the same for all four models, the differences of binding among different model reflected from our simulation results therefore indicate that, in addition to the number of binding sites, the spatial organization of ligands also plays an important role to regulate binding between receptors and ligands.
Another important feature is the internal flexibility of a tethered ligand assembly, with flexibility defined as the small range of conformational fluctuations around a given topological arrangement. The flexibility of a tethered ligand assembly is incorporated in our simulation as spatial variations of each binding site relative to its equilibrium position. Specifically, within each simulation time step, an additional operation was added to generate a small random perturbation along three translational and three rotational degrees of freedom for each binding sites in a ligand assembly. Fig 6a gives the comparison between a simulation in which flexibility was incorporated (red) and a simulation without flexibility (black). The third scenario of ligand model B2D2 (Fig 1d) was used for both simulations and identical values were assigned for all other parameters. The figure shows that flexibility not only leads to more interactions on average, but also causes larger fluctuations in the number of interactions during the simulation. We also changed the maximal ranges of translational and rotational perturbations in each simulation step to adjust the flexibility of the entire ligand assembly. The maximal range within which each ligand binding site in a tethered assembly can be randomly rotated was set from 0 to 30 degrees with an interval of 10 degrees. The maximal range of translational perturbation was set from 0 to 6nm, with an interval of 2nm. Simulations were generated for all combinations and the interactions between ligands and receptors were calculated. The overall results are presented in Fig 6b as a three-dimensional histogram. The figure suggests that binding of a multi-specific ligand assembly is promoted by the appropriate selection of its intramolecular flexibility. If the molecule is overly flexible; however, binding can be negatively affected. Overall, these studies illustrate that topology and flexibility of a multi-specific ligand can be fine-tuned to optimize its binding with cell surface receptors.
Binding of multivalent molecules is a ubiquitous phenomenon in living cells. For instance, intracellular signaling platforms such as apoptosome contain multiple subunits to amplify downstream signal transduction [43]. The cascade of these signaling pathways is initiated by the activation of various cell surface receptors through binding with their extracellular ligands. Similarly, the engagement of cell surface receptors and ligands can be spatially and temporally regulated when extracellular ligands are organized into multivalent assemblies, called multi-specific ligands. To probe the functional role of this multi-specificity in ligand-receptor interactions, a rigid-body based computational model has been developed. The model attempts to realistically simulate the process of binding between receptors and ligands to the greatest extent. To achieve this goal, our previously reported diffusion-reaction algorithm has been enhanced. The new method confines the diffusion of membrane receptors to a two-dimensional surface, while ligands are free to diffuse above the cell surface in three dimensions. The multi-specificity of ligands was implemented by incorporating spatial tethering of different binding sites, which takes both homogeneous and heterogeneous oligomerization into account. Although the model is coarse grained, basic structural details for each receptor and each binding site in a ligand can be captured, such as rotational diffusion and geometric constrains during binding. Finally, the proper selection of model parameters such as molecular size, diffusion coefficient and binding affinities, maximize the biological utility of our simulation results.
One of our major observations is the coupling effect between avidity of multiple binding sites and affinity of individual binding sites. When the individual binding affinities are weak, ligands dissociate from receptors relatively soon after they associate. The life-time of a ligand-receptor interaction is much shorter than the average time of ligand diffusion before the ligand can encounter with its binding partner. In another word, a ligand is very likely to diffuse away from surface before it can rebind to the receptor that it originally binds to. In this case, the tethering of different binding sites causes little effect. Therefore, no coupling was observed between binding sites in a multi-specific ligand (the lower left corners in Fig 3). When the binding affinities increase to the intermediate range, on the other hand, the interaction between one binding site in a multi-specific ligand starts to affect the binding of other sites. More specifically, the life-time of this intermediate-strength interaction is comparable to the average time of diffusion a ligand takes before it can encounter with its binding partner. We speculate that this further causes the following effects. Firstly, binding between any binding sites in a multi-specific ligand with their receptors simultaneously brings other binding sites in the ligand close to cell surface. In another word, the local concentration of different binding sites is increased due to the spatial tethering. Therefore, if the ligand dissociates from its original receptor, it will bind to other receptors with higher probability. Similar phenomena have been observed in the multivalent lectin-glycoconjugate interactions [44]. Moreover, binding between any binding sites in a multi-specific ligand with their receptors causes the entire tethered assembly to diffuse together with the receptors on cell surface, which provide better orientation of other binding sites in the ligand to their receptors. Additionally, a multi-specific ligand will leave the plasma membrane only if all its binding sites dissociate from their receptors, which effectively decrease the overall dissociation rate. Consequently, we observed that the interaction between one binding sites in a multi-specific ligand strengthens the binding of other sites. This effect is more evident when the avidity in a multi-specific ligand is increased.
However, when the binding affinities further increase to the very strong range, interestingly, we found the negative coupling between different binding sites in a multi-specific ligand (the upper right corners in Fig 3). This may be the consequence of the following reason. The life-time of a strong ligand-receptor interaction is much longer than the average time of ligand diffusion before the ligand can encounter with its binding partner. Moreover, the two-dimensional diffusions of receptors on plasma membrane are much slower than the three-dimensional diffusions of proteins in solvent environments. As a result, the binding of different sites in a tethered ligand to their cell surface receptors becomes competitive. In another word, if one site of a ligand binds to its target receptor, it will take very long time for other unbound sites in the same ligand to find their target receptors, as the entire ligand-receptor complex diffuses on cell surfaces. Meanwhile, the long dissociation time of the ligand-receptor complex, due to the strong affinity prevents other sites from diffusing back into the three-dimensional extracellular space and binding to their corresponding receptors. It needs to be noted that this kinetic trapping effect does not change the overall thermodynamics of the system. Therefore, when simulations reach infinite time, we should observe that most ligand sites can ultimately bind to their receptors due to the strong affinities. However, the negative coupling due to the kinetic issue has more functional relevance in the context of understanding the role of spatial organization in multi-specific ligands, because these biological processes occur within the physiologically meaningful time scale. It is reasonable to assume that both increase of encounter probability and decrease of overall dissociation of a multivalent complex are proportional to its internal structural flexibility, which has been validated by the further simulations. Our computational studies therefore provide quantitative insight into the general principles governing the binding between multivalent ligands and surface-bound receptors. In the future, additional features will be integrated into the model for the application to specific biological systems. For instance, more specific information about structural fluctuations between different binding sites of a ligand and the binding constants of wild-type or mutated ligand-receptor interactions can be achieved by higher-resolution simulation methods such as Brownian dynamic simulation [45–54]. These data can be fed into the current rigid-body based model by the further development of a multi-scale framework. Finally, it is worth mentioning that in some cases, binding of one ligand-receptor pair might change the affinity of other ligand-receptor pairs due to the conformational changes of these molecules upon binding. This effect is called allosteric regulation [55]. However, since molecules were simplified by rigid-bodies, the conformational changes within each ligand and receptor cannot be reflected by our model. Therefore, the impacts of allosteric regulation on ligand-receptor interactions were not taken into account here. The principles revealed in this study are purely based on the spatial organization of multi-specific ligands.
Future applications of our model include the design of multi-specific ligands to recognize specific cell types based on the differentiated expression levels of their surface receptors. There exist large ranges of expression level for membrane receptors in different types of cells. For instance, expression of immune receptors on the surfaces of different T cells are highly variable, such that a wide spectrum of antigens can be targeted [56]. In cancer biology, specific mutations lead to the overexpression of certain receptors, such as cell adhesion molecules on membrane [57], which is a hallmark to distinguish tumor cells from normal cells [58]. Therefore, understanding the quantitative relation between ligand binding specificity and receptor expression level is important to maximize drug efficacy and minimize off-target drug toxicity. If a ligand is monomeric, its binding probability depends only on its concentration and the expression level of its target receptor. Interestingly, by linking the ligand into a dimeric complex in which the second ligand subunit binds to a receptor with stable expression on cell surface, we show that the binding specificity of the first ligand not only depends on the expression level of its target receptor, but is also modulated by the binding affinity of the second ligand. These results provide insights to the practical strategies of next-generation drug design. By generating multi-specific ligands with design principles based on binding affinity, topology of binding sites and expression levels of their cognate receptors, we will be able to control the selectivity of these ligands for specific cell types. Conjugating these ligands with traditional cancer drugs may enable delivery to the target tissue with a much higher selectivity and reduced off-target effects [59]. Similarly, the incorporation of T cell receptor-specific recognition modules into tethered ligand assemblies may allow for the selective induction or suppression of disease-relevant T cells [60]. The selectivity associated with such reagents may reduce the extensive side effects associated with nearly all biologics-based immunotherapies, which elicit global immune modulation of the entire T cell repertoire [61]. The practical development of such ligand complexes could pave the way for a new generation of engineered immunotherapies.
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10.1371/journal.pntd.0007022 | Widespread circulation of West Nile virus, but not Zika virus in southern Iran | West Nile virus (WNV) and Zika virus (ZIKV) are mosquito-borne viral infections. Over the past few decades, WNV has been associated with several outbreaks involving high numbers of neuroinvasive diseases among humans. The recent re-emergence of ZIKV has been associated with congenital malformation and also with Guillain–Barre syndrome in adults. The geographic range of arthropod-borne viruses has been rapidly increasing in recent years. The objectives of this study were to determine the presence of IgG specific antibodies and the genome of WNV and ZIKV in human samples, as well as WNV and ZIKV genomes in wild-caught mosquitoes in urban and rural areas of the Hormozgan province, in southern Iran. A total of 494 serum samples were tested for the presence of WNV and ZIKV IgG antibodies using ELISA assays. One hundred and two (20.6%) samples were reactive for WNV IgG antibodies. All serum samples were negative for ZIKV IgG antibodies. Using the multivariable logistic analysis, age (45+ vs. 1–25; OR = 3.4, 95% C.I.: 1.8–6.3), occupation (mostly outdoor vs. mostly indoor; OR = 2.4, 95% C.I.: 1.1–5.2), and skin type(type I/II vs. type III/IV and type V/VI; OR = 4.3, 95% C.I.: 1.7–10.8 and OR = 2.7, 95% C.I.: 1.3–5.5 respectively, skin types based on Fitzpatrick scale) showed significant association with WNV seroreactivity. We collected 2,015 mosquitoes in 136 pools belonging to 5 genera and 14 species. Three pools of Culex pipiens complex were positive for WNV RNA using real-time reverse transcription polymerase chain reaction (rtRT-PCR). ZIKV RNA was not detected in any of the pools. All WNV ELISA reactive serum samples were negative for WNV RNA. In conclusion, we provided evidence of the establishment of WNV in southern Iran and no proof of ZIKV in serum samples or in mosquito vectors. The establishment of an organized arbovirus surveillance system and active case finding strategies seems to be necessary.
| In recent decades mosquito-borne viruses have reached and adapted to new habitats, and now they can be found in nearly all continents. Facilitated goods transportation, live stock exchange, people travelling more easily, and most importantly world climate alterations, might be some of the reasons for this mosquito habitat spreading. Emergence of WNV in North America, Europe, and most Mediterranean countries like Turkey, Greece and Israel is evidence of this spreading. Furthermore, emergence and re-emergence of some of these mosquito borne viruses in new areas may be accompanied with changes in their pre-known pathogenesis. Re-emergence of ZIKV in the South Pacific and America from 2007 to 2016 was accompanied with an increase in neurovirulent diseases and congenital malformations. In this study, we evaluated the presence of WNV and ZIKV via serological and genome detection in human samples and mosquitoes (viral genome analysis) from southern Iran. This region is on the coast with a warm and tropical climate suitable for inhabitation and expansion of the vectors harboring these two viruses. We caught a large spectrum of mosquitoes from these areas. After classification, we analyzed the mosquitoes’ pools for WNV and ZIKV genomic RNA. Our results showed that 20.6% of the studied human samples were IgG reactive to WNV while no antibodies against ZIKV were detected. We found WNV RNA genome in three mosquitoes’ pools. The genomic analysis was negative for ZIKV in both human and mosquito samples. Based on the results WNV is notably circulating in southern Iran; while no evidence of ZIKV infection in people or circulation in any of the vectors was observed.
| West Nile virus (WNV) and Zika virus (ZIKV) are mosquito-transmitted viruses from the genus Flavivirus. The geosgraphic range of many arthropod-borne viruses (arboviruses) has increased in recent decades, due in part to changing global climatic conditions, as well as increasing global travel of both humans and domestic animals [1, 2].
In 2007 the first major, but mild, ZIKV outbreak was reported on Yap Island in Micronesia in the western Pacific Ocean [3]. Before 2007, cases of ZIKV infection were detected only sporadically with mild symptoms in humans and because of that, ZIKV has been neglected since its discovery in 1947. However, the ZIKV disease outbreak in French Polynesia during 2012–2014 was accompanied by a high prevalence of Guillain-Barre syndrome in adults [4]. In addition, the ongoing ZIKV epidemic in Brazil has been associated with congenital infection, an unusual number of neonates with microcephaly and other central nervous system malformations [5, 6]. In 2015, the ZIKV epidemic spread from Brazil to 60 other countries and territories. Yet, active local virus transmission and cases of imported ZIKV infections are occurring all over the world [7–9].
ZIKV originally circulates in a sylvatic transmission cycle between non-human primates and forest-dwelling mosquito vectors. Humans are only accidentally infected in this sylvatic cycle. But after ZIKV adaptation to an urban cycle involving humans and domestic mosquitoes, humans are the primary amplifying hosts during epidemics. Several mosquito species have been found to be naturally infected with ZIKV, including some belonging to the genus Aedes (Ae.), such as Ae. aegypti, Ae. albopictus and Ae. Furcifer [10, 11]. As most studies have shown, Aedes mosquitoes are considered the primary ZIKV vectors; however, transmission of ZIKV may involve mosquitoes of other genuses, since the virus has also been isolated from Culex (Cx.) quinquefasciatus, Anopheles (An.) coustani and many other mosquito species in nature [12–14]. The range of natural hosts and vectors could expand through the virus spread and evolution. Any countries where Aedes spp., especially Ae.aegypti and Ae. Albopictus mosquitoes are present, have a high potential for geographic expansion of ZIKV [11].
WNV is the most widespread member of the Japanese encephalitis virus complex in the world. This virus expanded its range from a small area of sub-Saharan Africa to almost all continents in the last 25 years [15]. Several outbreaks involving high number of neuroinvasive disease cases have been reported among humans over the past few decades [16–18]. The enzootic cycle of WNV is maintained among ornithophilic mosquitoes and birds, whereas mammals, including humans and horses, are accidental hosts and since mammals do not develop sufficient viremia to support the transmission, they are dead-end hosts. The primary mosquito vectors are members of the Culex spp.; mostly, Cx. pipiens, Cx. univittatus, Cx. antennatus and Cx. vishnui complex. Infection with WNV is asymptomatic in most cases, while in approximately 20% of cases, infection results in West Nile Fever (WNF) and in less than 1% of cases it results in acute West Nile Neuroinvasive Disease (WNND) [19, 20].
Iran is located in the southeast part of the Middle East and it has been extremely affected by climate changes, favoring introduction of new mosquito-borne diseases or increasing their transmission rate. A recent study shows low invasive density levels of Ae. albopictus into the Sistan and Baluchestan province, southeastern Iran [21]. There is no obstacle for this potential primary ZIKV vector population to grow and expand in the near future. Furthermore, ZIKV positive serology has been reported in our neighboring country, Pakistan [22]. Although the potential risk of ZIKV introduction to Iran exists, no studies have been done on ZIKV infections in vector mosquitoes or in humans.
Serological surveillance data suggest human and animal exposure to WNV in some provinces of Iran as well as in some of our neighboring countries [23–26]. Recently, WNV RNA has been detected in mosquitoes’ pools of Ochlerotatuscaspius and Cx.pipiens specimens collected from wetlands in the northwest and north of Iran respectively [27, 28].
The objectives of this study were to determine the presence of IgG specific antibodies and the genome of WNV and ZIKV in human samples, as well as WNV and ZIKV genomes in wild-caught mosquitoes in urban and rural areas of the Hormozgan province, in southern Iran.
The study was conducted in urban and rural areas of the Hormozgan province located in southern Iran that comprises a total area of 70,697 km2 (Fig 1). Based on the 2016 general census data, there are 1.7 million people living in this province. It has hot, humid long summers and relatively arid and mild winters with limited rainfalls.
From September 2016 to June 2017, a total of 494 leftover serum samples were collected after an agreement with the governmental public laboratories at four major province counties, Khamir, Bandar Abbas, Bashagard and Jask (Fig 1). Sample size was calculated by the expected proportion of positive samples, based on previous studies performed in Iran [24, 29] using 0.05 level of significance resulting in a minimum of 300 samples needed. The samples belonged to people who used the laboratory services for various purposes, for example: routine check-ups, obtaining health certificates, follow ups of their chronic diseases, etc. At the time of initial sample collection, a verbal consent was obtained from the participants. For each serum sample, basic demographic information including age, sex, place of residence, skin type or complexion and occupation was obtained from the records prepared at the time of specimen collection. The skin complexion was identified using the Fitzpatrick skin type scale that identified six different numerical classification schemes for human skin color, ranging from type I being the fairest with the lowest scores to type VI being the darkest with the highest scores [30, 31]. Based on that we categorized the skin complexion into three groups; type I/II, type III/IV, and type V/VI. This data was collected to determine if non-protected exposure of the skin to the sun and to uncovered environment has a role in susceptibility to mosquito bites. The occupations were further grouped in seven main categories: child/student, house wife, office employee, freelancer, fisherman/sailor, worker and retiree. For statistical analysis these seven groups were allocated into three major categories based on where they performed their activities: mostly indoor, usually indoor and mostly outdoor. The sera were stored at -70°C.
Approval of the study protocol was received by the Ethics Committee of the Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, which waived the need for written consent for collection of leftover sera.
Mosquitoes were caught from several locations in four counties of Hormozgan from March 2016 to March 2017 using fixed white curtain and light traps. To minimize the sampling bias due to the increasing mosquitoes’ abundance, nets were changed every night. The insects were collected by a portable aspirator and transported to the lab where they were frozen in a −70°C freezer. The mosquitoes were placed on chilled tables and classified based on their morphological characteristics into different species and sexes. Well identified mosquitoes pooled according tocollection site, species and day of collection, into groups of one to 65 individuals, were immersed in RNase blocking solution and placed into screw-capped cryo-vials, stored and transported in liquid nitrogen gaseous phase to the Clinical Virology department at Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran for virological analysis.
Commercially available ELISA kits (ZIKV Euroimmun IgG ELISA kit, catalogue number: EI 2668–9601, and WNV Euroimmun IgG ELISA kit, catalogue number: EI 2662–9601, EUROIMMUN AG, Lübeck, Germany) were used to detect IgG antibodies against ZIKV and WNV. Each commercial kit includes positive and negative control samples. The assays were carried out at the Clinical Virology department. For each sample, a ratio of the extinction value of the control or patient sample over the extinction value of the calibrator was calculated according to the manufacturer’s instructions. Specimens with a value of ≥ 1.1 were considered positive for ZIKV IgG or WNV IgG antibodies. A value of ≥ 0.8 and < 1.1 was considered as an equivocal result and a value <0.8 was determined to be negative. The negative results were further categorized into three subgroups named negative (≥ 0.51 ≤ 0.79), low negative (≥ 0.21 ≤ 0.5), and very low negative (≥ 0.05 ≤ 0.2) to reflect the different background binding levels. All samples with borderline results were tested twice and samples with positive or equivocal results were grouped as reactive.
All samples considered reactive for WNV IgG antibodieswere evaluated with a commercial indirect immunofluorescence test (IIFT) kit (catalogue number: FI 2662–1005, EUROIMMUN AG, Lübeck, Germany) following the manufacturer’s instructions.
Mono species mosquitoes” pool were placed in poly propylene 15-mL tubes with 2 ml of DMEM medium containing 5% fetal bovine serum. Six 4-mm diameter dense glass beads were added to each tube, and mosquito pools were homogenized by hand shaking for about 2 min and then vortexing for 30 seconds. Mosquito homogenates were centrifuged for 5 min at 2500 × g at 4°C and supernatants were collected. Viral RNA was extracted from 200 μl of each mosquitoes’ homogenate or patient sera using the High pure Viral RNA Kit (catalogue number: 11858874001, Roche Diagnostics, Mannheim, Germany) according to the manufacturer’s instructions.
Commercially available rtRT-PCR primer/probe kits (Path-ZIKV-standard and Path-WNV-standard kits, Genesig, Primer design Ltd, Cambridge, United Kingdom) were used to detect and amplify ZIKV RNA and WNV RNA. All patients’ serum samples that were found positive with ELISA or IFT and all mosquitoes’ pools were subjected to the test. The tests were performed using an Applied Biosystem step one plus real-time PCR machine (Applied Biosystem, CA, USA). Amplification of ZIKV and WNV RNAs took place in a 20 μL single-tube, Superscript III Platinum one-step quantitative RT-PCR system (Catalogue number: 12574018, Invitrogen, Carlsbad, CA). Reactions contained 10.0 μL of 2X RT/PCR reaction mix, 1 μL primers/probe mix, 0.4 μL Superscript III RT/Platinum Taq mix, 0.4 μL ROX reference dye, and 5 μL of extracted sample RNA or serially diluted positive control copy number that were provided by the kits. The cycling conditions consisted of one cycle at 50°C for 30 min, one cycle at 95°C for 5 min, and 45 cycles at 95°C for 10 s and 60°C for 1 min. The test conditions for detection of WNA RNA was the same as above except for the concentration of primers/probe that was as previously described [32].
All statistical analyses were conducted using IBM SPSS Statistics version 22 (IBM Corp, Armonk, NY). A P-value of less than 0.05 was considered to be statistically significant. Binary logistic regression analysis was used to determine the relationship between the variables and seroreactivity for anti WNV. Odds ratios and 95% confidence intervals (CI) were calculated for univariate association of various demographic risks correlating with antibodies to WNV. All variables or confounders with statistically significant association in univariate analysis were entered into a 3 steps multivariable logistic regression model, in order to assess the impact of the separate risk factors on the dependent variable. In each step non-significant variable was taken out from the model and calculations were continued after.
A total of 494 serum samples were tested for the presence of ZIKV and WNV IgG antibodies using ELISA assay. Results showed that 102 (20.6%) samples were reactive (equivocal or positive) for WNV IgG antibodies in the ELISA test, of these 102 samples, 96 were also found positive by IIFT. These 6 IIFT negative serum samples were seroreactive by ELISA (4 were positive and 2 had equivocal results). All serum samples were negative for ZIKV IgG. Pattern of corrected ELISA OD levels values for ZIKV and WNV ELISA tests are shown in Fig 2. The age range of participants was between 1 and 86 years old with a mean age of 33.89, SD = 15.6 years (95% C.I. of mean: 32.5–35.3), with Skewness and Kurtosis of 0.896 and 0.517 respectively. Distribution of study participants by demographic characteristics is shown in Table 1.
According to univariate logistic regression WNV seroprevalenceis significantly associated with age (45+ vs. 1–25; OR = 4.1, 95% C.I.: 2.2–7.4), gender (male vs female; OR = 2, 95% C.I.: 1.2–3.2), residential areas (Bashagard and Bandar Abbas vs. Bandar Khamir; OR = 2.2, 95% C.I.: 1.1–4.2 and OR = 2, 95% C.I.: 1–3.8 respectively), occupation (mostly outdoor vs. mostly indoor; OR = 3.7, 95% C.I.: 1.8–7.7), and skin type (type I/II vs. type III/IV, type V/VI; OR = 3.8, 95% C.I.: 1.6–9.3 and OR = 2.9, 95% C.I.: 1.4–5.8 respectively) (Table 2).
By multivariable logistic analysis, only age (45+ vs. 1–25; OR = 3.4, 95% C.I.: 1.8–6.3), occupation (mostly outdoor vs. mostly indoor; OR = 2.4, 95% C.I.: 1.1–5.2), and skin type (type I/II vs. type III/IV, type V/VI; OR = 4.3, 95% C.I.: 1.7–10.8 and OR = 2.7, 95% C.I.: 1.3–5.5 respectively) showed significant association with WNV seroreactivity (The degrees of freedom for the model were 10, 8, and 6 for steps 1 to 3 of regression analysis, respectively, Table 3).
Among 2,015 mosquitoes (995 females and 1020 males) belonging to 5 genera and 14 species, were screened for ZIKV and WNV infections (Table 4, S1 Table). ZIKV RNA was not detected in any mosquitoes’ pools. Three of Cx. pipiens complex pools were positive for WNV RNA. First and second pools consisted mainly of females collected in the districts of Eidar and Sardasht, county of Bashagard; while the third pool had four females collected in the Khargo district of Bandar Abbas county. All WNV ELISA positive serum samples were negative for WNV RNA.
In our study ZIKV serology was negative for all individuals, indeed there is no evidence for past or recent infections with ZIKV in southern areas of Iran. Moreover, two major ZIKV mosquito vectors, Ae. Aegypti and Ae. Albopictous, were not among the 5 genera and 14 species of our collected mosquitoes, even though, recent reports have shown the potential for the presence of Ae. albopictous in Iran [21, 33]. In addition, all mosquitoes’ pools were found negative for ZIKV RNA using rt-RT PCR. Despite the description of ZIKV infection in our neighboring country Pakistan [22] we could not find any evidence of ZIKV infection in the area. The global climate changes severely affecting southern Iran will increase the invasion of ZIKV major vectors and facilitate their adaptation to this new environment. Moreover, the risk of accidental import of ZIKV vectors at the major commercial ports in southern Iran should be also considered. Therefore, establishment of a continuous mosquito surveillance system and controlling of the vector’s population at their mass gathering points seems to be necessary.
In this study we estimated a seroprevalence of 20.6% in WNV ELISA IgG among the studied population. In addition, WNV RNAs were detected in three Cx. Pipiens pools that were collected from Bashagard and Bandar Abbas. Molecular evidence of WNV in mosquitoes accompanied to high IgG seroprevalence in the present study, suggests high WNV circulation in southern Iran. Previous studies performed in some provinces of Iran have shown seropositivity to WNV in humans (ranging from 1.3% to 17.96%), equine (ranging from 2.8% to 23.7%) and birds (15%); however, they did not identify any possible vectors [23, 24,34, 35]. An earlier study in northwest Iran showed the presence of WNV genome in Ae. caspius [36]. Based on the present results, it is highly possible that WNV outbreaks had occurred in the past; although, there is no previous documentation supporting this idea. Despite the existence and circulation of WNV, no clinical cases have been described in Iran, so far.
Previous reports from around the world demonstrated that Ae. Aegypti and Ae. albopictus mosquitoes possess the ability to transmit ZIKV naturally or experimentally, but there is controversies about the ability of ZIKV transmission by other mosquitospecies. Of the collected mosquitoes in the present study 358 (17.67%) and 63 (3.12%) were Cx. quinquefasciatus and Ae. vexans respectively. It is unclear whether these endemic mosquitoes could serve as vectors to spread ZIKV in Iran or not. Different studies including those of Diallo et al., [10]; Guedes et al., [12], and Elizondo-Quiroga et al. [13] indicate that ZIKV has in fact been isolated from Cx. quinquefasciatus and Ae. vexans in nature; however, other studies have shown that most Culex spp. are not able to transmit the virus under laboratory conditions [37, 38]. Therefore, vector competency studies should be conducted on these endemic mosquito vectors in order to determine the possible ability of these mosquito species to transmit ZIKV.
Some studies showed that serologic evaluation of ZIKV infection is affected by cross-reactivity between antibodies of other flaviviruses mostly dengue viruses, namely in the world endemic areas. Serological and PCR studies have confirmed the presence of dengue virus infections in Iran, but most of them described the infection in association with travelling to hyper endemic areas. Indeed, travelers may play an important role in the epidemiology of dengue virus infections in Iran [39, 40]. In recent studies, it has been demonstrated that the ZIKV Euroimmun ELISA is specific and reliable when compared with other standard serologic methods [41, 42]. Moreover, we also did not detect any reactivity near or above threshold of borderline or positive zone for ZIKV antibodies.
To determine the presence of antibodies against WNV we used two different serological tests, ELISA and IIF. For the most part the results were in agreement (from 102 ELISA reactive samples 96 also showed positive signal by IIF test). Our results in univariate logistic regression showed that, seroprevalence of WNV is higher in males. Indeed, people’s lifestylein the study region is traditional, with most men doing outdoor jobs while most married women stay at home doing housekeeping work and raising children. Therefore, men are more exposed to potential mosquito bites and getting infected because they spend most of the daytime outdoors. Recent studies have been shown a relationship between WNV prevalence and gender [43, 44]. Consistent with our results, there are no significant differences between living in rural or urban areas, because there is no lifestyle disparities between these two populations in this region of the country. Based on the potential of being exposed to the WNV vectors, we categorized the population into three large job groups; first, those who mostly stay indoors, second, those who usually stay indoors and third, those who mostly stay outdoors. As the results show, people with outdoors jobs have significantly higher seroprevalence of WNV in comparison with those who mostly or usually do indoor activities. Similar results have been observed in other countries [45].
Our study shows a significant association of WNV seroreactivity with increase in age. The observed rate was four fold higher in the people who are 45 or older compared to those who are below 25. Impact of age and WNV seroreactivity is consistent with most previous seroprevalence studies in other countries [43, 46], and may be related to higher probability of experiencing and being exposed to WNV among older individuals during their lifetimes.
One important finding of the study is the significant differences in WNV seroprevalence among people with different skin types. According to our data, the risk of WNV infections among individuals who have type III/IV or V/VI skin complexion are three to four times higher than individuals with the skin type I/II, respectively. This result may be related to non-protected exposure of the skin to the sun and to uncovered environment.
In univariate analysis a significant relationship was found between the residential area and WNV seroreactivity. People residing in the Bashagard and Bandar Abbas had the highest seroprevalence of WNV antibodies when compared with those living in Jask and Khamir. This finding may be related to a mild difference in environmental factors including altitude, distance to the nearest wetland area and normalized difference vegetation index (NDVI) of the Bashagard and Bandar Abbas counties in comparison with the Jask and Khamir counties, which may have an effect on the ecology of the vectors. Similar effects of these environmental factors have been observed in previous studies [47].
A limitation of the present study is that we looked for the presence of WNV and ZIKV RNA in the sera samples. However, some reports suggest that the copy numbers of WNV and ZIKV genome is higher in whole blood or urine in comparison to sera samples [48]; which may explain in part why no RNA was detected in the sera samples.
In conclusion, our results provide the evidence of the establishment of WNV in southern Iran with a seroprevalence of 20.6% and no proof of ZIKV presence in mosquito vectors or serology markers in humans. Moreover, people who mainly work at open areas and the elderly are at higher risk of getting infected by WNV. In these study areas even though some people showed positive serum results for WNV infection, there were no recorded human WNV cases. As elucidated before, most people in southern Iran have traditional lifestyles where some moderate fever or temporary rashes do not make them visit medical facilities. Other important factors that might obscure not only WNV cases but also other mosquito-borne infections in these studied regions and even in other parts of Iran are the lack of an organized arbovirus surveillance system and active case finding and reporting strategies. These results generated new knowledge that is critical to better understand the epidemiology of the infection and the ecology of the vectors in the region. Furthermore, these findings will help to establish the basis for developing a better surveillance system with improved active case finding and reporting strategies for these arboviruses.
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10.1371/journal.pntd.0007669 | A novel MALDI-TOF MS-based method for blood meal identification in insect vectors: A proof of concept study on phlebotomine sand flies | Identification of blood sources of hematophagous arthropods is crucial for understanding the transmission cycles of vector-borne diseases. Many different approaches towards host determination were proposed, including precipitin test, ELISA, DNA- and mass spectrometry-based methods; yet all face certain complications and limitations, mostly related to blood degradation. This study presents a novel method for blood meal identification, peptide mass mapping (PMM) analysis of host-specific hemoglobin peptides using MALDI-TOF mass spectrometry.
To identify blood meal source, proteins from abdomens of engorged sand fly females were extracted, cleaved by trypsin and peptide fragments of host hemoglobin were sequenced using MALDI-TOF MS. The method provided correct host identification of 100% experimentally fed sand flies until 36h post blood meal (PBM) and for 80% samples even 48h PBM. In females fed on two hosts, both blood meal sources were correctly assigned for 60% of specimens until 36h PBM. In a validation study on field-collected females, the method yielded unambiguous host determination for 96% of specimens. The suitability of PMM-based MALDI-TOF MS was proven experimentally also on lab-reared Culex mosquitoes.
PMM-based MALDI-TOF MS analysis targeting host specific hemoglobin peptides represents a sensitive and cost-effective method with a fast and simple preparation protocol. As demonstrated here on phlebotomine sand flies and mosquitoes, it allows reliable and rapid blood source determination even 48h PBM with minimal material input and provides more robust and specific results than other currently used methods. This approach was also successfully tested on field-caught engorged females and proved to be a promising useful tool for large-scale screening of host preferences studies. Unlike other methods including MALDI-TOF protein profiling, it allows correct identification of mixed blood meals as was demonstrated on both experimentally fed and field-collected sand flies.
| Leishmaniases belong among the most important and yet neglected vector-borne diseases, transmitted mostly by bite of female phlebotomine sand flies. To understand role of different reservoir hosts in the transmission cycles, it is important to determine blood meal sources of bloodfeeding females. Most of currently used methods face challenges due to tiny volumes of engorged blood, in case of mammals also enucleated, as well as quick progress of blood digestion which leads to rapid DNA and protein degradation. New approach towards blood source determination presented in this study is based on MALDI-TOF mass spectrometry that identifies unique peptide sequences of host hemoglobins, showing high precision and sensitivity together with a longer time period for successful host determination when compared to nowadays standardly used DNA sequencing. It was tested and verified on engorged phlebotomine sand flies from both laboratory colonies and natural endemic areas and also on Culex mosquitoes and shall be universal to hematophagous insects. Beside blood meal identification, it allows also the use of both morphological and molecular methods (DNA- or protein-based) for the species identification of the analysed specimen.
| Phlebotomine sand flies (Diptera, Phlebotominae) are tiny nocturnal dipterans of high medical and veterinary importance as they are involved in transmission of several arboviruses, bacteria and protozoans that affect human and animal health. Among these, parasitic protozoans of the genus Leishmania are of paramount importance as they are causative agents of leishmaniases, a complex of neglected diseases with clinical manifestation ranging from cutaneous to mucocutaneous and visceral forms [1] that annually affect around 13 million people [2]. For better understanding of transmission cycles between vectors and their hosts, it is necessary to precisely identify their blood meal sources as the knowledge of reservoir animals, possible hosts and transmission dynamics of diseases is important for setting properly targeted control measures.
Phlebotomine females mostly require at least one blood meal to finish a gonotrophic cycle and lay eggs [3]. Moreover, during their life span, females can produce several egg clutches, which increases their need for blood meal and thus the possibility of disease transmission [4]. Females suck on average very small volumes of blood (up to 1.0 μl) depending on the species [5,6]. After feeding, blood is enclosed in a peritrophic matrix in mesenteron, digested by hydrolytic enzymes, mostly trypsin- and chymotrypsin-like endoproteases, and the remnants are later defecated. It was demonstrated that the speed of blood digestion significantly varies among different species, the digestion times are affected by many factors such as temperature and blood meal volumes [7,8].
Tiny volumes of blood meals together with variations in blood digestion and defecation pace pose complications to successful host identification. First methods used for blood source determination were serological techniques like precipitin test [9,10] or ELISA [11,12], which is still used nowadays. Later, DNA-based molecular methods were developed and are widely applied since; for phlebotomine sand flies the most often used is sequencing of cytochrome b (cyt b) gene [13,14], although this approach is laborious and quite expensive. Therefore, several other molecular techniques were introduced in order to simplify and optimize the workflow, but most of them were still DNA-based approaches such as multiplex PCR, PCR-RFLP, and qPCR [15]. As an alternative to DNA techniques, two approaches utilizing liquid chromatography-mass spectrometry (LC-MS/MS) were designed. The combination of non-targeted shotgun proteomics and mass spectral libraries enabled the detection of blood remnants of vertebrate hosts in ticks even six months after feeding [16]. Later, blood meals were identified by LC-MS/MS in triatomine bugs in a first application of this method on field-collected arthropod disease vectors [17]. However, none of these proteomics-based techniques became routinely applied to other hematophagous insects, probably because being time-consuming and technically demanding.
MALDI-TOF MS protein profiling represents nowadays an established method for species determination in many groups of organisms. Beside others, it was successfully applied on many hematophageous insects that serve as vectors of important infectious diseases [18] including sand flies [19]. In addition, this method was recently shown as a promising tool for host blood determination of both laboratory-reared [20] and field-collected mosquitoes [21]. Nevertheless, the approach provided successful host identification only for freshly engorged females in a short time window of 1–24 h PBM. It appears that the analysis of protein profiles of engorged mosquitoes that dynamically change as the blood digestion proceeds cannot successfully overcome a typical drawback of many other blood meal identification methods: relatively short time after the blood meal intake when conclusive identification is possible.
In the present study we applied MALDI-TOF protein profiling to determine blood meals of experimentally engorged sand flies and more importantly, we introduced a novel approach for blood meal determination, which employs peptide mass mapping (PMM)-based MALDI-TOF MS analysis [22] of host blood from abdomen digested by exogenously added trypsin. Obtained peptides, typically fragments of host hemoglobins, were detected by PMM and then sequenced on MALDI-TOF mass spectrometer, resulting in correct and reliable identification of blood origin. The efficiency and specificity of the method was first tested on sand fly females experimentally engorged on rabbit and three rodent species and its practical usefulness was illustrated in a blind study using field-collected sand fly females. Moreover, the applicability of this method on other insects was demonstrated on Culex mosquitoes. PMM-based MALDI-TOF mass spectrometry of trypsin-digested host blood was proven to be an accurate and robust approach for blood meal identification, especially useful for high numbers of samples collected during field surveys. The method is cost-effective and fast, requiring minimal material input and simple sample preparation, and unlike MALDI-TOF MS protein profiling it allows conclusive blood meal identification later after the blood meal intake. Although here tested on phlebotomine sand flies, the application of this method may be extended to other blood-sucking insects. Moreover, the utilization of only dissected abdomen enables other complementary methods to use remaining body parts for species identification, including DNA-based methods and vouchering of mounted head and genitalia for purposes of later morphological reference.
Animals used in this study were maintained and handled in the animal facility of Charles University, Prague in accordance with institutional guidelines and Czech legislation (Act No. 246/1992 and 359/2012 coll. on Protection of Animals against Cruelty in present statutes at large), which complies with all relevant EU guidelines for experimental animals. All experiments were approved by the Committee on the Ethics of Laboratory Experiments of the Charles University and were performed under the Certificate of Competency (Registration Number: CZ 02432).
All experiments were performed using sand flies from laboratory colonies reared under standardized conditions [23] in the insectary of the Department of Parasitology, Faculty of Science, Charles University. Specimens of four species were used (country of origin of females used to establish the colony is given in brackets): P. (Euphlebotomus) argentipes (India), P. (Larroussius) orientalis (Ethiopia), P. (Larroussius) perniciosus (Spain), and P. (Phlebotomus) papatasi (Turkey). Females were experimentally bloodfed on different animals from our husbandry, which were anaesthetized by ketamine/xylazine during blood feeding. All specimens were stored in 70% high quality ethanol (Merck KGaA) at -20°C.
Females of P. perniciosus were bloodfed on five different hosts: European rabbit (Oryctolagus cuniculus), Syrian hamster (Mesocricetus auratus), multimammate rat (Mastomys natalensis), Neumann’s grass rat (Arvicanthis neumanni) and SKH1 mouse (Mus musculus). Engorged females were kept under standard conditions and collected at various time points: 1, 12, 24, 36, 48, 60 h post-blood meal (PBM). All specimens (three samples for each collection time point and every host) were air-dried and dissected; abdomens were homogenized in 50 μl of 25% formic acid (Merck KGaA) and grounded in 1.5 ml Eppendorf tube for 15 s using a manual BioVortexer homogenizer (BioSpec) with sterile disposable pestles. The obtained homogenate was shortly centrifuged at 10 000 g for 15 s and 2 μl of the supernatant were mixed in a new microtube with 2 μl of MALDI matrix. One μl of the mixture was spotted on a steel target plate in duplicate and air-dried prior to MALDI-TOF MS analysis. The MALDI matrix was prepared fresh as an aqueous 60% acetonitrile/0.3% TFA solution of sinapinic acid (30 mg/ml) (Sigma-Aldrich).
For whole blood measurements, blood of all tested animals was stored with heparin at -20°C. Two μl of blood/heparin solution were resuspended in 100 μl of 25% formic acid (Merck KGaA), shortly centrifuged and mixed with the MALDI matrix as described above.
Females of P. perniciosus were fed on rabbit, Syrian hamster, Neumann’s grass rat and SKH1 mouse, females of P. orientalis on rabbit only. Engorged females of both species were kept under standard conditions and collected 12, 24, 36, 48 and 54 h PBM (six specimens per host and time point).
Prior to PMM analysis specimens were air-dried, dissected and separated abdomens were homogenized in 1.5 ml Eppendorf tubes for 15 s in 50 μl of distilled water (Merck KGaA) using a manual BioVortexer homogenizer (BioSpec) with sterile disposable pestles. After 15 s centrifugation of the homogenate at 10 000 g, 10 μl of the supernatant was transferred to a new microtube containing 10 μl of 50 mM N-ethylmorpholine acetate buffer (pH 8.1; Sigma-Aldrich) and 100 ng of sequencing grade trypsin (Promega). The obtained mixture was incubated at 37°C for 20 minutes. After the digestion, 0.5 μl of the mixture was deposited on a MALDI plate in duplicate, air-dried and overlaid with 0.5 μl of MALDI matrix (aqueous 50% ACN/0.1% TFA solution of α-cyano-4-hydroxycinnamic acid; 5 mg/ml; Sigma-Aldrich).
Females of P. perniciosus were first bloodfed on SKH1 mice, but after they started to take the blood meal, the feeding was interrupted and partially fed females were immediately moved to a second cage to continue the feeding on rabbit. Only females visibly bloodfed on mouse were transferred to feed on the rabbit for second feeding. The females which probed the rabbit and were therefore expected to feed on both hosts were kept under standard conditions and collected 12, 24, 36, 48 and 54 h PBM. One part of these females (10 specimens for each time point) was subjected to host identification using MALDI-TOF MS protein profiling and the rest was analysed by PMM-based approach (5 specimens for each time point). The samples for both types of mass spectrometric analysis were prepared according to protocols described above.
Females of P. argentipes and P. papatasi were bloodfed on BALB/c mice, and P. orientalis on rabbit; all engorged females were kept under standard conditions and collected 24, 48, 60, 72, 84 and 96h PBM. Three specimens of each sand fly species per time point were analysed in parallel by PMM-based method and DNA sequencing. The samples were first prepared according to the protocol for PMM and the residual 40 μl of the homogenates were then used for DNA isolation. DNA was isolated using QIAamp DNA Mini Kit (Qiagen) following the protocol for Blood and Body Fluid Spin Protocol. Isolated DNA was amplified by PCR using the modified vertebrate-universal specific primers targeting a segment of mitochondrial DNA cyt b gene, cyt_bb1 (5’-CCA TCC AAC ATY TCA DCA TGA TGA AA-3’) cyt_bb2 (5’-GCH CCT CAG AAT GAT ATT TGK CCT CA-3’) [24]. PCR amplification was performed in total volume of 25 μl and amplified products were visualised on 1% agarose gel. The obtained products were purified by QIAquick PCR Purification Kit (Qiagen) and directly sequenced in both directions using the primers used for DNA amplification (ABI Prism BigDye Terminator Cycle Sequencing Ready Reaction Kit). For identification of the vertebrate host species, sequences were blasted against the GenBank database using BLASTn program.
Protein mass spectra were measured on an Ultraflex III MALDI-TOF spectrometer (Bruker Daltonics) within a mass range of 3–25 kDa and with external calibration using the Bruker Protein Calibration Standard I. Each spectrum represented an accumulation of 1000 laser shots (20×50 laser shots from different positions of the sample spot). The generated spectra were visualized and compared with FlexAnalysis 3.3 software. MALDI Biotyper 3.1 software was further employed for data processing and for database creation of host blood protein profiles. At least three spectra for each host were used for the creation of reference MSP spectra with the following parameters: a maximum peak number of 100, S/N ratio above 3, and a minimum intensity of 1% of the most intense peak. The desired peak frequency for MSP reference spectra was 70%.
Peptide mass mapping spectra were acquired on an Ultraflex III MALDI-TOF instrument in the mass range of 700–4000 Da and calibrated externally using a peptide standard PepMix II (Bruker Daltonics). At least two peptides per sample were selected for MS/MS sequencing using LIFT technology. MS/MS data were searched against the SwissProt20171124 database subset of vertebrate proteins using in-house MASCOT search engine (Matrix Science) with the following search settings; enzyme: trypsin, variable modification: oxidation (M), MS mass tolerance: 20 ppm, MS/MS mass tolerance: 0.6 Da, number of missed cleavages: 1. MS/MS spectra with a MOWSE score over the threshold of 25 (calculated for the settings used) were considered as reliably identified. Peptide sequences of Neumann’s grass rat hemoglobin were derived by de novo MS/MS sequencing. One MALDI peptide mass map (sample EME8) was also measured on a Solarix FT-ICR mass spectrometer (Bruker Daltonics) with mass accuracy below 1 ppm.
The accuracy and usefulness of blood source identification by PMM-based approach was tested in a blind study carried on field-caught engorged females collected during a field survey in Greece (July 2017). Sand flies were sampled using carbon-dioxide (dry ice) baited CDC light traps (John W. Hock) operated overnight (approx. 5 p.m.-7 a.m.) and placed inside or next to animal shelters. Engorged females were separated and examined under a stereomicroscope; in total, 54 individuals from Greece were included in the assay. All specimens were kept in 70% pure ethanol (Merck KGaA) after the collections and stored at-20°C for several weeks prior to further analysis.
Females were processed as followed: head and genitalia were slide-mounted in CMCP-9 mounting medium (Polyscience) and morphological identification was carried out using published morphological keys [25]. The species determination was confirmed by MALDI-TOF MS protein profiling [19] using our in-house database currently comprising reference spectra of 23 different sand fly species. Abdomens were subjected to host blood determination by PMM-based method and using cyt b gene sequencing according to protocols described above.
Laboratory-reared females of Culex (Culex) quinquefasciatus were bloodfed on BALB/c mice. The engorged females were collected at 12, 24, 36, 48, 60 h PBM and stored in 70% high quality ethanol (Merck KGaA) at -20°C (five females for each time point). The specimens were air-dried, dissected abdomens were homogenized in 200 μl of water and further prepared for PMM analysis according to the protocol described above for sand flies.
To evaluate the potential of MALDI-TOF MS protein profiling for blood meal source identification in phlebotomine sand flies, protein spectra of homogenized abdomens of P. perniciosus females bloodfed on five different animals from our husbandry (SKH1 mouse, Syrian hamster, Neumann’s grass rat, multimammate rat or rabbit) were analysed. The spectra obtained from engorged abdomens collected 12 h PBM exhibited a high degree of reproducibility for each host tested and matched with whole blood protein patterns of animals used for sand fly feeding, indicating that the abdominal spectra were specific according to host blood origin. The comparison of spectra from engorged abdomens of all hosts revealed that the protein profiles were host-specific and distinguishable according to the source of blood meal (Figs 1 and S1). The obtained spectra were rather simple, with two dominating signals of alpha- (15 kDa) and beta-subunit (16 kDa) of hemoglobin [26] that is one of the most abundant proteins of vertebrate blood.
Protein profiles of abdomens from P. perniciosus females bloodfed on five experimental hosts sampled every 12 h from 1 h until 60 h PBM were subjected to MALDI-TOF MS to assess the effectivity of the method for determination of host blood origin in the course of time post blood feeding. As shown in Fig 2, the abdominal spectra of freshly engorged females were identical and stable from 1 h to 24 h PBM. Changes of protein patterns are evident at 36 h PBM and were more significant at 48 h and 60 h PBM time points with practically no detectable signals of intact hemoglobins. These alterations occurred regardless of the blood source (S2 Fig), presumably due to ongoing digestion of host blood in sand fly gut. Based on these results, a reference database was created from spectra of engorged abdomens collected 12 h PBM. Three samples for each collection time point and every host were further analysed by MALDI-TOF MS (in total 90 specimens) and the acquired protein profiles were queried against this database. The database search yielded 100% correct determination of blood meal source for all 45 specimens collected until 24 h PBM. For 36 h PBM time point, only 6 of 15 females gave accurate identification of blood meal origin. Remaining nine individuals were misidentified because of spectra altered by proceeding blood digestion. As expected, the substantially changed protein profiles made successful identification impossible for females collected 48 h and 60 h after feeding.
As an alternative to MALDI-TOF MS protein profiling, peptide mass mapping (PMM) using MALDI-TOF MS was adapted for blood source determination in sand flies.
Peptide mass maps of trypsin-digested abdomens of P. pernicious females fed on four different vertebrates (rabbit, Syrian hamster, Neumann’s grass rat and SKH1 mouse) were found distinct, displaying several host-specific signals of alpha- and beta-hemoglobin peptides (Figs 3 and S3). As hemoglobin sequences of potential host animals differ, characteristic peptide fragments serving as a unique host signature could be easily generated by trypsin digestion for each animal (Table 1). The sequencing of the fragments by tandem mass spectrometry (MS/MS) and subsequent database searching allowed to determine amino acid sequence of these discriminating peptides and thus to provide the correct assignment of blood meal origin. Interestingly, MS/MS sequencing was also able to distinguish isobaric peptides (those having the same mass) originating from different hosts (Fig 4).
To evaluate the usefulness of PMM-based methodology for assignment of host blood origin, females of P. perniciosus fed either on rabbit, hamster, Neumann’s grass rat or SKH1 mouse and P. orientalis engorged on rabbit were tested. The specimens of both sand fly species were sampled at 12, 24, 36, 48, and 54 h after feeding. The spectra of all females collected until 24 h PBM consisted exclusively of peptides of host hemoglobin as shown on tryptic maps of P. perniciosus fed on rabbit and mouse (Fig 3) and also demonstrated for P. orientalis engorged on rabbit (Fig 5). At 36 h PBM, minor changes were observed and tiny signals of the peptide fragments originating from sand fly body were detected. These peaks became more visible 48 h PBM and were quite dominant in the spectra of females collected 54 h after feeding regardless of the blood source and vector species (Figs 5 and S4). Nevertheless, the presence of arthropod-related signals did not prevent successful determination of blood meal origin because host identification using PMM-based approach, in contrary to MS protein profiling, was not dependent on overall spectrum pattern.
In the proof of concept study focused on determination of blood meal source in large group of sand fly females, six specimens per condition depicted above were subjected to PMM-based MALDI-TOF MS, namely 30 specimens of P. orientalis engorged on rabbit and sampled at five different points in time after feeding, and 120 females of P. perniciosus bloodfed on four animals (30 samples per host). Using PMM method, the origin of host blood was properly identified for all 90 specimens (100%) up to 36 h PBM. At 48 h PBM, 24 of 30 females (80%) yielded unambiguous assignment of blood meal source and finally 50% correct identification (15/30) was achieved for females collected 54 h after engorgement.
Females of P. perniciosus were bloodfed consecutively on two host animals (first on mouse, then on rabbit) and collected 12, 24, 36, 48, and 54 h PBM. One part of the samples was subjected to host identification using MALDI-TOF MS protein profiling, the second one was analysed by PMM-based MALDI-TOF mass spectrometry.
MALDI-TOF MS protein profiling failed to determine both hosts used for the feeding in all cases, probably because of the spectra complexity. As shown on S5 Fig, the spectra of females engorged on two animals consisted of hemoglobin peaks of both hosts and represented a superposition of individual host protein profiles. However, when searching such spectra against the reference database, only identification of one host was achieved. One host species was determined for all 10 females collected 12 h PBM (mouse in nine cases, once rabbit), whereas at the time point of 24 h PBM, only 3 of 10 females were assigned with a mouse as blood source (S1 Table). At later time points, significant alterations of protein profiles related to blood degradation were observed and no hosts were determined since 36 h PBM.
Compared to MALDI-TOF MS protein profiling, PMM-based approach provided more promising results in terms of detection of multiple blood sources in sand fly females (S1 Table). The method successfully revealed both hosts in 9 of 15 (60%) females until 36 h PBM. For the remaining 6 samples, only a single host was identified (five times mouse, once rabbit). Just one blood source was determined for 5 females collected 48 h after feeding. Four of them were assigned with a rabbit host even though they definitely fed on mice as well as they were optically observed as partially engorged after first exposure to a mouse host. This finding might be ascribed to a small volume of mouse blood engorged or eventually to a different digestion rate of mouse and rabbit blood. Finally, the spectra of the specimens sampled 54 h PBM did not contain any signals of hemoglobin peptides and therefore no hosts were identified.
The effectiveness of PMM-based MALDI-TOF mass spectrometry for identification of blood meal origin was compared with DNA sequencing, which nowadays represents the conventional method for host determination in arthropod vectors. In the comparative experiment, females of P. argentipes and P. papatasi bloodfed on BALB/c mouse, and P. orientalis engorged on rabbit were analysed in parallel by PMM approach and DNA sequencing; the species included into this assay were selected upon their known different pace of blood digestion, that was demonstrated to be fastest for P. argentipes and slowest for P. orientalis [6].
Both tested methods successfully assigned hosts of all females collected 24 h PBM, but remarkable differences were observed between these methods at 48 h PBM (Table 2). For this time point, PMM correctly determined blood meal sources of all samples of P. papatasi and P. orientalis, however in case of P. argentipes, just one of three specimens gave an identifiable spectrum due to advanced blood degradation. On the other hand, sequencing of cyt b gene was even more affected by blood digestion and yielded correct host identification only for single female of each sand fly species. For specimens sampled 60 h after feeding, no hosts were identified by DNA sequencing, whereas 3 of 9 females (two P. orientalis, one P. papatasi) were assigned with blood meal sources using PMM method. At later time points (72, 84 and 96 h PBM) no host identifications were possible, either using cyt b sequencing or by PMM approach.
The practical usefulness of PMM-based MALDI-TOF mass spectrometric method for host determination in entomological surveys was assessed in a blind test aimed at field-collected sand flies. In total, 54 field-caught engorged females from Greece (collected in 2017) were included in the study. In addition to assignment of blood meal origin using PMM-based method, sand fly species identification was carried out by combination of morphological analysis and MALDI-TOF MS protein profiling using our database of sand fly reference spectra. Thus, both host and sand fly identifications using MALDI-TOF mass spectrometric analyses (Fig 6), by cyt b gene sequencing or morphology were performed from single sand fly specimens.
For Greek field specimens, PMM-based approach successfully determined blood meal origin in 52 out of these 54 females (Tables 3 and S2). The method failed only in two cases, probably because of low volume of engorged blood or due to advanced blood digestion. According to the results obtained using PMM method, the most common hosts were sheep (39), followed by goats (7), dogs (3), and hen (1). Furthermore, mixed blood meals were detected in two specimens as these females presumably fed on goat as well as on sheep (S6 and S7 Figs). To verify the accuracy of the results from PMM-based MALDI-TOF mass spectrometry, DNA was isolated from all 54 females and a segment of host cyt b gene was attempted to amplify. Probably because of low DNA amount, only 32 specimens yielded DNA sequence matching to a host animal in GenBank repository (Table 3). In all 32 cases, DNA sequencing confirmed the blood meal origin identified by PMM indicating a nice agreement between the results provided by both methods. In case of mixed blood meals of two females, DNA sequencing was able to determine just goat. For the remaining 22 females, cyt b sequencing identified DNA sequence related to either sand fly or to human contamination.
Besides blood meal source determination, sand fly species identification of all Greek females was successfully performed by combination of morphological analysis and MALDI-TOF MS protein profiling (Table 3). P. neglectus (26) and P. tobbi (23) were revealed as the most abundant species, other species found were P. perfiliewi (4) and P. papatasi (1). Twelve of 54 analysed specimens had damaged peritrophic matrixes and blood present in thorax negatively affected their MALDI-TOF protein profiles. Only 7 of these 12 females yielded reliable sand fly determination with log score value (LSV) higher than 2.0. Four samples with low quality spectra gave correct, but uncertain species identification with LSV below 2.0, and one specimen was not assigned at all.
To demonstrate the universality of PMM-based MALDI-TOF mass spectrometry as a tool for host identification in blood-sucking insects, the method was also employed for determination of blood meal origin in mosquito specimens (C. quinquefasciatus). The females were bloodfed on mice and collected 12, 24, 36, 48, and 60 h PBM (5 specimens for each time point). Until 48 h after feeding, 19 of 20 engorged females were correctly assigned with a mouse host using PMM method, which was in accordance with the results obtained for sand flies. For one mosquito (48 h PBM) the determination was not possible anymore, most likely because of advanced blood degradation. At 60 h PBM time point the blood source of only one female was identified. Interestingly, abdominal peptide profiles of sand fly (P. perniciosus) and mosquito (C. quinquefasciatus) females fed on mice were found nearly identical (Fig 7), which confirmed that the spectra of trypsin-digested abdomens of freshly engorged females are characteristic for blood meal only and not affected by arthropod species.
To elucidate transmission cycles of vector-borne diseases and potential reservoir hosts it is necessary to choose a suitable method for blood meal identification in order to understand the feeding preferences of studied vectors. Accuracy, speed, and cost-effectiveness are the main attributes considered when selecting such method. A crucial parameter affecting the successful blood source determination is a time delay allowed after feeding which can undermine the success of the identification due to proceeding DNA and protein degradation. The second limitation stems from a considerably low volume of engorged blood, which in phlebotomine sand flies varies from 0.1 to 0.9 μl depending on a species [5,6]. Therefore, the detection of miniscule amounts of host DNA using DNA-based techniques remains quite challenging and susceptible to DNA contamination and may lead to co-amplification of both host and sand fly DNA during PCR followed by multiple sequence reading. Moreover, the low volume of enucleated mammalian blood is one of the reasons to use mitochondrial genes for DNA-based host identifications [15], which in case of accidental contamination may cause misidentification of blood meal source as human. In our work, we observed all these drawbacks characteristic for DNA techniques and the poor results of cyt b sequencing we obtained were in agreement with the previous studies struggling to identify blood meal sources of sand fly females using DNA sequencing [28–31].
An alternative approach taking advantage of protein mass spectrometry might overcome these obstacles. One of the first proposed LC-MS/MS techniques used direct spectral matching to detect remnants of blood in ticks and enabled to track the blood sources six months back after feeding [32]. Recently, successful blood meal source identification using LC-MS/MS analysis with subsequent database search was presented in the case of field-caught triatomine bugs [17]. Despite the high sensitivity, both LC-MS/MS-based methods are quite laborious and require expertized interpretation of results. On the other hand, MALDI-TOF MS protein profiling may be a promising choice due to its simple sample preparation, speed of analysis and easy data evaluation. In the last decade, it became an established method for routine species identification of different organisms including many parasitic arthropods [18] and it was also recently tested for host blood determination for the first time on Anopheles mosquitoes [20].
Our study proved that MALDI-TOF MS protein profiling can be applied to identification of blood meal origin also in phlebotomine sand flies. Until 24 h after feeding, the abdominal protein profiles of females experimentally engorged on five different mammalian hosts were well reproducible and distinguishable according to blood source. The spectra allowed differentiating even hosts that belong to the same family, like different rodent species belonging to Muridae (mouse, Neumann’s grass rat and multimammate rat). However, since 36 h PBM, obvious changes of protein spectra induced by blood degradation were observed which dramatically decreased the effectiveness of MALDI-TOF MS protein profiling for host blood identification similarly as reported for DNA-based techniques [33,34].
Beside the time frame within which the blood meal identification is still possible, another key parameter affecting the correctness of host identification is a specificity of the used method, which in case of MALDI-TOF protein profiling might not be sufficient to cope with intraspecific variability caused by genetic polymorphism or related to possible hemoglobin isoforms. Each single point mutation or a post-translational modification of hemoglobin sequence induces an inevitable shift of protein molecular weight that might unable determination of blood meal origin using the method relying on database search against reference protein masses. Vice versa, the host identification could also fail in those host organisms which, despite their hemoglobin sequences differing, have the same or very close total mass of hemoglobin subunits. In addition, a need for a specific database that would include protein spectra of most potential host species from a sampled geographical area is yet another limitation of the protein profiling approach.
To overcome these drawbacks, we introduced a novel method based on MALDI-TOF peptide mass mapping (PMM) mass spectrometry. The analysis is targeted on hemoglobin, one of the most abundant proteins in blood. Although highly conserved across the taxa, hemoglobin also displays small variations in the sequence that can be used for precise host blood identification at the species level [35]. The PMM method is primarily known as a tool for protein identification utilizing a proteolytic digestion of protein of interest followed by mass spectrometric analysis of resulting peptide mixture [22]. The measured masses of the detected peptides are compared to the predicted ones which can be generated from known protein sequence using a program such as PeptideMass [27]. The recorded spectrum, called peptide mass map, represents set of peptide signals characteristic not only for the protein in question, but also for the organism from which the protein originates. The sequences of these specific peptide fragments, here the peptides of host hemoglobin, are then determined by MS/MS sequencing using MALDI-TOF mass spectrometer in order to achieve the blood meal source identification. This approach, unlike MALDI-TOF MS protein profiling, does not need a creation of a specific database since it employs already existing public ones such as UniProt protein database. The proposed PMM-based MALDI-TOF MS method is very simple and fast not requiring any LC separation prior to MS like LC-MS/MS-based approaches. The sample is measured directly after trypsin digestion without any purification step and host identification of analyzed specimen is completed in less than one hour. As several individuals can be processed in parallel, the real throughput of the method is in fact much higher.
The effectiveness of PMM-based MALDI-TOF MS for blood meal source determination was first demonstrated in the proof of concept study using sand fly females fed on rabbit or various small rodents. In comparison to both MALDI-TOF MS protein profiling and DNA sequencing, the PMM-based approach was found less affected by blood degradation and allowed reliable host assignment for longer time period after engorgement. The method yielded confident blood meal identification until 48 h PBM and for some females even 54 h after feeding, which clearly outmatched the conventional PCR-based protocols [36,37]. In addition, it was revealed that the success rate of host identification for the later time points after feeding was in accordance with the speed of blood digestion of the analyzed sand fly [6]. The PMM-based technique also provided promising results in the analysis of mixed blood meals which represent a challenge in host blood identification. As reviewed [15], the successful detection of multiple blood sources depends on several factors such as engorged volume of individual blood meals, time span between feedings, and blood digestion rate, which differs among various bloods [38] and vector species [6]. The data obtained on P. perniciosus females experimentally fed consecutively on two host animals clearly showed a potential of the method for the identification of mixed blood meals as both hosts were uncovered in 60% of tested females (9 of 15) until 36h PBM. Only a single host (five times mouse, once rabbit) was determined for the remaining six specimens. The prevailing identification of a mouse host might be due to the fact that the females, after interrupting engorgement on mice, only probed the rabbit but did not properly imbibe the second blood meal.
The efficiency of PMM-based MALDI-TOF mass spectrometry was further validated by blood meal source determination of field-caught sand fly females, testing its usefulness in actual epidemiological surveillance study. In a blind study on sand flies originating from field collections in Greece, blood meal origin was reliably assigned for almost all (96%) specimens. Moreover, mixed blood meals were identified in two females from this field survey.
Beside blood meal analysis, the actual species identification of engorged females is also very important. As only dissected abdomen is utilized for blood meal origin determination using PMM-based MALDI-TOF mass spectrometry, the rest of the body is still available for sand fly identification and eventually also detection of pathogens like Leishmania by other methods. In our study, head and genitalia were slide-mounted for morphological analysis and thorax was used for species determination using MALDI-TOF MS protein profiling which successfully identified all engorged females except one from Greek field survey. Although 12 females had damaged peritrophic matrix, which resulted in visible blood traces in the thorax, 7 were conclusively identified with LSV score higher than 2.0 and four specimens yielded correct species assignment with LSV below 1.8 which was validated by morphology. Only one specimen was not identified at all due to low spectrum quality. These results are in contrast with a recent study, where authors reliably determined only 35% of engorged females and suggested that fresh engorgement of phlebotomine sand flies could compromise successful sand fly species identification by MALDI-TOF MS protein profiling [39]. Our findings however proved that MALDI-TOF MS protein profiling is the proper method not only for species identification of non-engorged specimens, but even of bloodfed sand fly females.
In conclusion, we present a novel approach for blood meal source identification of parasitic arthropods based on peptide mass mapping-based MALDI-TOF mass spectrometry of host-specific hemoglobin peptides. The method was developed on lab-reared specimens and further verified using field-collected samples. Although tested on phlebotomine sand flies, it could be universally applied to other hematophagous insects as we demonstrated by the analysis of experimentally bloodfed Culex mosquitoes. The method represents an efficient tool for accurate identification of host blood as it requires a minimal material input and simple and fast sample preparation, especially useful for high numbers of specimens collected during field surveys. Surpassing any currently used DNA- or protein-based methods, our approach allows reliable blood meal source determination until 48h after feeding and moreover, it is capable to detect and correctly identify also mixed blood meals. The specificity of the method enables to distinguish closely related hosts (goat vs. sheep, rodent species) and also to cope with potential hemoglobin isoforms and its allelic variants. Unlike in MALDI-TOF MS protein profiling, a prior creation of database that would include protein profiles of candidate hosts is not needed. The simple, rapid and cost-effective sample preparation that utilizes only abdomen enables to use remaining body parts for simultaneous sand fly species identification by other methods including vouchering of head and genitalia for morphological reference and isolation of DNA for species and blood meal identification by sequencing as well as possible PCR detection of Leishmania and other pathogens. All above described advantages of this novel method hopefully make it a promising future tool of choice for blood meal identification in sand flies as well as other hematophagous arthropods.
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10.1371/journal.pcbi.1000367 | A Structural Model of the Pore-Forming Region of the Skeletal Muscle Ryanodine Receptor (RyR1) | Ryanodine receptors (RyRs) are ion channels that regulate muscle contraction by releasing calcium ions from intracellular stores into the cytoplasm. Mutations in skeletal muscle RyR (RyR1) give rise to congenital diseases such as central core disease. The absence of high-resolution structures of RyR1 has limited our understanding of channel function and disease mechanisms at the molecular level. Here, we report a structural model of the pore-forming region of RyR1. Molecular dynamics simulations show high ion binding to putative pore residues D4899, E4900, D4938, and D4945, which are experimentally known to be critical for channel conductance and selectivity. We also observe preferential localization of Ca2+ over K+ in the selectivity filter of RyR1. Simulations of RyR1-D4899Q mutant show a loss of preference to Ca2+ in the selectivity filter as seen experimentally. Electrophysiological experiments on a central core disease mutant, RyR1-G4898R, show constitutively open channels that conduct K+ but not Ca2+. Our simulations with G4898R likewise show a decrease in the preference of Ca2+ over K+ in the selectivity filter. Together, the computational and experimental results shed light on ion conductance and selectivity of RyR1 at an atomistic level.
| Ryanodine receptors (RyRs) are ion channels present in the membranes of an intracellular calcium storage organelle, the sarcoplasmic reticulum. Nerve impulse triggers the opening of RyR channels, thus increasing the cytoplasmic calcium levels, which subsequently leads to muscle contraction. Congenital mutations in a specific type of RyR that is present in skeletal muscles, RyR1, lead to central core disease (CCD), which leads to weakened muscle. RyR1 mutations also render patients to be highly susceptible to malignant hyperthermia, an adverse reaction to general anesthesia. Although it is generally known that CCD mutations abort RyR1 function, the molecular basis of RyR1 dysfunction remains largely unknown because of the lack of atomic-level structure. Here, we present a structural model of the RyR1 pore region, where many of the CCD mutations are located. Molecular dynamics simulations of the pore region confirm the positions of residues experimentally known to be relevant for function. Furthermore, electrophysiological experiments and simulations shed light on the loss of function of CCD mutant channels. The combined theoretical and experimental studies on RyR1 elucidate the ion conduction pathway of RyR1 and a potential molecular origin of muscle diseases.
| Muscle contraction upon excitation by nerve impulse is initiated by a rapid rise in cytoplasmic Ca2+. In skeletal muscle, the rise in cytoplasmic Ca2+ is brought about by the opening of the ryanodine receptor (RyR1), which releases Ca2+ from intracellular stores [1],[2]. RyRs are large tetrameric ion channels (molecular weight of 2.26 MDa) present in the membranes of endoplasmic/sarcoplasmic reticulum. They have high conductance for monovalent (∼800 pS with 250 mM K+ as conducting ion) and divalent cations (∼150 pS with 50 mM Ca2+), while being selective for divalent cations (PCa/PK∼7) [3]. RyRs are important mediators of excitation-contraction coupling and congenital mutations of RyRs result in neuromuscular diseases such as malignant hypothermia and central core disease (CCD) [4].
Although RyRs are physiologically important, the molecular basis of their function is poorly understood. RyRs have unique properties such as their modes of selectivity and permeation not seen in other ion channels with known structures. Next to the putative selectivity filter (4894GGGIG), there are two negatively charged residues (D4899 and E4900) in RyR1 that are essential for normal selectivity and conductance [5]. K+ channels have an analogous selectivity filter, but in contrast to RyR1, have only one adjacent negative residue that is not even conserved while other Ca2+ channels have only one conserved negative residue in the equivalent position [6]. In the selectivity filter, mutations result in non-functional channels [4] leading to CCD. A structural model of the pore region that would reveal the location and function of these residues will be useful in understanding the role of these residues in channel function.
An early model of RyR ion permeation postulated potential barriers within the pore corresponding to three putative binding sites [7]. Without any knowledge of the structure of the pore, the model was able to quantitatively reproduce conductance data of various ions. A PNP-DFT (Poisson Nernst Planck-Density Functional Theory) model [8] accurately modeled the role of residues D4899 and E4900 in RyR1 in generating the high ion conductances of RyRs established by mutagenesis [5],[9]. Selectivity was attributed to charge-space competition, as Ca2+ could accommodate the most charge in least space compared to K+. However, since the channel model used in these simulations relied on a fixed structure, it could not predict changes due to mutations that potentially alter the structure of the channel.
Additionally, there are two homology models of the RyR pore region [10],[11] based on KcsA, a bacterial K+ channel whose solution structure is known [12]. Shah et al. [11] used bioinformatics approaches to construct models for RyR and the related inositol triphosphate channel. The luminal loop in their RyR model begins at 4890G resulting in the selectivity filter being 4890GVRAGG. However, mutagenesis has shown that residues I4897, G4898, D4899 and E4900 are important for channel conductance and selectivity, which suggests that they are part of the conduction pathway of RyR1 resulting in the predicted selectivity filter being 4894GGGIGDE. Welch et al. also constructed a homology model for the cardiac ryanodine receptor (RyR2) using the structure of the KcsA channel [10] and performed simulations to identify residues important for channel function. Their simulations failed to identify D4899 as an important residue for ion permeation contrary to what has been shown experimentally [5]. Furthermore, cryo-electron microscopy (cryo-EM) of RyR1 (which has revealed the pore structure at highest resolution yet) revealed significant differences between the pore region of KcsA and RyR1 [13].
Experimental structure determinations of the RyRs have been mainly performed by cryo-EM [14]–[17]. These studies revealed conformational changes that accompany channel opening [18] and the binding sites of various effectors of RyRs [19]–[21]. Cryo-EM has a resolution of ∼10–25 Å and thus is able to provide only limited structural information regarding the pore structure. Samso et al. [22] manually docked the KcsA pore structure into the transmembrane region of their cryoEM map of the intact closed RyR1. Furthermore, they predicted the presence of at least 6 transmembrane helices from simple volumetric constraints. Ludtke et al. [13] identified several secondary structure elements in their ∼10 Å resolution cryo-EM map of the closed RyR1. The pore-forming region as visualized by Ludtke et al. consists of a long inner helix made up of 31 residues and a pore helix made up of 15 residues that are presumably connected by a long luminal loop made up of 24 residues. Since the structure is derived from cryo-EM, the positions of pore residues' side chains and the structure of loops connecting the helices are unknown. We build a molecular model of the pore region of RyR1 based on their cryo-EM study by adding the luminal loop and the missing side chains of residues forming the helices of the pore. Furthermore, in our molecular dynamics simulations we examine the interactions of the pore region with mono- and divalent cations known to permeate the channel (Table 1).
We present in Figure 1 an atomistic model of the pore-forming region of the tetrameric RyR1 constructed from cryo-EM data [13]. Figure 1A shows two of the four inner helices and pore helices connected by a long luminal loop. Site-directed mutagenesis [22],[23] predicts that RyR1 has a selectivity filter (4894GGGIGD), which is analogous to K+ channels (Figure 1B). Since the pore helix immediately precedes the selectivity filter, we assign M4879-A4893 to the 15-residue pore helix. In K+ channels, the sequence GXXXXA in the inner membrane-spanning helix has been proposed to form the gating hinge [24]. The analogous glycine in RyR1 occurs in the 4934 position, which determines the sequence of the 40-residue inner helix as I4918-E4948. Thus, the pore corresponds to residues M4879-E4948 [13], which includes the putative selectivity filter. We construct the luminal loop by constraining the diameter of the selectivity filter at its luminal edge to 7 Å [25] (Figure 1B). In this model, the acidic residues important for channel function, D4899 and E4900 are located at the mouth of the pore and at the beginning of the selectivity filter.
Both the cytoplasmic and luminal faces of the pore are highly negatively charged (as seen in the surface representation in Figure 1C and 1D), which may allow cations to concentrate around the mouths of the pore. We also predict the negatively charged faces to exclude anions, which RyR1 is known not to conduct. Interestingly, with the exception of L4935 and L4943, the hydrophobic residues lining the helices face away from the water filled pore. In K+ channels, hydrophobic residues facing the pore are known to perform important functional roles (like stabilization of the inactivation gate [26]). The model exhibits structural similarities with the K+ channel MthK such as the positioning of the pore helix and the inner helix and the bending of the inner helix [27], although in RyR1 the pore is significantly wider. To model the interactions of RyR1 pore with ions and elucidate sites of high ion occupancy along the pore, we perform molecular dynamics simulations of RyR1 (see below).
We describe here some of the experimental characteristics of RyR1 mutants D4899Q and G4898R, which are both present in the selectivity filter. We compare in Figure 2 the ion permeation properties of wild type RyR1-WT [5] with RyR1-D4899Q [5] and CCD associated RyR1-G4898R [28] mutant channels. Proteoliposomes containing purified 30S channel complexes were fused with planar lipid bilayers and single channel currents were recorded in 250 mM KCl on both sides of the bilayer. Figure 2 shows representative single channel traces in presence of 2 µM cis (SR cytosolic) Ca2+ (left, upper traces) and following the subsequent addition of 10 mM trans (SR luminal) Ca2+ (left, lower traces). In presence of 2 µM Ca2+, WT and the two mutant channels showed rapid transitions between open and closed channel states. Reduction in cis Ca2+ from 2 µM to 0.01 µM reduced channel open probability for WT and D4899Q close to background levels (data not shown). In contrast, single channel activities of RyR1-G4898R did not respond to a change in cytosolic Ca2+ concentration from 2 µM to 0.01 µM (not shown). Ion currents through WT and mutant channels showed a linear voltage-dependence but differed in their magnitude (Figure 3, right panel). Averaged single channel conductances were 801 pS for WT, 164 pS for RyR1-D4899Q, and 352 pS for RyR1-G4898R. The Ca2+ selectivity of WT and the two RyR1 mutants was determined by recording current-voltage curves in 250 mM symmetrical KCl with 10 mM Ca2+ in the trans bilayer chamber. At 0 mV in presence of 10 mM trans Ca2+, WT exhibited averaged unitary Ca2+ current of −2.4 pA compared with −0.4 pA for RyR1-D4899Q and ∼0 pA for RyR1-G4898R. Addition of 10 mM Ca2+ to the trans chamber reduced single channel currents of WT and D4899Q at negative and positive potentials and the averaged reversal potentials (Erev) for WT and D4899Q were shifted by +9.5 mV and +1.9 mV, respectively (Figure 2, right panel). Applying constant field theory, a permeability ratio of Ca2+ over K+ (PCa/PK) of 7.0 and 1.0 is calculated for WT and D4899Q, respectively (Table 2). In contrast, addition of 10 mM trans Ca2+ did not generate a noticeable unitary Ca2+ current at 0 mV, and had no effect on ion currents or reversal potential of RyR1-G4898R. Taken together, the single channel data of Figure 2 indicate that the D4899Q mutation decreases K+ conductance and ion selectivity for Ca2+ over K+ compared to WT, without eliminating Ca2+ responsiveness. In contrast, the CCD associated G4898R mutation abolished Ca2+ responsiveness, Ca2+ permeation, and reduced ion conductance demonstrating that the G4898R mutation introduced major global conformational changes in RyR1.
Acidic residues lining the pore of the RyR channel have been assumed to be deprotonated at physiological pH. In support of this, site directed mutagenesis resulting in charge neutralization of acidic residues reduced ion conduction and selectivity [5]. Single channel experiments in the pH range of 6.5–9.4 (on the luminal side) on RyR1-WT were performed to probe the protonation status of luminal residues D4899 and E4900. Lack of a significant effect of a change of pH on K+ ion conductance and the permeability ratio of Ca2+ over K+ (Xu and Meissner, unpublished studies) indicate that the protonation status of D4899 and E4900 remained unchanged. Hence we assume in our MD simulations that these residues are deprotonated.
To model the interactions of RyR1 pore with ions, we perform molecular dynamics simulations of the RyR1 pore tetramer with 70 mM CaCl2 and 250 mM KCl. Ca2+ is present in the solution only on the luminal side. We plot the histogram of ion occupancies in the pore against the pore axis in Figure 3. Ca2+ shows highest occupancy at 77 Å along the axis of the channel, which corresponds to the position of D4899 in the selectivity filter (Figure 3C). We find the preferential occupancy ratio, R for the CaCl/KCl simulations to be 11.3±5.6 (Table 3). These results show a clear preference of Ca2+ over K+ in the selectivity filter. We hypothesize that this preferential localization of Ca2+ in the first binding site along the path from luminal to cytosolic side may play an important role in channel selectivity. To ensure that this preferential localization of Ca2+ is not due to selective exclusion of K+ ions in the filter, we perform simulations of the pore with only KCl present. These simulations show K+ occupancy to be highest at 81 Å (Figure 3B) and the total charge of the ions in the selectivity filter (Table 3) to be similar to simulations with CaCl/KCl, which shows that the binding sites are amenable to K+ in absence of Ca2+. As a control, we perform simulations with NaCl/KCl with the same starting configuration as the CaCl2/KCl simulations. We observe a decreased occupancy of Na+ when compared to Ca2+. Na+ is still preferred over K+ in the selectivity filter, if we take relative concentration of NaCl and KCl into consideration with R = 2.2 (Figure S1). Simulations with only CaCl2 show similar results (Figure 3D). The occupancy data qualitatively reflects the selectivity of RyR1 pore, which is in the order of Ca2+ being much greater than Na+ which is slightly greater than K+ [7].
Radial distribution functions (RDF) of the ions around the carboxyl oxygens of D4899 and E4900 reflect the affinities of various ions to the binding site. We plot the number of ions found at a distance r from the carboxyl oxygens of D4899 and E4900, to confirm that the peaks that we observe in ion occupancy plots is due to localization of the ions near these residues. From the RDFs, we find that Ca2+ ions exhibit the highest affinity for D4899 and E4900 followed by Na+ and then K+ (Figure 4A and 4B). It is experimentally observed that the conductance of the channel is highest for K+, followed by Na+ and then Ca2+ [7]. These two observations lead us to postulate that that tighter binding results in ions spending more time in the channel, thus giving rise to lower ion currents.
The higher occupancy of Ca2+ compared to K+ can be either primarily attributed to electrostatics (due to the presence of 8 acidic residues in the vicinity) or to electrostatics combined with the structure formed by the selectivity filter. By calculating the occupancy of ions inside the pore region corresponding to the selectivity filter (formed by 4894GGGIGDE of the tetramer, as shown in Figure 1B), we also consider if the pore structure contributes in concentrating the ions inside the pore. Although eight negative charges in a confined space would be more selective for Ca2+ than K+ as determined by a charge/space model [29], we find that in an all-atom model of RyR1 pore region, the selectivity filter is able to sample structures that support preferential localization of Ca2+ inside the pore (in the region corresponding to the selectivity filter). Thus, the preferential localization of Ca2+ over K+ is due to both the pore structure and electrostatics.
To confirm the preferential Ca2+ binding in the selectivity filter, we perform MD simulations on two experimentally characterized RyR1 mutants, D4899Q [5] and G4898R [28]. In single channel experiments, the RyR1-D4899Q exhibits a decreased selectivity for Ca2+ and lower K+ conductance (Table 2). The experimentally observed decreased selectivity of Ca2+ over K+ in RyR1-D4899Q can be rationalized from our simulations. In simulations involving D4899Q, the ion occupancy histograms show that the peaks for K+ and Ca2+ are outside the selectivity filter compared to that of the RyR1-WT (Figure 5D). We observe that the preference for Ca2+ over K+ in the selectivity filter decreases to R = 3.1±1.6 from R = 11.3±5.6 in RyR1-WT. Further, the total ionic charge in the selectivity filter is reduced (4.3 on an average compared to 7 in RyR1-WT). RDF of ions around Q4899 (Figure 4C) strongly suggests that there is no binding of K+ and Ca2+ at the site of mutation, as also seen in Figure 5D. In simulations of RyR1-D4899Q, the RDFs and ion histogram taken together prove that Ca2+ and K+ bind only at the end of the selectivity filter especially near E4900.
Transition of an ion from a fully hydrated state to inside the channel where hydration is low is facilitated by presence of high affinity binding sites near the entrance of the channel. According to our structural model of the RyR1 pore, D4899 and E4900 residues are present at the entrance of the channel and form the first binding site of ions as they traverse from the luminal side to the cytosolic side. Hence, mutation of glutamic acid to asparagine resulting in a net loss of 4 negative charges weakens the initial binding event of cations as they enter the pore, which would result in the decrease of overall conductance of cations. Another important observation is that when just one of the two acidic residues in the selectivity filter is neutralized, Ca2+ binding is affected much more than K+, whose binding remains the same as RyR1-WT (Table 3). These results suggest the requirement of higher magnitude of negative charges in the selectivity filter to bind Ca2+ efficiently.
Simulations with RyR1-G4898R show a decrease in preference of Ca2+ in the selectivity filter (Figure 5B). The preferential occupancy ratio of Ca2+ over K+ ions (R = 4.5±0.9) is lesser than RyR1-WT (R = 11.3±5.6). The highest occupancy of Ca2+ ions occurs near the edge of the selectivity filter similar to that of RyR1-D4899Q, which implies an exclusion of ions from the selectivity filter. The exclusion of Ca2+ ions is likewise seen from the RDF of ions around D4899 in RyR1-G4898R that shows decreased affinity of Ca2+ to D4899 compared to wild type (Figure 4D). Thus, the introduction of a basic residue next to the acidic residues of the selectivity filter decreases ion binding in the selectivity filter, with the effect on Ca2+ being greater than on K+.
Elucidation of the structure-function relationship in RyR1 necessitates an atomistic model of its pore region. We constructed a model of the pore region that identifies the positions of residues critical to channel function. Furthermore, molecular dynamics simulations help us confirm the potential binding sites of ions along the pore. Considering the permeation time for different ions in RyR, our molecular dynamics simulations cannot sample statistically significant number of permeation events. However, the correlation between preferential ion occupancies seen in our simulations and experimentally measured selectivity both in RyR1-WT and its mutants suggests that preferential Ca2+ binding to the selectivity filter is a necessary but not a sufficient condition for selectivity. The charge space competition (CSC) model [30]–[32] provides one explanation for the selectivity for Ca2+ over K+ and Na+. The model attributes Ca2+ selectivity to the ability of Ca2+ (with a higher charge) to neutralize the carboxylate rich selectivity filter of Ca2+ channels by occupying the same space as Na+ (or lesser space than K+). The ion occupancies seen in the selectivity filter in our simulations agree well with the CSC model for RyR1 [8],[29]. An important consequence of both models is the identification of sites on the pore (4899D and 4900E) that have preferential affinity for Ca2+ compared to K+ and are experimentally shown to be sensitive to mutations.
Even though the cryoEM studies of Ludtke et al. [13] provide strong evidence for the structure and the orientation of helices forming the pore region, assigning the right sequence to this structure is essential for our simulations. Several biochemical and bioinformatics studies have predicted the pore-forming region of RyR1 with good agreement between each other. Balshaw et al. [33] first proposed that the pore forming region in RyR1 is located around 4894GGGIGD due to the striking similarity between this sequence and the selectivity filter of K+ channels including KcsA. Zhao et al. [22] performed several mutations on this highly conserved region in RyR2 and observed dramatic effects on ion conduction properties. Gao et al. [23] performed functional studies on similar mutants in RyR1 that also highlighted the importance of these residues in ion conduction. Using the putative selectivity filter as an anchor and the predicted positions of the membrane spanning helix from hydrophathy plots, Welch et al. constructed a homology model of the RyR2 pore region from KcsA [10]. In K+ channels, the sequence GXXXXA in the inner membrane spanning helix has been proposed to form the gating hinge [24]. The analogous glycine in RyR1 occurs in the 4934 position, in center of the inner helix predicted by hydropathy plots [34]. Studies on triadin (a transmembrane protein known to interact with RyRs [35]) indicate that three of the acidic residues of RyR1 namely, D4878, D4907 and E4908 are essential for binding of triadin to RyR1 [36]. D4878 is located in the luminal loop that connects the pore helix to the rest of the transmembrane region of RyR1. D4907 and E4908 occur in the luminal loop connecting the pore helix to inner helix and are positioned after the selectivity filter. Binding of triadin in the luminal region of RyRs inhibits channel function experimentally, which can be inferred from our model too with respect to the positions of D4907 and E4908. Thus, the sequence assignments in our model are in good agreement with results of biochemical studies.
Although the cryo-EM data for the pore-region of RyR1 is obtained from the closed state of the channel, the inner helices of RyR1 resembles that of the potassium channels in the open state (MthK). The similarity in the inner helices of RyR1 and MthK implies that the inner helices of RyR1 need not undergo major conformational change during transitions from closed to open state. The structure of the selectivity filters of the closed and open states of potassium channels (MthK and KcsA) are essentially the same, which suggests that RyR1 selectivity filter may be modeled even from its overall closed state. Moreover, the results of our simulations are in good agreement with experimental data despite modeling the pore-structure from an overall closed state of the channel.
The residues forming the selectivity filter (GGGIGDE motif) are not the only determinants for selectivity and high conductance. There are acidic residues present in the inner helix, towards the cytosolic side, D4938 and D4945 whose mutation as predicted by PNP/DFT is experimentally shown to reduce K+ conductance and selectivity [8],[9]. The peaks in the K+ and Ca2+ occupancies in the cytosolic vestibule are found near the positions of these residues (Figure 4). Thus there are two regions in the pore that have high affinity for ions and are known to determine selectivity. One region is present in the luminal side along the selectivity filter, while the other is present in the cytoplasmic side of the pore. The presence of negatively charged sites on either side of the channel seen in our structural model is comparable to nicotinic acetylcholine receptor ion channel [37].
RyR1-G4898R is not responsive to Ca2+ and caffeine and does not bind ryanodine unlike RyR1-WT which points to major altered protein conformation of the mutant channel [28]. In single channel measurements, RyR1-G4898R mutant is constitutively open to K+ and it loses Ca2+ conductance and regulation by pharmacological agents. Since we model only the pore region, the present study cannot predict the global structural changes that occur due to G4898R mutation. However, the local structural changes in the selectivity filter and the consequent loss of ion binding as seen in our simulations could account for our experimental observations on RyR1 mutants. The selectivity filters of ion channels are highly dynamic as evinced experimentally by the flickering of ion currents in single-channel measurements and structurally by the preponderance of glycines. The long, polar side chain of arginine in the selectivity filter could interact with other regions of the channel resulting in a selectivity filter that is structurally different from RyR1-WT This distortion may render the selectivity filter in a conformation that does not allow Ca2+ conduction. In contrast to RyR1-G4898R, RyR1-D4899Q shows Ca2+ and caffeine dependent Ca2+ release and ryanodine binding comparable to RyR1-WT [5]. Maintenance of activity suggests that changes upon mutation are localized to the selectivity filter and our simulations can identify these changes.
Our simulations identify ab initio the preferential localization of Ca2+ ions near the side chain carboxyl groups of D4899, E4900, D4938 and D4945. This result is achieved without any prior knowledge of the ion positions with respect to the selectivity filter since the initial positions of ions in the simulations are all random. Without prior bias, we are able to reproduce Ca2+ occupancies up to 11.3 times higher than that of K+, which supports the accuracy of the model of a Ca2+ selective channel. RyRs are important players in excitation-contraction coupling, which is fundamental to muscle contraction for movement and heart function. A mechanistic model of RyR1 will help us understand not only a functionally unique ion channel, but also shed light on an important physiological process.
Our model is confined to the pore-forming region of the homotetrameric RyR1 channel. Both site directed mutagenesis and cryo electron microscopy (cryoEM) [13] have suggested that the RyRs have a pore architecture similar to K+ channels whose structure has been determined. Single particle cryo-EM studies on RyR1 detected several helix-like densities using the program SSEhunter [38],[39]. SSEhunter quantitatively identifies densities in a cryoEM map that may represent secondary structure elements and outputs the length and orientation of the secondary structures that can be unambiguously identified from the cryoEM map. This tool has been validated in many studies [40]–[42]. Two of these helices in each subunit, a long membrane spanning helix kinked in the middle (inner helix) and a short helix in the luminal side of the membrane (pore helix) face each other and form the backbone of the channel pore. At the resolution of the cryo-EM densities the side chains of the helices could not be resolved. The coordinates of the backbone atoms of the pore helix and the inner helix were obtained from the cryo-EM studies [13]. The kink in the middle of the inner helix (G4934) was proposed to be analogous to the gating hinge of MthK channel [27]. Sequence comparison indicates that 4894GGGIG motif is analogous to selectivity filter motif T[VI]GYG of K+ channels. Site directed mutagenesis suggests that 4899DE motif is also part of the selectivity filter of the RyRs.
The sequence used in constructing the pore region corresponds to M4879–E4948 (Swissprot ID: P11716) [13]. The amino acid sequence corresponding to M4879-A4893 is assigned to the pore helix while the sequence I4918-E4948 is assigned to the inner helix. In this sequence assignment, RyR1 has a long, 24-residue luminal loop (G4894-D4917), which is not visible in the cryo-EM reconstruction. Initial structure of the luminal loop is obtained by searching a database of loop structures found in SYBYL (Tripos, CA). To remove steric clashes between the loop and the helices, we further refine the loop conformation using the MODLOOP server [43], which predicts the loop conformations by satisfying spatial restraints. Using constrained all atom discrete molecular dynamics (DMD) [44], we perform simulations on the whole pore-forming region to constrain the distance between carboxyl oxygens of D4899 in the opposite monomers around 7 Å [25]. Finally, we optimize the rotamer states of all side chains using MEDUSA [45], a molecular modeling and design toolkit. Thus, we have directly used the structure of the helices identified by SSEhunter and the loop structure created using molecular modeling and low-resolution experimental constraints to create the final model of the pore structure of RyR1.
We perform molecular dynamics simulations using GROMACS [46],[47] with the OPLSAA force field [48] modified with additional parameters for lipids [49] and ion parameters of Aqvist [50]. The RyR1 pore is placed in a pre-equilibrated DPPC bilayer with explicit solvation using genbox program in GROMACS, which removes all the water and DPPC molecules that have clashes with the pore as it is placed in the bilayer. We use the SPC model for water [51]. The simulation system shown in Figure S2A consists of the pore-forming tetramer, 405 DPPC molecules, ∼14000 water molecules and ions to make a neutral system. The concentrations of K+ and Ca2+ used in different simulations are shown in Table 1. Berendsen weak temperature coupling [52] is used with a relaxation constant of 0.5 ps. A cut-off of 10 Å is used for Van der Waals interactions and long range electrostatics is treated with Particle Mesh Ewald [53] with a grid-spacing of 12 Å and a cutoff of 10 Å.
We perform the simulations at constant volume, with a vacuum of 2 nm thickness at the top and bottom of the simulation box, to maintain asymmetry of ion concentrations at the cytoplasmic and luminal side even in the presence of periodic boundary conditions [54]. In order to ensure that the introduction of a 2 nm slab of vacuum above and below our simulation system will not affect our results, we perform one set of simulations of RyR1-WT with KCl and CaCl2 by replacing the 2 nm slabs of vacuum with water molecules. We calculate the ion occupancy ratios and the histogram of ion occupation along the channel axis (Table S1 and Figure S3). These results do not change the primary conclusions of this study. We observe a preferential occupation of Ca2+ over K+ in the selectivity filter with a ratio of 6.9 (within 1 SD of simulations with the vacuum slabs). However, due to the periodic boundary conditions, replacing the vacuum slab with water eliminates the partition between the cytosolic and luminal compartments, which then removes the partition between what was originally two compartments, and hence the concentration gradient.
Since the pore forming region is a small part of the entire membrane-spanning domain, the dynamics of the helices would depend on interactions with other regions of the membrane-spanning domain, which is not included in the present model. Hence, we focus on studying the interactions of the ions with the luminal loop and also the dynamics of the selectivity filter and the luminal loop. Therefore, harmonic restraints with force constants of 1000 kJmol−1 nm−2 are imposed on the C, Cα, N, and O atoms of the pore lining helices and the pore forming helices during the simulations. In the complete channel, the pore forming helix may not be in direct contact with the membrane, but to ensure minimal contact of water with the membrane spanning, pore forming helix, we surround the pore forming helix with lipids. Applying harmonic constraints on pore forming region can have dramatic effects on observed ion binding events [55]. However, in our simulations, the harmonic restraints is placed only on the pore helix and the inner helix, while the luminal loop (containing the selectivity filter) is unrestrained. The narrowest region of the pore is formed by the luminal loop, which is flexible in our simulations. The cytoplasmic side of the pore, like MthK has a much bigger volume and we predict the movement of the helix to be minimal in the time scale of our simulations and hence potential artifacts on ion binding due to the restraints on the protein should be minimal.
The simulation system is first subjected to 1000 steps of steepest descent energy minimization. We then perform an equilibration run for 5 ns where the protein is restrained, while the lipids, water molecules and the ions are allowed to equilibrate around the protein. A 15 (or 25) ns production run is initiated after the equilibration run. The trajectory used for analysis corresponds to 5–15 (or 5–25) ns of the production run. We carry out simulations in many ionic conditions, namely CaCl2, KCl, NaCl, CaCl2+KCl, NaCl+KCl. The concentrations of ions used are shown in Table 1. Simulations are also performed on two pore mutants: RyR1-G4898R, RyR1-D4899Q.
We calculate ion occupancy as a function of z coordinate (along the axis of the pore) by counting the number of ions in the shaded region shown in Figure S1B. We calculate the histogram of ion occupancy with a bin width of 1 Å, which provides a picture of the ion binding sites and ion occupancies along the axis of the channel. To quantify the preferential localization of the ions, we calculate the ratio of number of each type of ion in the selectivity filter, while accounting for the differential concentration of each ionic species. We denote this ratio as R:where nsi is the number of ions of type i present in the selectivity filter, nti is the total number of ions of type i present in the luminal side. Since there are no ion translocation events during our simulations, nt remains constant throughout the simulation time. ns is calculated from the area under the curves shown in Figures 3 and 5, for each corresponding simulation.
To determine the specific binding locations of the ions, we calculate radial distribution functions (RDFs) of the ions around the side chain carboxyl oxygens of D4899 and E4900 of the selectivity filter.
To confirm that the system is equilibrated, we perform our analysis on shorter stretches (5 ns) of our trajectories and them with analysis performed over the whole length of the trajectories. The ion-occupancy histograms and the RDFs of the shorter stretches of the trajectory are similar to the longer complete trajectory, which confirms that the simulations are well equilibrated (data not shown).
Single channel measurements are performed using planar lipid bilayer method [5]. Proteoliposomes containing the purified recombinant RyR1s are added to the cis (SR cytosolic side) chamber of a bilayer apparatus and fused in the presence of an osmotic gradient (250 mM cis KCl/20 mM trans KCl in 20 mM KHepes, pH 7.4, 2 µM Ca2+). After the appearance of channel activity, trans (SR lumenal) KCl concentration is increased to 250 mM. A strong dependence of single channel activities on cis Ca2+ concentration indicates that the large cytosolic “foot” region faces the cis chamber of the bilayers. The trans side of the bilayer is defined as ground. Electrical signals are filtered at 2 kHz (0.5 kHz for Ca2+ currents at 0 mV), digitized at 10 kHz, and analyzed as described [5].
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10.1371/journal.pntd.0000272 | Conservation and Variability of Dengue Virus Proteins: Implications for Vaccine Design | Genetic variation and rapid evolution are hallmarks of RNA viruses, the result of high mutation rates in RNA replication and selection of mutants that enhance viral adaptation, including the escape from host immune responses. Variability is uneven across the genome because mutations resulting in a deleterious effect on viral fitness are restricted. RNA viruses are thus marked by protein sites permissive to multiple mutations and sites critical to viral structure-function that are evolutionarily robust and highly conserved. Identification and characterization of the historical dynamics of the conserved sites have relevance to multiple applications, including potential targets for diagnosis, and prophylactic and therapeutic purposes.
We describe a large-scale identification and analysis of evolutionarily highly conserved amino acid sequences of the entire dengue virus (DENV) proteome, with a focus on sequences of 9 amino acids or more, and thus immune-relevant as potential T-cell determinants. DENV protein sequence data were collected from the NCBI Entrez protein database in 2005 (9,512 sequences) and again in 2007 (12,404 sequences). Forty-four (44) sequences (pan-DENV sequences), mainly those of nonstructural proteins and representing ∼15% of the DENV polyprotein length, were identical in 80% or more of all recorded DENV sequences. Of these 44 sequences, 34 (∼77%) were present in ≥95% of sequences of each DENV type, and 27 (∼61%) were conserved in other Flaviviruses. The frequencies of variants of the pan-DENV sequences were low (0 to ∼5%), as compared to variant frequencies of ∼60 to ∼85% in the non pan-DENV sequence regions. We further showed that the majority of the conserved sequences were immunologically relevant: 34 contained numerous predicted human leukocyte antigen (HLA) supertype-restricted peptide sequences, and 26 contained T-cell determinants identified by studies with HLA-transgenic mice and/or reported to be immunogenic in humans.
Forty-four (44) pan-DENV sequences of at least 9 amino acids were highly conserved and identical in 80% or more of all recorded DENV sequences, and the majority were found to be immune-relevant by their correspondence to known or putative HLA-restricted T-cell determinants. The conservation of these sequences through the entire recorded DENV genetic history supports their possible value for diagnosis, prophylactic and/or therapeutic applications. The combination of bioinformatics and experimental approaches applied herein provides a framework for large-scale and systematic analysis of conserved and variable sequences of other pathogens, in particular, for rapidly mutating viruses, such as influenza A virus and HIV.
| Dengue viruses (DENVs) circulate in nature as a population of 4 distinct types, each with multiple genotypes and variants, and represent an increasing global public health issue with no prophylactic and therapeutic formulations currently available. Viral genomes contain sites that are evolutionarily stable and therefore highly conserved, presumably because changes in these sites have deleterious effects on viral fitness and survival. The identification and characterization of the historical dynamics of these sites in DENV have relevance to several applications such as diagnosis and drug and vaccine development. In this study, we have identified sequence fragments that were conserved across the majority of available DENV sequences, analyzed their historical dynamics, and evaluated their relevance as candidate vaccine targets, using various bioinformatics-based methods and immune assay in human leukocyte antigen (HLA) transgenic mice. This approach provides a framework for large-scale and systematic analysis of other human pathogens.
| Dengue viruses (DENVs) are mosquito-borne pathogens of the family Flaviviridae, genus Flavivirus, which are phylogenetically related to other important human pathogens, such as Yellow fever (YFV), Japanese encephalitis (JEV), and West Nile (WNV) viruses, among others. DENVs are enveloped, single-stranded RNA (+) viruses coding for a polyprotein precursor of approximately 3,400 amino acids, which is cleaved into three structural (capsid, C; precursor membrane and membrane, prM/M; envelope, E) and seven nonstructural proteins (NS1, 2a, 2b, 3, 4a, 4b and 5). Viral replication occurs in the cytoplasm in association with virus-induced membrane structures and involves the NS proteins. There are 4 genetically distinct DENV types, referred to as DENV-1 to -4, with multiple genotypic variants [1],[2]. DENVs are transmitted to humans primarily by Aedes aegypti mosquitoes and cause a wide range of symptoms from an unapparent or mild dengue fever (DF) to severe dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS) that may be fatal. It is estimated that more than 100 million people are infected each year, with up to several hundred thousand DHF/DSS cases [3]. To date, there is no licensed prophylactic vaccine and no specific therapeutic formulation available.
Adaptive immune responses include cellular responses to short peptides derived from self and foreign proteins by proteolysis. The peptides are presented to T-cell receptors (TCRs) by major histocompatibility complex (MHC) molecules, referred to as human leukocyte antigen (HLA) molecules in humans. HLA class I and class II molecules bind and present peptides to CD8 and CD4 T-cells, respectively, that play a critical role in antigen (Ag)-specific cytotoxic responses and the induction and maintenance of Ag-specific memory responses [4]–[6]. Peptides that are recognized by the T cells and trigger an immune response are referred to as T-cell determinants. One problem in developing a tetravalent DENV vaccine is the viral diversity [7], with rather low intra-type, but high inter-type variability, resulting in type-specific and type cross-reactive T-cell determinants [8]. This variability of related structures gives rise to a large number of variant peptide sequences with one or more amino acid differences that may function as alternative determinants, or altered peptide ligands [9], and affect anti-DENV host immunity [10],[11]. There is abundant evidence that interactions of memory T cells with peptide ligands bearing amino acid substitutions at TCR contact residues may alter T-cell activation and effector function [9], [12]–[15]. Even a single amino acid substitution can impair the function of T cells in a variety of ways, producing profoundly different phenotypes that range from modified stimulatory function to complete inhibition [14]. These findings suggest that infection or immunization with multiple DENV types, as is the case with some tetravalent vaccines, may lead to T-cell responses to variant peptides that might be deleterious. There is also the possibility that the altered-ligand phenomenon and cross-reactive T-cell responses, referred to as original antigenic sin, may play a role in DHF/DSS [7],[11],[16],[17]. Although the etiology of DHF and DSS is only partially understood, this consideration may have profound implications for the safety and efficiency of candidate vaccines.
The objective of this study was to search for sequence regions conserved across the majority of DENVs and representing potential immune targets [18]. Bioinformatics-based approaches were used to (a) extract all DENV sequences available in public databases, (b) identify and examine the structure-function relationship and distribution in nature of sequences that are highly conserved in the majority of DENVs (referred to as pan-DENV sequences), (c) analyze the variability of DENV sequences, and (d) examine the immune relevance of the conserved sequences as potential T-cell determinants that would be applicable to the majority of the human population worldwide [19]. We have also correlated the conserved DENV sequences to previously reported T-cell determinants and further identified novel candidate T-cell determinants by analyzing HLA-restricted immune responses in HLA transgenic mice.
The bioinformatics approaches and rationale for the methodology adopted in this study have been previously described [20] and are summarized in Figure 1.
DENV protein sequences were retrieved from the NCBI Entrez protein database in December 2005, and again in December 2007 for validation purposes, by use of a taxonomy ID search via the NCBI taxonomy browser [21]. The taxonomy IDs for DENV-1 to -4 were 11053, 11060, 11069 and 11070, respectively. The data for 2007 were processed separately from the 2005 dataset, but using identical procedures.
The sequences of the DENV proteins C, prM, E, NS1, NS2a, NS2b, NS3, NS4a, NS4b and NS5 were extracted from the database records (Dataset S1) by multiple sequence alignments, and application of the known cleavage sites obtained from the annotation of the GenPept [21] reference polyprotein sequences of DENV-1 to -4 (AAF59976, P14340, AAM51537, AAG45437, respectively), and from the literature [22]. Grouping of the sequences of each DENV type was performed by BLAST [23] followed by CLUSTALX 1.83 [24] multiple sequence alignments. Both full-length and partial sequences of each DENV protein were used for analysis, and identical sequences were not removed from datasets, unless otherwise indicated. All multiple sequence alignments were manually inspected and corrected for misalignments.
The DENV protein sequences were examined by a consensus-sequence based approach [25] to identify sequence fragments that were common across the 4 types. The consensus sequences for the proteins of each type (intra-type consensus) were first derived by multiple sequence alignments to select the predominant residue at each amino acid position. The 4 intra-type consensus sequences for a given protein (one from each type) were then aligned to reveal sequence fragments identical across each of the types that were at least 9 amino acids long. This minimum length was chosen because it represents the binding core length of a majority of HLA-restricted T-cell determinants [26]. Only sequence fragments that were identical in at least 80% of the sequences of each of the 4 types were retained for further analyses. Peptides with residue X in the alignment were ignored from the percentage representation (i.e. frequency) computation. The 80% intra-type representation cut-off was chosen because 44 of the 46 sequence fragments that were common across the 4 DENV types exhibited intra-type representation of ≥81%, and those two that did not had significantly lower representation (∼56–67%) in one of the 4 types.
Shannon information entropy [20],[27] was used to study the diversity of DENV protein sequences within each type (intra-type diversity) and across all DENVs (pan-DENV diversity) and to assess the predicted evolutionary stability of the identified pan-DENV sequences. All entropy analyses were carried out by using the in-house developed Antigenic Variability Analyser tool (AVANA) [28]. For immunological applications, the entropy measure for antigenic sequences was based on nonamer peptides [26], centered at any given position in the alignment. Applying Shannon's formula, the nonamer peptide entropy H(x) at any given position x in the alignment was computed bywhere p(i, x) is the probability of a particular nonamer peptide i being centered at position x. The entropy value increases with n(x), the total number of peptides observed at position x; it is also sensitive to the relative frequency of the peptides; such that it decreases when one peptide is clearly dominant (i.e. the position is conserved). Only sequences that contain a valid amino acid at position x were used for the entropy computation, and the alignment gaps were ignored. Although gaps tend to occur in high-diversity regions, proteins that have a high fraction of gaps have reduced statistical support, yielding an artificially low entropy value; for this reason, positions where more than 50% of sequences contained a gap were discarded. Because of the statistical nature of the entropy measure, both complete protein and shorter fragment sequences were used in this computation. The first and last 4 positions in the alignment of each protein were not assigned any peptide entropy value as they cannot be the center of a nonamer.
In theory, nonamer entropy values can range from 0, for a completely conserved nonamer peptide in all sequences analyzed, to 39 (log2 209); in practice, however, the upper bound is very much lower for alignments of closely related sequences. For finite-size sets of sequences, entropy computations are affected by the sequence count in the alignment. For an alignment of N sequences, alignment size bias is proportional to 1/N [29]. This relationship allows a correction for size bias by applying to each alignment a statistical adjustment that estimates entropy values for an infinitely-sized alignment with analogous peptide distribution. To obtain such an estimate, the alignment was repeatedly randomly sampled to create smaller alignments of varying size, whose entropy was measured. At each alignment position, the entropy of these subset alignments of size N was plotted against 1/N, using a linear regression to extrapolate the entropy estimate for N→∞. The regression's coefficient of determination (r2) was used as a goodness-of-fit of the resulting estimate. In this study, size bias correction was applied to all entropy calculations, so that alignment sequence counts could be ignored in comparisons. All entropy values reported are therefore infinite-size set estimates, rather than the values directly computed from the alignments.
Data from information entropy analysis were used to study the distribution of the representation of nonamer variant peptides in DENV sequences, within and across the types. For any given position x in the alignment, the combined representation of all nonamers, excluding the predominant peptide, was computed. The predominant nonamer was the peptide that was contained in the majority of the sequences at the position in the alignment. All the other peptides that differed by at least one amino acid from the predominant nonamer were defined as variants.
The known and putative structural and functional properties of pan-DENV sequences were searched in the literature and by use of the Prosite [30], via ScanProsite [31], and Pfam [32] databases. When possible, the sequences were mapped on the three-dimensional (3-D) structures of available DENV Ag in the PDB database [33] by use of ICM-Browser version 3.3 (www.molsoft.com). X-ray diffraction 3-D structures were visualized by use of the Corey, Pauling and Koltun (cpk) representation in the ICM-Browser.
Pan-DENV sequences that overlapped at least 9 consecutive amino acid sequences of other viruses and organisms were identified by performing BLAST search against viral protein sequences reported at NCBI (as of July 2007), excluding DENV sequences (parameters set: limit by Entrez query “txid10239[Organism:exp] NOT txid12637[Organism:exp]”; automatically adjust parameters for short sequences option enabled; low-complexity filter disabled; alignments: 20,000), and against protein sequences of all organisms excluding viruses (parameters set: limit by Entrez query “Root[ORGN] NOT Viruses[ORGN] NOT txid81077[ORGN]”; automatically adjust parameters for short sequences option enabled; low-complexity filter disabled; alignments: 20,000). The keyword “NOT txid81077 [ORGN]” was used to remove artificial sequence hits.
Both literature search and query against the Immune Epitope Database [34] (www.immuneepitope.org) were performed to detect reported immunogenic, human T-cell determinants (both class I and II) of DENV that either fully or partially overlapped with the pan-DENV sequences. In addition, dedicated algorithms based on several prediction models were used to identify candidate putative HLA-binding sequences to multiple HLA class I and II supertype alleles within the pan-DENV sequences. Putative HLA supertypes class I-restricted peptides were identified by use of NetCTL [35], Multipred [36], ARB [37], and class II-restricted peptides by Multipred and TEPITOPE [38]. Further, the intra-type representation of the putative T-cell determinants was analyzed.
The NetCTL 1.2 algorithm (www.cbs.dtu.dk/services/NetCTL/) predicts peptides restricted by 12 HLA class I supertypes (A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58 and B62). The algorithm integrates the predictions of HLA binding, proteasomal C-terminal cleavage and transport efficiency by the transporter associated with antigen processing (TAP) molecules. HLA binding and proteasomal cleavage predictions are performed by an artificial neural networks (ANN) method, while TAP transport efficiency is predicted using a weight matrix method. The parameters used for NetCTL prediction were: 0.15 weight on C terminal cleavage (default), 0.05 weight on TAP transport efficiency (default), and 0.5 threshold for HLA supertype binding, which was reported to be optimal (sensitivity (SN), 0.89 and specificity (SP), 0.94) in a large benchmark study containing more than 800 known class I T-cell determinants [35].
The TEPITOPE software (2000 beta version; courtesy of J. Hammer) utilizes quantitative matrix-based motifs, obtained from experimental scanning of the binding of P1-anchored designer peptides to soluble HLA-DR molecules in in-vitro competition assays, to predict peptides binding to 25 common HLA-DR alleles (DRB1*0101, *0102, *0301, *0401, *0402, *0404, *0405, *0410, *0421, *0701, *0801, *0802, *0804, *0806, *1101, *1104, *1106, *1107, *1305, *1307, *1311, *1321, *1501, *1502, and DRB5*0101) [38],[39]. The parameters for TEPITOPE predictions were: 5% quantitative threshold and putative determinants with a 10-fold inhibitory residue included. Nonamer peptides predicted to bind at least 10 out of the 25 HLA-DR alleles were selected as putative supertype-restricted determinants.
Multipred (research.i2r.a-star.edu.sg/multipred/) is a computational system for the prediction of peptides that bind to HLA class I supertypes A2 and A3 and class II HLA-DR supertype [36]. The HLA alleles selected to represent these supertypes by Multipred were as follows: A2 supertype, A*0201, *0202, *0203, *0204, *0205, *0206, *0207 and *0209; A3 supertype, A*0301, *0302, *1101, *1102, *3101, *3301 and *6801; DR supertype, DRB1*0101, *0301, *0401, *0701, *0801, *1101, *1301, and *1501. Hidden Markov model (HMM) and ANN methods are the predictive models of Multipred; both have been optimized and show similar performances [36]. The sum thresholds used for prediction of peptides restricted to the three HLA supertypes by ANN and HMM methods were: A2, 31.33 (ANN; SN = 0.80 and SP = 0.83) and 47.08 (HMM; SN = 0.80 and SP = 0.78); A3, 24.53 (ANN; SN = 0.90 and SP = 0.95) and 37.58 (HMM; SN = 0.80 and SP = 0.87); and DR, 23.42 (ANN; SN = 0.90 and SP = 0.92) and 51.08 (HMM; SN = 0.90 and SP = 1.00). Consensus predictions of the two methods were taken as final predictions for each HLA supertype.
The ARB matrix method (epitope.liai.org:8080/matrix/matrix_prediction.jsp) is based on a matrix of coefficients to predict IC50 values [37]. The HLA class I alleles predicted by ARB were grouped according to the current supertype classification [19],[40] and supertypes containing more than two alleles were selected, namely A2 (A*0201, *0202, *0203, *0206, and *6802), A3 (A*0301, *1101, *3101, *3301 and *6801), B7 (B*0702, A*3501, *5101, *5301, and *5401), and B44 supertypes (B*4001, *4002, *4402, *4403, and *4501). The prediction threshold value chosen for optimum sensitivity and specificity was IC50≤1000 nM and nonamer peptides predicted to bind 3 or more alleles of the supertype were considered as putative promiscuous HLA supertype-restricted determinants.
All experiments were approved by the Johns Hopkins University Institutional Animal Care and Use Committee. Murine H-2 class II-deficient, HLA-DR2 [41], HLA-DR3 [42],[43], HLA-DR4 (referred to as DR4/IE) [44] and HLA-DR4/human CD4 (huCD4) [45],[46] Tg mice were used, bred and maintained in the Johns Hopkins University School of Medicine Animal Facility. Specific pathogen-free (SFP) colonies were maintained in a helicobacter-negative mice facility. The HLA-DR expression of the experimental transgenic mice was evaluated by flow cytometry.
Mice were immunized subcutaneously at the base of the tail, twice at two weeks interval, with pools of overlapping peptides covering the DENV-3 protein (15–17 aa, overlapping by 10–11 aa) (Schafer-N Inc., Copenhagen, Denmark; BEI Resources, Manassas, VA). Peptide pools (73–155 peptides per pool) contained 1 µg of each peptide and were emulsified (1∶1) in TiterMax adjuvant (TiterMax USA, Inc.). An aqueous preparation of TiterMax (1∶1) was used as a negative control. Two weeks after the second immunization, the mice were sacrificed and HLA-DR-restricted CD4 T cell reponses were assessed by ex vivo IFN-γ ELISpot assay using CD8-depleted splenocytes. Each target peptide was tested in duplicate. Spot-forming cell (SFC) counts were normalized to 106 cells. The results were considered significant when the average SFC minus two standard deviations (SD) was greater than the average of the background plus two SD; and the average values were greater than 10 SFC per 106 splenocytes. The initial screening assays were performed with peptide matrices [47], followed by assays with the relevant individual peptides (Nascimento et al., manuscript in preparation).
A total of 9,512 and 12,404 complete and partial DENV protein sequences were collected from the NCBI Entrez protein database of December 2005 and 2007, respectively, representing an increase of approximately 30% (2892 sequences) in the 24-months interval (Table 1). The total number of sequences (2007) varied from 4,011 for DENV-2 to 1,415 for DENV-4 and from 3,845 for E to 523 for NS4a proteins. Most of the individual protein sequences originated from DENV strains that were unique variants with respect to the entire polyprotein, but were identical to other strains with respect to individual proteins [48].
The consensus-sequence approach [20],[25] identified a total of 44 pan-DENV sequences of at least 9 amino acids that were present in ≥80% of all sequences of each DENV type for both 2005 and 2007 datasets (Figure 2; Table S1). Strikingly, 34 of the 44 (∼77%) were conserved in ≥95% of all reported DENV sequences. The size of the pan-DENV sequences ranged from 9 to 22 amino acids, with a combined size of 514 residues, corresponding approximately to 15% of the complete DENV polyprotein (∼3390 amino acids) (Table 2). The vast majority (42/44) of the pan-DENV sequences were localized in the NS proteins, with 17, 12, 7 and 5 sequences found in NS5, NS3, NS1 and NS4b, respectively, and 1 in the NS4a protein. Notably, the remaining two pan-DENV sequences were localized in the E protein. No region of at least 9 amino acids and conserved in ≥80% of the sequences of each DENV type was found in the C, prM, NS2a and NS2b proteins. The largest size of the combined pan-DENV sequences was in the NS5 protein, representing a total of 215 amino acid positions covering ∼24% of the protein, followed by NS3, NS1 and NS4b with 122, 74 and 69 amino acid positions covering ∼20, ∼21 and ∼28% of the corresponding proteins, respectively. The two pan-DENV sequences in the E protein had a combined size of only 25 amino acids, corresponding to ∼5% of the protein.
In large-scale genomic analyses such as this study, biases may result from the collection of completely or partially overlapping redundant sequences, corresponding to identical or highly similar circulating DENV isolates sequenced by various dengue surveillance programs in different countries. Although to some extent this redundancy may be accepted as a reflection of the incidence of the corresponding DENV isolates in nature, we assessed its potential bias effect by repeating the analysis of conservation after discarding duplicate sequences from the datasets. The analysis of unique sequences identified all the pan-DENV sequences that were identified when including duplicates (Figure 2), except for NS112–20, NS125–35 and NS5597–616. Therefore, the presence of duplicates in the DENV datasets did not significantly affect the results. Although the removal of duplicates does not fully compensate for biases in the datasets, the removal of highly similar sequences, which may have been generated from relatively large sequencing efforts in single outbreaks, was deemed undesirable, since such arbitrary selection would introduce additional biases.
The evolutionary diversity of each DENV type, and the 4 types combined, was studied by use of Shannon information entropy [27], modified to examine the variability of nonamer peptide sequences, as described in the Methods. The entropy of the proteome of the recorded viruses of each type showed numerous long regions of low entropy (≤1), reflecting the relatively high degree of intra-type sequence conservation, in particular in the NS3, NS4b and NS5 proteins (Figure 3A–D). Overall, the average intra-type nonamer entropy values of the individual protein sequences of DENV-1, -2, -3 and -4 ranged from 0.2 for the DENV-4 NS4b to 1.0 for DENV-2 prM (Figure S1). Of note, however, were the marked differences in the relative degree of entropy of each protein between the 4 DENV types. For example, NS4b had the least diversity of the proteins of 3 types, but was replaced in DENV-2 by NS2b, which was the second most variable in DENV-3. The consequence of the differences in the sequences of each protein between the 4 types was a marked increase in the peptide entropy across the DENV 1-4 proteomes (Figure 3E), with average peptide entropy ranging from 1.6 for NS3 to 2.6 for NS2a (Figure S1), except for 44 sharply defined regions of low nonamer entropy (≤0.5) where the sequences were highly conserved in all DENVs (Figure 3E), with no significant difference between the 2005 and 2007 datasets (Table S2). Majority of the pan-DENV sequences had entropy values of ≤0.3, corresponding to the intra-type representation of ≥90%. Thus, the congruent consensus- and entropy-based analyses of the DENV nonamer peptides revealed highly conserved and evolutionarily stable pan-DENV sequences distributed in several viral proteins, despite the marked viral diversity defining multiple DENV types, genotypes and variants [49].
The combined representation of variant peptides that differed by at least one amino acid from the predominant peptide was also analyzed at each nonamer position. Examples of this analysis for DENV-3 proteins are shown in Table 3. Nonamers that lack entropy (zero entropy) have one sequence in all of the recorded virus isolates, and therefore have no variants. Positions with high entropy can contain many different variant peptides, each at lesser (or equal) frequency than the predominant peptide. The combined representation of variant peptides at each nonamer position across the proteome of each individual DENV type was generally low, representing less than 10% of the corresponding sequences, except for some positions where it was more than 50% (Figure 4A–D). Notably, the nonamer position with the highest combined variant representation for each DENV type was found in the nonstructural proteins and not the structural ones, with representation values ranging from ∼61 to ∼78% (DENV-1 NS5, DENV-2 NS5, DENV-3 NS2a, and DENV-4 NS1 and NS3 proteins). When representations of variants across all DENVs were calculated, the majority of all nonamer sites contained variants that together represented ∼60–85% of the total DENV sequences at that site (the highest representation of ∼85% was in the NS1 protein) (Figure 4E). This was in striking contrast to the 0 to ∼5% combined representation of variants at each nonamer position in the pan-DENV sequences, with no significant difference between the 2005 and 2007 datasets (Table S2). The majority of all nonamer sites in the pan-DENV sequences lacked variant or contained variants that together represented <1% of all recorded DENVs. These data further illustrate the extremely high genetic stability of the 44 pan-DENV sequences, among all recorded DENV sequences and demonstrate that irrespective of the high variability between the sequences of the 4 DENV types, the representation of variants in the pan-DENV sequences was almost negligible.
Highly conserved protein sequences are likely to represent critical sites and domains [50]. A search of the literature and the Prosite and Pfam databases [30],[32] revealed that 27 of the 44 pan-DENV sequences were associated with biological activities (Table S3); the functional significance of the remaining 17 pan-DENV sequences was not known. The two pan-DENV sequences in the E protein corresponded to the fusion peptide (positions 98 to 110) and dimerisation domain [51],[52]. In NS3, one pan-DENV sequence corresponded to the peptidase family S7 (Flavivirus serine protease) domain and comprised the His-51 catalytic residue [53], 3 sequences corresponded to known/putative Flavivirus Asp-Glu-Ala-Asp/His (DEAD/H) domain associated with ATP-dependent helicase activity [54], and two sequences were predicted to be required for cell attachment and targeting signal for microbodies. In NS5, one pan-DENV sequence corresponded to the conserved methyltransferase (MTase) S-adenosyl-L-methionine binding motif I (positons 77–86) involved in viral RNA capping [55], and two sequences corresponded to RNA dependent RNA polymerase (RdRp) domain [56]. Furthermore, 6 of the 27 pan-DENV sequences were predicted to exhibit post-translational modification(s), including N-glycosylation, protein kinase C and casein kinase II phosphorylation, N-myristoylation and/or amidation (Table S3).
It is generally recognized that amino acids buried inside proteins are subject to greater interactions and packing constraints [57] than those exposed on the outer surface. Although none of the DENV protein structures in the protein data bank (PDB) [33] was full-length, 19 of the 44 pan-DENV sequences could be mapped on the available crystallographic models of the E ectodomain (Accession No. 1OAN; 394 out of 493–495 residues), NS3 (1BEF and 2BMF, 181 and 451 out of 618–619 residues, respectively) and NS5 fragments (1R6A, 295 out of 900–904 residues). Eleven of the 19 pan-DENV sequences were buried, 2 partially exposed and 6 exposed at the surface of the corresponding structures (Figure S2). However, these results should be considered preliminary until full-length 3-D structures are available.
Twenty-seven (27) of the 44 pan-DENV sequences overlapped at least 9 amino acid sequences of as many as 64 other viruses of the family Flaviviridae, genus Flavivirus (Figure 5). Zika virus shared 22 of the 27 sequences; Ilheus and Kedougou viruses, 18; and representatives of some of the significant human pathogens, West Nile, St. Louis encephalitis, Japanese encephalitis, Yellow fever and Tick-borne encephalitis viruses, shared from 16 to 9 pan-DENV sequences. Thirteen (13) of the 27 sequences represented NS5, of which 9 were present in at least 27 Flavivirus species; 9 represented NS3, of which two were found in 35 and 23 species; one E sequence was found in 19 species; and the remaining were in NS1 and NS4b (Figure 6; Table S4). Five (5) of the 27 were associated with known biological activities (NS579–90 MTase, NS5658–670 RdRp, NS346–55 peptidase S7, NS3284–292 DEAD/H and E97–111 dimerisation/fusion domains). Interestingly, two sequences, NS3406–418 and NS5597–616, overlapped 9 amino acid sequences of the cell fusing agent virus polyprotein-like protein from the mosquito Aedes albopictus [58], and the phage-related tail fibre protein-like protein from the bacteria Chromohalobacter salexigens DSM 3043, respectively.
The representation of many of the pan-DENV sequences was high among known sequences of several of the highly studied Flaviviruses (Table S4): St. Louis encephalitis, West Nile, Japanese encephalitis, Murray Valley encephalitis, Usutu, Kokobera, Ilheus, Tick-borne encephalitis, Langat, Omsk hemorrhagic fever, Louping ill, Powassan, Kyasanur forest disease and Yellow fever viruses. Protein sequence data for the rest of the Flaviviruses that shared pan-DENV sequences was limited (<10 sequences) in the public database. Seven of the 27 pan-DENV sequences, NS112–20, NS3256–267, NS3383–392, NS3491–499, NS4b223–236, NS56–14 and NS5302–310, were present in a few species with less than 10 reported total sequences (Table S4).
Literature survey and database search revealed that 10 of the pan-DENV sequences (9 in NS3, one in E) overlapped at least 9 amino acids of 15 previously reported DENV T-cell determinants immunogenic in human, with their HLA restriction, when known, showed both class II (DR*15, DPw2) and class I (A*11) specificities (Table 4). Further evaluation of the immune-relevance of the pan-DENV sequences included a search for candidate putative promiscuous HLA supertype-restricted T-cell determinants within these regions by use of several computational algorithms: NetCTL [35], Multipred [36], ARB [37] and TEPITOPE [38]. Overall, 34 of 44 (∼77%) pan-DENV sequences (Figure 7), identified in the NS5, NS3, NS1, E and NS4a proteins were predicted to contain 100 supertype-restricted binding nonamers (Table S5). The majority (88/100) of the predicted promiscuous HLA-binding nonamers were present in ≥95% of the sequences of each DENV type (Table S6). Thirty-one (∼91%) of the 34 putative supertype pan-DENV sequences contained HLA-binding nonamers for multiple HLA supertypes. Clusters (hotspots) of two or more overlapping HLA-binder nonamer core peptides were present in 27 (∼79%) of the 34 putative supertype pan-DENV sequences. About half (14/27) of these clusters contained three or more nonamer binders overlapping by 8 amino acids, covering most or the entire corresponding conserved region.
The immunogenicity of the pan-DENV sequences was also analyzed by assay of peptide-specific HLA-restricted T-cell responses in murine H-2 class II-deficient, HLA-DR Tg mice expressing 3 prototypic HLA-DR alleles, corresponding to the divergent subgroups HLA-DR2 (DRB1*1501), HLA-DR3 (DRB1*0301), and HLA-DR4 (DRB1*0401). Mice were immunized with pools of overlapping peptides covering the sequences of the E, NS1, NS3, and NS5 proteins of DENV-3, and HLA-DR-restricted CD4 T-cell responses were assessed by IFN-γ ELISpot assays using CD8-depleted splenocytes. Thirty peptides eliciting positive T-cell responses in the HLA Tg mice contained 9 or more consecutive amino acids of 22 pan-DENV sequences, that were localized in the NS5 (11), NS3 (6), NS1 (4), and E proteins (one) (Table 5). Overall, 9, 10 and 18 peptides elicited positive responses in HLA-DR2, -DR3, and/or -DR4 Tg mice, respectively; 20 corresponded to sequences of NS5, 10 of NS3, 6 of NS1, and one of E. Furthermore, at least 7 of the pan-DENV sequences, all localized in the NS5 and NS1 proteins, contained promiscuous T-cell determinants for multiple HLA-DR alleles (Table 5). These data, together with those previously reported (Table 4), showed that a minimum of 26 of the 44 pan-DENV sequences, distributed predominantly in the NS5 and NS3 proteins, and to a lesser extent in NS1 and E, contained numerous HLA-restricted class II and/or class I determinants demonstrated by assays of T-cell responses in vivo.
In this study, we identified and characterized pan-DENV sequences that were highly conserved in all recorded DENV isolates. The large number of sequences analyzed (12,404 as of December 2007), and their wide distribution in terms of geography and time (1945–2007) (data not shown), offered information for a broad survey of DENV protein diversity in nature. The 44 pan-DENV protein sequences of at least 9 aa, covering 514 aa or about 15% of the complete DENV polyprotein of ∼3390 aa, were conserved in at least 80% of all recorded DENV sequences, and 34 of the 44 (∼77%) were conserved in ≥95% of DENV sequences. All the 44 were in the non-structural proteins except for the two E sequences. These conserved sequences have shown remarkable stability over the entire history of DENV sequences deposited in the NCBI Entrez protein database, as illustrated by their low peptide entropy values and variant frequencies. In addition, 27 of the pan-DENV sequences were conserved in 64 other Flaviviruses, as further evidence of prolonged evolutionary stability within this genus, as previously discussed [59]–[61]. Two are also present in the proteomes of the Aedes albopictus mosquito and the bacteria Chromohalobacter salexigens, possibly in keeping with recent reports of the genetic recombination between phyla [58]. It is likely that these pan-DENV sequences have been under selection pressure to fulfill critical biological and/or structural properties, some of which have been identified for the E (fusion peptide, dimerization domain), NS3 (peptidase S7, DEAD/H domains) and NS5 proteins (MTPase, RdRp domains) [51]–[56]. Hence, these conserved sequences are unlikely to significantly diverge in newly emerging DENV isolates in the future, and represent attractive targets for the development of specific anti-viral compounds and vaccine candidates.
There also is evidence that many of the conserved sequences are immunologically relevant. A majority (26/44) contained at least 9 amino acids overlapping with a total of 45 peptides that have been reported to be immunogenic in humans and/or HLA-DR Tg mice. In addition, putative T-cell determinants for 12 major HLA class I supertypes and for class II DR supertype, with broad application to the immune responses of human population worldwide, were predicted by computational analysis. Some of the putative T-cell determinants were predicted to be promiscuous to multiple HLA supertypes, in addition to multiple alleles of a given HLA supertype. Such a degree of promiscuity has previously been observed for DENV [62] and HIV peptides [63], among others. The existence of conserved T-cell determinants specific for multiple HLA supertypes further supports their evaluation as vaccine targets, since they would provide broader population coverage [63]. Many of the predicted HLA binding nonamers were localized in clusters, as we have also observed in HLA Tg mice immunized with WNV proteins and DNA encoding the SARS coronavirus N protein [64], and has been reported in studies of human immunodeficiency virus (HIV) type 1 proteins [65]–[68], the outer membrane protein of Chlamydia trachomatis [69], and other antigens [64].
The significant sequence variations between the proteins of the 4 DENV types represent a cardinal issue for the development of a tetravalent DENV vaccine that provides robust protection against each DENV type. Subtle amino acid substitutions within T-cell determinants restricted by a given HLA allomorph, such as in the event of sequential heterologous infections, or between a vaccine formulation and a subsequent natural infection [7], can dramatically alter the phenotype of the specific T cells, resulting in a wide range of effects from agonism to antagonism [9], [12]–[15]. Because of the extent of intra-type (1 to 21%) and inter-type (14 to 67%) amino acid variability among DENV isolates [48], many nonamer T-cell determinants contain single or multiple amino acid difference(s). When the 4 DENV types were analyzed together, a majority of the nonamer positions across the full proteome exhibited variants that together were present in ∼60 to ∼85% of all sequences. The frequencies of variant peptides across the 4 DENV types suggest that vaccine strategies incorporating whole DENV immunogens, such as inactivated and recombinant subunit vaccines, live attenuated viruses, or chimeric viruses expressing structural DENV genes, are likely to elicit T-cell responses to altered peptide ligands. This phenomenon is also likely to occur in individuals exposed to several Flaviviruses, such as DENV, JEV and YFV that are co-circulating in regions of Asia, India or South America, or following vaccination [70].
While the immune correlates of DENV protection remain poorly documented, there is evidence that both neutralizing antibody and specific T-cell responses are required [7],[71]. The incorporation of defined HLA-restricted T-cell determinants within DENV vaccine candidates might improve vaccine efficiency by increasing T-cell help to sustain a robust, long-lived immunity, and possibly through direct cytostatic and cytotoxic effects on infected cells. For tetravalent formulations, it may be relevant to focus primarily on sequences that are conserved in all 4 DENV types and to avoid the regions of T-cell immunity that are highly variable, unless they are strictly type-specific [17],[72]. The two pan-DENV E sequences (positions 97–111 and 252–261) and the exposed domain III of the E antigen (positions 300–400) [73],[74], are also candidate sequences for neutralizing antibody responses. An additional criterion for the selection of T-cell targets is the need for determinants with broad HLA representation, as it has been emphasized in the recognition of HLA supertypes [18]–[20]. Further investigations are needed to validate the immunogenicity of the candidate T-cell determinants in human subjects, and to identify sequences associated with deleterious T-cell responses.
The global approach described herein provides a framework and methodology for large-scale and systematic analysis of conserved sequences of other pathogens, in particular for rapidly evolving viruses such as influenza A virus [75] and HIV [63]. These studies will offer insights into their diversity and evolutionary history, together with providing critical data for rational vaccine development, structure-based design of candidate inhibitory compounds, and improvement of the current diagnostic methods.
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10.1371/journal.pcbi.0040002 | Genes and (Common) Pathways Underlying Drug Addiction | Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.
| Drug addiction has become one of the most serious problems in the world. It has been estimated that genetic factors contribute to 40%–60% of the vulnerability to drug addiction, and environmental factors provide the remainder. What are the genes and pathways underlying addiction? Is there a common molecular network underlying addiction to different abusive substances? Is there any network property that may explain the long-lived and often irreversible molecular and structural changes after addiction? These important questions were traditionally studied experimentally. The explosion of genomic and proteomic data in recent years both enabled and necessitated bioinformatic studies of addiction. We integrated data derived from multiple technology platforms and collected 2,343 items of evidence linking genes and chromosome regions to addiction. We identified 18 statistically significantly enriched molecular pathways. In particular, five of them were common for four types of addictive drugs, which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.
| Drug addiction, defined as “the loss of control over drug use, or the compulsive seeking and taking of drugs despite adverse consequences,” has become one of the most serious problems in the world [1]. It has been estimated that genetic factors contribute to 40%–60% of the vulnerability to drug addiction, and environmental factors provide the remainder [2]. What are the genes and pathways underlying addiction? Is there a common molecular network underlying addiction to different abusive substances? Is there any network property that may explain the long-lived and often irreversible molecular and structural changes after addiction? These are all important questions that need to be answered in order to understand and control drug addiction.
Knowing the genes and vulnerable chromosome regions that are related to addiction is an important first step. Over the past three decades, a number of technologies have been used to generate such candidate genes or vulnerable chromosome regions. For example, in hypothesis-driven studies, genes in different brain regions were selectively expressed, downregulated, or knocked out in animal models of addiction [3]. Recent high-throughput expression-profiling technologies such as microarray and proteomics analyses identified candidate genes and proteins whose expression level changed significantly among different states in addiction [4,5]. Finally, genetic studies such as animal Quantitative Trait Locus (QTL) studies, genetic linkage studies, and population association studies identified chromosomal regions that may contribute to vulnerability to addiction [6–8]. However, as addiction involves a wide range of genes and complicated mechanisms, any individual technology platform or study may be limited or biased [3,9–14]. There is a need to combine data across technology platforms and studies that may complement one another [3,15,16]. The resultant gene list, preferably in a database form with additional functional information, would be a valuable resource for the community. Systematic and statistical analysis of the genes and the underlying pathways may provide a more complete picture of the molecular mechanism underlying drug addiction.
Although different addictive drugs have disparate pharmacological effects, there are also similarities after acute and chronic exposure such as acute rewarding and negative emotional symptoms upon drug withdrawal [17]. Recently it was asked “Is there a common molecular pathway for addiction?” because elucidation of common molecular pathways underlying shared rewarding and addictive actions may help the development of effective treatments for a wide range of addictive disorders [17]. Several individual pathways have been proposed as common pathways [17]; however, they have not been studied systematically and statistically.
Key behavioral abnormalities associated with addiction are long-lived with stable and irreversible molecular and structural changes in the brain, implying a “molecular and structural switch” from controlled drug intake to compulsive drug abuse [18]. It was proposed that the progress of addiction may involve positive feedback loops that were known to make continuous processes discontinuous and reversible processes irreversible [19]. Once a common molecular network for addiction is constructed, we can look for the existence of positive feedback loops in the network and study the coupling between the loops. It may provide clues to explain the network behaviour and the addiction process.
As currently the information is scattered in literature, we retrieved and reviewed more than 1,000 peer-reviewed publications from between 1976 and 2006 linking genes and chromosome regions to addiction. In total, we collected 2,343 items of evidence linking 1,500 human genes to addiction. The detailed statistics is shown in Figure 1 and Table S1. A Knowledgebase of Addiction-Related Genes (KARG) is made publicly available at http://karg.cbi.pku.edu.cn. A description of the database statistics is given in Table S1, and the functional annotation fields are listed in Table S2. Two screenshots of the database user interface are shown in Figures S1 and S2. The interface supports browsing of the genes by chromosome or pathways, advanced text search by gene ID, organism, type of addictive substance, technology platform, protein domain, and/or PUBMED ID, and sequence search by BLAST similarity [20]. All data, database schema, and MySQL commands are freely available for download at http://karg.cbi.pku.edu.cn/download.php.
We analyzed in detail 396 genes that were supported by two or more independent items of evidence. We found that 18 pathways were statistically significantly enriched in addiction-related genes compared to the whole genome as background, including both metabolic and signalling pathways (Table 1). These pathways could be clustered into two categories: (i) upstream events of drug addiction including crosstalk among MAPK signaling, insulin signalling, and calcium signalling, which share properties with long-term potentiation; and (ii) downstream effects including regulation of glycolysis metabolism, regulation of the actin cytoskeleton, and apoptosis, which share components with a list of neurodegenerative disorders such as Huntington disease and amyotrophic lateral sclerosis. Gene Ontology enrichment analysis confirmed the findings (see details in Text S1 and Table S3).
Because we collected metadata about each item of evidence linking genes to addiction, in particular the nature of the addictive substance, we could ask next what are the pathways underlying addiction to each type of substance, and what are the common pathways among them. We identified five pathways shared by all four addictive substances (Table 2). Three of the pathways had been linked to addictive behaviors in previous studies and were statistically confirmed here. For example, “long-term potentiation” had been linked to addiction-induced adaptations in glutamatergic transmission and synaptic plasticity [21]. In particular, a core component of this pathway, CAMKII, had been reported to regulate neurite extension and synapse formation through regulation of the actin cytoskeleton [22], providing possible explanations for morphological changes triggered by addictive drugs [17]. This pathway was also considered a key molecular circuit underling the memory system, highlighting the possible shared mechanisms between drug addiction and the learning and memory system [23]. “MAPK signaling pathway” is another example, as previous studies had suggested its roles in regulating synaptic plasticity related to long-lasting changes in both memory function and addictive properties [24].
More interestingly, two other common pathways identified here had not been directly linked to addiction. “GnRH signaling pathway” was reported to activate gene expression and secretion of gonadotropins and regulate stress pathways in the hypothalamo-pituitary gonadal axis and mammalian reproduction [25]. It is reasonable to hypothesize that the pathway may also be involved in the regulation and control of certain emotional behaviors in addiction such as stress-induced drug-seeking. Another common pathway identified in our study, “Gap junctions”, can be regulated directly by three addiction-related kinases in the “long-term potentiation” pathway, PKA, PKC, and ERK. Since gap junctions are not only an important type of connection for neuroglial cells but also the most prevalent group of electrical synapses in the brain [26], this regulation may imply potential modification of cell communication in addiction. It would be interesting to investigate the roles of these pathways in future experimental studies.
A pathway is in itself a subjective concept, whereas the real systems are dynamic and include wide-ranging crosstalk among functional modules. Connecting the common pathways with additional protein–protein interaction data, we constructed a hypothetical common molecular network for drug addiction, shown in Figure 2 (see details in Text S2 and Figure S3).
From the common pathway network we identified four positive feedback loops, shown in Figure 2. We further observed that they interlinked with each other through CAMKII (Figure 2). Two of these positive feedback loops involved signal transduction and would be considered “fast” loops, whereas the other two loops involved transcription and translation and would be considered “slow” loops. It had been reported in a dozen systems, such as budding yeast polarization and Xenopus oocyte maturation, that coupled fast and slow positive feedback loops could create a switch that was inducible and resistant to noise and played key roles in discontinuous and irreversible biological process, features characteristic of addiction [27–29]. It was also known that activation of CAMKII played key roles in the development and maintenance of addiction states [30,31]. Disruption of dendritic CaMKII translation impaired the stabilization of synaptic plasticity and memory consolidation [32,33]. These evidences, taken together, suggested that the fast and slow positive feedback loops interlinked through CAMKII may be essential for the development and consolidation of addiction and may provide a systems-level explanation for some of the characteristics of addictive disorders.
The addiction-related genes, (common) pathways, and networks were traditionally studied experimentally. The explosion of genomic and proteomic data in recent years both enabled and necessitated bioinformatic studies of addiction. Integration of data from multiple sources could remove biases of any single technology platform, and statistical and network analysis of the integrated data could uncover high-level patterns not detectable in any individual study. For instance, our analysis revealed not only many pathways already implicated in addiction [34–38], but also new ones such as GnRH signaling pathway and gap junction, as well as the coupled positive feedback loops through CAMKII. They could serve as interesting hypotheses for further experimental testing.
The collection of addiction-related genes and pathways in KARG, the first bioinformatic database for addiction, is the most comprehensive to date. However, as new technologies continue to be developed and used, more and more genes will be linked to addiction. In 2004, a paper asked why proteomics technology was not introduced to the field of drug addiction [5]; since then eleven studies have identified about 100 differentially expressed proteins in drug addiction. Tilling-array technology, another new strategy for whole-genome identification of transcription factors binding sites, has been used to identify targets of CREB, an important transcription factor implicated in drug addiction [39]. In addition, as 100 K and 500 K SNP arrays have been introduced recently, whole genome association studies will also identify more closely packed and unbiased hypothesis-free vulnerable positions [40]. We will continue to integrate new data and update the gene list and molecular pathways toward a better understanding of drug addiction.
The data collection pipeline is summarized in Figure 1. The data and knowledge linking genes and chromosome regions to addiction were extracted from reviewing more than 1,000 peer-reviewed publications from between 1976 and 2006. This list of publications, available on KARG Web site at http://karg.cbi.pku.edu.cn/pmid.php, included recent review papers on addiction selected from results of PUBMED query ‘(addiction OR “drug abuse") AND review' as well as research papers selected from PUBMED query ‘(addiction OR “drug abuse") AND (gene OR microarray OR proteomics OR QTL OR “population association” OR “genetic linkage”)'. The data spanned multiple technology platforms including classical hypothesis-testing of single genes, identification of significantly differentially expressed genes in microarray experiments, identification of significantly differentially expressed proteins in proteomics assays, identification of addiction-vulnerable chromosome regions in animal QTL studies, genetic linkage studies, population association studies, and OMIM annotations [41]. From each publication we collected the genes, proteins, or chromosome regions linked to addiction, as well as metadata such as species, nature of the addictive substance, studied brain regions, technology platforms, and experimental parameters. For candidate genes or chromosomal regions identified in mouse or rat, we mapped them to human genes through ortholog mapping by Homologene or syntenic mapping, respectively [41]. For chromosome regions identified in genetic studies, we identified candidate genes when at least one positive marker lay (i) within the gene or (ii) in 3′ or 5′ flanking sequences that were contained on a block of high restricted haplotype diversity along with exon sequences from the same gene [8]. In total, we collected 2,343 items of evidence linking 1,500 human genes to addiction. Among them 396 genes were supported by two or more items of evidence (see full list in Table S4). This more reliable subset was used in subsequent analysis.
We used the FASTA sequences of the 396 human addiction-related genes as input to the KOBAS software, using all known genes in the human genome as background [42,43]. KOBAS had been shown to lead to experimentally validated pathways [44]. It maps the input sequences to similar sequences in known pathways in the KEGG database [45] (as determined by BLAST similarity search with evaluated cut off e-values <1e-5, rank ≤10), and then groups the input genes by pathways. Because some pathways are naturally large, they may appear highly represented in a random selection of genes or gene products. To resolve this, KOBAS selects the pathways that are more likely to be biologically meaningful by calculating the statistical significance of each pathway in the input set of genes or gene products against all pathways in the whole genome as background. For each pathway that occurs in the input genes, KOBAS counts the total number of genes in the input that are involved in the pathway, named m, and the total number of genes in the whole genome that are involved in the same pathway, named M. If input has n genes and the whole genome has N genes, the p-value of the pathway is calculated using a hypergeometric distribution:
KOBAS then performs FDR correction [42] to adjust for multiple testing. Pathways with FDR-corrected Q-value < 0.05 were considered statistically significantly enriched in the input set of addiction-related genes.
For each of the four addictive substances, cocaine, opiate, alcohol and nicotine, we input its list of related genes to KOBAS to identify the statistically significantly enriched pathways. Molecular pathways that were identified as significantly enriched for all four addictive substances were selected as common pathways for drug addiction.
We constructed a large molecular network of addiction-related genes with the nodes being the gene products and the links extracted from the KEGG database, the Biomolecular Interaction Network Database (BIND), and Human Interactome Map (HIMAP) [46]. The network was analyzed and visualized by Medusa [47]. We selected a more biologically meaningful sub-network representing only the common pathways identified above.
We developed a database with MySQL relational schema. Cross-reference to key external databases were included to integrate functional information about the genes, such as gene annotation [41], Gene Ontology annotation [48], interacting proteins [46], and functional domain annotations [49]. In addition, a link was given to the original literature reference in the NCBI PubMed database [41]. We implemented a Web-based user interface of the database using PHP and queries of the database using PHP/SQL query script. |
10.1371/journal.pntd.0002229 | Phylogenetic Findings Suggest Possible New Habitat and Routes of Infection of Human Eumyctoma | Eumycetoma is a traumatic fungal infection in tropical and subtropical areas that may lead to severe disability. Madurella mycetomatis is one of the prevalent etiologic agents in arid Northeastern Africa. The source of infection has not been clarified. Subcutaneous inoculation from plant thorns has been hypothesized, but attempts to detect the fungus in relevant material have remained unsuccessful. The present study aims to find clues to reveal the natural habitat of Madurella species using a phylogenetic approach, i.e. by comparison of neighboring taxa with known ecology. Four species of Madurella were included in a large data set of species of Chaetomium, Chaetomidium, Thielavia, and Papulaspora (n = 128) using sequences of the universal fungal barcode gene rDNA ITS and the partial LSU gene sequence. Our study demonstrates that Madurella species are nested within the Chaetomiaceae, a family of fungi that mainly inhabit animal dung, enriched soil, and indoor environments. We hypothesize that cattle dung, ubiquitously present in rural East Africa, plays a significant role in the ecology of Madurella. If cow dung is an essential factor in inoculation by Madurella, preventative measures may involve the use of appropriate footwear in addition to restructuring of villages to reduce the frequency of contact with etiologic agents of mycetoma. On the other hand, the Chaetomiaceae possess a hidden clinical potential which needs to be explored.
| Eumycetoma caused by Madurella mycetomatis is a common subcutaneous, mutilating fungal infection endemic in arid climate zones. Still there are many controversies on the route of infection, but traumatic inoculation of the subcutaneous tissue with the thorn or soil causative organism through minor skin trauma is a popular theory. This is due to the fact that, the origin and natural habitat of Madurella species, the prevalent mycetoma agents are still unknown. In order to predict the natural habitat of M. mycetomatis we investigated its phylogenetic relationships to species with known ecology. Two genes phylogeny based on LSU and ITS was performed for the species of the genus Madurella and representative genera from the family of Chaetomiaceae. Our findings confirmed that Madurella species are phylogenetically member of the family Chaetomiaceae. Members of this family are often found in dung and manure-enriched soil. We therefore suggest that animal dung, abundantly present in endemic villages, could be a possible niche for Madurella and plays an essential role in the onset of eumycetoma. This will help in understanding the origin of the disease and could be a base for future in depth study to investigate the presence of Madurella in dung from endemic areas.
| Eumycetoma is a subcutaneous disease with a high morbidity. It is prevalent in tropical and subtropical arid climate zones, with a focus in Northeastern Africa and particularly the Sudan [1]. Patients who develop advanced mycetoma of the extremities frequently become invalids due to the immobilizing nature of the disease (Fig. 1) [2]. Due to lack of social programs and poverty, patients become perpetually dependent on their family. Mycetoma can be caused by a variety of both bacteria (actinomycetoma) and fungi (eumycetoma) and is chronically progressive [1], [2]. eumycetoma is difficult to treat by chemotherapy, surgery frequently leads to mutilation, and relapse is common postoperatively. In the Sudan alone, 25% of the eumycetoma patients underwent amputation of the infected limb because of failure of therapy [3].
In order to reduce the morbidity of this disease, not only is an improvement in chemotherapy required, but also in the preventive measures. These might involve an efficient vaccine, as well as a reduction of contact with the causative agent. Gaining insight in the natural habitat of the prevalent Sudanese agent of mycetoma, Madurella mycetomatis, may lead to strategies to prevent introduction of causative agents into the skin and should reduce the burden of this disease in the endemic communities. However, the natural habitat of the prevalent Sudanese agent of mycetoma, Madurella mycetomatis, is unknown. The classical hypothesis is that aetiologic agents are traumatically introduced via thorn-pricks or with soil particles contaminated by the aetiologic agent, but M. mycetomatis has never been cultured from either thorns or soil. Madurella DNA was demonstrated in 17 out of 74 soil samples, and only in one out of 22 thorns tested [4]. Thus, the thorn-prick hypothesis seems less likely.
Madurella mycetomatis is thus far only known as sterile, melanized mycelium isolated from symptomatic patients. Isolates from subcutaneous infections that consist of dark hyphae are therefore routinely referred to as ‘Madurella’, while those forming compact clumps of cells are traditionally identified as ‘Papulaspora’. Still no form of propagation, either sexual or clonal, is known for these fungi, except for some occasional, undiagnostic phialide-like cells [5]. There are many more causative agents of subcutaneous disorders which lack identifiable sporulation in culture. Today, identification options of such poorly structured fungi have increased with the development of molecular diagnostics. It has become clear that non-sporulating fungi are phylogenetically quite diverse. The melanized species causing black-grain mycetoma worldwide belong to at least two different orders of ascomycetes: the Sordariales and the Pleosporales [6].
In the present study we apply morphology-independent techniques to classify sterile agents of mycetoma in a phylogenetic scaffold of the fungi. This should lead to a better understanding of their ecology and pathology. Non-sporulating clinical isolates, provisionally deposited in two reference laboratories under the generic names Madurella and Papulaspora, were analyzed using the universal fungal barcode gene rDNA partial large subunit (LSU) and the internal transcribed spacer (ITS) regions. Since Madurella mycetomatis is a member of the order Sordariales, Madurella pseudomycetomatis, M. fahalii and M. tropicana most likely belong to the same order [7]. Phylogenies based on the mitochondrial genome confirmed the relationship to the Sordariales. Shared synteny was observed of genes and tRNAs in the mitochondrial genomes of M. mycetomatis and Chaetomium thermophilum [8]. Chaetomium is a large genus of Sordariales with more than 100 described species [9], but only very few species have been sequenced yet. In the present study we sequenced reference and additional clinical isolates of Chaetomium (ITS and LSU). Further members of the family Chaetomiaceae (Sordariales), including representatives of the genera Achaetomium, Aporothielavia, Chaetomidium, and Thielavia were selected to build up a framework of neighboring species to Madurella. Notably nearly all these fungi are ascosporulating only, producing elaborate fruiting bodies which cannot be expressed in human host tissue. Loss of the fruiting body thus immediately leads to sterile, Madurella-like cultures, rather than to a conidial counterpart as is the case in the majority of filamentous fungi. Comparison of ecological habitats of Chaetomiaceae was done in order to predict aspects of possible sources and routes of transmission of Madurella species.
The analysis consists of 128 strains among which 60 strains of Chaetomiaceae contain presently available ex-type strains of described species deposited in the CBS culture collection. A total of 13 sterile filamentous isolates identified as Madurella, and one meristematic isolate, phenotypically identified as Papulaspora sp. were analyzed. The set was complemented with 54 clinical strains identified in this study (Supporting information; table S1). All clinical isolates included in our study were previously isolated from human sources and were taken from the CBS reference collection. Information on strains can be found at (www.cbs.knaw.nl)
About 10 mm3 fungal mass grown on agar surface were scraped in 2 ml screw cap vial containing 490 µl CTAB-buffer (2% CTAB, 100 mM Tris-HCL, 20 mM EDTA, 1.4 M NaCl) and 6–10 acid washed glass beads. In the subsequent step 10 µl of proteinase K (50 mg/ml) were added and the extraction buffer containing the sample vortexed for 2–5 minutes. The vials were incubated at 60°C for 60 minutes and vortexed again to ensure homogeneity of the sample. 500 µl of SEVAG (Chloroform∶Isoamylalcohol 24∶1) were added and the vials inverted repeatedly for at least two minutes. Vials were centrifuged at 14000 rpm (Eppendorf 5417R, Hamburg, Germany) for 10 minutes and the supernatant collected in new sterile vials with 0.55 volumes of ice cold 2-propanol and inverted several times. The precipitated total nucleic acids were centrifuged at 14000 rpm for 10 minutes. Finally, the pellets were washed with 70% ethanol, air- dried and re-suspended in 100 µl TE buffer.
The internal transcribed spacer (ITS) was amplified using the primers V9G and LS266 [10]. The resulting amplicons were bidirectionally sequenced with primers ITS1 and ITS4 [11]. The partial large ribosomal subunit (28S) was amplified with primer LR0R and LR5 and sequenced with the same primers [12]. A life Technologies Corp. 3730XL Sanger laboratory capillary electrophoresis system was used to retrieve the sequence data.
Trace files retrieved from bidirectional sequencing, were assembled and manually edited using Lasergene Seqman (DNASTAR, USA). A selection of 89 strains from the total data set was used for inferring the phylogenetic tree. Sequences were aligned with MUSCLE using the EMBL-EBI web server (http://www.ebi.ac.uk/Tools/msa/muscle/). A concatenated alignment was assembled for complete ITS (ITS1-5.8S-ITS2) and partial LSU sequences.
Bayesian and maximum likelihood analysis were performed with MrBayes v. 3.1.2 [13], and RAxML 7.2.8 respectively [14], [15]. MrBayes was run for 1 000 000 generations; one tree was saved per 100 of generations and burn-in was set for 25% of the saved trees. The 50% majority consensus tree was calculated and the final tree was edited using MEGA v. 5.05 [16]. Maximum likelihood was conducted using the CIPRES website (www.phylo.org), and GTR (General Time Reversible) model of nucleotide substitution was used; it is the only nucleotide substitution model in the RAxML software.
The analyzed data set comprised representative strains of the Chaetomiaceae [Sordariales] of both clinical and environmental origins (Supporting information; table S1).
Alignment of the combined genes sequences (ITS, LSU) consisted of 1,356 total characters in which 1029 were constant and 307 were variable. In our two-gene phylogeny most basal and internal branches show high Bayesian inference posterior probability values (BII PP) and maximum-likelihood bootstrap support (ML BS) respectively (Fig. 2). However, some internal branches of the Chaetomiaceae ingroup tree (split 0.88/-) comprising several clusters, e.g. for C. atrobrunneum and C. nigricolor (1.0/100), Chaetomium “sp. 1”, C. lucknowense, C. variosporum, Thielavia terricola and T. fragilis (0.96/46) as well as C. errectum and C. funicola (1.0/100), could not be fully resolved into dichotomies. The ingroup tree comprised a monophyletic cluster with four Madurella species with 1.0, 85% BII PP and ML BS, respectively, basal to Thielavia subthermophila (0.93/66). Madurella clustered within a large clade containing mostly environmental Chaetomium species which were distant from the type species of Chaetomium (C. globosum; Fig. 2). Madurella fahalii was identified as the closest taxon to the Chaetomiaceae at 6.0% ITS divergence from Chaetomium nigricolor. Papulaspora equi, known from three clinical isolates and identified by it is ex-type strain, was resolved basal to the grade comprising the Chaetomium/Chaetomidium/Thielavia/Madurella clades.
The data set contained 38 ex-type and authentic strains. Twenty-two of these were usable to define each as OTU's (Operational taxonomical unit), while 16 were found to be identical to other described species defined by an ex-type isolate. Seven species, as delimited by sequence data, comprised more than one ex-type strain having identical sequences, rendering these species as provisional synonyms. Groups of isolates identified as the classical species Chaetomium globosum, described in the 19th century without deposition of live material, did not contain an ex-type strain. In total, 29 Chaetomium species were judged to be distinct at the LSU/ITS level (Fig. 2), each being separated by several point mutations. Eight strains originating from clinical resources did not show identity to any known Chaetomium species and were therefore reported as ‘unknown Chaetomium sp.’ Three clinical isolates described as ‘Chaetomium sp. 1’, which had provisionally been identified as ‘Papulospora sp.’ on the basis of phenotypic characters, were found within the Chaetomium grade (Fig. 2, Supporting information; table S1).
All Achaetomium species were found to be synonyms of known Chaetomium species including ex-type strains of Achaetomium nepalense, A. thermophilum, and A. strumarium.
The origins of 128 strains analyzed are summarized in table S1 (supporting information). A large quantity (40.6%; n = 52), were of environmental origin; about 7.0% (n = 9) were derived from animal dung, mainly of herbivores such as antelopes, goats, elephants, hares and rodents, but also of carnivores such as foxes. A percentage of 16.4% (n = 21) originated from soil either mixed with dung or decayed plant material, or from rhizosphere; 10.9% (n = 14) were derived from putrid plant material. Several species (C. globosum, C. atrobrunneum) were repeatedly isolated from indoor environments such as mouldy rugs and mattresses.
A total of 54.7% (n = 70) of the overall analyzed strains were from clinical samples. Forty-five out of 112 Chaetomiaceae strains of Chaetomium, Chaetomidium and Thielavia were infection-related, of which 49 strains originated from humans and 5 were veterinary isolates. Five out of eight strains identified as C. atrobrunneum were obtained from deep localizations including sputum, bronchial lavages and brain. Chaetomium globosum was frequently isolated from clinical or veterinary sources (24 strains where information about the origin was available).
In general, the clinical isolates were predominately isolated from the respiratory tract (9.4%, n = 12), possibly as asymptomatic colonizers. A large number of strains (22.7%, n = 29) were isolated from superficial samples including skin, hair, nails and eyes. Five isolates (3.9%) were derived from brain of four humans and one horse, and five (3.9%) strains were recovered from blood and lymph nodes. Infections reported as being subcutaneous were exceptional (0.78%, n = 1 from a wound); none of these were associated with production of grains in tissue.
Within the Chaetomium grade, one unnamed ‘Chaetomium sp. 1’ and four Madurella species were exclusively from clinical origin. Strains of ‘Chaetomium sp. 1’ were mainly associated with eye infections. All 13 strains identified as Madurella were derived from rural patients with subcutaneous eumycetoma with grain production.
The genus Madurella, comprising the species M. mycetomatis, M. pseudomycetomatis, M. fahalii and M. tropicana, was found to cluster within the Chaetomiaceae. In contrast to Madurella, most species of this family are able to produce elaborate fruiting bodies with characteristically shaped setae and ascospores. The impressive morphology of the ascomata suggests that species should be easily distinguishable by microscopic morphology, using the available classical, richly illustrated monographs [9], [17]. However, judging from our phylogenetic data (Fig. 2), molecular taxonomy matches poorly with morphology. At the generic level, the distinction between Chaetomium, Achaetomium, Chaetomidium and Thielavia is ambiguous, since several species of these genera clustered amidst Chaetomium species. Sometimes several ex-type strains of described taxa were found to have identical ITS sequences, suggesting that names should be reduced to synonymy. It may be concluded that molecular classification of Chaetomiaceae is significantly different from conventional taxonomy and extensive revision is needed at generic as well as at species levels. The position of Madurella as a derived clade within the family is unambiguous, and unexpected.
Most members of the Chaetomiaceae lack anamorph sporulation, or some scattered, undiagnostic phialides are present at most [9]. Thus, if strains lose the ability to produce their elaborate ascomata, they cannot be recognized as a Chaetomium species by morphological means, as in Madurella. Most of the clinical Chaetomium strains analyzed in the course of this study produced ascomata in culture, but some had remained sterile. The clinical strains of Chaetomium were responsible for cutaneous or systemic phaeohyphomycoses, but never produced eumycetoma. In contrast, strains of the Madurella subcluster, with four different molecular siblings, were consistently associated with eumycetoma. They were all sterile or produced some undiagnostic, phialide-like cells. Large structures resembling fruiting bodies were occasionally observed in Madurella (Fig. 3), but these did not have the ability to produce ascospores. The Madurella clade is morphologically not so far away from remaining Chaetomiaceae, and the position of Madurella within the Chaetomiaceae thus is explainable. The clade deviates however by producing grains in host subcutaneous tissue.
A consistent human pathogen is thus introduced in the family Chaetomiaceae. Traditionally, most species of the family were considered to be insignificant as agents of human disease. Of the ∼100 Chaetomium species described to date only five have repeatedly been associated with infection [5]. The majority of Chaetomium clinical strains analyzed in this study were probably transient colonizers or agents of mild superficial disorders. Twenty seven were involved in onychomycosis or cutaneous and eye infections in otherwise healthy individuals. This matches with literature data [18], [19]. In our data, Chaetomium globosum showed a definite bias towards superficial infection, with 17 out of 29 strains analyzed (supporting information; table S1). The species is able to degrade keratin by production of extracellular keratinases [20]. Fatal, disseminated and cerebral infections by Chaetomiaceae have also been reported. In the literature about 20 deep and disseminated cases were described, nearly all in immunocompromised and severely debilitated patients [21], [22]. Several Chaetomium-like fungi thus show rather pronounced pathology, sometimes with species-specific predilections.
Grain formation in tissue by Chaetomiaceae other than Madurella is not known. A single case of chromoblastomycosis by Chaetomium funicola was reported by Piepenbring et al. [23]. The few subcutaneous cases [24] all showed hyphae in tissue rather than the compact grains of Madurella eumycetoma. In contrast to Madurella, none of the infecting Chaetomiaceae was exclusively clinical; all contained environmental strains as well. If agents of black-grain mycetoma have a relatively limited distribution in the phylogeny of Sordariales, i.e. are clustered within a single family, Chaetomiaceae, one may hypothesize that these fungi are predisposed to human infection and thus are likely to share a set of fundamental virulence factors. Many members of Chaetomiaceae have their natural habitat in soil or on mammal dung. A possible explanation of their recurrent virulence may lie in physiological properties such as growth at the human body temperature of 37°C, and the production of secondary metabolites such as inhibitors of chemokines and TNF-α [25], [26]. Particularly the fatal brain infections, which were repeatedly reported in Achaetomium strumarium (synonym of Chaetomium strumarium) [27], [28], in C. atrobrunneum [19], and in Thielavia subthermophila [21], all belonging to the Chaetomiaceae, are remarkable. The hidden clinical diversity of the Chaetomiaceae urgently needs to be explored.
The role of mammal dung and dung-enriched soil is one of the prime ecological niches in the order Sordariales, and this also holds true for Chaetomium [29] (supporting information; table S1). Some species in the current study exclusively grow in dung, such as Chaetomium homopilatum. Multiple Chaetomium and Thielavia species have been isolated in East Africa from different kinds of dung, ranging from cow and horse to more exotic types of dung such as of elephant and wildebeest [30]. Conversely, the position of Madurella in Chaetomiaceae is informative for the natural habitat of this pathogen. In the highly endemic area in Sudan, M. mycetomatis has as yet not been cultured, whereas the isolation of some other causative agents of mycetoma, Nocardia brasiliensis, Actinomadura madurae, and Streptomyces somaliensis has been successful [31]. The causative agent of eumycetoma Leptosphaeria senegalensis has been recovered from thorns of Acacia species in West and Central Sub-Saharan Africa [32]. Pseudallescheria boydii has been recovered from polluted soil samples all over the world, including the endemic mycetoma regions [33], [34], [35]. For Madurella mycetomatis numerous isolation attempts from environmental sources were without success [4], [36]. Thirumalachar et al. [37] reported M. mycetomatis from soil in India, but the identification was based on scant phenotypic characters only. The difficulty in recovering M. mycetomatis from soil might indicate that pure soil is not the natural habitat for this fungus. Other possible habitats were thorny plant thorns, as plant material was occasionally found in human tissue [36], but this remains exceptional. Based on our study, association with cattle dung now seems to be an alternative option. Madurella mycetomatis apparently needs other culture media for isolation. Enrichment with dung might be a successful strategy. This hypothesis may be extended to Madurella fahalii, M. tropicalis and M. pseudomycetomatis, which are endemic in the arid climate zone of Northeastern Africa and are exclusively known from human mycetoma.
Providing insight into the taxonomic position and possible natural habitat of Madurella species changes our view regarding routes of infection and prevalent risk factors for human mycetoma. The Gezira region in the Sudan is highly endemic for eumycetoma by M. mycetomatis [1]. Most inhabitants live on cattle and camel husbandry and agriculture [38]. Local villages are characterized by an abundance of cattle, goats, sheep, dogs, chickens and donkeys [39]. Cows are raised mainly for their milk and are kept in pens surrounded by walls made of mud or thorny bushes. The floors of the pens are paved with dry feces, thorns and trash [39], and some human settlements are made of dried cow dung. The family house is usually in direct contact with the pen. Inhabitants of the villages mostly are barefoot among the thorny bushes. Traumatic introduction of coprophilic fungi via thorn pricks is thus a plausible scenario. Given the low frequency of Madurella on thorns, contamination of dung and its role as an adjuvant in inoculation seems likely. If cow dung is an essential factor in inoculation by M. mycetomatis, preventative measures may involve the use of appropriate footwear in addition to restructuring of villages by stricter separation of animal husbandry and human settlement to reduce the frequency of contact with mycetoma etiologic agents.
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10.1371/journal.pgen.1004494 | Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population | Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.
| We explored the coding regions of 3,000 Finnish individuals with 3,000 non-Finnish Europeans (NFEs) using whole-exome sequence data, in order to understand how an individual from a bottlenecked population might differ from an individual from an out-bred population. We provide empirical evidence that there are more rare and low-frequency deleterious alleles in Finns compared to NFEs, such that an average Finn has almost twice as many low-frequency complete knockouts of a gene. As such, we hypothesized that some of these low-frequency loss-of-function variants might have important medical consequences in humans and genotyped 83 of these variants in 36,000 Finns. In doing so, we discovered that completely knocking out the TSFM gene might result in inviability or a very severe phenotype in humans and that knocking out the LPA gene might confer protection against coronary heart diseases, suggesting that LPA is likely to be a good potential therapeutic target.
| After widespread success with genome-wide association studies (GWAS) of common variants, several studies have recently begun to identify rare (with <0.5% allele frequency) and low-frequency (0.5–5%) variants in complex diseases and traits such as triglycerides [1], insulin processing [2], bone mineral density [3], Alzheimer's disease [4], impulsivity [5], and prostate cancer [6], some of which confer protection from disease [4]. Protective loss of function variants that can be tolerated in a homozygote state in humans are of particular interest as potential safe targets for therapeutic inhibition. Interestingly, many of these studies that have discovered rare and low-frequency variants use isolated populations that have undergone bottlenecks resulting in frequency enrichment of the associated variants. In contrast to the large number of extremely rare variants present in out-bred populations, such bottlenecked populations have a smaller spectrum of rare variation. This observation has been borne out in examples of Mendelian disease where, for example, Finns and Ashkenazi Jews have characteristic high incidence of recessive diseases because of the enrichment of specific mutations [7], [8], [9] – in the wider European population these same diseases are rarer and have mutational spectra involving a more diverse array of extremely rare mutations. It has not yet been assessed to which extent these population structures, so advantageous to Mendelian studies but of little importance to common variant GWAS, might generally improve the power to identify low-frequency loss-of-function (LoF) variants in studies of complex disease.
To explore this question, we used exome sequencing to characterize the allelic architecture of the Finnish population compared with a set of non-Finnish Europeans (NFEs) from the United States, Great Britain, Germany and Sweden. We demonstrate that Finns carry a significant enrichment of low-frequency (0.5–5%) LoF variation, defined here as nonsense and essential splice sites that are rare in NFEs. In addition to the isolate population structure, Finland has nationwide health records that provide decades of follow-up data that can be linked to epidemiological studies. The availability of nationwide health records in a population isolate structure triggered us to study the impact of low-frequency variants on risk factors and disease outcomes and their risk factors. The Sequencing Initiative Suomi (The SISu project) aims to combine these resources and build knowledge and tools for genome health initiatives. We genotyped 83 LoF variants discovered through our exome sequencing, in several large well-phenotyped population-based cohorts comprised of 36,262 Finns and tested for association to 60 quantitative traits and used data from the 13 disease outcomes assessed using the National Health Registers. We demonstrate that 5 of these variants have significant associations with clinically relevant phenotypes, illustrating the general value of the Finnish population for the study of low-frequency variants studies in complex as well as Mendelian diseases. We further confirm two LoF variants that significantly reduce lipoprotein(a) levels are associated with protection from cardiovascular disease.
As part of the SISu Project, we assembled 3,000 whole-exome sequences from Finns in projects including GoT2D, ENGAGE, migraine, METSIM and the 1000 Genomes Project along with 3,000 whole exome-sequences of NFEs from GoT2D, ESP, NIMH and 1000 Genomes project using the same data generation and processing pipelines (Table S1). The raw BAM files from these projects were compressed and re-processed at the Broad Institute and variant calling was performed in a unified manner to minimize potential batch effects. We compared the number and frequency of variable sites in 3,000 Finns and 3000 NFEs (Fig. 1A) and observed several expected hallmarks of the isolated bottlenecked Finnish population history. There was a depletion of ‘singletons’, or variants that were observed only once in 3,000 individuals, in Finns compared to NFEs. An average Finn had 3.7 times fewer singleton variants in these data (binomial P<1×10−6). On the other hand, there was an excess of low-frequency variants in Finns versus NFEs (binomial P<1×10−6), collectively suggesting that while most rare variants did not survive the bottleneck, the variants that did have become substantially elevated in frequency [10], while the rates of common variation were not different between Finns and NFEs. All these findings are consistent with an expected impact of the Finnish population bottleneck.
We then stratified the variants according to their functional annotations – LoF variants, missense variants and synonymous variants. We found a higher proportion of LoF variants in Finns compared to NFEs across the rare and low-frequency allelic spectrum (Fig. 1A, Table S2) and for missense variants predicted to be deleterious by PolyPhen2 (Fig. S1). We found a similar observation when comparing the Finns to an equivalent number of Swedes (Fig. S2). This is also a direct consequence of the bottleneck: alleles that are elevated in frequency through the bottleneck are drawn at random from extremely rare variants in the parental population, where there is a higher proportion of LoF variants that arose recently or were kept at low frequencies because of negative selection. This is clearly demonstrated with the decreasing proportions of LoF variants with increasing allele frequencies (Fig. 1B). The observation that LoF variants in the 0.5–5% range are enriched in Finns and our hypothesis that some of these variants might have health related phenotypic consequences, motivated the targeted association study described below (Fig. 2).
Despite the reduced overall variation in the isolated population, the existence of a greater number of low frequency LoF variants results in an average Finn harboring 0.16 homozygous LoF variants compared to only 0.095 in an average NFE, driven primarily by homozygosity in the 0.5 to 5% allele frequency range (Fig. S3B). These features of the Finnish population have already been well described as they pertain to Mendelian diseases: many characteristic “Finnish founder mutations” exist at unusually high frequencies, even up to 1%, for highly penetrant and reproductively lethal disorders while such variants are extremely rare or absent in NFEs [11]. We confirmed with simulations that while such variants are inevitably pushed to extremely low frequency after 1,000 or more generations, they can easily persist at frequencies between 0.1 and 1% up to 100 generations after a bottleneck (Fig. S4). Table S3 shows a table of a set of Finnish Disease Heritage (www.findis.org) variants and their population frequencies. The extent to which such variants contribute to more common diseases, either through highly-penetrant recessive subtypes or modest risk to carriers, will correspond to advantages in rare and low-frequency association studies in isolated populations.
Given our empirical observations of proportionally more LoF variants in the 0.5–5% allele frequency range in Finns, we next conducted a test of this hypothesis that some of the Finnish-enriched low-frequency LoF variants might have strong phenotypic effects. We successfully genotyped 83 low-frequency LoF variants (protein-truncating nonsense, essential splice site variants and frameshift variants) enriched in Finns based on their ability to multiplex in four Sequenom MALDI-TOF genotyping pools (Table S4). Of these 83 variants, 76 variants were more than 2-fold enriched and 26 were more than 10-fold enriched.in Finns vs. NFEs. Three genes (SERPINA10, LPA and FANCM) contained two LoF variants each; we combined these pairs and tested them as single composite LoF variants, resulting in a total of 80 independent LoF variants tested in this study. These 83 variants were genotyped in a total of 36,262 individuals from three population cohorts: FINRISK [12] (26,245 individuals), Health2000 (7,363 individuals) and Young Finns [13] (2,654 individuals).
As these three studies are population-based cohorts, we were able to assess whether any of the homozygous LoF variants result in such a severe phenotype that these individuals would not be able to participate in a population survey for instance, due to lethality in fetal life of early infancy. Study-wide, there was a modest excess of homozygotes of the variants (1.23-fold versus Hardy-Weinberg expectation) arising from within population substructure. A nonsense variant (Q246X) in the Translation Elongation Factor, Mitochondrial gene (TSFM) that is present at 1.2% allele frequency in Finns and absent in NFEs, was not found in a homozygous state in >36,000 Finns (Hardy Weinberg Equilibrium (HWE) P = 0.0077). This suggests that complete loss of TSFM might result in embryonic lethality, severe childhood diseases in humans, or that the individuals might not have been ascertained by the studies employed, i.e. if the individuals are too sick to be included in the studies. A lookup of this variant in another 25,237 Finnish samples in exome chip genotyping data from the GoT2D studies confirmed that the variant is present at 1.2% in Finns, but again with no homozygotes observed (combined HWE P = 1.6×10−4). Recessive missense variants in TSFM have been reported to result in mitochondrial translation deficiency [14], [15] and Finnish mitochondrial disease patients from two families have been identified with compound heterozygosity of this nonsense variant (each with a different second hit in TSFM) (personal communication) - lending strong evidence to the hypothesis that complete loss of this gene is not tolerated in humans. Neither did we observe strong associations for the TSFM Q246X heterozygotes across major diseases (Table S5).
Several other LoF variants occur in genes where recessive mutations have been noted to cause severe Mendelian diseases from the Online Mendelian Inheritance in Man database (OMIM) [16]. For instance, the Fanconi anemia complementation group M gene (FANCM) was initially discovered in one family with Fanconi anemia [17], but we did not observe any deficit of homozygous LoFs in FANCM from our dataset (expected = 5, observed = 7), which we would typically observe for a disease causing recessive variant. Furthermore, examination of the hospital discharge records did not provide any evidence for blood diseases, increased cancer events or any other chronic diseases in these individuals with homozygous LoFs in FANCM. We also had blood counts for two homozygote individuals. Both of them had normal hemoglobin, erythrocyte size and counts as well as leukocyte and thrombocyte counts. Singh et al. reported that the initial case that led to the association of FANCM with Fanconi anemia also harbor biallelic, functional mutations in FANCA, a well-established Fanconi anemia gene [18]. Our findings in this study, combined with the findings by Singh et al. do not support the hypothesis that FANCM is a Fanconi anemia gene but rather suggest that the initial FANCM association was not causative. In addition to FANCM, we further evaluated evidence for two other genes COL9A2 and DPYD that were previously implicated in other Mendelian diseases (Supplementary Methods).
The FINRISK cohort had collected 60 biochemical and physiological quantitative measurements of cardiovascular or immunologic relevance (Table S6), some of which are highly correlated. We tested the 80 variants across the 60 traits and report from this initial screen all associations with p<2×10−4 – that is, a value where we would expect only one chance observation in the entire study. In total, we observed 41 associations that exceeded this significance threshold (Table 1), far beyond the expected. If the phenotype was available in the Young Finns and Health 2000 cohorts, replication was attempted for these initial scan hits and significant associations are highlighted below when the combined p-value was smaller than a conservative study-wide Bonferroni-corrected threshold of 0.05/(80*60) = 1×10−5.
Three of these association have been previously reported and represent positive controls for our approach: a strong association for the 2 splice variants (c.4974-2A>G and c.4289+1G>A) in the Lipoprotein(a) gene (LPA) with lipoprotein(a) measurements in plasma (Pdiscovery = 2.17×10−81, Pdiscovery+replication = 1.53×10−117, combined = −0.64 or −8.77 mg/dL per allele, Table S7), the W154X variant in Fucosyltransferase 2 (FUT2) with increased Vitamin B12 levels [19] ( = 0.2, P = 3.7×10−26 or 43 pg/mL per allele, Table S8) and the R225X variant in the Citrate Lyase Beta Like gene (CLYBL) with decreased Vitamin B12 levels [20] ( = −0.2, P = 1.8×10−5 or −43 pg/mL per allele, Table S9) [21]. The boxplots for these associations are shown in Fig. S5.
In addition to a strong correlation between circulating lipoprotein(a) levels and cardiovascular disease, it has been previously reported that genetic variants that elevate circulating lipoprotein(a) levels are cardiovascular risk factors [22], [23]. The converse, critical for evaluation of the therapeutic hypothesis of inhibition, that lowering lipoprotein(a) levels can confer cardiovascular protection has not yet been evaluated. With access to National Health Records, we utilized the strong lipoprotein(a) lowering variants discovered here to evaluate the impact of lipoprotein(a) lowering via Mendelian randomization. Using a Cox proportional hazards model for incident cardiovascular disease in these cohorts (adjusted for age, gender and therapies), the composite LPA variant was found to protect against coronary heart disease (Hazard Ratio HR = 0.79, P = 6.7×10−3), demonstrating that lowering lipoprotein(a) levels are likely to confer protection for cardiovascular diseases. We adjusted the association for the composite LPA variant with a previously published risk variant (rs3798220) [22], but observed a similarly protective effect (N = 18,270, HR = 0.79, P = 0.014), suggesting that the splice variants are independent from the previously reported risk variants in LPA.
We confirmed this finding using three independent non-Finnish datasets: an early onset myocardial infarction dataset of 18,000 individuals and two studies from the Estonian Biobank (4,600 and 7,953 individuals respectively), which collectively replicated the observation that the LPA variants confer cardioprotective effect (OR = 0.87, P = 0.016). After meta-analyzing all the datasets, the final odds ratio was found to be 0.84 (P = 3×10−4, Fig. 3). We found 227 individuals who are homozygous or compound heterozygous for the two LPA splice variants with no evidence for increased morbidity or mortality based on National Health Records. This suggests that reduction of lipoprotein(a) is well-tolerated and might constitute a potential drug target for cardiovascular diseases. A survey across other diseases showed potential association between the LPA variants with acute coronary disease and myocardial infarction but not Type 2 Diabetes (Table S10). In addition, we surveyed the LPA variants across other cardiovascular risk factors and observed that the LPA variants were associated with mildly increased glucose levels but not high-density lipoproteins (HDL), low-density lipoproteins (LDL) or triglycerides (Table S11).
In addition, we observed novel associations for the FGL1, MS4A2 and ATP2C2 variants. The 1-bp c.545_546insA frameshift in the Fibrinogen-like 1 gene (FGL1) was associated with increased D-dimer levels ( = 0.21, P = 6.1×10−6 or 52.23 ng/mL per allele, Table S12). D-dimers are products of fibrin degradation and their concentration in the blood flow is clinically used to monitor thrombotic activity. The role of FGL1 in clot formation remains unclear: although FGL1 is homologous with fibrinogen, it lacks the essential structures for fibrin formation, with one study suggesting its presence in fibrin clots [24]. In addition, given prior links between variants associated with D-dimer levels and stroke, we utilized the same Mendelian randomization approach as for LPA above and found a nominally significant association between FGL1 c.545_546insA and increased risk of ischemic stroke (OR = 1.32, P = 0.024). If replicated, this would be consistent with modest risk increase for stroke that other variants associated to circulating D-dimer levels, such as reported for variants in coagulation Factor V, Factor III and FGA [25].
We found suggestive associations for the c.637-1G>A splice variant in the membrane-spanning 4-domains, subfamily A, member 2 gene (MS4A2) with triglycerides (Pdiscovery = 7.80×10−5, Pdiscovery+replication = 1.31×10−6, = 0.14 or 0.14 mmol/L per allele, Table S13). This observation is consistent with our previously published study of 631 individuals in the DILGOM subset of FINRISK showing that whole blood expression of MS4A2 was strongly negatively associated with total triglycerides ( = −1.62, P = 2.1×10−27, Fig. S6) [26] and a wide range of systemic metabolic traits [27]. A similar but insignificant trend was observed in 15,696 individuals from the D2D2007, DPS, FUSION, METSIM and DRSEXTRA cohorts ( = 0.04, P = 0.32). The MS4A2 gene encodes the β-subunit of the high affinity IgE receptor, a key mediator of the acute phase inflammatory response.
The c.2482-2A>C splice variant in the ATPase Ca++ Transporting Type 2C Member 2 gene (ATP2C2) was associated with increased systolic blood pressure (Pdiscovery = 1.25×10−5, Pdiscovery+replication = 1.3×10−6, = 0.12 or 2.13 mmHg per allele (an association that is undisturbed by correction for lipid lowering medication ( = 0.12, P = 1.75×10−5) or blood pressure lowering medication ( = 0.13, P = 1.3×10−5), Table S14). Based on its structure, ATP2C2 is predicted to catalyze the hydrolysis of ATP coupled with calcium transport. Interestingly, the ATP2C2 c.2482-2A>C variant is also significantly associated to several highly correlated immune markers, such as granulocyte colony-stimulating factor ( = 0.26, P = 6.98×10−7), interleukin-4 ( = 0.27, P = 2.48×10−6), interferon-γ ( = 0.26, P = 3.24×10−6) and interleukin-6 ( = 0.25, P = 4.58×10−6).
The empirical data of this study sheds light on an active debate in population genetics theory whether or not bottlenecked populations have an excess burden of deleterious alleles. Lohmueller et al. first observed that there were proportionally more deleterious variants in European American individuals compared to African American individuals [28]. They performed a series of forward simulations to demonstrate that such an observation is consistent with an Out-of-Africa bottleneck experienced by the European populations from which the European-American individuals descend, and illustrated that bottlenecked populations are likely to accumulate a higher proportion of deleterious alleles. A recent study by Simons et al. showed conflicting results suggesting that there are similar burdens of deleterious alleles in Europeans and West Africans and that demography is unlikely to contribute to the proportions of deleterious alleles in human populations [29].
The comparison of Finns, with a well-documented bottleneck, with non-Finnish Europeans here provides strong empirical data on these questions. While the distribution of common alleles, both synonymous and non-synonymous, is as expected unchanged by the bottleneck, when exploring the rare and low-frequency allelic spectrum where the Finns and NFEs demonstrate distinct distributions, we indeed observe a significant excess of deleterious variants in the Finns – despite the considerable deficit in variable sites in the population overall. This suggests that negative selection has had insufficient time to suppress the frequency of deleterious alleles dramatically elevated in frequency through the founding bottleneck, an observation that generalizes the intuitive understanding of the existence of characteristic and unusually common Mendelian recessive disorders in Finland. However, we note that while we observe a strong influence of the founding bottleneck, the observed results, particularly the proportional enrichment of rare deleterious variants, are also influenced by other elements in the unique history of the Finnish population and will not necessarily apply to all populations influenced by a bottleneck.
This excess of presumably deleterious variants motivated the subsequent association study and indeed, the absence of homozygotes at TSFM (contemporaneously identified as an early-onset mitochondrial disease gene) suggests that low-frequency variants in Finns, beyond those already identified in Mendelian disease, do include more unusually strong acting alleles than in non-founder populations. In this study, both replicated results and novel associations demonstrate the association of low-frequency LoF variants with various complex traits and diseases. In addition, we discovered a novel cardiovascular protective effect from splice variants in the LPA gene, suggesting that knocking down levels of circulating lipoprotein(a), or Lp(a), can confer a protection from cardiovascular diseases. Given that we detected numerous individuals in these adult population cohorts, healthy and in the expected Hardy-Weinberg proportions, carrying a complete knockout of LPA (homozygous or compound heterozygous for the 2 splice variants), this suggests that knocking out the gene in humans does not result in severe medical consequences. As such, this study provides data suggesting that LPA may be an effective target for therapeutic purposes.
As more Finnish samples are being sequenced, these enriched variants can also be imputed with high precision to the large number of existing samples with array-based GWAS genotypes. This advantage is likely to be more pronounced for the much larger pool of missense variation – while one can presume all LoF variants in a gene might have a comparable effect on phenotype (and thereby burden tests of LoF variants in an out-bred sample is not at a great disadvantage compared to isolated populations), it is evident that many rare missense variants within the same gene will not all have the same impact on gene function. Thus the ability to assess single low-frequency variants conclusively, especially since they will include an excess of damaging variants enriched through a bottleneck, rather than perform burden tests on heterogeneous sets of extremely rare variants, will offer substantial ongoing advantage to isolated population studies as indicated by these and other recent findings.
All research involving human participants have been approved by the Hospital District of Helsinki and Uusimaa Coordinating Ethical Committee, and all clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki.
Raw Binary Sequence Alignment/Map (BAM) files from the various projects were jointly processed at the Broad Institute and joint variant calling was performed on all exomes to minimize batch differences. Functional annotation was performed using the Variant Effect Predictor (VEP v2.5) tool from Ensembl (http://useast.ensembl.org/info/docs/tools/vep/). We modified it to produce custom annotation tags and additional loss-of-function annotations. The additional annotations were applied to variants that were annotated as STOP_GAINED, SPLICE_DONOR_VARIANT, SPLICE_ACCEPTOR_VARIANT, and FRAME_SHIFT and the variants were flagged if any filters failed. A loss-of-function variant was predicted as high confidence if there is one transcript that passes all filters, otherwise it is predicted as low confidence. In our genotyping study, we had used loss-of-function variants that were predicted to be high confidence. For quality control, we required all variants to pass the basic GATK filters and required all genotypes to have a quality score of ≥30, read depth of ≥10 and allele balance of between 0.3 and 0.7 for heterozygous calls and <0.1 for homozygous calls. Allele counts and frequencies were calculated within the 3,000 individuals for Finns and NFEs respectively.
To estimate the amount of substructure or homozygosity by descent, we fitted a regression model on all coding variants with the intercept set to 0, where q is the allele frequency of the alternate allele and FST is the proportion of allelic variance explained by population structure. Here we fit FST to capture the empirical departure from Hardy-Weinberg equilibrium arising from population substructure to insure this is not creating the observed difference between Finnish and NFE samples:Using the whole-exome sequencing data for the 3,000 NFEs, we estimated the parameters:Using the whole-exome sequencing data for the 3,000 Finns, we estimated the parameters:As shown, there is little substructure in the 3,000 Finns compared to the 3,000 NFEs, given that the estimates for FST are similar in both populations.
All frameshifts and loss-of-function single nucleotide variants with allele frequencies of 0.5–5% in Finns and at least 2-fold enriched in Finns compared to NFEs were selected for genotyping. To minimize the false positives in our variant selection, we performed Fisher's Exact Test for each variant between two independent NFE datasets and kept variants whose allele frequencies were highly concordant between the two NFE datasets (P>1×10−5). The high concordance between the allele frequencies in two independent NFE datasets ensures that the variants are unlikely to arise from alignment or sequencing artifacts and that these variants are unlikely to reside in a region of the exome that is difficult to sequence or genotype, which can result in highly variable allele frequencies from different experiments.
Genotyping was performed using the iPLEX Gold Assay (Sequenom Inc.). Assays for all SNPs were designed using the eXTEND suite and MassARRAY Assay Design software version 3.1 (Sequenom Inc.). Amplification was performed in a total volume of 5 µL containing ∼10 ng genomic DNA, 100 nM of each PCR primer, 500 µM of each dNTP, 1.25× PCR buffer (Qiagen), 1.625 mM MgCl2 and 1 U HotStar Taq (Qiagen). Reactions were heated to 94°C for 15 min followed by 45 cycles at 94°C for 20 s, 56°C for 30 s and 72°C for 1 min, then a final extension at 72°C for 3 min. Unincorporated dNTPs were SAP digested prior to iPLEX Gold allele specific extension with mass-modified ddNTPs using an iPLEX Gold reagent kit (Sequenom Inc.). SAP digestion and extension were performed according to the manufacturer's instructions with reaction extension primer concentrations adjusted to between 0.7–1.8 µM, dependent upon primer mass. Extension products were desalted and dispensed onto a SpectroCHIP using a MassARRAY Nanodispenser prior to MALDI-TOF analysis with a MassARRAY Analyzer Compact mass spectrometer. Genotypes were automatically assigned and manually confirmed using MassARRAY TyperAnalyzer software version 4.0 (Sequenom Inc.). The genotyped variants were then checked for concordance in allele frequencies with the exome sequencing data.
Data on disease status from National Health registers (Hospital Discharged Registers maintained by THL (Institute for Health and Welfare, Finland), Cause of Death Register, Statistics Finland and Prescription Medication Register, THL) for FINRISK, Health2000 and the Young Finns Study participants of this study were collected and curated. A description of each cohort is provided in the Supplement.
To analyze the effects of the LoF variants on gene expression, we used RNA sequencing data from two major studies: the GEUVADIS project [30] with RNA sequencing data from lymphoblastoid cell lines of 462 individuals participants from the 1000 Genomes Project [31]), and the GTEx project with RNA-sequencing data from a total of 175 individuals with 1–30 tissues each (http://www.broadinstitute.org/gtex/) [32]. The processing of the GEUVADIS data and the methods for allele-specific expression analysis are described in Lappalainen et al. [30] and the GTEx data were analyzed using similar methods. Allele-specific expression analysis was used primarily to capture nonsense-mediated decay. Additionally, to assess whether LoF variants lead to decreased exon expression levels overall or for individual exons, we calculated an empirical p-value for each exon of all the LoF genes with respect to all other exons genome-wide, denoting the proportion of all exons where carriers of the LoF variants are more extreme than in the each studied exon in LoF variant genes. The analyses were performed separately in each studied tissue: lymphoblastoid cell lines from the GEUVADIS data and nine tissues from the GTEx data. The significance threshold after correcting for the total number of tested exons across all tissues is 0.05/1070 = 4.67×10−5.
Inverse rank-based normalization was performed on the quantitative measurements in males and females separately, with linear regression residuals using age and age2 as covariates. Linear regression was then performed on the normalized Z-scores using R to obtain the statistics for the associations. We tested the correlations between the quantitative measurements and disease outcomes using two one-tailed t-tests to assess the significance of observing higher levels of the quantitative measurements in cases (individuals with the disease outcomes) versus controls (individuals without the disease outcomes), as well as lower levels of the quantitative measurements in cases versus controls. To test the association of the variants with the prevalent disease outcomes, we performed a logistic regression in R to obtain the reported statistics. In addition, a Fisher's Exact Test on the homozygous counts in cases and controls were performed to test for association with the homozygotes. The results for the LPA with cardiovascular disease association from MIGen ExA and the Estonian Biobank were meta-analyzed using METAL [33] and the combined results with FINRISK were obtained using the Fisher's Combined P method with 4 degrees of freedom.
We fit a linear model in which the log2-normalised gene probe expression of individual i was regressed on the LoF genotype, which was encoded as Xi = 0, 1 or 2 for the LoF genotypes −/−, +/− or +/+ respectively and association analysis of MS4A2 gene expression and triglycerides was performed as previously reported [26]. Briefly, we used a multivariate linear regression adjusted for age, gender, and use of cholesterol or blood pressure lowering medication. We further tested for association between MS4A2 c.637-1G>A and triglycerides using a 2-sided t-test.
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10.1371/journal.pntd.0001174 | Tissue and Stage-Specific Distribution of Wolbachia in Brugia malayi | Most filarial parasite species contain Wolbachia, obligatory bacterial endosymbionts that are crucial for filarial development and reproduction. They are targets for alternative chemotherapy, but their role in the biology of filarial nematodes is not well understood. Light microscopy provides important information on morphology, localization and potential function of these bacteria. Surprisingly, immunohistology and in situ hybridization techniques have not been widely used to monitor Wolbachia distribution during the filarial life cycle.
A monoclonal antibody directed against Wolbachia surface protein and in situ hybridization targeting Wolbachia 16S rRNA were used to monitor Wolbachia during the life cycle of B. malayi. In microfilariae and vector stage larvae only a few cells contain Wolbachia. In contrast, large numbers of Wolbachia were detected in the lateral chords of L4 larvae, but no endobacteria were detected in the genital primordium. In young adult worms (5 weeks p.i.), a massive expansion of Wolbachia was observed in the lateral chords adjacent to ovaries or testis, but no endobacteria were detected in the growth zone of the ovaries, uterus, the growth zone of the testis or the vas deferens. Confocal laser scanning and transmission electron microscopy showed that numerous Wolbachia are aligned towards the developing ovaries and single endobacteria were detected in the germline. In inseminated females (8 weeks p.i.) Wolbachia were observed in the ovaries, embryos and in decreasing numbers in the lateral chords. In young males Wolbachia were found in distinct zones of the testis and in large numbers in the lateral chords in the vicinity of testicular tissue but never in mature spermatids or spermatozoa.
Immunohistology and in situ hybridization show distinct tissue and stage specific distribution patterns for Wolbachia in B. malayi. Extensive multiplication of Wolbachia occurs in the lateral chords of L4 and young adults adjacent to germline cells.
| Most filarial nematodes contain Wolbachia endobacteria that are essential for development and reproduction. An antibody against a Wolbachia surface protein was used to monitor the distribution of endobacteria during the B. malayi life cycle. In situ hybridization with probes binding to Wolbachia 16S rRNA were used to confirm results. Only a few cells contain Wolbachia in microfilariae and vector stage larvae; this suggests that the bacteria need to be maintained, but may have limited importance for these stages. Large numbers of Wolbachia were detected in the lateral chords of L4 larvae and of young adult worms, but not in the developing reproductive tissue. Confocal laser scanning and transmission electron microscopy showed that Wolbachia are aligned towards the developing germline. It can be hypothesized that Wolbachia invade developing ovaries from the lateral chords. In inseminated females, Wolbachia were detected in the ovaries and embryos. In young males, Wolbachia were found in parts of the testis and in the lateral chords in the vicinity of testicular tissue but never in mature spermatids or spermatozoa. The process of overcoming tissue boundaries to ensure transovarial transmission of Wolbachia could be an Achilles heel in the life cycle of B. malayi.
| Filarial parasites infect more than 150 million people in tropical and subtropical countries and are responsible for important tropical diseases such as lymphatic filariasis (elephantiasis) and onchocerciasis (river blindness). Other filarial species are important veterinary pathogens (e.g. Dirofilaria immitis, the dog heartworm). Treatment of filarial infections in humans and animals is suboptimal, because available drugs do not efficiently kill adult worms. Most filarial species live in obligatory symbiosis with intracellular Wolbachia α-proteobacteria. Wolbachia are also present in many insect species, and they are among the most widely distributed bacteria that infect invertebrates. Wolbachia endosymbionts are necessary for development and reproduction of filarial nematodes, and they have been validated as a target for chemotherapy [1]. Tetracycline class antibiotics are active against Wolbachia, and depletion of endobacteria blocks reproduction and eventually kills adult worms in some filarial species [2], [3].
While Wolbachia DNA can be detected and quantified by PCR, microscopy provides important information on morphology and localization of bacteria in parasite tissues. Immunohistochemistry has been used for years to visualize Wolbachia in filarial worms, particularly in Onchocerca volvulus [2]. Brugia malayi is the only human filarial parasite that can be maintained in laboratory animals and for which all life cycle stages are relatively easily accessible. The population dynamics of Wolbachia during the development of B. malayi has been studied by quantitative PCR; for example, the number of Wolbachia exponentially increases soon after infection of the vertebrate host [4]. Recent studies have shown that Wolbachia are unevenly distributed in intrauterine embryos and that the bacteria are not always detected in germline precursor cells [5]. However, data on the histological distribution of Wolbachia during later development of B. malayi are scarce. While it is known that Wolbachia are present in developing embryos, the mechanism of this vertical transmission is poorly understood.
In situ hybridization has been used to study gene expression in filarial parasites such as B. malayi [6] and to detect Wolbachia in insects [7], [8], but it has not been used before to detect Wolbachia in filarial worms. In this paper, we have used optimized immunohistology, in situ hybridization, and transmission electron microscopy to systematically describe the distribution, the relative number and morphology of Wolbachia in different life stages and tissues of B. malayi. This work led to an interesting new hypothesis on the localization and migration of Wolbachia during development of filarial worms.
B. malayi worms were recovered from intraperitonial ( i.p.) infected jirds, 2, 5, 8 and 12 wks post infection (p.i.) as previously described [9]. Aedes aegypti mosquitoes containing different larval stages of B. malayi were available from a previous study. Parasite material was fixed either in 80% ethanol for immunohistology or in 4% buffered formalin for immunohistology or in situ hybridization. At least five blocks with four or more B. malayi worms each were examined for each time point. An extensive overview about the studied material and the methods performed is provided in a supplementary table (Table S1). Up to twenty serial sections of the same block were used for comparative studies of different staining procedures. For some blocks (especially those containing young adult worms) more than 60 sections (5 µm) were cut, but only a selection of sections was examined. For the ultrastructural analysis, 18 worms (39 and 56 days p.i., Table S1) were fixed in 2% paraformaldehyde/2.5% glutaraldehyde (Polysciences Inc., Warrington, PA, USA) in 100 mM phosphate buffer, pH 7.2 for 1 hr at room temperature.
A monoclonal antibody directed against the B. malayi Wolbachia surface protein (mab Bm WSP) was purified from culture supernatants kindly provided by Dr. Patrick J. Lammie, Atlanta [10]. Briefly, hybridoma supernatant was incubated overnight at 4°C with ammonium sulfate, pelleted, resuspended in water and dialyzed extensively against phosphate buffered saline. The antibody solution was concentrated to 5% of the original volume using Centricon Plus-20 columns (Millipore, Billerica, MA, USA) and the protein content was determined. A stock mab solution of 10 mg protein per ml was used to test dilution series of 1∶10 up to 1∶500. The best signal to background relationship was observed at a dilution of 1∶100, and this dilution was used for all further experiments.
The alkaline phosphatase anti-alkaline phosphatase (APAAP) technique was applied for immunostaining according to the recommendations of the manufacturer (Dako, Carpinteria, CA, USA) and as described earlier [11]. TBS with 1% albumin was used as negative control. Rabbit-anti mouse IgG (1∶25; Dako) was applied as secondary antibody and was bound to the APAAP complex. As substrate for alkaline phosphatase the chromogen Fast red TR salt (Sigma) was used and hematoxylin (Merck, Darmstadt, Germany) served as the counter-stain. Sections were examined using an Olympus-BX40 microscope (Olympus, Tokyo, Japan) and photographed with an Olympus DP70 microscope digital camera. For some fluorescent analysis wheat germ agglutinin (WGA 633, Invitrogen, Carlsbad, CA, USA) was used as membrane stain at 200 µg/ml for 10 minutes prior to mounting.
FITC conjugated anti-mouse IgG (1∶300; Sigma) was used as a secondary antibody for confocal laser scanning microscopy (LSM). Sections were examined with a Zeiss LSM 510 META (Zeiss, Jena, Germany) confocal laser scanning microcope equipped with a plan-apochromat 63× oil objective with an argon or helium/neon laser for excitation at 488 nm or 633 nm, respectively. Confocal Z slices of 0.8 µm were obtained using Zeiss LSM software. The Velocity program version 5.4.2 (Improvision, Lexington, MA, USA) was used for high resolution interactive 3D rendering. Sections were also examined using a wide field fluorescence microscope (WFFM, Zeiss Axioskop 2 MOT Plus) with plan-apochromat 100× oil, 63× or 40× objectives. Wide field fluorescence microscopy and LSM were performed at the Washington University Molecular Microbiology Imaging Facility (http://micro.imaging.wustl.edu/).
A 424 bp fragment of the 16S rRNA gene of Wolbachia of B. malayi was amplified (forward primer 5′CAGCTCGTGTCGTGAGATGT, reverse primer 5′ CCCAGTCATGATCCCACTT) and cloned into a dual promoter PCRII plasmid (Invitrogen). After linearization of the plasmid, probes (anti-sense) and negative controls (sense) were prepared with Megascript T7 and Sp6 high yield transcription kits according to the manufacturer's suggested protocol (Ambion, Invitrogen). For labeling of the probe a biotin-16 dUTP mix (Roche, Indianapolis, IN, USA) was used during in vitro transcription. The plasmid template was then removed by DNase digestion (Roche). The probes were concentrated by ethanol precipitation, re-suspended in DEPC-treated water, and stored at −20°C until use.
For staining, 5 µm thin paraffin sections were deparaffinized and partially digested with pepsin HCl for approximately 7 minutes. Sections were hybridized at 60°C overnight in a humid chamber with 1 µg of rRNA probe in hybridization buffer (50% formamide, 5XSSC, 0.3 mg/ml yeast tRNA, 100 µg/ml heparin, 1× Denhart's Solution, 0.1% CHAPS and 5 mM EDTA). A stringency wash was performed at 60°C for 30 min, and detection was performed using the ‘In situ Hybridization Detection System’ (K0601, Dako) which uses alkaline phosphatase conjugated streptavidin to localize biotinylated rRNA probes. Sections were incubated for 20 min with streptavidin-AP conjugate at room temperature. BCIP/NBT substrate solution was added for 10 to 30 min to localize binding of the probes.
Sections were deparaffinized and partially digested as described above and hybridized at 37°C overnight in a dark humid chamber using 200 ng of a custom made, labeled 30-mer antisense probe targeting the 16S rRNA of Wolbachia (wBm16S as, 5′Alexa 488-CAGTTTATCACTAGCAGT TTCCTTAAAGTC, Invitrogen). The complementary sense sequence was used as a negative control probe. One stringency wash was performed at 37°C for 30 minutes. Hybridization and stringency buffers were the same as described above. Finally sections were rinsed briefly in PBS and covered with a cover slip with ProLong Gold antifade reagent that contains DAPI (Invitrogen). This embedding reagent enables simultaneous fluorescence-based detection of condensed DNA in eukaryotic and prokaryotic organisms. Sections were examined using an Olympus-BX40 microscope equipped with the Olympus fluorescence filter 41001 (excitation 460–500 nm, emission 510–550 nm) for Alexa fluor or UN31000V2 (excitation 325–375 nm, emission 435–485 nm) for DAPI.
For ultrastructural analysis fixed samples were washed in phosphate buffer, embedded in agarose, and postfixed in 1% osmium tetroxide (Polysciences Inc.) for 1 hr as described previously [12]. Samples were then rinsed extensively in dH20 prior to en bloc staining with 1% aqueous uranyl acetate (Ted Pella Inc., Redding, CA, USA) for 1 hr. Following several rinses in dH20, samples were dehydrated in a graded series of ethanol and embedded in Eponate 12 resin (Ted Pella Inc.). Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome (Leica Microsystems Inc., Bannockburn, IL,USA), stained with uranyl acetate and lead citrate, and viewed on a JEOL 1200 EX transmission electron microscope (JEOL USA Inc., Peabody, MA, USA).
Different developmental stages were stained with mab Bm WSP (Fig. 1A, C–K). Results were confirmed by in situ hybridization or DAPI chromatin staining. Clusters of Wolbachia were detected in relatively few cells in microfilariae (Fig. 1A). The same staining pattern was observed by rRNA in situ hybridization with a probe for Wolbachia 16S rRNA (Fig. 1B). Wolbachia were sometimes detected in single cells of microfilariae within the midgut of mosquito vectors (Fig. 1C) or in sausage stage larvae and 2nd stage larvae in the mosquito thorax (Fig. 1D, E), but most of the cells in these larval stages were free of the endobacteria. Even in infective 3rd stage larvae the vast majority of cells were devoid of Wolbachia (Fig. 1F); Wolbachia in L3 were mainly present in the cells of the lateral chord, but not in internal organs (Fig. 1G).
The Wolbachia density at the anterior end of 4th stage larvae was low at 2 weeks p.i. in the vertebrate host (3–5 days after the molt), and no endobacteria were detected in the tissue around the pharynx (Fig. 1H). In contrast, large numbers of Wolbachia were detected in the developing lateral chords of the L4 midbody region (Fig. 1I–K).
In order to understand the distribution of Wolbachia in adult worms it is crucial to recall the anatomy and development of reproductive organs of filarial worms [13], [14]. The genital opening (vulva) lies close to the anterior end of the female worm, approximately at the level of the esophagus (Fig. 2A, B). The vagina leads into the bifurcated uterus which ends in the seminal receptacles. Theses organs are linked by oviducts with two ovaries that have an anterior growth zone, a maturation zone in the middle, and a posterior germinative zone. At 5 weeks p.i. in the vertebrate host, young adult female B. malayi worms are approximately 1.8 cm long and still growing. At that time point a massive accumulation of Wolbachia was observed, mainly in the lateral chords. Increased numbers of Wolbachia were observed in the lateral chords in the posterior end of the female which was still free of ovaries (Fig. 3A). Sections of the posterior part of the ovaries showed large numbers of Wolbachia in the adjacent lateral chords, but the ovaries themselves were free of Wolbachia (Fig. 3B, C, 4A). In the oocyte maturation and growth zones of the ovaries, Wolbachia were oriented within the lateral chords towards the pseudocoelomic cavity (Fig. 3D–G), and some sections showed Wolbachia in the periphery of the ovary (Fig. 3H, 4B, D, F). Two distribution patterns of Wolbachia were found in the lateral chords. Scattered Wolbachia were present in the apical part of the chords, and numerous clusters of Wolbachia were present basal border of the hypodermal chords adjacent to the ovaries (Fig. 3I). A similar staining pattern for Wolbachia was observed in the lateral chords in the midbody region of 5 week old females, but their empty uterus branches were always free of Wolbachia (Fig. 3J). In 8 week old female worms, less Wolbachia were detected in the lateral chords, but the mature ovaries in the posterior part of the worms were heavily infected with Wolbachia (Fig. 5A). These worms contained developing microfilariae, and the ovaries showed strong staining of nuclear chromatin (as determined by DAPI). Morula stage embryos were observed in the uterus with many Wolbachia, while in this region the number of endobacteria in the lateral chords was lower than in the distal parts of the lateral chords.
In 12 week old females numerous Wolbachia were observed in the lateral chords and the posterior parts of the ovaries, but bacteria densities in these areas were lower than in the lateral chords adjacent to the anterior ovary and oviduct (Fig. 5B,C,E, H). Numerous Wolbachia were detected in morula stage embryos in the uterus, but only a few were detected in the lateral chords of females at that level (Fig. 5D, F, G). Intrauterine spermatozoa surrounding degenerated oocytes in the seminal receptacle were free of Wolbachia, but serial sections showed some Wolbachia in the oocytes in this area (Fig. 5F,G). Stretched microfilariae in the vagina uterina contained Wolbachia in some cells, but the numbers were low compared to those in morula stage embryos. This suggests that Wolbachia may be necessary for rapid cell division which occurs in developing embryos but not in stretched microfilariae. The distribution of Wolbachia in the lateral chords was often asymmetrical, and this depended on the proximity to the reproductive system, body region and on the age of the worm.
The genital opening of the male worm lies at the posterior end and forms with the anus a cloaca (Fig. 2C). This is in stark contrast to the anatomy of females. A single vas deferens leads into a seminal vesicle that is connected to the testis; this can be subdivided into a growth zone, a maturation zone, and a germinative zone. In parallel to the distribution of Wolbachia in females, large numbers of endobacteria were observed in the lateral chords of 5 week old males, while the growing sections of the testes in the midbody region were free of Wolbachia (Fig. 6A). However, Wolbachia were present in 5 week males near the testes (Fig. 6B–E) and in the middle part of the testis itself (Fig. 6 F–J). No Wolbachia were detected within the vas deferens by immunohistology (Fig. 7A, D). In contrast, Wolbachia 16S rRNA was detected by in situ hybridization in the testis tissue surrounding the spermatocytes and in the periphery of the vas deferens that contained spermatids (Fig. 7B, C, E). Wolbachia were never observed in the spermatids or the spermatozoa.
Comparison of four different methods on consecutive sections (Figs. 3 D–G; 6 C–E; 6 F–J; 7A,B; 7D–G) revealed almost identical staining patterns for Wolbachia by immunohistology with mab Bm WSP, by in situ hybridization (using FISH and RNA in situ to detect Bm Wolbachia 16S rRNA), and by DAPI staining. Differences between immunohistology and 16S rRNA in situ detection were occasionally observed (Figs. 5B, C; 7A, B; 7D, E). In these cases in situ hybridization detected a strong Wolbachia 16S rRNA signal, while no or very little Wolbachia surface protein was detectable by immunohistology. This may indicate a small difference of gene expression pattern or of gene product stability of both markers, but was not noticed as confounding factor. In addition, the intestine of B. malayi was sometimes nonspecifically labeled by immunohistology because of endogenous alkaline phosphatase (e.g. Fig. 5A, F–H). This did not occur with in situ staining.
The mab Bm WSP immunohistology assay detects a protein on the surface of Wolbachia, and it is possible that this protein is not present on all Wolbachia cells. In contrast, the in situ hybridization assay detects expression of 16S rRNA in the cytoplasm of Wolbachia. Small subunit rRNA is known to be highly expressed during the exponential growth phase of bacteria, and that has been used as marker for viability [15]. Therefore the in situ assay is an excellent marker for Wolbachia growth, and it may be suitable for assessing both the presence and viability of Wolbachia. DAPI staining, which detects A-T rich regions in DNA, is an easy and quick method to detect Wolbachia in the lateral chords, since this syncytial tissue usually does not contain condensed filarial chromosomes (Figs. 3G; 7G). However, it is difficult to identify Wolbachia by DAPI staining in areas with condensed filarial chromosomes such as ovaries or in spermatids within the vas deferens (Figs. 3G; 7G). This problem can be solved by combining the DAPI stain for condensed DNA with immunohistology (Figs. 2J, 4D–G; 6H). This permits visualization of Wolbachia in the vicinity of filarial nuclei.
Confocal laser scanning microscopy was used to study the three dimensional distribution of Wolbachia in larvae and in developing reproductive tissue of young adult worms. Although Wolbachia numbers were increasing in the lateral chords in 4th stage larvae, no Wolbachia were observed in developing reproductive organs in L4. The higher resolution of LSM confirmed heavy Wolbachia loads in the lateral chords of young female worms (5 weeks) and relatively few endobacteria in the hypodermis (Fig. 4A). Entire oocytes could be examined for Wolbachia, because the size of oocytes is less than 5 µm and the scanned slices were 0.8 µm thick which is about the size of an endobacteria. The confocal examination of the distal end of the ovaries in 5 week old females confirmed the absence of Wolbachia from primary oocytes (Fig. 4A, D). A full LSM scan and rotation of the section show that Wolbachia were present also in the hypodermal pouches that form longitudinal lines in 5 week old female worms (video S1). A membrane stain helped to demonstrate that some Wolbachia were attached to the external membrane around the proximal ovary while other bacteria were actually in the ovary (Fig. 4B, C, videos S2, S3). The latter Wolbachia were always in the vicinity of large clusters of Wolbachia in the lateral chords adjacent to the ovaries in developing adult female worms (Fig. 4B, C). Wide field fluorescence microscopy using FITC labeled mab Bm WSP with a membrane stain and an overlay of the DAPI nuclear stain showed that Wolbachia are attached to the ovary membranes (Fig. 4D, E, F, G). It is possible that these endobacteria invade the ovaries of young females from the lateral chords. Wolbachia distribution in the developing ovaries was not uniform; in some cases, one branch was infected while the other branch was Wolbachia free (Fig. 4E).
Studies of the midbody region of 5 week old worms by transmission electron microscopy confirmed the presence of Wolbachia in the vicinity of developing reproductive tissues. Numerous rod-shaped and spherical Wolbachia were detected in the lateral chords in females, especially in adult worm tissues that are adjacent to developing ovaries. In some areas the hypodermal chord tissue was loose and vacuolized (Fig. 8A). The epithelial cells surrounding the basal lamina of the ovaries were occasionally also strongly vacuolized indicating tissue degeneration, and small, electron dense Wolbachia were detected in these vacuoles (Fig. 8B). Occasionally extracellular Wolbachia were seen in the pseudocoelomic cavity docking to the edge of the ovaries (Fig. 8C, D) or attached to the outer ovarian tissue (Fig. 8E, F). While most of the Wolbachia in the lateral chords were rod-shaped or spherical and up to 1 µm in length and 0.5 µm in diameter, the endobacteria in the pseudocoelomic cavity were condensed, bacillary in shape and only 0.15 to 0.5 µm in length (Fig. 8G–I). Within the ovaries, these small Wolbachia forms were observed in large vacuoles or in loose ovarian tissue (Fig. 8G, I) either as single bacteria or in groups (Fig. 8H).
In 5 week old male worms large clusters of large, rod-shaped or spherical Wolbachia were observed in the lateral chords in the vicinity of the testis (Fig. 9A). Small, bacillary Wolbachia forms were sometimes observed in the testis tissue. At the caudal end of the testis, close to the transition to the vas deferens, Wolbachia were observed in the inner tissue, sometimes in the vicinity of peripheral spermatids (Fig. 9B,C, D). These spermatids can be easily identified and differentiated from mature spermatozoa by their compact membranous organelles and the absence of major sperm protein complexes. Large amounts of membranous material were observed in the lumen between the spermatids and the inner testis epithelium. This material resembles degenerating Wolbachia (Fig. 9B, E–G) as they have been described previously [16]. Wolbachia were unambiguously identified in the reproductive tissue of young male worms, but not in the spermatids or spermatozoa.
Immunohistology has been extensively used to study Wolbachia and their clearance following chemotherapy in O. volvulus. Compared to O. volvulus, mature B. malayi have a thinner hypodermis and less pronounced lateral chords, and this can make the detection of Wolbachia more difficult. Our results demonstrate that the distribution and density of Wolbachia vary in different tissues and developmental stages. Our results are consistent with those from a PCR study that reported low amounts of Wolbachia DNA in vector stages and larger amounts in mammalian stages [4]. McGarry and co-workers reported an exponential increase in Wolbachia DNA in transmitted B. malayi L3 larvae as early as 7 days p.i. We detected large amounts of endobacteria by histology in the lateral chords of the midbody region in L4 larvae (14 d.p.i.). More Wolbachia were present in young adult worms at 35 d.p.i. in most parts of the lateral chords and also in an uneven distribution in the hypodermis.
Observations on Wolbachia density and tissue localization may lead to hypotheses regarding their potential function in filarial worms. Antibiotic treatment experiments have suggested that Wolbachia may play a crucial role in the molting process of filarial parasites [17], [18], [19], [20]. It appears clear that if Wolbachia have a direct function during molting, this function does not require localization in the vicinity of the filarial cuticle, since our localization results show that Wolbachia are not located near the cuticle during or immediately after molting. The distinct age and tissue specific distribution patterns of Wolbachia suggest also that the bacteria are not likely to be needed for housekeeping functions in all cell types of filarial nematodes. The absence of Wolbachia in the filarial nervous system, muscles, or the digestive systems suggests that Wolbachia are not needed for these functions. In adult worms the majority of mitochondria can be found in the periphery of the lateral chords, while the majority of Wolbachia are localized in or near the reproductive system. The differential distribution of Wolbachia and mitochondria within the lateral chord of filarial parasites has been reported previously [21]. Especially to the female worms the localization of Wolbachia in the lateral chords in vicinity of the reproductive system implies an important role of endobacteria for embryogenesis and intrauterine development. In agreement with this hypothesis tetracycline treatment to deplete Wolbachia in developing filarial worms has been shown to affect mainly females and causes a male-biased sex-ratio [20], [22].
The Wolbachia genome in B. malayi encodes complete pathways for the biosynthesis of nucleotides, riboflavin, flavin adenine dinucleotide and heme, which are missing or incomplete in the filarial genome [23]. A high demand for gene products (which may not be taken up from the mammalian host) from these pathways might be especially necessary during the development of the reproductive system in young adult worms. Furthermore, the phylogenetically old and tight association of filarial nematodes with Wolbachia during reproduction may have led to additional interdependencies that account for their mutualistic relationship. As hypothesized for Wolbachia in insects, it is possible that Wolbachia in filarial nematodes are especially important for pre-meiotic mitosis, meiosis, and meiosis associated processes [24], [25], [26], [27].
A recent study examined the dynamics of Wolbachia during intrauterine embryogenesis of B. malayi using Caenorhabditis elegans embryogenesis as a framework for the analysis [5]. Asymmetric Wolbachia segregation was observed that could explain the concentration of Wolbachia in the hypodermal chords. The early differential distribution of Wolbachia within embryonic cells corresponds well with the strong tissue specific distribution in later development described in our study. However, the authors also hypothesized that the asymmetric segregation pattern may be responsible for the presence of Wolbachia in the female germline [5]. This is in contrast to our results which clearly demonstrate the absence of Wolbachia in male and female reproductive tissue from the third stage larvae to the young adult worms. Since it is difficult or impossible to identify the germline cells or gender of microfilariae, vector stage first stage larvae, and second stage larvae of B. malayi, we cannot be sure when during development Wolbachia are lost in these cells.
The terminal ends of Brugia ovaries form the germinative zones which contain the mitotic growing oogonia [28]. Our study showed that these areas were free of Wolbachia in growing, young adult worms. Our results suggest that Wolbachia from adjacent lateral chords may cross tissue zones to infect cells in maturation zone 1 (which mainly contains primary oocytes in the pachytene stage of meiotic prophase I) and in maturation zone 2 (which contains oocytes in the remaining phases of meiosis I). The germinative zones of the ovaries seem to be populated by Wolbachia over a period of approximately three weeks following the L4–L5 molt. Large numbers of Wolbachia were present in the maturation zones of eight week or older female worms, while the attached growth zones which contain the secondary oocytes and the oviducts contained lower numbers of Wolbachia (see Fig. 5E). Fertilization precedes meiosis II in filarial nematodes [28]. Wolbachia were detected in secondary oocytes surrounded by spermatozoa and unfertilized oocytes within the seminal receptacle in mature females (see Fig. 5F, G).
The picture was similar in male worms. Wolbachia were not observed in the germinative zone of the testis. It is possible that Wolbachia from the lateral chords infect the primary spermatocytes in maturation zone 1, which are mostly in the pachytene stage of prophase of meiosis I [29]. This report is the first detection of Wolbachia in primary spermatocytes of developing male filarial nematodes. Although mature male worms have been previously examined for Wolbachia, prior studies did not report infection of the testis [30]. This is not contradictory to our findings, since Wolbachia appear to only infect the testis of immature adult stage B. malayi males and such worms were not studied previously. The spermatocytes of the adjacent growth zone and maturation zone 2 are difficult to differentiate morphologically, but larger secondary spermatocytes that have completed meiosis and the spherical spermatids which enter the vas deferens can be distinguished. Wolbachia were never seen in the spermatids or the mature spermatozoa. However, our in situ hybridization results clearly indicated the presence of Wolbachia 16S rRNA in the periphery of the seminal vesicle. This was confirmed by electron microscopy that showed Wolbachia in the inner epithelium of the testis or vas deferens, but not in the spermatids. These data may suggest that high Wolbachia densities are correlated with condensed chromatin and Wolbachia may be involved in chromosome segregation of filarial nematodes.
Our ultrastructural studies of young adult B. malayi confirm that Wolbachia are highly pleomorphic. This pleomorphism was recognized shortly after the discovery of endobacteria in filarial nematodes, and it has been suggested that Wolbachia may have a Chlamydia-like life cycle with small dense bodies as potential infectious forms [30], [31]. Chlamydia and filarial Wolbachia both have an obligatory intracellular life style and a small genome size due to the loss of a number of essential biosynthetic pathways. Both bacterial groups lack cell walls but retained a functional lipid II biosynthesis pathway [32]. It is also possible that Wolbachia share the requirement of Chlamydia for host cell sphingolipids supplied by the host cell Golgi apparatus and multivesicular bodies for activation [33]. Clearly, further studies are needed to assign functions to different morphological forms of Wolbachia during the filarial life cycle.
Based on our results we hypothesize that the genital primordium in larval B. malayi is devoid of Wolbachia and that reproductive tissues in young adult worms become infected with Wolbachia from adjacent lateral chords which have many Wolbachia. Prior studies have shown that newly introduced Wolbachia can cross several tissue planes and infect the germline in Drosophila [34]. This could be also the case in filarial Wolbachia, and it is possible that similar host signals trigger the germline tropism of Wolbachia in filarial worms and Drosophila. Previous studies have shown that a Wolbachia htrA serine protease can be found outside bacterial cells in filarial parasites. This protease and other secreted bacterial proteins may be involved in tissue invasion [35]. In addition to tissue lysis, motility of Wolbachia may be necessary for the bacteria to cross tissue boundaries. Actin-based motility occurs in Rickettsia and many other intracellular bacteria [36]. Orthologs of genes essential for actin-based motility have been found in the Wolbachia genome. Additional work will be needed to study the localization and timing of expression for these genes [23], [37].
Our ultrastructural results confirmed the presence of large clusters of Wolbachia in the lateral chords in the vicinity of the ovaries and in the outer ovary epithelium as previously described [30], [38]. The new finding reported here, is the detection of extracellular Wolbachia in the pseudocoelomic cavity in young females and the presence of Wolbachia in testis of developing male worms. In summary, this study shows the value of histological techniques such as immunohistology and in situ hybridization to study the tissue distribution of Wolbachia during the life cycle of filarial nematodes. Wolbachia infection was found to be highly cell and tissue specific. No Wolbachia were found in the developing reproductive organs in fourth stage larvae and freshly molted adult worms, which had heavy Wolbachia loads in the lateral chords. Wolbachia were detected in reproductive tissues with the onset of oocyte and sperm development, and infection of oocytes results in transovarial transmission of Wolbachia to the next generation.
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10.1371/journal.ppat.1002258 | Development of a Transformation System for Chlamydia trachomatis: Restoration of Glycogen Biosynthesis by Acquisition of a Plasmid Shuttle Vector | Chlamydia trachomatis remains one of the few major human pathogens for which there is no transformation system. C. trachomatis has a unique obligate intracellular developmental cycle. The extracellular infectious elementary body (EB) is an infectious, electron-dense structure that, following host cell infection, differentiates into a non-infectious replicative form known as a reticulate body (RB). Host cells infected by C. trachomatis that are treated with penicillin are not lysed because this antibiotic prevents the maturation of RBs into EBs. Instead the RBs fail to divide although DNA replication continues. We have exploited these observations to develop a transformation protocol based on expression of β-lactamase that utilizes rescue from the penicillin-induced phenotype. We constructed a vector which carries both the chlamydial endogenous plasmid and an E.coli plasmid origin of replication so that it can shuttle between these two bacterial recipients. The vector, when introduced into C. trachomatis L2 under selection conditions, cures the endogenous chlamydial plasmid. We have shown that foreign promoters operate in vivo in C. trachomatis and that active β-lactamase and chloramphenicol acetyl transferase are expressed. To demonstrate the technology we have isolated chlamydial transformants that express the green fluorescent protein (GFP). As proof of principle, we have shown that manipulation of chlamydial biochemistry is possible by transformation of a plasmid-free C. trachomatis recipient strain. The acquisition of the plasmid restores the ability of the plasmid-free C. trachomatis to synthesise and accumulate glycogen within inclusions. These findings pave the way for a comprehensive genetic study on chlamydial gene function that has hitherto not been possible. Application of this technology avoids the use of therapeutic antibiotics and therefore the procedures do not require high level containment and will allow the analysis of genome function by complementation.
| C. trachomatis is a major human pathogen for which there is no means of genetically manipulating its DNA. It is an obligate intracellular bacterium which has a complex developmental cycle that takes place in a specialized host cell cytoplasmic vacuole known as an inclusion. We have constructed a shuttle vector based on the chlamydial plasmid and developed a new approach to select genetically modified bacteria. It uses rescue by selection of stable infectious, penicillin-resistant C. trachomatis from a pool of non-dividing, non-infectious C. trachomatis (induced by penicillin arrest of the developmental cycle). The transformed C. trachomatis is also cured of its endogenous plasmid by the selection of the transforming vector. The vector was modified to express the green fluorescent protein (GFP) in both Chlamydia and E. coli. We have genetically manipulated a plasmid-free recipient strain of C. trachomatis and shown restoration of the ability of this recipient strain to synthesize and accumulate glycogen within inclusions upon acquisition of the shuttle vector. The ability to transform and manipulate C. trachomatis using a complementation based vector is an important advance that opens up the field of Chlamydia research.
| C. trachomatis is a major human pathogen with a unique intracellular developmental cycle [1], [2]. This cycle begins when the extracellular, infectious form of the microorganism, the EB, binds to susceptible host cells [3], [4]. EBs are taken up into a phagocytic vesicle which is modified to become a chlamydial inclusion where individual EBs differentiate into the metabolically active, replicative form of the microorganism, the RB [5]. RBs divide by binary fission and, when 8–10 divisions have elapsed, they differentiate back into EBs that are released by cell lysis [6].
C. trachomatis has a small and highly conserved genome of some 1,000 kb [7]. In addition, most C. trachomatis isolates carry a plasmid of 7.5kb [8] which encodes eight genes. All eight genes are transcribed [9] and translated during the developmental cycle [10]. Despite the availability of the plasmid as a potential vector, the development of a simple robust genetic transformation system for the chlamydiae has remained a significant challenge [11]. The demand for such a system is evidenced by the recent development of a means to mutate the C. trachomatis chromosome [12] but to reach its full potential this requires a complementary gene transfer system. An approach using chromosomal integration was used to make recombinants in C. psittaci EBs by allelic exchange using exogenous DNA introduced by electroporation [13]. This was limited to the 16S rDNA region and only allowed integration of a short 1 kb marker at extremely low efficiencies. C. psittaci requires high levels of containment and therefore is not readily available to the wider research community. Electroporation of EBs was used in 1994 in an attempt to transform C. trachomatis with an episomal vector based on the chlamydial plasmid [14]. No stable transformants were isolated although inclusions were present for up to four passages under chloramphenicol selection. There have been reports of plasmid free strains of C. trachomatis but only three viable, naturally occurring plasmid-free isolates of C. trachomatis have been described [15]–[17], thus most C. trachomatis isolates carry the 7.5 kbp plasmid, its biological function is unknown although its presence has for a long time been linked to the ability of C. trachomatis to synthesise glycogen [18]. We tried unsuccessfully to cure a lymphogranuloma venereum (LGV) strain of C. trachomatis L2 of its plasmid [19] but recently curing of the plasmid from a genital tract C. trachomatis D has been described [20]. This plasmid-cured strain and the naturally occurring plasmid-free strains do not stain for glycogen [21]; the plasmid does not encode glycogen synthesis genes thus the phenomenon involves complex interaction(s) between the plasmid and the chlamydial genome to elicit the glycogen staining phenotype [22]. However, formal proof associating this property with the plasmid alone is necessary but can only be achieved by re-introducing the plasmid into a plasmid-free strain. This has not been possible because of the absence of a means to genetically manipulate C. trachomatis.
We report here the first successful development of a simple, robust, reproducible plasmid-based genetic transformation system for C. trachomatis using penicillin selection and calcium chloride (CaCl2) treatment of EBs to render them competent. Penicillin causes C. trachomatis to enter a persistent non-infectious state [6], [23]–[25]; therefore our experimental plan was to select genetically stable, penicillin-resistant transformants by recovery of Chlamydiae from penicillin-arrested division. To demonstrate the effectiveness and reproducibility of the procedure, we have engineered a strain of C. trachomatis that is penicillin resistant and that expresses GFP. We have also proven the role of the chlamydial plasmid in glycogen biosynthesis by re-introducing the plasmid into a C. trachomatis strain that is plasmid-free (C. trachomatis L2 (25667R)) [15]. We have demonstrated that as a result of this genetic transformation the previously plasmid-free strain C. trachomatis L2 (25667R) acquired the ability to synthesize and accumulate glycogen within inclusions.
Our primary aim was to develop a transformation protocol using a chlamydial plasmid-based shuttle vector. Our scientific aim was to determine whether glycogen biosynthesis, a distinctive characteristic of C. trachomatis which has been linked to the presence of the plasmid, could be restored in a plasmid-free, glycogen-free variant of C. trachomatis, by re-introducing the plasmid DNA.
To enable the selection of transformants for plasmid vectors, antibiotic resistance markers offer the most attractive choices. There are a range of single gene antibiotic resistance markers that are safely used worldwide for the routine and stable transformation of bacteria. These markers are popular because it is not possible to generate transformants unless an intact gene is acquired thus eliminating the problem of background resistance through spontaneous mutation. The markers used in routine selection of bacterial transformants include tetracycline resistance, chloramphenicol resistance and β-lactamase (penicillin/ampicillin resistance) [26]. Tetracycline resistance markers have been used in allelic transfer experiments for chlamydiae [27], but tetracycline is a controversial choice because it is used routinely to treat C. trachomatis infections [28]. C. trachomatis is sensitive to chloramphenicol but it is difficult to reproduce a minimal inhibitory assay as this antibiotic causes mitochondrial stress [29] limiting chlamydial growth and is thus not so useful for selection and continuous passaging of transformants. By contrast, the effects of penicillin on C. trachomatis are well studied [6], [25], it is bacteriostatic giving a resistant phenotype and penicillin is not recommended for the treatment of chlamydial infections [30]. Following penicillin treatment, the developmental cycle is slowed and the transition to EBs ceases with the formation of giant, aberrant RBs. The normal developmental cycle resumes upon removal of penicillin (at low concentrations) from the culture media and the resultant normal chlamydial inclusions are easily detectable by phase contrast microscopy [6]. We reasoned that rescue of infectious C. trachomatis from the penicillin-induced aberrant developmental cycle through transformation and β-lactamase expression would present a distinctive phenotype that could be easily selected microscopically and transformants could be recovered under penicillin selection. We tested a range of penicillin concentrations on the growth of normal C. trachomatis L2 and determined that 10 units/ml of penicillin was a suitable concentration for inhibition of C. trachomatis and hence this was the antibiotic concentration chosen for the C. trachomatis transformation work. Penicillin resistance was introduced into a C. trachomatis plasmid (pL2) by ligating a pBR325 plasmid and the pL2 plasmid (pBR325::L2). This was a simple, standard recombinant plasmid from our collection that had an intact β-lactamase gene and the complete chlamydial plasmid. Ligation into the Bam HI site of pBR325 plasmid created an insertional inactivation of the tet gene but still conferred penicillin and chloramphenicol resistance. The Bam HI site in the pL2 plasmid is located in coding sequence 1 (CDS1), a region which is susceptible to mutation/deletion without affecting plasmid stability [8], [19]. The plasmid map is shown in Figure 1.
We chose the well-studied, laboratory-adapted strain C. trachomatis L2/434/Bu (ATCC VR902B) as the recipient strain because we already had a full complement of biological and genetic information for this strain (the complete genome sequence and defined proteome) [31], [32]. The vector with which we started the work contained the cognate pL2 plasmid cloned from C. trachomatis L2/434/Bu genomic DNA [33]. Furthermore, the LGV strains [34] that we used: C. trachomatis L2/434/Bu and (later) the plasmid-free C. trachomatis L2 (25667R) have relatively low particle to infectivity ratios [35] and do not need centrifugation to achieve efficient cell infection, thus these bacteria have a higher viability than standard genital tract isolates and have a faster developmental cycle giving a quicker turn around of experiments [4].
We wanted to investigate whether it was possible to transform EBs using a simple standard protocol and a defined buffer that could be reproduced in any laboratory. Standard bacterial transformations are based on the primary observation that bacteria treated with ice cold solutions of CaCl2 followed by brief heating can be induced to take up foreign DNA [36]. Therefore we initially used such a protocol, developed for E. coli, to attempt transformation of gradient purified C. trachomatis L2/434/Bu EBs and selected transformants with 10 units/ml penicillin. However, we found that heat shock was not necessary and that the whole transformation protocol could be achieved at room temperature (RT) simply by gently mixing EBs, vector DNA and McCoy cells in CaCl2/Tris buffer. This protocol is described in the Materials and Methods and the selection procedure is described in Table S1. For selection our reasoning was to allow recovery of transformants, without penicillin selection in the first round of infection (developmental cycle) and then apply selection with penicillin initially at 10 units/ml (10 units = 6 µg penicillin). After three rounds of selection, all the untransformed penicillin-inhibited C. trachomatis were lost and the culture was overgrown by penicillin-resistant transformants that appeared to grow with similar kinetics and inclusion morphology as the parental strain. It was possible to increase the concentration of penicillin to 20 and 100 units/ml at passages 3 and 4 to speed up the selection process. In each experiment regular observation of the cultures under phase contrast microscopy allows some flexibility in deciding precisely when the next passage is needed, taking into account the size, form and number of inclusion bodies observed at each stage.
The frequency of transformation, defined as the proportion of EBs or trypsinised cells receiving the shuttle vector DNA and still keeping the ability to start chlamydial proliferation, is a parameter that cannot be measured. The efficiency of recovery is dependent on the percentage of the transformed EBs present in the total population of EBs after each stage of selection. Each passage is performed by infecting cell cultures with cell lysates from the previous passage (see Materials and Methods, and Table S1). Following lysis of the McCoy cells, the untransformed Chlamydia, present as non-infectious RBs, fail to passage. Thus only transformed EBs and a diminishing number of carry-over untransformed EBs infect new host cells. The recovery increases with each passage until only transformed Chlamydia remain.
The pBR325::L2 transformed C. trachomatis L2/434/Bu strain was plaque purified (x3), expanded, and EBs and RBs were purified for detailed characterization. Southern blotting of chromosomal DNA purified from EBs with a vector probe (β-lactamase gene) and a chlamydial plasmid probe proved that transformation had occurred and revealed no changes in either the E. coli vector (pBR325) or the L2 plasmid (pL2), although by these passages the endogenous chlamydial plasmid had been eliminated from the transformed strain (Figure 2). There was no evidence for recombination/integration of the transforming plasmid with the recipient C. trachomatis chromosomal DNA.
The copy number of pBR325::L2 was similar to that of the endogenous pL2 as accurately measured by qPCR (Figure S1).
To investigate the effects of transformation by plasmid pBR325::L2 on the strain C. trachomatis L2/434/Bu, we compared the growth characteristics of the parental strain and the transformed strain with or without penicillin selection. These data are summarized in Figure 3 and clearly show that the parental strain (L2) grows well in McCoy cells, as previously demonstrated, and there was no recovery of infectious EBs in the presence of penicillin when it was added at 10 units/ml from the start of the developmental cycle.
By contrast, the transformed strain grew in both the presence (10 units/ml) and absence of penicillin (up to 8 passages) giving similar recoveries of infectious EBs at the end of developmental cycle. Nevertheless the presence of a large transforming plasmid (pBR325::L2) has a measurable effect and the developmental cycle was lengthened and the yield slightly reduced compared to the untransformed C. trachomatis L2/434/Bu. Transmission electron microscopy of infected cells at late stages of the developmental cycles show no obvious phenotypic differences for transformed or untransformed C. trachomatis in the absence of penicillin but in the presence of penicillin untransformed C. trachomatis produced large aberrant RBs whereas transformed C. trachomatis inclusions appeared normal (Figure S2).
The vector was recovered from pBR325::L2- transformed C. trachomatis L2 by genomic DNA preparation and analysed by direct sequencing. These data showed no evidence for the presence of the original pL2 and also confirmed that there were no rearrangements or recombination events with pL2 or within the pBR325::L2 vector, although there were a few base changes from the sequences deposited in the database for the original pBR325 vector (Figure S3). These were attributable to errors in the original sequencing and annotation rather than adaptive mutations or subsequent transformation of the DNA in to E. coli. Analysis of multiple colonies carrying the recovered pBR325::L2 from E. coli by mini-plasmid preparation and restriction digestion showed they were all clonal and appeared to be unchanged from the transforming vector. One of these recovered plasmids was also sequenced and was identical to the sequence obtained direct from the transformed C. trachomatis.
Immunoblotting of purified EBs and RBs with commercially available antibodies (Figure S4) showed the presence of both β-lactamase and chloramphenicol acetyl transferase enzymes consistent with the observation that pBR325::L2- transformed C. trachomatis L2 was, in contrast to the untransformed C. trachomatis L2, resistant to penicillin up to 100 units/ml and able to grow in medium containing chloramphenicol concentrations up to 3 µg/ml (data not shown).
Interestingly, both the processed and mature forms of β-lactamase were detectable by immunoblotting in EBs showing that the signal peptide [37] was functional in C. trachomatis and the pre-protein was cleaved completely in RBs (Figure S4A). Chloramphenicol acetyl transferase levels (as assessed by immunoblot) were the same in both RBs and EBs (Figure S4B).
The transformed C. trachomatis L2 grew under penicillin selection and whilst the presence of β-lactamase, as shown by Western blotting, indicated that resistance to penicillin is likely through the mechanism of action of this enzyme, it did not prove that the enzyme is active. Thus to prove formally that the resistance to penicillin was indeed due to acquisition of an active β-lactamase, purified RBs (the actively growing form of C. trachomatis) were assayed for β-lactamase activity. Figure 4 shows that pBR325::L2- transformed C. trachomatis RBs have β-lactamase activity whereas RBs from the untransformed parental strain do not.
These results, taken together, established that pBR325::L2 could be selected from a background of untransformed C. trachomatis and this vector could also replicate in E. coli, therefore providing the basis of a functional shuttle vector. They showed proof of principle that the functions for retaining and replicating the plasmid in C. trachomatis were unaffected in the shuttle vector and we have established that standard E. coli promoters for both β-lactamase and chloramphenicol acetyl transferase operate in C. trachomatis. Further, the β-lactamase is active and the type 1 signal peptidase mechanism from C. trachomatis [38] is able to cleave the β-lactamase precursor to a mature form of the correct molecular weight in RBs, the actively growing form of the micro-organism.
In our experiments deriving a transformation frequency as a percentage of EBs receiving vector DNA is not a relevant measure and we have no means of measuring how many survive selection in the first round of culture. We considered it might be possible to do this if we had a marker such as green fluorescence that we could use to identify live transformed Chlamydia.
To show that the transformants obtained in the first series of experiments were useful for general application and not just the outcome from a serendipitous choice of a single, vector configuration (pBR325::L2) it was essential to repeat the work but with more than one (and different) marker. For this purpose we chose the mutated plasmid from the Swedish new variant which has a characteristic 377 bp deletion in CDS1 and a 44 bp duplication in the 5′ terminus of CDS3 [8]. This plasmid was cloned into an E. coli vector able to express the GFP. The complete plasmid map of the shuttle vector carrying the mutated plasmid from SW2 is shown in Figure 5.
Plasmid pGFP::SW2 contains a β-lactamase gene together with a red-shifted green fluorescent protein gene fused to the chloramphenicol acetyl transferase gene under control of a neisserial promoter. The map of the original plasmid pRSGFPCAT that provided the fused rsgfp-cat cassette including the neisserial promoter is shown in Figure S5.
E. coli cells transformed with pRSGFPCAT are resistant to penicillin and chloramphenicol and fluoresce green under blue illumination (data not shown).
The plasmids and DNA used to make this final shuttle vector (pGFP::SW2) are summarized in Figure S6. The complete sequence and list of features for pGFP::SW2 are summarized in Figure S7.
Transformation of C. trachomatis L2/434/Bu with pGFP::SW2 was successfully achieved on six occasions and yielded a penicillin-resistant strain that had green fluorescent inclusions (Figure 6). Green fluorescent inclusions became visible at 24 h post infection, once the transformants had been selected by multiple passages; however, we were unable to observe green fluorescent inclusions in the first developmental cycle post-transformation.
The green fluorescent protein is expressed as a fusion protein with the chloramphencol acetyl transferase (GFPCAT). To show that the fluorescence observed in inclusions was derived from the GFPCAT fusion protein, transformed Chlamydia were immunoblotted with anti-GFP monoclonal antibodies as shown in Figure S8.
The ability to stain for the presence of glycogen in chlamydial inclusions is a property that has been linked to the presence of the plasmid although it has not been possible to prove formally the association as no system has previously existed for the introduction of plasmids into C. trachomatis [21]. The C. trachomatis L2 (25667R) strain is plasmid-free and not able to accumulate glycogen. We were unsure whether it would be possible to transform this strain as it was plasmid-free and may have lost the ability to retain the plasmid. Nevertheless, we attempted transformation of this C. trachomatis L2 strain with the vector pGFP::SW2 and obtained stable penicillin-resistant transformants that had green fluorescent inclusions. However, transformation of C. trachomatis L2 (25667R) strain with the basic cloning plasmid pSP73 alone did not yield transformants showing that the chlamydial plasmid (pSW2) was necessary to provide the replication functions of the shuttle vector pGFP::SW2 in C. trachomatis [8], [19]. We were able to recover the intact pGFP::SW2 vector from transformed C. trachomatis L2 (25667R) by genomic DNA preparation and re-transform the DNA into E. coli. Southern blotting of DNA extracted from transformed plasmid-free C. trachomatis L2 (25667R) confirmed the presence of pGFP::SW2 (Figure 7) and proved that transformation had occurred.
The pGFP::SW2-transformed C. trachomatis L2 (25667R) had inclusions which stained positive for glycogen (Figure 8). This observation confirms that glycogen biosynthesis is a trait that is dependent on the presence of the chlamydial plasmid. Every inclusion from the pGFP::SW2-transformed C. trachomatis L2 (25667R) stained for glycogen, demonstrating stable transformation at the individual level as well as the population level (as shown by the Southern blots).
Cells in culture and cells infected with C. trachomatis L2 were routinely visualized by phase contrast microscopy using a Nikon eclipse TS100 inverted microscope with fluorescence accessories. Fluorescence images were captured using a Leica DMRB microscope to visualize the expression of GFP in McCoy cells infected by pGFP::SW2-transformed C. trachomatis L2. Counting of inclusion forming units (IFU) to quantify chlamydial infectivity was performed on serial dilutions of C. trachomatis L2 in monolayers of McCoy cells grown in 96 well trays. For this assay inclusions were immunostained as previously described for C. abortus [39]. For transmission EM studies McCoy cells infected with C. trachomatis L2 at a multiplicity of infection (MOI) of 1 were grown in 6 well trays and 48 h post infection were fixed with 3% glutaraldehyde in 0.1% cacodylate buffer, processed as previously described [40] and photographed using an Hitachi H7000 electron microscope.
E. coli strain DH5α [41], [42] and its derivative strain E. coli ‘Top10’ from Invitrogen were used for the basic cloning and construction of vectors pRSGFPCAT and pGFP::SW2. The vector pBR325::L2 was constructed by ligation of pL2 cleaved by Bam HI from plasmid PDCPB [33] (this vector is equivalent to pBR322::L2) into Bam HI cleaved pBR325 (GenBank: L08855.1), this cloning was performed in E. coli strain HB101. The original preparation of the plasmid pBR325::L2 DNA (used to transform C. trachomatis L2 EBs) was performed in E. coli strain HB101, but all subsequent plasmid manipulations for transformation were performed using E. coli GM 2163. This strain is mutated for Dam, Dcm and Mcr methylation systems and was available from New England Biolabs (Cat. no. #E4105S). Plasmid pGFP::SW2 (Figure 5) was constructed from the C. trachomatis SW2 plasmid pSW2, pSP73 and pRSGFPCAT. The complete cloning strategy is summarised in Figure S6. Plasmid pRSGFPCAT is a small in-house vector based on a pUC origin of replication and carrying the cat gene fused to RSGFP and under the control of a neisserial promoter that is constitutively expressed in E. coli (refer to Figure S5 for sequence details). Briefly, the Bam HI fragment of pSW2 (GenBank: FM865439.1 - obtained by gel extraction from a C. trachomatis SW2 total genomic DNA Bam HI digestion) (Figure S6 panel A) was cloned into the unique Bam HI site of plasmid pSP73 (Figure S6 panel B) to give plasmid pSP73::SW2 (Figure S6 panel C). The Pst I/Sal I fragment of pRSGFPCAT (Figure S6 panel D) was then cloned into Pst I/Sal I backbone of pSP73 allowing selection of ampicillin resistant, chloramphenicol resistant green fluorescent colonies.
All genetic manipulations and containment work was approved under the UK Health and Safety Executive Genetically Modified Organisms (contained use) regulations 2000 notification no GM57,10.1 entitled ‘Genetic transformation of Chlamydiae’.
McCoy cells were used for propagation of C. trachomatis and for the transformation studies. Two strains of C. trachomatis were used as recipient strains for transformation in this study: C. trachomatis L2/434/Bu (ATCC VR-902B) which carries a 7.5 kb plasmid (pL2) and C. trachomatis L2 (25667R) which has no plasmid [15]. Both strains were confirmed as pure, clonal isolates by 3 rounds of plaque purification and, together with the McCoy cells, were regularly tested for mycoplasma contamination by fluorescence microscopy using Hoechst no. 33258 staining and by VenorGem Mycoplasma PCR detection (Minerva Biolabs, Berlin, Germany) according to the manufacturer's instructions. The McCoy cells were grown in Dulbecco's modified Eagles' medium (DMEM) supplemented with 10% fetal calf serum (FCS). Cell concentrations were determined by staining with Trypan Blue and counting in a haemocytometer. Cells were infected with C. trachomatis by overlay of the inoculum for 1 h in medium containing cycloheximide (1 µg/ml) and gentamicin (25 µg/ml). On completion of the developmental cycle and immediately prior to host cell lysis, infected monolayers were detached with trypsin/EDTA buffer (from Invitrogen, Cat. # 25300-054) and EBs were harvested in DMEM containing 10% FCS at 3,000 g for 10 min. The C. trachomatis-infected cell pellet was suspended in a solution of 10% PBS in water and homogenized in a Dounce homogenizer to break open the cells and release the EBs. Cell debris was removed by centrifugation at 250 g for 5 min and the supernatant containing partially purified EBs was mixed with an equal volume of phosphate/sucrose buffer (16 mM Na2HPO4 pH 7.1 and 0.4 M sucrose, abbreviated as 4SP), and was stored at −80°C.
The C. trachomatis strains were plaque purified as previously described [31], [43]. Briefly a single plaque was picked, cultured and purified to clonality by two further rounds of plaquing. The plaque-purified Chlamydia were then used to make stock preparations. Large scale cultures were prepared for the production of EBs and RBs which were purified by two cycles of Urografin (Schering Healthcare, UK) density gradient centrifugation as previously described [31].
A simple protocol was developed where C. trachomatis L2 EBs were first mixed with plasmid DNA and then used to infect freshly trypsinised McCoy cells (MOI = 2.5). The preparation of EBs, vector DNA and cells followed by the transformation protocol is described in detail below.
1. Preparation of EBs for transformation: McCoy cells were grown in 6× T75 flasks, and infected with C. trachomatis L2 in fresh medium (DMEM + 10%FCS) containing 1 µg/ml of cycloheximide, and grown in 37°C, 5% CO2 incubator for 2 days at an MOI = 2 giving ∼90% cells infected. The cells were bulk harvested using cell scrapers, and then spun at 3500 rpm for 10 min. The cell pellet was saved, resuspended in 1 ml of cold 10% PBS, and transferred into a bijoux tube with glass beads. The cells were then lysed by vortexing for 1 min. The cell debris was removed by spinning at 1000 rpm for 5 min. The supernatant was saved (∼1 ml) and mixed with 1 ml of 4SP. The inocula were divided into 100 µl aliquots and stored at −80°C.
2. The vector DNA was extracted from overnight E. coli cultures (GM2163 strain) using the PureYield Plasmid Midiprep System (Promega Cat. No. A2492). The quality and the concentration of the DNA were evaluated by agarose gel electrophoresis and NanoDrop 1000 Spectrophotometer (from Thermo Scientific). The DNA was used at concentration ∼0.5–1 µg/µl.
3. McCoy cells were prepared for transformation when cells were ∼70% confluent in a T75 flask, the medium was removed and the cells were washed twice with 5 ml Dulbecco's PBS (DPBS) (from Invitrogen, Cat. # 14190-094). Then 2 ml Trypsin/EDTA buffer was added to cover the cells. Trypsinisation was allowed to proceed at RT for 5 min and when the cells were released from the flask, 10 ml of medium was added with a pipette, and the medium was washed up and down with the pipette to release the remaining cells and to break up clumps. The cells were transferred to a plastic universal and spun in a bench top centrifuge (Beckman Coulter Allegra X-15R) at 1000 rpm for 5 min, the medium was removed and the pellet briefly rinsed and then resuspended in 5 ml DPBS and pelleted again at 1000 rpm for 5 min. The DPBS buffer was discarded and cells resuspended in CaCl2 buffer for transformation.
4. Transformation is performed as follows: 10 µl Chlamydia EBs (1×107 IFU) and 10 µl plasmid DNA (6 µg) were mixed in a total volume of 200 µl CaCl2 buffer (10 mM Tris pH 7.4 and 50 mM CaCl2) and then incubated for 30 min at room temperature. Freshly trypsinised McCoy cells (4×106), resuspended in 200 µl CaCl2 buffer were then added to the plasmid/EB mix and incubated for a further 20 min at room temperature with occasional mixing. 100 µl of this mixture was then added to a single well in a six well tray together with 2 ml of pre-warmed DMEM + 10% FCS. The cells were allowed to settle and incubated at 37°C in 5% CO2 for 2 days without cycloheximide or penicillin. The infected cells from each well were harvested individually by scraping cells with a 1 ml filter tip and then lysed by vortexing with glass beads. The cell debris was removed by spinning at 1000 rpm for 5 min. The supernatant was saved (∼2 ml) and mixed with 2 ml of 4SP and stored in −80°C freezer (this was called T0).
5. Passage and selection. The T0 inocula were used to infect McCoy cells in a T75 flask (passage 1). Potential transformants were grown in medium containing cycloheximide (1 µg/ml) and selected with 10 units/ml of penicillin G (Sigma product no. P3032). Under these conditions, most inclusions were large and vacuolar as previously described [6]. Chlamydia were grown for two days before harvesting as ‘T1’ and this was used to infect McCoy cells as passage 2 in a T25 flask and selected with 10 units/ml of penicillin. Passaging was continued for 2–4 times in T25 flasks with 10 units/ml of penicillin until only normal inclusions were recovered. The passage and selection procedures are summarized in Table S1.
The genomic DNA of C. trachomatis L2, C. trachomatis L2 (25667R) and pBR325::L2- transformed C. trachomatis L2 (or L2, L2P- and L2/pBR325::L2 in short) were extracted from Chlamydia inocula (collected from infected McCoy cells in T75 flasks) using Wizard Genomic DNA Purification Kit (Promega, Cat. No. A1120). The genomic DNA of pGFP::SW2- transformed C. trachomatis L2 and transformed C. trachomatis L2 (25667R) (or L2/pGFP::SW2 and L2P-/pGFP::SW2 in short) was extracted from Chlamydia inocula (collected from a well of infected McCoy cells in 6-well tray) using NucleoSpin Tissue (Fisher Scientific, Cat. No. NZ74095250). The genomic DNA extracted from transformed C. trachomatis (L2/pBR325::L2, L2/pGFP::SW2 or L2P-/pGFP::SW2) were used for sequencing and the transformation of E. coli to recover the shuttle plasmids.
Restriction endonuclease digests of chlamydial genomic DNA were separated on agarose gels and then transferred to membranes using standard techniques [26]. The DNA used as probes in Figure 2 were either a ∼550 bp PCR amplicon for the β-lactamase gene using primer pair AmpF 5′-TTACCAATGCTTAAT-3′ and AmpR 5′-TACTCACCAGACACAG-3′) using pBR325::L2 as template or the whole recombinant chlamydial plasmid (pL2) released from the cloning vector pBR325::L2 by complete digestion with Bam HI (the insert was separated from the cloning vector by gel electrophoresis and the 7.5 kb fragment then eluted). DNA fragments were labeled with [α-32P]deoxy-CTP using a random primer labeling kit (Promega). The labeled products were purified by gel filtration on Sephadex G50. Membranes were pre-hybridized, hybridized overnight and then washed according to the manufacturer's standard conditions at 65°C. Dried membranes were exposed to Kodak XAR-5 film. In Figure 7 blots were probed with nonradioactive, digoxigenin-11-dUTP-labeled probes (Random primed DNA labelling) and chemiluminescence detection with CSPD (Roche Diagnostics Ltd., Product No. 11 585 614 910). The gfp probe was a 739 bp Bam HI/Bgl II fragment from pGFP::SW2, which annealed to a 1445 bp fragment of Bam HI digested pGFP::SW2 or a 3625 bp fragment of Bgl II digested pGFP::SW2. The pSW2 probe template was a 2518 bp Eco RI fragment from pGFP::SW2 (between pSW2 CDS4 and CDS7), which annealed to a 7169 bp fragment of Bam HI digested pGFP::SW2 or a 5555 bp fragment of Bgl II digested pGFP::SW2. The ompA probe template was a 1022 bp PCR product from C. trachomatis L2 (25667R) using primers PCTM3 (5′- TCCTTGCAAGCTCTGCCTGTGGGGAATCCT-3′) and NR1 (5′-CCGCAAGATTTTCTAGATTTC-3′) based on the C. trachomatis L2/434/Bu ompA sequence, this probe annealed to a 8837 bp fragment of Bam HI digested genomic DNA or a 5518 bp fragment of Bgl II digested genomic DNA.
McCoy cells grown to confluence in 96 well trays were infected with C. trachomatis L2/434/Bu transformed by pBR325::L2 at MOI = 1.0. EBs were allowed to adsorb to cells for 1 h at 37°C; cells were then washed with PBS to remove any residual unadsorbed EBs. The infected cells (performed in quadruplicate) were overlaid with 100 µl culture medium and incubated at 37°C in 5% CO2. At 65 h post infection, when the developmental cycle had completed, samples were stored (after snap freezing) at −80°C. For penicillin - treated cultures, medium containing 10 units/ml penicillin G was added at the time of infection. Chromosomal and plasmid DNA was extracted in a microplate format following a well described protocol. The residue was then resuspended in 100 µl nuclease-free water. Samples were diluted 1 in 100 prior to quantitative real time polymerase chain reaction (qPCR) analysis. A quantitative real-time PCR protocol was used to determine the absolute number of chlamydial plasmids and genomes in samples using 5′- exonuclease (TaqMan) assays with unlabelled primers andcarboxyfluorescein/carboxytetramethylrhodamine (FAM/TAMRA) dual-labeled probes as has been described previously.
Proteins from purified EBs and RBs were run on 10% SDS PAGE gels and the proteins were electroblotted onto a BioRad Immun-Blot PVDF (polyvinylidene difluoride) membrane for 1 h at 15 V. The membrane was incubated in a solution of PBS supplemented with 0.05% Tween-20 (PBS-T) and 5% dried milk for 30 min at room temperature. Commercially available antibodies that recognize the enzymes β-lactamase (AbCam mouse monoclonal Cat. No. ab12251) and chloramphenicol acetyl transferase (Sigma anti-CAT antiserum from rabbit product no. C9336) were used at a concentration of 10 µg/ml, the anti-GFP mouse monoclonal antibodies (Roche Cat no. 11814 460 001) were used at 0.4 µg/ml in a solution of PBS-T supplemented with 1% dried milk, and incubated with the membrane for 1 h at RT. Following extensive washing with PBS-T the membrane was incubated for 1 h at room temperature with horseradish peroxidase-conjugated goat- anti-mouse or goat- anti-rabbit antibody (BioRad cat nos. 172-1011 and 172-1019) at the recommended dilution. After further washing with PBS-T the membrane was incubated in Pierce ECL Western Blotting Substrate (Thermo Scientific product no. 32106) as described by the manufacturer's instructions and exposed to Kodak BioMax XAR film.
Nitrocefin changes from yellow to red in the presence of β-lactamase, therefore it can be used as an indicator of β-lactamase activity. A standard Nitrocefin assay was set up in 1 M Phosphate buffer (K2HPO4.3H20; KH2PO4) pH 7.0 using Nitrocefin (0.5 mg/ml) as described by the manufacturer (Calbiochem, Darmstadt, Germany). Equal amounts of gradient-purified C. trachomatis RBs (as determined by qPCR assay) from pBR325::pL2- transformed C. trachomatis L2/434/Bu and the wild–type parental C. trachomatis L2/434/Bu, and E. coli and pBR325::pL2- transformed E. coli were used in this assay and samples were taken at 5 min intervals for OD readings at 486 nm.
McCoy cells grown to confluence in 24 well trays were infected with C. trachomatis at MOI = 1. EBs were allowed to adsorb to cells for 1 h at 37°C and the infected cells were washed with PBS to remove any non-adsorbed EBs. The cells were then overlaid with culture medium or culture medium containing penicillin G at 10 units/ml and incubated at 37°C in 5% CO2. The infection was stopped at 8-hourly time points (8, 16, 24, 32, 40, 48, 56, 64 and 72 h) by scraping up the cells and vortexing with glass beads. The samples were then rapidly frozen and stored at −80°C.
C. trachomatis strains were cultured in McCoy cells on coverslips. Briefly, infected cells bearing C. trachomatis inclusions were washed with PBS and then fixed to coverslips with ice-cold methanol. The coverslips were stained with 5% iodine stain (containing both potassium iodide and iodine in 50% ethanol) for 10 min. The stain was then changed for 2.5% iodine stain for 10 min and mounted in 5% iodine stain in glycerol (1:1) for photomicroscopy.
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10.1371/journal.pntd.0005768 | Cross-recognition of a pit viper (Crotalinae) polyspecific antivenom explored through high-density peptide microarray epitope mapping | Snakebite antivenom is a 120 years old invention based on polyclonal mixtures of antibodies purified from the blood of hyper-immunized animals. Knowledge on antibody recognition sites (epitopes) on snake venom proteins is limited, but may be used to provide molecular level explanations for antivenom cross-reactivity. In turn, this may help guide antivenom development by elucidating immunological biases in existing antivenoms. In this study, we have identified and characterized linear elements of B-cell epitopes from 870 pit viper venom protein sequences by employing a high-throughput methodology based on custom designed high-density peptide microarrays. By combining data on antibody-peptide interactions with multiple sequence alignments of homologous toxin sequences and protein modelling, we have determined linear elements of antibody binding sites for snake venom metalloproteases (SVMPs), phospholipases A2s (PLA2s), and snake venom serine proteases (SVSPs). The studied antivenom antibodies were found to recognize linear elements in each of the three enzymatic toxin families. In contrast to a similar study of elapid (non-enzymatic) neurotoxins, these enzymatic toxins were generally not recognized at the catalytic active site responsible for toxicity, but instead at other sites, of which some are known for allosteric inhibition or for interaction with the tissue target. Antibody recognition was found to be preserved for several minor variations in the protein sequences, although the antibody-toxin interactions could often be eliminated completely by substitution of a single residue. This finding is likely to have large implications for the cross-reactivity of the antivenom and indicate that multiple different antibodies are likely to be needed for targeting an entire group of toxins in these recognized sites.
| Although snakebite antivenom is a 120-year-old invention, saving lives and limbs of thousands of snakebite victims every year, little is known about the mechanisms and molecular interactions of how antivenoms neutralize snake toxins. Antivenoms are produced by immunizing large animals with cocktails of snake venoms resulting in antibodies recognizing toxic as well as non-toxic venom proteins to variable degrees. As a result, high doses of antivenom are needed for treating a snakebite victim, causing more severe adverse reactions due to a high burden of heterologous antivenom proteins. For the first time, we have characterized the antibody recognition sites on hundreds of pit viper toxins using high-throughput peptide microarray technology and an antivenom specific for three pit vipers inflicting a high number of bites in Central America. Most pit viper toxins are enzymes known to have a catalytic site important for toxicity. However, our results suggest that the employed antivenom generally does not target such sites, but instead inhibits toxicity by binding to alternative sites, possibly causing conformational shifts in the toxin structures or interference with toxin-target recognition. The identification of these toxin-specific recognition sites may explain why the antivenom is effective against certain snakebites from pit vipers whose venoms are not part of the immunization mixture.
| Snakebite envenoming constitutes a serious public health problem on a global basis [1–3]. It primarily affects impoverished populations living in rural settings of Africa, Asia, and Latin America [4]. It is estimated that about 70,000 snakebite cases occur in Latin America every year, although it is likely that the actual magnitude of the problem is higher owing to the poor records of these accidents in many countries [5].
Parenteral administration of animal-derived antivenoms is the centerpiece of snakebite envenoming therapy. In Latin America, several laboratories are manufacturing antivenoms against the most relevant venomous snake species [6,7]. The vast majority (> 95%) of envenomings in Latin America are caused by species classified in the family Viperidae, subfamily Crotalinae, commonly referred to as pit vipers [5]. Most antivenoms against pit viper envenomings are polyspecific, meaning that venoms from more than one species are used in the immunization process. The resulting antivenom is therefore effective against bites from a range of snake species. This is crucial owing to the difficulty of species identification upon a snakebite. In Central America and Mexico, polyspecific antivenoms are produced by immunizing horses with mixtures of venoms of Bothrops, Crotalus, and Lachesis. In general, polyspecific antivenoms manufactured in various Latin American countries have shown ability to cross-neutralize several heterologous venoms, i.e. venoms not used in the immunization schedule. This phenomenon is referred to as para-specificity, and is especially prominent for species that belong to the Bothrops genus (lance-headed vipers) [8–12]. However, para-specific antigenic recognition and neutralization of venoms is not always observed at the intra-generic level, and cannot be assumed a priori only on the basis of taxonomy [13,14]. For venoms of the American Micrurus elapids (coral snakes), a marked antigenic divergence has been documented, where antivenoms raised against particular species failed to cross-neutralize congeneric venoms [15–19]. Similarly, cases of antigenic divergence leading to lack of cross-recognition of toxins among venoms of viperid species have been described, although these are mainly explained by the existence of certain toxins that are not widespread across all taxa. For example, the polyspecific Crotalinae antivenom prepared in Costa Rica using venoms of Bothrops asper, Crotalus simus, and Lachesis stenophrys [20] as immunogens, cross-neutralizes the venoms of the local Bothriechis (palm vipers) species (B. lateralis, B. schlegelii, B. supraciliaris), except for B. nigroviridis. The venom from the latter species contains a high proportion of a lethal 'crotoxin-like' phospholipase A2 (PLA2) named nigroviriditoxin, which is not cross-recognized by antivenom antibodies targeting heterologous PLA2s [21].
Para-specificity of an antivenom has traditionally been assessed by in vivo studies in mice and supported by a variety of immunological techniques. In the more recent years, a standardized method, referred to as “antivenomics” [22], combining affinity chromatography with proteomic identification of antigens, has gained widespread acceptance. Overall, the information provided by such immunological analyses reveals if cross-recognition occurs between venom components on a protein family level, indicating the existence of antibody recognition sites (epitopes) shared between heterologous toxins.
To gain molecular level insight into para-specificity, antivenom cross-recognition of individual toxins can be assessed using synthetic peptides representing linear elements of B-cell epitopes on the toxins [23,24]. Despite the inability to evaluate discontinuous epitopes, a growing number of studies have proven the usefulness of linear epitope analyses in antivenom research. Linear elements of epitopes have been found and the ability of synthetic mimicking peptides to induce a neutralizing antibody response have been demonstrated, opening new possibilities to improve the efficacy of snakebite antivenoms in the near future [25–31]. Due to technological limitations of traditional peptide synthesis and cellulose-bound peptide arrays (spot-synthesis) and the high number of overlapping peptides needed to perform such meticulous experiments, reported studies have each focused on no more than five toxins. However, by harnessing high-density peptide microarray technology, we recently enabled high-throughput molecular level study of para-specificity of toxins and antivenoms by characterizing epitopes in 82 related toxins from African Dendroaspis (mamba) and Naja (cobra) species [32].
In this study, we scaled up the high-density peptide microarray method to identify linear elements of epitopes in 702 pit viper toxins and 168 partial toxin sequences obtained from the UniProtKB database [33], using the Costa Rican polyspecific Crotalinae antivenom as probe [34]. Even though sequence data is only available for 69 of the 151 described pit viper species, the large scope of the study allows in-depth characterization of linear elements of epitopes. With its high number of investigated toxins and broad species coverage, this study is thus the largest of its kind performed to date.
The polyspecific Crotalinae antivenom (hereafter called ‘antivenom’) analyzed here (batch 5500914POLQ, expiry date: September 2017) was produced at Instituto Clodomiro Picado, University of Costa Rica, by immunization of horses with a mixture of Bothrops asper, Crotalus simus, and Lachesis stenophrys venoms as described elsewhere [20]. An immunoglobulin preparation obtained from the plasma of non-immunized horses, processed in the same manner as the antivenom, was used as a negative control. The antivenom and the plasma of non-immunized horses were not prepared specifically for this study.
An in silico library of peptides was generated to span the entire length of the 702 pit viper toxins and 168 partial toxin sequences that were available in the UniprotKB database at the time of the microarray design (February 2015). The library consisted of 174,797 15-mer peptides derived from the primary sequences of each toxin by displacement of the running window by one amino acid residue allowing overlap of 14 residues for neighboring peptides. The four toxin sequences containing unspecified residues had such residues replaced with glycines in their sequences prior to generation of peptides. The array of peptides was curated for redundant (non-unique) peptide sequences, leaving 82,423 unique 15-mers, which were included in five replicates. The individual peptides in the library were assigned random positions on the microarray to minimize local intensity biases.
The peptide microarray was produced by Schafer-N (Copenhagen, Denmark) using mask-less photolithographic synthesis adapted to solid-phase peptide synthesis with the C-terminal residue linked to the surface of the array, as previously described [35]. The microarray was first incubated for 1.5 hours with 50 μg/mL naïve horse IgG in 0.05 M Tris/acetate (Trizma base, Sigma-Aldrich), pH 8.0, 0.1% v/v Tween 20, 1 g/l Bovine serum albumin (dilution and washing buffer). After washing, the array was incubated with 1 μg/ml of goat anti-horse IgG (H+L) conjugated with AlexaFluor 647 (Jackson ImmunoResearch, 108-605-003) at room temperature for 1.5 hours. Washing procedure was repeated to remove unbound conjugates and an image was recorded using an InnoScan900 microarray scanner (Innopsys) with an excitation wavelength of 635 nm. The microarray was then washed and incubated with 50 μg/mL antivenom for 1.5 hours, followed by further washing and a second incubation with the same antibody conjugate as above, washed and recorded. Fluorescence intensity for each peptide field was calculated from the resulting image files using proprietary software by Schafer-N.
The 15-mer peptide KKKRKKKRKKKRKKK was synthesized in 852 fields and used to define the corners of the microarray grid, as this peptide is highly prone to unspecific antibody binding. The effect of two successive binding events for peptides prone to unspecific binding was determined using the signals from corner peptide fields as the difference in the median signal in each recording. This re-incubation effect results from interaction of antivenom antibodies with free binding sites on bi-valent secondary anti-horse antibodies already attached to naïve IgG and additional binding of antivenom antibodies to free peptides on the microarray surface. The re-incubation effect was found to provide 4.3 times higher signals in the second incubation than in the first. To remove such artefacts, the antivenom signal was subtracted by the background signal multiplied by the re-incubation effect.
Aiming at reducing the impact of outliers on the downstream data analysis, the median signal of five replicates of each peptide was determined after both binding of naïve IgGs and antivenom IgGs. The signal medians were mapped to each toxin by the position of the N-terminal residue in the original protein sequences. As linear elements of epitopes are usually between 4 and 12 amino acid residues in length [35], true epitopes result in high signals across overlapping peptides. Therefore, the running median of signals was calculated by taking the signals of the nearest preceding and subsequent 15-mer peptide into account.
The likelihood of each signal to be a result of specific antibody recognition was determined using the one-sided Z-test, assuming the corrected running median of random peptides follows a normal distribution (with mean of 0). The standard error of five replicates was found to increase with signal strength, which is why a conservative estimation approach for the standard error of the running median was followed, using the signal intensities of corner peptides as they showed a high level of unspecific antibody binding. By random sampling with replacement (bootstrapping) from the two empirical distributions of signal intensities of corner peptides (background and antivenom), the standard deviation of the background-corrected running median score was determined. The sampling process was repeated 50,000 times, resulting in a standard deviation of 12.31. The results for each unique peptide-running median pair (n = 84,023) were corrected for multiple comparisons using Benjamini-Hochberg procedure for controlling the false discovery rate and significance threshold was set to α = 0.05, resulting in a significant corrected running median score to be above 37.94. This number is referred to as the significance threshold.
Protein family affiliation for each toxin was obtained from UniprotKB and a multiple sequence alignment of each protein subfamily was constructed using Clustal Omega [36]. The corrected running median scores were mapped to the alignment using the position of the N-terminal residue of each 15-mer. In case of alignment gaps, no data was attached to the given positions for corresponding toxins. The corrected running median scores assigned to positions in multiple sequence alignments were visualized as signal profiles [32], and any peaks above the significance threshold and present across more than ten toxins were investigated further. The toxin with the highest peak was used to identify overlapping 15-mer peptides of relevance to an epitope. The core motif of the linear epitope was extracted and the sum of scores of all 15-mers containing the motif was determined. This procedure was performed for similar 15-mer peptides across all toxins in each multiple sequence alignment. A peptide was considered similar to a recognized peptide if no more than three residues were different between the pair. The resulting core sequences, including sum of scores, were analyzed using the SigniSite 2.1 algorithm to identify residues associated with high or low sum of scores [37]. Like above, the significance threshold was set to α = 0.05. The results were illustrated as sequence logos using the R “ggseqlogo” package [38].
The core residues found across each series of recognized overlapping peptides were assigned a value corresponding to the sum of running median scores obtained for the given peptides. The values were mapped using the R “Rpdb” package to a crystal structure pdb-file (when available), or alternatively to a homology model constructed using CPHmodels [39]. Metalloprotease-domains: P83512 from B. asper mapped to RCSB entry: 2w15 [40]; J3SDW8 from C. adamanteus homology model based on RCSB entry 2ERQ [41]. Serine proteases: Q072L7 from Lachesis stenophrys and J3S832 from Crotalus adamanteus homology models based on RCSB entry 2AIP [42].
Aiming at gaining molecular insights into antivenom para-specificity, linear elements of epitopes were identified using a custom designed high-density peptide microarray. The setup included overlapping 15-mer peptides from the 702 full-sequence pit viper toxins available in UniprotKB at the time of the experiment. The microarray was designed similarly to a previous study of mamba and cobra toxins [32]. The median signal intensity of each peptide was determined and mapped to the toxin sequence. Potential linear epitope elements were identified when the signal of two successive 15-mers was found to pass the significance threshold (see method section for details). Following this approach, at least one linear epitope element was identified in 337 out of 702 full-sequence toxins. Furthermore, linear epitope elements were identified in 53 out of 168 toxin sequences classified as incomplete, meaning that linear elements of epitopes might exist in the un-sequenced (and therefore not investigated here) parts of the toxins. Segmented into protein families, the results are summarized in Fig 1A.
Proteomics-based “venomic” studies of pit viper venoms have revealed that their most abundant toxins belong to a limited number of protein families. In general, the main toxic effects exerted by Latin American pit viper venoms are caused by zinc-dependent snake venom metalloproteases (SVMPs), phospholipases A2 (PLA2s), and snake venom serine proteases (SVSPs), although other components may contribute to the overall toxicity [46–49]. These less predominant toxin families include cysteine-rich secretory proteins (CRISPs), C-type lectin and lectin-like proteins, L-amino acid oxidases, vasoactive peptides, and crotamine in the case of some Crotalus (rattlesnake) species [50,51]. Members of each of the mentioned protein families are represented in this study, however, the detailed epitope characterization in the following will focus on the 501 full-sequence toxins and 124 partial toxin sequences from the three major protein families.
The three snake venoms used in the immunization mixture for production of the antivenom are well-described on the protein family level [43–45] as illustrated in Fig 1B. However, only 11 complete toxin sequences and 4 partial sequences (8 being full sequences and 4 partial sequences from B. asper, 3 full sequences from L. stenophrys, and none from C. simus) were available in the UniprotKB database, when this study was designed (the toxin entry C0HK50 from C. simus was added afterwards and is not part of the study). This limitation on antigen sequences is compensated by the inclusion of a vast number of homologous sequences in each of the medically relevant protein families (see Fig 1A).
Imposing information of the snake genera to the results for the full-sequence toxins in Fig 1A results in a more detailed overview of para-specificity for the antivenom (Fig 2). In this overview, each group of toxins from the same species and protein family (or subgroup when applicable) is colored to reflect the proportion of toxins for which one or more linear elements of an epitope were identified.
In some toxin families, no linear elements of epitopes were identified (e.g. the 22 members of the crotamine family), while all members of e.g. the nine L−amino acid oxidase family had linear epitope elements that were recognized by the antivenom. As an epitope is a surface area in three-dimensional space, an epitope might not contain sufficiently long linear parts to be detected in this setup. This also means that no recognition of peptides from e.g. the crotamine family does not prove that crotamine-specific antibodies are absent or that the antivenom will display poor clinical efficacy against venoms containing these toxins. However, poor retention of crotamines has previously been reported in antivenomic studies with the same antivenom and is congruent with the absence of crotamine in the venom of C. simus from Costa Rica used in the horse immunization procedure for antivenom manufacture (Fig 1B) [45]. In general, the lack of peptide interactions with the antivenom antibodies might be due to absence of common linear elements between different toxin epitopes or due to low immunogenicity of whole groups of toxins.
The protein family of SVMPs is known to be among the key toxins responsible for the toxicity of pit viper venoms and these enzymes are known to induce degradation of collagen in the vascular basement membrane resulting in local hemorrhage, as well as in other local and systemic pathological effects [48,52]. The binding data for each of the 223 (174 full sequence) SVMPs was aligned to obtain a holistic understanding of recognition by the antivenom antibodies. Due to variations in domain composition between the individual subfamilies of SVMPs, each domain was investigated separately. The hallmark metalloprotease (M) domain is omnipresent in immature form of all SVMP family members. However, disintegrins and DC fragments derived from some SVMP P-II and P-III subgroups members do not contain the M-domain in mature form, since it is cleaved post-translationally [53]. Consequently, only 167 sequences (full and partial combined) of M-domains were available for this study. Alignment of the resulting background-corrected running median scores for each 15-mer peptide results in the signal profile in Fig 3A. Here, four segments (highlighted in gray boxes) in the first half of the signal profile were found to contain a peak shared between ten or more sequences.
Two of the M-domains originate from B. asper venom employed in the immunization mixture: A P-I (P83512) and a P-II (Q072L5) SVMP. The signal plots of the B. asper toxins (blue and green line in Fig 3A) show high signals in all the highlighted segments except for segment 4. Furthermore, B. asper toxins are recognized at four additional sites, which are not commonly shared by other M-domains and therefore not discussed here. As these two toxins were present in the immunization mixture, the finding that they are among the best recognized M-domains is expected and can be regarded as support for the methodology.
Multiple sequence alignments of the core residues of the overlapping peptides in each of the four selected segments in Fig 3A are represented as sequence logos in Fig 3B. These sequence logos show the general level of residue conservation across the investigated toxins. Only representing the sequences of toxins recognized by the antivenom, the sequence logos in Fig 3C contain the residues that are most likely to play a role as part of epitopes. For segments 1, 3, and 4, a high level of conservation is observed for several positions in the sequence logos, indicating that antivenom recognition and thereby cross-recognition is easily lost when the residues are substituted. This stands in contrast to segment 2, where the antivenom antibodies recognize multiple different motifs.
Using non-recognized toxin sequences similar to the ones recognized by the antivenom, the effect of naturally occurring amino acid substitutions in the investigated positions can be examined. As a measure representing the level of antivenom binding to each toxin sequence, the sums of the running median signals for the series of overlapping peptides were determined. Hereafter, the SigniSite 2.1 algorithm [37] was applied to perform residue level genotype-phenotype correlations and thereby identify amino acid residues significantly associated with lack of antibody binding. The resulting Z-scores obtained for each residue in each position reflect the strength of residue association with either high or low antivenom binding. The Z-scores represented as special sequence logo are found in Fig 3D. In such sequence logo, conserved residues–which might also be essential to antibody recognition–will not show, as such residues are found both in the top as well as in the bottom of the list of peptides ordered according to the determined score by the SigniSite algorithm. Consequently, Fig 3D must be viewed together with Fig 3C to obtain comprehensive understanding of the binding preferences of the antivenom antibodies.
Mapping of the identified linear epitope elements discovered across SVMP M-domains to three-dimensional structures can reveal details about possible mechanisms of neutralization. Several crystal structures of the previously mentioned P-I SVMP from B. asper, P83512, are available [40]. The core residues found across each series of recognized overlapping peptides were assigned a value corresponding to the sum of running median scores obtained for the given peptides and mapped to a crystal structure of P83512 in Fig 4A. The structural mapping reveals that the recognized residues are proximally located to each other in space and arranged around a small α-helix (commonly referred to as α-1) containing the residues corresponding to the highly-recognized segment 1 in Fig 3. The position in the so-called “upper main molecular body” of the M-domain is far away from both the active site cleft (see also annotations in Fig 4A) and the irregularly folded flexible region in the C-terminal “lower sub-domain”, known to be important for substrate recognition [54,55], and which has been suggested to determine the hemorrhagic potential of some PI SVMPS [56]. Similar to the other three sequence segments, the spatial position of segment 4 in Fig 3 (not recognized in P83512) was also mapped to the upper main molecular body (Fig 4B) and not to parts of the M-domain directly involved in catalysis.
Identification of linear elements of one or more overlapping epitopes distant from the catalytic site might possibly be of limited therapeutic relevance. It is in principle possible that neutralizing epitopes may exist that have too short linear elements to be detected in this analysis. However, three of the linear epitope elements identified here have previously been found to be neutralizing epitopes when studied in rabbits immunized with either isolated P-I SVMPs or mixtures of epitope-mimicking peptides: A study investigating a P-I SVMP from Lachesis muta (P22796) also identified segment 1 and 2 as important for antivenom recognition [29]. Immunization with three 12-mer peptides, of which none were overlapping with the catalytic site and one contained segment 1, was sufficient to produce neutralizing antibodies. Furthermore, the second 12-mer peptide corresponded to a non-significant peak centered around 15-mer number 69 in the signal profile for P22796. A similar study on a P-I SVMP from Bothrops atrox (P85420) determined segments 2 and 3 to be sufficient in raising a protecting antibody response against the toxin when rabbits were immunized with the corresponding synthetic peptides [31]. In both studies only immunization with combinations of peptides were reported.
Binding of antibodies to the four segments distant from the active site may neutralize enzymatic activity and henceforth toxicity by the following hypothetical mechanisms: 1) Steric hindrance, i.e. binding of a large 150 kDa antibody sterically hinders the much smaller 22 kDa M-domain from interacting with collagen (or other relevant) substrate. Such effect can be large when binding at exosites important for interaction with the relevant tissue target [57,58]; 2) Allosteric effect, i.e. antibody binding at distant sites alters the conformation of the toxin, thus inactivating the enzyme by distortion of the active site. This has been documented for an antibody targeting the upper molecular body of a human membrane metalloproteinase [59]; or 3) Since antibodies are bivalent, the binding may induce cross-linking of several toxin molecules, thus precluding them from reaching or interacting with their targets. Alternatively, cross-linking can lead to formation of larger protein complexes that are more easily cleared by the victim’s immune system. Additionally, antibody binding will also have a profound effect on toxin pharmacokinetics, which may lead to a reduction in toxicity.
Looking at the individual linear elements of epitopes, segment 1 of the aligned signal profiles in Fig 3A (corresponding to the red helix in Fig 4A) shows a very high level of antivenom recognition. 102 out of 168 toxin sequences had one or more peptides above the significance threshold in this segment. Of the eight residues making up the linear epitope element, only two are found not to be exposed at the surface of the toxins (Fig 4A), namely the residues in position 11 and 15. These residues might still be important for forming the helical structure, explaining why only a very limited selection of hydrophobic residues is found in the positions across all toxins (Fig 3B). From the sequence logo in Fig 3C, representing only the recognized toxins in the alignment, very few substitutions were tolerated by the corresponding antibodies. However, many of the residues in the helix were also rather conserved (Fig 3B). This finding can potentially explain the para-specificity of the antivenom and why the antivenom has previously been found to bind all or most P-I and P-III SVMPs from several investigated American pit viper venoms [34]. However, even within the Bothrops genus, which generally contains an “ADHR(M/I)FTK” motif in segment 1, non-recognized versions of M-domains violating the pattern exist. This means that having origin from a Bothrops snake is not sufficient for a toxin to contain an antibody binding version of segment 1 (although it is likely). As a result, para-specificity of the antivenom to this specific segment of the M-domain is a property which cannot be predicted simply based on phylogenetic relationship.
In the remaining three segments in Fig 3B between 13 and 33 toxins are recognized. Of particular interest, none of the two B. asper M-domains (Fig 3A) or in fact any M-domains originating from Bothrops or Lachesis species are recognized in segment 4. Here, the top scoring toxins originate from Gloydius brevicaudus and Crotalus adamanteus (example in Fig 4B), pointing at a likely origin of this linear epitope element to result from antibodies targeting C. simus SVMPs. The difference in the antibody recognition profiles between the individual toxins highlights that the expanded “epitope recognition space” of the antivenom obtained from immunizing with a broad range of related toxins (multiple venoms) reduces the likelihood that any given toxin of the same family “goes unnoticed” by the antivenom. However, it is interesting that all identified linear epitope segments are located around the small helix constituting segment 1, far away from the enzymatic site involved in toxicity and tissue damage. This contrasts with elapid neurotoxins, where binding of antivenom antibodies was detected at the functional site [32]. Even though the link to neutralization must be explained by other factors as discussed above, the current findings shine light upon possible mechanisms for para-specificity of the antivenom.
To understand para-specificity of P-II and P-III SVMPs, it is not sufficient to investigate the M-domain alone. Nonetheless, the binding data for the disintegrin(-like) (D) domain present in both P-II and P-III and the cysteine-rich (C) domain in P-III SVMPs shows only one linear epitope element above the significance threshold (Fig 5). Based on previous reports [34] and the observation that no significant signals were seen for the D-domain sequences, we conclude that disintegrins are likely to be of low immunogenicity. Since the vast majority of PII SVMPs are cleaved post-translationally to release disintegrins [60], and the M-domain in these proteins is degraded, horses receive poorly immunogenic, low molecular weight disintegrins instead of whole PII SVMPs when immunized with the venom.
One C-domain, originating from the mRNA transcript sequence B0VXU4 from Sistrurus catenatus edwardsii, is strongly recognized with running median signals above 200 in the area outlined by 15-mer number 80–89 (Fig 5B). The core sequence FCFPNK is unique to this entry. However, from a BLAST search against all toxins of the study, the similar permutated sequence FFCPNK is found as part of a recognized sequence in several SVSPs and the shorter sequence FPNK is found in the likewise well-recognized C-terminal of the acidic D49 PLA2 (P84651) from Lachesis stenophrys (venom included in immunization mixture). In conclusion, the recognition of B0VXU4 is likely to be a stochastic event resulting from immunization with a similar sequence in another context.
Taken together, the results for the M-, D- and C-domains indicate that SVMP P-III group members are recognized almost exclusively at the M-domain part. However, as epitopes without sufficiently long linear elements to be identified in this analysis may potentially exist, and as some SVMP P-IIIs are post-translationally modified, additional recognition of the D- or C-domain cannot be excluded. The most extreme post-translational modification is observed for the subgroup P-IIId, where a snake venom C-type lectin-like domain is added. Nonetheless, limited antibody binding is also observed for the C-type lectin-like toxins investigated in this study (See overview in Fig 2).
PLA2s are found in all pit viper venoms as well as in many other viper and elapid snake venoms. Neutralization of this toxin family is generally very important for an antivenom to be effective in the clinical setting. For an antivenom to have broad coverage across several pit viper venoms, PLA2 recognition by the antivenom antibodies must be maintained when variations among the individual toxin sequences exist. Aiming at understanding antivenom recognition of PLA2s, 267 PLA2 sequences (of which 212 are full sequences) were included in the experimental setup, and a residue-level investigation of antibody binding was performed, resembling the analysis of SVMPs in the previous section.
Prior to the analysis, the PLA2s were divided into five groups based on the descriptions of the well-characterized full-sequence toxins and clustering of all PLA2 sequences, where unannotated sequences were grouped together with similar sequences. The groups include the acidic and basic catalytically active PLA2s, which both have a conserved calcium-coordinating aspartic acid in position 49 (D49 groups). The third group consists of basic PLA2-like toxins, which have the aspartic acid crucial for enzymatic activity replaced, typically with a lysine in position 49. Therefore, this group is named the K49 group of myotoxins [61]. The neurotoxic “crotoxin(-like)” D49 PLA2s, which contain acidic and basic subunits, were grouped independently from the other acidic and basic D49 PLA2s, resulting in a total of five groups. The antivenom was found not to recognize linear epitope elements of the individual groups of PLA2s equally well (see overview in Fig 2 and Fig 6A). The basic D49 and K49 groups were recognized reasonably well, while the acidic D49 and the neurotoxic crotoxin(-like) PLA2s were recognized to a much lower degree. This general finding can be explained by the composition of the immunization mixture (Fig 1), as basic K49 PLA2s constitute nearly half of the entire PLA2 content, while C. simus is the only one of the three snakes containing neurotoxic crotoxins–and only in low abundance (3.8% dry weight) in venom of the adult snakes used in immunization [45]. Moreover, the antivenom has previously been found not to neutralize neurotoxic effects of crotoxins in three investigated Crotalus species [34], and not to neutralize a crotoxin-like PLA2 heterodimer described in the venom of Bothriechis nigroviridis [21].
The signal profiles of the aligned PLA2s in Fig 6A reveal recognition of 15-mers in three separated segments. Most (34 out of 60) toxins recognized in the first segment were found to belong to the basic D49 group of which 60% (34 out of 57) were recognized at the N-terminal site. Furthermore, an additional 20 PLA2s from the acidic D49 and basic D49 (crotoxin) groups were found to closely resemble the recognized D49 sequences at this site. None of the 6 basic K49 toxins recognized in the segment were found among the top 50 toxins with highest binding signal, showing that most antibodies targeting this site are antibodies targeting D49 PLA2s. Three D49 toxins originating from venoms (B. asper and L. stenophrys) used in the immunization mixture were included in the study. However, none of the recognized toxins in the first segment originate from Bothrops or Lachesis snakes, despite that 47 Bothrops and 7 Lachesis PLA2 sequences covered the region. On the other hand, 7 Crotalus toxins were among the recognized toxins, indicating that the antibodies targeting this segment are likely to be a result of the presence of C. simus D49 PLA2s in the immunization mixture. A previous study investigated the different recognition profiles of two antivenoms prepared by immunization with either four Bothrops species or a Crotalus snake [23]. The study involved peptides derived from three individual PLA2s (one from each of the non-crotoxin groups) from B. jararacussu venom using a low-resolution variant of the analysis performed in this study. Although the investigated toxins were of Bothrops origin, a high level of binding for the anti-crotalus antivenom was observed at the N-terminal end of the basic D49 PLA2. Taken together with our results, this indicates that the N-terminal basic D49 PLA2s from Crotalus snakes might generally be more immunogenic than the Bothrops counterparts.
The second segment in Fig 6 was mainly recognized for a subset of toxins from the acidic D49 group with no toxins originating from the immunization venoms. The origin of the antibody response towards this site is unclear, as both one Bothrops enzyme and one Crotalus enzyme are recognized here. Due to the low level of information on this linear epitope element, it is not discussed further here.
The third and last segment in Fig 6A, corresponding to the C-terminal residues, was more difficult to define compared to any other linear epitope element identified in the study. As many very different sequences were recognized by the antivenom, the information content of the sequence logo in Fig 6C was low in most positions. Also, the borders of the segment were difficult to define, as the signal profile of the 31 toxins found to be recognized in the area peaked at various positions between peptide number 97 and 196. In extreme cases, significant signals were observed for 13 overlapping peptides, meaning that only 2 residues were shared between all 13 peptides, and that two or more linear epitope elements are likely to exist in the area. In contrast to segment 1, dominated by basic D49 PLA2s, 18 of the 31 toxins recognized in segment 3 were found to be members of the basic K49 group. Furthermore, 9 of the recognized toxins belong to the acidic D49 family. Comparing the core sequences of the recognized acidic D49 and the basic K49 toxins, only one lysine was found to be conserved. We therefore conclude that at least two populations of antibodies with very different binding preferences, but recognizing topologically equivalent sites, are likely to exist in the antivenom.
Looking at which species the recognized toxins in segment 3 originate from, both antibodies recognizing K49 and antibodies recognizing acidic D49 PLA2s appear to be induced by the B. asper proteins in the immunization mixture. This conclusion is based on the recognition of 11 Bothrops toxins (4 acidic D49 and 7 basic K49 PLA2s), including two B. asper K49 PLA2s, while no Crotalus or Lachesis toxins were recognized in this segment. The anti-Bothrops antivenom of the low-resolution study, previously discussed, was also found to recognize peptides in the C-terminal end of K49 PLA2s, while the anti-Crotalus antivenom did not recognize this part of any of the Bothrops jararacussu toxins [23].
The C-terminal region of the basic K49 myotoxins is amphiphilic, contains several positively charged residues, and is known to be critical for toxicity by non-enzymatic disruption of the plasma membrane of skeletal muscle fibers [61]. Antivenom recognition to this site is therefore an example of neutralization by binding directly at the toxic site. Furthermore, the C-terminal region of the B. asper K49 myotoxin P24605 has also previously been found to be a neutralizing epitope in mice and rabbits [24,25]. As a passing remark, three other batches of the horse antivenom showed poor recognition of the C-terminal region of P24605 [24], which is in agreement with the findings in the present study, where the toxin was the only B. asper K49 PLA2 with a signal profile not passing the significance threshold.
Linear epitope elements were detected in 2 out of the 6 described PLA2 sequences from the venoms used for immunization–an intriguing result. It is possible that the 3 B. asper toxins with signal profiles below the significance threshold are not present in sufficient concentration in the immunization mixture to elicit a strong enough antibody response for this analysis to detect binding. Alternatively, these toxins might contain epitopes with no linear parts (traditionally referred to as “conformational epitopes”). Previously, two neutralizing monoclonal antibodies developed to bind two B. asper K49 myotoxins were found not to bind the toxins when the toxins had been denatured, thus suggesting discontinuous epitopes [62]. Alternatively, an explanation for the lack of recognized linear element for 4 of the of B. asper PLA2s could be that many PLA2s are simply poor immunogens as observed for other PLA2s [63].
SVSPs are enzymes interfering with the blood-clotting system and generating vasoactive mediators from endogenous precursors [64,65]. For many pit vipers, SVSPs are major contributors to venom toxicity, and it is therefore relevant to understand how antivenom antibodies can recognize and neutralize this toxin family. In this study, 135 SVSP sequences were investigated, of which 115 are complete sequences covering the entire enzyme in mature form. Binding of antivenom antibodies to SVSP-derived 15-mer peptides were obtained for more than half (62) of the full-sequence toxins (Fig 1). Mapping of signals from antibody recognition of 15-mer peptides to a multiple sequence alignment of SVSPs shows binding to four individual segments in close proximity to each other (Fig 7).
The core residues of segment 3 and 4 in Fig 7A turned out to border each other (Fig 7B–7D). Further investigation of the results revealed a large overlap between the toxins recognized in these two segments, as 11 out of the 12 SVSPs recognized in segment 4 were also recognized in segment 3, while an additional 18 toxins were solely recognized in segment 3. Of these 11 toxins recognized in both segments, 9 were found to originate from venoms used in the immunization mixture or from related species of the same genera. This finding indicates that antibodies might target two separate linear elements of the toxins used for immunization. As the residues from position 77–84 in the multiple sequence alignment are poorly conserved, and since the recognized residues are generally not among the most frequently observed residues (Fig 7B and 7C), antibody binding to the linear epitope element in segment 4 is easily lost to naturally occurring sequence variation.
Mapping of the identified linear epitope elements to structural models of SVSPs (Fig 8) reveals that all segments are accessible to antibodies, but also that only segment 4 is overlapping residues of the enzymatic cleft. Binding of antivenom antibodies at the enzymatic site will inhibit function of the aspartic acid of the catalytic triad, thereby neutralizing the toxins. However, binding to segment 3 was more prominent (Fig 7A), and a higher number of toxins were recognized at this site compared to segment 4. A study of antibody binding to a similar site in a human serine protease, the hepatocyte growth factor activator (HGFA), sheds light on a possible mechanism of neutralization [66]. In the study, the said site was found to be an allosteric site, where binding of monoclonal IgG antibodies induced a conformational change incompatible with substrate binding at the enzymatic site. As the overall structures of HGFA and SVSPs are conserved, the reported allosteric mechanism of serine protease inhibition may likely explain how antivenom antibodies neutralize SVSPs by binding to segment 3.
The possible therapeutic relevance of segment 1 and 2 is not possible to assess based on prior studies and it is outside of the scope of this study. Disregarding the neutralization potential of antibodies binding at these two segments, segment 2 was by far the most commonly recognized site of the two, with 27 recognized toxins, compared to 8 toxins in segment 1. A remarkably low number of only 3 toxins were recognized in both segment 2 and segment 3 or 4. This indicates that segment 2 might constitute an alternative epitope, which is mostly found in toxins not recognized in segment 3 or 4. Of the 27 recognized toxins in segment 2, 9 belong to Crotalus species, while only one originates from a Bothrops species and none from Lachesis species. It is therefore likely, that this linear epitope element is a result of immunization with SVSPs from C. simus venom (present in venom (Fig 1), although no SVSP sequences available from this species).
The high-density peptide microarray methodology employed here does not take post-translational modifications into account. For most members of the PLA2 family this is not an issue as they rarely have post-translational modifications [67]. However, PII and PIII SVMPs, and especially SVSPs are commonly N-glycosylated. In extreme cases in SVSPs, sugar moieties can constitute more than half of the molecular mass [64,68]. Yet, none of the recognized toxins contain an asparagine residue subject to glycosylation in any of the antibody binding peptides identified here, meaning that the data should contain no false positives from this effect. N-glycosylation is frequently found across SVSPs in alignment position number 20, 81, 99, 100, 132, 148, and 231. This could potentially explain the complete lack of antibody recognition to the second half of the alignment, as the horse antibodies may recognize epitopes comprising the foreign (snake-type) N-glycosylations and not the “naked” peptides.
Focusing on the well-recognized segments 3 and 4, for which mechanisms of neutralization can be deduced, their high variability may have large implications for obtaining a broad-acting pit viper antivenom. Both traditional antivenom production, developments within novel immunization strategies employing venoms, recombinant toxins, synthetic peptides, or even DNA/RNA immunization techniques, and development of novel recombinant snakebite therapies based on mixtures of monoclonal antibodies [7] may need to bind a diverse set of toxins with a high degree of amino acid variation in these sequence segments. Therefore, it is likely that multiple different antibodies are needed to target and neutralize an entire group of toxins in these strategic sites. However, future studies with all the identified linear epitope elements are needed to verify if they correspond to neutralizing epitopes.
The data presented here reveals important molecular details for understanding the paratope-epitope interactions between antivenoms and linear elements of viperid venom toxins, and contributes to explain the cross-reactivity, i.e. paraspecific recognition, often observed between antibodies and venom proteins from species not included in the immunization mixture.
Most of the venom protein families discussed in this work are enzymes, and they either exert their toxic effects locally, in the case of SVMPs and PLA2s that induce local hemorrhage and necrosis, or systemically, as in the case of enzymes that act on clotting factors or SVMPs that induce systemic hemorrhage. These toxins are readily recognized by antivenom antibodies at various segments of their sequence, but seldom in their “catalytic site”, which in these enzymes is largely responsible for toxicity. This reveals an important difference between the SVMP and PLA2 toxin families and neurotoxins from the three-finger toxin (3FTx) and Kunitz-type inhibitor (including dendrotoxins) families. Whereas the latter families are recognized by antivenoms in their toxic sites [32], most of the linear epitope elements of the dominant viperid toxin enzyme families are found in sites different from the catalytic site. This could suggest that these enzymatic toxins may be neutralized via other effects such as steric hindrance or allosteric effects. Alternatively, since enzymatic toxins often have molecular regions, which could be exosites, that enable them to recognize relevant tissue targets [53,57,58], it is likely that antibodies recognizing epitopes outside the catalytic site may be de facto neutralizing. Binding to exosites may preclude interaction between these toxic enzymes and their targets in the plasma membrane of cells, extracellular matrix proteins, or blood clotting factors, even if their catalytic sites are not blocked or disrupted. Likewise, in the case of toxic PLA2 homologues devoid of enzymatic activity, antibody neutralization occurs through the binding of regions, which determine the ability of toxins to interact with targets in the muscle cell plasma membrane, as in the case of K49 PLA2s. It is also possibly that, instead of inhibiting enzymatic activity, neutralizing antibodies derive their therapeutic effects via the removal of toxin-antibody complexes through endocytosis by immune cells [69]. However, the extent of such neutralization mechanism is not possible to address based on the current study.
Our observations highlight a complex molecular immunological scenario for snake venom toxins, in which antivenom antibodies may exert their neutralizing ability by several mechanisms depending on the nature of the toxin family.
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10.1371/journal.pgen.1004091 | Arabidopsis AL PHD-PRC1 Complexes Promote Seed Germination through H3K4me3-to-H3K27me3 Chromatin State Switch in Repression of Seed Developmental Genes | Seed germination and subsequent seedling growth define crucial steps for entry into the plant life cycle. For those events to take place properly, seed developmental genes need to be silenced whereas vegetative growth genes are activated. Chromatin structure is generally known to play crucial roles in gene transcription control. However, the transition between active and repressive chromatin states during seed germination is still poorly characterized and the underlying molecular mechanisms remain largely unknown. Here we identified the Arabidopsis PHD-domain H3K4me3-binding ALFIN1-like proteins (ALs) as novel interactors of the Polycomb Repressive Complex 1 (PRC1) core components AtBMI1b and AtRING1a. The interactions were confirmed by diverse in vitro and in vivo assays and were shown to require the AL6 N-terminus containing PAL domain conserved in the AL family proteins and the AtRING1a C-terminus containing RAWUL domain conserved in animal and plant PRC1 ring-finger proteins (including AtRNIG1a/b and AtBMI1a/b). By T-DNA insertion mutant analysis, we found that simultaneous loss of AL6 and AL7 as well as loss of AtBMI1a and AtBMI1b retards seed germination and causes transcriptional derepression and a delayed chromatin state switch from H3K4me3 to H3K27me3 enrichment of several seed developmental genes (e.g. ABI3, DOG1, CRU3, CHO1). We found that AL6 and the PRC1 H3K27me3-reader component LHP1 directly bind at ABI3 and DOG1 loci. In light of these data, we propose that AL PHD-PRC1 complexes, built around H3K4me3, lead to a switch from the H3K4me3-associated active to the H3K27me3-associated repressive transcription state of seed developmental genes during seed germination. Our finding of physical interactions between PHD-domain proteins and PRC1 is striking and has important implications for understanding the connection between the two functionally opposite chromatin marks: H3K4me3 in activation and H3K27me3 in repression of gene transcription.
| Seed germination is a key developmental transition during the plant life cycle. During this process, seed developmental genes are progressively turned off to allow proper seedling growth and development. Molecular mechanisms involved in establishing the repression state of seed developmental genes, however, are still poorly known. Here we demonstrated that the Arabidopsis PHD-domain H3K4me3-binding ALFIN1-like proteins (ALs) physically interact with the Polycomb Repressive Complex 1 (PRC1) core components AtBMI1b and AtRING1a. Loss of AL6 and AL7 as well as loss of AtBMI1a and AtBMI1b causes transcriptional derepression and a delayed chromatin state switch from H3K4me3 to H3K27me3 enrichment of several seed developmental genes (e.g. ABI3, DOG1, CRU3, CHO1). Furthermore, GFP-AL6 and LHP1-myc directly bind at ABI3 and DOG1 loci. We propose that AL PHD-PRC1 complexes, built around H3K4me3, lead to a switch from the H3K4me3-associated activation to the H3K27me3-associated repression of seed developmental genes. Our study unravels a striking connection between PHD-domain proteins and PRC1, two types of readers for the functionally opposite chromatin marks. The newly uncovered mechanism is relevant to understand reprogramming of gene activity, which is at the heart of plant growth phase transitions and of cell differentiations in plants and other organisms.
| Timely transition from a growth-arrested seed to a growing seedling is a key process during the plant life cycle, which has great importance in plant adaptation to environmental conditions [1]. Seed germination, a developmental process spanning from initial seed hydration to embryonic root emergence, and subsequent seedling growth requires silencing of seed developmental genes, e.g. ABSCISIC ACID INSENSITIVE 3 (ABI3) and DELAY OF GERMINATION 1 (DOG1) in Arabidopsis thaliana. ABI3 belongs to the plant-specific B3 domain transcription factor family and regulates the expression of a number of genes involved in various aspects of seed development [2]–[4]. Among those, the 12S seed storage protein encoding genes CRUCIFERIN1 (CRU1/CRA1), CRU2 and CRU3/CRC are bound by ABI3 [5], likely representing direct targets of ABI3. Although the precise function of the DOG1 protein remains unknown, DOG1 is a major quantitative trait locus for seed dormancy and its transcript as well as protein levels are tightly regulated during seed development [6]–[8]. Among other regulatory genes, the AP2 family transcription factor gene CHOTTO1/AINTEGUMENTA-LIKE5 (CHO1/AIL5) and the antioxidant gene CYSTEINE PEROXIREDOXIN 1 (PER1) also show specific seed expression patterns and regulate Arabidopsis seed germination under certain laboratory conditions, such as nitrate, salt and glucose treatments [9], [10].
Recent studies implicate the requirement of the repressive histone mark H3K27me3 in silencing of seed developmental genes during seedling growth [11]–[15]. The H3K27me3 mark is established by the Polycomb Repressive Complex 2 (PRC2), which is conserved in animals and plants [16]. Arabidopsis PRC2 mutant seedlings show substantially reduced levels of H3K27me3 and ectopic expression of seed developmental genes, including ABI3, DOG1, CRU1, CRU3, and CHO1 [12]. Loss of function of the Arabidopsis ATP-dependent chromatin remodeler PICKLE also affects H3K27me3 deposition and ABI3 repression [11], [15], [17]. It is generally considered that H3K27me3 provides a docking site for the PRC1 complex, which subsequently conducts H2A monoubiquitination and establishes a repressive chromatin configuration [18]. PRC1 components diverge considerably between animals and plants, and the most conserved components are the ring-finger proteins RING1 and BMI1 (reviewed in Molitor and Shen [19]). The Arabidopsis double mutants Atring1a Atring1b and Atbmi1a Atbmi1b display pleiotropic phenotypes, including ectopic embryonic callus formation and ABI3 derepression in seedlings [20]–[23]. In spite of the importance of PRC2 and PRC1 in silencing, however, how seed developmental genes timely switch from activation to a repression chromatin state is not understood.
In this study we identify the ALFIN1-like (AL) PHD-domain proteins, which bind H3K4me2/me3 [24], as novel partners of AtRING1 and AtBMI1. We provide evidence supporting a role of AL PHD-PRC1 complexes during the chromatin state switch from H3K4me3-associated transcriptional activation to H3K27me3-associated transcriptional repression of seed developmental genes, i.e. ABI3, DOG1, CRU1, CRU3, CHO1 and PER1, which is necessary for proper seed germination and early seedling growth.
During a yeast two-hybrid screen using AtRING1a as bait, we isolated from an Arabidopsis seedling cDNA library three positive clones corresponding to AL6. AL6 encodes a small protein of 256 amino acids, which contains a PHD domain at the C-terminus (Figure 1A and Figure S1). The AL group proteins are found in the green lineage including algae and plants but not in fungi and animals. The Arabidopsis genome contains seven homologs denoted AL1 to AL7 ([24]; Figure S1). In the yeast two-hybrid test, AL1, AL2, AL5, AL6 and AL7 each interact with AtRING1a and AtBMI1b. Deletion analysis revealed that the N-terminal region of AL6 excluding the PHD domain could bind the C-terminal region of AtRING1a excluding the RING domain (Figure 1B). It is worth noting that the N-terminal region of AL6 could also interact with ALs, suggesting that ALs might form homo- and/or hetero-dimers. The N-terminus of AL6 contains a domain of so far unknown function (DUF3594), which is specifically conserved in the AL group proteins and thus likely represents a novel protein-binding domain (Figure S1). We named this domain PAL for PHD-associated AL domain. The C-terminus of AtRING1a contains the RAWUL domain, an ubiquitin-like domain conserved in animal and plant PRC1 ring-finger proteins including AtRING1a, AtRING1b, AtBMI1a, and AtBMI1b [25]. Our data suggest that PAL and RAWUL may form a protein-protein interaction module. In agreement with previous observations [20]–[22], AtRING1a could interact with itself and AtBMI1b, and this interaction was shown here to occur via the N-terminal region containing the RING domain (Figure 1A and 1B).
Next, we verified interactions between ALs and PRC1 ring-finger proteins by several independent techniques. Using in vitro pulldown assays, we observed that GST-fused AL2, AL6 or AL7 but not GST alone could pulldown FLAG-AtRING1a from total protein extracts of transgenic plants (Figure 2A). Similarly, GST-AtBMI1a or GST-AtBMI1b could pulldown GFP-AL6 (Figure 2B). In co-immunoprecipitation (CoIP) experiments, FLAG-AtRING1a was detected in the GFP-AL6 immunoprecipitated fraction from transgenic plants expressing FLAG-AtRING1a and GFP-AL6 (Figure 2C). Finally, using Fluorescence Lifetime Imaging Microscopy (FLIM) analysis, we detected interaction of AtRING1a-GFP with RFP-AL1, RFP-AL2, RFP-AL6 or RFP-AL7 but not with AtRING1a-RFP or RFP-AtRING1a, as well as interaction of AtBMI1b-GFP with RFP-AL1, RFP-AL2, RFP-AL6, RFP-AL7 or AtRING1a-RFP (Figure 2D).
Collectively, these data firmly establish that AL proteins are interactors of the PRC1 ring-finger component proteins, AtRING1 and AtBMI1.
AL gene expression was detected ubiquitously but at varied levels in different plant organs (Figure 3A). Hereinafter we focused on the functional characterization of AL6 and AL7, two genes preferentially expressed in seeds and encoding two proteins with 84% identity at the amino acid sequence level, which are grouped in a separate clade from the other AL proteins according to phylogenetic analysis (Figure S2). T-DNA insertion mutants, al6 and al7, were obtained and shown to display knockdown of AL6 and AL7, respectively (Figure 3B). Under standard growth conditions, the al6 and al7 mutants as well as the al6 al7 double mutant showed a normal growth phenotype. However, under osmotic treatments with salt or mannitol, the al6 al7 double mutant but not the single mutants showed a delay of seed germination compared to the wild-type Col-0 (Figure 3C and 3D). This mutant phenotype could be rescued by transformation with pAL6:GFP-AL6 (Figure 3C), indicating that AL6 and AL7 act redundantly and knockdown of both AL6 and AL7 function has caused the mutant phenotype.
The Atbmi1a Atbmi1b mutant also displayed a delay in seed germination in our assays (Figure 3D). Interestingly, compared to that of al6 al7, the exponential phase of the Atbmi1a Atbmi1b germination rate curve started earlier after stratification, but reaching a comparatively lower maximum percentage value. This observation suggests that AL6/7 may be involved primarily in initiation and AtBMI1a/b in maintenance of the germination process. We also obtained the quadruple mutant al6 al7 Atbmi1a Atbmi1b and showed that it is drastically impaired in both germination initiation time and maximum percentage of germination rate (Figure 3D). The enhanced germination defects observed in the quadruple mutant further suggest that other ALs and PRC1 ring-finger proteins may also participate in germination regulation.
As expected, the six examined seed developmental genes ABI3, DOG1, CRU1, CRU3, CHO1 and PER1 displayed transcriptional repression during the 72 hours after stratification (HAS; Figure S3A and S3B). Notably, AL5, AL6 and AL7 genes, but not AL1 and AL2 genes, also showed transcription repression during seed germination (Figure S3C). In line with the germination delay, salt treatment inhibited the repression of seed developmental genes as well as that of AL5, AL6 and AL7 albeit to a less extent (Figure S3). Meanwhile, it is worth to note that AL1, AL2, AL6 and AL7 are well expressed after seed germination and along different plant developmental stages (Figure S4). Next, we investigated expression levels of the six seed developmental genes in the al6 al7 and Atbmi1a Atbmi1b mutants. Because the salt treatment drastically varied the germination time of Col-0, al6 al7 and Atbmi1a Atbmi1b, to minimize secondary effects we choose to perform molecular analyses using seeds/seedlings germinated on normal media where wild-type and mutants only exhibit very little differences (Figure 3D). As shown in Figure 4, all six seed developmental genes showed higher expression levels in the al6 al7 and Atbmi1a Atbmi1b mutants as compared to Col-0 at 72 HAS, with some genes (e.g. DOG1, CRU1, CRU3, PER1) also elevated earlier at 24 HAS. These data demonstrate that both AL6/7 and AtBMI1a/b are involved in repression of seed developmental genes during germination and early seedling growth.
To investigate the mechanism of seed gene repression, we performed chromatin immunoprecipitation (ChIP) analysis on H3K4me3 and H3K27me3 levels during seed germination. ChIP fractions were analyzed using PCR primers covering the promoter, UTR and gene body regions of ABI3 (Figure 5A) or DOG1 (Figure 5B). In wild-type Arabidopsis prior to seed germination (0 HAS), we detected relatively high levels of H3K4me3 at gene body chromatin regions but low levels at the promoter and 3′-UTR chromatin regions. H3K4me3 levels decreased drastically upon germination (24 HAS) and reached nearly background levels in 3-day-old seedlings (72 HAS). In contrast, H3K27me3 levels were very low at 0 HAS and 24 HAS but very high at 72 HAS. These data indicate that ABI3 and DOG1 repression is associated with the removal of the active transcriptional mark H3K4me3 and the establishment of the repressive transcriptional mark H3K27me3. Compared to Col-0, al6 al7 and Atbmi1a Atbmi1b mutants showed overall higher levels of H3K4me3 and lower levels of H3K27me3 at the ABI3 and DOG1 chromatin (Figure 5), indicating a delay in the H3K4me3-to-H3K27me3 switch during seed germination and early seedling growth. A similar delay was also observed on CRU3 and CHO1 chromatin (Figure S5). Together these data indicate that AL6/7 as well as AtBMI1a/b are necessary for the timely switch from H3K4me3-associated activation to H3K27me3-associated repression of seed developmental genes during germination and early seedling growth.
To examine whether AL6 binds directly to chromatin of seed developmental genes, we performed ChIP analysis using an anti-GFP antibody on the pAL6:GFP-AL6 complemented al6 al7 line. GFP-AL6 enrichment at ABI3 (Figure 6A) and DOG1 (Figure 6B) was undetectable in seeds at 0 or 24 HAS but was significantly elevated in seedlings at 72 HAS. Similarly, the H3K27me3-reader component of the PRC1 complex LHP1 [19] was observed to bind ABI3 and DOG1 chromatin in seedlings at 72 HAS (Figure 6), when using anti-myc antibody on the pLHP1:LHP1-myc complemented lhp1 line [26]. These data indicate that AL6 and LHP1 directly bind chromatin of ABI3 and DOG1. While the PHD-domains of AL1, AL4 and AL7 [24] as well as the full-length AL1, AL2, AL6 and AL7 proteins (Figure S6) bind H3K4me3 in vitro, we failed to detect GFP-AL6 binding of the ABI3 and DOG1 chromatin in seeds at 0 or 24 HAS when H3K4me3 levels are high. This inconsistency might be explained by failure to detect GFP-AL6 binding because of technical limitations associated with difficulties in ChIP analysis using seed material. Alternatively or additionally, the GFP-AL6 binding of H3K4me3-rich chromatin might be unstable and occur transiently prior to a more stable AL6-PRC1 chromatin association during ABI3 and DOG1 repression.
On the basis of our study, we propose a model for chromatin state switch of seed developmental gene silencing during seed germination (Figure 7). Seed developmental genes (e.g. ABI3 and DOG1) are actively expressed in seeds and marked by the transcriptional activation chromatin marker H3K4me3. During germination, H3K4me3 provides a docking site for AL proteins (e.g. AL6, AL7, and possibly other ALs) via the conserved PHD domain recognition, which subsequently recruits the PRC1 ring-finger components RING1 (AtRING1A/B) and BMI1 (AtBMI1A/B) through physical protein-protein interactions. The recruitment of RING1 and BMI1 favours H3K27me3 deposition by PRC2 through two possible mechanisms. In the first case, RING1 directly recruits PRC2. In line with this, a physical interaction between AtRING1a and the PRC2 core component CURLY LEAF (CLF) was detected in a previous study [20]. Alternatively, PRC2 may be recruited through LHP1. In line with this, physical protein-protein interactions were observed between LHP1 and RING1 or BMI1 [20]–[22] and between LHP1 and the PRC2 component MULTICOPY SUPPRESSOR OF IRA 1 (MSI1) [27]. LHP1, via its chromodomain, binds H3K27me3 [28], [29], forming a positive feedback loop of PRC2-mediated H3K27me3 deposition and enhancing stable AL PHD-PRC1 complex formation. Stable repression of seed developmental genes guarantees timely seed germination and proper seedling growth. Beyond seed germination, the PRC1 components LHP1 and AtBMI1 had been reported as required for H3K27me3 deposition and repression of some target genes in roots and plants, likely involving other additional regulatory proteins [23], [27].
In Arabidopsis, the seven AL proteins together with the two ING (INhibitor of Growth) proteins form a family of small (235 to 270 aa in length) proteins containing a single PHD domain. The ING group proteins are conserved in plants, animals and fungi; the animal ING proteins bind H3K4me3 via their PHD domain and act as components of histone acetylase/deacetylase or chromatin-remodeling complexes involved in multiple critical processes (reviewed in Guérillon et al. [30]). The AL and ING proteins contain PHD domains of a similar primary sequence and tertiary structure, and consistently AL proteins also bind H3K4me3 ([24]; Figure S6). Distinctively, however, the ING proteins harbor at the N-terminus a conserved NCR (Novel Conserved Region) domain necessary for protein-protein interaction [30] whereas the AL proteins have a PAL domain, which is specific for plant proteins. Our study demonstrates that ALs could form dimers and could bind both AtBMI1 and AtRING1 (Figures 1 and 2). The AL6 N-terminus containing PAL is sufficient for binding with ALs and AtRING1a, indicating PAL as a novel protein-protein interaction domain. The finding of ALs as interactors of PRC1 components opens a new horizon for understanding the mechanistic function of this family of PHD-domain proteins.
While the function of the two Arabidopsis ING genes is unknown so far, our study has revealed a redundant function of AL6 and AL7 in the regulation of seed germination. The AL6 and AL7 proteins show the highest sequence homology among all AL proteins, and both AL6 and AL7 genes are expressed at high levels in seeds. While the al6 and al7 single mutants were normal, the al6 al7 double mutant displayed a delay in germination under osmotic stress growth conditions (treatment with salt or mannitol; Figure 3). Similarly, the Atbmi1a Atbmi1b double mutant also displayed a germination delay. This is consistent with the proposed function of AL6/7 and AtBMI1a/b working in complexes to promote seed germination. The enhanced germination defects observed in the al6 al7 Atbmi1a Atbmi1b quadruple mutant indicate that AL6/7 and AtBMI1a/b may also work in parallel pathways and/or that other ALs as well as AtBMI1c may be involved in seed germination regulation. Furthermore, AL6/7 likely act earlier than AtBMI1a/b during seed germination. This is further evidenced by later growth phenotypes: the Atbmi1a Atbmi1b plants are characterized by the presence of ectopic embryonic traits [21], [22] whereas the al6 al7 mutant plants are morphologically indistinguishable from Col-0. In line with the importance of AtRING1 within the PRC1 complex, the loss of both AtRING1a and AtRING1b caused ectopic embryonic callus formation in seedlings as well as many other growth and developmental defects [21]. Phenotype differences exhibited by the lhp1 [31], Atring1a Atring1b [20], Atbmi1a Atbmi1b [21], [22] and al6 al7 (this study) mutants also pinpoint to additional independent functions of LHP1, RING1, BMI1 and ALs in diverse plant growth and developmental processes.
AL6/7 and AtBMI1a/b promote seed germination likely through repression of seed developmental genes. Consistently, the al6 al7 mutant and the Atbmi1a Atbmi1b mutant showed derepression of ABI3, DOG1, CRU1, CRU3, CHO1 and PER1 (Figure 4). The expression of these seed developmental genes has been previously shown to negatively regulate seed germination [4], [7], [9], [10]. Quantitative differences in gene expression and the stress-inducible nature of these genes in seed germination regulation might explain the al6 al7 mutant phenotype observable under osmotic stress conditions. The detected gene derepression was more severe and persisting in Atbmi1a Atbmi1b than in al6 al7. This indicates that although AL6/7 and AtBMI1a/b form protein complexes, they play distinct roles: AL6/7 acts early and transiently, whereas AtBMI1a/b are necessary for establishing stable repression. We propose that AL6/7 bind H3K4me3 and recruit AtBMI1a/b, facilitating H3K27me3 deposition and stable repressive AL PHD-PRC1 complex formation. Prior to seed coat rupture, all four analyzed target loci (i.e. ABI3, DOG1, CRU3 and CHO1) were highly enriched in H3K4me3 and depleted of H3K27me3 (Figure 5), which is consistent with their high transcriptional activities. Upon seed germination, H3K4me3 levels decreased and H3K27me3 levels increased, leading to a repressive chromatin state characterized by background levels of H3K4me3 but high levels of H3K27me3. The switch from H3K4me3 to H3K27me3 is correlated with the seed germination time when assayed under varied physiological conditions [14]. Loss of AL6/7 or AtBMI1a/b caused a delay in the switch from H3K4me3 to H3K27me3, which is in agreement with the observed seed germination delay phenotypes. High levels of H3K27me3 at seed developmental genes were maintained by PRC2 throughout the subsequent seedling and vegetative growth stages [12], [13]. Consistently, binding of the PRC1 H3K27me3-reader component LHP1 was detected at ABI3 and DOG1 (Figure 6) as well as at CRU3 and CHO1 loci ([29]; http://epigenomics.mcdb.ucla.edu/cgi-bin/hgTracks).
It is worth noting that there is a marked correlation of gene conservation during evolution. ABI3 sequence homologues are found in evolutionarily distant species, including green algae, mosses, gymnosperms and angiosperms [32]. AL homologues also appeared together with green algae and are widespread in the green lineage. PRC1-related sequences have emerged later during green lineage evolution: BMI1 homologues are found as early as the mosses whereas RING1 and LHP1 homologues are found only in angiosperms [19]. This indicates that H3K4me3 and AL readers are established early whereas H3K27me3 and PRC1 readers appeared later in the evolution of seed plants. It will be of interest to investigate AL and BMI1 function in mosses to examine the hypothesis that the ABI3-regulatory pathway is ancient and acquired for desiccation tolerance beyond seed germination control [33], [34]. This will also examine minimum component composition required for AL PHD-PRC1 complex function.
Our finding of physical interactions between PHD-domain AL proteins and PRC1, two types of readers for the functionally opposite chromatin marks, H3K4me3 and H3K27me3, respectively, is novel and intriguing. In general, genome regions contain either the active mark H3K4me3 or the repressive mark H3K27me3. Bivalent configuration containing both H3K4me3 and H3K27me3 has been first reported in animal stem cells and is thought to maintain developmental genes in a silenced state poised for activation upon cell differentiation [35], [36]. A transient bivalent-like state with AL-H3K4me3 and PRC1-H3K27me3 interactions could exist at seed developmental genes during germination. Most remarkably, however, AL-H3K4me3 interaction likely serves to recruit PRC1 to previously active seed developmental genes, leading to the switch from on to off of transcription of these genes. In animals, the Polycomblike (PCL) proteins bind the active mark H3K36me3 via Tudor domain and physically interact with PRC2 to implement de novo repression of previously active embryonic stem cell-specific genes during transition to cell differentiation (reviewed in Abed and Jones [37]). The Arabidopsis PHD-domain proteins VRN5 and VIN3 form complexes with PRC2, which may act similarly to animal PCL-PRC2, in establishing repression of FLOWERING LOCUS C (FLC) for plant vegetative-to-reproductive growth phase transition [38]. Distinctively, our study proposes recruitment of PRC1 via AL-H3K4me3 interaction (Figure 7), which challenges the classic hierarchical paradigm where PRC2 is recruited prior to PRC1. In line with our proposition, two recent studies have shown that AtBMI1 positively regulates H3K27me3 enrichment at several genes including ABI3 in 10-day-old plants [23] and LHP1 is required for H3K27me3 enrichment at flower gene loci in roots [27]. It is reasonable to speculate that different PRC1-associated factors may be involved in repression of different genes in different types of plant cells/tissues. Also an increasing number of studies in animals start to reveal varied composition of PRC1/PRC1-associated complexes as well as examples of H3K27me3 (PRC2)-independent functions of some PRC1 complexes [39], [40]. Future studies are necessary to investigate PRC1 composition and spatial-temporal dynamic complex assembly in vivo, which remain poorly documented so far.
In conclusion, our study demonstrates that the PRC1 core components AtBMI1 and AtRING1 physically interact with the PHD domain H3K4me3-binding proteins ALs and that the loss of AL6 and AL7 partly mimics the Atbmi1a Atbmi1b mutant phenotype in seed developmental gene derepression and seed germination delay. Our data supports a model in which the AL PHD-PRC1 complexes built around H3K4me3 lead to a switch from the H3K4me3-associated active to the H3K27me3-associated repressive transcription state of seed developmental genes. Reprogramming of gene activity is a mandatory step to allow plant growth phase transitions as well as cell differentiations in plants and animals. Our newly discovered mechanism may extend to other plant targets and be relevant to Polycomb silencing in other organisms. Our results also raise new questions. For instance, which enzymes are involved in the removal of H3K4me3? Whether the timely order of PRC1 and PRC2 recruitment is target context dependent? How is the target specificity determined? Answering these questions will undoubtedly shed further light on the molecular mechanisms of chromatin state switch, which is at the heart of gene reprogramming and cell differentiation.
Full-length ORFs of AL1, AL2, AL5, AL6 and AL7 were PCR-amplified from Arabidopsis cDNA using gene specific primers (Table S1), cloned into pGEMT-easy (Promega) and then into pGBKT7 and/or pGADT7 vectors (Clontech, http://www.clontech.com) using BamHI and XhoI restriction enzyme sites. pGBKT7-AtRING1a, pGBKT7-AtBMI1a and pGBKT7-LHP1 constructs have been described previously [20], [21]. The truncated constructs AtRING1aN (aa 1–218), AtRING1aM (aa 116–303), AtRING1aC (aa 238–522), and RING (aa 116–218) were obtained through PCR amplification and cloning. The truncated construct AL6N (aa 1–162) was recovered from the yeast-two-hybrid cDNA library screen using the bait plasmid pGBKT7-AtRINGA1a. The various combinations of pGBKT7- and pGADT7-based constructs were introduced into Saccharomyces cerevisiae strain PJ69-4a, selected on synthetic defined (SD) medium lacking Leu and Trp (SD-LT), and assayed for protein-protein interaction by growth on SD lacking Leu, Trp and Ade (SD-LTA). Details of constructs are described in SI Materials and Methods.
All Arabidopsis thaliana lines were derived from the Columbia ecotype (Col-0). T-DNA insertion mutants were obtained for AL6 (SALK_040877) and AL7 (SALK_032503) from the Arabidopsis Biological Resource Center (ABRC, http://www.arabidopsis.org). The double mutant al6 al7 was generated by crossing of the single mutants. The Atbmi1a Atbmi1b mutant [21] and transgenic Arabidopsis lines expressing FLAG-AtRING1a [20] or LHP1-myc [26] have been previously described. To generate the transgenic line expressing GFP-AL6, AL6 cDNA was first introduced into the Gateway entry plasmid pENTR-3C and then by recombination into pB7WGF2 (http://gateway.psb.ugent.be/), resulting in the GFP-AL6 fusion driven by the 35S promoter. Next, the AL6 endogenous promoter sequence (1311 bp upstream of the start codon) was PCR-amplified and used to replace the 35S promoter in pB7WGF2:GFP-AL6 by cloning into HindII and SpeI restriction sites, resulting in pAL6:GFP-AL6. pAL6:GFP-AL6 plasmid was introduced into Agrobacterium tumefaciens (GV3101) and the resulting strain was used to transform Arabidopsis. Transgenic homozygous lines containing a single T-DNA insertion were obtained.
The AL1, AL2, AL6 and AL7 cDNAs were cloned into BamHI and XhoI sites of pGEX-4T-1 expression vector. pGEX-4T-1-AtBMI1a and pGEX-4T-1-AtBMI1b constructs have been previously described [21]. All constructs were introduced into E. coli Rosetta (DE3) strain in which glutathione-S-transferase (GST) and GST-fusion proteins were expressed and purified. Total protein extracts from two-week-old Arabidopsis seedlings expressing FLAG-AtRING1a or GFP-AL6 were used in pulldown assays performed as previously described [41]. The pulldown fractions were analyzed by Western blot using monoclonal antibodies against FLAG (Sigma Aldrich) or GFP (Miltenyi).
Two-week-old Arabidopsis seedlings expressing GFP-AL6 and FLAG-AtRING1a or FLAG-AtRING1a alone were ground in liquid nitrogen and proteins were extracted in lysis buffer (50 mM Tris-HCl pH 7, 150 mM NaCl, 10% glycerol, 4 mM MgCl2, 0.5% Triton X-100, 1 mM DTT, anti-complete proteinase (Roche) and DNaseI (Fermentas)). The crude protein extract was filtered through Miracloth, cleared by centrifugation (20 mins, 10000 g) and subsequently pre-cleared for 1 hour with magnetic protein A beads (Magna-ChIP, Millipore). A fraction was conserved as input control. IP was carried out overnight at 4°C using polyclonal anti-GFP antibodies (Invitrogen) in combination with magnetic protein A beads. Beads were washed 3 times for 10 min in lysis buffer. Immunoprecipitated proteins were eluted by boiling, separated by 10% SDS-PAGE and detected by Western blotting using HRP conjugated anti-GFP (Miltenyi) or anti-FLAG (Sigma Aldrich) antibodies.
The AL1, AL2, AL6, AL7, AtRING1a, AtBMI1a, AtBMI1b and LHP1 cDNAs were PCR-amplified and introduced into the Gateway system and cloned as 3′ or 5′ in-frame fusions to RFP or GFP sequences in plant expression vectors downstream of the 35S promoter (pB7WGF2; pB7FWG2; pH7WGR2; pH7RWG2; http://gateway.psb.ugent.be/). Plasmids were introduced into A. tumefaciens (GV3101). Bacteria cultures grown overnight were centrifuged and pellets resuspended in 10 mM MgCl2 to an optical density of 0.5 at 600 nm and induced with 200 µM acetosyringone. Leaves of 4–5 week old Nicotiana benthamiana plants were coinfiltrated with an equimolar bacterial suspension of the two constructs to be tested. Confocal laser scanning images of protein co-localization and FLIM data were recorded 2 days post-infiltration (LSM-700, Carl Zeiss; LIFA frequency domain fluorescence lifetime imaging system, Lambert Instruments). The percentage of GFP fluorescence lifetime decay was calculated relative to the absence of RFP fusion protein as an average of 3 biological replicates, each recording over 30 nuclei. Proteins were considered to interact if the presence of RFP-tagged proteins decrease GFP fluorescence lifetime by more than 5%, a reference value established according to the negative control: RFP with GFP.
Seeds were chlorine gas surface sterilized and sown on petri dishes containing the growth media: Murashige and Skoog (MS) salts, 0.8% agar with or without addition of 100 mM NaCl or 200 mM mannitol. To synchronize germination, seeds were stratified after sowing for 3 days at 4°C and subsequently transferred to a growth chamber (23°C, photoperiod 16 h light, 8 h dark). Germination rates were scored daily for 12 days following stratification. Seeds were considered to have germinated when radicle emergence was visible under a dissecting microscope.
RNA was extracted from Arabidopsis seeds/seedlings at 0, 24 and 72 HAS as previously described [42]. Reverse transcription was performed using Superscript III reverse transcriptase (Invitrogen). Relative levels of cDNA were quantified with SYBR-Green I master mix in the LightCycler 480-2 according to the manufacturer's instructions (Roche). RT-PCR primers were designed with the aid of the Universal ProbeLibrary Assay Design Center (Roche, http://www.roche-applied-science.com). All primer sequences are listed in Table S1. The efficiency of each primer pair was calculated by LinRegPCR [43]. cDNA levels were normalized to internal reference genes At4g34270 and At4g26410 which are transcriptionally stable during germination [44].
ChIP was performed as previously described [45] with minor modifications required for adaptation of the protocol for seed tissue. Fixation time was extended to one hour and chromatin was pre-cleared with protein-A beads. Chromatin was immunoprecipitated overnight using antibodies against H3K4me3 (Millipore), H3K27me3 (Millipore), myc (Roche), GFP (Invitrogen) or without antibodies as negative control. Buffers described in Berr et al. [45] were supplemented with various detergents, i.e. 0.01% SDS and 0.1% Triton X-100 were added to low and high salt wash buffers and 1% sodium deoxycholate was added to LiCl wash buffer.
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10.1371/journal.ppat.1000403 | trans-Complementation of an NS2 Defect in a Late Step in Hepatitis C Virus (HCV) Particle Assembly and Maturation | Recent studies using cell culture infection systems that recapitulate the entire life cycle of hepatitis C virus (HCV) indicate that several nonstructural viral proteins, including NS2, NS3, and NS5A, are involved in the process of viral assembly and release. Other recent work suggests that Ser-168 of NS2 is a target of CK2 kinase–mediated phosphorylation, and that this controls the stability of the genotype 1a NS2 protein. Here, we show that Ser-168 is a critical determinant in the production of infectious virus particles. Substitution of Ser-168 with Ala (or Gly) ablated production of infectious virus by cells transfected with a chimeric viral RNA (HJ3-5) containing core-NS2 sequences from the genotype 1a H77 virus within the background of genotype 2a JFH1 virus. An S168A substitution also impaired production of virus by cells transfected with JFH1 RNA. This mutation did not alter polyprotein processing or genome replication. This defect in virus production could be rescued by expression of wt NS2 in trans from an alphavirus replicon. The trans-complementing activities of NS2 from genotypes 1a and 2a demonstrated strong preferences for rescue of the homologous genotype. Importantly, the S168A mutation did not alter the association of core or NS5A proteins with host cell lipid droplets, nor prevent the assembly of core into particles with sedimentation and buoyant density properties similar to infectious virus, indicating that NS2 acts subsequent to the involvement of core, NS5A, and NS3 in particle assembly. Second-site mutations in NS2 as well as in NS5A can rescue the defect in virus production imposed by the S168G mutation. In aggregate, these results indicate that NS2 functions in trans, in a late-post assembly maturation step, perhaps in concert with NS5A, to confer infectivity to the HCV particle.
| Worldwide, more than 200 million people are chronically infected with hepatitis C virus (HCV) and thus at risk for fatal liver disease. New, virus-specific therapies are under development by the pharmaceutical industry that target aspects of virus entry and genome replication. However, the process of viral assembly and release from the cell remains cloaked in mystery and thus difficult to target for drug development. Here we describe studies that reveal a previously unrecognized role for the nonstructural protein 2 (NS2) of HCV during a late step in this process. We show that mutations in the carboxy-terminal domain of NS2 disrupt a maturation step that is required to convert a viral assembly intermediate to a fully infectious virus particle. Additional genetic evidence suggests that this maturation step occurs in concert with another nonstructural protein, NS5A. This study thus sheds new light on the role of nonstructural proteins in assembly and maturation of infectious HCV particles and suggests new targets for drug development.
| Persistent infection with hepatitis C virus (HCV) is a common cause of chronic liver disease, and may lead to clinically severe cirrhosis and/or hepatocellular carcinoma [1],[2]. Over the two decades that have elapsed since its discovery [3], much has been learned about HCV, the proteins it encodes, and their roles in genome replication. However, investigation of the complete viral life cycle became possible only recently with the development of tractable cell culture-based infection systems [4]–[7]. The availability of these systems has accelerated studies of the mechanisms underlying virus assembly and egress as well as cell entry. In addition to the roles expected of the structural proteins (core, E1, E2) in viral assembly, a number of recent studies suggest that nonstructural proteins, such as NS3 and NS5A as well as p7 and NS2, are also critically important in this process [6]–[16].
NS2 is a small membrane-bound protein with one or more trans-membrane domains [17],[18]. Up until recently, the only known function of NS2 has been its role in polyprotein processing as part of the auto-protease that cleaves in cis between NS2 and NS3. However, data from several studies that have used cell culture infection systems suggest the specific involvement of NS2 during the assembly of infectious HCV particles. We showed that compensatory mutations accumulating within the N-terminal domain of NS2 following transfection of an inter-genotypic, 1a/2a chimeric viral RNA enhanced the specific infectivity of secreted virus particles [14]. Subsequently, the deletion of NS2 sequence was shown to block infectious virus production by an otherwise viable di-cistronic HCV RNA that no longer required the NS2-NS3 auto-protease for genome amplification [12]. A structure-function analysis of NS2 has also shown that several amino acid residues within the first trans-membrane domain of NS2 are critical for assembly and release of infectious particles [19]. These studies strongly support a role for NS2 during virus assembly. However, the details of that role and how NS2 might act during the production of infectious virus particles remain unknown.
Here, we show that Ala or Gly substitutions of the Ser-168 residue of NS2 impair infectious virus production without affecting HCV RNA replication or cis-processing of the virus polyprotein. Other experiments described here reveal that the defect in infectious virus production can be complemented in trans by expression of wild-type NS2 protein in a genotype-specific manner, and that this defect in virus production occurs at a step late in the process of viral assembly and egress, after the interaction of core and NS5A with host cell lipid droplets and the formation of rapidly sedimenting, core-protein containing particles.
As indicated above, previous studies suggest that NS2 is likely to have a role in virus assembly and/or release that is distinct from its role during polyprotein processing and genome-amplification as an auto-protease [12], [14], [19]–[22]. Proteins are often modulated by phosphorylation to regulate multiple functions and, since it has been reported that NS2 may be phosphorylated by the host cell kinase CK2 [23], we set out to determine whether NS2 phosphorylation influences the production of infectious HCV particles in cell culture. To this end, we mutated the Ser-168 residue in NS2, which is putatively targeted by CK2 for phosphorylation [23], in two different infectious HCV cDNA clones, one derived from the genotype 2a JFH1 virus, and the other a chimeric genome (HJ3-5) which is comprised of the core-NS2 sequence of the genotype 1a H77 virus in the background of JFH1 with compensatory mutations in E1 (E1-Y170H) and NS3 (NS3-Q221L) [14],[16]. The resulting mutants were named JFH1(SA) and HJ3-5(SA), respectively (Figure 1A). Synthetic RNA derived from either of the parental cDNA clones efficiently produces infectious HCV following transfection into human hepatoma cells (FT3-7 cells, a Huh7 sub-clone). However, the S168A mutation completely abolished infectious virus production by the chimeric HJ3-5 RNA, and significantly reduced the yield of infectious virus released from cells 48 h post-transfection of JFH1 RNAs (Figure 1B). In contrast to lysates of cells transfected with the parental RNAs, in which we could readily detect infectious virus, only a low titer of infectious virus was present in lysates of cells transfected with JFH1(SA) RNA; there was no infectious virus at all in lysates of cells transfected with HJ3-5(SA) RNA, which encodes the genotype 1a NS2 (Figure 1B). These results indicate that Ser-168 of NS2 is involved in a step in infectious virus production that occurs prior to the release of virus from cells. Similar results were obtained with viral RNAs mutated to encode a Gly residue at position 168 in NS2 (HJ3-5(SG) and JFH1(SG) mutants, respectively) (see below).
The impaired production of infectious virus associated with the S168A mutation was not due to an effect on genome replication, since cells transfected with the S168A mutants accumulated HCV RNA at a rate similar to that of wt genomes over the first 48 h post-transfection (Figure 1C). The abundance of the S168A mutant RNA declined after 48 h in transfected cells. In contrast, the increase in abundance of the wt JFH1 or HJ3-5 RNA was sustained beyond 48 h up to 96 h post transfection. This difference in the kinetic of HCV RNA accumulation over time between the S168A mutant and wt genomes is similar to that we have observed previously between an assembly-defective inter-genotypic chimeric RNA (HJ3, Figure 1A) and its derivatives, such as HJ3-5, which are assembly competent due to a compensatory Q221L mutation in the NS3 helicase [16]. Consistent with these results, the S168A mutation did not impair polyprotein processing at the NS2-NS3 junction. This was assessed by determining the self-cleavage activity of an NS2-NS3-NS4A segment derived from the HJ3-5 chimera, with or without the S168A mutation, following its translation in vitro in a cell-free system (Figure 1D). Taken together, these results indicate that the S168A mutation results in a defect late in the virus life cycle, involving either the assembly and/or maturation of infectious particles prior to their release from the cell.
As discussed above, the NS2 S168 residue has been suggested to be targeted by the host cell kinase CK2 for phosphorylation [23]. Since the results above indicate that removal of this putative CK2 target site ablates production of infectious virus, we determined whether pharmacological inhibition of CK2 would similarly prevent the release of infectious virus from cells transfected with viral RNA. However, since CK2 phosphorylation of NS5A Ser-457 is essential for infectious virus production [8], it was necessary to first eliminate the requirement for CK2 phosphorylation at this site. To accomplish this, we introduced an NS5A Ser-457 to Asp mutation in both the HJ3-5 and JFH1 genomes. This mutation has been shown to render the production of virus from a chimeric genotype 2a RNA insensitive to pharmacologic inhibition of CK2, or siRNA knockdown of CK2 expression [8]. These mutated RNAs, HJ3-5/5A-S457D and JFH1/5A-S457D were transfected into FT3-7 cells, which were then treated with a range of concentrations of the CK2 inhibitor, DMAT (2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole), from 4 to 48 h post-transfection. As shown in Figure 1E, DMAT treatment up to 1 µM concentration had no effect on release of infectious virus by either RNA. While we observed a >50% reduction of virus yield by cells treated with 10 µm DMAT, this concentration of the inhibitor causes nonspecific cytotoxicity [8]. These results are consistent with previous findings by Tellinghuisen et al. [8]. They suggest that CK2-mediated phosphorylation of NS2 Ser-168 residue is not essential for the production of infectious virus, and that there is an alternative explanation for the impact of the S168A (or S168G) mutation on this process.
To characterize this defect in virus assembly, we designed a series of experiments aimed at determining whether the S168A assembly defect can be complemented in trans by wt NS2 expressed from an alphavirus replicon. Toward that end, we constructed subgenomic Venezualan equine encephalitis virus (VEE) replicons with dual sub-genomic promoters that mediate the expression of NS2 and drug selection marker puromycin N-acetyltransferase (PAC) [24]. Several different replicons were constructed, including replicons expressing the genotype 1a H77 NS2 (Flag-H77-NS2), genotype 2a JFH1 NS2 (Flag-JFH1-NS2), and a chimeric NS2 (Flag-H/J-NS2) comprising N-terminal H77 sequence and C-terminal JFH1 sequence fused at a site of natural recombination [14],[16],[25] (Figure 2). These NS2 proteins were fused to a Flag or Myc tag at their N-terminus, while a fourth construct, sig-H77-NS2-Flag, included the upstream NS2 signal sequence (sig) and was fused to a Flag tag at its C-terminus. We also constructed similar replicons containing the S168A or S168G mutations in NS2. FT7-3 cells were transfected with each of the VEE replicon RNAs, and stable cell lines were selected in the presence of puromycin.
Interestingly, immunoblot analyses of extracts from VEE replicon cells suggested that there is an intrinsic difference in the stability of the wt H77 and JFH1 NS2 proteins. In multiple attempts to establish stable VEE replicon cell lines, the abundance of JFH1 NS2 was always greatest, followed by the chimeric H/J-NS2, and then H77 NS2 (Figure 2B). Similar results were obtained whether the NS2 proteins were fused to either a Flag or Myc tag at the N-terminus. Franck et al. [23] reported previously that the NS2 protein expressed by the genotype 1a HCV-H strain (which is closely related to the H77 virus) is unstable, and that its abundance could be increased by treatment with the proteasome inhibitor, MG132. This suggests that the genotype 1a NS2 protein may be degraded in a proteasome-dependent fashion. Franck et al. [23] also showed that the S168A mutation enhances the stability of HCV-H NS2. Consistent with this, we observed that the S168G mutation modestly increased the abundance of the H77 NS2 protein expressed from VEE replicons (Figure 2C, compare lanes 3 versus 5). MG132 treatment of VEE replicon cell lines significantly enhanced the abundance of the wt H77 NS2 and, to a lesser extent, the related S168G mutant (lanes 3 versus 4 and 5 versus 6). This was the case with both N- and C-terminally Flag-tagged proteins, whether or not the upstream signal sequence was expressed with NS2 (lanes 1 versus 2). However, it had no effect on the abundance of the JFH1 NS2 protein (lanes 7 versus 8). Inclusion of the S168G mutation also did not enhance JFH1 NS2 abundance (data not shown).
Interestingly, MG132 treatment resulted in the appearance of a ∼14 kDa NS2 degradation product in cells containing the wt Flag-H77-NS2 replicon, while this was not observed with cells expressing wt JFH1 NS2 (lanes 4 versus 8). The H77 NS2 degradation product was derived from the N-terminus of NS2, since it was detected in Flag immunoblots only when Flag was fused at the N-terminus of NS2 (Flag-H77-NS2) and not the C-terminus (sig-H77-NS2-Flag) (lanes 4 versus 2). Moreover, it was not detected in MG132-treated cells expressing the H77 S168G mutant, Flag-H77-NS2(SG) (lanes 4 versus 6). A similar degradation product was not detected in cells expressing wt JFH1 NS2 upon MG132 treatment (lane 8). These results suggest that the H77 NS2 protein is intrinsically less stable than JFH1-NS2, probably because it is more prone to proteasome-mediated degradation. They also suggest the possibility that H77-NS2 undergoes a cleavage event leading to a ∼14 kDa fragment that is degraded by the proteasome. Importantly, the S168G mutation appears to prevent that cleavage and stabilize the H77 NS2 protein. While pulse-chase experiments and experiments to determine whether the H77 NS2 molecule is ubiquitinated are ongoing, these results indicate important differences in stability of the H77 and JFH1 NS2 proteins that may in part explain the difference in the impact of the S168A mutation on infectious virus production by the JFH1(SA) and HJ3-5(SA) RNAs shown in Figure 1B.
To ascertain the potential for trans-complementation of the S168A-imposed defect in virus assembly, we transfected the JFH1(SA) and HJ3-5(SA) RNAs into the NS2-expressing cell lines shown in Figure 2 and monitored release of infectious virus into the extracellular milieu. The presence of a replicating VEE replicon had no effect by itself on the assembly-defective phenotype of the S168A JFH1 mutant, as there was no difference in the extracellular virus yield when the JFH1(SA)-mutant RNA was transfected into the parental FT3-7 cells or cells containing a control, green fluorescent protein (GFP)-expressing VEE replicon (VEE-GFP) (Figure 3A). When JFH1(SA) RNA was transfected into cells expressing Flag-JFH-NS2, the JFH1 infectious virus yield was increased over 10-fold compared to normal FT3-7 cells and restored to the level observed in cells transfected with wt JFH1 RNA (Figure 3A). Transfection of the JFH1(SA) mutant into replicon cells expressing the wt H77 NS2 resulted in no increase in infectious virus production (Figure 3A). Expression of either the N- or C-terminal Flag tagged versions of H77 NS2 rescued the production of infectious virus by cells transfected with HJ3-5(SA) RNA, resulting in a greater than 100-fold increase in infectious virus yields (Figure 3B). Although VEE replicon cells expressing JFH-NS2 also promoted virus production by the HJ3-5(SA) RNA, the virus yield was only 10% that obtained in the Flag-H77-NS2 expressing cells, even though Flag-JFH-NS2 protein expression was much higher (Figures 2 and 3B). VEE replicon cells expressing JFH1-NS2(SA) or H77-NS2(SA) were unable to support the production of virus by JFH1(SA) or HJ3-5(SA) RNA, respectively, confirming that functionally active NS2 protein is required for the trans-complementation of virus production (Figure 3A and 3B). In aggregate, these results indicate that expression of wt NS2 is capable of trans-complementing an NS2-specific defects, and that this trans-complementation activity is genotype-, or possibly strain-specific.
We next assessed the ability of the chimeric H/J-NS2 protein to trans-complement the virus assembly defect in the JFH1(SA) and HJ3-5(SA) mutants. As shown in Figure 4C and 4D, cells expressing the chimeric (H/J)-NS2 were capable of supporting the production of infectious virus by either JFH1(SA) or HJ3-5(SA) RNA,although to a lesser extent than cells expressing the homologous JFH1 or H77 NS2.
We previously reported that several naturally evolving compensatory mutations in the N-terminal domain of NS2 enhanced the production of infectious virus (but not viral genome replication) by a chimeric HCV genome (H-(NS2)-J RNA, referred to herein as HJ2) [14], in which sequence encoding the structural proteins of the genotype 1a H77 virus had been placed within the context of the JFH1 genome, and in which the NS2 protein was identical to the H/J-NS2 chimera expressed by the VEE replicons (Figure 1A). To determine whether the positive effect of these compensatory NS2 mutations could be re-created in the trans-complementation system, we constructed a VEE replicon expressing H/J-NS2 containing one such mutation, NS2 I30T (I839T in the H77 polyprotein sequence). Cells expressing this mutated chimeric NS2 (Myc-H/J-NS2-I30T) produced a 6- to 8-fold higher yield of infectious virus when transfected with HJ3-5(SA) RNA, compared to cells expressing the chimeric NS2 without the compensatory mutation (Figure 4D). Significantly, the presence of this mutation had no effect on the efficiency of virus production in cells transfected with the JFH1(SA) RNA (Figure 4C), again indicating the genotype- (or strain-)specific nature of trans-complementation.
In aggregate, these data indicate that both the N-terminal and C-terminal domains of NS2 are somehow involved in processes required for production of infectious virus particles. Of note, however, is that our prior work suggests that the NS2 I30T mutation does not influence assembly per se, but rather enhances the specific infectivity of released virus particles, suggesting an effect on a late, post-assembly maturation step [14].
To better define the NS2 domains required for trans-complementation of S168A-mediated defect in virus production, we constructed a series of VEE replicons expressing H77-NS2 protein mutants with N- and C-terminal deletions of increasing length. Most of the N-terminal deletion mutants were unstable, and their expression could not be detected by immunoblotting or immunofluorescence microscopy (even with MG132 treatment) in cells transfected with the replicons (data not shown). However, the C-terminal deletion mutants were expressed at an abundance similar to that of the wt protein (Figure 5A and 5B). Nonetheless, when the HJ3-5(SA) mutant RNA was transfected into VEE replicon cells expressing these C-terminal deletion mutants, none were capable of trans-complementing the S168A defect in virus production. These data indicate that NS2 contains an essential domain within its C-terminal 71 residues that is required for production of infectious virus particles, consistent with the phenotype of the S168A and S168G mutants.
As described previously, the chimeric HJ3-5 RNA used in the experiments described above does not produce infectious virus particles in the absence of one or more compensatory mutations within the NS3 helicase domain (Q221L in HJ3-5, Figure 1A). These NS3 mutations are required for the intracellular assembly of core protein-containing particles that have the sedimentation properties of infectious virions [14],[16]. The ability of these mutations to compensate for the assembly defect in the parental chimeric RNA (H-(NS2/NS3)-J, referred to herein as HJ3) (Figure 1A), has thus defined an important role for NS3 during an early step in infectious virus production that involves particle assembly [14],[16]. To ascertain whether the Ala or Gly substitution at Ser-168 of NS2 causes a similar early defect in particle assembly, or acts at a later, post-assembly step, we characterized the sedimentation properties of core protein present in extracts of HJ3-5 and HJ3-5(SG) RNA transfected cells. This was accomplished by rate-zonal centrifugation of cell extracts in sucrose gradients, followed by immunoblotting individual gradient fractions to detect the presence of the core protein. These experiments were carried out in cells containing the GFP- or H77-NS2-expressing VEE replicon. Results were compared with those from cells transfected with the assembly-defective HJ3 chimeric RNA as a control.
Results from this series of experiments are shown in Figure 6A. Consistent with previously reported data [16], peak core protein abundance was found in fractions 6–12 (from the top) of gradients loaded with extracts of HJ3-transfected cells (“slow sedimenting”) (Figure 6A, lower panel). In contrast, the bulk of the core protein present in lysates of cells transfected with HJ3-5 (which contains the compensatory Q221L mutation) sedimented more rapidly, and was present in gradient fractions 12–19 (“fast sedimenting”) [16]. These latter fractions also contained the peak HJ3-5 infectious virus titer (HJ3 produces no infectious virus) (Figure 6A, upper panel). Importantly, although some core was found at the very top of the gradient (fraction 1), a significant portion of the core protein present in lysates of GFP-expressing cells transfected with HJ3-5(SG) RNA was also present in fractions 12–19 (Figure 6A). This result indicates that the S168G mutation does not prevent the assembly of fast-sedimenting virus-like particles, despite the absence of infectious virus production. Thus, the S168G mutant appears to be defective in a late, post particle-assembly step, in virus production.
Despite the assembly of core into fast-sedimenting intracellular particles, we were not able to detect the release of a measurable amount of core protein from cells transfected with the HJ3-5(SG) mutant using a sensitive ELISA assay (Figure 6B). In contrast, core protein was readily detected in supernatant media from cells transfected with the parental HJ3-5 RNA (Figure 6B). Taken together with the results shown in Figure 6A, these results suggest that at least one of the functions of NS2 protein is to confer infectivity to pre-assembled core containing particles, and that in the absence of this, these “fast sedimenting” non-infectious particles are prevented from being released from the cell. Consistent with this interpretation, the amount of core protein present in lysates of cells transfected with the S168G mutant was over 10-fold the limit of detection in the ELISA assay, and thus at least 10-fold greater than the abundance in the supernatant fluids (Figure 6B). In contrast, the concentration of core was slightly greater in the supernatant fluids than in extracts from the HJ3-5 transfected cells. Gastaminza et al. [26] recently demonstrated that high density, infectious, intracellular HCV particles are prone to degradation in a proteasome-independent manner unless they undergo a maturation step that is necessary for secretion [26]. The data shown in Figure 6A and 6B are consistent with this model for virus production, and suggest that NS2 plays a key role in the transition from non-infectious, intracellular particles to infectious particles capable of secretion.
When we similarly analyzed lysates of H77-NS2 expressing replicon cells transfected with the HJ3-5(SG), we found that the infectious virus produced by trans-complementation banded within fractions 12–19, similar to virus produced by the parental HJ3-5 RNA in GFP-expressing cells (Figure 6A, upper panel). Consistent with these results, a significant proportion of the core protein was also detected in these fractions (Figure 6A, lower panel). However, a substantial amount of the core protein also sedimented slowly (fractions 3–11), consistent with the relatively low efficiency of trans-complementation and the 40-fold lower titer of infectious virus produced compared with wt HJ3-5 RNA (see legend to Figure 6A). We observed similar results, including a substantial proportion of core protein within fractions 12–19, when we analyzed lysates of VEE H77-NS2(SG) replicon cells transfected with the HJ3-5(SG) RNA (Figure 6A, bottom panel). These results provide compelling evidence that the NS2 S168G mutation does not prevent the assembly of rapidly-sedimenting, core-containing particles despite the inability of RNA bearing this mutation to produce infectious virus. While the distribution of the rapidly sedimenting core antigen associated with the noninfectious particles that formed in the HJ3-5(SG) RNA-transfected cells across fractions 12–19 of these gradients was very similar to the distribution of core associated with the infectious particles formed in cells transfected with the assembly-competent HJ3-5 RNA, these experiments do not exclude significant differences in the composition of these particles.
To further characterize the non-infectious and trans-complemented, infectious HJ3-5(SA) particles, we ascertained their buoyant density by equilibrium centrifugation of cell lysates in iodixanol density gradients and compared these results with intracellular HJ3-5 particles. As shown in Figure 6C, the densities of the infectious intracellular particles derived from the HJ3-5 and trans-complemented HJ3-5(SG) RNA were indistinguishable, with peak infectivity for each banding between 1.104 and 1.121 g/cm3. Most of the core protein present in these lysates banded at the same density as infectious particles, while lesser amounts were found in less dense fractions (1.028–1.047 g/cm3) (Figure 6C). Very similar results were obtained in immunoblots of fractions from gradients loaded with the non-infectious HJ3-5(SG) particles. These results indicate that the buoyant density of the non-infectious HJ3-5(SG) particle is similar to the infectious particles produced by trans-complemented HJ3-5(SG) RNA and the parental HJ3-5 genome, despite the absence of infectivity.
Recent reports indicate that the NS5A protein plays a critical role in the assembly of HCV particles by recruiting replication complexes to core protein localized on the surface of lipid droplets where particle assembly takes place [9],[10],[27]. Since the results shown in Figure 6 suggest that the mutations in Ser-168 of NS2 impact a later, post-assembly step in the production of infectious virus, we considered it likely that these mutations would not interfere with the association of NS5A with the core protein on lipid droplet, despite the ability of the mutations to ablate infectious virus production from HJ3-5 RNA. This was confirmed by laser-scanning confocal microscopy of FT3-7 cells transfected with the HJ3-5 or HJ3-5(SG) RNAs, which revealed co-localization of core and NS5A protein on lipid droplets in cells transfected with either RNA (Figure 7A). Thus, the NS2-S168G mutation has no impact on the interaction of core with NS5A on lipid droplets. This is consistent with the rate-zonal gradient analysis shown in Figure 6A, which indicates that replication of the HJ3-5(SG) RNA leads to the assembly of core protein-containing particles with sedimentation properties similar to the infectious virus particles produced from the wt genome. The colocalization of core and NS2 on lipid droplets was unchanged when the HJ3-5(SA) mutant RNA was transfected into cells containing a replicon expressing JFH1-NS2 (Figure 7B).
Confocal microscopy also demonstrated that the Flag-JFH1-NS2 protein expressed from VEE replicons was distributed in a reticular pattern throughout the cytoplasm, but with particularly strong staining near the nuclear membrane (Figure 8A and 8B). Flag-H77-NS2 showed a similar distribution (data not shown), but a much lower signal intensity consistent with lower protein abundance determined in immunoblots (Figure 2). Since an assembly-defective core mutant was recently shown to be rescued by a second-site mutation within NS2 [15], we used confocal microscopy to determine whether there was co-localization of these two proteins in VEE Flag-JFH1-NS2 replicon cells transfected with the HJ3-5(SA) RNA. As shown above, the expression of the JFH1 NS2 protein in these cells rescues the ability of the HJ3-5(SA) RNA to produce infectious particles (Figure 3B). However, there was no demonstrable co-localization of NS2 and core in these cells (Figure 8A, upper panels), nor in JFH1 NS2-expressing cells transfected with JFH1(SA) RNA (Figure 8B, upper panel).
The reticular staining pattern observed for NS2 in these studies resembles that of E1 and E2, which localize to the ER membrane [28]. Consistent with this, there was strong co-localization of E2 and NS2 when the Flag-JFH1-NS2 replicon cells were transfected with either HJ3-5(SA) or JFH1(SA) (Figure 8A and 8B, middle panels). This was confirmed by a quantitative pixel analysis of these confocal microscopic images (see the panels to the right in Figure 8A and 8B). Since most E2 protein does not co-localize with core in JFH1 infected cells [28], the co-localization of NS2 with E2 is consistent with the absence of co-localization with core. Quantitative pixel analysis also suggested partial co-localization of NS2 with NS5A in these experiments, but to a lesser extent than with E2 (we used Myc-JFH1-NS2 replicon cells in this experiment, as this allowed simultaneous labeling of NS5A with rabbit antibody and NS2 with murine anti-Myc) (Figure 8A and 8B, lower panels). Taken together, these results suggest that NS2 and E2 localize to membranes of the peri-nuclear ER, distinct from core which strongly localizes to the lipid droplet. NS5A associates with core on the lipid droplet, but also is found in association with NS2, a fact that may be important in particle assembly.
To better understand how Ser-168 might function to confer infectivity on pre-assembled HCV particles, we transfected cells with the HJ3-5(SA) RNA and attempted to isolate revertants with second-site mutations capable of rescuing the ability of the HJ3-5(SA) RNA to produce infectious virus. In multiple transfection experiments, infectious virus began to be released from cells after 6–7 cell passages. However, sequencing of RNA extracted from these viruses invariably revealed reversion of the S168A mutation to the wt sequence (a single nucleotide change is sufficient). Different results were obtained with HJ3-5(SG), in which two nucleotide substitutions are required to change the codon (‘GGG’) from Gly back to Ser. We successfully isolated revertants in 4 independent transfection experiments, with release of infectious virus first detected between cell passages 7 to 11 (Table 1). Sequencing of these viruses revealed mutations at Leu-174 of NS2 in 3 out of 4 independent transfection experiments: this residue was changed to Val in 2 experiments, and to a mixture of Leu and Ile in the third experiment, in concert with a mixture of Trp and Arg at Arg-68 in the protease domain of NS3. (The HJ3-5(SG) clone used in these experiments was found subsequently to contain an adventitious NS5A mutation, Thr-115 to Ala; this had partially or completely reverted to the wild-type Thr sequence in virus from each of these 3 transfection experiments). The only mutation found in infectious virus recovered from the fourth transfection experiment was in NS5A, in which Val-464 was changed to Leu. These mutations are summarized in Table 1.
To confirm that these mutations render the HJ3-5(SG) RNA capable of producing infectious virus, we independently introduced the NS2-L174V and NS5A-V464L mutations into the HJ3-5(SG) construct (resulting in HJ3-5(SG)/2-L174V and HJ3-5(SG)/5A-V464L, respectively) and assessed the ability of the modified RNAs to produce infectious virus by measuring infectivity in supernatant fluids 2 days after transfection. The NS2-L174V mutation, which is only 6 residues distant from the S168G mutation in HJ3-5(SG), resulted in robust production of infectious virus, fully restoring the ability of the HJ3-5(SG) RNA to produce infectious virus (Figure 9). Interestingly, the JFH1 NS2 protein already contains Val at residue 174. Therefore, it is tempting to speculate that the lesser inhibition of virus production we observed when we placed the S168A mutation in the JFH1 compared to the HJ3-5 background (Figure 1B) may reflect this natural polymorphism.
The NS5A-V464L mutation also restored the ability of the HJ3-5(SA) RNA to produce infectious virus. However, although the NS5A-V464L mutation enhanced virus production at least 1000-fold, infectious virus yields from the HJ3-5(SG)/5A-V464L RNA were about 10-fold less than with wt HJ3-5 RNA (Figure 9). This genetic interaction between NS2 and NS5A is consistent with a direct physical interaction of the two proteins as suggested by the partial co-localization of NS2 and NS5A detected by confocal microscopy (Figure 8A and 8B, lower panels). Interestingly, the V464L mutation is close to a putative CK2 phosphorylation site in the C-terminal domain of NS5A (Ser-457) that regulates the production of infectious JFH1 virus [8]. It also has been identified during selection of JFH1 variants capable of producing higher yields of infectious virus in cell culture [29].
NS2 is a small trans-membrane protein that is located between the structural and nonstructural proteins within the HCV polyprotein [17],[18]. Until recently, the only known role for this protein in the viral life cycle was its auto-protease activity, which cleaves the polyprotein between NS2 and NS3 [20],[22],[30]. However, along with newly recognized roles for two other nonstructural proteins, notably NS5A [8]–[10] and NS3 [16], in viral particle assembly, there is growing evidence that NS2 also has essential function(s) that are required for the production of infectious virus [12],[14],[15],[19]. In the work described here, we have shown that a highly conserved amino acid residue within NS2 (Ser-168) is essential for the production of infectious virus particles, as the mutation of this residue to Ala or Gly either eliminated or substantially reduced the yield of infectious virus released by cells transfected with viral RNAs containing sequences encoding either genotype 1a or 2a NS2 protein (Figure 1B). Furthermore, we show that the defect in infectious virus production that is caused by mutation of Ser-168 is within a late step in this process, one that occurs subsequent to the assembly of core-protein containing particles, as non-infectious, intracellular particles with sedimentation properties and buoyant densities similar to infectious virus still formed within cells transfected with the mutant RNAs (Figure 6A). Consistent with this, core and NS5A proteins were co-localized, as they normally are, on the surface of lipid droplets in HJ3-5(SG) transfected cells (Figure 7A). Thus, the defect imposed by the Ser-168 mutations appears be within a maturation step that confers infectivity on previously assembled particles. Since the non-infectious, core-containing particles that are assembled by these NS2 mutants are not released from the cell (Figure 6B), this maturation process may be required for their release from the cell. The data presented here thus reveal a novel role for NS2, most likely in cooperation with NS5A (see below), during the maturation of HCV particles late in the process of virus assembly and release.
Our focus on the Ser-168 residue in NS2 was prompted by the report of Franck et al. [23], who suggested that this residue (in the genotype 1a HCV-H strain of HCV) is targeted by the host kinase, CK2, for phosphorylation. However, although we found that both S168A and S168G mutations ablated production of infectious virus by HJ3-5 RNA (which contains NS2 sequence from H77) (Figure 1B), the lack of a specific effect of DMAT on production of infectious virus by this RNA strongly suggests that phosphorylation of Ser-168 by CK2 is not required for this process (Figure 1E). These latter data are consistent with recent work by Tellinghuisen et al. [8], who identified a CK2 phosphorylation site in NS5A and reported that treatment with a CK2 inhibitor did not impair infectious virus release by a genotype 2a RNA (J6/JFH1 chimera) provided the NS5A residue targeted by CK2 was first mutated to Asp. Importantly, we found that mutations at NS2 Ser-168 significantly impaired infectious virus production by viral RNAs containing either the genotype 1a (HJ3-5) or 2a (JFH1) NS2A sequence. Taken together, these data suggest that the NS2 Ser-168 mutations are likely to inhibit the production of infectious virus by a mechanism other than interference with CK2 phosphorylation of this residue.
As described by Franck et al. [23], we observed that the genotype 1a H77 NS2 protein was subject to proteasome-mediated degradation (Figure 2). While Franck et al. suggested that CK2 phosphorylation of Ser-168 regulates the stability of the H77 NS2 protein, our data suggest that the protein is partially, but not completely, stabilized by a Gly substitution at this residue (Figure 2C). In contrast, the wt genotype 2a JFH1 NS2 does not appear to be subject to proteasome-mediated degradation (Figure 2C), although it shares the same putative CK2 target site, Ser-168 in NS2, along with other HCV sequences [23]. The difference in the intrinsic stability of the NS2 proteins from these two distinct viral strains may account, at least in part, for the differences we observed in the magnitude of the impact of S168A or S168G mutations on infectious virus production by the HJ3-5 and JFH1 RNAs. The S168A mutation completely abolished infectious virus production by HJ3-5, while causing an incomplete loss of infectious virus yield from the JFH1 RNA (Figure 1B). However, we also observed that the H77 NS2 could trans-complement the production defect in HJ3-5(SA) but not JFH1(SA) RNA, while the JFH1 NS2 can rescue virus production by either RNA (although with a greater effect on the JFH1(SA) defect) (Figure 3A and 3B). These differences in NS2 trans-complementation cannot be attributed simply to differences in NS2 stability, and suggest more fundamental differences in NS2-protein interactions required for particle maturation. Whether these differences are restricted to these two specific strains of HCV, or instead reflect broader, genotype-specific differences (genotype 1a versus 2a), will require further study.
Importantly, second site mutations involving either Leu-174 in NS2 (residue 938 in the polyprotein), or Val-464 in NS5A (residue 2440) were capable of rescuing the defect in virus production mediated by the S168G mutation (Figure 9). An X-ray crystallographic reconstruction of the NS2 auto-protease domain suggests that both Ser-168 and Leu-174 are surface-exposed residues [20]. Therefore, it seems reasonable to speculate that Ser-168 and Leu-174 may be involved in protein-protein interaction(s) required for the maturation of previously assembled particles and leading to the production of infectious virus. The fact that the V464L mutation in NS5A also rescues infectious virus production by the NS2 S168G mutant suggests the possibility that this putative NS2 interaction could involve NS5A (Figure 9). While other explanations are possible for the genetic interaction we observed between NS2 and NS5A, a close physical interaction is suggested by the partial co-localization of NS2 and NS5A identified by confocal microscopy (Figure 8). Whether or not a direct interaction occurs between NS2 and NS5A, the rescue of the late NS2 defect in virus production by a second-site mutation in NS5A indicates that NS5A acts at multiple, temporally distinct points in the production of infectious virus particles. First, in an early step in virus assembly, NS5A is recruited to the core protein which decorates the surface of lipid droplets [8]–[10]. This is a pre-requisite for particle assembly, but it is not sufficient, as we have recently described an NS3 defect in infectious virus production that ablates intracellular particle assembly but does not impair the association of NS5A with core on lipid droplets [16]. The data we have presented in this paper indicate that NS2 acts at a later step in the process of infectious particle production, in association with NS5A but subsequent to the earlier involvement of core, NS5A and NS3, to confer infectivity on previously assembled particles in a maturation step that appears to be required for release of virus to the extracellular environment. This hypothetical sequence for the contribution of individual nonstructural proteins to infectious particle assembly and release is summarized in Figure 10.
HCV is classified within the family Flaviviridae, and thus it is related phylogenetically to yellow fever virus (YFV) as well as Kunjin virus. It is interesting to note that the NS2A proteins of YFV and Kunjin have also been implicated in virus assembly and release, and in both cases this function can be trans-complemented by expression of the wt protein [31],[32], as we have shown here for HCV. Although the flavivirus NS2A proteins differ substantially from the NS2 protein of HCV both in sequence and functions within the viral life cycle, it is not unreasonable to suspect that they may share some mechanistic features in common during the process of virus assembly and release. In the case of the Kunjin NS2A protein, its role in infectious virus assembly and release appears to be closely linked to the production of specific virus-induced membrane alterations [32]. We observed very strong co-localization of the HCV NS2 protein with the major envelope protein, E2, in VEE replicon cells during infectious virus production (Figure 8A and 8B, middle panels). E2 and NS2 were both prominently localized to membranes of the peri-nuclear ER, suggesting the possibility of an interaction between NS2 and E2 during the maturation process. NS2 may interact with E1 as well, since we showed recently that a mutation in E1 (Y170H) functions cooperatively with a mutation in NS2 (I30T), but not P7 (Y31C), to enhance the infectious yield of virus produced from a genotype 1a/2a chimera ([14] and Yi M, Lemon SM, unpublished data). One possibility is that NS2 might facilitate a rearrangement of the envelope proteins following assembly of the particle that confers or enhances infectivity. Such an envelope rearrangement would not be without precedent among the flaviviruses, and could be required for infectivity of the HCV particle. Additional studies of the HCV assembly and release process will be required to assess this possibility, as well as a detailed characterization of the structure of the infectious virus particle.
The parental and chimeric HCV plasmids used in these studies have been described previously: pHJ3-5 (pH-(NS2/NS3)-J/YH/QL) was derived from pHJ3 (pH-(NS2/NS3)-J) by introducing two compensatory mutations, one located within E1 (Y361H) and the other in NS3 (Q1251L), that allow for assembly of infectious virus particles following transfection of Huh7 cells [14],[16]. The NS2 S168A mutation was introduced into the pHJ3-5 and pJFH1 plasmids resulting in pHJ3-5(SA) and pJFH1(SA) by QuikChange mutagenesis (Stratagene) and confirmed by sequencing analysis. Similar methods were utilized to construct and validate the NS2 S168G and the various NS5A mutants described in the text. VEE replicons expressing various NS2 proteins were constructed using p5′VEErep/L/GFP/Pac [24], a generous gift from Dr. Ilya Frolov (University of Texas Medical Branch at Galveston). To facilitate these constructions, a PacI site was introduced at the end of the GFP coding sequence in this plasmid using QuikChange mutagenesis (p5′VEErep/L/GFP/Pac v.2). NS2 coding sequence was amplified from pH77S or pJFH1 by PCR using primers that were designed to introduce XbaI and PacI sites at the 5′- and 3′-termini of the NS2 sequence. Additional sequences encoding Flag or Myc tags were placed at the N- or C-terminal end of NS2, as shown in Figure 2A. The NS2 fragments were digested with XbaI and PacI before being ligated to two DNA fragments (PacI/MluI and MluI/XbaI) derived from p5′VEErep/L/GFP/Pac v.2, resulting in the NS2-expressing VEE replicon constructs. The NS2-S168A and S168G mutations were introduced subsequently by QuikChange mutagenesis. To introduce the NS2-L174V mutation, HJ3-5 was digested with BglII/MluI and ligated to a PCR fragment containing both NS2-S168G and L174V mutations. C-terminal NS2 deletion mutants ΔC63, ΔC119, ΔC147 were constructed using QuikChange mutagenesis. The integrity of the constructs was validated by DNA sequencing of the manipulated regions.
FT3-7 and Huh-7.5 cells are clonal derivatives of Huh7 human hepatoma cells [14],[33]. They were grown in DMEM containing 10% FBS and 1× penicillin/streptomycin at 37°C in a 5% CO2 environment. VEE replicon cells were cultured in the same medium plus 1 µg/ml puromycin (Invitrogen).
HCV RNAs were transcribed in vitro and electroporated into cells as described previously [34]. In brief, for electroporation,10 µg of in vitro-synthesized HCV RNA was mixed with 5×106 FT3-7 cells in a 2-mm cuvette and pulsed twice at 1.4 kV and 25 µF in a Gene Pulser II (BioRad) apparatus. Cells were subsequently seeded into 12-well plates for analysis of HCV RNA. For virus production, transfected cells were seeded into 25-cm2 flasks and fed with medium containing 10% FBS. Cells were split every 3–4 days.
Total RNA was isolated from cell lysates using an RNeasy kit (Qiagen) in accordance with the manufacturer's instructions. RNA was isolated from cell culture supernatants and gradient fractions (see below) using a QIAamp viral RNA kit (Qiagen). For monitoring RNA replication in transfected cells, we assayed viral RNA abundance in a quantitative real-time RT-PCR reaction carried out in a Bio-Rad iQ5 Real-time PCR Detection System using Taq-Man chemistry and the forward primer HCV84FP (5′-GCCATGGCGTTAGTATGAGTGT-3′), reverse primer, HC300R (5′-CCCTATCAGGCAGTACCACAA-3′), and detection probe: FAM (6-carboxy-fluorescene)-TCTGCGGAACCGGTGAGTACACC-DBH (dual-labeled probe Black Hole Quencher)-1.
For virus titration, 100-µl aliquots of serial 10-fold dilutions of supernatant cell culture fluids (clarified by low-speed centrifugation), clarified freeze-thaw cell lysates, or iodixanol or sucrose gradient fractions (see below), were inoculated onto naïve Huh-7.5 cells seeded 24 h previously into 8-well chamber slides (Nalge Nunc) at 3×104 cells/well. Cells were maintained at 37°C in a 5% CO2 environment and fed with 200 µl of medium 24 h later. Following 48 h additional incubation, cells were fixed in 1∶1 methanol-acetone at room temperature for 9 min, then stained with monoclonal antibody C7-50 to core protein (Affinity BioReagents, 1∶300) for 2 h at 37°C, washed with PBS twice, and incubated with fluorescein isothiocyanate-conjugated goat anti-mouse immunoglobulin G (Southern Biotech, 1∶100) for 30 min at 37°C. Clusters of infected cells staining for core antigen were considered to constitute a single infectious focus-forming unit (FFU), as described previously [4],[7],[14]. Infectivity titers (FFU/ml) were calculated from the results obtained with sample dilutions yielding 5 to 100 FFU.
Immunoblots of cell lysates were probed with antibody to core (C7-50, Affinity BioReagents; 1∶30,000 dilution), anti-Flag M2 (Sigma, F3165; 1∶1000 dilution), anti-Myc (Sigma, M5546; 1∶1000 dilution) followed by horseradish peroxidase-conjugated anti-mouse IgG (Southern Biotech, no. 1030-05; 1∶30,000). Proteins were visualized by chemiluminescence using reagents provided with the ECL Advance kit (Amersham Biosciences).
For in vitro translation of HCV polyprotein segments expressing NS2-NS3-NS4A derived from HJ3 with or without the NS2-S168A mutation, 1 µg of in vitro transcribed RNA was used to program in vitro translation reactions in rabbit reticulocyte lysate (Promega) in a 50 µl reaction mixture containing 2 µl of [35S]-methionine (1,000 Ci/mmol at 10 mCi/ml), 2.5 µl of canine pancreatic microsomal membranes and 1 µl of an amino acid mixture lacking methionine at 30°C for the indicated time. Reactions were stopped by the addition of SDS sample buffer and boiling for 10 min. Translation products were separated by SDS-PAGE followed by autoradiography.
Transfected cells were seeded onto 8-well chamber slides and 2–3 days later washed three times with PBS, fixed with 4% paraformaldehyde, and permeabilized with 0.1% Triton X-100 for 10 min [27]. Cells were labeled with monoclonal antibody C7-50 to core protein (Affinity BioReagents, 1∶400) and rabbit polyclonal antibody to NS5A (a generous gift of Dr. Craig Cameron, 1∶300 dilution) followed by goat anti-mouse immunoglobulin G conjugated to Alexa-488 and goat anti-rabbit immunoglobulin G conjugated to Alexa-594 (Invitrogen, 1∶200). Neutral lipids present in lipid droplets were visualized by staining with LipidTOX Deep Red (Invitrogen). Nuclei were visualized by counterstaining with DAPI (1∶1000). To detect NS2 protein containing Myc- or Flag-tag sequences, cells were incubated monoclonal anti-Myc antibody (Sigma, M5546; 1∶100 dilution) or Rabbit anti-Flag antibody (Sigma, F7425; 1∶100 dilution). E2 protein was detected using the human monoclonal antibody CBH-7 (kindly provided by Steven Foung, Stanford University) at a 1∶100 dilution. Slides were examined with a Zeiss LSM 510 Meta laser-scanning confocal microscope.
Cell lysates were prepared for intracellular virus infectivity assays as described by Gastaminza et al. [35]. Briefly, cell pellets harvested after trypsinization were resuspended in complete media, washed twice with PBS, lysed by four cycles of freezing and thawing, and clarified by centrifugation at 4000 rpm for 5 mins. A 250 µl volume of lysate was loaded on a preformed continuous 10 to 50% sucrose gradient prepared in TNE (10 mM Tris, pH 8.0, 150 mM NaCl, 2 mM EDTA) and centrifuged for 1 h at 40,000 rpm (∼200,000×g) in a SW41 Ti rotor at 4°C. A total of 40 fractions (300 µl each) were collected from the top of the gradient and subjected to immunoblot analysis, to determine the distribution of core protein, and virus titration to determine the location of infectious virus within the gradient. The density of gradient fractions was estimated from the refractive index determined with a Milton-Roy refractometer.
Three days following electroporation of cells with HCV RNA, clarified cell lysates (prepared after multiple freeze-thaw cycles as described above) were layered on top of 10 to 40% iodixanol gradients prepared in Hanks balanced salt solution. Gradients were centrifuged in an SW60 rotor (Beckman Coulter) at 45,000 rpm for 16 h at 4°C, and nine fractions (500 µl each) were collected from the top of the tube. The density of gradient fractions was estimated from the refractive index determined with a Milton-Roy refractometer.
Core protein was quantified in gradient fractions using the Ortho Trak-C ELISA kit (Ortho-Clinical Diagnostics) with minor modifications to the manufacturer's recommended procedures. Cell culture supernatant or lysates derived from FT3-7 cells transfected with HJ3-5 with or without the NS2-S168G mutation were pre-diluted 10- to 200-fold in the dilution buffer supplied with the kit (so that OD490 values were within range of the standard solutions) prior to incubation in the ELISA plates at room temperature for 1 h. The quantity of core protein present in fractions was estimated from the OD490 by reference to a standard curve.
Cells were treated with the CK2 inhibitor, 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole (DMAT, EMD Biosciences), as described by Tellinghuisen et al [8]. FT3-7 cells were treated with a range of concentrations (0.1, 1, 10 µM) of DMAT beginning 4 h after electroporation with HCV RNA, with treatment continued until 24 h post-electroporation. We measured the titer of infectious virus present in the cell culture supernatant at 48 h post-transfection.
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10.1371/journal.ppat.1000008 | Adenylyl Cyclase α and cAMP Signaling Mediate Plasmodium Sporozoite Apical Regulated Exocytosis and Hepatocyte Infection | Malaria starts with the infection of the liver of the host by Plasmodium sporozoites, the parasite form transmitted by infected mosquitoes. Sporozoites migrate through several hepatocytes by breaching their plasma membranes before finally infecting one with the formation of an internalization vacuole. Migration through host cells induces apical regulated exocytosis in sporozoites. Here we show that apical regulated exocytosis is induced by increases in cAMP in sporozoites of rodent (P. yoelii and P. berghei) and human (P. falciparum) Plasmodium species. We have generated P. berghei parasites deficient in adenylyl cyclase α (ACα), a gene containing regions with high homology to adenylyl cyclases. PbACα-deficient sporozoites do not exocytose in response to migration through host cells and present more than 50% impaired hepatocyte infectivity in vivo. These effects are specific to ACα, as re-introduction of ACα in deficient parasites resulted in complete recovery of exocytosis and infection. Our findings indicate that ACα and increases in cAMP levels are required for sporozoite apical regulated exocytosis, which is involved in sporozoite infection of hepatocytes.
| Malaria is transmitted through the bite of an infected mosquito that deposits Plasmodium sporozoites under the skin. These sporozoites migrate from the skin into the circulation and then enter the liver to start a new infection inside hepatocytes. Sporozoites have the capacity to traverse mammalian cells. They breach their membranes and migrate through their cytosol. This process is required for infection of the liver and triggers the exposure of adhesive proteins in the apical end of sporozoites, a process that facilitates invasion of hepatocytes. We found that elevations of cAMP inside sporozoites mediate the exposure of adhesive proteins and therefore the infection process. Mutant sporozoites that do not express adenylyl cyclase, the enzyme that synthesizes cAMP, are not able to expose the adhesive proteins and their infectivity is reduced by half. Reinsertion of adenylyl cyclase gene in the mutant sporozoites recovers their capacity to expose adhesive proteins and to infect hepatocytes, confirming the specific role of this protein in infection. These results demonstrate the importance of cAMP and the exposure of adhesive proteins in sporozoites, but also show that Plasmodium sporozoites have other mechanisms to invade host hepatocytes that are not inhibited in the mutant parasites.
| Plasmodium, the causative agent of malaria, is transmitted by the bite of infected mosquitoes that inoculate the sporozoite form of the parasite in the host. Sporozoites rapidly migrate to the liver, where they infect hepatocytes, replicate and develop into merozoites, the blood-stage form of the parasite. Plasmodium belongs to the phylum apicomplexa, a group of parasites that share conserved mechanisms of motility and cell invasion machinery [1]. Apical exocytosis is another common feature that has been characterized in Toxoplasma tachyzoites [2] and sporozoites from Eimeria [3], Cryptosporidium [4] and Plasmodium [5]. This process has been most extensively studied in Toxoplasma tachyzoites, where active invasion of host cells involves the secretion of transmembrane adhesive proteins from the micronemes, which congregate on the anterior surface of the parasite and bind host receptors to mediate apical attachment [6]. One of these adhesive proteins, MIC2, which plays a central role in motility and invasion [7] is closely related to Plasmodium Thombospondin-Related Anonymous Protein, TRAP (also known as Sporozoite Surface Protein 2, SSP2) [8], which is also exposed in the apical end of the parasite upon microneme exocytosis [5],[9] and is also required for Plasmodium sporozoite motility and invasion [10].
While in Toxoplasma tachyzoites microneme secretion is strongly up-regulated upon contact with the host cell, in Plasmodium sporozoites contact with host cells is not sufficient to activate this process and migration through cells is required to induce apical regulated exocytosis [9]. Sporozoites of different human and rodent Plasmodium species have the ability to migrate through host cells. Sporozoites enter and exit cells by breaching the plasma membrane of the traversed cell. This process results in sporozoites traversing host cells by moving through their cytosol without any surrounding membranes. Ultimately, sporozoites establish infection in a final hepatocyte through formation of a vacuole within which the parasite replicates and develops [9]. Migration through host cells induces apical exocytosis in Plasmodium sporozoites, resulting in the exposure of high concentrations of TRAP/SSP2 in the apical end of the parasite [9]. This process, similarly to Toxoplasma secretion of MIC2 [7], is thought to facilitate invasion of the host cell [9].
During migration through host cells sporozoites are not surrounded by any host membranes, and as a result, they are in direct contact with the cytosol of the host cell [11]. Incubation of Plasmodium sporozoites with a lysate of host cells activates apical exocytosis in the parasite, suggesting that host cell molecules induce the activation of exocytosis in migrating parasites [9]. We have studied the role of uracil nucleotides in sporozoite exocytosis, since these molecules induce exocytosis in other cellular systems [12] and are found in the cytosol of mammalian cells in high concentrations. We found that uracil and its derived nucleoside and nucleotides (UMP, UDP and UTP) at the physiological concentrations found in the cytosol of mammalian cells, activate apical regulated exocytosis and increase the infectivity of sporozoites [13]. Since sporozoites are in contact with the cytosol of the traversed host cells, it is likely that the high concentrations of uracil derivatives that they would encounter, probably participate in the activation of sporozoites during migration through cells. Addition of uracil derivatives in vitro induces apical regulated exocytosis within the first ten minutes after addition of the stimulus [13]. In certain mammalian cell types, UTP and UDP can activate signaling cascades by binding to P2Y receptors, which in turn can activate adenylyl cyclase and increase cyclic adenosine monophosphate (cAMP) levels. Activation of P2Y receptors by nucleotides leads to exocytosis in different cells from insulin release from pancreatic islet β cells to the release of histamine from mast cells [14].
Here we have analyzed the role of the cAMP signaling pathway in sporozoite apical exocytosis and infection. We found biochemical evidences indicating that increases in cAMP levels in sporozoites mediate apical regulated exocytosis, which activates sporozoites for host cell invasion. By creating a parasite line deficient in adenylyl cyclase α (ACα), we confirmed that the cAMP signaling pathway is essential to induce apical exocytosis, which is activated during migration through cells. In addition, this recombinant parasite provides a tool to determine the precise contribution of apical exocytosis to sporozoite infection. A role for migration through cells and apical regulated exocytosis in infection was proposed before [9], but it had been questioned in view of transgenic sporozoites that were able to infect cells in vitro without performing the previous migration step [15]. Here we show that apical regulated exocytosis contributes significantly to host cell invasion, but the parasite seems to have alternative mechanisms to establish successful infections in host cells.
To investigate the signaling pathways mediating Plasmodium sporozoite exocytosis, we used a mix of uracil and its derivatives (uridine, UMP, UDP and UTP) at the concentrations normally found in the cytosol of mammalian cells (described in Experimental Procedures), which induce exocytosis in sporozoites [13]. Apical regulated exocytosis has been characterized in Plasmodium sporozoites by the exposure of high concentrations of TRAP/SSP2 in the apical end of the parasite and also by the release of this protein into the medium [9]. We confirmed that exocytosis occurs at the apical end of the sporozoite by staining the trails left behind after gliding motility. Trails are always next to the posterior end because sporozoites move with their apical end in the front (Fig. S1).
We first investigated whether cAMP induces or modulates sporozoite regulated exocytosis by preincubating P. yoelii sporozoites with a membrane permeant analogue of cAMP. Exocytosis is quantified as the percentage of sporozoites that present a defined accumulation of extracellular TRAP/SSP2 in their apical end [9]. We found that 8Br-cAMP induces sporozoite exocytosis to a similar level than uracil derivatives. Addition of both stimuli to sporozoites did not increase the level of exocytosis (Fig. 1A), suggesting that both stimuli may be using the same pathway to induce exocytosis. As an alternative way to increase cytosolic cAMP in sporozoites, we used forskolin, an activator of the enzyme that synthesizes cAMP, adenylyl cyclase (AC). This treatment also induced apical regulated exocytosis in sporozoites (Fig. 1B). Incubation of sporozoites with MDL-12,330A, an inhibitor of AC [16] prevented activation of exocytosis by uracil derivatives (Fig. 1B). We confirmed that these treatments did not increased sporozoite lysis compared to control (Table S1 and Fig. S2).
Genetically manipulated sporozoites that are deficient in their capacity to migrate through cells (spect-deficient) infect hepatic cell lines in vitro, questioning the role of migration through cells in the activation of sporozoites for infection [15]. To analyze the exocytosis response of these sporozoites, we stimulated them with uracil derivatives or treatments that modulate cAMP levels. Incubation of P. berghei wt or spect-deficient sporozoites with uracil derivates induced apical regulated exocytosis. However, forskolin and 8-Br-cAMP did not induce exocytosis in spect-deficient sporozoites and MDL-12,330A only has a partial effect in the inhibition of exocytosis (Fig. 1C). These results suggest that, in contrast to wt P. berghei sporozoites, spect-deficient sporozoites do not use cAMP-mediated signaling pathways to activate exocytosis.
We have used the rodent malaria parasites P. yoelii and P. berghei as a model for P. falciparum, the human parasite responsible for the mortality associated with this disease. P. falciparum sporozoites also migrate through host cells [11], a process that induces apical regulated exocytosis in this species of the parasite [13]. Similar to the rodent parasites, uracil and its derivatives induce exocytosis in P. falciparum sporozoites [13]. We found that elevated cAMP levels also induce exocytosis in P. falciparum sporozoites and that exocytosis induced by uracil derivatives is inhibited by MDL-12,330A (Fig. 1D), suggesting that this pathway is conserved in the human and murine parasites.
To directly demonstrate that cAMP levels are increased in P. yoelii sporozoites in response to exocytosis-inducing stimuli, we measured cAMP concentration in sporozoites after incubation with uracil derivatives. Salivary glands dissected from uninfected mosquitoes and processed in a similar way, were used as negative control. We found that uracil derivatives significantly increase the levels of cAMP in sporozoites (Fig. 1E). No increases were found when control material from uninfected mosquitoes was stimulated with uracil derivatives (not shown).
Migration through host cells induces sporozoite apical regulated exocytosis, which activates sporozoites for infection. Stimulation of exocytosis by other means, such as host cells lysate [9] or uracil derivatives [13], overcomes the need for extensive migration through cells and increases infection. To test whether stimulation of exocytosis by increases in intracellular cAMP in the sporozoite would also overcome the need for migration through host cells before infection, we incubated P. yoelii sporozoites with forskolin or 8Br-cAMP to induce regulated exocytosis before addition of sporozoites to intact Hepa1-6 cells. Migration through host cells is determined as the percentage of cells that are wounded by sporozoite migration and as a result become positive for a soluble impermeant tracer (dextran) [17]. We found an increase in the number of infected cells, indicating that stimulation of regulated exocytosis by cAMP in sporozoites increases their infectivity (Fig. 2A, black bars). In addition, activation of sporozoite exocytosis with increased cAMP levels reduces sporozoite migration through host cells, confirming that such extensive migration is no longer necessary when exocytosis is induced by elevations in the level of cAMP (Fig. 2A, white bars). These results indicate that cAMP-induced exocytosis contributes to the activation of sporozoites for infection.
Since sporozoites appear to activate the cAMP signaling cascade to stimulate apical regulated exocytosis, inhibition of cAMP production in sporozoites by MDL-12,330A, the inhibitor of AC, should decrease their infectivity. We actually found a significant reduction in their infectivity after treatment with this inhibitor (Fig. 2B). MDL-12.330A does not appear to have a toxic effect on sporozoites, since migration through cells was not affected (Fig. 2B).
We also observed that gliding motility of sporozoites is greatly decreased 18 to 24 min after addition of the exocytosis inducing stimulus (UD or forskolin), but not during earlier time points, while exocytosis is presumably occurring (0 to 8 min after addition of the stimulus) (Fig. S3).
The major downstream effector of cAMP is protein kinase A (PKA), a serine/threonine kinase that activates other kinases and transcription factors in the cell. This protein is likely to be present in Plasmodium because PKA activity has been detected in P. falciparum during the blood stage of the parasite [18],[19] and there is a gene sequence with high homology to PKA expressed in P. falciparum and conserved in all species of Plasmodium analyzed [20],[21], however no functional assays have yet determined the PKA activity of this putative protein. To investigate whether sporozoite exocytosis is mediated by PKA activity, we treated sporozoites with H89, a PKA inhibitor already shown to inhibit this kinase in a different stage of the parasite [18],[19]. We found that H89 inhibits sporozoite exocytosis induced by uracil derivatives (Fig. 3A), suggesting that this process is mediated by the activation of PKA. The infectivity of sporozoites pretreated with H89 is reduced, probably as a consequence of the inhibition of exocytosis (Fig. 3B), while parasite migration through host cells is not affected, confirming that H89 treatment is not toxic for sporozoites (Fig. 3C).
Activation of PKA should occur after cAMP has been generated in the signaling cascade. To analyze this step of the pathway, we pretreated sporozoites with H89 before increasing cAMP levels with the addition of 8Br-cAMP. As expected, we found that exocytosis was completely inhibited (Fig. 3D), suggesting that PKA is activated down-stream of cAMP. Incubation of sporozoites with genistein, an inhibitor of tyrosine kinases, did not affect regulated exocytosis (Fig. 3E), indicating that tyrosine kinases are not involved in the signaling cascade. In fact, no sequences with homology to tyrosine kinases have been found in the Plasmodium genome [20].
To strengthen the evidence that the cAMP signaling pathway mediates the activation of exocytosis in sporozoites and reduce the probability of inhibitors affecting exocytosis due to non-characterized effects of the drugs, we used alternative inhibitors with unrelated chemical structures from the ones used before to inhibit adenylyl cyclase and PKA. We found similar inhibitory results using 2′, 5′-Dideoxyadenosine or SQ22536, which inhibit adenylyl cyclase. The addition of a competitive inhibitor of cAMP (cAMP Rp-isomer), which inhibits PKA, also results in inhibition of apical regulated exocytosis in sporozoites (Fig. 3F).
Since cAMP signaling appears to mediate the activation of apical exocytosis, we searched for ACs in the malaria genome. Two different genes with high homology to ACs (ACα and ACβ) have been identified in Plasmodium. In particular, ACα was shown to have AC activity in P. falciparum [22],[23]. Interestingly, ACα genes from Plasmodium, Paramecium and Tetrahimena are closely related and their sequence includes a domain with high homology to K+ channels [23]. In Paramecium, where the purified AC protein also has K+ channel activity, generation of cAMP is regulated by K+ conductance [24]. It is thought that ACα presents a transmembrane K+-channel domain and an intracellular AC domain, which are functionally linked [25].
Since cAMP in Plasmodium sporozoites induces apical exocytosis, we first tested whether extracellular K+ is required for this process. In fact, sporozoites must remain in a high K+ environment during migration through cells, because the cytosol of eukaryotic cells has high concentrations of this ion [26]. The existence of K+ channels has been predicted for Plasmodium parasites from electrophysiological [27] and genomic sequence data [20].
To determine whether extracellular K+ is required for sporozoite exocytosis, we stimulated exocytosis in P. yoelii sporozoites in regular medium (containing K+) or in K+-free medium. We found that exocytosis stimulated with uracil derivatives was inhibited in K+-free medium (Fig. 4A). To confirm that sporozoites were not impaired by the incubation in K+-free medium, we transferred sporozoites to regular medium after the K+-free medium incubation. We found that exocytosis in these sporozoites was similar to exocytosis in sporozoites that were never incubated in K+-free medium (Fig. 4B).
Exocytosis was inhibited when sporozoites were pre-incubated with different K+-channel inhibitors (Fig. 4C,D), suggesting that K+ is required for the activation of exocytosis. We also analyzed the requirement for extracellular K+ in sporozoite exocytosis induced by 8Br-cAMP or forskolin. We found that in these cases extracellular K+ is not required (Fig. 4E,F), suggesting that extracellular K+ is required upstream cAMP in the signaling cascade. Removal of K+ from the medium may alter the electrochemical gradient of sporozoites affecting UD-induced exocytosis. However, since the response to forskolin and 8Br-cAMP in K+ free medium is not affected, it suggests that the sporozoite exocytosis pathway is perfectly functional in the absence of extracellular K+. Also, the viability (Table S1) and capacity of exocytosis response (Fig. 4B) of sporozoites after this treatment was found to be unaffected.
A Ca++ ionophore can induce apical regulated exocytosis in P. yoelii [9], suggesting that Ca++ signaling may be involved in exocytosis. We first compared the magnitude of the cAMP-induced to the Ca++-induced exocytosis, finding similar results (Fig. 4G). To study whether Ca++ is also involved in the signaling induced by UD, we induced exocytosis with UD in Ca++-free medium. We found that exocytosis is not inhibited in Ca++-free medium (Fig. 4H), suggesting that extracellular Ca++ is not required for this process. However, we found a strong inhibition of exocytosis when sporozoites were incubated with a membrane-permeant Ca++ chelator, suggesting that intracellular Ca++ is required for exocytosis (Fig. 4I). A possible model for the signaling mediating exocytosis is proposed (Fig. 4J).
Since Plasmodium sporozoite regulated exocytosis requires both extracellular K+ and cAMP, we decided to test whether ACα is involved in the process of sporozoite exocytosis and activation for infection by producing recombinant parasites deficient for this enzyme. We identified the sequence encoding PbACα, the P. berghei orthologue of PfACα, in the PlasmoDB database (http://www.plasmoDB.org/). Complete PbACα sequences were retrieved from Sanger sequencing genomics project (http://www.sanger.ac.uk/). We found that PbACα is 60% identical to PfACα at the amino-acid level of the full-length predicted protein, and 79% in the AC catalytic domain.
Microarray analysis had detected expression of PfACα in sporozoites [28]. To analyze the expression of PbACα, we isolated mRNA from P. berghei sporozoites and performed reverse transcription followed by PCR. We also found expression of this gene in sporozoites (Fig. 5A). Thus, we decided to pursue a targeted gene disruption at the blood stages to study the importance of ACα for the Plasmodium pre-erythrocytic life cycle stages. We created two independent cloned lines of P. berghei parasites that are deficient in ACα (PbACα-) by using targeted disruption of the ACα gene through double crossover homologous recombination (Fig. 5B). PbACα-deficiency of the mutant parasites was confirmed by RT-PCR and Southern Blotting (Fig. 5C).
We examined the phenotype of PbACα- parasites during the Plasmodium life cycle. We compared the two PbACα- lines with WT P. berghei parasites also cloned independently. PbACα- parasites were indistinguishable from WT parasites in growth during red blood cell stages in mice (Fig. 6A). We next analyzed parasite growth in the mosquito by determining oocyst development and sporozoite salivary gland invasion. Similar oocyst and salivary gland sporozoite numbers were obtained for PbACα- and the WT control, indicating that PbACα is not involved in oocyst development and sporozoite salivary gland invasion (Table 1).
Gliding motility, the characteristic form of substrate-dependent locomotion of salivary gland sporozoites, was unaffected in PbACα- parasites. Stimulation of gliding motility with albumin [29] was also similar in WT and PbACα- sporozoites (Fig. 6B). We also tested whether deletion of the ACα gene affect sporozoites ability to migrate through cells. We found that the cell-traversal activity of PbACα- sporozoites was slightly lower, but not significantly different from WT sporozoites (Fig. 6C).
We then tested whether apical regulated exocytosis was affected in PbACα-sporozoites. Activation of exocytosis by the mix of uracil derivatives or by forskolin, was greatly reduced in the two different clones of PbACα- sporozoites analyzed (Fig. 7A). Addition of a membrane permeant analogue of cAMP (8-Br-cAMP), which induces exocytosis in WT parasites, also stimulated exocytosis in PbACα- sporozoites (Fig. 7B). This result indicates that all sporozoite components required for exocytosis downstream of cAMP are functional in PbACα- sporozoites; however, the lack of ACα inhibits proper response upon activation with uracil derivatives or activators of AC activity. Migration through host cells induces apical regulated exocytosis in Plasmodium sporozoites [9]. To confirm that ACα is also required for exocytosis stimulated by migration through hepatocytes, we measured the response of WT and PbACα- sporozoites after migration through Hepa1-6 cells. We found that regulated exocytosis was not activated in sporozoites deficient in ACα (Fig. 7C).
To examine the role of apical regulated exocytosis and ACα in sporozoite infection, we first analyzed the infectivity of PbACα- sporozoites in vitro using Hepa1-6 cells. We found that PbACα- sporozoites are approximately 50% less infective than WT sporozoites (Fig. 7D). As the infectivity of Plasmodium sporozoites can be noticeably different depending on each particular mosquito infection, we repeated the experiment using sporozoites from three different batches of infected mosquitoes. Similar results were found, confirming that PbACα- sporozoites have reduced infectivity in hepatocytes (not shown).
We also tested the infectivity of PbACα- parasites in vivo in C57/Bl6 mice, which are highly susceptible to infection by P. berghei sporozoites [30]. To quantify the infectivity of PbACα-, we used real time PCR to measure parasite load in the liver by determining the levels of the parasite-specific 18 S rRNA [31]. Remarkably, 50% decrease of parasite rRNA was detected by this method (Fig. 7E). We repeated the experiment using sporozoites from three different batches of infected mosquitoes finding similar results (not shown). These results suggest that Plasmodium sporozoites use apical regulated exocytosis to infect host cells and that ACα is an important protein involved in Plasmodium liver infection.
To confirm that the phenotype observed in the PbACα- sporozoites is caused specifically by depletion of the PbACα gene, we complemented one of the PbACα- parasite lines with ACα. The correct replacement event was confirmed by PCR and Southern blot hybridization (Fig. 8A). No differences were found between the complemented parasite line and WT or PbACα- parasites during blood stage infection in mice or in mosquito oocyst development and salivary gland sporozoite numbers (not shown). We found that apical regulated exocytosis response to uracil derivatives was recovered in the complemented sporozoites (Fig. 8B). The infectivity of sporozoites was restored by complementation of the PbACα gene (Fig. 8C), confirming the role of PbACα in sporozoite exocytosis and infection.
The role of exocytosis of apical organelles in invasion of host cells has been extensively studied in Toxoplasma tachyzoites. Our knowledge of Plasmodium sporozoite exocytosis and infection is less advanced, as this parasite stage can only be obtained by dissection of infected mosquitoes, and this procedure provides limited numbers of sporozoites. Sporozoite purification methods have been recently developed (S. L. Hoffman, personal communication) allowing us to use highly purified P. falciparum sporozoites in our studies. Gene deletion technology has opened the possibility of dissecting the role of complex pathways into their individual protein components. Using a rodent malaria model we have first identified that the cAMP signaling pathway is involved in Plasmodium sporozoite exocytosis. The similar response observed in P. falciparum sporozoites suggests that the cAMP-dependent signaling pathway leading to exocytosis is conserved in the human parasite. Based on these results, we have generated a transgenic parasite that is deficient in an essential protein in the cAMP signaling pathway. This approach allowed us to evaluate the role of apical regulated exocytosis in hepatocyte infection by sporozoites in vitro and in vivo using a mouse model.
Regulated exocytosis in mammalian cells is frequently triggered by an elevation of intracellular Ca2+ levels and is modulated by cAMP, which acts synergistically with Ca2+, but cannot induce exocytosis by itself. However, in some specific cell types exocytosis is triggered solely by elevations in cAMP concentrations [32]. Increases in cytosolic Ca2+ induced with ionophores can induce exocytosis in Plasmodium sporozoites [9], suggesting that Ca2+ stimulation is also sufficient to induce this process. The signaling pathways of Ca2+ and cAMP are interrelated inside eukaryotic cells [33]. In particular, in P. falciparum blood-stages, a cross-talk between Ca2+ and cAMP has been observed, where increases in cAMP induce the elevation of intracellular Ca2+ concentrations through the activation of PKA [18]. Our results suggest that the cAMP and Ca2+ pathways are also interconnected in the sporozoite stage and that intracellular, but not extracellular Ca2+, is required for exocytosis.
When exocytosis is inhibited by the AC or the PKA inhibitors, the reduction in sporozoite infectivity is comparatively lower than the reduction in exocytosis. Similar results were obtained with the PbACα- sporozoites, where exocytosis is reduced to background levels, but infection is reduced by 50%. Taken together these results suggest that sporozoites have alternative pathways to invade host hepatocytes that do not require apical regulated exocytosis. However, we cannot exclude the possibility that low levels of exocytosis that cannot be detected in our assays still occur in the PbACα- sporozoites and are sufficient to mediate infection of hepatocytes.
The analysis of host cell molecules required for sporozoite infection has provided evidence that sporozoites use more than one unique pathway to achieve hepatocyte infection [34], suggesting that sporozoites may take advantage of this phenomenon to overcome polymorphisms in host receptors or to escape from immune mechanisms inhibiting one particular pathway of infection.
We had previously observed that activation of sporozoite exocytosis increases their infectivity and reduces the need for migration through cells [9]. Here we confirmed that activation of exocytosis by cAMP-mediated pathways increases exocytosis infectivity reducing migration through cells. Accordingly, inhibitors of this pathway inhibit sporozoite exocytosis and decrease their infectivity. Interestingly, spect-deficient sporozoites, which do not migrate through host cells [15], responded to uracil derivatives but were not able to respond to either an activator of AC or to a permeant analogue of cAMP, suggesting that cAMP-induced signaling leading to exocytosis is different in these mutant sporozoites. The positive exocytosis response observed in the presence of the inhibitor of AC, suggests that these parasites are able to respond to uracil derivatives by activating cAMP-independent pathways that are not normally activated in wt sporozoites, where cAMP is required for exocytosis. It is still not clear how this relates to their impaired capacity to migrate through cells, but suggests that they may up-regulate the alternative mechanisms that are independent of migration through cells and exocytosis to infect hepatocytes. These results are consistent with the concept that sporozoites can use alternative pathways to invade hepatocytes, as the infection experiments with PbACα- sporozoites suggest.
Apical regulated exocytosis in the transgenic parasites deficient in ACα is dramatically decreased in response to uracil derivatives or migration through host cells, indicating that ACα is necessary to induce high levels of exocytosis and confirming the essential role of the cAMP signaling pathway in this process. Complementation of the genetically deficient parasites with the ACα gene confirms that the defect in exocytosis and infection observed in PbACα- sporozoites is caused by deletion of the ACα gene and not by other modifications resulting from the genetic manipulations of these parasites.
Two genes with high homology to ACs have been identified in the Plasmodium genome: ACα and ACβ [25]. ACα activity as an AC has been demonstrated for P. falciparum, where the catalytic domain was expressed independently [22]. A second putative AC gene, called ACβ, has been identified in the Plasmodium database. We tried to generate ACβ-deficient parasites; however the ACβ gene seems to be essential for the asexual blood-stages of Plasmodium.
ACα- sporozoites are able to stimulate exocytosis in response to the permeant analogue of cAMP, but not to forskolin, the activator of ACs, confirming that the defect is caused by the lack of a functional AC and can be compensated by artificially increasing intracellular concentrations of cAMP. The results obtained with PbACα- sporozoites also suggest that ACα is sensitive to forskolin stimulation, as the increase in exocytosis induced by this drug is lost in the genetically deficient sporozoites. Since AC activity is insensitive to forskolin in asexual blood-stages [35] and ACβ is preferentially expressed in this stage of the parasite cycle [25], it seems likely that ACβ, rather than ACα, is required for cAMP formation during erythrocyte infection. We also found that the growth of PbACα- parasites in the asexual blood-stages was indistinguishable from control, consistent with the lack of activity of ACα during this stage.
Interestingly, the ACα gene contains a N-terminal domain with high homology to voltage-gated K+ channels. Other apicomplexans and also the ciliates Paramecium and Tetrahymena have an ACα gene homologous to the one in Plasmodium [23]. In Paramecium it has been demonstrated that the purified ACα protein also has K+ channel activity, and the generation of cAMP is regulated by K+ conductance [24]. Although functional K+ channel activity has not been demonstrated for ACα in Plasmodium, our results are consistent with a role for K+ conductance in sporozoite exocytosis. Uracil derivates do not induce exocytosis in K+ free medium, but activation of AC with forskolin or addition of the permeant analogue of cAMP overcomes the requirement for extracellular K+. Therefore, it seems likely that increased K+ permeability may induce activation of ACα and synthesis of cAMP.
Hepa 1-6 (ATCC CRL-1830), a hepatoma cell line derived from a C57L/J mouse, which is efficiently infected by rodent malaria parasites [36] was used for in vitro hepatocyte infections. Plasmodium yoelii yoelii sporozoites (cell line 17× NL), P. berghei ANKA wt and spect-1 deficient sporozoites [15] and the NF54 isolate [37] of P. falciparum were used to produce sporozoites in A. stephensi mosquitoes. Salivary glands were dissected from the mosquitoes. The P. falciparum sporozoites were extracted from the salivary glands, purified, and cryopreserved. Prior to being used in assays, the sporozoites were thawed and suspended in RPMI medium.
Exocytosis was induced by incubation of sporozoites with a mixture of the physiological concentrations of uracil derivatives (ICN Biomedicals) consisting of 180 µM uracil, 280 µM uridine, 300 µM uracil monophosphate (UMP), 50 µM uracil diphosphate (UDP) and 30 µM uracil triphosphate (UTP) was prepared in RPMI 1640 and pH adjusted to 7.
Sporozoites (105 P. yoelii, P. berghei or 5 × 104 P. falciparum) were centrifuged for 5 min at 1800 × g on glass coverslips before addition of uracil derivatives or conditioned medium. After incubation at 37°C for 1 h, sporozoites were fixed with 1% paraformaldehyde for 10 min (non-permeabilizing conditions) before staining for surface TRAP/SSP2 with the monoclonal antibody (F3B5) for P. yoelii, PfSSP2.1 for P. falciparum [38] and a specific TRAP/SSP2 rabbit anti-serum for P. berghei. Sporozoite regulated exocytosis was quantified as the percentage of total sporozoites that present a TRAP/SSP2 stained ‘cap’ in their apical end. Results are expressed as the average of triplicate determinations counting at least 50 sporozoites for each condition. Background level exocytosis was measured by staining sporozoites after dissection from mosquitoes, before incubation in vitro. Background exocytosis was always lower than 8% and was subtracted from all values. All experiments were performed twice showing similar results.
4 × 105 P. yoelii sporozoites were incubated alone or with the different exocytosis stimuli for 1 h at 37°C before spinning at 20,000 g for 10 min. The supernatants were collected and separated in a 7.5% gel in reducing conditions. After semi-dry transfer to a PDVF membrane, proteins were stained with anti-P. yoelii MTIP antiserum followed by anti-rabbit conjugated to horseradish peroxidase. Bound antibodies were detected by chemiluminescence using ECL (GE Healthcare Bio-Sciences).
Sporozoites (105) were incubated with 100 µM forskolin, 100 µM MDL-12.330A, 500 µM 8Br-cAMP, 10 µM H89, 30 µM genistein, 100 nM charybdotoxin, 50 µM SQ22536, 50 µM 2′, 5′-Dideoxyadenosine, 5 µM Adenosine 3′, 5′-cyclic monophosphorothioate 8Br-Rp-isomer, 1 nM margatoxin, 20 µM BAPTA, ionomycin 1 µM (all from Calbiochem) before addition or not of uracil derivatives for 1 h, followed by fixation and quantification of exocytosis. For exocytosis assays sporozoites were pretreated with the drug for 15 min and concentrations were kept constant throughout the experiment. For infection and migration, treatment with drugs was performed for 15 min before washing and spinning sporozoites on Hepa1-6 cells grown on coverslips placed in 24-well dishes containing 1 ml of culture medium/well. For assays in K+-free medium: 105 P. yoelii sporozoites were incubated for 45 min in regular medium (RPMI 1640, that contains 5.3 mM KCl and 100 mM NaCl), K+-free medium (modified RPMI 1640 with no KCl and 110 mM NaCl to maintain osmolarity) in the presence or absence of stimulus, before fixation and quantification of exocytosis. To assay sporozoites viability after incubation in K+-free medium, sporozoites centrifuged at 20,800 g and resuspended in regular medium with uracil derivatives to induce exocytosis. All experiments were performed twice showing similar results.
Intracellular levels of cAMP in P. yoelii sporozoites were determined using a cAMP Biotrack Enzymeimmunoassay system from Amersham Bioscience. For each sample 2 × 106 P. yoelii sporozoites were incubated with uracil derivatives for 45 min at 37°C. The experiment was performed twice showing similar results.
Sporozoites (105 sporozoites/coverslip) were added to monolayers of 2 × 105 Hepa1-6 cells for 1 h in the presence of 1 mg/ml of rhodamine-dextran lysine fixable, 10,000 MW. Sporozoites breach the plasma membrane of host cells during migration and as a result fluorescent dextran enters in their cytosol, allowing detection of wounded cells [17]. Cells were washed and incubated for another 24 hours before fixation and staining of infected cells with the mAb (2E6) recognizing HSP70 to detect infected cells [39], followed by anti-mouse IgG-FITC antibodies. Migration through host cells is quantified as percentage (or total number) of dextran-positive cells. Infection was quantified as the number of infected cells per coverslip or per 50 microscopic fields. For transwell filter assays Hepa1-6 cells (5×105) were cultivated on 3 µm pore diameter Transwell filters (Costar, Corning, New York) until they form a continuous monolayer. Empty coverslips were placed underneath the filters. P. berghei sporozoites (2×105) were added to filter insets containing Hepa1-6 cells. Coverslips were fixed after 2 h of incubation with sporozoites, before staining for surface TRAP/SSP2. All experiments were performed twice showing similar results.
P. yoelii sporozoites were incubated with the indicated drugs for 20 min before addition of propidium iodide (1 µg/ml) for 10 min. Sporozoites were washed and observed directly with a fluorescence microscope. Propidium iodide positive sporozoites were considered dead and quantified. At least 100 sporozoites were counted in each condition.
Live P. yoelii sporozoites were observed directly under the microscope in a heated stage at 37°C before or after addition of different stimuli. As control, the same volume of medium with the same solvent used for the stimuli was added. At least one hundred sporozoites were counted in each condition and they were classified as immobile, twisting or gliding, depending on their type of motility observed.
To disrupt the ACα locus an ACα replacement vector was constructed in vector b3D.DT.̂H.̂Db (pL0001, MRA-770) containing the pyrimethamine-resistant Toxoplasma gondii (tg) dhfr/ts gene. To complement ACα into the genome of PbACα- parasites, a vector was constructed with the human (h) dhfr selectable marker and two fragments of 4.3kb (5′) and 0.5 kb (3′) of the ACα gene of P. berghei. The linearized vector can integrate in ACα. Further details are described in Fig. 5. P. berghei-ANKA (clone 15cy1) was used to generate PbACα-parasites. Transfection, selection, and cloning of PbACα- parasites was performed as described [40]. Two clones (C1 and C2) were selected for further analysis. PbACα- C1 parasites were transfected with the complement vector to create ACα- complement. Selection of transformed parasites was performed by treating infected animals with WR99210 (20 mg/kg bodyweight) as has been described [41]. One parasite clone (Cmp) in which the ACα gene was integrated into the ACα locus was selected for further analysis. Correct integration of constructs into the genome of transformed parasites was analyzed by RT-PCR and Southern analysis of restricted DNA. PCR on DNA of WT and ACα− parasites was performed by using primers specific for the WT 5′ (flG1F 5′-AGCGCATTAGTTTATGATTTTTG-3′ and flG1R 5′-TTGTGAATTAGGGATCTTCATGTC-3′; amplifying a fragment of 0.7 kb) and WT 3′ (flG2F 5′-ATGCGCAAACCCGTTAAAT-3′ and flG2R 5′-TTTGATTCATTCCACTTTCCA-3′; amplifying fragment of 0.7 kb) and disrupted 5′ (flG1F and Pb103 5′-TAATTATATGTTATTTTATTTCCAC-3′; amplifying a fragment of 0.8 kb) and disrupted 3′ (flG2R and Pb106a 5′-TGCATGCACATGCATGTAAATAGC-3′; amplifying fragment of 0.9 kb) locus. PCR on DNA of complement was performed by using primers specific for INT3′ (Pb106a and flG4R 5′-GCAGAGAGAGCGTTAAAAACTATTG-3′, amplifying a fragment of 1.0 kb). RT-PCR was performed on RNA isolated from WT sporozoites. Primers 02-F (5′-AGGGTGACATTGAAGGGATG-3′) and 02-R (5′-ATTCCTCGGGATATTCCACC-3′) were used to amplify cDNA or genomic DNA derived from the PbACα gene, amplifying a fragment of 270 bp and 658 bp, respectively.
Genomic DNA of P. berghei (2 µg) was digested with HincII / EcoRI or NheI / EcoRI, separated on 0.9% agarose gel and then transferred onto a nylon membrane. DNA probe was labeled with digoxigenin using the DIG PCR labeling kit (Roche Diagnostics) using genomic DNA as template with the following primer pair, 5′-TCCTTCGTGGAATTTACACTTG-3′ and 5′-CCAGACGAGGAACTAATGCAG-3′. Signals were detected using the DIG/CPSD system (Roche Diagnostics).
Parasitemia in mice was determined by examination of a Giemsa-stained blood smear. Oocyst formation and sporozoite development were quantified in infected Anopheles stephensi mosquitoes as described [42]. The number of salivary gland sporozoites per mosquito was determined by dissecting salivary glands from 10 infected mosquitoes in each condition [43]. Blood stage infections were studied in mice (male Swiss Webster or C57/Bl6 mice, 20–25 g) infected with 200 µl of blood at 0.5% parasitemia. Experiment was performed twice showing similar results.
Gliding motility of sporozoites was analyzed by counting the average number of circles performed by single sporozoites [44]. Sporozoites (2 × 104) were centrifuged for 10 min at 1,800 × g onto glass coverslips previously coated with anti-CS 3D11 antibody, followed by incubation for 2 h at 37°C and staining with biotin-labeled 3D11 antibody followed by incubation with avidin-FITC for sporozoite and trail visualization. Quantification was performed by counting the number of circles performed by 100 sporozoites in three independent coverslips. When indicated 3% mouse albumin was present in the assay.
Hepa1-6 cells were cultivated on 3 µm pore diameter Transwell filters (Costar, Corning, New York) until they form a continuous monolayer. Empty coverslips were placed underneath the filters. Sporozoites (2×105) were added to filter insets containing Hepa1-6 cells or no cells. Coverslips were fixed after 2 h of incubation with sporozoites, before staining for surface TRAP to determine exocytosis. Experiment was performed twice showing similar results.
Groups of three C57/Bl6 mice were given i.v. injections of 20,000 sporozoites. 40 h later, livers were harvested, total RNA was isolated, and malaria infection was quantified using reverse transcription followed by real-time PCR [31] using primers that recognize P. berghei–specific sequences within the 18S rRNA 5′-AAGCATTAAATAAAGCGAATACATCCTTAC and 5′-GGAGATTGGTTTTGACGTTTATGT. Experiment was performed three times showing similar results.
P. falciparum ACα: UniProtKB/TrEMBL accession number: Q8I7A1. PlasmoDB identifier: PF14_0043
P. berghei ACα: PlasmoDB identifier: PB001333.02.0. Complete PbACα sequences (contig 1047, 5680) were retrieved from Sanger sequencing genomics project. P. falciparum PKA: PlasmoDB identifier PFI1685w. |
10.1371/journal.pgen.1002120 | Independent Chromatin Binding of ARGONAUTE4 and SPT5L/KTF1 Mediates Transcriptional Gene Silencing | Eukaryotic genomes contain significant amounts of transposons and repetitive DNA elements, which, if transcribed, can be detrimental to the organism. Expression of these elements is suppressed by establishment of repressive chromatin modifications. In Arabidopsis thaliana, they are silenced by the siRNA–mediated transcriptional gene silencing pathway where long non-coding RNAs (lncRNAs) produced by RNA Polymerase V (Pol V) guide ARGONAUTE4 (AGO4) to chromatin and attract enzymes that establish repressive chromatin modifications. It is unknown how chromatin modifying enzymes are recruited to chromatin. We show through chromatin immunoprecipitation (ChIP) that SPT5L/KTF1, a silencing factor and a homolog of SPT5 elongation factors, binds chromatin at loci subject to transcriptional silencing. Chromatin binding of SPT5L/KTF1 occurs downstream of RNA Polymerase V, but independently from the presence of 24-nt siRNA. We also show that SPT5L/KTF1 and AGO4 are recruited to chromatin in parallel and independently of each other. As shown using methylation-sensitive restriction enzymes, binding of both AGO4 and SPT5L/KTF1 is required for DNA methylation and repressive histone modifications of several loci. We propose that the coordinate binding of SPT5L and AGO4 creates a platform for direct or indirect recruitment of chromatin modifying enzymes.
| Transposons and other repetitive elements occupy vast areas of the eukaryotic genomes. They pose a threat to genome integrity but at the same time regulate expression of many genes and have been proposed to be a major factor contributing to genome evolution. One of the processes responsible for controlling activity of transposons and other repetitive elements is transcriptional gene silencing. This process uses small interfering RNA and long non-coding RNA to recruit enzymes that establish repressive chromatin modifications. Several proteins have been identified to be needed for siRNA–mediated transcriptional silencing in Arabidopsis thaliana, however for many of them their position in the silencing pathway is unknown. One of those proteins is SPT5L/KTF1, a homolog of an elongation factor associated with RNA Polymerase II. Here we establish the position of SPT5L in the silencing pathway and propose the molecular mechanism of its function. This gives further knowledge of the mechanism of transcriptional gene silencing and is important to understand how transposons are controlled.
| Eukaryotic genomes contain significant amounts of transposons and other repetitive DNA elements, which usually remain transcriptionally inactive. Efficient silencing of transposon transcription is essential for preventing their mobility and for maintaining genome integrity [1]. Transposon silencing has also been hypothesized to regulate expression of genes that contain transposable elements in their promoters and to facilitate the evolution of genomes [2].
Transposons are silenced at both transcriptional and post-transcriptional levels by mechanisms that involve small interfering RNAs (siRNAs) [3]. These 20–25-nt RNA molecules are generated by the RNase III enzyme Dicer and provide sequence specificity for effector complexes mediating RNA cleavage and/or the establishment of chromatin modifications that silence transcriptional activity [3]. In Arabidopsis thaliana, single-stranded RNA precursors for siRNA biogenesis are produced by RNA Polymerase II (Pol II) or RNA Polymerase IV (Pol IV), while the second strand is synthesized by RDR2 (RNA-Dependent RNA Polymerase 2). DCL3 (Dicer-like 3) cleaves double-stranded RNA into siRNAs that are then incorporated into ARGONAUTE4 (AGO4) [4], [5]. This mechanism seems to be similar in maize where homologs of RDR2 and Pol IV have been shown to be involved in transcriptional gene silencing [6]–[8].
Recognition of target loci by AGO4-siRNA complexes requires sequence identity between siRNAs and the genomic loci. These loci, however, are often actively transcribed, and it is not clear if siRNAs base-pair interact with DNA or nascent RNA transcripts [3], [9]. The latter possibility is well supported in Schizosaccharomyces pombe where loci subject to siRNA-mediated transcriptional silencing are actively transcribed by RNA Polymerase II [10]–[12]. The central role of nascent transcripts in recognition of siRNA targets in S. pombe was observed by the ability of Argonaute proteins to cleave RNA. This ability is required for the establishment of repressive chromatin modifications [13]. Moreover, tethering Argonaute and siRNA-containing RITS (RNA-induced initiation of transcriptional gene silencing) complex to nascent transcripts is sufficient for the initiation of repressive chromatin modifications and transcriptional silencing [14].
This mechanism may be similar in Arabidopsis where transcriptional silencing requires a specialized RNA Polymerase complex known as RNA Polymerase V (Pol V) [15]–[17]. Pol V produces non-coding transcripts in otherwise silent chromatin, and its activity is required for the establishment and maintenance of repressive chromatin modifications [18]. Pol V-produced non-coding transcripts physically interact with AGO4 and recruit siRNA-AGO4 complexes to their targets [19]. Additionally, transcriptional silencing of several loci needs AGO4 slicer activity [20], suggesting that in plants siRNAs may recognize their targets by base-pair interactions with Pol V transcripts [19].
RNA Polymerases and AGO4 are assisted in their functions by several other known protein components of the plant silencing system, all of which are required for efficient establishment and maintenance of transcriptional silencing [5]. One of them is SPT5L (Suppressor of Ty insertion 5 - like; also known as SPT5-like or KTF1), a homolog of SPT5 Pol II-associated elongation factor. It was shown to contain a domain rich in WG/GW repeats that facilitate physical interaction with AGO4 [21]–[23]. Because SPT5L interacts with RNA but is not required for the accumulation of Pol V-dependent transcripts, it was hypothesized to work downstream of Pol V and recruit AGO4 to Pol V-transcribed loci [22], [24].
Despite the recent progress in understanding the mechanisms of transcriptional gene silencing, it is not known how siRNAs work with Pol V transcripts, AGO4 and other proteins to recruit chromatin modifying enzymes to their target loci in chromatin. It is unknown how chromatin-bound AGO4 recruits enzymes that establish repressive chromatin modifications. It is unknown if other protein components of the silencing system help AGO4 recruit chromatin modifying enzymes. It is also unknown in what order proteins involved in silencing are recruited to chromatin. Here we try to resolve the mechanism of siRNA-mediated recruitment of chromatin modifying enzymes to chromatin and the function of SPT5L in this process. We show that SPT5L physically interacts with chromatin and that SPT5L works downstream of Pol V but does not require 24-nt siRNA. SPT5L and AGO4 are recruited to chromatin in parallel and at least partially independently of each other and both are needed for DNA methylation and repressive histone modifications at several loci. We propose that the coordinate binding of SPT5L and AGO4 creates a platform for direct or indirect recruitment of chromatin modifying enzymes.
The interaction of SPT5L with AGO4 [21], [22] suggested that like AGO4 [19], SPT5L may bind loci targeted by siRNA-mediated transcriptional gene silencing. We first used chromatin immunoprecipitation (ChIP) with anti-SPT5L antibody to test if SPT5L binds chromatin. Subsequent real-time PCR demonstrated recovery of IGN5 and solo LTR DNA from Col-0 wild type at much higher levels than from spt5l mutant which represents the background level (Figure 1C, 1D). This shows that SPT5L physically interacts with IGN5 and solo LTR loci which are known to be transcribed by Pol V and silenced by the siRNA-mediated transcriptional gene silencing pathway [18], [19], [25], [26]. There was, however, no enrichment on the control Actin 2 and Tubulin 8 (TUB 8) loci (Figure 1A, 1B), which are transcribed by Pol II and not occupied by components of the silencing pathway [18], [27]. This suggests that SPT5L is present at the loci undergoing transcriptional silencing and that its function in silencing is most likely direct.
Interaction of SPT5L with chromatin was also demonstrated at IGN20, IGN22, IGN23, IGN25 and IGN26 (Figure 1E–1I), which have been identified in a genome-wide screen of Pol V occupancy (A. Wierzbicki, R. Lister, B. Gregory, J. Ecker and C. Pikaard, unpublished data), suggesting that SPT5L binding may be a general feature of Pol V-transcribed loci.
SPT5L interacts with chromatin (Figure 1C–1I) as well as with AGO4, Pol V complex and Pol V transcripts [21]–[23]. SPT5L is also not required for the accumulation of Pol V-dependent transcripts at IGN5, IGN6 or AtSN1 [22]. This suggests that SPT5L should work downstream of Pol V. To test this prediction we assayed Pol V binding to chromatin by ChIP with antibody against NRPE1, the largest subunit of Pol V. Subsequent real-time PCR demonstrated recovery of DNA from Col-0 wild type at much higher level than from the nrpe1 mutant at IGN5, solo LTR and AtSN1 loci but not at Actin 2 or Tubulin 8 loci (Figure 2A–2E) demonstrating that Pol V binds chromatin at IGN5, solo LTR and AtSN1 loci. DNA recovery from spt5l mutant was comparable to Col-0 wild type (Figure 2A–2E) showing that SPT5L is not needed for Pol V binding to chromatin. Interestingly, Pol V binding to chromatin was reproducibly increased at solo LTR locus in ago4 mutant (Figure 2C), indicating that AGO4 may inhibit Pol V binding to chromatin possibly by affecting initiation and/or elongation of Pol V transcription. We conclude that SPT5L does not work upstream of Pol V in siRNA-mediated transcriptional gene silencing pathway.
Because both Pol V and SPT5L are required for DNA methylation at several silenced loci [21]–[23], SPT5L may be functionally dependent on Pol V and/or Pol V transcription. To test this possibility we performed western blot with anti-SPT5L antibody in nrpe1 mutant background. Accumulation of SPT5L was strongly reduced in the nrpe1 mutant (Figure 1J). To test if nrpe1 mutation affects accumulation of SPT5L mRNA or SPT5L protein stability, we assayed SPT5L RNA using real time RT-PCR. Accumulation of SPT5L RNA was not reduced in the nrpe1 mutant (Figure 1K) indicating that Pol V is needed for SPT5L protein stability. This behavior of SPT5L in nrpe1 mutant is reminiscent of reduced AGO4 protein stability in mutants that reduce siRNA production [28]. Interestingly, we observed a slight increase in SPT5L RNA level in the nrpe1 mutant which may be explained by the presence of an AtMU10 transposon in SPT5L coding region. Overall, these results suggest that SPT5L is functionally dependent on Pol V.
We further tested the functional relationship between Pol V and SPT5L by performing ChIP with anti-SPT5L antibody in nrpe1 mutant background. Consistent with the reduced stability of SPT5L in nrpe1 (Figure 1J), DNA recovery from Pol V-transcribed loci was reduced to the level observed in the spt5l mutant (Figure 1C–1I). This result may be explained by the overall reduction in the amount of SPT5L. However, a similar reduction in the SPT5L protein accumulation in rdr2 mutant (Figure 1J) did not affect SPT5L binding to chromatin (see below). This suggests that nrpe1 may affect the ChIP signal not only by destabilizing SPT5L, but also by affecting its ability to bind chromatin. Because SPT5L does not work upstream of Pol V and is functionally dependent on Pol V, we conclude that SPT5L works downstream of Pol V and/or Pol V transcription and may be recruited to chromatin by Pol V.
The recruitment of SPT5L to chromatin by Pol V (Figure 1, Figure 2) is consistent with the interaction of SPT5L with Pol V transcripts and AGO4 [21], [22]. There are at least two explanations of SPT5L function in the establishment of siRNA-mediated transcriptional gene silencing. SPT5L may be recruited by Pol V and then help recruit AGO4-siRNA complexes. Alternatively, AGO4-siRNA may recognize target loci and then recruit SPT5L which further recruits chromatin modifying enzymes. To test the latter possibility we performed ChIP with αSPT5L antibody in the ago4 mutant. DNA recovery of all tested Pol V-transcribed loci was comparable from Col-0 wild type and the ago4 mutant (Figure 1C–1I). This shows that binding of SPT5L to chromatin was not affected in the ago4 mutant, and suggests that SPT5L is not recruited to its target loci by AGO4-siRNA complexes. We conclude that SPT5L does not work downstream of AGO4 in the siRNA-mediated transcriptional gene silencing pathway.
Having concluded that SPT5L does not work downstream of AGO4, we tested the alternative hypothesis that SPT5L may work upstream of AGO4 by binding Pol V and/or Pol V transcripts and recruiting AGO4 to chromatin. To test this possibility we performed ChIP with anti-AGO4 antibody. As demonstrated by real-time PCR we recovered DNA from wild type plants above the background level observed in the ago4 mutant at IGN5 and solo LTR (Figure 3C, 3D) as well as at IGN20, IGN22, IGN23, IGN25 and IGN26 loci (Figure 3E–3I). This indicates that AGO4 binds chromatin at all tested Pol V-transcribed loci. In the spt5l mutant total accumulation of AGO4 protein was not affected (Figure 3J). At all assayed Pol V-transcribed loci AGO4 binding to chromatin in the spt5l mutant was reproducibly above the background level observed in the ago4 mutant indicating that AGO4 is able to bind chromatin in the absence of SPT5L (Figure 3C–3I). Interestingly, we observed that the intensity of AGO4 binding to chromatin is slightly reduced in the spt5l mutant at solo LTR, IGN20, IGN22, IGN23, IGN25 and IGN26 (Figure 3C–3I). This indicates that although SPT5L is not required for AGO4 recruitment to chromatin, it enhances AGO4 chromatin binding. Alternatively, most loci may be occupied by two pools of AGO4. One being SPT5L-dependent and other recruited to chromatin independently of SPT5L.
We conclude that SPT5L is not required for recruitment of a pool of AGO4 to specific loci in chromatin and therefore does not work upstream of AGO4 in the siRNA-mediated transcriptional gene silencing pathway. Since SPT5L also does not work downstream of AGO4, they are most likely recruited in parallel and at least partially independently of each other.
The parallel and independent recruitment of SPT5L and AGO4 to chromatin suggests that they are both guided by the interactions with Pol V complex and/or Pol V transcripts. To test if SPT5L is also guided by siRNA we used ChIP to assay SPT5L binding to chromatin in rdr2, a mutant in an RNA-dependent RNA polymerase responsible for production of the majority of 24-nt siRNA [29]. The rdr2 mutation reduced the stability of SPT5L protein (Figure 1J, 1K) but did not cause reduction in DNA recovery of the tested loci after ChIP (Figure 4C–4I). This suggests that although RDR2 increases the amount of SPT5L protein, the chromatin-bound fraction of SPT5L is not affected by the rdr2 mutation. This also suggests the presence of siRNA-dependent pool of SPT5L that does not physically interact with assayed Pol V-transcribed loci.
These results demonstrate that binding of SPT5L to chromatin is not affected in the rdr2 mutant and suggest that RDR2-dependent siRNA is not required for SPT5L binding to chromatin. In contrast, RDR2 is necessary for proper establishment of DNA methylation at AtSN1, IGN5, IGN25, IGN23, IGN26, solo LTR and IGN22 (Figure 4J); demonstrating that all assayed loci are in fact targets of the siRNA-mediated transcriptional gene silencing pathway. We conclude that SPT5L is recruited to chromatin in a manner independent of 24-nt siRNA.
Parallel and at least partially independent recruitment of SPT5L and AGO4 by Pol V suggests that at Pol V-transcribed loci none of them is sufficient for the establishment and maintenance of silent chromatin modifications. To further test this possibility we assayed several Pol V-transcribed loci for DNA methylation side-by-side in nrpe1, ago4 and spt5l mutants using DNA methylation-sensitive restriction endonucleases. Methylation of cytosines in HaeIII, AluI or AvaII restriction sites blocks the enzymes from cutting and allows amplification of the genomic region by PCR. However, unmethylated sites are cleaved and PCR amplification fails. All three enzymes recognize asymmetric (CHH) methylation at tested loci. Consistently with previous reports, DNA methylation was strongly reduced at AtSN1 locus in both ago4 [19], [24], [30] and spt5l mutants [21]–[23] and at IGN5 locus in ago4 mutant [19] (Figure 5A). DNA methylation was also reduced at IGN5 locus in spt5l mutant and at IGN23, IGN25 and IGN26 loci in both ago4 and spt5l mutants (Figure 5A, 5B). Importantly, in all these cases reduction of DNA methylation was comparable in ago4 and spt5l mutants (Figure 5A, 5B) suggesting that neither AGO4 nor SPT5L is sufficient for the establishment of asymmetric DNA methylation at Pol V-transcribed loci.
We also tested the effect of nrpe1, ago4 and spt5l mutations on dimethylation of lysine 9 of histone H3 (H3K9me2). At IGN5 and IGN26 loci, H3K9me2 was reduced in all three mutants (Figure 5D, 5F) showing that both AGO4 and SPT5L are required not only for the establishment and/or maintenance of DNA methylation but also H3K9me2. We conclude that at least at a subset of loci SPT5L and AGO4 work together to recruit repressive chromatin modifications. We propose that it is the coordinate action of SPT5L and AGO4 that directly or indirectly recruits de novo DNA methyltransferase DRM2 and H3K9 methyltransferases.
While AtSN1, IGN5, IGN23, IGN25 and IGN26 loci require both AGO4 and SPT5L for repressive chromatin modifications (Figure 5), soloLTR has been shown to be methylated independently of SPT5L [21], [23]. We confirm this result and further show that solo LTR and IGN22 which, like other Pol V-transcribed loci, are methylated in a Pol V and AGO4-dependent manner (Figure 6A) did not show reduction of DNA methylation on AluI or AvaII sites in the spt5l mutant (Figure 6A). This suggests that there is some significant locus specificity in SPT5L contributions to DNA methylation. Furthermore, H3K9me2 was reduced at both soloLTR and IGN22 in nrpe1 and ago4 mutants but not in the spt5l mutant (Figure 6B, 6C). Also acetylation of histone H3 (H3Ac) at solo LTR was increased in nrpe1 and ago4 but not in spt5l (Figure 6D). This demonstrates that the locus-specific function of SPT5L affects not only DNA methylation but also H3K9me2 and H3Ac.
The requirement of SPT5L for repressive chromatin modifications (Figure 5, Figure 6) does not correlate with the extent of partial SPT5L-dependency of AGO4 binding to chromatin (Figure 3). It suggests that the pool of AGO4 that is bound to chromatin in an SPT5L-dependent manner is not required for silencing. This is consistent with our interpretation that AGO4 and SPT5L are recruited to chromatin in parallel and independently of each other.
Our findings establish the order of events leading to siRNA-mediated establishment of transcriptional silencing. This process is initiated by recognition of silencing targets and production of two classes of non-coding RNA. The first class is siRNA which is produced from double-stranded RDR2 products by DCL3 and becomes incorporated into AGO4 and possibly also AGO6 and AGO9 [4], [5], [24]. The second class is long non-coding RNA produced by Pol V and/or Pol II [18], [26]. Pol V transcription is initiated independently of siRNA and Pol V transcripts most likely are not precursors for siRNA biogenesis [18], [31]. Pol V recruitment to chromatin and transcription requires the presence of DMS3, DRD1 and RDM1, which either help initiate Pol V transcription or assist elongation of Pol V transcripts [18], [19], [32].
Pol V transcription is followed by association of two RNA-binding proteins with chromatin (Figure 7). First is AGO4 which is recruited to chromatin by Pol V transcripts and uses the incorporated siRNA to provide sequence-specificity of silencing [19]. The second is SPT5L (Figure 1, Figure 2), which is recruited to chromatin by an unknown mechanism, possibly involving interactions between SPT5L and Pol V complex and/or with Pol V transcripts [22], [23]. SPT5L binds chromatin independently of 24-nt siRNA (Figure 4) and is likely a general factor associated with transcribing Pol V and its transcripts [21]–[23]. Since SPT5L binds chromatin in the absence of AGO4 (Figure 1), and the functional pool of AGO4 is able to bind chromatin in the absence of SPT5L (Figure 3), we concluded that they are recruited to chromatin in parallel and independently of each other. Both AGO4 and SPT5L are required for the establishment and/or maintenance of DNA methylation and repressive histone modifications at the majority of tested loci (Figure 5). This suggests that both are needed for the recruitment of enzymes establishing repressive chromatin modifications.
Because AGO4 and SPT5L bind chromatin independently of each other, and, at the majority of tested loci both are required for establishment and maintenance of silencing, we propose that AGO4 and SPT5L create a binding platform for the recruitment of chromatin modifying proteins. One possibility is that both weakly interact with a downstream protein but the interaction becomes strong enough to recruit chromatin modifying enzymes only when both are present. Alternatively, AGO4 may be a sole interacting partner of downstream proteins but SPT5L, which has a C-terminal domain rich in WG/GW motifs, interacts with AGO4 and alters its conformation to facilitate the recruitment of chromatin modifying enzymes.
Our results show that there are loci where DNA methylation is established in a Pol V, AGO4 and SPT5L-dependent manner (Figure 5A), but these loci have an overall low level of H3K9me2 and no change in the histone modifications in tested mutants (IGN23 in Figure 5E). It suggests that the de novo DNA methyltransferase DRM2 is likely the chromatin modifying enzyme directly recruited by the AGO4-SPT5L platform. It is also possible that DRM2 may be recruited indirectly by another protein that binds the AGO4-SPT5L platform.
Binding of AGO4 and SPT5L to chromatin is mediated by multiple protein-protein and protein-RNA interactions. These interactions may mediate recruitment of proteins to specific genomic regions and/or stabilize binding after recruitment by an independent mechanism.
SPT5L binding to chromatin occurs downstream of Pol V and is most likely mediated by protein-RNA interaction between SPT5L and Pol V transcripts [22]. Like canonical SPT5, SPT5L may also form a heterodimer with SPT4 [33]. Alternatively, SPT5L may be recruited to chromatin by protein-protein interaction with Pol V complex as suggested by identification of SPT5L in Pol V holoenzyme [23] and interactions between yeast SPT5 as well as bacterial homolog of SPT5, nusG, with RNA polymerases [34], [35]. It is also possible that SPT5L is recruited to chromatin by interacting with both Pol V transcripts and Pol V complex. All these mechanisms explain the AGO4-independent binding of SPT5L to Pol V-transcribed loci.
Interaction with Pol V transcripts seems to be the major factor recruiting AGO4-siRNA to chromatin [19]. AGO4 also interacts with WG/GW-rich C-terminal domains of Pol V and SPT5L [21], [22], [36]. Because Argonautes contain only one WG/GW binding pocket [37] these interactions may be employed sequentially. First, they help recruit AGO4 to chromatin by interaction with Pol V and then they stabilize the binding of AGO4 to chromatin on its target loci by interaction with SPT5L. It is consistent with our observation that AGO4 binding to chromatin is slightly reduced in the spt5l mutant (Figure 3).
We show that SPT5L contributes to regulation of siRNA-mediated transcriptional silencing in a highly locus-specific manner. This is demonstrated by the observation that two of the tested loci require Pol V and AGO4 but not SPT5L for establishment and/or maintenance of repressive chromatin modifications (Figure 6). It could be explained by presence of the canonical SPT5 at a subset of silenced loci. However, both loci are occupied by SPT5L in wild type plants (Figure 1) suggesting that SPT5L is in fact involved in their silencing. Only when SPT5L is mutated, the canonical SPT5 is able to compensate the deficiency at these particular loci. Alternatively, it is possible that the observed locus-specificity of SPT5L is caused by the presence of both Pol V and Pol II at a subset of loci [26]. Pol II-bound canonical SPT5 may be able to compensate the lack of Pol V-bound SPT5L. The mechanism deciding locus specificity of the SPT5L function remains unknown.
Our results also suggest the presence of two pools of AGO4: SPT5L-dependent and SPT5L-independent. Because both pools are detectable at loci that are silenced in a SPT5L-independent manner, the SPT5L-dependent pool of AGO4 is likely not required for silencing. It may be recruited independently of siRNA by direct interaction with SPT5L and may have some other, yet unknown and locus-specific functions.
Arabiodopsis thaliana nrpe1 (nrpd1b-11), dms3-4, and ago4-1 introgressed into Col-0 background were described previously [19], [38]. rdr2-1 mutant was obtained from J. Carrington. spt5l-1 (rdm3-3; SALK_001254) mutant line, affinity-purified anti-SPT5L (anti-KTF1), affinity-purified anti-Pol V (anti-NRPE1) and affinity-purified anti-AGO4 antibodies were described previously [19], [22], [39]. Mouse monoclonal anti-H3K9me2 antibody (cat. #ab1220) was obtained from Abcam, rabbit polyclonal anti-H3Ac antibody (cat. #06-599) was obtained from Millipore.
ChIP was performed essentially as described [18], [19]. Detailed ChIP protocol is included in the Text S1. ChIP samples were amplified in triplicate in Applied Biosystems 7500 real time PCR machine and obtained data were analyzed using comparative CT method relative to inputs [40]. All ChIP experiments were performed in three independent biological replicates. Results from every biological replicate were normalized to Col-0 wild type and normalized data were used to obtain averages and standard deviations that show fold difference between analyzed strains. Normalized data were subsequently multiplied by average ChIP signal level of Col-0 wild type. This way data are corrected for variability in overall signal strength between independent experiments, the unit is %input and presented data reflect the relative signal strength observed at particular loci. Standard deviations for Col-0 wild type are not available because Col-0 wild type was used to normalize data.
For DNA methylation analysis genomic DNA was extracted from above-ground tissue of 2-week old plants using DNeasy Plant Mini Kit (Qiagen). 100 ng of genomic DNA was digested with 10u of HaeIII, AluI or AvaII restriction enzymes (NEB) for 20 min. After heat-inactivation of the enzyme DNA was amplified using 0.75u Platinum Taq (Invitrogen).
Total RNA was extracted from 2-week old plants using RNeasy Plant Mini Kit (Qiagen) and amplified using SuperScript III Platinum SYBR Green One-Step qRT-PCR Kit (Invitrogen) in Applied Biosystems 7500 real time PCR machine.
Oligonucleotide primers used in this study are shown in Table S1.
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10.1371/journal.pcbi.1005352 | Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective | Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.
| Iron is required for cellular metabolism and growth, but can be toxic due to its ability to cause high oxidative stress and consequently DNA damage. To prevent damage, all organisms that require iron have developed mechanisms to tightly control iron levels. Dysregulation of iron metabolism is detrimental and can contribute to a wide range of diseases, including cancer. This paper presents a predictive mathematical model of iron regulation linked to iron utilization, oxidative stress, and the oncogenic response specific to normal breast epithelial cells. The model uses a discrete modeling framework to generate novel biological hypotheses for an investigation of how normal breast cells become malignant cells, capturing a breast cancer phenotype of iron homeostasis through overexpression and knockout simulations. The new biology discovered is (1) IRP2 overexpression alters the iron homeostasis pathway in breast cells, without affecting the oxidative stress response or oncogenic pathways, (2) an activated oncogenic pathway disrupts iron regulation in breast cancer cells.
| Every aerobic organism requires iron for energy production, DNA synthesis, oxygen transport, and cellular respiration. However, this essential element has the potential to exist in various oxidation states and can enable the formation of reactive oxygen species. To avoid iron toxicity, all organisms requiring iron have developed a complex machinery to tightly control iron at both the systemic and the cellular levels. Our goal here is to understand how in cancer this machinery is altered. Dysregulation of iron metabolism in cancer is well documented, and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression [1]. Recently, it was observed that reduced levels of ferroportin, a cellular iron exporter, were associated with poor clinical outcome [2]. In the same study, a direct relationship between intracellular iron and tumor growth was demonstrated, and in subsequent work it was shown that high expression levels of the major iron importer, transferrin receptor 1, and reduced levels of the gene HFE, were also associated with poor prognosis in breast cancer patients [3].
In a previous study, we constructed a dynamic mathematical model of the core iron homeostasis control system in normal breast epithelial cells [4]. This choice of cell type was motivated by our interest in the role of intracellular iron homeostasis in the pathogenesis of breast cancer. For the core control system we have focused on the proteins responsible for iron import, export, and sequestration, together with the iron regulatory proteins and the labile iron pool. We validated the model using experimental data from overexpression of ferroportin. Our analytical arguments and extensive simulations demonstrated that the model reaches a unique stable steady state for any choice of parameters, agreeing with experimental evidence that cellular iron is tightly controlled [5].
We hypothesized there that major signaling pathways activated in cancer disrupt this iron regulatory network. To test this hypothesis, it was necessary to first connect the core iron network to known molecules whose expression levels are altered in cancer. Here, we build and analyze an intracellular mathematical model specific to normal breast epithelial cells that dynamically links iron metabolism to species from iron utilization, the oxidative stress response, and oncogenic pathways. The model has allowed us to highlight dynamical features of the system and identify key players in the system that lead to different phenotypes without having to perform lengthy laboratory experiments. We have validated the model using experimental data and literature, and confirmed that the iron homeostasis pathway can be modified by activating an oncogenic pathway.
Several models related to iron homeostasis have been developed and analyzed. Some are aimed at systemic iron homeostasis, consisting of a number of compartments that capture the amount of iron at a particular location [6–10]. Other models are cell type specific (kidney) [11] or organ specific (liver) [12]. One of the first intracellular models of iron metabolism was proposed by Omholt et al. [13], but this model did not explicitly include proteins responsible for iron export and sequestration. Our earlier model [4] included this additional feedback structure and was further considered by Mobilia et al. [14], where the authors concentrated on the same five species but a different system of differential equations. While all of these models are valuable and address specific questions, none of of them, including our earlier model, connect the iron network to an oncogenic pathway. The approach of identifying and uniting different biochemical pathways was previously explored by Funke et al. [15], where the authors attempted to explain Parkinson’s disease by considering gene products involved in disease, and also included iron. We took a similar approach by producing one coherent model integrating several pathways connected to the iron network. Our new model has a potential for further inclusion of other pathways to produce a more comprehensive picture of dysregulation of iron metabolism in cancer.
This section provides biological background about iron metabolism and its connection to some oncogenic pathways.
Based on the known biology described in the previous section, we have constructed a network model, depicted in Fig 1. We have incorporated simplifications, as follows. Recall that HO-1 is part of the iron utilization pathway, and thus this enzyme is modeled in our network as a separate node. On the other hand SOD, CAT and GPx, which can eliminate specific reactive oxygen species, are represented as a single node, labeled Antioxidant enzymes (AE). Similarly, (O2·)−, H2O2 and ·OH are modeled as one species, labeled ROS in our network (Fig 1). Oncogenic pathways and reactive oxygen species (ROS) have a close and intricate relationship. Our model is not detailed enough to capture all the complexities of their interactions, but we do include many known established connections. In particular, it has been shown that activated Ras induces the production of ROS, which is required for oncogene-mediated cellular transformation and Ras dependent proliferation [59–62]. Moreover, there is a direct induction of EGFR by endogenous H2O2 and a localized generation of H2O2 by EGFR through an NADPH oxidase (Nox)-mediated process [48, 63]. Extracellular-signal regulated kinases (ERKs) and Ras are also involved in the oxidative pathway by activating Nrf2 [64–66].
The model in Fig 1 was built in a very general way, and is based primarily on the pertinent literature, including several connections derived from different cell types. For clarity, we refer to the network in Fig 1 as the normal cell network.
We describe a discrete dynamic model of the network in Fig 1, based on an encoding of the regulatory logic for each node through a “logical” update rule. This type of model is qualitative, in the sense that each species can assume a finite set of states rather than quantitative concentrations of molecular species. For this study, we adopted a ternary logic, an extension of Boolean logic. Our choice of ternary logic was motivated by the fact that iron levels cannot be viewed as either ON = 1 or OFF = 0. The iron homeostasis pathway is the major focus of our study and both low and high levels of iron are detrimental, so that it is tightly controlled. Additionally, IRP2 at both low and high activity levels does alter the iron pathway [68]. With only two states it would not be clear when IRP2 operates at low activity levels, as it would be represented the same way as normally active protein. For our model to be able to differentiate between iron homeostasis (normal levels of iron) and low/high iron levels as well as activities/concentration levels of various proteins, we chose to represent each species by three levels: low, normal and high. In the language of logical models the state of a particular species is described by 0 if the species is low/inactive, by 1 if at normal/intermediate activity, and by 2 if high/active.
In analogy to the Boolean formalism, we can compute the future state of a species at time step t + 1 using the states of other species at time step t. Fundamental OR and AND gates for two species X and Y are defined as max{X, Y} and min{X, Y}, respectively, where X, Y ∈ {0, 1, 2}. To differentiate from the Boolean OR and AND gates, we denote these gates by Max and Min, respectively. The NOT gate (denoted here by X ¯) is defined by inverting the input, i.e., leaving 1 unchanged and inverting 0 and 2. For a concrete example, consider heme in Fig 1. It is produced through ALA synthase (ALAS1) but inhibited by HO-1. Then the logical function (update rule) that predicts how much heme is present at time t + 1 can be computed as follows:
heme(t+1)=Min(ALAS1(t),HO−1¯(t)).
This means that, if HO-1 was 0 (low) and ALAS1 was 2 (high) at time t, then heme will be 2 (high) at time t + 1.
Based on the biological knowledge described in the previous section, we translated the interactions of the normal cell network into logical functions (see Table 1). One caveat about logical models that is not present for Boolean models is that species can change for example from a low state to a high one in one time step, skipping intermediate concentrations. This is biologically unrealistic. Thus, to address the continuity issue we have also implemented a methodology commonly used for logical models that takes into account the previous state of the regulated species (see [69] for details). For purposes of simulation, we converted the logical rules into polynomial functions to obtain a so-called polynomial dynamical system (PDS). A description of the construction of the PDS and the entire system can be found in the Materials and Methods section and in the supplemental file S1 PDS, respectively.
To analyze the dynamic properties of the model we simulated the entire state space and computed the basins of attraction of the system. For this purpose, we used an encoding of the model as a polynomial dynamical system, as described above, and customized scripts written in Perl and Python (see Materials and Methods section). The size of the model’s state space is 324 = 282, 429, 536, 481, where 24 is the number of species in the network and 3 is the number of states (low, medium, high) per species. We employed a synchronous update schedule for the species in the network; all species were updated simultaneously based on the states of their input species at the previous time step. Each state leads to another state, eventually converging to a steady state or a limit-cycle (a set of recurring states), which are called attractors. A collection of initial states that lead to a particular attractor is termed the basin of attraction. Under this scheme, each state belongs to the basin of attraction of only one attractor: a point attractor (steady state) or a cycle attractor (limit-cycle). These attractors correspond to different phenotypes in the biological context and can describe various behaviors of the system such as homeostasis.
We simulated the normal cell model and also investigated the long-term behavior of this model under different conditions, namely, the effects of knockout (k/o) or overexpression (o/e) of one or more species. To simulate these experimental conditions, we set the update rule for a particular species to a constant equal to 0 or 2, respectively. In other words, regardless of the input (regulators), the species of interest will always stay at the chosen level. Our results are summarized in Fig 2, which shows all the species and their long-term behavior. Simulations were performed by exhaustively enumerating the transitions of the model on all possible 324 states. The normal cell network has no cycle attractors and reaches a unique stable steady state (point attractor) indicating that all species are at their respective normal levels regardless of the initial starting state (Fig 2 top line of the heat map labeled Normal).
It is well-known that iron metabolism in breast epithelial cells is differentially regulated as cells transition to malignancy. Determining the causes for this altered phenotype is complicated by the complexity of iron regulation and its connection to several other processes, such as response to oxidative stress and changes in iron consumption [80], as well as crosstalk with oncogenic pathways. Integrating these different influences on the iron phenotype in normal and malignant cells can benefit greatly from a systematic approach through dynamic mathematical modeling, beyond the network approach taken in [80]. The model presented here is a first step toward a comprehensive understanding of the iron phenotype of cells as it changes in breast cancer. We have chosen to construct a qualitative model of an intracellular iron network (Fig 1) to capture its fundamental dynamic features (attractors). The main reason for our choice of modeling platform is that our current knowledge of the kinetics involved in these different processes as well as mechanisms underlying these complex reactions is very limited, so that a quantitative model, such as a system of ordinary differential equations is more challenging to construct.
We have validated our model using both experimental data and information from the literature not used in model construction. In particular, we have experimentally validated the model prediction that IRP2 overexpression in the normal cell network only alters the iron homeostasis pathway, leaving the other model components unchanged. Also, our model agrees with the current literature that overexpression of mitochondrial ferritin (Ftmt) increases both IRPs and TfR1, decreases cytosolic Ft and reduces cytosolic and mitochondrial iron pools [71]. In addition, we have shown that shutting down trafficking of iron into the mitochondria, together with Ras overexpression and Cat reduced bioactivity, does lead to the observed cancer phenotype of the iron homeostasis pathway. However, it might be possible that further refinements of the model can lead to the required phenotype by altering only the oncogenic pathway.
Not all known information about the normal and cancer phenotypes can be captured by the model, however. This is likely due to the fact that some key features of this system are not represented completely, such as an iron-sensing regulator in the mitochondria and iron-sulfur cluster (ISC) synthesis. It has been suggested that frataxin, a nuclear-encoded mitochondrial protein, may act as an iron-sensing regulator and even function as a switch between heme and ISC synthesis [81–83]. At this stage, one cannot determine whether it is frataxin or some other iron sensor/regulator, but we have suggested the possibility of a mitochondrial iron-sensing node in our current model (depicted as a question mark in Fig 4(a)). This adjusted normal cell model also reaches a unique stable steady state agreeing with our model discussed in the Results section (Fig 4(b)). Additionally, we simulated two more models using the following perturbations: (i) knockout of the sensor node and (ii) overexpression of Ras and of the sensor node, and low bioactivity of CAT (2nd and 3rd rows in Fig 4(b)). Interestingly, the sensor node k/o model agrees with experimental data that in frataxin k/o mice heme is decreased, TfR1 is upregulated and iron uptake via Mfrn is increased, leading to cytosolic iron-deficiency and mitochondrial iron overload [84, 85]. This strongly indicates that there is a sensor/regulator, and thus further refinements of the model can provide insight into mitochondrial iron regulation and utilization, and potentially suggest new experiments that can validate new connections. The latter model produced the same cancer phenotype of the iron homeostasis pathway (see Eq (2)) and also implied that cancer cells have reduced heme biosynthesis. Furthermore, we note that the latter model allows Ftmt and ALAS1 from the iron utilization pathway to have high expression levels (compare 9th row in Fig 2 to row 3 in in Fig 4(b)). While we do not have much evidence about Ftmt in cancer, there are some studies about ALAS1 in lung cancer. It was found that ALAS1 protein levels were substantially increased in non-small-cell lung cancer cells compared to normal cells [86]. This suggests the possibility to expand the cancer phenotype of the iron homeostasis pathway to the iron utilization pathway. Of course, one can simulate a model by setting various proteins to their respective observed levels, but then we gain no information about the drivers that change iron metabolism in cancer. Ideally, we would like to include other pathways implicated in breast cancer to capture different molecular subtypes of breast cancer and iron cancer phenotypes associated with them.
We begin by defining a set of rules that describe various relations between molecular species, from which we then build the entire model. If species X is, inducing species Y (X → Y) or species X is inhibiting species Y (X ⊣ Y) then we represent these relationships via a transition table as depicted in Table 2.
Notice that inhibition in Table 2 is just a logical NOT gate, denoted here by X ¯. The other two fundamental gates, OR and AND, for two species X and Y regulating species Z (X → Z ← Y), are defined as max{X, Y} and min{X, Y} respectively, for X, Y ∈ {0, 1, 2}, and denoted here by Max and Min. We can express the above gates as polynomials over a finite field on three elements, F 3. If we limit the exponent of each variable in a polynomial to be less than or equal to 2, then one can show that any logical rule constructed from these three operations has a unique polynomial representation, using
x¯=2+2xMax(x,y)=x2y2+x2y+xy2+2xy+x+yMin(x,y)=2x2y2+2x2y+2xy2+xy. (3)
One can check that polynomials given by Eq (3) agree with definitions of fundamental gates as described in the paragraph above, e.g., max{1, 2} = 2 and Max(1, 2) = (1)2(2)2 + (1)2(2) + (1)(2)2 + 2(1)(2) + 1 + 2 = 2, where the right-hand side is computed modulo 3. Various adjustments to the strength of a particular regulation can be made by altering entries in the Table 2. For example, it has been suggested that IRP1, when active, contributes less to the regulation of ferritin (Ft) than IRP2 [68] (see Table 3). These tables mean that when IRP2 = 2 (active) it will inhibit Ft, whereas when IRP1 = 2 (active) it will have a lesser effect on Ft.
Thus, we can represent regulation of Ft by IRP2 in Table 3 using Eq 3:
IRP2¯=2+2⋅IRP2.
Now, for IRP1 regulating Ft according to this new adjustment, one can also find a polynomial representing Table 3 (left table). For convenience, whenever we use an adjusted regulation we will place an asterisk (*) in front of the variable inside the logic gate.
*IRP2¯=2+2⋅(IRP1)2.
To match current biological knowledge we have adjusted regulation of IRP1 for TfR1 and Fpn as well [68]. The transition table for Fpn is similar to Ft. For IRP1 regulating TfR1: when IRP1 = 2, then Tfr1 = 1, while, when IRP1 is 0 or 1 then TfR1 is also 0 or 1, respectively. Additionally, we modified regulation of Keap1 by Nrf2 to reflect current literature [44]. For Nrf2 regulating Keap1 we have that when Nrf2 = 0 then Keap1 = 1, while when Nrf2 is 1 or 2 then Keap1 is also 1 or 2, respectively.
To make sure that we preserve continuity (i.e., each species changes at most one unit in one time step), we are going to employ methodology as described in [69]. The underlying reasoning is that this can be accomplished by taking into account the previous state (e.g., concentration or activity) of the regulated species, in effect adding a self-regulation loop to each network node. The future value of the regulated species under continuity is computed as follows. Let fxi be the update function for xi. To ensure that each variable changes at most 1 unit, define a function h(xi, fxi) for the future value of the variable xi:
h ( x i , f x i ) = x i + 1 if f x i > x i x i if f x i = x i x i - 1 if f x i < x i (4)
LIP, heme, and ROS do not undergo self-degradation/self-regulation and hence we do not apply continuity to these species. In order to compute final polynomials, we are going to make use of the following property of finite fields:
Remark 0.1 If h : F p n → F p is any function then there is a polynomial g : F p n → F p so that h(x) = g(x) for all x ∈ F p n.
One can find g by using the following formula,
g ( x ) = ∑ c ∈ F p n h ( c ) ∏ j n ( 1 - ( x j - c j ) p - 1 ) , (5)
where h(c) is the update function as defined by Eq (4), c is a vector of input variables, and the right-hand side is computed modulo p.
All of these logic gates, transition tables describing different strength of regulation and continuity, are then appropriately translated into final polynomial functions over a finite field with three elements. These polynomial functions then form what is called a polynomial dynamical system (PDS) over a finite field. Below, we fully describe a construction of the update function for ferritin (Ft) in our network (see Fig 1). The entire PDS system can be found in the supplemental file S1 PDS.
The attractors of the models were found using 2 algorithms: the attractor finder by random sampling (Algo. 1) that is written in Perl and the attractor finder by iterating over all possible states (Algo. 2) that uses a custom written Python package. The codes can be found at https://github.com/LoLab-VU/LogicalModel. Models that were used for simulations are located in the same directory under NewModels_2015_12_18 and NewModels_2015_8_17 folders. Supporting file S1 Simulations provides additional o/e and k/o simulation results using attractor finder by random sampling. The index for the order of variables is available from row 21 to row 45. After 3,000 random sampling, the basin size of the attractor is specified in the table.
The first program requires a model file, the number of states and a sampling size, which is 100,000 here. We randomly selected 100,000 states and stored the attractor states to have a broad perspective on the possible attractors of a model. It was utilized to test which overexpression and knockout models could be potential cancer models. To ensure that we know all attractors of the models of interest, we ran the second program, which requires a model file and number of states. Optional arguments include start and end states and an option to create images of attractor states. The model file is parsed and compiled into an executable function with Cython [87]. We iterated through all possible states of each model (3N), storing only the attractor states. Simulations were performed in parallel using mpi4py [88] running on large cluster computers.
Algorithm 1 Pseudo code for attractor finder by random sampling
1: procedure For i in 100,000 ▹Iterate over 100,000 randomly selected states
2: sampled = empty set
3: state = changebase(random(3N))
4: while state ∉ sampled do
5: sampled.add(state)
6: state = update(state) ▹Update function is the compiled model
7: state.pop() ▹Returns the last state added to sampled
Algorithm 2 Pseudo code for attractor finder by iterating over all possible states
1: procedure For i in 3N ▹Iterate over all possible states
2: sampled = empty set
3: state = changebase(i)
4: while state ∉ sampled do
5: sampled.add(state)
6: state = update(state) ▹Update function is the compiled model
7: state.pop() ▹Returns the last state added to sampled
MCF10A, non-tumorigenic immortalized human mammary epithelial cells were obtained from the Wake Forest University Comprehensive Cancer Center Tissue Culture Core facility. The cells were maintained in a suggested condition by ATCC.
To overexpress IRP2 in MCF10A cells, the lentiviral vector pSL2-IRP2 [68] was applied. Briefly, MCF10A cells were infected with the concentrated viral particles from pSL2-IRP2 and pLS2 empty vector (as a control). The infection efficiencies for both infections were over 90% based on GFP fluorescence in cells. The cell lysates were harvested for subsequent analysis seven days after infection.
Western blotting was performed as previously described [68]. Antibodies: GAPDH (Fitzgerald), TfR1 and c-Myc (Invitrogen), IRP2 and EGFR (Santa Cruz Biotechnology), Keap1(Cell Signaling Technology), HO-1 and IL-6 (Abcam), ferritin H ([89]).
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10.1371/journal.pgen.1003309 | Complex Patterns of Genomic Admixture within Southern Africa | Within-population genetic diversity is greatest within Africa, while between-population genetic diversity is directly proportional to geographic distance. The most divergent contemporary human populations include the click-speaking forager peoples of southern Africa, broadly defined as Khoesan. Both intra- (Bantu expansion) and inter-continental migration (European-driven colonization) have resulted in complex patterns of admixture between ancient geographically isolated Khoesan and more recently diverged populations. Using gender-specific analysis and almost 1 million autosomal markers, we determine the significance of estimated ancestral contributions that have shaped five contemporary southern African populations in a cohort of 103 individuals. Limited by lack of available data for homogenous Khoesan representation, we identify the Ju/'hoan (n = 19) as a distinct early diverging human lineage with little to no significant non-Khoesan contribution. In contrast to the Ju/'hoan, we identify ancient signatures of Khoesan and Bantu unions resulting in significant Khoesan- and Bantu-derived contributions to the Southern Bantu amaXhosa (n = 15) and Khoesan !Xun (n = 14), respectively. Our data further suggests that contemporary !Xun represent distinct Khoesan prehistories. Khoesan assimilation with European settlement at the most southern tip of Africa resulted in significant ancestral Khoesan contributions to the Coloured (n = 25) and Baster (n = 30) populations. The latter populations were further impacted by 170 years of East Indian slave trade and intra-continental migrations resulting in a complex pattern of genetic variation (admixture). The populations of southern Africa provide a unique opportunity to investigate the genomic variability from some of the oldest human lineages to the implications of complex admixture patterns including ancient and recently diverged human lineages.
| The Khoesan have received recent attention, as they are the most genetically diverse contemporary human populations. However, Khoesan populations are poorly defined, while archeological evidence suggests a once broader dispersal of click-speaking southern African foragers. Migrations into the regions populated by contemporary Khoesan involved agro-pastoral Bantu around 1,500 years ago, followed over a millennium later by the arrival of European colonists establishing a halfway station for a maritime route between Europe and the East, which led to unions between diverse global populations. Using almost a million genetic markers for 103 individuals, we confirmed a significant Khoesan contribution to five southern African populations. The Ju/'hoan show genetic isolation (early divergence from all other modern humans), carry no significant non-Khoesan contributions, and unlike most global populations lack signatures of gene-based adaption to agriculture. The !Xun show two distinct Khoesan prehistories; while comparable to the female-derived Khoesan contribution to the amaXhosa Bantu, the male-derived Bantu contribution to the !Xun most likely represents cultural-driven gender-biased gene-flow. Emanating largely from male-derived European ancestral contributions, the Basters showed the highest maternal Khoesan contribution, while the Coloured showed the largest within population and regional-associated variability. The unique admixture fractions of the two latter populations reflect both early diverged and recently diverged human lineages.
| Southern Africa is home to populations carrying significant human genomic variation. The analysis of patterns of DNA variation, have placed modern human origins within Africa [1], with the most divergent contemporary lineages found in the indigenous Khoesan inhabitants of southern Africa [2]–[6]. Defined by their use of clicking languages and a foraging-based subsistence, contemporary Khoesan are largely restricted to the greater Kalahari regions of Namibia and Botswana. Representing a collection of isolated subpopulations with dwindling numbers and subpopulation extinctions, the Khoesan population identifier once represented a broader geographical dispersal reaching the most southern tip of Africa. Historical migrations into southern Africa including agro-pastoral Southern Bantu from a western/central African homeland beginning roughly 1,500 years ago [7], [8], followed over a millennium later by the arrival of European settlers and East-Indian slaves [9], shaped the ancestral contributions of contemporary southern Africans. These intra- and inter-continental contributions led to historical events that perpetuated population dispersals, isolations and assimilation between populations, ultimately giving rise to complex genomic admixture. The pattern of genomic variation in contemporary southern African populations thus resulted from unions between the most diverse genomes found within Africa to the least differentiated as represented by populations impacted by a severe founder effect (bottleneck) associated with the out-of-Africa dispersal [2], [10]–[13].
Determining the ancestral origins of contemporary southern African admixture is limited by a number of factors including the availability of well-characterized subjects, limited availability of genomic data for appropriate founder populations, biases in current content genotyping arrays and analytical challenges. Lack of genomic data for southern African populations defined based on linguistics and culture broadly as Bantu and Khoesan, has perpetuated biases. To minimize these limitations, we leveraged genotype information from the largest current content array dataset that was available at the time the study was initiated in 2010, interrogating over 1 million genome-wide data points (Illumina HumanOmni1-Quad BeadChips). The 103 individuals in this study represent five southern African populations defined as Khoesan, specifically Ju/'hoan and !Xun, Southern Bantu, specifically amaXhosa, and European-initiated admixed populations, specifically Coloured and the newly described Baster population (Figure 1). At the time of submission there had been limited largely gender-specific analyses performed for pooled subsets of Southern Bantu [14], [15], while gender-specific [14], [16] and more extensive analysis for the Coloured had focused on non-regional sub-structure [17], [18]. While we previously considered the extent of whole exome diversity between two Ju/'hoan and a single !Xun, providing limited genome-wide analysis using the smaller 500 K Illumina arrays [5], no study had determined possible admixture contributions to these foraging-based populations. We merge our data with the only Khoesan-derived genome-wide dataset, the South African #Khomani [4]. The availability of globally relevant genomic data (published and from the Illumina iControl Database) provides a means to predict contributing migratory homogenous founder populations (specifically as a result of Bantu migration and European colonization), which most closely represent historical events that have impacted relations between southern African populations (Figure 1). In contrast, identifying indigenous founder contributions is more problematic. Contemporary Khoesan populations have either themselves experienced varying degrees of non-Khoesan contribution, or may not accurately represent the likely lost ancient ancestral lineages that once thrived along the southern coast of Africa at the time of non-Khoesan arrival. A major goal of our study was therefore to define a Khoesan population with negligible non-Khoesan contribution. Using anthropological, cultural, linguistic, as well as personal interactions within the remaining Khoesan communities of Namibia, the Ju/'hoan and !Xun were identified as likely candidates. Identifying early human divergence and unique forager-based genomic signatures, we further assess the significance of ancestral contributions within our study sample using multiple analytical approaches, while providing significant insights into the history of the region.
Before one can assess complex admixture fractions, it is critical to determine the validity of the study sample to be tested, paying particular attention to the potential for sampling biases, while assessing limitations within available genotyping content. Additionally, the identification of the most homogenous founder representative populations is critical. While the availability of globally relevant datasets allows for the assessment of non-regional founders, the identification of a homogenous indigenous founder population is currently not available. We provide evidence that supports the Ju/'hoan in this study as a likely representation of a homogenous Khoesan ancestral lineage. Using this data we provide multiple complimentary approaches to assess southern African admixture fractions.
Our data suggests that the Ju/'hoan represent the most likely homogenous contemporary Khoesan population. Two factors that set the Khoesan apart from other global populations include early divergence and forager substituted by hunting existence. We use genomic data to look for signatures that differentiate the Ju/'hoan in this study based on these criteria.
Although previous studies have considered the role of admixture in shaping the genetic diversity among southern Africans, in particular the Coloured, no study has assessed (i) the significance of these contributions, (ii) how this admixture has shaped or contributed to distinct population subgroups among southern Africans, or (iii) the possibility that southern Africans may be harboring ancient vestiges of a ‘lost’ or understudied source of genetic diversity. The extent of admixture within people today defined broadly as Khoesan complicates these analyses, further compounded by subject heterogeneity. We attempt to assess sources of admixture and heterogeneity and ultimately identify and characterize a Khoesan-representative population that displays little to no significant non-Khoesan ancestral contribution. Such a population we identify as the Ju/'hoan.
This study suggests that the Ju/'hoan form a unique ancestral population for the human lineage, distinct (i.e., most dissimilar) from all contemporary populations for which data is currently available, including other forager populations. Gender-specific analysis confirms genetic isolation of the Ju/'hoan from non-Khoesan populations, while autosomal analysis shows no significant non-Ju/'hoan ancestral contribution. While the rest of the world was driven into agriculture at the end of the Last Glacial Maximum [48], the Ju/'hoan appear to have maintained their hunter-gatherer based subsistence. Significant agricultural-driven genomic signatures were absent from the study subjects, while previously described functionally significant ancestral forager-based alleles were identified. One of the most interesting findings to emerge from our analysis of foraging versus agricultural genome profiles was a potential for an increased chemical dependency for tobacco. We observed heavy tobacco usage by all study participants, both male and female. Historical accounts include the successful use of tobacco as a means of trade or coercion of indigenous Khoesan by European settlers [49]. Anthropological observational studies suggest an unusual devotion of Ju/'hoan to master the difficult task of tobacco cultivation over food-based cultivation when minimal farming is adopted [50]. Our data therefore suggests that the Ju/'hoan have not had adequate time to adapt to selective pressure associated with the use of tobacco. The significance of genes associated with inflammatory, autoimmune or immune diseases, being significantly enriched between forager and agriculturalist requires further investigation. Coined ‘the harmless people’ [51], it may not be surprising that we found a greater representation of loci associated with mood-based disorders. Physical characteristics within the Ju/'hoan with possible links to enriched pathways include (i) maintaining both thermal and fluid homeostasis within desert climates, (ii) the need for rapid wound repair, and (iii) a possible state of semi-erection in males. The latter, a locally accepted trait, has been documented in Bushmen rock art [52] and reported as a defining characteristic [53].
Unlike the Ju/'hoan, the !Xun exhibit significant male-derived non-Khoesan African ancestral contribution to their gene pool. While autosomal marker analysis suggests roughly 20.5% non-Khoesan admixture, Y-chromosomal analysis suggests a possible East African Nilotic contribution, although extended autosomal substructure analysis suggests a proto-Bantu and Sandawe contributions while excluding for a Nilotic contribution. Evidence for Bantu migration into the northern Kalahari region of Namibia appears as early as the 7th century [54]. Bantu-Khoesan interaction is evident by the introduction of iron-based arrow tips and cooking utensils, as well as the use of cultivated tobacco by the Khoesan, and conversely the inclusion of clicks within the non-click languages of early Bantu immigrants, for example isiXhosa (the language of the amaXhosa). The possibility of a pre-Bantu, likely east African migration into the region requires further investigation. The Ju/'hoan-Yoruba differentiating AIMs defined two unique !Xun subgroups suggesting independent genomic prehistories. The Ju/'hoan-ancestral !Xun share on average 54.8% (range 43.6–67.5%) of their genomic heritage with contemporary Ju/'hoan, and include the Angolan !Xun from this study (Figure 4C). In contrast, we identify a new non-Ju/'hoan (range 0.9–5.3%) ancestral contribution to 50% of the !Xun, averaging 71.1% (range 65.9–76.9%) (Figure 4D). We suggest that the !Xun identifier as used today incorporates different Khoesan prehistories, one independent from contemporary Ju/'hoan. Interestingly, the non-Khoesan African contribution to the !Xun appears to be uniform with ancestral signatures shared by contemporary Bantu and Sandawe. Our data therefore suggests that these two independent !Xun lineages carry the same non-Khoesan African contributions. The amaXhosa Bantu carry an almost equal ancient ancestral Khoesan contribution, while AIMs analysis suggests that this contribution is largely non-Ju/'hoan. It is highly feasible to assume that the southward migration of the amaXhosa along the eastern coast would constitute differing Khoesan contribution from the more westerly located inland Ju/'hoan. This observation is further supported by the lack of L0k mtDNA representation within the amaXhosa. Further analysis would be required to determine the relationship between the Khoesan contribution to the amaXhosa and the ‘unknown’ !Xun lineage identified in this study.
While the !Xun and amaXhosa show evidence for historical admixture, inter-continental migrations to the region has led to the emergence of more recent admixture. Considering a highly variable non-Khoesan contribution to the #Khomani, the Coloued and Baster populations represent a complex admixture pattern that transverses both the earliest and the most recently diverged human lineages. Defining and tracing such significant ancestral contributions provides a unique model not only to track human expansion and prehistories, but also define gene regions undergoing selection [55]–[57] and recombination [58], [59]. The datasets presented in this study provide a unique resource for further genomic analyses. In the Ju/'hoan we speculate that the fraction of ROH has been lowered as a result of early divergence with other populations, while increased as a result of a smaller effective population size (Ne). Unlike cosmopolitan societies, the maintenance of population size is an essential survival mechanism for foragers. As a result of varied contribution of ancestrally distinct chromosomal segments, contemporary southern African populations would display admixture-based recombination, decreasing total ROH. The complex ‘Khoesan-African-Asian-European’ ancestral admixture fractions of the Baster and Coloured would be further impacted by gender-specific meiotic recombination rates [60]. The observation of gender biased ancestral contributions include a paternally-driven ‘African non-Khoesan’ contribution to the !Xun, maternally-driven ‘Khoesan’ contribution to the amaXhosa, and maternally-driven ‘Khoesan’ and paternally driven ‘non-African’ (likely European) contribution to the Baster and Coloured.
Although previous studies have looked at the ancestral contributions to the Coloured [17], [18], no studies have to date addressed complex admixture within the Basters. Emerging from a common historical background to the Coloured, the Baster population have since the late 1800 s distinguished themselves as independent from the Coloured, migrating to the now Baster nation of Rehoboth in Namibia [9]. In contrast to the Coloured we show the Baster population to carry the largest Khoesan-derived maternal contribution (91.7% compared to 64.3% in the Coloured) and the largest paternal European-derived contribution (93.3% compared to 71.8%), while autosomal marker analysis confirmed increased ‘Khoesan’ and ‘European’ contributions and decreased ‘Asian’ and ‘African non-Khoesan’ contributions. Geographic distribution of the ‘African non-Khoesan’ admixture fraction showed an increased contribution and significance from west to east (Baster, NC-, D6- to EC-Coloured, Figure S7), with significance of the Bantu-derived fraction (1.6%, 5.8%, 15.4% and 16.6%, respectively) based on nine ancestral fractions (Figure 2B) and mirroring Bantu population distributions (Statistics South Africa Census 2011 and Community Survey 2007, (http://www.statssa.gov.za)). The most significant ‘Asian’ contribution was found within persons who were residents of District Six. Previously a residential region of Cape Town, District Six was geographically located at the heart of the Dutch-East Indian slave trade [43], [47]. In this study we define an almost equal ‘broadly Indian’ and ‘Sino-Tibetan’ contribution to the D6-Coloured. Besides fixation for the dry earwax allele in the Han Chinese and Koreans, an elevated frequency (71%) has been reported for the Indian Dravidian inhabitants of Tamil Nadu (correlating to the DR-S-LP3 population from this study) [41]. Lack of this allele in our subjects alludes to a non-Dravidian Indian contribution which was further supported by non-contributing independent GC4 Dravidian subgroup substructure.
Since the submission of this paper, two publications have emerged that have addressed genomic variation within the southern African region we studied. The first assessed ∼500 K custom designed variants including study subjects described as Ju/'hoan and !Xun (!Xuun) and grouped together as Kx'a speakers [61]. Significant findings consistent with our analyses include ∼20% non-Khoesan contribution to the !Xun (after fixing non-Khoesan contribution to the Ju/'hoan at 6%), while confirming minimal admixture contribution within the Ju/'hoan. Additionally this study dates the !Xun African non-Khoesan-mixture time to around 450 years ago and implies an ancient genetic link between southern and Eastern Africa. Our observation for a predominance of the East African Nilotic (non-Bantu) E1b1b Y-chromosomal haplogroup within the !Xun may provide further confirmation for a southern-eastern link, although our autosomal analysis suggests that this link is more likely related to the Sandawe and not the Nilotic peoples. No ancestral link was observed between east Africans and the Ju/'hoan from our study. The second paper looked at ∼2.3 million variants including study subjects described as Ju/'hoan, !Xun, Coloured (Colesburg), Coloured (Wellington) and undefined South African Bantu-speakers [62]. Consistent with our findings and the first paper, this study depicts the Ju/'hoan as a relatively homogenous population, while depicting a non-Khoesan contribution to the !Xun. In contrast to both studies, we suggest additional !Xun substructure and present the notion of two distinct !Xun prehistories. Our assumption is that contemporary !Xun represent a unique ancestral Khoesan lineage with an ancient non-Khoesan African (predominantly Bantu) contribution, with one subgroup having shared an ancient genetic link with the Ju/'hoan while the other remained genetically isolated from the Ju/'hoan. Notably the second study reports a predominance of Angolan !Xun study representation, represented in our study by the Ju/'hoan-ancestral genetic link. Further between study confirmation includes the representation of a South Asian (Indian) contribution to the Coloured, in particular the Wellington-Coloured (approximately 60 miles from Cape Town and District Six) compared with the Colesburg-Coloured (approximately 500 miles from Cape Town and District Six), with minimal East Asian ancestral contribution. Unlike the Wellington-Coloured, however, no subject in this study presented with non-African ancestry (Bantu and/or Khoesan). A single individual from District Six lacked any observable Khoesan contribution. No distinction was made for the Southern Bantu included in the latter study, so no correlation could be made with regards to the amaXhosa. The availability of new southern African datasets will allow for a more comprehensive analysis of population substructure within the region.
This study demonstrates both ancient and recent admixture within southern Africans. Cautionary concerns include: (i) bias in current content arrays towards non-African populations will greatly impact inferences about diversity among southern Africans, while lack of rare allele representation would diminish an ability to separate southern African subpopulations, (ii) lack of an available common ancestral genome that truly represents the earliest modern humans results in biases in methods used to attain divergence times among populations, (iii) inferences regarding population structure and recent admixture events are currently based on analyses of data reflecting contemporary genetic variation between populations, which is still largely lacking for the region of Southern Africa, and (iv) this is confounded by lack of data for populations that may actually be extinct. Taking these cautionary observations into consideration, we present an analysis of a set of individuals that, to the best of our knowledge, most accurately defines a homogenous ancestral Khoesan contribution, the Ju/'hoan. Additional cultural differences that may have restricted interbreeding between our Ju/'hoan and local agro-pastoral groups include; economic distinction (those without and those with possessions), language (Khoesan versus Bantu), social practices (egalitarian versus patriarchal society), kinship (bilineal/patrilineal versus matrilineal), marital locality (matrilocal versus patrilocal), and marital practices (monogamy versus polygamy, and no bridal payment versus a bridal payment). While a recent study acknowledges western influences as a result of the establishment of a Ju/'hoan ‘reserve’ near Tsumkwe [62], for this reason we actively avoided recruitment within the immediate vicinity of Tsumkwe. In contrast to the Ju/'hoan, we describe not only a ‘African non-Khoesan’ (almost equal proto-Bantu and Sandawe) contribution to the !Xun, but define two distinct !Xun lineages, with, and largely without, a shared Ju/'hoan ancestry. Additionally we describe a new population with complex ancient and recently diverged genomic contribution, the Basters of Namibia. Sharing a history with the South African Coloured, population-defining genetic signatures include increased significance of Khoesan and European contribution with gender-specific bias to a maternal and paternal contribution, respectively. In contrast, while we confirm increased ‘African non-Khoesan’ (largely Bantu and to a lesser extent Sandawe) and ‘Asian’ (Indian and Indonesian) contribution to the Coloured, we demonstrate significant regional-based ancestral differences which would have important implications for gene mapping studies that rely on self-reported ancestry among Coloured and non-Coloured populations. As inter- and intra-continental migration increases globally, so will the impact of admixture on disease gene mapping studies. The dataset presented provides an opportunity to investigate the impact of arguably some of the most diverse genomic contributions within single population identifiers.
The study was approved by the Ministry of Health and Social Services in Namibia, the human Research Ethics Committee at the University of Stellenbosch, South Africa (Project # N08/03/072), the Institutional Review Board Committee at the J. Craig Venter Institute (IRB# 2010-126) and previously the Human Research Ethics Committee at the University of New South Wales Australia (HREC# 08244). Consents were acquired either via verbal or written documentation with the understanding that the data generated will be made freely available to the scientific community as a collective. There are no known cultural limitations that would prohibit open access of the data.
Genomic DNA was isolated from whole blood using the QIAamp DNA Blood Mini kit or the FlexiGene DNA kit (QIAGEN) and quantified on the NanoDrop spectrophotometer (Thermo Scientific). A total of 105 subjects were genotyped using the Illumina HumanOmni1-Quad Beadchips. The Illumina GenomeStudio software (version1.7.4) was used for the data analysis with a GenTrain score of 0.5 as the minimum for inclusion. Excluding indels, mtDNA, Y-chromosome markers and no-calls resulted in 927,298 variants. Two subjects (both Ju/'hoan) were excluded as a result of likely first cousin relatedness based on the estimation of the probable number of shared alleles at any given marker, identity by descent (IBD) values of 0.4351 and 0.4129. Genotype calls have been made available without restrictions at http://www.jcvi.org/cms/research/projects/southern-african-genome-diversity-study/ for the complete dataset of 103 subjects according to population identifiers.
MT-haplogroup specific markers (Table S1) were identified using the phylogenetic tree (www.phylotree.org) build 13 [68], amplified and Sanger sequenced. Briefly, PCR was performed using the FastStart Taq system (Roche), product cleanup using the ExoSap method and Sanger Sequencing using the BigDye terminator cycling kit and the ABI 3100 Genetic Analyzer (Applied Biosystems). Paternally derived Y-chromosome haplogroups were assessed from a total of 1,283 Y-chromosome markers represented in the Illumina Human Omni1-Quad array content. Markers were identified from the Y-Chromosome Consortium (YCC) 2008 nomenclature [69]. E1b1b-specific marker M215 (rs2032654) was determined by amplicon-specific Sanger sequencing. All primer information and amplification conditions are available within Table S14.
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10.1371/journal.ppat.1004690 | Rational Development of an Attenuated Recombinant Cyprinid Herpesvirus 3 Vaccine Using Prokaryotic Mutagenesis and In Vivo Bioluminescent Imaging | Cyprinid herpesvirus 3 (CyHV-3) is causing severe economic losses worldwide in common and koi carp industries, and a safe and efficacious attenuated vaccine compatible with mass vaccination is needed. We produced single deleted recombinants using prokaryotic mutagenesis. When producing a recombinant lacking open reading frame 134 (ORF134), we unexpectedly obtained a clone with additional deletion of ORF56 and ORF57. This triple deleted recombinant replicated efficiently in vitro and expressed an in vivo safety/efficacy profile compatible with use as an attenuated vaccine. To determine the role of the double ORF56-57 deletion in the phenotype and to improve further the quality of the vaccine candidate, a series of deleted recombinants was produced and tested in vivo. These experiments led to the selection of a double deleted recombinant lacking ORF56 and ORF57 as a vaccine candidate. The safety and efficacy of this strain were studied using an in vivo bioluminescent imaging system (IVIS), qPCR, and histopathological examination, which demonstrated that it enters fish via skin infection similar to the wild type strain. However, compared to the parental wild type strain, the vaccine candidate replicated at lower levels and spread less efficiently to secondary sites of infection. Transmission experiments allowing water contamination with or without additional physical contact between fish demonstrated that the vaccine candidate has a reduced ability to spread from vaccinated fish to naïve sentinel cohabitants. Finally, IVIS analyses demonstrated that the vaccine candidate induces a protective mucosal immune response at the portal of entry. Thus, the present study is the first to report the rational development of a recombinant attenuated vaccine against CyHV-3 for mass vaccination of carp. We also demonstrated the relevance of the CyHV-3 carp model for studying alloherpesvirus transmission and mucosal immunity in teleost skin.
| Common carp, and its colorful ornamental variety koi, is one of the most economically valuable species in aquaculture. Since the late 1990s, the common and koi carp culture industries have suffered devastating worldwide losses due to cyprinid herpesvirus 3 (CyHV-3). In the present study, we report the development of an attenuated recombinant vaccine against CyHV-3. Two genes were deleted from the viral genome, leading to a recombinant virus that is no longer capable of causing the disease but can be propagated in cell culture (for vaccine production) and infect fish when added to the water, thereby immunizing the fish. This attenuated recombinant vaccine also had a drastic defect in spreading from vaccinated to non-vaccinated cohabitant fish. The vaccine induced a protective mucosal immune response capable of preventing the entry of virulent CyHV-3 and is compatible with the simultaneous vaccination of a large number of carp by simply immersing the fish in water containing the vaccine. This vaccine represents a promising tool for controlling the most dreadful disease ever encountered by the carp culture industries. In addition, the present study highlights the importance of the CyHV-3 - carp model for studying alloherpesvirus transmission and mucosal immunity in teleost skin.
| Aquaculture is currently one of the world’s fastest growing food production sectors, with an annual growth rate of 6.2% between 2000 and 2012 [1]. Global aquaculture currently provides half of the fish consumed worldwide. However, aquaculture is suffering important economic losses due to outbreaks of infectious and parasitic diseases [2–4], which are promoted by high rearing densities under artificial conditions [5], the efficient abiotic vector properties of water [5], and the international trade of genitors and fingerlings [6].
Inland aquaculture of freshwater finfishes dominates global aquaculture, representing 57.9% (38.6 million tons) of global production of aquaculture, far greater than mollusks (22.8%), crustaceans (9.7%), and mariculture of finfishes (8.3%) and other aquatic animals (1.3%) [1]. The common carp (Cyprinus carpio) is one of the oldest cultivated freshwater fish species. In China, carp cultivation dates back to at least the 5th century BC, and in Europe carp farming began during the Roman Empire [7]. Common carp is currently one of the most economically valuable species in aquaculture; it is one of the main fish species cultivated for human consumption, with worldwide production of 3.8 million tons (the third most important species based on the number of tons produced) in 2012, representing 9.8% of all freshwater fish production and US$5.2 billion [8]. Common carp is also produced and stocked in fishing areas for angling purposes. In addition, its colorful ornamental varieties (koi carp) grown for personal pleasure and competitive exhibitions represent one the most expensive markets for individual freshwater fish, with some prize-winners being sold for US$10,000-1,000,000 [9].
Koi herpesvirus (KHV), also known as cyprinid herpesvirus 3 (CyHV-3; species Cyprinid herpesvirus 3, genus Cyprinivirus, family Alloherpesviridae, order Herpesvirales), is the etiological agent of an emerging and mortal disease in common and koi carp [10]. Since its emergence in the late 1990s, this highly contagious disease has caused severe economic losses worldwide in both common and koi carp culture industries [9,11,12]. Outbreaks of CyHV-3 disease are associated with a mortality rate of 80–100% [9]. As an example, outbreaks of CyHV-3 in Indonesia in 2002 and 2003 caused an estimated economic loss of US$15 million [13,14].
The economic losses caused by infectious and parasitic diseases in aquaculture have motivated the development of efficient prophylactic vaccines [2,3]. In addition to the safety/efficacy issues that apply to all vaccines independent of the target species (humans or animals), vaccines for fish and production animals in general are under additional constraints. First, the vaccine must be compatible with mass vaccination and administered via a single dose as early as possible in life. Second, the cost-benefit ratio should be as low as possible, implying the lowest cost for vaccine production and administration [5]. Ideally, cost-effective mass vaccination of young fish is performed by bath vaccination, meaning that the fish are immersed in water containing the vaccine [15]. This procedure allows vaccination of a large number of subjects when their individual value is still low and their susceptibility to the disease highest [15]. The use of injectable vaccines for mass vaccination of fish is restricted to limited circumstances, i.e. when the value of an individual subject is relatively high and when vaccination can be delayed until an age when the size of the fish is compatible with their manipulation [16,17].
Various anti-CyHV-3 vaccine candidates have been developed [18–27]. Injectable DNA vaccines are efficacious under experimental conditions [21,22] but incompatible with most of the field constraints described above (i.e., the value of an individual common carp is very low and they should be vaccinated when only a few grams). In contrast, attenuated vaccines could meet these constraints but raise safety concerns, such as residual virulence, reversion to virulence, and spreading from vaccinated to naïve subjects. A conventional anti-CyHV-3 attenuated vaccine has been developed by serial passages in cell culture and UV irradiation [18–20,26,27]. This vaccine is commercialized in Israel by the KoVax Company for the vaccination of koi and common carp by immersion in water containing the attenuated strain and was recently launched in the US market under the name Cavoy but was withdrawn from sale after just a year. This vaccine has two major disadvantages. First, the attenuated strain has residual virulence for fish weighing less than 50 g [18,27]. Second, the determinism of the attenuation is unknown, and consequently, reversions to a pathogenic phenotype cannot be excluded [28].
Due to scientific advances in molecular biology and molecular virology, the development of attenuated vaccines is evolving from empirical to rational design [28–30]. A viral genome can be edited to delete genes encoding virulence factors in such a way that reversion to virulence can be excluded. This approach has been tested for CyHV-3 by targeting different genes thought to encode virulence factors, such as open reading frame (ORF) 16, ORF55, ORF123, and ORF134, which encode a G protein-coupled receptor, thymidine kinase (TK), deoxyuridine triphosphatase, and an Interleukine-10 (IL-10) homolog, respectively. Unfortunately, none of the recombinants lacking these genes express a safety/efficacy profile compatible with use as an attenuated recombinant vaccine [23,24,31].
In the present study, we took advantage of a recombinant strain that was obtained by accident. This strain was deleted of three genes and expressed a safety/efficacy profile compatible with use as an attenuated vaccine. Using prokaryotic recombination technologies, a series of recombinants was produced to identify the determinism of the attenuation and to improve further some properties of the vaccine candidate. Based on the results, a double deleted strain was selected as a vaccine candidate. The safety and efficacy of this strain as a recombinant attenuated vaccine against CyHV-3 were investigated using various approaches, including an in vivo bioluminescent imaging system (IVIS). Taken together, the results of the present study demonstrate that this vaccine candidate is appropriate for safe and efficacious mass vaccination of carp against CyHV-3, inducing a protective mucosal immune response at the portal of entry. In addition to its importance for applied research, the present study is also important for fundamental research by demonstrating the potential of the CyHV-3 - carp model for studying the transmission of members of the familly Alloherpesviridae and mucosal immunity in teleost skin.
In an earlier study, we produced a CyHV-3 recombinant strain lacking ORF134, which encodes a viral IL-10 [31]. This recombinant was produced using a bacterial artificial chromosome (BAC) clone of the CyHV-3 FL strain and prokaryotic recombination technologies. To reconstitute infectious viral particles encoding a wild-type ORF55 locus (encoding TK) in which the BAC cassette was inserted, the FL BAC ORF134 Del galactokinase (galK) plasmid (deleted for ORF134) was co-transfected with the pGEMT-TK plasmid in permissive Cyprinus carpio brain (CCB) cells. For one of the viral clones we obtained, this procedure led to unexpected non-homologous recombination between the pGEMT vector and the beginning of ORF57, leading to reversion to a wild-type ORF55 locus and deletion of CyHV-3 genome from coordinates 97001 to 99726. This deleted region encodes most of ORF56 and the beginning of ORF57 (Fig. 1A). Despite this triple deletion (ORF56-57 and ORF134), this strain replicated efficiently in vitro, reaching a titer of 106 plaque forming units (pfu)/ml. Moreover, it expressed in vivo a safety/efficacy profile compatible with use as an attenuated vaccine for vaccination of carp against CyHV-3 by immersion in water containing the virus.
Here, we produced and tested a series of recombinant strains to establish the contribution of the ORF134 and the ORF56-57 deletions to the observed safety/efficacy profile of the triple deleted recombinant described above. Based on our recent study demonstrating that ORF134 is not essential for virulence in vivo [31], we hypothesized that the attenuated phenotype of the triple deleted strain was mainly, if not exclusively, determined by the ORF56-57 deletion. However, deletion of ORF134, which encodes an IL-10 homolog, could potentially contribute to the safety observed for the triple recombinant and/or the immune response it induced. To test these hypotheses, two groups of recombinants were produced independently using BAC cloning technologies according to the strategy described in Fig. 1B. Both groups encoded the ORF56-57 deletion (Fig. 1A) encompassing coordinates 97001 to 99750. This deletion was slightly longer on the 3’ end than the deletion observed in the triple deleted strain described above in order to remove two potential alternative ATG start codons present in the beginning of ORF57. In addition to the ORF56-57 deletion, one of the groups also encoded an ORF134 deletion. When reconstituting infectious virions from recombinant BAC plasmids, the BAC cassette was removed by homologous recombination, leading to a wild-type ORF55 locus, or by cre-loxP-mediated excision, leading to a truncated ORF55 locus (TKtrunc genotype). The molecular structures of all recombinant strains were controlled by SacI restriction fragment length polymorphism (RFLP), Southern blot analysis (S1 Fig.), and sequencing of the genome region encoding ORF55 to ORF57. Strains encoding wild-type loci had a sequence identical to the reference sequence available in the GenBank (Accession number NC_009127.1) and all recombinant strains had the expected modified sequences. All recombinants were tested for their virulence (i.e., safety) and their ability to induce immune protection against a lethal challenge (i.e., efficacy) (Figs. 2 and S2). Independent of the ORF55 genotype (wild-type versus truncated (TKtrunc)) and ORF134 genotype (wild-type versus deleted (Δ134)), all recombinants encoding the double ORF56-57 deletion (Δ56-57) expressed comparable safety/efficacy profiles. Fish infected with the WT (Fig. 2) or the TKtrunc (S2 Fig.) strains exhibited all clinical signs associated with CyHV-3 disease, including apathy, folding of the dorsal fin, hyperemia, increased mucus secretion, skin lesions, suffocation, erratic swimming, and loss of equilibrium. Independent of the dose tested, mortality ranged from 47 to 87%. In contrast, all double ORF56-57 deleted (Δ56-57) strains expressed an attenuated phenotype. No clinical signs were observed in fish inoculated at 4 or 40 pfu/ml, and only a few fish expressed transient mild hyperemia and folding of the dorsal fin at the higher dose (400 pfu/ml). Importantly, all fish exhibited unaltered swimming and feeding behavior. These observations were confirmed by the survival rate, which was very high in all groups (a single fish died in the group inoculated with the highest dose of the Δ56-57Δ134 strain). To investigate if the fish initially inoculated with strains encoding the ORF56-57 deletion developed a protective anti-CyHV-3 immune response, they were challenged at 21 and 42 days post-primary infection (dppi) by cohabitation with fish that were freshly inoculated with the parental FL strain (Fig. 2 and S2 Fig.). Though mock-infected fish were very sensitive to this challenge (reaching 100% mortality rate in nearly all cases), fish previously infected with strains encoding the ORF56-57 deletion at 400 pfu/ml did not express the disease and had a survival rate of 100%. Fish initially inoculated at 40 pfu/ml exhibited also very high survival rates (100% or 93%). Fish initially inoculated at 4 pfu/ml exhibited partial protection ranging from 27% to 80% survival rate. Interestingly, the challenges performed 21 dppi led to comparable results 42 dppi, indicating early onset of protective immunity. The results presented above demonstrate that the double ORF56-57 deletion correlated with a safety/efficacy profile compatible with use of the encoding strain as an attenuated vaccine. Additional truncation of ORF55 and/or deletion of ORF134 did not improve the safety/efficacy profile. The latter result is consistent with our recent study demonstrating that ORF134 does not affect CyHV-3 virulence [31] as observed for other virally encoded IL-10 homologs [32].
Taking into account the results for safety and efficacy, as well as the stability of the ORF56-57 deletion observed when the virus was passed extensively in cell culture (demonstrated by sequencing of both ORFs after passage of the virus under industrial conditions corresponding to vaccine production, Master Seed Virus +5; sequences observed were identical to initially edited sequences), the Δ56-57 strain was selected as an attenuated recombinant vaccine candidate against CyHV-3 disease.
Recently, we demonstrated the usefulness of the IVIS to study the portal of entry and spreading of CyHV-3 into its host [33–36]. In the present study, we exploited this technology to investigate the safety of the Δ56-57 strain. To achieve this goal, a recombinant strain encoding both the ORF56-57 deletion and a luciferase (Luc) expression cassette inserted in a previously studied insertion site [33], hereafter called the Δ56-57 Luc strain, was produced by homologous recombination in eukaryotic cells (S3 Fig.). The molecular structure of this strain was confirmed by RFLP, Southern blot analysis (S4 Fig.), and full-length genome sequencing. Full length genome sequence of WT Luc and Δ56-57 Luc strains have been deposited in the GenBank (Accession numbers KP343683 and KP343684, respectively). The two strains share a number of defects in genes other than those noted in other strains. This includes one point deletion in ORF129 (GGGG>GGG), one inversion/deletion in ORF122, one deletion affecting the ends of ORF27 (already mutated in some other CyHV-3 strains) and ORF28. However, since these mutations are present in both WT Luc and Δ56-57 Luc strains, they are not relevant to the attenuated observed phenotype.
Before its use in vivo, the Δ56-57 Luc strain was tested in vitro. First, even if the insertion of the Luc expression cassette in a wild type strain was previously shown to have no effect on viral growth in cell culture and virulence in vivo [33], we tested whether this insertion affects viral growth for the Δ56-57 genotype (Fig. 3A). Replication of the Δ56-57 Luc strain was comparable to that of the Δ56-57 strain. Consistent with an earlier report [33], replication of the WT Luc strain was comparable to that of the WT strain. These data demonstrate that insertion of the Luc cassette between ORF136 and ORF137 had no effect on viral growth. However, this experiment revealed that both strains encoding the ORF56-57 deletion replicated at a significantly lower level than the strains encoding the ORF56-57 wild-type genotype (p<0.0001 at 2 and 4 days post-infection (dpi), and p = 0.0012 at 6 dpi). Second, to investigate whether the ORF56-57 deletion affects the expression of the Luc expression cassette, CCB cells were infected at different multiplicity of infection (MOI) with the WT Luc strain and Δ56-57 Luc strain (Fig. 3B). IVIS analyses performed 12 and 24 hours post-infection (hpi) demonstrated that the transduced Luc expression was comparable between the two strains. In contrast, analyses performed 48 hpi revealed significantly faster replication of the WT Luc strain compared to the Δ56-57 Luc strain (p<0.0001). Taken together, the results demonstrate that Δ56-57 Luc strain replication in vitro is comparable to that of the Δ56-57 strain, and it transduces Luc expression comparably to the WT Luc strain. These data validate the use of these two Luc recombinant strains to investigate the effect of the double ORF56-57 deletion on the pathogenesis of CyHV-3. This question was investigated by performing the experiment described in Fig. 4.
Fish were inoculated by immersion in water containing the WT Luc strain or Δ56-57 Luc strain. At different times post-infection (restricted to 8 dpi for the WT Luc strain to precede the peak of mortality), fish were collected and each fish analyzed by the IVIS (Fig. 5), qPCR (Fig. 6), and histopathological examination (Fig. 7). The results of these read-outs were presented throughout the figures using a distinctive constant symbol for each analyzed fish according to the viral strain inoculated and the time post-infection at which the fish was collected. This mode of presentation allows identification of all results obtained for a particular fish in the figures. The skin has been shown to be the major portal of entry of CyHV-3 after inoculation by immersion in water containing the virus [33]. Consistent with this earlier report, we observed that, independent of the inoculated virus, all tested fish expressed light foci on their skin as early as 2 dpi (Fig. 5). Though the signal increased in intensity from 2 to 6 dpi on fish inoculated with the WT Luc strain, it remained stable for 12 dpi on fish inoculated with the Δ56-57 Luc strain. Global statistical analysis of the data obtained during the first 8 dpi demonstrated no significant difference in the number of positive fish between the two virus strains, as all fish were positive, but significantly less light emission was observed for fish infected with the double ORF56-57 deleted recombinant. After initial replication in the skin, the WT Luc strain spread and replicated rapidly in all tested organs, and all fish were positive in all organs at 6 dpi (Fig. 5). These results are consistent with earlier reports [33,34]. Spreading of the Δ56-57 Luc strain within infected fish was significantly delayed, reduced both quantitatively (i.e., number of positive fish) and qualitatively (i.e., intensity of the signal), and transient (Fig. 5, compare left and right columns). The reduced ability of the Δ56-57 Luc strain to spread into infected fish was further supported by qPCR analysis performed on the gills and heart (Fig. 6). Compared to fish infected with the WT Luc strain, fish infected with the double ORF56-57 deleted variant expressed significantly lower viral loads. Taken together, the results in Fig. 5 and 6 demonstrate that the Δ56-57 Luc strain was as capable of entering fish as the wild-type control strain, but it had a reduced ability to spread in the infected animal, thereby explaining its attenuated phenotype. Finally, as the gills have been shown to support drastic anatomopathological modifications during CyHV-3 disease, they were examined at different times post-infection with the two viral strains (Fig. 7). Examination of both gill rakers and gill lamellae led to the conclusion that the Δ56-57 Luc strain induced much less severe lesions than the WT Luc strain, as demonstrated by the significantly fewer positive fish (gill lamellae but not gill rakers) and significantly lower lesion scores (gill lamellae and gill rakers). These results support the attenuation conferred by the double ORF56-57 deletion and justify the selection of the Δ56-57 strain as a recombinant attenuated vaccine candidate for mass vaccination of carp by immersion in water containing the vaccine.
A key safety aspect of an attenuated recombinant vaccine is the possible spread from vaccinated to unvaccinated naïve cohabitant subjects. This aspect is particularly important for vaccination in aquaculture, where water can act as an efficient abiotic vector. To address this issue, we used the two recombinant Luc strains described above to investigate the effect of the double ORF56-57 deletion on the ability of CyHV-3 to spread from newly infected fish to naïve sentinel fish (Fig. 8). The spread of CyHV-3 was studied using two experimental settings designed to allow transmission of the virus through infectious water (water sharing) or through infectious water and physical contact between infected and naïve sentinel fish (tank sharing). Fish were infected with either the WT Luc strain or the Δ56-57 Luc strain for 2 h in water containing the virus. After rinsing in fresh water, infected fish were distributed in tanks (Fig. 8) for water sharing and tank sharing experiments. The IVIS analysis of initially infected fish 2 dpi (n = 4 for each virus strain) demonstrated that they all expressed luciferase activity on their skin as described in Fig. 5 (2 days post-infection, skin analysis), thereby demonstrating successful infection. The IVIS analysis of sentinel fish was performed after different periods of cohabitation to investigate potential spreading; the experiment was stopped at day 14 for the WT Luc strain because the vast majority of fish were dying from the infection. Under water sharing conditions, no transmission was detected for the Δ56-57 Luc strain over 18 days. In contrast, for the WT Luc strain, positive fish (2 out of 5) were detected as early as 6 days of cohabitation. Both the number and radiance of positive fish increased with time. Under tank sharing conditions, erratic and rare transmission was observed for the Δ56-57 Luc strain. Only two of the 25 analyzed fish were positive respectively after 10 and 18 days of cohabitation. In contrast, naïve sentinel fish cohabiting with fish infected with the WT Luc strain became infected as early as 6 days of cohabitation. Taken together, the results demonstrate that the double ORF56-57 deletion drastically impaired the ability of CyHV-3 to spread from freshly infected fish to naïve sentinel fish.
The experiments presented above demonstrate the usefulness of the IVIS for studying the safety of an attenuated recombinant vaccine candidate. In the last section of this study, we also used the IVIS to characterize the immune protection conferred by the Δ56-57 strain (Fig. 9). Fish were vaccinated by immersion in water containing two different doses of the Δ56-57 strain. Six weeks post-primary infection, fish were challenged with the WT Luc strain and analyzed by the IVIS at 2, 4, and 8 days post-challenge. Analyses performed on day 2 post-challenge revealed few vaccinated fish (2 fish vaccinated at 40 pfu/ml and 1 fish vaccinated at 400 pfu/ml) expressing a low luciferase signal close to the threshold determined for the mock-infected/mock-challenged group. None of the fish analyzed on day 2 post-challenge expressed a positive signal in internal organs. No positive signal was observed on the skin or internal organs of vaccinated fish at later time points, independent of the dose used for vaccination. In contrast, mock-infected fish challenged with the WT Luc strain expressed increasing light intensity according to time post-infection. The data demonstrate that vaccination with the Δ56-57 strain induces a protective mucosal immune response at the portal of entry.
In the present study, we took advantage of an accidentally obtained recombinant CyHV-3 strain that exhibited a safety/efficacy profile compatible with use as an attenuated vaccine. This recombinant strain had deletions at three loci (ORF56-57, and ORF134). To determine the role of the double ORF56–57 deletion in the phenotype and to improve further the quality of the vaccine candidate, a series of deleted recombinants was produced and tested in vivo. The Δ56-57 strain with a deletion encompassing ORF56 and ORF57 was selected and characterized as an attenuated recombinant vaccine candidate against CyHV-3. This strain exhibited properties compatible with use as an attenuated recombinant vaccine for mass vaccination of carp by immersion in water containing the virus. It replicated efficiently in vitro, though at a lower level than the parental wild-type strain, expressed a safe attenuated phenotype, and induced a protective mucosal immune response against a lethal challenge by blocking viral infection at the portal of entry.
Deletion of ORF56 and/or ORF57 has not been reported previously for CyHV-3. Sequencing of the Cavoy attenuated anti-CyHV-3 vaccine demonstrated that it encodes a wild-type ORF56 and ORF57 sequence identical to the reference sequence available in the GenBank (Accession number NC_009127.1, sequencing from coordinates 96370 to 101248), whereas immunofluorescent staining of infected cells demonstrated the expression of both proteins (S5 Fig.). The proteins pORF56 and pORF57 have unknown functions. They both lack a signal peptide and transmembrane domain. Though pORF57 is one of the most abundant proteins of the CyHV-3 virion, pORF56 is a non-structural protein [37,38]. Extensive bioinformatic analyses did not generate a hypothesis concerning their putative functions. Homologs of CyHV-3 ORF56 and ORF57 are found in orthologous positions in the two other cyprinid herpesviruses (CyHV-1 and CyHV-2) [39], among which CyHV-2 (also known as goldfish hematopoietic necrosis virus) is responsible for a severe disease initially reported in goldfish (Carassius auratus auratus) that recently emerged in gibel carp (Carassius auratus gibelio) [40]. Based on the positional orthology of ORF56 and ORF57 in cyprinid herpesviruses, the ORF56-57 deletion reported here for CyHV-3 can likely be exploited to produce attenuated CyHV-1 and CyHV-2 recombinant vaccine candidates. Homologs of CyHV-3 ORF57 are also present in the cyprinivirus anguillid herpesvirus 1 (AngHV-1) [41] and a more distantly related virus, crocodilepox virus (CRV) [39,42], which suggests an ancestral origin for the gene. AngHV-1 infects European and Japanese eel (Anguilla anguilla and Anguilla japonica) and is responsible for mortalities of up to 30% in cultured and wild eel populations [43]. As part of a follow-up project of the present study, ORF56 and ORF57 single deleted recombinants were produced and were tested in vivo. The data obtained demonstrate that most of the attenuation observed for the double deletion ORF56-57 relied on the deletion of ORF57. These data identify the ORF57 homologue encoded by AngHV-1 (ORF35) as an obvious locus for production of an attenuated recombinant vaccine candidate.
Our earlier finding based on a wild-type CyHV-3 strain suggested that the skin is the major portal of entry after inoculation by immersion in water containing the virus [33]. After initial replication in the skin, wild-type CyHV-3 spread rapidly to virtually all organs (Fig. 5, left column). Soon after the skin, the gills, followed by other organs, support viral infection [34,44,45]. Even if the skin was always the first organ to express luciferase, the early positivity of the gills led to the hypothesis that they could represent an alternative and possibly parallel portal of entry for the virus [45–47]. The present study of the tropism of the Δ56-57 strain demonstrated that it also spreads from the skin to all tested organs. However, compared to the wild-type strain, its systemic spread to the other organs was much slower, and its replication was reduced in intensity and duration (Figs. 5 and 6). The slower spread of the Δ56-57 vaccine strain within infected fish allowed better discrimination of the portal(s) of entry from secondary sites of infection. Though the skin of all fish was positive as early as 2 dpi, all of the other tested organs (including gills and gut) were positive in the majority of fish after 6 dpi. These data further demonstrate that the skin is the major portal of entry of CyHV-3 after infection by immersion and suggest that the other organs (including gills and gut) represent secondary sites of replication.
The double ORF56-57 deletion reduced the ability of the virus to spread within infected fish and impaired virus transmission from infected fish to naïve sentinel fish. In the present study, we designed two tank systems to test the ability of CyHV-3 strains to spread from infected to naïve sentinel subjects through indirect or indirect-and-direct contact (Fig. 8). The absence of detectable transmission under water sharing conditions and the very low level of transmission detected under tank sharing conditions suggested that fish inoculated with the vaccine candidate strain excrete very low amounts of infectious particles during the 18 days following vaccination. Combined with the observation that all vaccinated fish supported skin infection during the first 8 dpi, these data demonstrate that the ORF56-57 deletion drastically impaired viral excretion by infected subjects. Notably, the spread of the vaccine candidate strain was tested in the present study by cohabitation of fish immediately after vaccination. This is in contrast to previous studies that tested spreading by starting cohabitation weeks after vaccination, when replication of the attenuated vaccine strain was ending [27].
In addition to its relevance for applied research, the CyHV-3 - carp model has numerous qualities as a subject of fundamental research [9,11,12]. First, it is phylogenetically distant from the vast majority of herpesviruses studied so far, thereby providing an original field of research [10,39,48]. Second, it can be studied in laboratories by infection of its natural host (homologous virus-host model). Third, it allows the study of the complete infectious cycle of an alloherpesvirus, including transmission from infected to naïve animals. Transmission is an essential step in the biological cycle of pathogens and acts as a main motor of their evolution. However, very few laboratory models currently studied among members of the Herpesvirales allow the study of transmission [49]. Here, we report two systems allowing the study of CyHV-3 transmission by indirect or direct contact between infected and naïve sentinel fish. They will be useful for understanding the biology of an alloherpesvirus and for evaluating the ability of prophylactic strategies to inhibit the spread of wild-type strains in a fish population. The difference in transmission kinetic observed between the two systems demonstrated that direct contact between subjects promotes transmission of CyHV-3. Early replication of the virus at the portal of entry should contribute not only to the spread of the virus in infected fish, but also to the spread of the virus in the fish population. As early as 2 to 3 dpi, infected fish rubbed themselves against each other or objects. This behavior could contribute to skin-to-skin transmission [35]. Later during infection, transmission could also occur when uninfected fish peck macroscopic skin herpetic lesions developed by infected fish, thereby being infected by both skin to skin contact and infection of the pharyngeal periodontal mucosa [34]. These data highlight the importance of inducing a mucosal immune response through vaccination against CyHV-3.
Immunization of carp by immersion in water containing the vaccine candidate strain induced protective mucosal immunity, preventing replication of a challenging strain. Vaccination by immersion has several advantages. Firstly, it is compatible with mass vaccination. Secondly, in contrast to injection based vaccination, it is not restricted by a minimum fish size. Though the experiments reported in the present study were performed with fish older than 6 months, the safety and the efficacy of the Δ56-57 strain has been demonstrated in younger fish (4 months old fish, mean weight of 1.3 g). These data demonstrated that the Δ56-57 strain is compatible with vaccination of carp soon after they acquired a competent adaptive immune system (at the end of the second month of life [50,51]). Thirdly, in contrast to the oral route, which favors the greediest fish, vaccination by immersion delivers a comparable dose to each fish. Finally, it induces antigen exposition and, hopefully, an adaptive immune response at the portal of entry used by the pathogen. The data presented in Fig. 9 demonstrate that the adaptive immune response induced by the vaccine candidate prevented infection of a challenging strain at the portal of entry. The mucosal immunity induced against CyHV-3 at the portal of entry could contribute not only to protecting vaccinated fish from CyHV-3 disease (clinical protection), but also to preventing them from transmitting virulent circulating strains (sterile immunity), thereby inducing herd immunity. This hypothesis is currently being tested using the two tank systems described above. The skin of teleost fish is a pluristratified epithelium composed exclusively of living cells covered by a mucus layer [35]. The large surface area of this mucosa, combined with its easy access, promoted the study of its innate and adaptive immune components. Interest in teleost skin as a model for studying comparative mucosal immunity recently increased with the discovery of a new immunoglobulin isotype, IgT (or IgZ) [52–54], specialized in mucosal immunity [55–58]. This specific mucosal adaptive immune response further supports the importance of antigen presentation at the pathogen’s portal of entry to induce topologically adequate immune protection capable of blocking pathogen entry into the host [59,60]. Future studies are required to unravel the mechanisms underlying the mucosal immune protection conferred by the anti-CyHV-3 vaccine candidate developed in the present study. However, this study provides an original model for studying epidermal mucosal immunity against an infectious agent in teleosts.
Since it was first described in the late 1990s, CyHV-3 rapidly spread to different continents, causing severe financial losses in the common carp and koi culture industries worldwide [9]. In addition to its negative economical and societal impacts, CyHV-3 also has a negative environmental impact by affecting wild carp populations [61,62]. Thus, CyHV-3 rapidly became the subject of applied research aiming to develop diagnostic methods and a safe/efficacious vaccine. In the present study, we used BAC cloning mutagenesis and IVIS technology to develop and characterize the first rationally designed attenuated recombinant CyHV-3 vaccine compatible with mass vaccination. In addition, the present study demonstrated the importance of the CyHV-3 - carp model as an interesting and original fundamental subject of research. This model allows the study of the complete infectious cycle (including transmission from infected to naïve animals) of an alloherpesvirus by infection of its natural host. Furthermore, infection of carp by CyHV-3 represents a unique model for studying skin mucosal immunity in teleosts in response to a natural infection.
CCB cells [63] were cultured in minimum essential medium (Sigma) containing 4.5 g/L glucose (D-glucose monohydrate; Merck) and 10% fetal calf serum (FCS) as described previously [24]. The CyHV-3 FL strain was isolated from the kidney of a fish that died from CyHV-3 infection and previously used to produce the FL BAC plasmid [24]. The FL BAC revertant ORF136 Luc strain (called WT Luc in the present study) of CyHV-3 was derived from the FL BAC plasmid by prokaryotic mutagenesis [33]. The Cavoy strain was cultured from the Cavoy vaccine (Novartis).
Common carp (Cyprinus carpio carpio) were kept in 60 L tanks at 24°C. Water parameters were checked twice per week. Microbiological, parasitic, and clinical examinations of the fish just before the experiments demonstrated that they were healthy. All experiments were preceded by an acclimation period of at least 2 weeks.
Two modes of inoculation were used: inoculation by immersion in infectious water and inoculation by cohabitation with newly infected fish. Fish were inoculated by immersion in water (volume adapted based on fish size and fish number to use a biomass around 10%) containing the virus for 2 h under constant aeration. At the end of the incubation period, the fish were returned to 60 L tanks. For inoculation by cohabitation, newly infected fish were produced by immersion of naïve fish for 2 h in water containing 200 pfu/ml of the FL strain. At the end of the incubation period, newly infected fish were released into the tank of fish to be contaminated at a ratio of 2 newly infected fish per 15 fish to be contaminated.
The experiments, maintenance and care of fish complied with the guidelines of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes (CETS n° 123). The animal studies were approved by the local ethics committee of the University of Liège, Belgium (Laboratory accreditation No. 1610008, protocol No. 1059). All efforts were made to minimize suffering.
Different recombinant BAC plasmids were produced using the FL BAC [24] and FL BAC ORF134 Del plasmids [31] as parental plasmids (Fig. 1B). Recombinant plasmids were produced based on the strategy described in Fig. 1B using galK positive/negative selection in bacteria as described previously [64]. Recombination cassettes encoding galK were produced by PCR using the primers listed in Table 1 and the pgalK vector as the template. The ORF56-57 Del cassette consisted of 250 bp upstream (coordinates 96751-97000) and 249 bp downstream (coordinates 99751-100000 with deletion of base 99760) of the ORF56-57 deletion (Fig. 1A, ORF56-57 deletion). To reconstitute the infectious virus, the recombinant BAC plasmids were co-transfected in CCB cells using polyethylenimine (3 μg polyethylenimine per 1 μg DNA) with either the pGEMT-TK plasmid or pEFIN3 NLS Cre (molecular ratio 1:75) [24]. Transfection with pGEMT-TK plasmid induced recombination upstream and downstream of the BAC cassette, leading to its complete removal and consequent reversion to a wild-type TK locus (FL BAC revertant strains). Transfection with pEFIN3 NLS Cre induced expression of a nuclear Cre recombinase and cre-loxP-mediated excision of the BAC cassette. Viruses reconstituted (FL BAC excised strains) by this procedure express a truncated form of TK due to a 172 bp foreign sequence of the BAC cassette left in the ORF55 locus. Independent of the method, EGFP-negative plaques (the BAC cassette encodes an EGFP expression cassette) were picked and amplified.
The Δ56-57 Luc strain was produced by co-infection of CCB cells with two parental strains (S3 Fig.). CCB cells were superinfected at the MOI of 10 pfu/cell with the WT Luc strain and Δ56-57 strain (ratio 1:1). When all cells expressed a cytopathic effect, the supernatant containing progeny virions was collected and submitted to limiting dilution of clone virions. The recombinant viral clone expressing both Luc and ORF56-57 deletion was selected by successive screening with the IVIS and PCR genotyping, respectively.
Mouse polyclonal antibodies (pAb) directed against the unstructured domain (IUPred, http://iupred.enzim.hu) of pORF56 encoded by coordinates 98049-99398 (GenBank accession number NC_009127.1) were produced by DNA immunization using a customized commercial service (DelphiGenetics). Mouse monoclonal antibodies (mAb) directed against pORF57 were selected from a bank of mAbs raised against CyHV-3 structural proteins; mAb 6B2 recognizes an epitope expressed in the last 165 amino acid residues of pORF57 (genomic coordinates 100309-100803, GenBank accession number NC_009127.1).
Cells were fixed in PBS containing 4% (w/v) paraformaldehyde at 4°C for 15 min and then 20°C for 30 min. After washing with PBS, samples were permeabilized in PBS containing 0.1% (v/v) NP-40 at 37°C for 15 min. Immunofluorescent staining (incubation and washes) was performed in PBS containing 10% FCS (v/v). Mouse pAb raised against pORF56 (diluted 1:500), mAb 6B2 raised against pORF57 (diluted 1:2500), and rabbit pAb raised against CyHV-3 structural proteins (diluted 1:1500) were used as the primary antibodies. The primary antibody was incubated at 37°C for 1 h. Alexa Fluor 488 goat anti-mouse immunoglobulin G (H+L) and Alexa Fluor 568 goat anti-rabbit immunoglobulin G (H+L) (Invitrogen) were used as the secondary antibodies. The secondary antibody was incubated at 37°C for 30 min. After washing, cells were mounted using Prolong Gold Antifade Reagent with DAPI (Invitrogen).
CyHV-3 recombinants were characterized by RFLP using SacI digestion, Southern blot analyses [31,33], sequencing of regions of interest (ROIs), and for some recombinants, full-length genome sequencing. For full-length genome sequencing, DNA (500 ng) was sheared by sonication to an average size of 400 bp and prepared for sequencing using a KAPA library preparation kit (KAPA Biosystems, Woburn, MA, USA). The fragments were A-tailed, ligated to the NEBnext Illumina adaptor (New England Biolabs, Ipswich, MA, USA), and amplified by PCR. Index tags were added by six cycles of PCR using KAPA HiFi HotStart and NEBnext indexing primers. The samples were analyzed using a MiSeq DNA sequencer running v2 chemistry (Illumina, San Diego, CA, USA). Approximately 1 million 250-nucleotide paired-end reads were obtained per sample. The reads were prepared for assembly using Trim Galore v. 0.2.2 (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore). A scaffold was assembled de novo for the WT Luc strain using AbySS [65] and finished by referencing GenBank accession number NC_009127.1. Sequence accuracy was checked by assembling the reads against this sequence using BWA v. 0.6.2-r126 [66] and visualizing the alignment using Tablet v. 1.13.08.05 [67]. The other sequences were obtained by assembling the relevant reads against conceptual modifications of the sequence of the WT Luc strain using BWA and checking it using Tablet. In all viral genomes, two regions were of undetermined length: an A repeat and a GA repeat located at 32540–32465 and 177568-177730 nt, respectively, in NC_009127.1. The full length genome sequence of the WT Luc and Δ56-57 Luc strains were deposited in the GenBank (Accession numbers KP343683 and KP343684, respectively).
Triplicate cultures of CCB cells were infected at a MOI of 0.1 pfu/cell. After an incubation period of 2 h, cells were washed with PBS and overlaid with Dulbecco’s modified essential medium (DMEM, Sigma) containing 4.5 g of glucose/L and 10% FCS. Supernatant was removed from the infected cultures at successive intervals (0, 2, 4, and 6 dpi) and stored at-80°C. The titration of infectious viral particles was determined by duplicate plaque assays in CCB cells as described previously [31,33].
Firefly (Photinus pyralis) luciferase was imaged using an IVIS (IVIS spectrum, Caliper LifeSciences) as described previously [33–35]. For cell culture analysis, the culture medium was replaced with fresh medium containing D-luciferin (150 μg/ml) (Caliper LifeSciences). Analyses were performed after an incubation period of 5 min at room temperature. For in vivo analyses, fish were anesthetized with benzocaine (25 mg/L of water). Fifteen minutes before bioluminescence analysis, D-luciferin (150 mg/kg of body weight) was injected into the peritoneal cavity. After 15 minutes, the fish were analyzed in vivo lying on their right and left sides and ex vivo after euthanasia. Dissected organs were analyzed independent from the body. All images presented in this study were acquired using a field view of C or D, a maximum auto-exposure time of 1 minute, a binning factor of 4, and a f/stop of 1. The relative intensities of transmitted light from bioluminescence and scales were determined automatically and represented as a pseudo-color image ranging from violet (least intense) to red (most intense) using Living Image 3.2 software. ROIs were drawn manually by surrounding the organs or body outline, and the average radiance (p/sec/cm2/sr) was taken as the final measure of the bioluminescence emitted over the ROI.
The virus genome was quantified by real-time TaqMan PCR as described previously [31], by amplifying fragments of the CyHV-3 ORF89 and carp glucokinase genes. The primers and probes are listed in Table 1.
Gills were dissected immediately after euthanasia and fixed in 4% buffered formalin before embedding in paraffin [31]. Five-micrometer sections were stained with hematoxylin and eosin, mounted, and examined by microscopy. Three independent observers scored the lesions in a blind test mode using a 4-step scale. For each sample, a score was attributed for the gill rakers and gill lamellae. When at least two observers agreed on a grade, the corresponding grade was attributed. In a few exceptional cases, when all three examiners scored differently, additional analyses were performed until achieving a majority. The grading system evaluated the degree of epithelial hyperplasia, the presence of intra-nuclear viral inclusions, and cell degeneration (Fig. 7A). Briefly, grade 0 = physiological state; grade 1 = mild hyperplasia without evidence of degenerated cells and viral inclusions; grade 2 = severe hyperplasia and presence of few degenerated cells and viral inclusions; and grade 3 = the presence of abundant degenerated cells and viral inclusions (gill lamellae and gill rakers), massive epithelial hyperplasia filling the entire secondary lamellae inter-space (gill lamellae), and ulcerative erosion of the epithelium (gill rakers).
The number of positive fish or positive organs (qualitative data) according to the two viral strains was compared by a permutation test (Figs. 5–8). Briefly, all occurrences were recorded in a dataset (observed dataset). A series of 10,000 random repetitions of the same procedure was performed to allocate positive events to the two groups, creating the shuffled datasets. For each dataset (observed dataset and shuffled datasets), the global difference between the two groups was calculated by summing the daily observed differences. The proportion of shuffled datasets with a global difference greater or equal to the global difference in the observed dataset was then taken as the p-value. Histopathological grading results were compared by a permutation test after calculating the value per group and per day corresponding to the sum of all individual grades obtained in each group (Fig. 7). Viral growth (Fig. 3A), IVIS (Figs. 3B, 5, and 9B), and qPCR results (Fig. 6) were compared as quantitative data (level of positivity) using two- or three-way ANOVA with interactions followed by post-hoc t-test. The variables used and comparisons retained for statistical illustrations are described in their respective figure legends. Presence or absence of statistical significance is represented as follows, ns: not significant, * p<0.05, ** p<0.01, *** p<0.001.
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10.1371/journal.pntd.0001807 | Secondary Mapping of Lymphatic Filariasis in Haiti-Definition of Transmission Foci in Low-Prevalence Settings | To eliminate Lymphatic filariasis (LF) as a public health problem, the World Health Organization (WHO) recommends that any area with infection prevalence greater than or equal to 1% (denoted by presence of microfilaremia or antigenemia) should receive mass drug administration (MDA) of antifilarial drugs for at least five consecutive rounds. Areas of low-antigen prevalence (<1%) are thought to pose little risk for continued transmission of LF. Five low-antigen prevalence communes in Haiti, characterized as part of a national survey, were further assessed for transmission in this study. An initial evaluation of schoolchildren was performed in each commune to identify antigen-positive children who served as index cases for subsequent community surveys conducted among households neighboring the index cases. Global positioning system (GPS) coordinates and immunochromatographic tests (ICT) for filarial antigenemia were collected on approximately 1,600 persons of all ages in the five communes. The relationship between antigen-positive cases in the community and distance from index cases was evaluated using multivariate regression techniques and analyses of spatial clustering. Community surveys demonstrated higher antigen prevalence in three of the five communes than was observed in the original mapping survey; autochthonous cases were found in the same three communes. Regression techniques identified a significantly increased likelihood of being antigen-positive when living within 20 meters of index cases when controlling for age, gender, and commune. Spatial clustering of antigen-positive cases was observed in some, but not all communes. Our results suggest that localized transmission was present even in low-prevalence settings and suggest that better surveillance methods may be needed to detect microfoci of LF transmission.
| Lymphatic filariasis (LF) is among the leading causes of disability among tropical diseases and is caused by a mosquito-transmitted parasite but can be prevented using mass drug therapy and vector-control. In recent years, an international effort has been mounted to eliminate LF. In order to focus limited resources on areas with the highest disease burden, the World Health Organization (WHO) has suggested that mass drug treatment programs be focused in areas with >1% prevalence of the infection, working under the assumption that areas with <1% prevalence are equivalent to areas of limited or no transmission. We carried out an additional assessment in low-prevalence areas and observed evidence of active transmission and clustering of antigen-positive persons. Our results imply that a 1% infection threshold may not be sufficient to capture all remaining reservoirs of transmission.
| Lymphatic filariasis (LF) is one of 13 neglected tropical diseases (NTDs) known to chronically infect some of the worlds' poorest individuals [1]. While LF has been shown to be endemic in over 80 countries world-wide, it is one of six diseases that were deemed to be eradicable in 1993 by the International Task Force for Disease Eradication [2]. Since LF was made a priority by the World Health Organization (WHO) in 1997, there has been much progress in the control and elimination of LF across the globe [3]. In 2000, the WHO developed the Global Programme for the Elimination of Lymphatic Filariasis (GPELF) and established a goal to eliminate LF by 2020. A “two-pillar” approach has been implemented for the control and elimination of LF that focuses on the interruption of transmission through Mass Drug Administration (MDA) and limiting the disability caused by infection through morbidity management programs. The original definition of elimination used by the WHO was based on the demonstration that the microfilaria (Mf) or antigen prevalence at the community level was <1% and that the cumulative incidence in children born after the start of a MDA was less than 1 per 1000 children.
National mapping of lymphatic filariasis identifies areas requiring MDA by utilization of tests for microfilaremia or antigenemia. The immunochromatographic test (ICT), a rapid antigen test, is considered to be one of the most practical tools for the rapid mapping of endemic areas [4]. Mapping methods are generally based on convenience sampling to identify implementation units (administrative units, identified by the Ministries of Health, as the unit for implementing MDA) in need of MDA [5], [6]. These approaches to mapping facilitate programmatic decision-making, but because of the known heterogeneity of LF, microfoci of LF transmission may be missed when overall infection prevalence is low. Strategies for defining residual foci of transmission in low-prevalence settings are relevant to the global elimination effort, both from the perspective of targeting communities for MDA and for understanding surveillance requirements following cessation of MDA.
Haiti is one of only four LF-endemic countries in the Americas and bears 90% of the LF disease burden in the region [7]. The LF-causing filaria Wuchereria bancrofti has been historically documented in Haiti as far back as the 1700s, primarily transmitted by the Culex quinquefasciatus species [7]. Based on nation-wide mapping carried out in 2001, antigen prevalence ranged from zero to 45% among 6 to 11 year olds and 88% of the 120 communes that had been defined at the time had a prevalence greater than 1% [8]. In the current study, we conducted follow-up surveys to analyze potential transmission of LF in five communes that did not exceed this 1% threshold. Antigen surveys were performed in schools and selected antigen-positive children were defined as index cases for subsequent community surveys. Households near the residence of index cases were mapped and persons from a random sample of these households were tested for antigenemia using the ICT rapiddiagnostic. The analysis was designed to determine if active transmission of LF occurred in these settings and if infection prevalence exceeded the 1% threshold for MDA in some communities.
Surveys were conducted according to study protocols approved by the Centers for Disease Control and Prevention (CDC) and University of Notre Dame Institutional Review Boards (IRBs) and the Ethics Committee of Ste. Croix Hospital. During previous research in the area, researchers observed low rates of literacy in the population; therefore, all information regarding the study was translated into Haitian Creole, and verbal consent and assent were requested from parents and participants in both the school and community surveys. The consent form was read to potential study participants and/or parents. The reader of the consent form and a witness were then asked to sign the form to indicate the subject's agreement, in accordance with the IRB-approved protocol. Before surveys began in the schools, approval was obtained from the Haitian Ministry of Health and Population (MSPP), the Ministry of Education, department directors, and schoolmasters. Subsequently, meetings were held with parents to provide an opportunity to explain the survey and the potential risks and benefits to their child. Prior to the community survey, community leaders were informed of the survey's procedures and provided approval for the study to commence. At the time of the survey, household members were informed of survey objectives and procedures, at which time oral consent or assent was obtained for all participants.
In 2001, national mapping for LF was performed, as previously described, using antigen testing of 100–250 schoolchildren aged 6–11 years of age per commune (a district-sized political entity in Haiti) across all Haitian communes [8]. Since financial resources were limited, communes of highest antigen prevalence (≥10% prevalence) were prioritized for MDA. Our evaluation focused on five, predominantly rural, communes in which antigen prevalence in the survey was ≤1%: Grand Goâve (0.8%), Hinche (1.0%), Thomazeau (0.6%), Moron (0.8%), and St. Louis du Sud (0.4%).
Within each of the communes, five to seven schools were selected for antigen testing. These public and private schools were in urban and rural areas. Following the acquisition of informed consent and assent, as detailed above, blood samples were collected from students and tested as described below. ICTs were performed on all consenting children on site and additional blood was taken for ELISA testing upon return to laboratory facilities. Questionnaires were administered to a parent or guardian of ICT-positive children. The questionnaires were designed to identify autochthonous cases—defined as those children thought to have acquired the infection in the community where testing was conducted based on responses to questions about the absence of travel and residence in the same community for the past five years.
Five to eight ICT-positive children were chosen from each commune as index cases for that area. Index cases were children identified as antigen-positive by ICT in the school survey, with recoded GPS coordinates who responded to the questionnaire, and, when possible, were chosen based on those with confirmatory ELISA results. The index cases became central points in the subsequent community surveys.
For the community surveys, households of index cases defined the center for each testing radius. Circles of 50–75 meters were used in more densely populated urban or peri-urban areas, and circles of 100–250 meters were used in sparsely-populated rural settings. After index cases were identified, all consenting members of index households, and a systematic random sample of the neighboring households were selected for testing by ICT. All neighboring houses within the test radius were mapped using global positioning system (GPS) TerraSync (Sunnyvale, CA). In an effort to test 100 persons per community, approximately 20 households were chosen, based on an estimate of five persons per household (unpublished data). To select these 20 houses, the total number of houses in the zone was divided by 20 to determine the sampling interval. Houses were selected from a numbered list using a randomly selected starting point and this sampling interval. The questionnaire and methods used for blood collection and testing were the same as those used for the school survey. A total of 1,633 persons of all ages were evaluated in the community survey. For our study, subjects were included in the analysis if they had not been previously defined as an index case in the school study, received an ICT test result, and GPS coordinates were available for their households (n = 1290).
A blood specimen was collected from each person tested in the school or community survey. Filarial antigen-status was determined by ICT (Binax, Portland, ME) by trained laboratory personnel at the time of blood collection. Finger prick blood (100 µl) was collected and results were read at 10 minutes following application to the antigen test card. Technicians were supervised, and all efforts were made to uphold the quality of the test in regards to environment and timing. An additional 200 µl of blood was collected for confirmatory antigen testing. Collected blood was stored overnight at 4°C. The tubes were centrifuged the following day and the collected sera were stored in the field at 4°C for several days until return to Hôpital Ste. Croix where they were stored at −20°C. Sera were used for subsequent serologic assays, including confirmation of antigen status for persons with positive or questionable ICT results by Og4C3 antigen enzyme-linked immunosorbent-assay (ELISA, TropBio, Queensland, Australia). For the school survey and the subsequent community survey, ICT results were used as the indicator of antigen status for all persons; furthermore, autochthonous index cases were confirmed as antigen-positives by Og4C3 ELISA, the current gold standard for identifying circulating filarial antigens. Antigen-positive persons were treated with a single dose of diethylcarbamazine (DEC, 6 mg/kg). Due to the low percentage of ICT positive persons in each commune and the logistic difficulty of night blood surveys, microfilaria levels were not assessed.
Data were analyzed using SAS 9.3 (Cary, NC, USA), Epi Info 6 (CDC, Atlanta, USA) and ArcGIS (v. 9.3.1, Environmental Systems Research, Inc., Redlands, CA, USA). Univariate, Mantel Haenszel chi-square and logistic regression techniques were utilized.
The outcome of interest for this analysis was antigen positivity as denoted by the ICT results performed in the field. Two separate case definitions were employed as indicators of possible exposure in this analysis. The definition of index case only required a positive ICT among children tested in the school survey and the availability of GPS data. Index cases would therefore serve as potential, but unconfirmed, reservoirs of infection. A second, more stringent case definition was applied for children with positive Og4C3 results and who were defined as autochthonous cases based on their answers to the survey. These individuals were referred to as autochthonous index (AI) cases. The exposure of interest was the distance from each person tested to the nearest index or autochthonous index case. In order to determine the ordinal categories which best represent the distance to cases, a sensitivity analysis was performed for dichotomized distances of 10, 20, 40, 80 and 160 meters. Analysis of distance when using the AI case definition revealed no antigen-positive persons in the 59–99 m group, so the categories of 59–99 and 100+ meters were combined into a 60+ meter group, which was then used as the referent for the crude and multivariate regression analyses.
Potential confounders and effect modifiers, including age, gender and commune were also considered based on previous literature and anticipated heterogeneity among the communes. For the purpose of modeling, age was dichotomized into <15 years and ≥15 years, with the age of 15 or younger to denote school-aged children.
A spatial cluster analysis was performed on mapped households in the four communes recording antigen positivity. The analysis tested the spatial clustering of antigen-positive persons (excluding index cases) through the use of a Bernoulli model in SatScan (version 9.1.1, Boston, MA). A separate cluster analysis was performed for each of the four communes that included confirmed antigen-positives to better elucidate micro-clusters. Both general and isotonic simulations were performed on the commune-specific data, the latter of which accounts for the inverse relationship between risk and distance from the center of the cluster [9]. This type of simulation holds biological plausibility in representing the transmission patterns of vector-borne diseases.
Of the 2,639 children tested (age range 4–17) in the school survey 67 (2.8%) were antigen-positive by ICT (Figure 1). Positive ICTs were observed for all five communities; however, following confirmatory ELISA testing, only 3 of the 5 communes presented higher antigen prevalence than previously observed in the original national survey. The school survey was used as guide for selecting the areas for the subsequent community survey. Each of the 67 ICT positives was followed up with confirmatory ELISA testing and was given a questionnaire to determine if their infection was autochthonous in origin. Using the aforementioned case definitions, 30 children were identified as index cases and 11 were identified as autochthonous index (AI) cases (see Figure 1 for derivation of case definition from source population).
While ICT results showed antigen positive cases from the original school survey in Moron, none of those results were confirmed by ELISA testing. Furthermore, the community survey showed no positive ICTs, thus all points within the Moron commune were excluded from the remaining analysis. Among the remaining communes, based on the results of the community survey, antigen prevalence was highest in Grand Goâve (4.35%), and lowest in St. Louis du Sud (0.82%). Females were slightly more represented in the tested population, but this difference was not statistically significant (Table 1). The community survey included a broad range of ages, from 2 to 90 years old (mean = 24). Overall, antigen prevalence in young children ages zero to four years was higher than expected (1.2%) for an area at low risk for transmission, antigen prevalence increased to 3.0% in older children (5 to 9 years old), after which point antigenemia was relatively stable for older age groups (Figure 2). Antigen prevalence in the community survey was related to commune and the distance from the index case (p = 0.0044), but not to gender or to living in an urban versus rural environment (Table 1).
A sensitivity analysis was conducted to evaluate the relationship of distance from an index case to antigen positivity. The relationship was strongest when a distance of 20 meters from the index case was employed (p = 0.0004), and was therefore used as the cutoff for the distance variable (Table 2). Antigen prevalence was highest for persons living within 20 meters of the index case, with decreasing antigen prevalence as distance from an index case increased (Figure 3). Furthermore, 40% of the persons testing antigen positive were found to live within 20 m of an index case.
Crude odds ratios were calculated to evaluate the likelihood of being antigen-positive compared across the individual covariates: distance from index case, age, gender, locale (meaning urban or rural habitation) and commune. A distance of less than 20 m from an index case produced a crude prevalence odds ratio of 4.99 [95% CI 1.60, 15.51] when compared with distances of 100 m or more from an index case. All other individual covariates were evaluated for significance, but none besides distance of less than 20 m was statistically significant.
Multivariate logistic regression techniques were applied and evaluated for collinearity, interaction and confounding, and the final model is presented in Table 3 where the exposure of interest is distance from an index case. The odds of positive antigen status among persons living within 20 meters of an index case is 5.41 [95% CI 1.64, 17.83] times the odds of positive antigen status among persons living 100 meters or more from an index case, when controlling for age, gender and commune. The communes of Grand Goâve and Hinche showed significantly higher odds ratios for antigen prevalence (5.72 [95% CI 1.26, 25.90], and 7.17 [95% CI 1.53, 33.50] respectively) compared to Thomazeau.
The parallel analysis using the AI case definition determined that there were no AI cases in the initial school survey in Moron and St. Louis du Sud, thus both were excluded from further analysis. Similar results were obtained between the index and the AI case definitions in both the crude and multivariate analyses (Table 4). A proximity of less than 20 m to an AI case had a statistically significant increased odds of being antigen positive compared to distances of 60 meters or more from the AI case (cPOR 6.76 [95% CI 2.31, 19.78]) in the crude analysis, while no other covariates yielded statistically significant results. Multivariate logistic regression techniques identified an even larger increased odds of antigen positivity with close proximity to AI cases (6.70[95% CI 2.02, 22.21]) when controlling for age, gender and commune; and the communes of Grand Goâve and Hinche showed slightly higher odds of being ICT-positive when compared to Thomazeau; all of which were statistically significant.
Spatial analyses were carried out in each commune, looking at the clustering of antigen-positive persons compared to the total number of persons tested. The Bernoulli model analyzed spatial clustering of cases and non-cases from a total of 319 households, each with an average of four people tested per household. Results shown in Table 5 demonstrate statistically significant clustering of cases in Hinche and Thomazeau, when evaluated at the 5% significance level in both the general and isotonic Bernoulli analyses. Examples of clustering can be seen in Figure 4.
The original mapping for Haiti, carried out in 2001, identified the communes of Grand Goâve, Hinche, Moron, St. Louis du Sud and Thomazeau as areas of low antigen prevalence (≤1%). As transmission of lymphatic filariasis was presumed to not be occurring, the original conclusion was that MDA was not required in these areas. A subsequent school survey was conducted to determine if there was evidence of ongoing transmission in such areas. The results from our survey showed both higher than expected (>1%) antigen prevalence in three of the five communes and unexpected presence of autochthonous cases (Figure 1). These observations provide evidence that transmission of LF is occurring in settings previously identified as below the prevalence threshold that would trigger a MDA. We do not know whether the results we have observed in low-prevalence areas reflect historic foci not detected through previous surveys, or recently established transmission as a consequence of population migration, or expansion of vector populations [10]. These results were shared with the MSPP and led to the decision to carry out MDA across all Haitian communes, independent of the initial mapping results.
The statistically significant clustering of antigen-positive cases and increased odds of antigen positivity that were observed in the crude and multivariate analyses, as a function of distance to index and AI cases, all suggest that transmission might be occurring in microfoci, that is, among people living in very close proximity to one another, posing challenges for current mapping strategies. The model demonstrated a statistically significant increased likelihood of having a positive ICT result when residing within 20 meters of an index or AI case controlling for age, gender and commune, suggesting that antigen-positive children can serve as indicators of microfoci of transmission, and that proximity to these microfoci may be associated with the risk of acquiring LF.
Risk associated with proximity to infected persons becomes of particular interest as communities see fewer and fewer instances of new infections. Different studies have reached independent conclusions regarding the possible risk. One study from Brazil found that one antigen-positive individual did not seem to pose a significant risk for transmission, as no one in the vicinity had become infected in the 10 years he lived in this non-endemic community [11]. However, this study was only observational and based on a single individual and therefore may not be generalizable to the overall population.
Washington et al. addressed the probability of acquiring LF as a function of the distance from antigen-positive cases through an analysis of changes in antifilarial antibodies [12]. This study determined that for every 10 meter increase in distance from an antigen-positive case, there was a 5.6% decrease in IgG1 antibody levels, when controlling for age, gender and treatment status (p = 0.04) [12]. These observations coupled with our present study indicate substantial risk with spatial proximity to an antigen-positive person in both exposure as well as acquisition of LF arguing that clustering may play a substantial role in transmission dynamics.
The latest published research pertaining to the clustering and identification of “hotspots” of LF was performed by Joseph et al [13], in which researchers examined the spatial clustering of LF in Samoa. This study looked at the clustering not only antigen positive, but also microfilaremic and antibody positive persons. Their results revealed statistically significant clustering of antigen positive individuals in three of the tested communities with radii ranging from 0 to 1160 meters. Our study complements this work by documenting the presence of clustering prior to the implementation of MDA in low prevalence settings.
The final multivariate logistic regression model included commune as a significant variable, consistent with the conclusion that differences in transmission exist between communes (Table 3). Differences in the transmission of LF are likely due to different physical environments or population densities, either of vectors or humans, which may be more compatible with transmission of LF and could warrant further exploration. We did not collect data on mosquito densities for this study; however, it is likely that clustering and dispersal of infections are influenced by the behavior and flight range of the vector mosquito as well. Future studies also should examine the micro-environment surrounding the households of study participants to better address possible heterogeneity.
This study is a preliminary analysis of the factors associated with antigen prevalence in five communes in Haiti in 2003, and the findings may not be generalizable to all endemic settings. Infection risk could not be established definitively in this cross-sectional study, and we suggest a cohort study be conducted in order to confirm these results. The cross-sectional study design also does not allow for chronology to be established so there is no way to determine if cases identified in the school survey were infected before or after their ICT-positive neighbors. Thus, although we can argue that antigen-positive children were indicators of community infection, we could not determine the actual reservoir or source of this transmission. Due to the low percentage of ICT positive persons in each commune and the logistic difficulty of night blood surveys, microfilaria levels were not assessed. While generally highly specific, the ICT is not considered the most accurate test for LF infection because of problems with test interpretation in the field [14]. Testing using Og4C3 ELISA provides a quantitative measure of circulating antigens and is generally accepted as being a more sensitive test of antigenemia; however, due to financial and logistical considerations, the ICT was used for all participants in the study.
Presence of microfilaria as well as biting rates from the vector would allow for the calculation of rate of transmission, and transmission risk; however, such tests were not performed for this study. We used antigen positivity as an indicator of transmission in lieu of the acquisition of such entomologic data which can be challenging and expensive to collect, especially in the context of short term surveys.
Since this study was carried out in low-prevalence settings, there were few persons found to be antigen-positive. This is a challenge of sampling in a low-prevalence setting and it is accompanied by decreased statistical power. Lastly, in order to evaluate the exposure of interest, we required that GPS coordinates be available, in addition to ICT results. Since this information was not available for all study participants, our sample size was reduced.
We have demonstrated that transmission, using antigen prevalence as a proxy, is occurring in areas that had previously been categorized as areas with low risk of LF transmission, suggesting that areas of low-prevalence may not be without transmission risk. The nation-wide mapping techniques in 2001 revealed a prevalence of ≤1% antigenemia for all five communes in our study, but we observed prevalence values ranging from zero to 5.6% in the school survey and 0 to 4.35% in the community survey within those same 5 communes.
The identification of autochthonous index cases indicates that transmission is occurring at the level of microfoci. Since our analysis revealed that living within 20 meters of an index case significantly increased the likelihood of being antigen-positive, such microfoci may represent a particular challenge in terms of surveillance following MDA. MDA is expected to reduce infection prevalence to the point that only small isolated foci of transmission are expected to remain. The concern that these foci might represent groups of persons who are systematically noncompliant has been hypothesized on several occasions [15], [16], [17]. How long such microfoci can persist is unknown. In any case, the presence of such foci should be addressed and the school survey strategy we have described may represent one approach to detecting such foci through active surveillance, as proposed by Huppatz et al. [18]. Such efforts will be aided by the development of new and more sensitive diagnostic tools, based on the detection of parasite-specific antibody [18], [19].
Ramaiah et al. reported residual microfilaria prevalence ranging from 0.03 to 0.43% in the population when tested annually over a period of 20 years post MDA [20]. It is impractical to require a surveillance period of 20 years post MDAs; however, increasing the period of surveillance from five years to ten years, prior to certification, might be necessary to ensure that transmission has indeed stopped or at least slowed to a point which cannot sustain the filarial lifecycle. Additional research is needed to address this issue. With a comprehensive program and stringent monitoring and evaluation of remaining infection we can make greater strides towards the elimination of lymphatic filariasis.
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10.1371/journal.pbio.2006812 | Attention promotes the neural encoding of prediction errors | The encoding of sensory information in the human brain is thought to be optimised by two principal processes: ‘prediction’ uses stored information to guide the interpretation of forthcoming sensory events, and ‘attention’ prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction (or ‘representation’) neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for ‘feature information’ in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.
| The human brain is theorised to operate like a sophisticated hypothesis tester, using past experience to generate a model of the external world, testing predictions of this model against incoming sensory evidence, and generating a ‘prediction error’ signal that updates the model when predictions and sensory evidence do not match. In addition to predicting the content of sensory signals, an optimal system should also predict the reliability (or ‘precision’) of those signals to minimise the influence of unreliable sensory information. It has been proposed that attention optimises this process by boosting prediction error signals, which are coded as the difference (or ‘mismatch’) between predicted and observed stimulus features. Accordingly, this theory predicts that attention should increase the selectivity for mismatch information in the neural response to surprising stimuli. We tested this hypothesis in human participants by training a decoding algorithm to identify ‘mismatch information’ in the brain, recorded by electroencephalography (EEG), following the presentation of surprising stimuli that were either attended or ignored. We found that attention did indeed increase the selectivity for mismatch information in the neural response, supporting the notion that attention and prediction are intricately related processes.
| Perception is believed to arise from a process of active inference [1], during which the brain retrieves information from past experiences to build predictive models of likely future occurrences and compares these predictions with incoming sensory evidence [2,3]. In support of the idea that prediction increases the efficiency of neural encoding, previous studies have demonstrated that predicted visual events typically evoke smaller neural responses than surprising events (e.g., evoked activity measured in terms of changes in electrical potential or blood oxygen level dependent [BOLD] response; for a review, see [4]). Recent studies have shown that selective attention can increase [5] or reverse [6] the suppressive effect of prediction on neural activity, suggesting that attention and prediction facilitate perception [7] via synergistic modulation of bottom-up sensory signals [8–11]. It remains unclear, however, what type of information is modulated in the interaction between attention and prediction. This question is important because different predictive coding models make distinct predictions about how information is transmitted through the cortical hierarchy [3,8,12,13]. Here, we used forward encoding models to assess selectivity for two distinct types of information in the neural response to surprising stimuli—feature and mismatch information—and to test the effect of attention on these two informational codes.
A prominent version of predictive coding theory claims that top-down prediction signals ‘cancel out’ bottom-up sensory signals that match the predicted content, leaving only the remaining prediction error to propagate forward and update a model of the sensory environment [2,8,9]. Because error propagation is thought to be associated with superficial pyramidal cells [9], and these cells are thought to be primarily responsible for generating EEG signals [14,15], this theory predicts that surprising events will increase the selectivity of EEG responses to the difference between predicted and observed stimulus features, i.e., mismatch information. Furthermore, a recent extension of this theory suggests that selective attention optimises the expected precision of predictions by modulating the synaptic gain (postsynaptic responsiveness) of prediction error units [8]—i.e., neurons coding for behaviourally relevant prediction errors should be more responsive than those coding for irrelevant prediction errors. On this account, attention should further increase selectivity for mismatch information in the neural response to surprising stimuli relative to unsurprising stimuli. Here, we call this account the ‘mismatch information model’.
Alternative predictive coding models [12,13,16] propose that predictions—as opposed to prediction errors—are propagated forward through the visual hierarchy, and it is these prediction signals that are modulated by attention. For example, the model proposed by Spratling [12] simulates the common physiological finding that attention to a stimulus enhances the firing rate of neurons tuned to specific stimulus features (e.g., orientation or colour for visual neurons) and has been shown to be mathematically equivalent to the biased competition model of attention [17–20]. In line with these alternative models, we investigated a second hypothesis—here termed the ‘feature information model’—which proposes that the interaction between attention and prediction at the level of neural responses is driven by changes in feature-specific information in the brain.
Here, we tested whether the feature information model or the mismatch information model provides a better account of the neural coding of surprising stimuli in the human brain and examined the influence of selective attention on each of these two neural codes. Participants attended to, or ignored, periodic streams of visual gratings, the orientations of which were either predictable, surprising, or unpredictable. We applied forward encoding models to whole-brain neural activity measured using EEG to quantify the neural selectivity for information related to the grating orientation and the mismatch between the predicted and observed grating orientations. We show that surprising stimuli evoke neural responses that contain information related to the difference between predicted and observed stimulus features, consistent with the mismatch information model. Crucially, we also find that attention increases the selectivity for mismatch information in the neural response to surprising stimuli, supporting the hypothesis that attention increases the gain of prediction errors [8].
We recorded brain activity using EEG as human observers (N = 24) undertook a rare-target detection task (see Methods; Fig 1). Participants fixated centrally and were presented with a periodic stream of gratings (100 ms duration, 500 ms interstimulus interval, 415 gratings per block) in one of two conditions (randomised across blocks). In ‘roving standard’ blocks [21] (see Fig 1A), grating orientation was repeated between 4 and 11 times (‘standards’) before changing to a new orientation (‘deviants’, pseudorandomly selected from one of nine orientations, spanning 0° to 160° in 20° steps). Grating orientation was thus ‘predictable’ for standards and ‘surprising’ for deviants. In ‘equiprobable blocks’ [22] (see Fig 1B), gratings changed orientation on every presentation and thus could not be predicted (‘unpredictable’ controls). Attention was manipulated by having participants either monitor the grating stimuli for rare targets with a different spatial frequency (‘grating task’, gratings ‘attended’) or ignore the gratings and instead monitor for rare fixation-dot targets with decreased contrast (‘dot task’, gratings ‘ignored’).
Participants completed the grating task and dot task in separate sessions, approximately one week apart (session order counterbalanced). At the beginning of each session, participants completed three practice blocks of the specified task, during which target salience levels were titrated to approximate a target detection rate of 75% (see Methods). Participants were then fitted with a 64-electrode EEG cap before completing 21 test blocks. One participant detected fewer than 50% of targets in both tasks and was therefore excluded from all further analyses. The remaining participants detected an equivalent percentage of targets in the grating task (75.64% ± 1.76%, mean ± SEM) and dot task (72.73% ± 2.54%; t[22] = 1.57, p = 0.13, BF10 = 0.12) and also produced similar numbers of false alarms in each (20.43 ± 3.79 and 22.57 ± 5.47, respectively; t[22] = −0.41, p = 0.684, BF10 = 0.18), suggesting that difficulty was well matched between attention conditions.
EEG data were preprocessed offline using EEGlab [23] and epoched according to the onset of each grating (see Methods for details). Statistical analyses were conducted using cluster-based permutation tests in Fieldtrip [24]. S1 Fig shows the main effects and interactions for the factors of attention and prediction on event-related potentials (ERPs). Briefly, ERPs were modulated by both attention (86–434 ms, cluster-corrected p < 0.001; S1A and S1C Fig) and prediction (39–550 ms, cluster-corrected p < 0.001, S1A Fig). Follow-up analyses of the simple effects of prediction revealed that deviants elicited larger responses than both standards (39–550 ms, cluster-corrected p < 0.001; S1A and S1D Fig) and controls (324–550 ms, cluster-corrected p = 0.002; S1A and S1E Fig). The difference between deviants and controls emerged later and was smaller than the difference between deviants and standards, consistent with the notion that the former comparison reflects the pure effects of prediction (‘genuine’ mismatch response [MMR]) [22], whereas the latter comparison confounds the effects of prediction with those of adaptation to the standard (‘classic’ MMR, see [4] for a review).
We also observed an interaction between attention and prediction (180–484 ms, cluster-corrected p < 0.001; S1A Fig). Follow-up analyses revealed that attention increased both the classic MMR (176–469 ms, cluster-corrected p < 0.001; S1F and S1G Fig) and the genuine MMR (176–550 ms, cluster-corrected p < 0.001; S1H and S1I Fig). In the attended condition, both the classic and the genuine MMRs emerged approximately 200 ms after stimulus onset over posterior-lateral (PO7, PO8) electrodes (S1B Fig, solid green and yellow lines, respectively). Whereas the onset of the genuine MMR is consistent with previous literature [22], the classic MMR we report here emerged slightly later than what has typically been reported previously (about 150 ms; for a review see [4]). We note, however, that at least one previous study reported a visual MMR beginning as late as 250 ms [25], highlighting the variable nature of this component.
In the ignored condition, we observed classic and genuine MMRs (S1B Fig, dotted green and yellow lines, respectively) with positive polarities over posterior (PO7, PO8) and frontal (Fz) electrodes, respectively. In contrast, previous studies have typically (but not always; see [5]) reported MMRs with negative polarities, even in the absence of attention [4]. A number of differences between previous studies and our own could explain this discrepancy (e.g., stimuli, interstimulus interval, presentation duration, task, etc). In particular, we used large sinusoidal gratings (11° of visual angle) to optimise orientation decoding, in contrast to previous studies that presented much smaller oriented bars (about 3°–4° of visual angle, e.g., [22,26]). Thus, the stimuli in the current study likely activated a larger area of visual cortex than those used in previous studies, which produced a different dipole (or combination of multiple dipoles) and associated projection to scalp electrodes (due to the complex folding structure of the cortex, [4]) than has previously been observed. Indeed, close inspection of the ERPs seems to indicate the presence of a single dipole projecting to frontal and posterior electrodes (note the highly similar pattern of activity between electrodes Fz and Pz, but with opposite sign, S1A Fig), which has not typically been observed in previous studies (e.g., note the relatively uniform responses across the scalp in [22,27,28]).
The feature information model predicts that the orientation-selective neural response to surprising stimuli (deviants) will be different than that of control stimuli. To investigate this hypothesis, we used a forward encoding model to estimate orientation selectivity from neural activity measured with EEG (see Methods for details). Briefly, we used multivariate regression to transform activity in electrode space into an orientation-selective ‘feature space’ [29–32], comprising nine hypothetical ‘orientation channels’ matching those presented in the experiment (0°–160°, in 20° steps). For each orientation channel, we modelled the expected activation across trials by convolving the presented orientation with a canonical orientation-selective tuning function. We then regressed this pattern of expected activity against the EEG data, separately for each time point (−100 to 550 ms after stimulus onset) to produce a weight matrix that converted multivariate activity in electrode space into activity in the specified orientation channel. The spatial weights for each orientation channel were then inverted to reconstruct the forward model (hence why these models are also called ‘inverted encoding models’, e.g., [34]) and were applied to an independent set of test trials (using a cross-validation procedure) to estimate activity across all orientation channels. As shown in Fig 2A, the forward encoding approach reconstructed distinct response profiles for each of the nine grating orientations presented to participants. Orientation channels were then realigned for each trial such that the presented orientation channel was centred on 0°, and activation patterns were averaged across trials in each condition. The forward encoding model revealed an orientation-tuned response throughout the epoch (Fig 2B and 2C). This response emerged soon after stimulus onset, peaked at about 130 ms, and declined gradually until the end of the epoch.
To quantify the effects of attention and prediction on orientation response profiles, we fitted the condition-averaged orientation channel responses with an exponentiated cosine function [33,34] using least squares regression:
y(x)=Aeҡ(cos2(x−μ)−1)+B
such that y is the predicted orientation channel activity in response to a grating with orientation x, A is the peak response amplitude, ҡ is the concentration (i.e., inverse dispersion; a larger value corresponds to a ‘tighter’ function), μ is the centre of the function, and B is the baseline offset (see Methods).
Attention increased the amplitude of orientation response profiles (219–550 ms, cluster-corrected p < 0.001; Fig 3A and 3B) but did not modulate the tuning concentration (all clusters p > 0.104). There was a significant main effect of prediction on the amplitude of orientation response profiles late in the epoch (324–550 ms, cluster-corrected p < 0.001; S2C and S2D Fig), as well as a nonsignificant but trending cluster early in the epoch (94–145 ms, cluster-corrected p = 0.154; S2C Fig, cluster not shown). Follow-up analyses revealed that orientation response profiles evoked by standards (0.11 ± 0.01 arbitrary units [a.u.]) were smaller than those of both deviants (0.25 ± 0.03 a.u.; t[22] = −4.32, p < 0.001, BF10 = 1,469.10) and controls (0.22 ± 0.03 a.u.; t[22] = −3.79, p < 0.001, BF10 = 156.16; S2C and S2D Fig). Crucially, the amplitudes of orientation response profiles evoked by deviants and controls were equivalent (t[22] = 0.78, p = 0.443, BF10 = 0.19; Fig 3A, S2C and S2D Fig). Finally, there was no effect of prediction on the concentration of orientation response profiles (all clusters p > 0.403) and no interaction between attention and prediction on either the amplitude (cluster-corrected p = 0.093, S2E and S2F Fig) or concentration (no clusters found) of orientation response profiles.
To determine the scalp topography that was most informative for orientation decoding, we calculated univariate sensitivity separately for each electrode across all trials and averaged across time points in the significant main effect of attention (see Methods). As revealed in Fig 3C, posterior electrodes were the most sensitive to orientation information, as would be expected for a source in visual cortex.
The mismatch information model proposes that prediction errors are represented in populations of neurons tuned to the difference between predicted and observed stimulus features. According to this model, therefore, surprising stimuli (deviants) should produce a more mismatch-selective neural response than control stimuli. Furthermore, if attention enhances the gain of prediction errors [8], we should expect an interaction between attention and prediction, such that attention enhances the amplitude of mismatch response profiles evoked by deviants more than that of controls, because deviants should evoke a larger prediction error [2]. To investigate these hypotheses, we trained a separate forward encoding model, as described above, on the angular difference between gratings (deviants or controls) and the preceding stimuli. That is, deviants were coded according to the difference between the deviant orientation and the preceding standard orientation, and controls were coded according to the difference between successive control orientations. For example, if a horizontally oriented deviant (0°) was preceded by a standard that was oriented at 40° (clockwise of horizontal), it would be coded as a mismatch of −40° (0°–40°).
As shown in Fig 3D and 3E, we were able to reconstruct mismatch response profiles for attended deviants. By contrast, mismatch response profiles were clearly weaker in response to controls and ignored deviants. There was a significant main effect of attention on the amplitude of mismatch response profiles (attended > ignored, 188–550 ms, cluster-corrected p = 0.002; Fig 3D, grey bar along x-axis). There was also a significant main effect of prediction (deviant > control, 113–550 ms, cluster-corrected p < 0.001; Fig 3D, solid black bar along x-axis), suggesting that prediction error is encoded according to the mismatch between predicted and observed features. Crucially, attention and prediction interacted to influence the amplitude of mismatch response profiles (332–480 ms, cluster-corrected p = 0.031; Fig 3D, dotted black bar along x-axis). As can be seen in Fig 3D and 3E, attention enhanced the amplitude of deviant mismatch response profiles but had little effect on those evoked by controls, supporting the hypothesis that attention boosts prediction errors [8].
The concentration of mismatch response profiles was not modulated by attention (all clusters p > 0.888) or the interaction between attention and prediction (all clusters p > 0.615), although we did find a significant main effect of prediction on the concentration of mismatch response profile fits (controls > deviants, 344–422 ms, cluster-corrected p < 0.001). Because controls seemed to produce negligible mismatch response profiles during this time period (yellow lines, Fig 3D), however, we followed up this result by averaging MMR amplitudes across the significant timepoints and comparing these values to zero with a t test and Bayes Factor analysis (uniform prior, lower bound: 0, upper bound = 0.3). We found that control mismatch response profile amplitudes (0.005 ± 0.023 a.u.) were equivalent to zero (t[22] = 0.19, p = 0.848, BF10 = 0.11), suggesting that the observed effect on concentration was more likely an artefact of the fitting procedure than a true effect of prediction on mismatch response profiles.
We calculated the sensitivity of each electrode to mismatch information in trials that contained attended deviants, and collapsed across the significant interaction between 332 and 480 ms. As revealed in Fig 3F, posterior electrodes were again the most informative, but the topography of mismatch sensitivity was weaker and more sparsely distributed than that of orientation decoding (Fig 3C).
Next, we investigated whether the number of preceding standards was related to the amplitude of mismatch response profiles (putative prediction errors). Repeated presentations of the standard are thought to increase the strength of the memory trace, resulting in larger prediction errors to a subsequent surprising stimulus [35]. Mismatch response profiles evoked by attended deviants were grouped according to the number of preceding standards (4–7 repetitions versus 8–11 repetitions) and fitted with exponentiated cosine functions (see Methods). As can be seen in Fig 4A and 4B, increasing the number of standard repetitions also increased the amplitude of mismatch response profiles (387–520 ms, cluster-corrected p = 0.050). This finding is consistent with the notion that successive standards allow a more precise prediction to be generated, which results in enhanced prediction errors when violated. Finally, there was no effect of the number of standard repetitions on the concentration of mismatch response profiles (cluster-corrected p = 0.314).
We also tested whether larger deviations from the prediction increased selectivity for mismatch information. Mismatch response profiles of attended deviants were grouped according to the angular difference between the deviant and preceding standard (i.e., the original mismatch values entered into the encoding model) and fitted with exponentiated cosine functions (variable centre, see Methods). There was a significant main effect of deviation magnitude on mismatch response profile amplitude (215–410 ms, cluster-corrected p = 0.004). As shown in Fig 4C, the amplitude of mismatch response profiles increased with the absolute deviation angle (±80° > ±60° > ±40° > ±20°), supporting the notion that larger angular deviations (from the predicted orientation) produce more prediction error. A second cluster emerged later in the epoch (465–550 ms, cluster-corrected p = 0.031), which followed a similar pattern but with the amplitude of the ±40° and ±60° responses reversed. Individual mismatch response profiles were typically centred on the orthogonal deviation angle (90°, Fig 4D). This pattern of results differs from the individual orientation response profiles (Fig 2A), which were (approximately) centred on the presented orientation.
In a final step, we investigated whether the spatial maps that produce mismatch response profiles are stable or evolve dynamically over time. We used the same encoding analysis as above, with the exception that the trained weights at each time point were tested on all time points in the epoch [30,36] (see Methods). This produced a train time × test time generalisation matrix of mismatch channel responses, to which we fitted exponentiated cosine functions. Fig 5 shows the mismatch selectivity (response profile amplitude) for attended and ignored deviants, generalised across time. As revealed in Fig 5A, the mismatch response profile evoked by attended deviants generalised across the latter part of the epoch (black outline surrounding large red patch in upper right quadrant between approximately 200 and 550 ms, cluster-corrected p = 0.010), indicating that the spatial map associated with mismatch information was relatively consistent throughout this period. Note also that this pattern of generalisation was asymmetrical (triangular-shaped rather than square-shaped). Specifically, spatial maps trained at late timepoints (e.g., between 400 and 450 ms) generalised to early (test) time points (e.g., between 250 and 300 ms), but training at early timepoints did not generalise equally well to late timepoints. Since asymmetrical generalisation can indicate differences in signal-to-noise ratios between time points [36], this finding suggests that the strength of prediction error signals may have increased toward the end of the epoch. It is also worth noting that the apparent generalisation of spatial maps trained at stimulus onset (ttrain = 0) to later times in the epoch (about 200–550 ms, red patch along the x-axis) was not significantly different from zero (no clusters found in this region) and produced high residuals in the function fits (see S3 Fig), suggesting that this pattern represents noise. Finally, the mismatch response profile evoked by ignored stimuli (Fig 5B) did not generalise across time points (all clusters p > 0.935) and was significantly smaller than that of attended stimuli (significant difference denoted by the opaque patch in Fig 5C; p = 0.026).
Here we set out to determine what type of information is modulated in the interaction between attention and prediction [8]. To achieve this, we used forward encoding models of EEG data to quantify the selectivity for orientation and mismatch information in the neural responses to surprising and unpredictable stimuli in the well-established roving oddball paradigm [21,37]. Relative to unpredictable stimuli (controls), we found that EEG responses to surprising stimuli (deviants) were equally selective for orientation information, but more selective for information related to the difference between predicted and observed stimulus features. These results are consistent with the mismatch information model and support the idea that top-down prediction signals ‘cancel out’ matching bottom-up sensory signals and leave only the remaining prediction error to propagate forward [2,3,8,9]. Crucially, we also found that attention increased the selectivity for mismatch information in neural responses to surprising but not control stimuli. This finding demonstrates that attention boosts mismatch information evoked by surprising stimuli (putative prediction errors) and is consistent with a recent version of predictive coding theory that proposes attention optimises the expected precision of predictions by increasing the gain of prediction errors [8].
We found no difference between orientation response profiles evoked by surprising and unpredictable stimuli (a prediction of the feature information model), suggesting that the increase in EEG activity that is typically observed with surprise is not coded according to stimulus features. This finding contradicts predictive coding models in which predictions (or ‘representations’) of stimulus features are passed up the visual hierarchy [12,16,17]. Because feedforward connections largely originate primarily from superficial pyramidal cells and it is this activity that is measured with EEG [9,14,15], these models would predict that surprise changes the feature selectivity of EEG responses: a finding we do not observe here. This finding might also seem to contradict a recent study that demonstrated greater selectivity for orientation information in early visual cortex BOLD activity following presentation of a predicted grating, relative to a surprising grating [38]. Since BOLD activity indirectly measures the activity patterns of heterogenous populations of neurons, however, this change in feature selectivity could have reflected a change in either of the two neuronal populations proposed to underlie predictive coding—predictions or prediction errors. The latter interpretation is inconsistent with the results of the present study, which suggests that prediction errors are encoded according to the mismatch between predicted and observed stimulus features, and not the features themselves. The former interpretation (i.e., that predictions are coded according to the stimulus features) fits well with a recent study that showed prediction induces feature-specific templates immediately prior to stimulus onset [31]. Thus, a parsimonious account of the literature to date suggests that predictions and prediction errors are represented in the brain via distinct neural codes: whereas predictions are represented according to stimulus features, prediction errors are represented according to the mismatch between predicted and observed stimulus features.
In a recent study by our group [39], we observed a decrease in orientation selectivity in the neural response to predicted stimuli, relative to surprising stimuli, shortly after stimulus onset (79–185 ms). Here, we observed a similar (but nonsignificant) trend in the same direction (standards < deviants) at approximately the same time (94–145 ms, S2C Fig, cluster not shown). Close inspection of the present results, however, suggests that some orientation information evoked by the previous standard was still present in the brain at the onset of the subsequent standard (indicated by the above-zero amplitude of the orientation response to standards at stimulus onset, t = 0 ms, S2C Fig), which may have obscured detection of the early effect reported in Tang and colleagues [39]. The present results revealed a late effect of prediction (standards < deviants, 324–550 ms, S2C and S2D Fig) that was not observed in our previous work [39]. Since a critical difference between the two studies was the number of times identical stimuli could be presented consecutively (no more than twice in the previous study), we speculate that the late effect observed here might reflect the minimal amount of model updating required after the presentation of a precisely predicted stimulus.
We also found that attention increased the amplitude of orientation response profiles (Fig 3A and 3B), consistent with previous studies that applied forward encoding models to human functional MRI (fMRI) [34,40] and time-frequency-resolved EEG data [29]. The present study replicates and extends these studies with the application of forward encoding models to time-resolved EEG recordings (resulting in <30 ms temporal resolution after smoothing), demonstrating that attention increases feature selectivity in the human brain from approximately 200 ms after stimulus onset.
Crucially, we also tested the interactive effects of attention and prediction on information processing in the brain. There was a large and significant effect of attention on mismatch response profiles in response to surprising but not unpredictable stimuli (beginning around 150 ms after stimulus onset and reaching significance from about 350 ms). This finding demonstrates that attention boosts mismatch prediction errors evoked by surprising stimuli and is consistent with a recent iteration of predictive coding theory according to which attention optimises the expected precision of prediction errors [8]. Previous studies have found evidence for an interaction between attention and prediction in both the auditory [5] and visual [6,41] modalities. These studies used activation-based analyses to compare differences between predicted and unpredicted stimuli at the level of overall neural activity but did not investigate what type of information is modulated in the interaction between attention and prediction. In contrast, the present study used information-based analyses [42] to identify specific patterns of neural activity that are associated with orientation-mismatch information in the brain, and showed that selectivity for this type of information (but not feature information) is increased with attention. Thus, the present study provides clear support for the hypothesis that attention boosts the gain of prediction errors [8]. It will be important for future research to investigate whether the interactive effects of attention and prediction on mismatch information is contingent on the type of attention (e.g., feature-based versus spatial attention) or prediction (e.g., rule-based versus multimodal cue-stimulus predictions; [31,43]).
We found that the magnitude of mismatch response profiles correlated with the number of preceding standards (Fig 4A and 4B). Previous work in the auditory domain demonstrated that successive repetitions of the standard evoke progressively increased responses to a subsequent attended deviant [35]. Here, we find a corollary for this effect in the visual domain and demonstrate that the neural activity modulated by the number of preceding standards is likely encoded as mismatch information. This finding is also consistent with the notion that repeating the standard allows a more precise prediction to be generated, which results in a larger prediction error to a subsequent surprising stimulus [44].
We also found that mismatch response profiles increased with the magnitude of the mismatch between predicted and observed stimulus features (Fig 4C). Previous work in the auditory domain has demonstrated a correlation between deviation magnitude and the amplitude of the neural response to deviants (i.e., the mismatch negativity) [45]. Here, we demonstrate a relationship between deviation magnitude and selectivity for mismatch information (as opposed to activation levels) in the visual domain, suggesting that the magnitude of mismatch information might be used by the brain to guide updating of the predictive model. Since the present study investigated mismatch signals with respect to a continuous and circular feature dimension (i.e., orientation), it will be important for future research to extend the current line of research to noncircular (e.g., luminance, auditory frequency) and categorical (e.g., facial emotions) feature dimensions.
There was a lateral shift in the response profile of individual mismatch channels toward the orthogonal (90°) channel (Fig 4D). The extent of this effect depended on the deviation magnitude, with large deviations (±40°–80°) being predominantly stacked over the 90° channel and smaller deviations (±20°) being more closely aligned with their veridical mismatch angle (Fig 4D). We speculate that this might indicate a qualitative difference in the way that small and large prediction errors were treated by the brain in the present study. Small deviations may have resulted in updating and retention of the current model (via a near-veridical mismatch signal), whereas large deviations may have resulted in the wholesale rejection of the current model (via a generic mismatch signal) in favour of an alternative model that represents the deviant stimulus. In the latter case, the magnitude of the (orthogonal) mismatch channel response might represent an efficient code that the brain utilises to select from a number of likely alternative models.
A number of recent studies failed to find an interaction between the effects of attention and prediction on stimulus information in the brain [31,38,46]. If predictions are encoded according to stimulus features, as we argue above, these null findings contradict the theory that attention boosts predictions [47]. In contrast, we show that prediction errors, represented according to the mismatch between predicted and observed stimulus features, are enhanced with attention. Although the present study cannot speak to the activity of single neurons, we note that the emerging picture is consistent with the notion that predictions and prediction errors are represented in distinct populations of neurons [2] that encode two distinct types of information and are differentially influenced by attention. Under this framework, feature information encoded by prediction units would be immune to attention, whereas mismatch information encoded by prediction error units would be enhanced by attention. Future research could test these hypotheses at the single-cell level, for example by using single-unit electrode recordings or 2-photon calcium imaging to assess whether different neurons within a given cortical area satisfy these constraints.
The study was approved by The University of Queensland Human Research Ethics Committee (approval number: 2015001576) and was conducted in accordance with the Declaration of Helsinki. Participants provided informed written consent prior to commencement of the study.
Twenty-four healthy participants (11 female, 13 male, mean = 23.25 years, SD = 9.01 years, range: 18 to 64 years) with normal or corrected-to-normal vision were recruited via an online research participation scheme at The University of Queensland.
Stimuli were presented on a 61 cm LED monitor (Asus, VG248QE) with a 1,920 × 1,080 pixel resolution and refresh rate of 120 Hz, using the PsychToolbox presentation software [48] for Matlab (version 15b) running under Windows 7 with a NVidia Quadro K4000 graphics card. Participants were seated in a comfortable armchair in an electrically shielded laboratory, with the head supported by a chin rest at a viewing distance of 57 cm.
During each block, 415 gratings with Gaussian edges (outer diameter: 11°; inner mask diameter: 0.83°; spatial frequency: 2.73 c/°; 100% contrast) were presented centrally for 100 ms with a 500 ms ISI. Grating orientations were evenly spaced between 0° (horizontal) and 160° (in 20° steps). Eighteen (18) gratings in each block (2 per orientation) were presented with a higher spatial frequency (range: 2.73–4.55 c/°, as per staircase procedure, below), with a gap of at least 1.5 seconds between any two such gratings. We used a modified de Bruijn sequence to balance the order of grating orientations across conditions, sessions, and participants. Specifically, we generated two 9-character (orientation) sequences without successive repetitions (e.g., ABCA, not ABCC)—one with a 3-character subsequence (504 characters long) and another with a 2-character subsequence (72 characters long)—and appended two copies of the former sequence to three copies of the latter sequence (1,224 characters in total). This master sequence was used to allocate the order of both deviants and controls in each session (using different, random start-points) and ensured that each orientation was preceded by equal numbers of all other orientations (up to 2+ preceding stimuli) so that decoding of any specific orientation could not be biased by the orientation of preceding stimuli.
In roving oddball sequences, the number of Gabor repetitions (i.e., standards) was balanced across orientations within each session, such that each orientation repeated between 4 and 11 times according to the following distribution: (31, 31, 31, 23, 5, 5, 5, 5), respectively. During each block, the fixation dot (diameter: 0.3°, 100% contrast) decreased in contrast 18 times (contrast range: 53%–98% as per staircase procedure, below) for 0.5 seconds (0.25-second linear ramp on and off). Contrast decrement onsets were randomised separately for each block, with a gap of at least 1.5 seconds between any two decrement onsets.
Participants attended two testing sessions of 60 minutes’ duration, approximately 1 week apart, and completed one of two tasks in each session (Fig 1, session order counterbalanced across participants). For the grating task, participants were informed that approximately 1 out of 20 of the gratings would be a target grating with a higher spatial frequency than nontargets and were asked to press a mouse button as quickly as possible when they detected a target grating; all other gratings were to be ignored. For the dot task, participants were informed that the fixation dot would occasionally decrease in contrast and were asked to press a mouse button as quickly as possible when they detected such a change. Participants initially completed three practice blocks (3.5 min per block) with auditory feedback (high or low tones) indicating missed targets and the accuracy of their responses. During practice blocks in the first testing session, target salience (spatial frequency or dot contrast change, depending on the task) was adjusted dynamically using a Quest staircase procedure [49] to approximate 75% target detection. During practice blocks in the second testing session, target salience was adjusted to approximate the same level of target detection observed in the first testing session. Participants were requested to minimise their number of false alarms. After the practice blocks, participants were fitted with an EEG cap (see ‘EEG data acquisition’) before completing a total of 21 test blocks (3 equiprobable, 18 roving standard, block order randomised) without auditory feedback. After each block, participants were shown the percentage of targets correctly detected, the speed of these responses, and how many nontargets were responded to (false alarms).
Participant responses were scored as hits if they occurred within 1 second of the onset of a target grating in the grating task, or within 1 second of the peak contrast decrement in the dot task. Target detection was then expressed as a percentage of the total number of targets presented in each testing session. One participant detected less than 50% of targets in both sessions and was removed from further analysis. Target detections and false alarms across the two sessions were compared with paired-samples t tests and Bayes Factors. Bayes factors allow for quantification of evidence in favour of either the null or alternative hypothesis, with B01 > 3 indicating substantial support for the alternative hypothesis and B01 < 0.33 indicating substantial support for the null hypothesis [50]. Bayes factors were computed using the Dienes [50,51] calculator in Matlab, with uniform priors for target detection (lower bound: −25%; upper bound: 25%) and false alarms (lower bound: −50; upper bound: 50).
Participants were fitted with a 64 Ag-AgCl electrode EEG system (BioSemi Active Two: Amsterdam, the Netherlands). Continuous data were recorded using BioSemi ActiView software (http://www.biosemi.com) and were digitized at a sample rate of 1,024 Hz with 24-bit A/D conversion and a 0.01–208 Hz amplifier band pass. All scalp electrode offsets were adjusted to below 20 μV prior to beginning the recording. Pairs of flat Ag-AgCl electro-oculographic electrodes were placed on the outside of both eyes, and above and below the left eye, to record horizontal and vertical eye movements, respectively.
EEG recordings were processed offline using the EEGlab toolbox in Matlab [23]. Data were resampled to 256 Hz and high-pass filtered with a passband edge at 0.5 Hz (1691-point Hamming window, cut-off frequency: 0.25 Hz, −6 db). Raw data were inspected for the presence of faulty scalp electrodes (2 electrodes, across 2 sessions), which were interpolated using the average of the neighbouring activations (neighbours defined according to the EEGlab Biosemi 64 template). Data were re-referenced to the average of all scalp electrodes, and line noise at 50 and 100 Hz was removed using the Cleanline plugin for EEGlab (https://www.nitrc.org/projects/cleanline). Continuous data were visually inspected, and periods of noise (e.g., muscle activity) were removed (1.4% of data removed in this way, across sessions).
For artefact identification, the cleaned data were segmented into 500 ms epochs surrounding grating onsets (100 ms pre- and 400 ms post-stimulus). Improbable epochs were removed using a probability test (6 SD for individual electrode channels, 2 SD for all electrode channels, 6.5% of trials across sessions), and the remaining data were subjected to independent components analyses (ICAs) with a reduced rank in cases of a missing EOG electrode (2 sessions) or an interpolated scalp electrode (2 sessions). Components representing blinks, saccades, and muscle artefacts were identified using the SASICA plugin for EEGlab [52].
For further analysis, the cleaned data (i.e., prior to the ICA analysis) were segmented into 800 ms epochs surrounding grating onsets (150 ms pre- and 650 ms post-stimulus). Independent component weights from the artefact identification process were applied to this new data set, and previously identified artefactual components were removed. Baseline activity in the 100 ms prior to each stimulus was removed from each epoch. Grating epochs were then separated into their respective attention and prediction conditions. Epochs in the grating task were labelled as ‘Attended’ and epochs in the dot task were labelled as ‘Ignored’. Epochs in the roving oddball sequence were labelled as ‘Deviants’ when they contained the first stimulus in a repeated train of gratings and ‘Standards’ when they contained a grating that had been repeated between 5 and 7 times. Epochs in the equiprobable sequence were labelled as ‘Controls’.
Trials in each attention and prediction condition were averaged within participants to produce ERPs for each individual. The effect of attention was assessed using a two-tailed cluster-based permutation test across participant ERPs (Monte-Carlo distribution with 5,000 permutations, pcluster < 0.05; sample statistic: dependent samples t statistic, aggregated using the maximum sum of significant adjacent samples, psample < 0.05). Because there were 3, rather than 2, levels of prediction, we tested the effect of prediction with a cluster-based permutation test that used f-statistics at the sample level and a one-sided distribution to account for the positive range of f-statistics (Monte-Carlo distribution with 5,000 permutations, pcluster < 0.05; sample statistic: dependent samples f-statistic, aggregated using the maximum sum of significant adjacent samples, psample < 0.05). Simple contrasts between prediction conditions (deviants versus standards, and deviants versus controls) were tested using two-tailed cluster-based permutation tests (with the same settings as used to investigate attention). The interaction between attention and prediction was assessed by subtracting the ignored ERP from the attended ERP within each prediction condition and subjecting the resulting difference waves to a one-tailed cluster-based permutation test across participant ERPs (Monte-Carlo distribution with 5,000 permutations, pcluster < 0.05; sample statistic: dependent samples f-statistic, aggregated using the maximum sum of significant adjacent samples, psample < 0.05). The interaction effect was followed up by comparing difference waves (attended minus ignored) between deviants and standards, and between deviants and controls (two-tailed cluster-based permutation tests, same settings as above).
To investigate the informational content of orientation signals, we used a forward encoding model [29,53] designed to control for noise covariance in highly correlated data [31,54] (https://github.com/Pim-Mostert/decoding-toolbox), such as EEG. We modelled an idealised basis set of the 9 orientations of interest (0°–160° in 20° steps) with nine half-wave rectified cosine functions raised to the 8th power, such that the response profile associated with any particular orientation in the 180° space could be equally expressed as a weighted sum of the nine modelled orientation channels [29]. We created a matrix of nine regressors that represented the grating orientation presented on each trial in the training set (1 = the presented orientation; 0 = otherwise) and convolved this regressor matrix with the basis set to produce a design matrix, C (9 orientation channels × n trials). The EEG data could thus be described by the linear model:
B=WC+N,
such that B represents the data (64 electrodes × n trials), W represents a spatial weight matrix that converts activity in channel space to activity in electrode space (64 electrodes × 9 orientation channels), and N represents the residuals (i.e., noise).
To train and test the forward encoding model, we used a 3-fold cross-validation procedure that was iterated 100 times to increase reliability of the results. Within each cross-validation iteration, the experimental blocks were folded into thirds: one-third of trials served as the test set, and the remaining two-thirds served as the training set, and folds were looped through until each fold had served as a test set. Across successive iterations of the cross-validation procedure, the number of trials in each condition was balanced within folds by random selection (on the first iteration) or by selecting the trials that had been utilised the least across previous folds (subsequent iterations).
Prior to estimating the forward encoding model, each electrode in the training data was de-meaned across trials, and each time point was averaged across a 27.3 ms window centred on the time point of interest (corresponding to an a priori window of 30 ms, rounded down to an odd number of samples to prevent asymmetric centring). Separately for each time point and orientation channel of interest, i, we solved the linear equation using least square regression:
wi=Btrainctrain,iT(ctrain,ictrain,iT)−1,
such that wi represents the spatial weights for channel i, Btrain represents the training data (64 electrodes × ntrain trials), and ctrain,i represents the hypothetical response of channel i across the training trials (1 × ntrain trials). Following Mostert and colleagues [54], we then derived the optimal spatial filter vi to recover the activity of the ith orientation channel:
vi=∑˜i−1wiwiT∑˜i−1wi,
such that Σi is the regularized covariance matrix for channel i, estimated as follows:
∑˜i=1ntrain−1εiεiT
εi=Btrain−wictrain,i,
such that ntrain is the number of training trials. The covariance matrix ∑~i was regularized by using the analytically determined shrinkage parameter [31]. Combining the spatial filters across each of the nine orientation channels produced a channel filter matrix V (64 electrodes × 9 channels).
Ctest=VTBtest,
such that Btest represents the test data at the time point of interest (64 electrodes × ntest trials), averaged over a 27.3 ms window (as per the training data). Finally, the orientation channel responses for each trial were circularly shifted to centre the presented orientation on 0°, and the zero-centred responses were averaged across trials within each condition to produce the condition-average orientation channel response (Fig 3B).
To assess information related to the mismatch between predicted and observed stimulus features (Fig 3D and 3E), we computed a second forward encoding model as above, with the exception that now the regression matrix represented the difference between the current grating orientation (deviant or control) and the previous grating orientation (standard or control, respectively). That is, a grating at 60° orientation that followed a grating at 20° orientation would be coded as 40° (current minus previous orientation).
To assess the dynamic nature of mismatch response profiles (Fig 5), we trained the weight matrix, W, at a single time point in the training set, B1 (using a 27.3 ms sliding window) and then applied the weights to every third time point in the test set, B2 (using a 27.3 ms sliding window). This process was repeated for every third time point in the training set, resulting in a three-dimensional matrix that contained the population response profile at each cross-generalised time point (9 orientations × 66 training time points × 66 testing time points).
Previous studies have utilised a number of different methods to quantify the selectivity of neural response profiles [30,31]. Because we were interested in characterising the properties of neural response profiles, we opted to fit an exponentiated cosine function to the modelled data [33,34] using least square regression:
y(x)=Aeҡ(cos2(x−μ)-1)+B
such that y is the predicted orientation channel activity in response to a grating with orientation x; A is the peak response amplitude, ҡ is the concentration parameter, μ is the centre of the distribution, and B is the baseline offset. Fitting was performed using the nonlinear least square method in Matlab (trust region reflective algorithm). The free parameters A, ҡ, and B were constrained to the ranges (−0.5, 2), (1.5, 200), and (−1.0, 0.5), respectively, and initiated with the values 0.5, 2, and 0, respectively. The free parameter μ was constrained to be zero when quantifying mean-centred orientation or mismatch response profiles (which should be centred on zero, Figs 3, 4A and 4B). When quantifying individual (uncentred) mismatch channel response profiles (Fig 4C and 4D), the free parameter μ was allowed to vary between −90° and 90°. To reduce the likelihood of spurious (inverted) fits, the parameter search was initiated with a μ value centred on the channel with the largest response.
The main effects of attention and prediction on orientation or mismatch response profiles were assessed with cluster-based permutation tests across participant parameters (amplitude, concentration). The interaction effects (between attention and prediction) on orientation and mismatch response profiles were assessed by first subtracting the ignored response from the attended response and then subjecting the resulting difference maps to cluster-based permutation tests. In cases where two levels were compared (i.e., the main effect of attention on orientation response profiles, and all effects on mismatch response profiles), we used two-tailed cluster-based permutation tests across participant parameters (Monte-Carlo distribution with 5,000 permutations, pcluster < 0.05; sample statistic: dependent samples t statistic, aggregated using the maximum sum of significant adjacent samples, psample < 0.05). In cases where three levels were compared (i.e., the main effect of prediction and the interaction effect on orientation response profiles), we used one-tailed cluster-based permutation tests across participant parameters (Monte-Carlo distribution with 5,000 permutations, pcluster < 0.05; sample statistic: dependent samples f-statistic, aggregated using the maximum sum of significant adjacent samples, psample < 0.05) and followed up any significant effects by collapsing across significant timepoints and comparing individual conditions with paired-samples t tests and Bayes Factors (uniform prior, lower bound: −0.3 a.u., upper bound: 0.3 a.u.).
To determine which electrodes were most informative for the forward encoding analyses, we tested the sensitivity of each electrode to both orientation and mismatch information (Fig 3C and 3F). The baseline-corrected signal at each electrode and time point in the epoch was regressed against a design matrix that consisted of the sine and cosine of the variable of interest (orientation or mismatch), and a constant regressor [30]. We calculated sensitivity, S, using the square of the sine (βSIN) and cosine (βCOS) regression coefficients:
S=√(βSIN2+βCOS2).
S was normalised against a null distribution of the values expected by chance. The null distribution was computed by shuffling the design matrix and repeating the analysis 1,000 times. The observed (unpermuted) sensitivity index was ranked within the null distribution (to produce a p-value) and z-normalised using the inverse of the cumulative Gaussian distribution (μ = 0; σ = 1). The topographies shown in Fig 3C and 3F reflect the group averaged z-scores, averaged across each time period of interest.
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10.1371/journal.ppat.1000643 | Mycobacteria Exploit Host Hyaluronan for Efficient Extracellular Replication | In spite of the importance of hyaluronan in host protection against infectious organisms in the alveolar spaces, its role in mycobacterial infection is unknown. In a previous study, we found that mycobacteria interact with hyaluronan on lung epithelial cells. Here, we have analyzed the role of hyaluronan after mycobacterial infection was established and found that pathogenic mycobacteria can grow by utilizing hyaluronan as a carbon source. Both mouse and human possess 3 kinds of hyaluronan synthases (HAS), designated HAS1, HAS2, and HAS3. Utilizing individual HAS-transfected cells, we show that HAS1 and HAS3 but not HAS2 support growth of mycobacteria. We found that the major hyaluronan synthase expressed in the lung is HAS1, and that its expression was increased after infection with Mycobacterium tuberculosis. Histochemical analysis demonstrated that hyaluronan profoundly accumulated in the granulomatous legion of the lungs in M. tuberculosis-infected mice and rhesus monkeys that died from tuberculosis. We detected hyaluronidase activity in the lysate of mycobacteria and showed that it was critical for hyaluronan-dependent extracellular growth. Finally, we showed that L-Ascorbic acid 6-hexadecanoate, a hyaluronidase inhibitor, suppressed growth of mycobacteria in vivo. Taken together, our data show that pathogenic mycobacteria exploit an intrinsic host-protective molecule, hyaluronan, to grow in the respiratory tract and demonstrate the potential usefulness of hyaluronidase inhibitors against mycobacterial diseases.
| Mycobacterium tuberculosis and Mycobacterium bovis are major bacterial pathogens that kill approximately 2 million people annually by causing tuberculosis. The M. tuberculosis complex has several strategies to parasitize the host. After infection is established, these pathogens are rarely eliminated from the host, and nowadays approximately a third of the world's human population is infected with the Mycobacterium tuberculosis complex. The elucidation of the parasitic mechanisms of the M. tuberculosis complex is important for the development of novel strategies against the disease. The major portal entry of M. tuberculosis complex is through the respiratory tract. On the surface of the airway, hyaluronan retains bactericidal enzymes so that they are “ready-to-use”, protecting tissues from invading pathogens. Furthermore, fragmented hyaluronan produced as a result of infection is used by the immune system as a sensor of infection. Thus, hyaluronan plays a pivotal role in host defenses in the respiratory tract. However, in this study, we observed that the M. tuberculosis complex utilizes hyaluronan as a carbon source for multiplication. We also found that the M. tuberculosis complex has hyaluronidase activity and showed that it is critical for hyaluronan-dependent growth of the M. tuberculosis complex. This study demonstrates a novel parasitic mechanism of the M. tuberculosis complex and suggests that mycobacterial hyaluronidase is a potential drug target.
| Infectious diseases caused by mycobacteria are serious threats to human health. Tuberculosis is caused by infection with mycobacteria, most frequently with Mycobacterium tuberculosis but also with Mycobacterium bovis, Mycobacterium africanum, Mycobacterium microti, and Mycobacterium canetii and kills around 2 million people annually. Leprosy is caused by Mycobacterium leprae and the globally registered prevalence of leprosy was around 22,000 cases at the beginning of 2006.
The major portal of entry for mycobacterial pathogens is through the respiratory tract. The primary phase of the infection begins with inhalation of bacteria, which are then phagocytosed by alveolar macrophages in the periphery of the lungs. In addition, several lines of evidence indicate that mycobacteria interact with epithelial cells in the respiratory tract [1]–[4]. The recent reports show the significant role of type II pneumocytes in the pathology of tuberculosis [3],[5],[6]. The onset of mycobacterial diseases frequently occurs after a long latent phase. Mycobacteria are an intracellular bacterium, multiplying within host cells, but also grow extracellularly [7],[8].
Macrophages phagocytose mycobacteria through interaction with several cell surface receptors, including complement receptors, mannose receptors, surfactant protein A, scavenger receptors, and Fc receptors [9]. By contrast, mycobacteria attaches to or invades lung epithelial cells through interactions with glycosaminoglycans (GAG) [10]. M. tuberculosis, M. bovis bacillus Calmette-Guerin (BCG), and M. leprae produce two types of GAG interacting adhesins, heparin-binding hemagglutinin (HBHA) [10],[11] and mycobacterial DNA-binding protein 1 (MDP1, also called histone-like protein and laminin-binding protein in M. leprae) [1],[12]. HBHA is secreted to the extracellular milieu from mycobacteria [13], whereas MDP1 is tightly attached on the mycobacterial cell wall [14].
We previously demonstrated that hyaluronan is a major portal for infection of mycobacteria into A549 human lung epithelial cells by interacting with MDP1 [1]. Hyaluronan is a nonsulfated linear GAG composed of thousands of repeating units of GlcNAc- (beta-1, 4)-GlcUA- (beta-1, 3) and is synthesized by 3 isoforms of hyaluronan synthases (HAS), designated HAS1, HAS2, and HAS3 in both mice and humans [15]–[18]. In vertebrates, hyaluronan is a ubiquitous structural component of the extracellular matrix, and is abundant in the chondral and vitreous tissues. Recent findings demonstrated that hyaluronan has a pivotal role in diverse dynamic biological functions such as embryonic development [19], cell migration [20],[21], tumor transformation, [22],[23], wound healing [24], and inflammation [25]–[27].
On the mucosal surface of the airway, hyaluronan retains bactericidal enzymes so that they are “ready-to-use”, protecting mucosal tissues from invading pathogens [28]. Furthermore, in the alveolar tracts, released fragmented HA stimulates innate immune responses by activating Toll-like receptor 2 and 4 dependent pathways and initiating lung inflammation [25]. By contrast, during resolution of respiratory inflammation, immuno-stimulatory hyaluronan is taken up via the hyaluronan receptor CD44 on alveolar macrophages [26]. Thus hyaluronan plays a pivotal role in host defenses in the respiratory tract, but its role in mycobacterial infection had not been elucidated so far. In this study, we analyzed the role of hyaluronan after mycobacterial infection was established.
A549 cells, a type II human lung epithelial cell line, were exposed to recombinant BCG expressing luciferase (rBCG-Luc) under the control of the HSP60 promoter [14] at a multiplicity of infection (MOI) of 10 for 16 hours. Cells were then washed and various doses of hyaluronan added into the culture. Growth of BCG was monitored by luciferase activity at each time point, which is indicative of viable bacteria [14],[29]. We found that exogenously added hyaluronan enhances bacterial growth in a dose-dependent manner (Figure 1A). We also confirmed this effect by counting viable bacteria using a colony forming units (CFU) assay (Figure 1C).
In our experimental setting, around 60% of the bacteria adhere to the cell surface and the remaining 40% are internalized by the cells [1]. Therefore, we next examined whether hyaluronan enhances extracellular or intracellular growth by treatment with gentamicin, which kills extracellular but not intracellular bacteria. After infection, we added gentamicin (50 µg/ml) into the culture for 6 hours and then added hyaluronan after removing gentamicin. The results showed that gentamicin treatment abrogated the growth of BCG (Figure 1B), indicating that bacterial growth occurred extracellularly. The enhanced effect of hyaluronan on bacterial growth was also abolished by gentamicin treatment (Figure 1B). This suggests that hyaluronan enhances growth of BCG attached to these cells.
We next examined if the same effects of hyaluronan can be seen in M. tuberculosis growth after infection to A549 cells. We infected M. tuberculosis H37Rv to A549 cells, then added hyaluronan, and monitored growth by counting colony-forming units (CFU). Similar to the case of BCG, we found that presence of hyaluronan enhances the growth of M. tuberculosis in a dose dependent manner (Figure 1D). Gentamicin treatment also abrogated the growth of M. tuberculosis and growth-enhancing effect of hyaluronan.
To determine why hyaluronan enhances the growth of BCG, we hypothesized that BCG can utilize it as a carbon source because hyaluronan is a polymer of disaccharides. We cultured BCG-Luc in 7H9 based carbon-starved broth in the presence (0.5 mg/ml) or absence of hyaluronan. As expected, in the carbon-starved media BCG did not grow, while the addition of hyaluronan supported the growth of BCG (Figure 2A), demonstrating that BCG can utilize hyaluronan as a carbon source.
We next compared hyaluronan with other GAG in terms of their growth supporting effect. BCG-Luc was cultured in 7H9-based carbon starved media or media including 0.5 mg/ml of each GAG as the sole carbon source. The results showed that BCG did not grow in the media supplemented with heparin or heparan sulfate. Both hyaluronan and chondroitin sulfate encouraged the growth, but hyaluronan sustained higher growth rates of BCG than chondroitin sulfate (Figure 2A). We also demonstrated that the growth supporting effect of hyaluronan is comparable to an equivalent amount of glucose (0.5 mg/ml) (Figure 2B).
In order to evaluate uptake of hyaluronan during hyaluronan-dependent growth of mycobacteria, we cultured BCG in the presence of 3H-labeled hyaluronan in the media containing hyaluronan as a sole carbon source. As shown in Figure 2C, live BCG incorporated hyaluronan, whereas heat-killed bacteria did not, showing actual uptake of hyaluronan into bacteria.
We next assessed the action of hyaluronan in the growth of virulent M. tuberculosis (strain H37Rv), and environmental mycobacterial species such as M. smegmatis (strain mc2155) and M. avium (ATCC25291). In carbon-starved media, none of the three strains grew. However, M. tuberculosis H37Rv, along with BCG, multiplied in the media containing hyaluronan as a sole carbon source while neither M. smegmatis nor M. avium proliferated. After 12 days culture, optimal density (OD) at 630 nm of M. tuberculosis culture increased to 0.32±0.038 from 0.01 (day 0). We then compared hyaluronan and other GAGs in terms of growth supportive effects on M. tuberculosis. Similar to the case of BCG, hyaluronan most effectively enhanced the growth of M. tuberculosis among tested GAGs (Figure 3).
Because hyaluronan is a long chain consisting of the repeat of two monosaccharides at over 2×105 Da, we hypothesized that extracellular cleavage of the polymer would be required before taken up by cells. Therefore, we next assessed hyaluronidase activity in mycobacteria. Hyaluronan was incubated in the presence or absence of cell lysates derived from BCG before precipitation by phenol/chloroform extraction. Precipitates were then fractionated by polyacrylamide gel electrophoresis (PAGE) and visualized by alcian blue staining as described previously [30]. Hyaluronan was separated into discrete ladder-like bands by electrophoresis after incubation with BCG lysate (Figure 4A), demonstrating that BCG possesses hyaluronidase activity.
We then addressed whether hyaluronidase activity is crucial for hyaluronan -dependent growth of mycobacteria. L-Ascorbic acid 6-hexadecanoate (Vcpal) is shown to be a potent inhibitor of hyaluronidase [31]. We investigated the effect of Vcpal on hyaluronidase activity of BCG and found that hyaluronidase activity was abolished in the presence of 25 µM Vcpal (Figure 4A, lane 4).
We next examined the effects of Vcpal on the growth of BCG. BCG-Luc was cultured in modified 7H9 media containing hyaluronan (0.5 mg/L) as the sole carbon source or 7H9-ADC complete media, which contains Tween 80, glycerol, and dextrose as carbon sources and BSA. We found that 25 µM Vcpal did not change the growth rate of BCG in 7H9-ADC complete media, while it abolished the growth of BCG in the media containing hyaluronan as the sole carbon source (Figure 4B).
We also examined the effect of Vcpal on the growth of M. tuberculosis. M. tuberculosis H37Rv was cultured in the media with or without Vcpal (50 and 100 µM). Vcpal suppressed the growth of M. tuberculosis in the media containing hyaluronan as a sole carbon source but not the growth in conventional 7H9-ADC media (Figure 4C). Other hyaluronidase inhibitors, such as apigenin and quercetin [32], also inhibited hyaluronan dependent growth of M. tuberculosis as shown in Figure S1. These results indicate that hyaluronidase activity is essential for both BCG and M. tuberculosis when utilizing hyaluronan as a carbon source.
We next examined whether Vcpal suppresses the enhancing effect of hyaluronan on the growth of BCG after attachment to A549 epithelial cells. After exposure to BCG-Luc, hyaluronan was added with or without Vcpal (25 µM) into the culture and growth of BCG was monitored by measuring luciferase activity. After 6 days culture, RLU values of BCG-Luc increased to 36.6±7.5 RLU or 52.6±18.7 RLU in the absence or presence of hyaluronan, respectably. Adding Vcpal abrogated the enhanced effects of hyaluronan (29.3±2 RLU), demonstrating that BCG utilized exogenously added hyaluronan as a carbon source after infection to A549 cells.
This work so far on the growth of mycobacteria has been performed with hyaluronan purified from human umbilical cord (Sigma). In order to elucidate whether mycobacteria can use hyaluronan actually synthesized in situ by mammalian cells, we employed the previously established stable human HAS1–3 expressing rat 3Y1 fibroblasts [15]. 3Y1 rat fibroblasts do not produce detectable hyaluronan themselves but each transfectant produces different sized hyaluronan. Both HAS1 and HAS3 transfectants secret hyaluronan with broad size distributions with molecular masses between 2×105 to ∼2×106 Da, while the HAS2 transfectant secretes extremely large hyaluronan at an average molecular mass of >2×106 Da [15]. We analyzed the level of hyaluronan production by utilizing a hyaluronan-binding protein (HABP)-based ELISA assay and confirmed that the HAS2 transfectant produced high levels of hyaluronan (235.7 µg/mL in the culture media), while the HAS3 transfectant synthesized the smallest amount of hyaluronan (15.9 µg/mL). The HAS1 transfectant produced moderate levels of hyaluronan (85.3 µg/mL), and the empty vector transfectant did not produce detectable amounts of hyaluronan.
Each human HAS transfectant was exposed to BCG-Luc and the growth kinetics of the bacteria were monitored by luciferase activity. The results showed that BCG grew after attachment to 3Y1 cells transfected with HAS1 and HAS3 but not with HAS2 or empty vector (Figure 5A). In addition, we found that hyaluronidase treatment of HAS1 transfected cells enhanced the growth of BCG (Figure 5B). These results suggest that shorter sized chains of hyaluronan are preferential for BCG growth.
We also monitored the growth of M. tuberculosis H37Rv after infection to these HAS transfectant cells. Along with the case of BCG, HAS1 and HAS3 but not HAS2-tranfectants supported the growth of M. tuberculosis (Figure 5C).
To see if hyaluronan is present at the site of infection of M. tuberculosis, we assessed the expression of hyaluronan synthases (HAS1, HAS2, and HAS3) in the lungs of BALB/c mice infected with the M. tuberculosis H37Rv strain, using the low-dose aerosol infection model. Total RNA was extracted from the lungs after 1, 3, 5, 7, 14, and 21 days of infection, and analyzed for HAS1, HAS2, and HAS3 mRNA transcription by reverse transcriptase-polymerase chain reaction (RT-PCR) (Figure 6A). The data showed that HAS1 mRNA expression increased after infection and was maintained at all time points (Figure 6A).
We next determined if hyaluronan is present in alveoli using biotin-conjugated hyaluronan-binding protein (HABP) and histochemical analysis. Before infection, hyaluronan was located on the surface of the airways and alveoli (Figure 6B). After M. tuberculosis infection, hyaluronan levels were profoundly increased and accumulated in the granulomatous legion (Figure 6B). Taken together, these data indicate that the major hyaluronan synthase in the lungs is HAS1 both before and after M. tuberculosis infection and hyaluronan accumulates in the tuberculosis lesion.
M. tuberculosis-infected mice had numerous sites of granulomatous inflammation in their lungs but in primates, tuberculosis granulomas are well-organized and tighter. We next studied hyaluronan in the lung granuloma of M. tuberculosis H37Rv-infected rhesus monkeys by staining with alcian blue, which is commonly used dye to detect GAG. The dye stained the surrounding region of well-organized granuloma (Figure 7A) and the staining was largely abolished by treatment with hyaluronidase (Figure 7B), showing that hyaluronan is a major GAG surrounding granuloma. Acid-fast bacilli (arrow heads in Figure 7C) were located in alcian blue stained areas, thus suggesting a strong correlation between the localization of the tubercle bacilli and hyaluronan.
Finally, we addressed the effect of Vcpal on the growth of BCG in BALB/c mice. Mice were infected with BCG intravenously through their tail veins. One day after BCG challenge, the hyaluronidase inhibitor Vcpal (0.4 or 1.64 mg/dose) was injected every day thorough the tail veins for 14 days. Two days after the final injection, the mice were euthanized and viable bacteria counts were determined by the CFU assay. As a positive control, we also treated mice with amikacin (Amk), which kills extracellular but not intracellular mycobacteria, by an intramuscular injection. The results showed that Vcpal apparently suppressed growth of BCG in the lungs, similar to Amk (Figure 8).
Although hyaluronan is crucial for both structural and physiological properties in the alveolar spaces, its role in mycobacterial infection was previously unknown. We demonstrated before that hyaluronan is the major attachment site of both BCG and M. tuberculosis in the infection of A549 cells, which itself produced hyaluronan [1] probably depending on HAS3 and HAS2 (Figure S2). In this study, we further extended our research and studied the role of hyaluronan after infection was established.
First, we examined the effect of hyaluronan on the growth of BCG after infection of A549 cells. BCG is an attenuated strain of the virulent M. bovis and is a live vaccine against tuberculosis. Because BCG bacilli share biological and pathological characteristics [33] and over 99.5% of their genome with that of M. tuberculosis [34], BCG is frequently utilized for the analysis of virulence of M. tuberculosis.
Utilizing BCG, we first found that exogenously added hyaluronan enhances the growth of BCG after incubation with A549 cells. We found that gentamicin treatment abrogated the growth of both BCG and M. tuberculosis, showing that these mycobacteria grow outside A549 cells. By contrast, this BCG strain (Pasteur) and M. tuberculosis H37Rv grew inside J774 mouse macrophages. These data apparently suggest that intracellular spaces in A549 cells are not suitable for the growth of mycobacteria.
Mycobacteria are intracellular pathogens and survive in macrophages by blocking phagosome-lysosome fusion (P-L fusion) at the stage of Rab5–Rab7 conversion [35]–[37]. Mycobacteria can infect non-professional epithelial cells in addition to alveolar macrophages. However, the exact mechanisms of how mycobacteria invade and persist or are killed in epithelial cells are unknown. Clemens and Horwitz demonstrated that mycobacterial phagosomes acquired Rab7 in HeLa epithelial cells, suggesting that P-L fusion is not efficiently blocked. Furthermore, Takeda's group recently found that type II pneumocytes produce antimicrobial peptides, secretory leukocyte protease inhibitor and Lipocalin 2, which have potent anti-mycobactericidal activities [5],[6]. Such bactericidal molecules may contribute to the inhibition of intracellular growth of mycobacteria within type II pneumocytes. These data suggest that intracellular trafficking of mycobacteria-containing vacuoles and intracellular states of mycobacteria are different from that in macrophages.
We found that both BCG and M. tuberculosis grew in the media containing hyaluronan as the sole carbon source (Figure 2A and 3). In addition to hyaluronan, mammals synthesize several GAGs, but hyaluronan most strongly supported the growth of BCG among GAGs and is comparable with glucose (Figure 2). By contrast, environmental mycobacteria, such as M. smegmatis and M. avium, failed to use hyaluronan as a carbon source. These data help us to understand why pathogenic mycobacteria have the ability to adhere to hyaluronan and metabolize it. It is reasonable to assume that this property is a great advantage, allowing them to grow in the hyaluronan-rich respiratory organs of their hosts.
Because hyaluronan is a long carbon chain, we considered that cleavage must be an essential step for its use as a carbon source, and indeed found hyaluronidase activity in BCG (Figure 4). Although certain other species of bacterial pathogens, such as Streptococcus, Staphylococcus, and Streptomyces, produce hyaluronidases [38], there has been no report of hyaluronidase of mycobacteria. This is the first report showing hyaluronidase activity in mycobacteria.
There are two main groups of hyaluronidases identified to date. One group is endo-β-N-acetyl-hexosaminidase or endo-β-glucuronidase, which degrades hyaluronan by hydrolysis [39]. These enzymes are distributed in some vertebrates including mouse and human. Others are lyase type hyaluronidase that degrade hyaluronan by β-elimination [39]. Bacterial hyaluronidases are lyases, which are unstable but have stronger activity than those of vertebrates, and generate unsaturated products, which is more suitable for energy supply than saturated hyaluronan. Therefore, it is reasonable to consider that mycobacteria have the lyase type of hyaluronidase. Although hyaluronidase is not yet described in the genome of either M. tuberculosis [33] or BCG [34], there are approximately 40 lyases. One of these lyases may be responsible for degradation of hyaluronan. Defining which enzyme is responsible for cleavage of hyaluronan is next important issue. Most hyaluronidases in mammals and bacteria display redundancy in recognition of their GAG substrates. Our data show that chondroitin sulfate also supported the growth of BCG (Figure 2). This may imply that hyaluronidase(s) of BCG cleave chondroitin sulfate as well.
Hyaluronan possesses many properties in vivo and it is believed that these biological activities are dependent on its size [40]–[42]. Although hyaluronan is composed of simple repeating disaccharides, its secondary structure is flexible. It is affected by the numbers of intramolecular hydrogen bonds, their location, and hydrophobic interactions [43],[44], all of which are increased as the size of the chains increase. Dynamic laser light-scattering analysis showed that the rod-like structure of low molecular weight hyaluronan changes to a stiff coil structure beyond a molecular weight of 1×105 Da [45]. Taken together, it is conceivable that hyaluronan synthesized by HAS1 and HAS3 exhibits a different structure from that synthesized by HAS2. Employing HAS transfectants, we found that both BCG and M. tuberculosis utilize hyaluronan synthesized only by HAS1 or HAS3 for multiplication (Figure 5A and 5C).
The fact that BCG and M. tuberculosis grow when co-cultured with HAS1 and HAS3 but not HAS2 transfected cells (Figure 5A and 5C) suggests that HAS1 and HAS3-synthesized hyaluronan supports the growth of mycobacteria in the human body. We founds that HAS1 is the major hyaluronan synthase in M. tuberculosis-infected mouse lungs (Figure 6A). HAS1 is expressed in immune cells, such as dendritic cells and T cells [46]. To clarify what kind of cell expresses HAS1 during mycobacterial infection is the next important issue.
In spite of the importance of hyaluronan in host protection in the lungs, its role in mycobacterial diseases had not been elucidated. In this study, we demonstrated that BCG and M. tuberculosis can utilize it as a carbon source. Hyaluronan was observed in the granulomatous region of mice lungs infected with M. tuberculosis (Figure 6). Furthermore, M. tuberculosis bacilli were residing in the region where hyaluronan was located in the lungs of monkeys that had died from tuberculosis (Figure 7). We also showed that blocking hyaluronidase inhibited in vivo multiplication of BCG (Figure 8). These results suggest that pathogenic mycobacteria have evolved to exploit the intrinsically host-protective molecule, hyaluronan as a nutrient to grow. Similar behavior of pathogenic mycobacteria was observed during infection of macrophages, that is, BCG is phagocytized in a cholesterol-dependent manner [47] and utilizes cholesterol as a carbon source to survive in activated macrophages [48]. It is likely that mycobacteria developed several strategies to obtain nutrients under nutrient-limited conditions.
After digestion of hyaluronan, it must be incorporated into mycobacteria through specific receptors or membrane proteins. Based on our results and consideration, hyaluronidase and a potential transporter of fragmented hyaluronan of pathogenic mycobacteria are potential drug targets.
All animals were maintained under specific pathogen-free conditions in the animal facilities of Osaka City University Graduate School of Medicine and in a biosafety-level-3 facility at The Research Institute of Tuberculosis according to the standard guidelines for animal experiments at each institute.
RPMI 1640 media, L-glutamine, fetal bovine serum, HEPES, hyaluronan from human umbilical cord, heparin from porcine intestinal mucosa and heparan sulfate from bovine kidney were purchased from Sigma-Aldrich (St. Louis, MO). Chondroitin sulfate A and C were purchased from Calbiochem (Gibbstown, NJ). For conventional culture of mycobacteria, Middlebrook 7H9 medium (Becton Dickinson) supplemented with 0.085% NaCl, 10% albumin-dextrose-catalase (BD Biosciences), 0.2% glycerol, and 0.05% Tween 80 (7H9-ADC) or 7H11-agar supplemented with 0.085% NaCl, 10% oleic acid-albumin-dextrose-catalase (BD Biosciences), and 0.2% glycerol (7H11-OADC) were used. 7H9 medium (Becton Dickinson) supplemented with 0.085% NaCl and 0.1% albumin was used as a carbon-starved 7H9 medium.
A549 cells were grown in RPMI 1640 medium containing 10% heat-inactivated fetal bovine serum, 2 mM L-glutamine, 25 mM HEPES and 5.5×10−5 M 2-mercaptoethanol (complete culture medium) at 37°C in an atmosphere of 5% CO2. Cells were suspended at 2×105/ml in complete culture medium and 1 ml of cell suspension was dispensed into individual wells of a 24-well polystyrene plate (BD Biosciences, San Jose, CA). Plates were incubated at 37°C for 24 h and were washed with serum-free RPMI 1640 medium to remove nonadherent cells. Wells were then refilled with 1 ml of complete culture medium. M. bovis BCG or M. tuberculosis cell suspension was prepared as described previously [1]. The bacterial cell suspension was added to A549 cells at multiplicities of infection (MOI) of 10. After 16 (BCG) or 4 (M. tuberculosis) h incubation, unbound bacteria were removed by washing with serum-free RPMI 1640 three times. After adding 1 ml of fresh complete culture medium to each well, hyaluronan solution was added to final concentrations ranging from 5 to 500 µg/ml. Cells were collected periodically for luciferase or CFU assays.
Construction of BCG expressing luciferase was described previously [1]. Luciferase activity was measured using the luciferase assay system from Promega (Madison, WI) according to the manufacturer's protocol on a Wallac 1420 manager as described previously [14].
A549 cells in 96-well polystyrene plates (8×104/well) were infected with BCG-Luc or M. tuberculosis at MOI of 10 at 37°C. After 16 (BCG) or 4 (M. tuberculosis) h, the monolayers were washed three times with RPMI 1640 medium to remove extracellular bacteria. Fresh complete culture medium containing 1 mg/ml of hyaluronan and 50 µg/ml of gentamicin were added to each well (200 µl/well) and incubated at 37°C. Cells were collected periodically for detection of luciferase activity of BCG-Luc or CFU assay of M. tuberculosis.
BCG-Luc or M. tuberculosis was adjusted to a concentration of 1×104 CFU/ml in carbon-starved 7H9 medium described previously [14], and 200 µl of bacterial cell suspension was added to 96-well polystyrene plates. Heparin, heparan sulfate, chondroitin sulfate, hyaluronan or glucose was added to appropriate wells to a final concentration of 500 µg/ml. Plates were incubated at 37°C and bacterial cells were collected periodically for detection of luciferase activity of BCG-Luc or CFU assay of M. tuberculosis.
BCG Pasteur was grown aerobically in 7H9-ADC medium at 37°C. Cells were then collected by centrifugation and half of the cells were heat-killed by heating at 65°C for 30 min. Then bacteria were washed, resuspended by carbon-starved 7H9 medium and adjusted to an optical density at 600 nm of 0.07. One hundred microliters of cell suspension was added to 100 ml of carbon-starved 7H9 with or without 6 mg of 3H-labeled hyaluronan and 14 mg of non-labeled hyaluronan (final concentration of 100 mg/L of total hyaluronan). Cells were then incubated at 37°C. After incubation, cells were harvested by use of a Scatron Harvester (Scatron) onto a glass fiber filter. The incorporated radioactivity was measured in a gamma counter (ALOKA ARC-2000).
M. tuberculosis strain H37Rv, M. smegmatis strain mc2155 and M. avium strain type4 were grown in carbon-starved 7H9 medium containing 0.5 mg/ml of hyaluronan, and the cultures were monitored periodically for their optical density at 600 nm (M. tuberculosis and M. smegmatis) or CFU (M. tuberculosis and M. avium).
BCG was grown in 7H9-ADC medium to mid-log phase. After incubation, bacterial cells were harvested, washed three times with ice-cold PBS (pH 6.0) and resuspended in the same buffer. To disrupt bacterial cells, the cell suspension was added to a screw-capped tube containing glass beads (diameter, 1.0 mm) and the tube was oscillated on a Mini-Bead Beater (Cole-Parmer). The tube was centrifuged at 10,000×g for 10 min, and the supernatant containing the bacterial protein extract was collected into a new tube. The protein solution was then mixed with 1 mg/ml of hyaluronan in PBS (pH 6.0) at 37°C. After incubation for 24 h, the solution was mixed with an equal volume of phenol to remove protein. The mixture was centrifuged at 10,000×g for 10 min and the supernatant was collected for PAGE analysis.
PAGE analysis of hyaluronan was performed as previously described by Ikegami-Kawai et al. [30] with minor modifications. The PAGE mini-slab gels contained 12.5% acrylamide, 0.32% N, N′-methylene bis-acrylamide in 0.1 M Tris-borate-1 mM Na2EDTA (TBE, pH 8.3). For the electrophoretic run, samples containing hyaluronan were mixed with one-fifth volume of 2M sucrose in TBE and 10 µl of the mixtures was applied directly to the gel. Bromophenol blue in TBE containing 0.3 M sucrose was used as a tracking dye, but was generally applied to a well with no sample. The gels were electrophoresed at 300 V for approximately 70 min using TBE as a reservoir buffer. After electrophoresis, the gels were stained with alcian blue as described previously [30]. Briefly, the gels were soaked in 0.05% Alcian blue in distilled water for 30 min in the dark and destained in water for 30 min.
BCG-Luc or M. tuberculosis H37Rv was suspended in 7H9-ADC, carbon-starved 7H9 or carbon-starved 7H9 containing 0.5 mg/ml of hyalurona to a final concentration of 1×104 CFU/ml and 200µl of each suspension was added to 96-well polystyrene plates. Vcpal was added to each well. Bacterial cells were then incubated at 37°C and were collected periodically for detection of luciferase activity for BCG-Luc or CFU assay for M. tuberculosis. Similarly, M. tuberculosis H37Rv was incubated in the media containing 0.5 mg/ml hyaluronan in presence or absence of 0.1 or 0.5 mM of apigenin or quercetin. After incubation for 7 days, living bacterial number was determined by CFU assay.
The expression of hyaluronan synthase genes in the lung tissues of mice aerogenically challenged with the virulent M. tuberculosis strain H37Rv was determined by RT-PCR. Seven-week-old of female BALB/c mice were aerogenically infected with the M. tuberculosis strain H37Rv (2×102 CFU/mouse) using a Glas-Col chamber. At different time points, 3 mice per group were euthanized and, the lungs were homogenized in PBS containing 0.05% Tween 80. The homogenates were centrifuged, and the pellets were processed to isolate total RNA using the RNeasy mini kit (QIAGEN, West Sussex, UK) according to the manufacturer's instruction. One microgram of total RNA was reverse transcribed using Super Script II RNase H reverse transcriptase (Invitrogen). The cDNA was then subjected to RT-PCR. The following primer pairs were used: β-actin, 5′-TGGAATCCTGTGGCATCCATGAAAC-3′ (F) and 5′-TAAACGCAGCAGCTCAGTAACAGTCCG-3′ (R); HAS1, 5′-GCTCTATGGGGCGTTCCTC-3′ (F) and 5′-CACACATAAGTGGCAGGGTCC-3′ (R); HAS2, 5′-TGGAACACCGGAAAATGAAGAAG-3′ (F) and 5′-GGACCGAGCCGTGTATTTAGTTGC-3′ (R); HAS3, 5′-CCATGAGGCGGGTGAAGGAGAG-3′ (F) and 5′-ATGCGGCCACGGTAGAAAAGTTGT-3′ (R). The amplification procedure involved initial denaturation at 94°C for 4 min followed by 35 cycles of denaturation at 94°C for 1 min, annealing of primers at 57°C for 1 min and primer extension at 72°C for 3 min. After completion of the 35th cycle, the extension reaction was continued for another 7 min at 72°C.
Total RNA was extracted from A549 cells by RNeasy mini kit (QIAGEN) and then 1 µg of total RNA was reverse transcribed using Super Script II RNase H reverse transcriptase (Invitrogen). The cDNA was then subjected to RT-PCR. The following primer pairs were used: β-actin, 5′-GATCATTGCTCCTCCTGAGC-3′ (F) and 5′-CACCTTCACCGTTCCAGTTT-3′ (R); HAS1, 5′- ACTCGGACACAAGGTTGGAC -3′ (F) and 5′- TGTACAGCCACTCACGGAAG -3′ (R); HAS2, 5′- ATGCATTGTGAGAGGTTTCT -3′ (F) and 5′- CCATGACAACTTTAATCCCAG -3′ (R); HAS3, 5′- GACGACAGCCCTGCGTGT -3′ (F) and 5′- TTGAGGTCAGGGAAGGAGAT-3′ (R). The amplification procedure involved initial denaturation at 94°C for 10 min followed by 40 cycles of denaturation at 94°C for 1 min, annealing of primers at 56°C for 1 min and primer extension at 72°C for 2.5 min.
The M. tuberculosis H37Rv challenge infection study of in rhesus male monkeys was performed previously [49]. The lung of non-vaccinated monkeys that died of tuberculosis 3 month after intratracheal challenge of 3,000 CFU/lung of M. tuberculosis H37Rv were immediately removed and fixed with 15% formalin for 10 days. Three animals' lungs were embedded in paraffin blocks and used in this study as well.
After deparaffinization by washing with xylene and ethanol, the tissue sections were washed in TBS and incubated with fresh TBE containing 0.05 mM of Pronase K (Dako) for 60 min at room temperature. After washing with TBS containing 1% bovine serum albumin, the slides were incubated with 3% bovine serum albumin in TBS for 30 min at room temperature to block non-specific binding sites. The slides were then washed with TBS twice for 10 min and incubated with the biotinylated hyaluronan-binding protein (HABP) probe at a concentration of 2 mg/ml in TBS for 60 min at room temperature. Following washing in TBS, the slides were incubated with a streptavidin-peroxidase reagent and the staining developed using DAKO Cytomation LSAB-system AP (Dako). The slides were then washed with distilled water and counterstained with Mayer's hematoxylin. Paraffin sections were also stained with alcian blue (Sigma) pH 2.5 (3% acetic acid) for 5 min. The slides were counterstained with nuclear fast red (Biomeda) and mounted with Gel/Mount (Biomeda). For GAG digestion, 0.5 mg/ml (10 U/ml) Streptomyces hyaluronidase was added for 30 min at 37°C before alcian blue staining. The slides were stained by Ziehl-Neelsen technique using carbol-fuchsin and malachite green (Sigma).
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10.1371/journal.pcbi.1003069 | The Impact of a Ligand Binding on Strand Migration in the SAM-I Riboswitch | Riboswitches sense cellular concentrations of small molecules and use this information to adjust synthesis rates of related metabolites. Riboswitches include an aptamer domain to detect the ligand and an expression platform to control gene expression. Previous structural studies of riboswitches largely focused on aptamers, truncating the expression domain to suppress conformational switching. To link ligand/aptamer binding to conformational switching, we constructed models of an S-adenosyl methionine (SAM)-I riboswitch RNA segment incorporating elements of the expression platform, allowing formation of an antiterminator (AT) helix. Using Anton, a computer specially developed for long timescale Molecular Dynamics (MD), we simulated an extended (three microseconds) MD trajectory with SAM bound to a modeled riboswitch RNA segment. Remarkably, we observed a strand migration, converting three base pairs from an antiterminator (AT) helix, characteristic of the transcription ON state, to a P1 helix, characteristic of the OFF state. This conformational switching towards the OFF state is observed only in the presence of SAM. Among seven extended trajectories with three starting structures, the presence of SAM enhances the trend towards the OFF state for two out of three starting structures tested. Our simulation provides a visual demonstration of how a small molecule (<500 MW) binding to a limited surface can trigger a large scale conformational rearrangement in a 40 kDa RNA by perturbing the Free Energy Landscape. Such a mechanism can explain minimal requirements for SAM binding and transcription termination for SAM-I riboswitches previously reported experimentally.
| Folding dynamics is crucial for RNA function. Riboswitches are a classic example. A typical riboswitch senses the cellular concentration of a small molecule. By refolding itself into a new structure, the riboswitch converts that information into changes in rates for synthesis of related metabolites. Understanding how the small molecule physically changes RNA structure can help us to target riboswitches, which occur mainly in bacteria, for drug design, or to engineer new riboswitches. This understanding has been blocked because 1) we cannot view intermediate stages experimentally and 2) simulations cannot reach the timescale for the structural conversion. Recent advances in RNA structure modeling enable us to model intermediate states. A new computer specialized for long timescale molecular dynamics (MD) simulations, called Anton, helps us to extend the simulation timescale. We modeled intermediate riboswitch structures, focusing on a reduced segment of the structure-switching region, in order to reduce the time required for a transition. We simulated an MD trajectory in which a small molecule converted the structure of this reduced switching region. Some steps in riboswitch structural transitions are therefore accessible to the newly extended MD timescale. Wider availability of resources like Anton can aid the advancement of riboswitch engineering and novel antibiotic design.
| Riboswitches reveal the versatility of Ribonucleic Acid (RNA) folding, and its remarkable biological impact. They are folded mRNAs that sense cellular metabolite levels and control expression of downstream genes [1]–[4]. Design of altered or novel riboswitches has been suggested for bioengineering applications [5]–[9]. Riboswitches also represent an important target for the design of novel antibacterials [10]–[12].
Riboswitches contain an aptamer, which recognizes and binds the metabolite. This binding triggers conformational rearrangement of the expression platform, which controls gene expression. Like other transcriptional riboswitches, the SAM-I riboswitch secondary structure is rearranged upon ligand binding [2], [13], [14]. The P1 and terminator (T) helices form in the ligand-bound state (Figure 1). This bound state is called the transcription OFF state since the terminator stops transcription. Without ligand the antiterminator (AT) helix forms, preventing formation of P1 and T helices, and allowing transcription (the transcription ON state).
SAM-I and other riboswitches raise the question–how can a small molecule binding to a limited contact surface cause a major folding rearrangement of a much larger RNA? Addressing this question requires consideration of conformational dynamics. X-ray studies of riboswitches have largely focused on the ligand-bound aptamer, truncating the expression domain to suppress conformational dynamics [15]–[18]. Such dynamic behavior is problematic for high resolution structure determination.
All-atom MD simulations are a major workhorse to tackle conformational dynamics [19]–[25]. Such methods have been applied to riboswitches, working largely with aptamer X-ray coordinates. These studies have revealed further insights into ligand recognition [26]–[30], the role of ions [25], [31], and contrasted dynamic properties in the liganded and unliganded states [27], [29], [30], [32], [33]. Larger-scale, slower dynamic processes have required coarse-grained modeling or directed simulations using biased force fields [34], [35].
Until recently, however, all-atom MD simulations using unbiased force fields have been generally limited to time scales less than microseconds. The birth of a specialized machine designed for MD simulation-Anton [36], [37] has increased the timescale limitation up to 200 times compared to simulations with conventional High Performance Computing (HPC) machines. Recent advances in software development and RNA structure modeling have improved the building of RNA models [38]–[48]. Together with enhanced sampling techniques [49]–[52] these modeling tools extend the accessible conformational space beyond that available within even the extended MD timescales.
Here we employ all-atom MD simulations to observe the direct effects of ligand binding on the equilibrium between alternative SAM-I riboswitch base pairing configurations. A large gap remains between the timescale required for strand migration (perhaps ms-seconds [53], [54]) and even the extended Anton MD timescale. We bridge this gap by bypassing the “nucleation” step in strand migration to simulate propagation-presumably a more rapid step. To generate “pre-nucleated” starting models for intermediate states, we capitalize on the recent advances mentioned above for sampling of RNA conformations.
We focus on the relationship between SAM binding and P1 helix propagation, or strand migration from an AT to a P1 helix (also termed the “switching” event [29] or “conformational collapse” [55]). We observed a strand migration event in the presence of SAM converting 3 AT helix base pairs (characteristic of the unbound riboswitch ON state) to competing P1 helix base pairs (characteristic of the OFF state). Overall, our simulations predict that SAM perturbs the reduced Free Energy Landscape (FEL) in a manner that favors conformations with expanded P1 helix base pairing and reduced AT pairing within the competing region, for certain starting geometries. Based on this simulation, we propose a mechanism for ligand-induced conformational switching which is consistent with reported requirements for SAM-I riboswitch function.
For SAM binding to fully convert an AT helix to a P1 helix may require at least milliseconds, judging from NMR measurements on an analogous strand-switching RNA [53], or longer based on a strand displacement assay [54]. We reasoned that the most rapid effect of SAM binding on the riboswitch would take place if the ligand bound to an intermediate conformation, hybridizing elements of the ON and the OFF state. Figure 2 shows a schematic of the strategy that we used to generate a starting configuration for our MD simulation. In the ON state, one strand of the P1 helix pairs with a downstream segment of the expression domain (removed in the crystallized RNAs) to form the AT helix. We initiated our simulation with a truncated segment fixing a partial P1 helix (two base pairs), and a partial AT helix (seven base pairs). In between a 4 nucleotide competition region can form either a P1 or AT helix. We call this “hybrid” construct 6P1_11AT, since it has the potential to form up to 6 P1 base pairs and up to 11 AT base pairs. Our simulations start with three of the four switching base pairs as AT helix, and a boundary nucleotide residue (U110) is positioned equally close to its putative AT or P1 binding partners.
This choice of starting structure allowed us to 1) Work with a segment that was shown experimentally to bind SAM, 2) Include the minimal nucleated P1 helix known to bind SAM, and 3) Maximize the potential number of AT base pairs with the potential to switch to P1 pairing (see “Details of MD simulations” in Supplementary Information (text S1)).
We used MC-Sym [40], [56] to sample the placement of the AT helix in the 3D structures and the geometry of the boundary region with the nucleated P1. Previously we showed experimentally that SAM binds to hybrid constructs [57]. Though reduced in affinity, the SAM binding to the hybrids has similar dependence on Mg2+, and similar sensitivity to mutations as with the aptamer. Therefore we assumed that the folding of the portion of the SAM/hybrid riboswitch complex outside of the strand switching region, henceforth referred to as the “aptamer core”, is similar to that in the X-ray structure of the aptamer domain.
Since the AT helix approximates a canonical A form geometry, the critical local region to be sampled is the three nucleotide segment A109, U110 and A111. These three nucleotides act as a hinge to bridge the partial P1 helix and the nearly complete AT helix. Additionally, an explicit triplet constraint was applied on the three nucleotides highlighted in purple in Figure 2A and 2B (A4, U110 and A136). Two adenosines compete for base pairing with a U (Figure 2B). The scripts used to generate the models can be found in the SI.
An overview of the outcome from MC-Sym sampling is shown in Figure 2 using the pseudo-dihedral angle [58]. Monitoring of the pseudo-dihedral angle (Figure 2C) indicates that MC-Sym has focused on the populated geometries according to the known structures, but also has sampled exhaustively the full range of geometries (Figure 2D). There is a region (between 80 and 170 degrees) that is rarely sampled due to steric clash with the P3 helix coordinates (Figure 2D). Therefore, the results demonstrate that MC-Sym can sample a wide range of the conformational space, while placing the AT helix without steric clashes.
Three criteria were used for selecting MC-Sym generated models for MD simulations: 1) Calculated potential energy should be favorable, 2) The SAM binding pocket must be accessible and 3) Coaxial stacking should be present between the P1 and AT helices. The latter constraint was based on experimental observations that SAM binding at µM affinity was detected for RNA constructs which allow the potential for such stacking (“3P1_10AT”), but not for those which do not (“3P1_9AT”) [57]. For reasons explained in supplementary information, we used the Amber99bsc0 force field with the generalized Born (GB) implicit solvent model to calculate free energy. Two (model 51 and model 55) out of the top five ranked in terms of free energy satisfied all the three criteria. Figure S1A shows calculated free energies, while Figure S1B highlights the coaxial stacking for these two models as measured by internucleotide vdW energies. The local geometry of the switching region is displayed schematically for these two structures in Figure 3A and global folds are shown in Figure 3B. The main difference between these two models is that the unpaired 5′ strand of the P1 helix is placed in the two different grooves of the AT helix–in the minor groove of the AT helix for model 51, and in the major groove for model 55 (Figure 3A).
The geometry sampled in model 51 and 55 resembles an RNA triple helix composed of poly(U)-poly(A)-poly(U) from a crystal structure [59]. With limited experimental data, these two models are rationalized as potential models for the intermediate or “transition state” between ON and OFF state.
Table 1 lists MD trajectories included in this study, using model 51 and 55 and the X-ray coordinates (3NPB) [60] as starting models. Different trajectory evolutions are observed for model 51 with or without SAM. Strikingly, formation of a complete P1 helix (all 6 Watson-Crick base pairs) is observed at ∼1.3 µs for the simulation in the presence of SAM (see Movie S1). Figure 4A displays the time evolution of RMSD for individual base pairs with reference to that in the P1 helix of the X-ray structure. A small RMSD value (deep blue) indicates that the geometry of the nucleobases in a single base pair is close to that observed for the Watson-Crick base pair in the crystal structure. Monitors of classical Watson-Crick hydrogen bonding presence for the base pairs in the P1 helix and the AT helix are presented in Figure S2.
As is apparent from Figure 4A and Figure S2, the time at which the P1 helix was completely formed can be located as indicated with the red arrow in Figure 4A. The lifetime of this conformation spans from frame 6544 to frame 6635 (18.2 ns). The top 4 base pairs in the P1 helix (base pair 1 to 4) maintain the P1-like conformation corresponding to the crystal structure during the remaining simulation in the presence of SAM. Additionally, the electrostatic interactions between the sulfur atom of SAM and the carbonyl oxygen atoms of two U nucleotide residues persist through out the simulation (Figure 4B) as observed in the repeated simulation on the aptamer domain of the yitJ SAM-I riboswitch (3NPB in the presence of SAM in Table 1).
The short life span of the fully formed P1 helix is linked to fraying of the closing base pair (base pair 6). The two participating nucleotides flip to a cross-strand stacking conformation. The adjacent base pair (base pair 5) is disrupted shortly after the loss of the closing base pair, but reappears at 1.9 µs for 300 ns (altogether 2250 out of 9097 snapshots after the strand migration event display this base pair). The two bases remain proximal (Figure 4C), however, and flip between states involving alternative hydrogen bonding patterns (Movie S1, Figure S3).
A similar plot for the AT helix pairs shows that the destabilization of AT base pairs 1 to 3 precedes complete P1 formation (Figure 4A, Figure S2). The disruption of this AT region is not due to the deficiency in modeling the AT helix since the simulation on the same model without SAM maintains the geometry close to a standard A-form helix for 2 (base pair 2 and 3) out of these three base pairs. Moreover, the 2 base-pair starting partial P1 helix is unstable in the absence of SAM in model 51 (Figure 4A, Figure S2).
During the interval leading up to the strand migration event, P1 helix base pairs 4–6 show a slowly rising trend in RMSD relative to the X-ray coordinates (Figure 4A, Figure S3). Thus the strand migration event is preceded by a fluctuation in which the corresponding nucleotide residues explore a “transition state”. This fluctuation coincides with the loss of AT helix base pairs 2 and 3, which otherwise block P1 helix propagation through P1 base pairs 5 and 6. The RMSD for P1 helix base pairs 3–6 goes down, in some cases dramatically, at the time of the strand migration. The two terminal base pairs drift towards configurations which show only a slightly smaller RMSD relative to X-ray coordinates than at the start. The RMSD of backbone atoms relative to the X-ray coordinates, however, decreases and remains low after the strand migration event (Figure S3).
Complete P1 formation does not take place in model 55 within the time scale (1.467 µs) accessible so far for this simulation when SAM is present. Base pair 3 in the P1 helix gets trapped in a state with base pair geometry close to an AU Hoogsteen base pair (U•A cis W.C./Hoogsteen and class XXIII according to reference [61]) (Figure S4). In addition, this state is stabilized by a new hydrogen bond interaction between A4 and SAM, which is not sampled during the simulation of the aptamer domain (the construct for the X-ray study) in the presence of SAM (Figure S4B, C).
In Figures 5 and 6 we visualize the conformational pathways observed for the various trajectories for model 51. The overall fraction of hydrogen bonds in Watson-Crick base pairs from the P1 helix and from the AT helix are used as generalized coordinates. Figure 5 displays the conformational trajectories for a simulation started from model 51 only, and a second simulation from model 51 in complex with SAM.
The results suggest that starting model 51 locates at a branch point in the FEL. The formation of a stable AT helix (high probability for AT Helix Hydrogen Bonding-vertical axis) is favored in the absence of SAM, while the presence of SAM allows model 51 to navigate to other transient states and eventually leads to sampling of the conformation with a complete P1 (high probability of P1 helix formation-horizontal axis, Figure 5). However, the event of complete P1 helix formation is short-lived (Figure 5, Figure 6A, B). Therefore, a third simulation restarted from a snapshot with complete P1 at frame 6615 of the first trajectory in the presence of SAM (the snapshot with the lowest RMSD relative to the X-ray coordinates) was performed to evaluate the stability of this conformation (Figure 6 C, D). A 1.767 µs trajectory starting from frame 6615 in simulation of model 51 with SAM, only samples the bottom part of a deep energy funnel populated by an ensemble with complete P1 helix (Table 1, Figure 6E).
Interestingly, P1 helix base pairing is also relatively stable (persists through the simulation) when frame 6615 with a complete P1 helix is used as the starting coordinates for a simulation without SAM present (Table 1, Figure 6F). When snapshot 9974, with a five base pair P1 helix, is used as starting coordinates, the 5 P1 helix base pairs again remain relatively stable over the course of the trajectory (Table 1, Figure 6G). In this case, however, opening of some individual P1 helix base pairs is observed, particularly towards the end of the trajectory with SAM absent (Table 1, Figure 6H). In the latter trajectory, at least one P1 helix base pair reverts to AT base pairing.
In SAM-I aptamer X-ray structures a Mg2+ ion observed near the SAM binding site and phosphate moieties in J1/2 and J3/4 [60], [62]–[64]. We monitored the contact distances between this Mg+2 and phosphates in J1/2 in the various trajectories of the SAM-I riboswitch aptamer and hybrid starting models with and without SAM. Our previous simulation on another aptamer of SAM-I riboswitch—metF from T.tengcongenesis [31] indicated cooperativity between this Mg2+-phosphate coordination complex and SAM-leading to stabilization of tertiary interactions. Similarly, this effect was also observed in simulations of model 51 and 55 in the presence of SAM (Figure 7). The presence of SAM is correlated with the maintenance of short magnesium contacts with J1/2, while these contact distances increase during the simulations without SAM. Contact distances between Mg2+ and phosphates in J3/4 are almost constant in the presence and absence of SAM. For the yitJ aptamer the correlation between the presence of SAM and short Mg2+ contact distances with J1/2 is still maintained (Figure S5, but contact distances with phosphates on J3/4 begin to increase in the absence of SAM. Additionally, for restarted simulations of frame 6615 and 9974, the contacts of this Mg2+ ion with J1/2 are still maintained even in the absence of SAM (Figure S6). Overall, these results confirm the stable coordination between the Mg2+ ion and J3/4 in the absence of SAM, and the tendency of SAM contact to stabilize an additional coordination with J1/2.
Movies S2, S3, S4, S5, S6, S7 also highlight base moieties attached to nucleotide A7/9 in J1/2 and A80/82 in J3/4. Our earlier study also observed transient formation of a non-adjacent dinucleotide stack between nucleotide bases in J1/2 and J3/4 in simulations with and without SAM [31]. Of the ∼12 X-ray SAM-I riboswitch coordinate sets [60], [62]–[64] all except one (pdb id 3GX3, with SAH bound) show the two nucleotide bases pointing to the same region outside the helix, with the respective bases within 3–7 angstroms proximity. In this study we again observed transient formation of dinucleotide stacking with and without SAM for model 51 and the aptamer, but with alternating stacking geometries (Movies S2, S3, S4, S5, S6, S7). Predominantly the two nucleotides were positioned with favorable stacking energies (Figure S7) but little effect was observed from SAM binding.
Overall, we can summarize the results with model 51 MD trajectories as the following: 1) In the absence of SAM, 2–3 starting P1 helix base pairs appear to be unstable, whereas a long AT helix remains stable up to the terminal base pair; 2) In the presence of SAM, a strand migration event is observed after ∼1.3 µs leading to transient formation of a full 6 base pair P1 helix, at the expense of competing AT helix base pairs; 3) Terminal base pairs within the fully formed P1 helix form transiently in the original simulation, but appear relatively stable in a new trajectory using the snapshot with fully-formed P1 helix as the starting point; 4) The fully formed P1 helix is also relatively stable in a trajectory which starts with the same snapshot even in the absence of SAM. 5) In a trajectory starting with 5 P1 base pairs with SAM, hydrogen bond contacts corresponding to the five base pairs appear slightly more stable than they do in one starting from the same snapshot without SAM.
By contrast, trajectories starting with model 55 result in a conformation in which the competing base pair at the boundary between P1 and AT base pairs forms a non-Watson-Crick pair, while other P1 and AT base pairs are stable. Taken together, these findings indicate that SAM binding promotes P1 helix base pairing at the expense of AT helix pairing, but with qualifications. Certain starting geometries, such as that in which the 5′ nucleotides reside near the major groove of the AT helix (as in model 55), may be slow to convert to the P1 helix-forming conformation. Our original simulation of model 51 in the presence of SAM resulted in 4 stable P1 helix base pairs. Thus, SAM binding may have its strongest direct stabilization of P1 helix base pairs near the SAM binding site.
All of these results are consistent with experimental evidence. A minimum length of P1 helix is necessary for SAM binding [13], [65], though the presence of a partial AT helix can restore µM SAM binding with a P1 helix as short as 2 base pairs [57]. The latter study indicated that SAM binding affinity increases in model systems as the P1 helix is extended and the AT helix shortened. There are also indications that P1 helix dynamics are reduced by SAM binding [17], [54], [65] for truncated aptamers.
Earlier we proposed that SAM contacts with J1/2 and indirect stabilization of Mg2+ contacts with J1/2 enhance P1 helix formation [31], and that the contacts with J1/2 block formation of competing conformers [66]. Our simulation suggests that additional enhancement of P1 helix formation arises through direct contact with SAM. The importance of these electrostatic SAM-P1 helix contacts for mediating the ligand binding specificity has been established experimentally [63].
As observed in our earlier simulations [31], direct contacts between SAM and the key G11 nucleotide within the P1 helix are persistent throughout these extended timescale simulations. In addition, we observed shorter contact distances between a bound Mg2+ and at least two electronegative functional groups on J1/2 in the presence of SAM during the simulations starting with model 51 and with the aptamer, than in the absence of SAM (Figures S4, S5, S6). The Mg2+ ion site which we have monitored here, observed in the original X-ray structures, is suspected to form an inner sphere coordination complex [31], [67].
Movies shown in supplementary materials (Movies S2, S3, S4, S5, S6, S7) vividly illustrate the interplay between SAM, Mg2+, and the backbones of the J1/2 and J3/4 junctions. Movies without SAM show the Mg2+ surrounded by phosphates from J3/4, with particularly stable coordination with phosphates 81 and 83 (83 and 85 in the aptamer). In the presence of SAM, G9/11 O6 is anchored in a bridging position between the Mg2+ and phosphate groups in J1/2. A recent study identified a cooperative effect between Mg2+ and SAM in SAM-I riboswitch folding, and proposed a role for the same core Mg2+ in pre-organizing folding intermediates for SAM binding [55]. Movies S2, S3, S4, S5, S6, S7 provide a striking illustration of a potential mechanism to explain this cooperativity. This coordination complex could induce a reorientation of the P1 helix, as reported [65], by fixing the position of J1/2.
Favorable non-adjacent dinucleotide stacking between nucleotide bases in J1/2 and J3/4 is observed in model 51 with SAM, but with altered geometry in the absence of SAM (Movies S2, S3). Altogether, these observations leave an open question as to the role that non-adjacent dinucleotide stacking may play in pre-positioning J1/2 and J3/4 in a manner that is favorable to aptamer formation and P1 helix formation specifically.
The simulation of model 51 in this study shows that the stabilization of the partial P1 helix by SAM anchors the 5′ strand of the P1 helix in an orientation that enables this single strand region to compete over the AT helix. This model is reminiscent of an NMR study on a small RNA system showing that the stabilization of a pre-formed helical region by a tetra loop increases the rate of conversion between two different hairpin folds [53]. In the riboswitch, SAM stabilization of the nucleated P1 helix may play a similar role.
Simulations on the same starting coordinates in the absence of SAM displayed the loss of all P1 helix base pairing. Conformations with three or fewer base pairs in the P1 helix may not be stable enough to prevent the formation of the AT helix in the absence of SAM. When a snapshot with fully formed P1 helix (frame 6615) was used as the starting structure an MD trajectory displayed a relatively stable P1 helix even in the absence of SAM. When frame 9974 with 5 P1 helix base pairs was used as the starting structure, all 5 P1 helix pairs remained stable in the presence of SAM. With these starting coordinates, however, as the simulation time approached 1 µs, the P1 helix began to show some instability in the absence of SAM. Therefore, differing degrees of shift of the conformational equilibrium amongst a series of conformational intermediates toward the OFF state with SAM facilitate the SAM-I riboswitch function as a dimmer switch. The most dramatic SAM binding effect is on a hybrid conformer with minimal P1 helix base pairing.
In the biological context, it is proposed that SAM binding takes place soon after the transcription of the aptamer-forming segment [68]. The conformation is then locked before full transcription of the antiterminator, a mechanism similar to that indicated for other transcriptional riboswitches [69], [70]. Such a mechanism is highly sensitive to the concentrations of reaction components-a recent report indicated that nucleotide levels dramatically alter the degree of kinetic control of a lysine riboswitch within active transcription complexes [71]. For the yitJ SAM-I riboswitch, moreover, partial AT helix formation can take place in the non-overlapping region, even in the presence of a full P1 helix. Our simulations indicate that the presence of SAM would prevent strand invasion by this partial AT and dissociation of the P1 helix in this scenario.
High resolution structures of riboswitch aptamers with and without ligand have led to the proposal that many fold according to the “conformational capture” mechanism [72], [73]. Typically this mechanism is described as selection by the ligand of a single bound conformer amongst a range of conformations being sampled by the unliganded substrate (Figure 8A). Inclusion of a portion of the expression domain, however, leads to more dramatic effects of ligand on RNA folding. Secondary structure calculations predict that an equilibrium Boltzmann ensemble for the yitJ SAM-I riboswitch includes some hybrid conformations with partial P1 and partial AT helix [57], [66]. A “capture” of these intermediates, according to simulations here, would facilitate rapid propagation of a longer P1 helix. This event would free a sufficient segment of the 3′ strand of the AT to nucleate the downstream Terminator sequence. By contrast, our simulations indicate that in the absence of SAM the AT helix could displace the nucleated P1 helix within the intermediates.
A more precise description of conformational capture in this scenario would be selection of a region of conformational space by the ligand, which then chaperones the RNA towards the aptamer configuration (Figure 8B). In panel B of the figure, free energy is now the relative free energy of the total system, including ligand as well as RNA plus solvent and ions. Conformations that can bind SAM have reduced free energy relative to those for which RNA and SAM are not in contact, and the reduction in free energy for each conformer is proportional to favorable free energy of binding. We hypothesize that the aptamer folding rate would be accelerated by this mechanism because during the Levinthal sampling process the FEL region that can initiate aptamer formation is widened.
Simulations of RNA folding kinetics [74] concluded that a strand migration pathway would lead to the fastest transition rate for an inter-conversion between two hairpins. Kinetic folding studies for a number of riboswitches [75]–[79] indicate that P1 helix formation takes place during later stages of the folding pathway. Our simulations and the experimental findings in our previous study [57] therefore raise the possibility of a role for SAM in accelerating P1 helix formation, by facilitating strand migration as the aptamer folding pathway. In this scenario, SAM binding could still facilitate aptamer formation after a portion of the expression domain has been transcribed. In vitro kinetics of SAM-I riboswitch folding and transcription termination would then be highly sensitive to mutations in the expression domain, as has been reported for a transcriptional lysine riboswitch [71]. SAM effects on P1 vs. AT length could be tested through NMR measurements on partially labeled SAM-I riboswitch hybrid constructs, or through NMR methods designed to detect minor conformers [80], [81].
In previous work we showed that altered base pairing in the unliganded yitJ SAM-I riboswitch as compared to the bound state extends beyond the P1/AT helix switch [57], [66]. The question of the impact of the ligand on the riboswitch conformation is therefore related to large-scale alterations in RNA folding. Although great advancement has been achieved to speedup MD simulations, a complete simulation of folding/unfolding for RNA of this size (∼40 kDa) is still not possible. The P1/AT helix switching event alone may take ms or longer, according to data on analogous model systems [53]. This is because the nucleation of the transient state takes up most of the folding time. The propagation step can be faster than the overall folding rate by four orders of magnitude [82]. Therefore, MC-Sym was used to sample a discrete conformational space aiming to identify candidate transient state models with atomic details to bypass the most time-consuming part of the simulation.
In the SI, we discuss measurements from the literature for folding and conformational switching for a range of RNAs. Overall, considering the literature data and estimating a rate constant based upon snapshots observed in the model 51 trajectory as transition states (Tables S1 and S2), the microsecond regime appears plausible for the strand migration within the three base pair stretch simulated in this study. Extension of the MD timescale to the microsecond regime by using Anton now appears to make some intermediate steps of strand migration accessible. The adequacy of force fields and parameters for long timescale simulations for RNA is relatively untested as compared to protein MD [83]. Nonetheless, it seems improbable that a strand exchange observed only in the presence of ligand, and leading to decreased RMSD relative to X-ray coordinates (Figure 4, Figure S3), is solely a result of instabilities or imperfections of the force fields [84]. The two terminal base pairs of the P1 helix are transient in the original model 51 simulation with SAM, although the RMSD relative to the X-ray structure for each base pair remains lower than before the strand invasion. This observation may reflect instabilities in the force field, a genuine tendency towards “fraying” [85], [86], or a longer simulation may be required to reach a thermodynamically stable state. The success of this study in observing a strand migration event should motivate efforts to optimize and validate parameters and protocols for long timescale MD simulations for RNA.
The atomic models for the RNA construct described in Figures 2 and 3 were generated using MC-Sym [40] installed locally. The aptamer core (highlighted in blue in the figure) was modeled using its counterpart in the known structure of the yitJ SAM-I riboswitch (PDB ID: 3NPB) [60]. The other parts of the construct were built from the library of small fragment RNA structures, known as Nucleotide Cyclic Motifs (NCMs) [56]. An explicit triplet constraint was applied on the three nucleotides highlighted in purple (A4, U110 and A136). In this way we sampled the 3D space in which these three nucleotides are proximal to each other. In these three nucleotides, the two As are competing for base pairing with a U. The scripts used to generate the models can be found in Appendix S2. Different RMSD threshold values were tested to ensure exhaustive sampling in the local region bridging the partial P1 and the AT helix (A109, U110 and A111). Models with small differences (low pairwise RMSD) in pseudo-dihedral angle of the A109-A111 region were filtered out. Energy minimizations (max step is 2000 or gradient tolerance <1.0) were performed on the atomic structures of the models generated from MC-Sym runs using Nucleic Acid Builder (NAB) [87]. AMBER99bsc0 force field [88] and Generalized Born model [89] with an inverse Debye-Huckel length of 0.19 Å−1 [90] were used in the energy minimization procedure. 149 models were generated in this step. This energy minimization is mainly to rebuild the chain connectivity for models generated from MC-Sym without introducing the sampling effect of the force field. Thus, we used MC-Sym to sample the possible placement of the AT helix in the 3D structures and the geometry of the potential nucleation site of the P1 helix close to the SAM binding pocket. The modeling assumed that the folding of the aptamer core is similar to that in the crystal structure of the aptamer domain. After the energy minimization step, models with high van der Waals energy were filtered out. There are two reasons for high van der Waals energy: 1) steric clashes that cannot be released by energy minimization, 2) broken chain connectivity that cannot be bridged during energy minimization.
Models were chosen following the three criteria listed in the results section under “Selection of starting models for MD simulation”. For the models in the presence of SAM, the ligand was placed in the binding pocket while maintaining most of the interactions (except the contacts with the end base pair AU in the partial P1 helix) observed in the crystal structure of the aptamer domain complex (PDB: 3NPB). The simulations are run on Anton [36]. The equilibrated structures for Anton were prepared using local HPC clusters following the MD protocol as described in our previous study [31] (also see “Details of MD simulations” in Text S1). The trajectory was recorded for every 200 ps.
The definition of hydrogen bond probability (HBP) of the hydrogen bond i at time t is similar to that in reference [91]:(1)where , and is defined as(2)Here is the distance between hydrogen and hydrogen bond acceptor, is the angle of hydrogen bond donor, hydrogen and hydrogen bond acceptor and scaling constant Å. In the reference state Å and rad. The list of hydrogen bonds monitored is listed in Table S3.
|
10.1371/journal.pcbi.1000120 | Using Expression Profiles of Caenorhabditis elegans
Neurons To Identify Genes That Mediate Synaptic Connectivity | Synaptic wiring of neurons in Caenorhabditis elegans is largely
invariable between animals. It has been suggested that this feature stems from
genetically encoded molecular markers that guide the neurons in the final stage
of synaptic formation. Identifying these markers and unraveling the logic by
which they direct synapse formation is a key challenge. Here, we address this
task by constructing a probabilistic model that attempts to explain the neuronal
connectivity diagram of C. elegans as a function of the
expression patterns of its neurons. By only considering neuron pairs that are
known to be connected by chemical or electrical synapses, we focus on the final
stage of synapse formation, in which neurons identify their designated partners.
Our results show that for many neurons the neuronal expression map of C.
elegans can be used to accurately predict the subset of adjacent
neurons that will be chosen as its postsynaptic partners. Notably, these
predictions can be achieved using the expression patterns of only a small number
of specific genes that interact in a combinatorial fashion.
| Synaptic wiring in the nematode Caenorhabditis elegans is
largely invariant between individuals, suggesting that this wiring is
genetically encoded. This is in essence the chemoaffinity hypothesis suggested
by Roger Sperry. However, proving this hypothesis in model organisms and
detecting the identities of the genes that determine the presence or absence of
synaptic connections is a major challenge. C. elegans provides
a unique opportunity to examine this hypothesis due to the availability of both
its neuronal wiring diagram and neuronal gene expression map. In this study we
show that the neuronal gene expression profiles can be used to predict the
subset of adjacent neurons that each neuron will connect to with good accuracy.
We further identify a small set of putative genes on both sides of the synapses
that interact in a combinatorial fashion and mediate the neuronal partner
selection process. The modular design in which a small set of components is
reutilized throughout the network is common with other known biological systems
and raises the possibility of a similar design in neuronal networks of more
complex organisms.
| The nervous system of Caenorhabditis elegans has exactly 302 neurons
with a simple gross morphology, often having only a single, unbranched process.
Processes run together in parallel bundles, forming synapses to adjacent processes.
The neuronal bodies and their processes are found in characteristic positions and
similar sets of synaptic connections are seen in different individuals and among
sets of homologous cells (e.g., cells that are bilaterally symmetrical to each other
in the worm's body) [1]. Furthermore, most of the neurons are connected to
a subset of about 50% of the neurons that are in physical proximity to
them and this subset is fairly constant from animal to animal [2],[3]. These observations raise
the fundamental question in neuroscience: What are the rules that govern nervous
system connectivity and how are these rules encoded in the genome?
The development of the nervous system can be divided into three phases: The
generation of the correct cells in the right temporal and spatial locations, the
outgrowth of nerve processes, and the formation of synapses. The first phase is
determined by the lineage of the organism, which positions the neurons at the right
temporal and spatial locations. The second phase depends mostly on the growth cone
which migrates through the animal, spinning out the nerve process behind it. The
third phase depends on short range communication and is feasible only between
neurons that are in physical proximity. All of these phases show a high degree of
specificity [1],[2].
Here, we focus on the third phase in which a neuron “chooses” its
synaptic partners from among the neurons that are in physical proximity to it. A
classical hypothesis for this phase with many empirical proofs is Sperry's
chemoaffinity hypothesis [4]–[6], which states that the
wiring is “activity-independent,” i.e., that each neuron links
to a postsynaptic target by selective attachment mediated by specific chemical
molecular identifiers. These molecular identifiers are encoded in the genome [7], label
the neurons, and determine their chemical affinity. Candidate genes which may
constitute the molecular identifiers are the Dscam gene in
drosophila
[8] and the
Protocadherin (Pcdh) proteins in humans [9]. In C.
elegans, the most unequivocal proof for the existence of such molecular
identifiers was demonstrated for a single neuron (HSNL) [10], where it was shown that
the transmembrane proteins syg-1 and syg-2,
members of the immunoglobulin superfamily, bind together and guide the neuron to
form the correct synapses.
The relationship between connectivity and gene expression in C.
elegans was recently explored in two studies. Kaufman et al. [11] was
the first study to demonstrate a correlation between gene expression and neuronal
connectivity using a covariation correlation analysis. They also showed that the
expression signature of each neuron can be used to predict its outgoing connectivity
signature using the k-nearest neighbors method, i.e., neurons that
express similar sets of genes tend to choose similar sets of synaptic partners. A
similar result was separately shown for the incoming connectivity. They used feature
selection to find a small set of genes whose expression carries most of the neuronal
connectivity information. However, their approach does not provide predictions on
the way in which these genes interact to mediate synaptic connectivity.
In a closely related study, Varadan et al. [12] applied an entropy
minimization approach to identify sets of synergistically interacting genes whose
joint expression pattern predicts the existence of a synapse with minimum
uncertainty. They provide a single rule, composed of two genes in the presynaptic
region and two genes in the postsynaptic region whose joint expression predicts the
existence of a synapse with minimum uncertainty. This rule achieved significantly
smaller entropy than that expected by chance, but its predictive ability was not
examined in a cross-validation scheme.
A common feature in both of the above studies [11],[12] is the attempt to
predict the formation of a chemical synapse between any pair of neurons in the worm
based on the expression pattern of the genes, regardless of their spatial location.
Here, we propose to integrate the spatial locations of neurons into this prediction
task, by limiting the predictions to pairs of neurons that are certain to be in
physical proximity to each other in the worm's body (since they are
connected by chemical or electrical synapses). By doing so, we shift the focus from
genes whose expression affects synaptic connectivity through mechanisms such as
lineage, axonal guidance and neuronal migration to genes whose expression has a role
in the crosstalk of the neurons in the final stage of the chemical synapse formation
when neurons identify their designated partners.
Our study has two complementary goals. First, we wish to explore whether the gene
expression signature of the neurons carries significant information on the subset of
adjacent neurons that are chosen as their postsynaptic partners. Second, we wish to
find a subset of genes and specific rules of interactions among them that with high
confidence predict the choice of chemical synaptic partners. We combine the gene
expression patterns of neurons with the neuronal wiring diagram, and apply a
probabilistic learning algorithm for detecting the subset of relevant genes and
their combinatorial logic, while incorporating the physical proximity of the
neurons.
Our results confirm that neuronal gene expression can be used to accurately predict
the choice of synaptic partners and that only a few genes with specific interaction
patterns are sufficient to make these predictions. We suggest that this small number
of genes imply that there may be a general genetic mechanism that wires the nervous
system of the worm and that deeper understanding of this mechanism may contribute to
the understanding of the development of nervous systems in higher organisms.
Our goal is to model the dependence of the chemical synapse formation on the
expression patterns of the genes in the neurons. To this end, we introduce a
variable representing the chemical synapse formation between neurons and try to
predict its value based on a stochastic logical function of the expression of the
genes in both the presynaptic and postsynaptic neurons. We chose a model that is
based on a probabilistic decision tree, which uses the expression pattern of genes
in adjacent neurons to regress upon the chemical synapse formation variable. This
model has two important virtues which make it suitable for our task. First, it
permits context specific independencies: rather than maintaining a complete tree
with all the possible splits for gene expression levels, it maintains only the
branches which are relevant. For example, consider a simple mechanism of
lock-and-key molecular identifiers such that only when the presynaptic neuron
expresses a lock molecule and the postsynaptic neuron expresses a key molecule, a
synapse would be formed between them (Figure 1A). However, if a neuron does not express the lock then it will
not form a synapse onto its neighbors, regardless of the expression of the key.
Thus, the decision tree branch that corresponds to the scenario in which the lock is
not expressed in the presynaptic neuron should not be split again by the key
expression in the postsynaptic neuron (Figure 1B). In this case, in the context in which the lock is not
expressed in the presynaptic neuron, the formation of a synapse between adjacent
neurons is independent of the expression of the key in the postsynaptic neuron. This
way, the context specific independencies reduce the number of model parameters to
only those that are relevant, making the model both more intuitive to interpret and
easier to robustly learn from the data.
The second virtue of our model is its probabilistic nature, which is important given
that both the wiring diagram and the available gene expression patterns are crude
and noisy [11]. In addition, although largely constant, the wiring
diagram between animals displays some variability, which may be a consequence of a
nondeterministic selection of neuronal partners based on their chemical affinities
or a consequence of other mechanisms of synaptic plasticity such as Hebb law for
activity-dependent synaptic formation [13]. For these reasons, a probabilistic model seems
appropriate, since it can account for the noise and inherent variability in the
problem.
Our probabilistic decision tree is an instantiation of a probabilistic graphical
model, or Bayesian network. Specifically, we chose the tree-structured conditional
probability distribution (tree-CPD) that was introduced by Friedman and Goldszmidt
[14]. This tree-CPD assigns a conditional probability to
every leaf. Thus, every pair of neighboring neurons is mapped to a single leaf based
on the genes that they express and the probability of synapse formation between them
is obtained from that leaf. For example, in the tree-CPD of Figure 4F, if the postsynaptic neuron expresses
hmr-1 and the presynaptic neuron does not expresses npr-1, then the probability of
chemical synapse in this direction is 0.92. This probability is independent of
akt-1, glr-1, cdh-3, osm-6, and unc-4, although these genes affect the probability
of chemical synapse formation in other contexts.
We use both the gene expression signature of the neurons and the synaptic
connectivity network to learn the model. Since many genes have nearly identical
expression patterns, we clustered the neuronal expression patterns of the 251 genes
in the dataset into 133 expression classes, thereby removing redundancies in the
dataset (see Materials and Methods section).
Recall that we wish to focus on the last phase of synaptic connectivity, in which
neurons perform crosstalk with each other in order to correctly choose their
designated synaptic partners. Thus, ideally, we should choose every ordered pair of
neurons that are spatially proximal (such that a chemical synapse could be created
between them) at some stage of development as an example to learn from. However,
lacking detailed geometric coordinates of the neuronal processes, we use the
connectivity pattern itself to approximate the physical proximity of any two
neurons. Specifically, we define two neurons as being in the same neighborhood if
they are connected by a chemical synapse in either direction or by an electrical
synapse (gap junction). According to this definition, neurons in the same
neighborhood are certainly close enough to form synapse in either direction (Figure 2). Our approximation may
miss negative examples in cases where two neurons that are close enough to form
chemical synapse do not form any synapse in either direction. To further validate
that our results are not biased due to this approximation, we compared them (below)
to the results achieved by applying the same learning process under a more relaxed
assumption according to which two neurons are considered spatially proximal if they
are both connected by an electrical or chemical synapse to each other or to another
neuron in the network.
To learn the tree-CPD model, we used a Bayesian score [15] and a two phase
tree-CPD construction heuristic [14]. The Bayesian score exhibits a tradeoff between
the fit to the data and the complexity of the model, a desirable property that
prevents overfitting. The two phase tree-CPD construction heuristic is designed to
prevent the learning process from getting stuck in local minima by scanning the
space of tree-CPDs in a way that allows temporary reduction of the score (see Materials and Methods section).
We first tested whether the model learned from this data indeed demonstrates that the
gene expression signature of the neurons has predictive power regarding the subgroup
of adjacent neurons that will be chosen as the postsynaptic partners of every
neuron. We used the tree-CPD as a classifier which predicts the presence or absence
of a synapse for each ordered pair of neurons, and extended it by AdaBoost [16], a
boosting algorithm designed to improve the accuracy of classifiers. In general,
AdaBoost is an iterative algorithm that iteratively learns a new tree-CPD on a
reweighed dataset, where the reweighting in each learning iteration is done in a way
that shifts the focus from the correctly classified examples (easy examples) to the
wrongly classified ones (hard examples). The final classifier is a weighted majority
vote of all of the tree-CPDs that were learned (see Materials and Methods section). To assess the quality of the classifier,
we compared its accuracy using the standard area under the ROC curve (AUC) for
5-fold cross-validation, to the accuracy obtained for randomized datasets, in which
neurons identities were shuffled [11],[12], or in which the examples signs (presence or
absence of a synapses) were shuffled (see Materials
and Methods section).
We find that our boosted tree-CPD classifier predicts the formation of synapses with
an AUC of 0.84±0.008, significantly better than the AUC of
0.71±0.005 achieved on the randomized datasets (Figure 3). The use of boosted decision trees
allows us to achieve high performance with shallow tree-CPDs, compared to using
nonboosted classifiers (Text S1 and Figure S1).
This high performance is independent of the maximal depth of the tree and requires
less than 30 boosting iterations to reach maximal performance (Figure S2).
The performance obtained for repeating the same experiment under the relaxed
proximity assumption described above was AUC of 0.78±0.01 for the real
dataset compared to AUC of 0.64±0.008 for the randomized dataset.
Although the performance on both the real and randomized datasets has decreased (due
to the 10 fold increase in the number of negative examples while maintaining the
same number of positive examples as before), the significance of the results has
remained the same. These results therefore show that a probabilistic classifier can
predict neuronal connectivity from neuronal expression patterns with good accuracy,
thereby achieving the first goal of our study.
We next asked whether we can identify a set of genes and specific rules of
interactions among them that explain the choice of chemical synaptic partners with
high confidence. The model learned above provides predictions about such putative
genes with specific interaction patterns. However, the set of these putative genes
and the way they interact may vary for different divisions of the data into train
and test sets, raising the question of how confident we are in the set of rules that
were learned. To examine the confidence of the rules that were learned, we used a
standard nonparametric bootstrap [17] approach of tree-CPDs, in which at each bootstrap
iteration we learn a tree-CPD on resampled data and in the end examine the number of
times in which a rule was learned. Thus, after N bootstrap
iterations we gather N tree-CPDs, and the confidence of each rule
can then be estimated by the fraction of tree-CPDs that contain it (we used
N = 1000). We repeated the
bootstrap procedure without restricting the maximal depth of the learned tree, and
with different constraints on the maximal depth of the leaves, from 1 to 6. Figure 4A–E shows the
most confident rules that we learned with a confidence greater than 0.3. When the
maximal depth was allowed to be greater than 5, no high confidence rules were
learned. Figure 4F shows how all
of these rules can be concisely combined into one single tree-CPD.
The fact that our approach extracted a set of rules with high confidence and that
they can be concisely represented by a single decision tree demonstrates that we can
indeed identify a subset of genes and interaction rules among them that predict
neuronal connectivity. We next examined the specific set of gene clusters that were
extracted in high confidence rules. Note that each cluster is represented by a
single gene but may contain several genes. We examine all the genes in each cluster
since our model cannot distinguish between them (see Discussion section).
The most confident cluster of genes that affect the chemical synapses is in the root
of our resulting tree (Figure
4F). It is represented by the hmr-1 gene. This cluster
contains two genes that have a similar expression pattern in the neurons of the
worm. These genes are unc-55 and hmr-1.
Unc-55 encodes a nuclear hormone receptor. It was shown in
[18]
that unc-55 is essential for the producing the synaptic pattern
that distinguishes ventral D motor neurons from the dorsal D motor neurons.
Hmr-1 gene encodes two isoforms of a classical cadherin that
contain extracellular cadherin and a highly conserved intracellular domain. Cadherin
superfamily molecules are known to be involved in many biological processes, such as
cell recognition, cell signaling, cell communication, morphogenesis, angiogenesis,
and possibly even neurotransmission [19]. Furthermore, in humans, the Protocadherins,
which are a subfamily of the Cadherin superfamily, have been proposed to constitute
the molecular identifiers of Sperry's chemoaffinity hypothesis [9].
Indeed, this gene is predicted to function as a calcium-dependent, homophilic
cell–cell adhesion receptor. It was also predicted to be required for
mediating cell migrations and for fasciculation and outgrowth of a subset of motor
neuron processes [20].
The akt-1 gene cluster appears in the first level of the resulting
tree in the context where the hmr-1 gene is not expressed. It
contains both the akt-1 and the akt-2 genes which
encode an ortholog of the serine/threonine kinase Akt/PKB that functions to regulate
processes such as dauer larval development and salt chemotaxis learning [21],[22]. In
addition, they genetically interact with the insulin signaling pathway which was
shown to be essential for ensuring that the nervous system is wired correctly during
development in Drosophila
[23]. The
rest of the clusters that are part of high confident rules contain only one gene
which is also the representative of these clusters.
In the context where the hmr-1 gene is expressed we find the
npr-1 gene. It encodes a predicted G protein-coupled
neuropeptide receptor that is homologous to the mammalian neuropeptide Y receptor.
Npr-1 affects some aspect of
unc-6/netrin-mediated branching of motor neurons, as strong
npr-1 mutations can suppress abnormal migration of ventral
nerve cord neurons induced by overexpression of unc-6 lacking
domain C [24].
As we continue to traverse over the resulting tree, we encounter the
cdh-3 gene next. It encodes a member of the cadherin superfamily.
Unlike the hmr-1 gene it encodes a nonclassical cadherin (fatlike
cadherin) that has a very large extracellular region. Cdh-3 was
shown to affect morphogenesis of tail epithelia and excretory function [25].
Cdh-4, the only other fatlike cadherin gene in the C.
elegans genome was shown to control axon guidance, cell migration and
pharynx development [26].
Further down the tree, the glr-1 gene encodes an AMPA ionotropic
glutamate receptor subunit. Glr-1 activity is required for
mediating some behavioral responses [27]. Its expression is dependent on the homeodomain
protein encoded by unc-42
[28] that
is required for axonal pathfinding of neurons. In wild-type worms, the axons of AVA,
AVD, and AVE lie in the ventral cord, whereas in unc-42 mutants,
the axons are anteriorly, laterally, or dorsally displaced, and the mutant worms
have sensory and locomotory defects [29].
The osm-6 gene encodes a protein that is localized to cytoplasm,
including processes and dendritic endings where sensory cilia are situated. Mutation
in this gene causes defects in the ultrastructure of sensory cilia and defects in
chemosensory and mechanosensory behaviors [30]. It was shown that
sensory activity affects sensory axon development [31] and that disruptions to
this activity may alter neuronal connectivity [32].
Finally, in the last level of our resulting tree we find the unc-4
gene. It encodes a homeodomain transcription factor with orthologs in
Drosophila and vertebrates. A mutation in the
unc-4 gene alters the pattern of synaptic input to one class of
motor neurons in the C. elegans ventral nerve cord. It was shown
that unc-4 is required for establishing the identity of the A class
motor neurons DA and VA, and is thus required for movement, axon guidance, and
synapse formation [33].
Thus, examining the single tree that contains the rules that were extracted with high
confidence (Figure 4F), we find
that its set of genes or their orthologs in other species have all been previously
implicated as having a direct or indirect role in neuronal connectivity, which
combined with the robustness with which they are predicted in our tree, increases
our confidence in their role in the process.
In this study we performed a systematic search for genes that mediate the last phase
of chemical synaptic partner selection, while incorporating geometrical constraints
on neuronal connectivity. We demonstrated that combination of expression patterns
can be used to predict chemical synapse connectivity with good accuracy. We
highlight specific genes and provide the combinatorial logic by which these genes
may interact to specify the formation of a chemical synapse between neighboring
neurons.
A key observation of our study is that neuronal wiring can be predicted by logical
combination of a small number of genes. This finding was partly biased by the search
for small decision trees but the fact that it achieves good accuracy supports its
validity. An alternative design could have used hundreds or thousands of different
genes to achieve the same connectivity, for example, one gene for each synapse. Our
result is supported by the observation of White [2] that if a neuron is for
some reason (mutation or variation between isogenic individual) created in a
slightly different surrounding than usual with a slightly different set of close
neurons, it creates a different set of synapses. If every synapse was encoded in the
genome independently by an independent set of genes, this would not be the case. The
modular design we find is similar to other biological systems, such as signal
transduction cascades, where the mapping between signal inputs to the cells and
their response in highly different pathways and cells is carried out by a small set
of core modules [34]. It may be that this modular design, observed
here in the context of neuronal wiring, is more optimal or evolvable than the
alternatives. It also raises the possibility that the genetic mechanism for neuronal
wiring in C. elegans is rather similar to the mechanism in more
complex organisms, but this hypothesis should of course be reexamined when similar
data becomes available for more complex organisms.
Despite its predictive power, our approach has several limitations. Currently, both
the connectivity network and the gene expression pattern are crude and noisy [11] and
some important pieces of information are missing. The most prominent limitation of
our model is its inability to infer the causal relationship between gene expression
and synapse formation. In the absence of temporal or interventional data, our model
cannot distinguish between genes that are responsible for chemical synaptic
specificity and genes that are over- or underexpressed in either side of a chemical
synapse due to its formation. Another limitation of our model is that it cannot
distinguish between genes that are directly responsible for synaptic specificity and
genes that have only indirect affect on this process within the same gene cluster.
This distinction can sometimes be made manually by examining the expression patterns
of the genes in nonneuronal cells or by examining the relevant literature.
One of the strengths of our approach is that it can be easily extended to deal with
many types of additional data. For example, the gene expression in individual cells
is measured by GFP fluorescence or by immunostaining. These levels are of course not
binary (on or off), but they appear as such in the single database that is currently
available [11]. Future large-scale work could solve this problem by
systematic detection of the continuous expression pattern of genes in a uniform way
[35],[36]. By minor modifications to the tree-CPD
representation and learning procedure, we can apply our method to learn nonbinary
tree-CPDs and automatically detect the thresholds on the expression level by which a
split should be made.
An interesting observation by White et al. [3] is that the neuron groups
AVD, AVE and AVB all have extensive synapses onto AVA along the cord (each neuron
group consists of neurons with similar morphologies and connectivity patterns and
denoted by an arbitrary three-letter name [3]). However, in the nerve
ring, processes from these cells do not form such synapses even though they are
accessible to AVA (i.e. are adjacent to its processes). One possible explanation for
this is time. It is possible that the genetic signal for synapse formation is
changed at a specific time point during development and that this change affects
only newer processes. Another possible explanation could be signals that are
localized to specific regions of the cell. Knowing the specific time each synapse
was created and the specific adjacent set of neurons in conjunction with the
specific (preferably, intracellular) expression pattern of all the genes in the
neighborhood at that specific time would lead to the most comprehensive and complete
picture. All of this data could be easily incorporated into the data instances from
which we learn with relatively minor changes. Such timing information may also
address the problem of cause and effect that currently cannot be disentangled by our
approach. Solving this problem would lead to the most convincing proof for the
determination of neuronal wiring by gene expression patterns in C.
elegans.
This work combines two types of input data: the gene expression signature of the
neurons and the synaptic connectivity network. For the Boolean single-cell gene
expression signature of the neurons we have used the data provided by Varadan et
al. [12]. This data was extracted from WormBase (http://www.wormbase.org version WS180), the main public
repository of the C. elegans's genetic data, using a
stringent mining criteria and was manually curated.
The single-cell gene expression data in WormBase was gathered from many studies
that read the GFP levels from transgenic worms in which a GFP gene was inserted
downstream to the promoter of the investigated gene or stained the worm with a
specific protein antibody in different developmental stages. This data is
considered crude and noisy due to inaccuracies in the gathering process of the
data from the animal and due to its discretization into a Boolean expression of
“on” and off”.
As a preprocess stage we eliminated all the genes that were expressed in less
than 2% of the neurons since they carry little information for our
computation. In order to avoid instability of the results due to genes that have
very similar expression pattern over the neurons, the remaining 251 genes were
clustered using hierarchical clustering. First the Hamming distance (the
percentage of neurons that disagree on the expression) between every pair of
expression patterns was calculated, then a nearest neighbors algorithm was used
to construct a linkage tree. This tree was divided into 133 expression classes
by applying a cutoff of 0.8 to the inconsistency coefficient [37] of
its edges. The average Hamming distance between different genes in the same
class was 1.7% and only 5% of the expression classes
contain more than 4 genes. The typical expression pattern of an expression class
that contains more than one gene was set to be the same as the expression
pattern of the gene that has the minimum average Hamming distance from all other
genes in this class. The final gene set and their assignment to expression
classes are listed in Table S1.
For the synaptic connectivity network we used a version of the pivotal works of
White et al. [3] and Hall and Russell [38] that was recently
compiled by Chen et al. [39]. This version contains the complete
connectivity of 280 nonpharyngeal neurons and it is publicly available at
Wormatlas (http://www.wormatlas.org/). We have used this synaptic
connectivity network to build the set of weighted data instances from which we
learn our model. The weight of a positive data instance (i.e. data instance for
positive example) is proportional to the number of chemical synapses that were
observed in this direction, whereas the weight of the negative data instance is
set to 1. The biological motivation for the use of weights is that the number of
identical synapses in the same direction is positively correlated with its
invariability between isogenic individuals. Specifically, some of the small,
single synapses are not present in some individuals and therefore may be less
significant [3] while on the other hand a broad core of
connections that are constant in all the individuals in the population includes
most of the strong synaptic connections containing many synapses [1].
To obtain balance between the weights of the positive and the negative data
instances, the weights of the positive data instances were normalized such that
their sum would equal the sum of weights of the negative data instances. As a
result, the final data instances set contained 4574 weighted examples composed
of 48% positive and 52% negative, each carrying
50% of the total weights.
Learning the tree-CPD model from the input data requires two components. The
first is a scoring scheme that measures the goodness of fit of the model and
enables the comparison of two different models. The scoring method that we used
is the Bayesian score [15]. This score is a standard and a principled
way to tradeoff model complexity and fit to data, thus it relaxes the necessity
of Varadan et al. in [12] to predetermine the number of expected
interacting genes. For detailed explanation about the Bayesian score and
comparison to the maximum likelihood score which is a scoring method that does
not tradeoff model complexity and fit to the data see Text S2 and
Figure
S3.
The second component that is required is a search heuristic to scan the
exponentially large model space in order to find the highest scoring model. We
have adopted the approach of Friedman and Goldszmidt [14] which was
inspired by Quinlan and Rivest [40]. According to this approach, the tree is
learned in two phases. In the first phase, the tree is grown in a top-down
fashion, starting from the trivial empty tree and growing till the maximal tree
is learned. In each step of this phase, we split one leaf of the tree using the
variable that induces the best scoring tree. During this process there might be
some splits that will reduce the score of the tree, but we do not stop if it
happens, since further growth of the tree might compensate for this temporary
reduction of the score. In the second phase, we trim the tree in a bottom-up
manner. We start from the leaves and climb to the root, checking for each inner
node of the tree if the replacement of the subtree rooted at it with an empty
tree will increase the score. If it does, we trim the tree at that node and
continue. The downhill splits we are willing to take during the first phase
prevent the learning process from getting stuck at every local minima of the
search space, like most of the greedy search heuristics for learning decision
trees [14].
We have used the standard boosting algorithm AdaBoost introduced by Freund and
Schapire in 1995 [16] to improve the classification accuracy of the
tree-CPD. The main idea of AdaBoost is to change the weights of the training
data according to the success in their classification. In each round, the
weights of incorrectly classified examples are increased so that in the next
round, the tree-CPD has to focus on the hard examples. The final combined
classifier is a weighted majority vote of all the tree-CPDs from all the
iterations. A pseudocode that summarizes this procedure is given in Protocol
S1. An important advantage of AdaBoost compared to other methods such as
neural networks and support vector machines is that it works well without fine
tuning and no sophisticated nonlinear optimization is necessary. It also tends
not to overfit the data [41],[42]. In fact, Adaboost
in conjugation with decision trees was described as the best
“off-the-shelf” classifier in the world [41].
The performance of the model was measured using a standard 5-fold
cross-validation scheme. In this procedure, we randomly partitioned the data
into five equal parts. We then made some small adjustments to the partition in
order to eliminate dependencies as described below and learned a model on each
of the five subsets of four parts and tested its performance on the held out
subset. The final performance estimator is an average of the performance of the
five estimators obtained.
To avoid dependencies between the train and test sets that might bias the
results, the partition of the data into train and test sets must consider the
symmetries of the connectivity diagram of C. elegans since
symmetrical neurons tend to form similar connections [1] and often express
similar sets of genes. The main symmetry axis in the worm is the
left–right axis and the secondary symmetry axis which appears
especially in the pharynx is the dorsal–ventral axis. Thus, for some
neurons there is even a 6-fold symmetry! In addition, for several neurons
(especially for motorneurons) there is longitudinal duplication throughout the
ventral and dorsal cord. The nomenclature of the neurons suggested by white et
al. [3]
captures these symmetries. E.g. the IL1 group of neurons consists of the
symmetrical neurons: IL1DL, IL1DR, IL1L, IL1R, IL1VL and IL1VR. The last two
letters show the symmetry where D, V, L, and R stand for Dorsal, Ventral, Left,
and Right, respectively.
To eliminate the dependence of the train and test sets, Kaufman et al. [11]
used only the neurons from right side of the worm. However, this approach does
not eliminate dependencies of the dorsal–ventral symmetry axis and the
amount of data that remains for learning is reduced significantly. We have used
a different approach, in which if (X,Y) is an example in the train set than
every pair of (X′,Y′), (X′,Y) and (X,Y′)
will also be in the train set, where X′ and Y′ are neurons
that were assigned by white et al. to the same group of neurons as X and Y,
respectively. This approach uses all the data and eliminates the bias that might
be caused by the known symmetries.
Prediction accuracy of the model was measured by the standard area under the
receiver operating characteristic (ROC) curve. The ROC curve plots the fraction
of true positives versus the fraction of true negatives for a binary classifier,
while its discrimination threshold varies. The area under the ROC curve (AUC) is
a measure that intuitively can be interpreted as the probability that when we
randomly pick one positive and one negative example, the classifier will assign
a higher score to the positive example than to the negative one.
Statistical significance of the prediction performance was calculated against two
empirical null distributions: the shuffled expression and the shuffled
connectivity distributions. The first was constructed by repeating the
prediction procedure 50 times, each time with neuronal identities reshuffled.
This empirical null distribution was used in previous studies [11],[12]. The motivation
behind this test is to evaluate whether the prediction accuracy obtained for the
real data can be attributed to real dependence between the expression profiles
of the neurons and synaptic connectivity, or if it is a result of the properties
of the input data such as the number of different expression patterns, the
degree distribution of the network, etc. Indeed, the best AUC that was achieved
for this empirical null distribution was 0.63 (Figure 3). This AUC is significantly above
the 0.5 score that a pure random guess would achieve. This means that even if
there was no real relation between gene expression and chemical synapse
formation, it is possible to find a model that is this good just by chance due
to the properties of the input data. To better understand this, think of the
extreme case of a starlike network in which there is one neuron that is
postsynaptic to all other neurons in the network and that there are no other
synapses in the network. If, after the shuffling of the identities of the
neurons, this single neuron expresses a gene X that no other neuron expresses
(it is not unreasonable if there are enough, different, gene expression
patterns) then the rule: “if a neuron expresses gene X than it will be
postsynaptic to every other neuron in its neighborhood” will have
strong evidence in both the train and the test sets, regardless of the partition
of the examples into train and test. As a consequence the classifier that is
learned on the train set will achieve AUC that is greater than 0.5 on the test
set, even though the identities of the neurons were shuffled.
The second distribution was constructed by repeating the prediction procedure 50
times, each time with the signs of the examples reshuffled, while maintaining
the same amount of positive and negative examples for each neuron. In other
words, each neuron chooses to create a chemical synapse to a random subset of
the neurons in its neighborhood while the size of this random set is equal to
the number of neurons it chooses in the real data. The motivation behind this
distribution is to test whether or not each neuron chooses to form synapses with
a subset of its neighboring neurons based on their gene expression profile. The
significance of the result with respect to this second empirical null
distribution is generally lower (Figure 3), since much of the relation between gene expression and
synaptic connectivity from the real data is maintained due to the limited
shuffling (there is a correlation of ∼0.6 between the real data and each
shuffled data from this distribution).
To evaluate the confidence of the rules that we learned we used a nonparametric
Bootstrap. According to this method, we generated many resampled versions of the
data and learned a model from them. This way we collected many reasonable models
for the real data. The confidence of a rule is the percentage of models that
agree with it. Each resampled version of the data was generated by resampling
the data instances with replacement for m times, where
m is the number of data instances in the data, therefore it
is expected to contain about 63.2% of the data instances and the rest
are duplicates. A pseudocode that summarizes this procedure is given in Protocol
S2.
The confidence of complex rules tends to be smaller relative to simpler rules due
to several reasons: First, the deeper the tree-CPD, the larger the search space
is and the probability to learn exactly the same rules in different bootstrap
iterations decreases. Second, decision trees are inherently unstable [41],
i.e. slight perturbation of the data may lead to a different learned tree
especially when the tree is deep. Third, the gene expression data is highly
correlated. Although we aggregated highly correlated gene expressions into
expression classes there still exists correlation between these expression
classes. Closely related expression classes may switch roles in tree-CPDs that
are learned on different resampling of the data. |
10.1371/journal.pntd.0005522 | Detecting and enumerating soil-transmitted helminth eggs in soil: New method development and results from field testing in Kenya and Bangladesh | Globally, about 1.5 billion people are infected with at least one species of soil-transmitted helminth (STH). Soil is a critical environmental reservoir of STH, yet there is no standard method for detecting STH eggs in soil. We developed a field method for enumerating STH eggs in soil and tested the method in Bangladesh and Kenya. The US Environmental Protection Agency (EPA) method for enumerating Ascaris eggs in biosolids was modified through a series of recovery efficiency experiments; we seeded soil samples with a known number of Ascaris suum eggs and assessed the effect of protocol modifications on egg recovery. We found the use of 1% 7X as a surfactant compared to 0.1% Tween 80 significantly improved recovery efficiency (two-sided t-test, t = 5.03, p = 0.007) while other protocol modifications—including different agitation and flotation methods—did not have a significant impact. Soil texture affected the egg recovery efficiency; sandy samples resulted in higher recovery compared to loamy samples processed using the same method (two-sided t-test, t = 2.56, p = 0.083). We documented a recovery efficiency of 73% for the final improved method using loamy soil in the lab. To field test the improved method, we processed soil samples from 100 households in Bangladesh and 100 households in Kenya from June to November 2015. The prevalence of any STH (Ascaris, Trichuris or hookworm) egg in soil was 78% in Bangladesh and 37% in Kenya. The median concentration of STH eggs in soil in positive samples was 0.59 eggs/g dry soil in Bangladesh and 0.15 eggs/g dry soil in Kenya. The prevalence of STH eggs in soil was significantly higher in Bangladesh than Kenya (chi-square, χ2 = 34.39, p < 0.001) as was the concentration (Mann-Whitney, z = 7.10, p < 0.001). This new method allows for detecting STH eggs in soil in low-resource settings and could be used for standardizing soil STH detection globally.
| Intestinal worm infections are common in populations living in tropical, low-income countries. People primarily become infected when they consume intestinal worm eggs from contaminated water, hands, and food. Intestinal worm eggs are transmitted from infected people and spread through the environment, particularly via soil. There is no standard laboratory method for counting intestinal worm eggs in soil, which is a major barrier to comprehensive research on the transmission of infection. We tested different laboratory protocol steps to extract soil-transmitted helminth eggs, which is one type of intestinal worm, from soil and propose a new, fast, and efficient field method. We tested the method in Kenya and Bangladesh and found that soil contamination with helminth eggs was prevalent in both study areas. We propose that environmental contamination be included in discussions about intestinal worm transmission, control, and elimination, especially in areas with low infection prevalence. The method we propose will help researchers assess soil contamination, which can be used to examine the effectiveness of intestinal worm transmission control measures.
| Almost one quarter of the world’s population is infected with at least one species of soil-transmitted helminth (STH) [1]. South Asia, Southeast Asia, and Sub-Saharan Africa are the regions with the highest prevalence [1]. Ascaris and Trichuris infection is spread via an environmentally-mediated fecal-oral transmission route, through ingestion of a larvated egg that has incubated in soil. Hookworm infection is spread by larvae, hatched from eggs after incubation in the soil, penetrating the skin. One hookworm species, Ancylostoma duodenale, can also be transmitted by ingestion of a larvae [2]. Although soil is the main environmental reservoir of STH eggs, there is no standard method for enumerating STH eggs in soil. In contrast, there is a relative abundance of data on global STH infection prevalence as measured by detection of eggs in stool, in part, because there are standard stool analysis methods that are relatively fast and appropriate for resource-constrained settings [3].
A standard method for counting STH eggs in soil would be valuable for examining soil as a key step in the transmission pathway, and to compare field studies that have estimated the prevalence of STH in soil. Previous field methods have focused on three main steps: sieving, flotation, and microscopy. The number and size of sieves used varies. In general, a large sieve is used to remove large soil particles and a small sieve is used to retain STH eggs. Flotation methods can vary based on the flotation solution and the flotation time. Many flotation solutions have been used, including magnesium sulfate [4–6], zinc sulfate [7–9], sodium nitrate [10], sugar [11–16], and salt [17]. Magnesium sulfate has been recommended by the US Environmental Protection Agency (US EPA) for detection of Ascaris in wastewater and biosolid samples [6]. Zinc sulfate has been commonly used for flotation of parasite eggs in stool samples and is very effective, but zinc is toxic to aquatic life and requires safe disposal. Sugar is inexpensive and easily accessible, but it can distort STH eggs and make microscopic identification difficult [18]. Sugar solutions also attract flies and are susceptible to microbial growth, requiring the addition of an antimicrobial substance such as formaldehyde [19]. Salt is inexpensive and easily accessible, but the specific gravity reaches a maximum at ~1.2. Although this specific gravity should be high enough to recover Ascaris, Trichuris, and hookworm spp., it may not be high enough to recover heavier parasite eggs, such as Taenia spp. [20].
Differences in the egg flotation step are also attributed to the use of passive versus centrifugal floatation methods. Passive flotation can be useful in low-resource settings because it does not require access to a centrifuge, but it may not be feasible for a large number of samples because it requires more time. Methods that use centrifugal flotation report varied centrifuge speed, number of flotation steps, and flotation times. Additionally, some methods rely on adherence to a coverslip while centrifuging, which can reduce processing time, but it is not known how this affects the recovery efficiency [21]. As reviewed by Collender et al., there is limited research on the impact of different steps in the protocol on the recovery efficiency of the method [21], which may have contributed to the lack of a standard protocol. Additionally, many methods with published recovery efficiencies use Toxocara eggs [21] as a test organism instead of Ascaris eggs, which are slightly smaller and denser than Toxocara [20].
Another challenge is that there are few references that provide guidance on correctly identifying STH eggs in soil through microscopy. Researchers need substantial experience reviewing slides from soil samples before they are able to distinguish the different STH egg types from the debris and animal parasite eggs common in soil samples. The most widely used resource for STH egg identification is a bench guide created by the World Health Organization for identification of parasites in clinical samples [22]. There is also a guide for analysis and identification of helminths eggs in wastewater [18], but there is no similar resource for identification of parasites in soil samples. Laboratory technicians familiar with identification of STH eggs in stool samples are not qualified to analyze soil samples without additional training, as soil contains different life stages of STH eggs, non-STH eggs, and debris.
Molecular methods, such as DNA extraction followed by quantitative polymerase chain reaction (qPCR), offer the potential to reduce human error in egg identification compared to microscopy. Molecular methods are under development for detection of STH in stool and biosolids [23,24]. Additionally, two recent studies used molecular methods for detection of hookworm species and Ascaris lumbricoides in soil [25,26]. Molecular methods should be also be created and tested for detection of Ascaris and Trichuris in soil. Potential complications with developing and employing molecular methods in the field for enumerating STH eggs in soil include: cost, accessibility of reagents and equipment, inhibition of assays from humic and fulvic acids present in soil [27], and ability to distinguish viable and non-viable eggs [28]. These issues must be resolved to make molecular detection of STH in soil a feasible method for low-resource settings.
The goal of this study was to develop and evaluate a field method based on direct microscopy for quantifying STH eggs in soil. After reviewing previous field methods and their limitations, we tested the impact of different protocol steps on the egg recovery efficiency in the laboratory to inform an improved protocol. The aim was to shorten the processing time and make the method easier to implement in remote field laboratories, without negatively impacting recovery efficiency. We assessed the feasibility of using the new method in a low-resource setting by field testing it in Kenya and Bangladesh. Using data from our field tests, we also compared the prevalence and concentration of STH eggs in soil in Kenya and Bangladesh.
The study procedures were approved by the Stanford Institutional Review Board (Protocol Numbers 23310 [Kenya] and 25863 [Bangladesh]), the Kenya Medical Research Institute (KEMRI) Ethical Review Committee (SSC Number 2271), and the International Center for Diarrheal Diseases Research, Bangladesh (icddr,b) Ethical Review Committee (PR-11063).
We based our initial method [29] on the US EPA method for detecting and enumerating Ascaris eggs in wastewater, sludge, and compost [6], which has not been previously validated for use with soil. The US EPA method employs a series of sample concentration and flotation steps. After the laboratory processing, Ascaris eggs are counted using microscopy. The main benefits of this method are that it is a standard method for biosolids in the US and the recovery efficiency is high. For example, a recent study found the efficiency of the method for recovering helminth eggs from composted feces and sugarcane husk was 71.6% [30]. The main challenge of the US EPA method is that it is time consuming; the protocol takes approximately three days. Therefore, we reduced the time and number of settling steps to reduce the overall processing time while varying several protocol elements such as sieve size and surfactant and flotation solutions to enhance recovery efficiency.
We performed experiments at Stanford University to determine the Ascaris egg recovery efficiency of protocol variations by analyzing seeded samples. We collected organic loam and sand from two different locations at Stanford University to use in the experiments. Ascaris suum eggs were purchased from Excelsior Sentinel, Inc. (Trumansburg, NY). Eggs were collected from intestinal contents of infected pigs and preserved in 0.1 N sulfuric acid. Ascaris suum eggs have been used in other laboratory experiments as a proxy for Ascaris lumbricoides eggs because they have a lower health risk to humans, they are easily procured, and they are morphologically identical to Ascaris lumbricoides [24,31,32]. Eggs were stored at 4°C prior to use. To seed soil samples, we counted a 1 mL aliquot of eggs suspended in distilled water (mean = 931 eggs, standard deviation = 128 eggs) under a microscope and rinsed them into a 50-mL centrifuge tube containing 15 g of soil. The initial concentration of seeded STH eggs in soil was approximately 62 eggs/g wet soil. Seeded soil samples were left to sit for one day at room temperature prior to laboratory processing to allow the eggs to percolate into the soil and adhere to soil particles. Each recovery efficiency experiment was conducted on three independent samples (experimental triplicates).
We focused our experiments on three different aspects of the protocol and a total of eight processing steps associated with these to assess their impact on egg recovery: (1) egg detachment (choice of surfactant, stir-plate mixing), (2) concentrating the sample (settling time, settling volume, sieve size), and (3) egg flotation (flotation time, specific gravity of the flotation solution). We also tested different soil textures, loam and sand, to determine the effect of soil type on the recovery efficiency.
Based on the results from our recovery efficiency experiments, we developed an improved protocol and field-tested the method in Bangladesh and Kenya. We added a 15 g aliquot of soil to a 50 mL centrifuge tube to process soil samples for enumeration of STH eggs with the improved method. Then, we added surfactant, 1% 7X (MP Biomedicals, Santa Ana, CA), to each sample, bringing the volume up to the 35 mL line, and vigorously shook the samples by hand for two minutes. We rinsed the sides and cap of the tube with 1% 7X, added 1% 7X to the 45 mL line on the centrifuge tube, and left the samples to soak overnight. The next morning, we hand shook each sample for one minute, vortexed for 15 seconds, and poured through a stainless steel size 50-mesh sieve (300 μm, H&C Sieving Systems, Columbia, MD). We rinsed the sample through the sieve with 1% 7X and rinsed the bottom of the sieve with 1% 7X to capture any eggs stuck to the sieve. The settling volume was around 150 mL. We left the samples to settle for 30 minutes and then vacuum aspirated the supernatant. We poured the remaining sample into two 50-mL centrifuge tubes, filled the tubes to the 40 mL line with 1% 7X, and centrifuged (Sorvall Legend XT, Thermo Scientific, Waltham, MA) at 1000 x g for 10 minutes. We gently poured off the supernatant without disturbing the soil pellet, added 5 mL of zinc sulfate solution (ZnSO4 heptahydrate, 1.25 specific gravity) as flotation solution, vortexed for 30 seconds, and added additional zinc sulfate solution up to the 40 mL line. We centrifuged at 1000 x g for 5 minutes and then poured the supernatant through a fine stainless steel 500-mesh sieve (25 μm, H&C Sieving Systems, Columbia, MD). We rinsed the contents of the sieve into a clean 50 mL centrifuge tube (CELLTREAT, Shirley, MA) using distilled water. Then, we repeated this flotation step a second time using a clean sieve. We centrifuged the solution at 1000 x g for 5 minutes to settle the helminth eggs. We removed the supernatant using a clean 25 mL serological pipette until only 1 mL of solution remained. We transferred the final solution to a Sedgwick Rafter slide (1 mL cell volume, Wildco, Yulee, FL) using a pipettor (1000 μL fixed volume pipette, Cole Parmer, Vernon Hills, IL).
We examined slides using a microscope (M10 series, Swift, Schertz, TX) under 10x magnification to count Ascaris, Trichuris and hookworm eggs. We returned samples that contained any STH eggs back to their centrifuge tubes by rinsing the slide with distilled water and added 4 mL of 0.1 N sulfuric acid. We incubated these tubes at 28°C for 28 days. We reexamined these samples after incubation to determine egg viability by counting the number of larvated eggs that remained. Larvated eggs were considered viable whereas fertilized but non-larvated eggs were considered non-viable because they did not develop during the incubation period.
To determine moisture content, we dried an aliquot of each soil sample. Moisture content can vary widely based on local conditions, so it is necessary to report concentrations in terms of mass of dry soil. A 15 g aliquot of soil (wet weight) was placed on foil and oven dried overnight for at least 16 hours at 110°C in a gravity convection oven. Samples cooled for 10 minutes on a countertop before weighing to determine dry weight.
We field tested the improved protocol in Bangladesh from June to August 2015, during the rainy season, and in Kenya from August to November 2015, during the dry season and the beginning of the short rainy season. Field staff obtained written consent from all study participants on a prior visit, as well as oral consent on the day of soil collection. We collected soil samples from 100 rural households in Kakamega in western Kenya and 100 rural households in Mymensingh, Tangail, and Kishoreganj districts in central Bangladesh. We selected households based on their proximity to our field laboratories and their participation in an ongoing intervention trial [40]. We collected soil samples at the primary house entrance, either directly adjacent to the doorway or within 2 meters of the doorway if there was no soil in front of the doorway. Field staff collected soil by scraping the entire surface layer within a 900 cm2 area using a clean metal spade and scooping the soil into a sterile Whirlpak bag (118 mL capacity, Nasco, Fort Atkinson, WI). We collected approximately 50 g (wet weight) of soil from each household. Field staff transported samples at room temperature to our field laboratory and stored them in a 4°C refrigerator before laboratory processing began. Field staff in Bangladesh followed the same sample collection protocol as in Kenya, except that samples were transported on ice to the field laboratory.
Laboratory staff processed all samples using the improved protocol. In Kenya, we processed 7% of samples (7 out of 100 total samples) with a laboratory replicate. We counted 44 samples both pre- and post- incubation to determine the percentage of viable eggs and to act as quality assurance and quality control. We also took photos of the first egg seen in a sample for each type of egg for additional review. To ensure consistency across countries, both laboratory teams shared and reviewed each other’s egg photos. In Bangladesh, we made a few adaptations to the lab protocol used in Kenya. First, we increased the settling time from 30 minutes to at least 1 hour. Second, we oven dried 5 grams of soil instead of 15 grams to determine the moisture content. Third, we incubated all samples immediately after processing, instead of counting eggs pre- and post-incubation, due to logistical constraints. Finally, we did not characterize the soil texture of any of the Bangladeshi samples. In Bangladesh, we processed 9% of the samples (9 out of 100 total samples) with a laboratory replicate to assess the variability of the method, and two lab technicians counted 17% (17 out of 100 total samples) of samples in duplicate to assess inter-counter variability for quality assurance and quality control.
We calculated recovery efficiency by dividing the final egg count by the initial egg count. We analyzed the results of the recovery efficiency experiments using two-sided t-tests to compare the experiments that had just one variation in the protocol (settling time, soil texture, sieve size, flotation time and number of steps, stir-plate mixing, settling volume and time, flotation solution specific gravity); we compared recovery efficiencies from three experimental triplicates to another three experimental triplicates. We assessed the difference in the prevalence (the proportion of positive samples) of any STH in soil in Kenya and Bangladesh using a chi-square test and the difference in the total concentration of STH eggs per dry gram of soil using a Mann-Whitney test. Any p-value less than 0.05 was considered to be statistically significant. We analyzed the recovery efficiency experiment results using Excel 2013 and the field results using STATA version 13.
We compared the use of 0.1% Tween 80 and 1% 7X in experiments 1 and 2; 1% 7X significantly improved Ascaris recovery by 16.2 percentage points over 0.1% Tween 80 (two-sided t-test, t = 5.03, p = 0.007) (Table 1: 2A vs 1A). This was the only change to the protocol that resulted in a statistically significant change in recovery efficiency; however, we made several other adaptions to the protocol based on the magnitude of the difference in recovery efficiencies and time savings. The impact of soil texture on recovery efficiency was assessed in experiments 2 and 3; the recovery efficiency was higher by 14.6 percentage points (two-sided t-test, t = 2.56, p = 0.083) when using sandy soil compared to loamy soil. The recovery efficiency using a 400-mesh and 500-mesh sieve was similar (experiments 2 and 4). In experiments 4 and 5, we compared using two 5-minute flotation steps and one 10-minute flotation step; using two 5-minute flotation steps resulted in an increase of 9.5 percentage points in recovery efficiency (two-sided t-test, t = 1.67, p = 0.171), so we adopted this flotation protocol. We compared the protocol without a stir-plate mixing step to the protocol with it (experiments 5 and 6), and although we found a slight decrease in recovery efficiency of 8.1 percentage points (two-sided t-test, t = 1.43, p = 0.226), we decided to remove this step to save time. Comparing experiments 6 and 7, there was no loss of recovery efficiency when we reduced the settling volume and time (two-sided t-test, t = 0.62, p = 0.601), so we adopted these changes. We compared 1.25 specific gravity with 1.2 specific gravity as a flotation solution and found a 10.2 percentage point increase in recovery efficiency when two 5-minute flotation steps were used (two-sided t-test, t = 3.65, p = 0.068) (experiments 7 and 10); we therefore decided to use a specific gravity of 1.25. The final, improved method had a significantly higher recovery efficiency (72.7%) than the initial method (37.2%) (two-sided t-test, t = 9.83, p < 0.001) (experiments 10 and 1). The differences between the initial and improved method are detailed in Fig 3.
The prevalence of any STH eggs from our study area in Kenya was 37%. Ascaris was most common (22%), followed by Trichuris (21%) (Table 2). No hookworm eggs were found. Some Ascaris eggs were larvated (34.8%) when we isolated them from soil, but few Trichuris eggs were larvated (6.5%) prior to incubation. Most Ascaris eggs were viable (99.3%) and the majority of Trichuris eggs were viable post-incubation (71.6%) (Table 2). The median concentration in positive samples was 0.15 eggs/g dry soil (mean = 0.46 eggs/g dry soil) or 2 eggs/sample (mean = 6.1 eggs/sample). The soil texture of most soil samples in Kenya was either sandy loam (60%) or clay loam (31%). The mean moisture content of all samples in Kenya was 9.1%. The mean difference in egg counts before and after incubation was 3 eggs per sample.
The prevalence of any STH eggs in soil from our study area in Bangladesh was 78% (Table 2). Sixty-seven percent of samples contained Ascaris and 36% of samples contained Trichuris, while hookworm eggs were not detected (Table 2). Ascaris eggs had a similar viability as Trichuris eggs. (Table 2). The median concentration of positive STH eggs in soil was 0.59 eggs/g dry soil (mean = 1.6 eggs/g dry soil) or 8 eggs/sample (mean = 21 eggs/sample). The mean moisture content of all samples in Bangladesh was 19.1%. Soil samples from Bangladesh were significantly more likely to have STH eggs than soil samples from Kenya (chi-square, χ2 = 34.39, p < 0.001). Bangladeshi soil also had a significantly higher concentration of STH eggs as compared to Kenyan soil (Mann-Whitney, z = 7.10, p < 0.001). Slides counted twice by different enumerators had consistent counts, with a mean 9% difference in counts and an overall difference of about 1 egg per sample. The variation of egg counts in laboratory replicates was about 4 eggs/sample.
This paper presents an improved method for enumerating STH eggs in soil that is appropriate for use in resource-constrained settings. This field method is relatively fast; approximately 20 samples can be processed in a day and a half. In comparison, the original US EPA method takes at least 3 days to complete the full protocol on 10 samples. The method also has a higher recovery efficiency of 73% compared to previously published field methods. A recent review of previous methods for detecting STH eggs in environmental media demonstrated a median method recovery efficiency of 25% [21]. Our new protocol is comprehensive in that it includes STH egg enumeration, identification and viability determination, as well as soil moisture content measurement and soil texture classification.
Our recovery experiments in the lab identified one protocol step that affected recovery efficiency. We found that using 1% 7X instead of 0.1% Tween 80 significantly increased the egg recovery efficiency of the method. This result is consistent with a published method for enumerating Ascaris in hand rinse samples [33]. No other changes to the protocol were statistically significant; however, the recovery efficiency was significantly higher for the improved method that included several alterations than for the original method.
In our field tests, we found that the study area in Bangladesh had a higher prevalence and concentration of STH eggs in soil than the study area in Kenya. One factor that could affect the egg prevalence in soil is the infection prevalence of STH in the study area. STH infection is widely geographically variable [41–45], so it is important to examine the same study area for infection prevalence. An alternative explanation is that latrine access and sanitation infrastructure may be different in the two study areas. Also, flooding during the monsoon season in Bangladesh may spread waste from pit latrines and fecal sludge ponds to the surrounding areas. Other factors that are expected to influence the soil prevalence include sanitation behaviors and environmental conditions; these aspects are the focus of ongoing studies in both locations.
There are several limitations of our recovery efficiency experiments and field tests. We only tested one STH egg concentration (approximately 67 eggs/g wet soil) during the recovery efficiency experiments, and the concentration that was used to seed the samples was higher than what we typically found in soil in Kenya and Bangladesh. Recovery efficiency has been shown to change with the initial concentration of eggs in soil; one study found that recovery efficiency was inversely proportional to the egg concentration. Although they did not test concentrations as low as those that we would expect to see in naturally contaminated samples, this may indicate that the recovery efficiency of our method could be higher than 73% for samples with a low-concentration of STH eggs [17]. Another limitation is that we performed most of our recovery efficiency experiments with loamy soil, one experiment with sandy soil, and no experiments with clay soil. It is likely that the recovery efficiency would be highest in sand, followed by loam and then clay [21]. Thus, at a minimum it is important to note the soil texture when analyzing soil samples for the presence of STH eggs. Ideally, the impact of soil type on recovery efficiency should be measured in future studies, using samples from the actual field sites. Also, we had wide ranges in recovery efficiency between triplicates, limiting our power to detect statistically significant differences between protocol variations. Similarly, the ordering of our experiments may have affected the difference in recovery efficiency between steps because we did not test all potential combinations of the different steps. Finally, we did not test the recovery efficiency of the method for Trichuris or hookworm as, unlike Ascaris, these eggs cannot be easily procured in the United States; the recovery efficiency of the protocol may be different for these STH eggs than the value we report for Ascaris.
We did not detect hookworm in any of the soil samples. It is unclear whether hookworm was not present in our study areas or whether the protocol is not appropriate for detecting hookworm. As hookworm larvae hatch from eggs in the soil rather than in the human large intestine, we could expect to detect both eggs and larvae in the soil. Two studies that used sieving, centrifugation, and flotation steps similar to our protocol recovered hookworm eggs from seeded soil samples [4,7]. In one of these studies, 58% of the samples contained only larvae [4]. Our method should work in principle for recovering hookworm larvae if the samples are examined by microscopy immediately after processing, although it needs to be confirmed that larvae are not retained by the 50 mesh sieve (pore size of ~ 300 um) as infective filariform larvae are ~ 600 um in length. Also, hookworm eggs are fragile [21] and may be damaged by storage and processing. Thus, since the samples in Bangladesh were only enumerated after incubation, it was not expected to find hookworm eggs. Future work is needed to develop methods that are also effective for recovering hookworm eggs and larvae.
It should be noted that STH eggs from humans can be morphologically similar or identical to STH eggs from animals. For example, Ascaris lumbricoides eggs from humans are morphologically identical to Ascaris suum eggs from pigs [46]. Also, Trichuris trichiura eggs from humans appear like Trichuris suis eggs from pigs [47]. Trichuris vulpis eggs from dogs are larger than other Trichuris eggs, but there can be some overlap in the size ranges [48]. We identified STH eggs in our study based on a set of standard criteria (Fig 2). Although microscopy tends to be more feasible in low-resource settings than molecular methods, human error may occur and it is difficult to completely rule out accidental enumeration of morphologically similar eggs from animal sources. This is a particular concern for studies that seek to understand human fecal contamination in the environment or exposure to human STH eggs. More work should therefore be done to develop molecular methods for Ascaris and Trichuris in soil that can differentiate between eggs from human and animal hosts. In particular, inhibition from compounds in soil needs to be addressed before these assays can be deployed. Our cleaning and concentration method could be used in combination with molecular methods to reduce inhibition and increase the volume of processed soil before performing DNA extraction and PCR for detection of STH eggs.
The method presented here can be used to examine STH soil contamination to better understand STH transmission. It is relatively fast and efficient compared to other methods, making it more feasible for high-throughput processing in the field. A standard method for enumerating STH in soil will allow comparison of the prevalence and risk factors of soil contamination with STH across different settings, e.g. household sanitation practices (presence and type of latrine, management of child feces), community-level practices (presence of open drains, locations where fecal sludge is disposed or reused), and climatic and environmental effects. Soil contamination measurements can also be an effective tool for evaluating interventions aimed at reducing STH transmission.
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10.1371/journal.pgen.1007163 | Identification of an elaborate NK-specific system regulating HLA-C expression | The HLA-C gene appears to have evolved in higher primates to serve as a dominant source of ligands for the KIR2D family of inhibitory MHC class I receptors. The expression of NK cell-intrinsic MHC class I has been shown to regulate the murine Ly49 family of MHC class I receptors due to the interaction of these receptors with NK cell MHC in cis. However, cis interactions have not been demonstrated for the human KIR and HLA proteins. We report the discovery of an elaborate NK cell-specific system regulating HLA-C expression, indicating an important role for HLA-C in the development and function of NK cells. A large array of alternative transcripts with differences in intron/exon content are generated from an upstream NK-specific HLA-C promoter, and exon content varies between HLA-C alleles due to SNPs in splice donor/acceptor sites. Skipping of the first coding exon of HLA-C generates a subset of untranslatable mRNAs, and the proportion of untranslatable HLA-C mRNA decreases as NK cells mature, correlating with increased protein expression by mature NK cells. Polymorphism in a key Ets-binding site of the NK promoter has generated HLA-C alleles that lack significant promoter activity, resulting in reduced HLA-C expression and increased functional activity. The NK-intrinsic regulation of HLA-C thus represents a novel mechanism controlling the lytic activity of NK cells during development.
| It has been proposed that the human HLA-C gene evolved in higher primates to serve as a ligand for the KIR family of inhibitory receptors for MHC class I that are expressed by natural killer (NK) cells and regulate their activity. NK cell potential is determined by the level of MHC class I on surrounding cells and on the NK cell itself. We have uncovered a highly complex system regulating HLA-C expression in NK cells. A NK-specific promoter produces a large array of differentially-spliced transcripts that vary in their ability to be translated into HLA-C protein. As NK cells differentiate and become more cytotoxic, the level of HLA-C expression increases, and this correlates with an increased abundance of translatable HLA-C mRNAs. A subset of HLA-C alleles have a promoter polymorphism that abrogates its activity, resulting in NK cells that are unable to upregulate HLA-C levels, and consequently, possess increased functional activity. Overall, our findings provide insight into the mechanisms of NK cell development, as well as a method to identify individuals with high NK activity, that may provide superior outcomes in hematopoietic stem cell transfer.
| Natural Killer (NK) cells use two major receptor systems to detect alterations in the expression of MHC class I on potential target cells: the CD94:NKG2A receptor recognizing non-classical HLA-E, and the MHC class I receptors represented by Ly49 in the mouse and KIR in humans [1]. The recognition of HLA-E by NKG2A is dependent on the presentation of the MHC class I leader peptide, and thus surveys cells for the presence or absence of MHC class I expression in general. In contrast, each Ly49 or KIR is specific for a subset of MHC class I molecules, providing a more precise detection of alterations in the expression of individual MHC class I genes. Several studies have demonstrated a switch from NKG2A expression to Ly49/KIR expression as NK cells mature [2–4]. The measurement of HLA expression levels by mass spectroscopy of peripheral blood lymphocytes revealed that HLA-A/B/C levels are at least 25 times higher than that of HLA-E [5], suggesting that the level of inhibitory signaling by MHC class I receptors may increase as NK cells mature and switch from NKG2A recognition of HLA-E to KIR-mediated HLA binding.
The education of NK cells by MHC class I is currently an area of intensive research [6–8]. The interaction of inhibitory MHC class I receptors with their ligands has been shown to augment NK cell potential, leading to higher lytic activity and cytokine secretion. The dynamic nature of NK cell education has been revealed by transfer of NK cells into a novel MHC environment, leading to a change in their responsiveness [9–11]. A recent study of human NK cell education has indicated a role for NK cell-intrinsic expression of HLA in the tuning of NK cell activity, as silencing of HLA expression in primary NK cells reduced their function [12]. The role of the human HLA-C gene in NK cell education is of particular interest, as it appears to have developed primarily as a ligand for the KIR2D family of receptors [13,14]. Whereas only small subsets of HLA-A and HLA-B alleles possess KIR ligands, all HLA-C alleles are recognized. Furthermore, HLA-A or HLA-B cell surface expression levels are 13–18 times higher than HLA-C [5], consistent with a primary role of HLA-C in tuning NK cell responsiveness rather than presenting antigen to T cells. Evolutionary selection for an optimal level of KIR:HLA interaction is implied by the observed allelic variation of KIR cell surface expression levels and differences in ligand affinity of KIR alleles for HLA molecules [15]. Recent studies have also revealed variability in the level of cell surface expression of HLA-C alleles, indicating that variation in ligand levels may also be involved in the tuning of NK responsiveness [16,17].
In order to gain insight into the mechanisms underlying allele-specific differences in HLA-C expression, we conducted a detailed analysis of polymorphisms in predicted transcription factor (TF) binding sites in the 1.5 kb region upstream of the HLA-C coding region. Several TF sites were identified that possessed a disruptive single nucleotide polymorphism (SNP) associated with reduced promoter activity [18]. However, a SNP that disrupted a consensus Ets-binding site located approximately 1.3 kb upstream of the HLA-C start codon was not associated with altered promoter activity in the panel of cell lines studied. The detailed analysis of this region described in the current study reveals that the upstream Ets site is contained within an NK-specific promoter that produces translatable full-length HLA-C transcripts. The presence of these transcripts is associated with a higher level of HLA-C expression on NK cells. Disruption of the Ets site by a SNP in the HLA-C*02/*05/*07/*08 alleles results in the loss of NK-specific transcripts and decreased HLA-C expression. The analysis of NK cells from individuals homozygous for the Ets-disrupting SNP revealed that the loss of NK-specific HLA-C transcripts is associated with higher functional activity. The presence of NK-specific control elements in the HLA-C gene supports a central role for this gene in the development and regulation of NK cells.
A detailed analysis of the HLA-C gene region located ~1300 bp upstream of the start codon using the UCSC Genome Browser (http://genome.ucsc.edu/), revealed the presence of two spliced transcripts (GenBank numbers: DA932871, DA955942) that initiated 14 and 22 bp downstream of the polymorphic Ets element, suggesting the presence of a promoter in this region (Fig 1A). Both of these transcripts were obtained from a human spleen oligo-capped EST library, indicating that they represent true transcription start sites (TSS). The putative promoter region upstream of these TSS contains predicted AP1, SP1, and Ets elements that have previously been associated with the promoters of genes expressed by NK cells [19] (Fig 1A), suggesting that an NK-specific promotor may be present. Furthermore, a single nucleotide polymorphism in the Ets site of four HLA-C alleles (C*02, C*05, C*07, C*08) is predicted to abrogate Ets binding to this site and might affect promoter activity. The homologous region of the HLA-A gene has several nucleotide differences that disrupt the AP1-binding site, and the SP1/Ets element is replaced by binding sites for XBP1 and RORα (Fig 1A), indicating a distinct function for this region in the HLA-A gene. Transcripts from this region of HLA-A were observed in a macrophage EST library (GenBank numbers: BP306201, BP300425, BP297407), suggesting that a macrophage-specific promoter is present in this region of the HLA-A gene. This observation is consistent with the evolution of this region from supporting an antigen presentation function of HLA-A in macrophages to a role for HLA-C in NK cell function.
A panel of human tissue RNAs was tested for the presence of transcripts initiating from the putative upstream HLA-C promoter. Fig 1B shows the results of RT-PCR using a forward primer downstream of the observed initiation sites, but preceding a consensus splice donor site, and a reverse primer in exon 1. The strongest signal was found in spleen RNA, with weaker signals found in bone marrow, lung, and uterus, suggestive of NK-specific transcription. Only a very faint band is present in thymus, excluding T cells as a significant source of transcripts. Comparison of the level of PCR products generated from purified peripheral blood NK cell (pNK) cDNA with bone marrow, spleen, thymus, and purified monocyte cDNA, demonstrated that NK cells are the principal source of HLA-C upstream transcripts (Fig 1C). We will henceforth refer to the novel upstream HLA-C promoter as the NK-promoter (NK-Pro).
Interestingly, different patterns of amplified bands were observed in the tissues that produced NK-Pro transcripts (Fig 1B and 1C). Sequencing of the RT-PCR products from purified peripheral blood NK cells, spleen, and the YT human NK cell line revealed a large repertoire of alternatively spliced mRNAs (Fig 2). The highly variable nature of NK-Pro transcripts and the presence of a large 5´-UTR region containing competing initiation codons could provide an additional mechanism of modulating HLA-C protein expression, or it could reflect an enhancer/repressor function for the -1300 element, rather than a promoter capable of producing translatable HLA-C mRNAs. In order to assess the translatability of NK-Pro transcripts, full-length HLA-C cDNAs were generated from bone marrow, spleen, and peripheral blood NK cells from multiple donors possessing a variety of HLA-C alleles. Sequencing of the full-length products revealed additional alternative splicing events that could impact the translation of HLA-C (Fig 3). The most abundant bone marrow transcript retained intron 1, and was therefore untranslatable. Splice forms lacking exon 1 were observed in spleen and NK cell cDNA. Exon 1 contains the leader sequence of HLA-C. However, exon 2 contains an in-frame start codon, suggesting that an intracellular HLA-C molecule could be made. Fig 4A summarizes all of the exons observed. HLA-C transcripts initiating at the upstream promoter contain 1–3 additional non-coding exons that have been named -1a1-7, -1b1-6, and -1c1-4, with subscripts indicating differing exon sizes due to the use of alternative splice acceptors or donors for each exon. In addition, the size of the first HLA-C coding exon varies due to the presence of 7 alternative splice acceptors that can be used, so we have also named exon 1 isoforms as 11−7. Interestingly, it appears that there has been selection for distinct exon variants in certain alleles, such as the observation of the -1b2 exon only in the HLA-C*06 and C*12 alleles, which is likely due to the presence of a SNP in these two alleles that generates a stronger splice donor consensus (G|GT versus A|GT in other alleles). The -1b3 and -1b4 exons are only found in cDNAs originating from the HLA-C*01 or *04 alleles, since the key G nucleotide of the consensus GT splice donor is only present in these alleles. Only the HLA-C*01, C*03, C*04, and C*14 alleles can generate exon 12, due to a G to A nucleotide substitution that creates a splice acceptor site. The complex splicing patterns observed in HLA-C distal transcripts are not seen in HLA-A, which produces only one distal transcript containing two invariant untranslated 5´ exons (GenBank BP306201), indicating that modulation of the 5´-UTR structure has evolved specifically in the HLA-C gene.
Notably, there appears to be tissue-specific differences in exon usage of HLA-C NK-Pro transcripts (Figs 3 and 4B). Many of the NK-Pro transcripts in bone marrow retained intron 1, whereas a large 1.3 kb first exon (-1a7) was only observed in spleen, and most of the transcripts in peripheral blood NK cells contained a small 81 bp exon 1 (11) and no intron retention was observed. The highly variable and tissue-specific splicing patterns observed for HLA-C NK-Pro transcripts suggests that HLA-C expression levels in NK cells from various tissues could be distinct.
In order to directly address the translatability of the alternatively spliced HLA-C mRNAs, full-length cDNAs were cloned into the pEF6 mammalian expression vector, transfected into the JAR trophoblast cell line that lacks HLA-C expression, and HLA-C protein levels were assayed by Western blot (Fig 4C and 4D). To evaluate the effect of 5´-UTRs of differing size on expression, a series of four constructs were tested, ranging from the full 1.3 kb UTR, to the minimal UTR generated by transcription from the proximal promoter. Fig 4C shows that the level of HLA-C protein produced decreased with increasing UTR size, suggesting that the variable splice forms with differing 5´-UTR sequences could “tune” the levels of HLA-C protein. Fig 4D demonstrates that exon 1 is required for HLA-C expression. None of the splice forms lacking exon 1 produced detectable HLA-C protein, indicating that the skipping of this exon results in an inefficiently translated mRNA or an unstable protein product. Furthermore, removal of exons -1a and -1b from a full-length NK-Pro transcript containing exon 1, so that the cDNA started at exon -1c, substantially decreased protein levels, suggesting that the mRNA secondary structure of the 5´-UTR of NK-Pro transcripts may prevent an ATG start codon in exon -1c from competing with the downstream HLA-C start codon. In addition, the cDNA containing the full 1.3 kb 5´-UTR was translatable (exon -1a7, Fig 4C), even though it contained multiple alternative 5´-ATG codons, further supporting a role for RNA secondary structure. The significant differences in HLA-C mRNA splicing in different tissues and at distinct stages of NK differentiation imply that changes in spliceosome function may play an important role in NK cell maturation or function.
The subset of untranslatable NK-Pro transcripts generated by skipping of exon 1 could potentially represent a mechanism to control HLA-C expression levels during NK development. In order to address the possibility of differential HLA-C mRNA splicing during NK development, HLA-C levels and the splicing patterns of NK-Pro transcripts were analyzed in NK subsets representing different stages of NK cell differentiation. Fig 5A shows the HLA-C expression levels in peripheral blood NK subsets from 7 individuals. There is a clear increase in HLA-C expression on the more mature CD56dim NK cells relative to the less differentiated CD56bright population in all subjects tested. As CD56dim NK cells differentiate further, they acquire KIRs and CD57 [4]. This late-stage differentiation was also accompanied by an increase in HLA-C expression (Fig 5A and 5B). Analysis of educated NK cells (NK cells that express KIR molecules capable of recognizing self) revealed higher levels of HLA-C on educated NK cells as compared to non-educated or KIR-ve NK cells (Fig 5C), indicating that high HLA-C expression occurs in highly functional, mature NK cells. FACS analysis of lymphocytes from various human tissues showed that increased expression of HLA-C is found on CD56dim NK cells from multiple tissues, and the relative increase in HLA-C levels on KIR-expressing NK cells from tissues is similar to that seen in peripheral blood NK cells (Fig 5D). Furthermore, the level of HLA-C expression varies widely between tissues, which may reflect tissue-specific splicing (Fig 3) or differences in the activity of NK-Pro.
In order to determine if differential splicing of the NK-Pro transcript is associated with the changes in HLA-C expression observed, RT-PCR of NK-Pro transcripts was performed on RNA isolated from sorted CD56bright versus CD56dim peripheral blood NK cells (Fig 5E) or CD56dim/CD57-negative versus CD56dim/CD57-positive NK cells (Fig 5F). Fig 5E shows that the fraction of HLA-C NK-Pro mRNAs that contain exon 1 increases as cells progress from CD56bright to CD56dim NK. Fig 5F reveals an increase in the number of splice variants containing exon 1 in CD57-positive NK as compared to CD57-negative NK. Taken together, these results are consistent with a model whereby skipping of exon 1 results in the generation of non-productive transcripts, and represents a mechanism that prevents NK-Pro transcripts from generating increased HLA-C levels in immature NK cells. The comparison of splicing isoforms observed in Fig 5E and 5F also demonstrates the high degree of variability in splice forms observed with different HLA-C alleles. The donor analyzed in Fig 5E is homozygous for the HLA-C*06 allele, and a relatively simple splicing pattern is observed. In contrast, Fig 5F represents NK cells from a HLA-C*15/C*16 heterozygote, and a much greater number of distinct splice forms are observed, none of which are in common with the HLA-C*06 isoforms.
In order to confirm the functionality of the predicted TF-binding sites in the NK-specific promoter and the effect of the Ets site SNP, an electromobility-shift assay (EMSA) was performed with oligonucleotides spanning the predicted AP1, SP1, and Ets TF-binding sites shown in Fig 1A. The AP1 site bound c-Fos and JunB proteins present in YT nuclear extract (Fig 6A). The combined SP1:Ets site bound both SP1 and the Ets family member Elf-1. The SNP in the NK-Pro Ets site was predicted to disrupt binding of Ets family members to this site, and EMSA with a probe containing the disruptive SNP demonstrated that the altered site had greatly diminished binding to both Elf-1 and SP1, most notably in the YT human NK cell line, indicating a cooperative interaction between these TFs (Fig 6B). A cooperative interaction between Ets family members and SP1 has been observed in many promoters [20,21], and tandem SP1/Ets sites have been identified in many genes, including the CD16 promoter [19]. The strong reduction in TF binding observed as a result of the A to G substitution in the Ets site (Fig 6B), suggests that transcriptional activity should be affected by this SNP found in the HLA-C*02/*05/*07/*08 alleles.
The central role of an Ets site capable of binding Elf in conferring NK cell/T cell-specific promoter activity has been observed previously for the MUNC4D gene [22], and the in vitro analysis of NK-Pro indicated that an intact Ets site was required for TF binding to the SP1/Ets site. Therefore, we predicted a significant loss of NK-Pro transcription in HLA-C alleles with a disrupted Ets site. RT-PCR of the HLA-C NK-Pro transcript using RNA isolated from purified peripheral blood NK cells obtained from donors selected for the presence of alleles containing an intact or disrupted NK-Pro Ets site revealed that an intact Ets site was associated with the production of high levels of NK-specific transcripts (Fig 7A).
Individuals that were homozygous for any combination of the HLA-C alleles that lacked an intact Ets-binding site (HLA-C*02/05/07/08), had greatly reduced/absent HLA-C NK-Pro transcripts. Interestingly, in some of the individuals that lacked HLA-C NK-Pro transcripts, homologous transcripts derived from the HLA-B*08 gene were detected, suggesting the presence of NK-Pro activity in some HLA-B alleles. A Blast search of GenBank revealed that the HLA-B*14 allele contained an intact Ets site. However, HLA-B*08 and all other HLA-B alleles in GenBank contained the same SNP in the Ets site found in HLA-C*02/05/07/08. Interestingly, three of the donors shown in Fig 7A that did not produce any detectable transcripts had the HLA-B*14 allele, indicating that the Ets site is not associated with the production of distal transcripts in the HLA-B gene. This suggests that there are multiple nucleotide changes relative to HLA-B that are associated with NK-specific activity in the HLA-C gene in addition to the Ets site.
Fig 7B shows a FACS analysis of HLA-C expression by T, B, and NK cell subsets performed on peripheral blood from the same individuals tested for NK-Pro transcripts by RT-PCR. The presence of an intact Ets site in NK-Pro was associated with significantly higher expression of HLA-C by NK cells. A weaker effect of the Ets SNP was observed in T cells, indicating some activity of this element in T cells. However, there was no difference between intact versus disrupted Ets alleles with regard to HLA-C expression on B cells. It therefore appears that the -1300 element evolved in order to generate higher levels of HLA-C expression on NK cells. The functional effect of enhanced HLA-C expression by NK cells could be manifested in numerous ways. High HLA-C levels could protect NK cells from fratricide mediated by other NK [23], create a higher threshold for NK activation due to cis recognition, or it could lead to increased NK function as predicted by the observation that reduction of NK cell HLA expression can reduce NK activity [12].
The functional consequences of high versus low HLA-C expression on NK cells was studied by comparing CD107a expression triggered by interaction of NK cells from high or low HLA-C expressing donors with 721.221 target cells. All donors possessed the KIR2DL3, KIR2DL1, and KIR3DL1 genes present on the KIR-A haplotype to ensure that receptors for both HLA-C1/C2 and HLA-Bw4 were present. Both Bw4 and Bw6-homozygous individuals were tested in the assay in order to control for possible KIR3DL1/HLA-B effects. After a 5-hour incubation with target cells, NK cells from individuals with high HLA-C expression had significantly lower CD107a expression in the CD56dim subset than donors with a disrupted HLA-C NK promoter and lower HLA-C expression (Fig 7C). The increased degranulation response in individuals with reduced HLA-C expression due to the absence of upstream transcripts implies an important role for cis expression of HLA-C in determining the functional activity of NK cells.
The presence of an NK-specific promoter coupled with an elaborate alternative splicing mechanism to control the translatability of HLA-C mRNA implies an important role for endogenous NK cell HLA-C protein in the development and/or function of NK cells. The increased NK activity we have observed in individuals unable to upregulate HLA-C levels on mature NK cells suggests that endogenous HLA-C plays a role in the tuning of NK cell function. These results are consistent with the evolution of the HLA-C gene to function as a ligand for the KIR family of MHC class I receptors expressed by human NK cells. The change in RNA splicing patterns and the increase in HLA-C expression levels as NK cells mature provides additional evidence for a role of HLA-C in controlling human NK cell function. It is remarkable that alternative RNA splicing generates both on/off (intron retention and exon skipping) and rheostat-like (variation in 5´-UTR length) control of HLA-C expression in NK cells, consistent with endogenous HLA-C levels playing an important role in NK function or differentiation. There is a considerable body of evidence demonstrating an association of increased NK cell activity with the presence of KIR that recognize self HLA (NK education). However, this correlation has been attributed to the recognition of HLA on target cells. MHC class I on the NK cell surface has been shown to control NK activity in mouse NK cells, and this is explained by the ability of Ly49 to recognize class I MHC in cis due to the presence of a flexible stalk in the Ly49 proteins. Although cis interactions with MHC have been demonstrated for the Ly49 receptors [24], and cis interaction is required for murine NK cell licensing [25], there has been no direct evidence of cis interaction of KIR with HLA. Since KIR do not have a flexible stalk, it is believed that cis interaction of KIR with HLA on the cell surface does not occur. It may be possible, however, for inhibitory signaling to occur in endosomes if both KIR and HLA are present. Vesicles containing target cell HLA-C have been observed in KIR2DL1-expressing NK cells, indicating acquisition and internalization of ligand by KIR [26]. It is therefore possible that high levels of endogenous NK cell HLA-C may contribute to inhibitory signaling in these endosomes. The results presented here, together with the previous observation that modulation of HLA expression in NK cells suppresses their activity [12], strongly suggests that KIR:HLA interactions are occurring in human NK cells. The maturation of NK cells from a CD56bright to a CD56dim phenotype is associated with the acquisition of lytic activity [27]. HLA-C levels increase in CD56dim NK cells as well as mature CD57-positive NK cells, and this upregulation is associated with higher levels of translatable NK-Pro HLA-C transcripts due to increased inclusion of exon 1. Therefore, it appears that HLA-C levels increase when the NK cell acquires lytic activity, or alternatively the acquisition of lytic activity is triggered by increased HLA-C levels.
At first glance, the increased functional activity of NK cells with reduced HLA-C due to the lack of NK-Pro transcripts seems to be at odds with the observed increase in HLA-C levels on NK cells as they become more mature and functionally active. However, these results are consistent with a model wherein the upregulation of HLA-C in mature NK cells occurs to regulate their function rather than having a direct effect on education. KIR gene expression is activated by a stochastic mechanism in developing NK cells, and there is sequential receptor acquisition until a sufficient inhibitory signal is achieved, which results in a fully functional, educated NK cell [28]. The observed increase in HLA-C expression that occurs with increasing numbers of expressed KIR genes in an NK cell suggests that increased HLA-C levels are a result of NK cell education, rather than driving it. In order for upregulated levels of NK cell HLA-C to play a role in NK education, it would have to occur in the uneducated NK population. Furthermore, it has been shown that differing levels of HLA-C have no effect on the licensing/education of NK cells [29]. The higher levels of HLA-C observed on educated versus uneducated NK cells, further supports upregulation of HLA-C on NK cells as a product of NK education rather than a cause. Fig 8 shows a schematic detailing the changes in NK-Pro mRNA structure and HLA-C expression as NK cells differentiate. The immature CD56bright, NKG2A+ve, KIR-ve cells have low levels of HLA-C produced primarily from proximal promoter transcripts, since NK-Pro cDNAs in immature cells are largely untranslatable. At the intermediate stage of differentiation, KIR expression is stochastically activated in CD56dim cells, and the presence of a ligand for the expressed KIR produces an educated NK cell. This leads to upregulated HLA-C in the mature NK cell due to an increase in the level of translatable HLA-C NK-Pro mRNAs. Increased levels of HLA-C in the mature NK cell are predicted to control lytic activity due to cis inhibitory signaling.
The tissue-specific differences in mRNA splicing of the NK-Pro transcript and the differences in HLA-C protein expression on NK cells from various tissues that we have observed could represent a tuning mechanism that regulates the responsiveness of NK cells in certain tissues. It will therefore be of interest to examine if the observed differences in HLA-C levels on NK cells from different tissues correlates with their ability to recognize and lyse targets with reduced HLA-C expression. The increased activity of NK cells with low HLA-C expression due to the presence of an inactivating SNP in NK-Pro suggests that the threshold of NK cell activation is lower, which might also produce a state of decreased lytic potential over time due to more frequent degranulation. Conversely, an increased accumulation of granules, and thus lytic potential would be predicted to occur in NK cells with a high level of HLA-C. Testing the serial-killing activity of high versus low HLA-C expressing NK cells could address this possibility.
Allele-specific differences in 5´-UTR exon content also imply selection for an optimal level of HLA-C expression in NK cells, perhaps in conjunction with allelic differences in KIR-binding affinity, in order to achieve an appropriate level of inhibitory signaling. A detailed examination of NK cell HLA-C levels in individuals that are homozygous for HLA-C alleles with distinct splicing patterns and comparing expression levels with the affinity of the allele for the corresponding KIR would be required to investigate this.
The existence of HLA-C alleles of both C1 (C*07/C*08) and C2 (C*02/C*05) supratypes with an inactivated NK-specific promoter suggests that there may be circumstances wherein the absence of upregulated expression of NK cell HLA-C would be beneficial. It will be of interest to determine whether there are any associations of NK-Pro deficient HLA-C alleles with clinical outcomes in infectious disease or bone marrow transplantation.
HeLa, EL-4, MOLT-4, and the JAR/BeWo human trophoblast cell lines were obtained from ATCC (Manassas, VA, USA) and grown in the recommended media. YT cells were cultured in RPMI 1640 media containing 10% fetal bovine serum, 100 U/ml penicillin, 100 U/ml streptomycin, sodium pyruvate and L-glutamine.
Healthy volunteers were recruited through the NCI-Frederick Research Donor Program (http://ncifrederick.cancer.gov/programs/science/rdp/default.aspx). The KIR and HLA genotype of each donor was determined as previously described [30]. NK cells were separated from the peripheral blood of healthy donors by Histopaque (Sigma-Aldrich, St Louis, MO, USA) gradient centrifugation using the RosetteSep Human NK Cell Enrichment Cocktail (STEMCELL Technologies, Vancouver, BC, Canada). Human tissue samples were obtained from surgically removed tissue (liver, spleen, endometrium, decidua, tonsil) at the Karolinska University Hospital, Stockholm, Sweden. Written and oral informed consent was obtained from all patients, and the study was approved by the Regional Ethics Review Board, Stockholm, Sweden (approval numbers: 2017-1659-32, 2013/2285-31/3, 2006/229-31/3, 2013/1324-31/2, 2017-649-31/1). Lymphocytes from liver, decidua, and endometrium were isolated using enzymatic digestion as previously described [31]. Tonsil and spleen were mechanically dissociated using scalpels followed by filtration. Mononuclear cells were obtained by density centrifugation using Histopaque (Sigma-Aldrich).
Total RNA from 20 human tissues (Human total RNA master panel II) was obtained from Clontech (Mountain View, CA, USA). Total RNA from purified NK cells or the YT cell line was isolated from 1–5 x 106 cells with the RNeasy kit (Qiagen, Valencia, CA, USA). A cDNA synthesis reaction was carried out using Random Hexamer primer, Taqman Reverse Transcription Reagents kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s instructions. A forward primer in exon -1a of the NK-Pro transcript (5´-AGAAGGGCTGGAGAAGCAGGAG-3´) was used together with an exon 13 reverse primer upstream of the major HLA-C TSS (5´-GGACTGCGGAGACGCTGATTGG-3´) for the initial detection of NK-Pro transcripts. Additional HLA-C specific exon-1a forward (5´-GGGATGAGAGGGGCAGASAG-3´) and exon 2 reverse (5´-GTGCCTGGCGCTTGTASTTC-3´) primers were used to confirm the NK-specificity of HLA-C transcripts (Fig 1C). For full-length transcripts, an alternative 3´ primer located immediately following the HLA-C stop codon was used (5´-GTCCCACACAGGCAGCTGTCTC-3´). To assay the level of exon 1 skipping, an exon 2 reverse primer was used (5´-GAACTGCGTGTCGTCCACGTAG-3´). PCR products were cloned into the pCR2.1-topo vector (Invitrogen, Carlsbad, CA, USA) and sequenced. The sequences of the alternatively spliced NK-Pro transcripts have been deposited in GenBank, and can be found under accession numbers MF536989-MF536999 for the peripheral blood NK cell cDNAs, and accession numbers MF563479-MF563493 for the spleen and bone marrow cDNAs.
Full-length HLA-C cDNAs with varying 5´-UTR lengths were cloned into the pEF6/V5-His TOPO-TA vector (Thermo Fisher Scientific, Waltham, MA, USA) and verified by sequencing. 1 ug of each construct was transfected into the human JAR trophoblast cell line using HilyMax Transfection Reagent (Dojindo Molecular Technologies Inc., Rockville, MD, USA). Cells were harvested with Nonidet-P40 (NP-40) lysis buffer (1% NP-40, 50 mM Tris-HCl, pH 8.0, 150 mM NaCl) supplemented with complete mini protease inhibitor cocktail tablets (Roche Diagnostics, Indianapolis, IN, USA), and protein concentrations were determined using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific). Equal amounts of total protein were separated using sodium dodecyl sulfate-PAGE (SDS-PAGE) on 4–12% Tris-Glycine gels (Invitrogen) and transferred to Immobilon-P membrane (Sigma-Aldrich). Membranes were blocked for one hour in 5% milk solution in PBST (Phosphate Buffered Saline, pH 7.4, 0.1% Tween 20) and subsequently probed for 1.5 hours with an anti-HLA-C antibody (Abcam, Cambridge, MA, USA; ab126722) diluted 1:5000 in 5% milk PBST solution. Blots were washed 4 times for 5 minutes each with PBST and then were incubated for 20 minutes with an anti-rabbit HRP-linked IgG antibody (Cell Signaling Technology, Danvers, MA, USA) diluted 1:20,000 in 5% milk PBST solution. Blots were washed 4 times for 5 minutes with PBST and proteins were visualized using Amersham ECL Western blotting detection reagents (GE Healthcare, Pittsburgh, PA, USA). Normalization for protein loading was accomplished by first stripping blots with SDS stripping buffer for 10 minutes and then washing 3 times with PBS, pH7.4 for 5 minutes. Blots were then probed for 1 hour with a monoclonal anti-β-Actin antibody (Sigma-Aldrich A2228) at a concentration of 1 μg/ml followed by an anti-mouse HRP linked IgG antibody (Cell Signaling Technology) for 20 minutes diluted 1:20,000 in 5% milk PBST. Levels of β-Actin were visualized using Amersham ECL Western detection (GE healthcare).
Nuclear extracts were prepared from cell lines using the CellLytic NuCLEAR extraction kit (Sigma-Aldrich). Protein concentration was measured with a Bio-Rad protein assay, and samples were stored at −70°C until use. Double-stranded DNA oligonucleotide probes containing either the AP1 site (5´-GACACGACCTGAGTCACATTAGC-3´) or the the combined SP1/Ets site (5´-GACATGGGCAGGAAGTGAGGGAC-3´) were synthesized (IDT, Newark, NJ, USA). Probes were labeled with α-[32P]deoxycytidine triphosphate (3000 Ci/mmol; PerkinElmer, Waltham, MA, USA) by fill-in using the Klenow fragment of DNA polymerase I (Invitrogen). 32P-labeled double-stranded oligonucleotides were purified using mini Quick Spin Oligo Columns (Roche). DNA–protein binding reactions were performed in a 10-μl mixture containing 5 μg nuclear protein and 1 μg poly[dI-dC] (Sigma-Aldrich) in 4% glycerol, 1 mM MgCl2, 0.5 mM EDTA, 0.5 mM DTT, 50 mM NaCl, 10 mM Tris-HCl (pH 7.5). Nuclear extracts were incubated with 1 μl 32P-labeled oligonucleotide probe (10,000 cpm) at room temperature for 20 min and then loaded on a 5% polyacrylamide gel (37:5:1). Electrophoresis was performed in 0.5x TBE for 2 h at 130 V, and the gel was visualized by autoradiography.
Lymphocytes isolated from whole blood via Ficoll-Paque Plus gradient centrifugation (GE Healthcare) from 16 healthy volunteers were stained extracellularly with antibodies to determine expression of HLA-C on various subsets. The antibodies used were: CD3 [clone UCHT1] BV605, CD19 [clone HIB19] PE-Cy7, CD56 [clone HCD56] BV711 (BioLegend, San Diego, CA, USA); CD4 [clone SK3] PE-Cy7, CD8 [clone RPA-T8] APC (BD Biosciences, Franklin Lakes, NJ, USA); HLA-C [clone DT9] conjugated to AlexaFluor488. Data from the samples collected on a BD LSRFortessa flow cytometer were analyzed using FlowJo v10.1. Mononuclear cells from additional blood donors and from indicated tissues were analyzed by flow cytometry at the Karolinska Institutet, Stockholm, Sweden. The following additional antibodies were used: CD3 [clone UCHT1] PE-Cy5 (Beckman Coulter, Brea, CA, USA); CD19 BV510 (Biolegend); NKG2A [clone Z199] BB515, CD56 [clone NCAM16.2] BUV737, CD57 [clone NK-1] BV605, HLA-C [clone DT9] PE, KIR2DL2/3/S2 [clone CH-L] BUV395 (BD Biosciences); KIR2DL1/S1 [clone EB6] PC-5.5 (Beckman Coulter); KIR3DL1 [clone DX9] BV421 (Biolegend); KIR2DS4 [clone REA860] PE-Vio615 (Miltenyi Biotec, Cambridge, MA, USA). Live/Dead marker Aqua (Thermo Fisher Scientific) was also used. Samples were acquired on a 5 laser BD LSRFortessa flow cytometer and analyzed using FlowJo v10.1.
The frequency of degranulated human NK cells was quantitated by flow cytometric detection of cell surface CD107a. Purified donor human NK cells (105) were co-cultured with 721.221 tumor cells in a round bottom 96-well plate at an effector target ratio of 1:1 in RPMI 1640 supplemented with 10% FBS, 10 mM Hepes, 2 mM L-glutamine, 0.1 mM nonessential amino acids, 1 mM sodium pyruvate, Penicillin 100 U/ml, Streptomycin 100 ug/ml, and 100 IU/ml recombinant human interleukin 2 (Tecin; Teceleukin, obtained from the Biological Resources Branch of the National Cancer Institute). Anti-human CD107a PE or BV421 (Biolegend) was added at 3 ul to 200 ul of the co-culture and the mixture was gently mixed by pipetting and then spun down at 50 g for 5 minutes and incubated for 1 h at 37°C in 5% CO2 after which Golgi-Stop (BD Biosciences) was added and cells were incubated for an additional 4 h at 37°C in 5% CO2. Cells were washed with sorter buffer (HBSS containing 0.5% BSA, 1mM EDTA, 25mM Hepes, 0.05% sodium azide) and Fc receptors were blocked with human FcR Blocking Reagent (Miltenyi Biotec) for 5 minutes and then then stained with anti-human CD56 PE or APC (clone HCD56, Biolegend) for 10 minutes. Stained cells were washed twice with sorter buffer and fixed with BD Cytofix (BD Biosciences) for 20 minutes. Cells were washed twice with sorter buffer and flow cytometric analysis was performed on a LSRFortessa instrument (BD Biosciences). Degranulated NK cells were analyzed using FlowJo software by gating on single cells (FSC-H x FSC-A) in the lymphocyte gate and then the frequency of CD56dim CD107a+ cells was quantitated.
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10.1371/journal.pgen.1005973 | FgPrp4 Kinase Is Important for Spliceosome B-Complex Activation and Splicing Efficiency in Fusarium graminearum | PRP4 encodes the only kinase among the spliceosome components. Although it is an essential gene in the fission yeast and other eukaryotic organisms, the Fgprp4 mutant was viable in the wheat scab fungus Fusarium graminearum. Deletion of FgPRP4 did not block intron splicing but affected intron splicing efficiency in over 60% of the F. graminearum genes. The Fgprp4 mutant had severe growth defects and produced spontaneous suppressors that were recovered in growth rate. Suppressor mutations were identified in the PRP6, PRP31, BRR2, and PRP8 orthologs in nine suppressor strains by sequencing analysis with candidate tri-snRNP component genes. The Q86K mutation in FgMSL1 was identified by whole genome sequencing in suppressor mutant S3. Whereas two of the suppressor mutations in FgBrr2 and FgPrp8 were similar to those characterized in their orthologs in yeasts, suppressor mutations in Prp6 and Prp31 orthologs or FgMSL1 have not been reported. Interestingly, four and two suppressor mutations identified in FgPrp6 and FgPrp31, respectively, all are near the conserved Prp4-phosphorylation sites, suggesting that these mutations may have similar effects with phosphorylation by Prp4 kinase. In FgPrp31, the non-sense mutation at R464 resulted in the truncation of the C-terminal 130 aa region that contains all the conserved Prp4-phosphorylation sites. Deletion analysis showed that the N-terminal 310-aa rich in SR residues plays a critical role in the localization and functions of FgPrp4. We also conducted phosphoproteomics analysis with FgPrp4 and identified S289 as the phosphorylation site that is essential for its functions. These results indicated that FgPrp4 is critical for splicing efficiency but not essential for intron splicing, and FgPrp4 may regulate pre-mRNA splicing by phosphorylation of other components of the tri-snRNP although itself may be activated by phosphorylation at S289.
| In eukaryotic organisms, many genes containing introns that need to be spliced by the spliceosome after transcription. Among all the spliceosome components, Prp4 is the only protein kinase. Unlike other organisms, deletion of the FgPRP4 kinase gene was not lethal in the wheat scab fungus Fusarium graminearum. In this study, we found that FgPRP4 is not essential for intron splicing but important for splicing efficiency. The Fgprp4 mutant was not stable and produced spontaneous suppressors recovered in growth rate. Suppressor mutations were identified in the PRP6, PRP31, BRR2, and PRP8 orthologs, key components of the U4/U6-U5 complex in the spliceosome and FgMSL1 by candidate gene or whole genome sequencing. We also showed that the N-terminal 310 amino acid region of FgPrp4 plays a critical role in its localization and functions of FgPrp4 and identified S289 as a critical phosphorylation site. Overall, our result indicated that FgPrp4 is important for splicing efficiency, possibly by phosphorylation of other spliceosome components.
| Pre-mRNA splicing is mediated by the spliceosome that is formed by ordered interaction of the U1, U2, U4/U6, U5 snRNPs, and non-snRNP proteins [1]. U1 and U2 first interact with the 5’-splice site (5’-ss) and the branch point (BP) of the introns in pre-mRNA to generate the A complex. The A complex is then converted to the pre-catalytic B-complex by the integration of the preformed U4/U6-U5 tri-snRNP. Activation of the B-complex involves the unwinding of U4/U6 and dissociation of U1 and U4. Whereas the activated B-complex catalyzes the first step of splicing, the C complex catalyzes the second step of splicing to form mature mRNA [2].
Unwinding of U4/U6, a critical step during B-complex activation, is catalyzed by the Brr2 DExD/H-box family RNA helicase that recognizes the single-stranded region of U4 next to the Stem I of the U4/U6 [2]. The helicase activity of Brr2 is regulated by Prp8 and Snu114 to prevent premature unwinding of U4/U6 [1,2]. Prp6 and Prp31 also are two essential components of the U4/U6-U5 tri-snRNP. However, unlike Prp8 and Brr2, they lack structural domains with defined biochemical functions. Prp6 and Prp31 are associated with pre-catalytic spliceosomal complexes [3] but not with the activated- or post-catalytic spliceosomal complexes [4–7]. Prp6 interacts with the U4/U6 specific protein Prp31 and the U5 proteins Brr2 and Prp8 [8,9].
Many components of the spliceosome are conserved in eukaryotic organisms [10]. However, the budding yeast Saccharomyces cerevisiae, a model for studying spliceosome and intron splicing, lacks a distinct ortholog of Prp4, which is the only serine/threonine protein kinase among the spliceosome components [11]. In the fission yeast Schizosaccharomyces pombe, prp4 is an essential gene required for intron splicing [11]. It phosphorylates the non-SR protein Prp1 and its kinase activity is essential for G1-S and G2-M transition in the cell cycle [12]. In humans, hPrp4 is specifically associated with the U4/U6 and U4/U6-U5 RNPs. It functionally interacts with hPrp6 (human ortholog of S. pombe Prp1), Prp31, Brr2, and Prp8, and plays an essential role in the catalytic activation of B-complex [3]. Phosphorylation of hPrp6 and hPrp31 by hPrp4 is required for stable integration of the tri-snRNP into the B-complex, and it has been characterized by phosphoproteomics analysis [11].
Whereas Prp4 is essential in S. pombe, deletion of its orthologous gene appears to be not lethal in Fusarium graminearum because the putative Fgprp4 deletion mutant was identified in a systematic characterization study of its protein kinase genes. F. graminearum is the predominant species causing Fusarium head blight (FHB), one of the most important diseases on wheat and barley [13,14]. It causes severe yield losses and contaminates infested grains with harmful mycotoxins, including zearalenone and trichothecene mycotoxin deoxynivalenol (DON), a potent inhibitor of eukaryotic protein synthesis [15,16].
The PRP4 orthologs are well conserved in filamentous fungi but none of them have been functionally characterized, including the model organisms Neurospora crassa and Aspergillus nidulans. To our knowledge, Fgprp4 is the only null mutant that is available for this well-conserved protein kinase gene among all the eukaryotic organisms. In this study, we further characterized the function of FgPRP4 in intron splicing and suppressor mutations of the Fgprp4 mutant. Our results showed that FgPrp4 is critical for splicing efficiency and FgPrp4 may regulate pre-mRNA splicing by phosphorylation of other tri-snRNP proteins. FgPrp4 itself may be phosphorylated at the N-terminal region by autophosphorylation or other protein kinases.
The Prp4 ortholog in F. graminearum (Fg04053) shares 57% identity with Prp4 of S. pombe but their homology is mainly in the kinase domain. Although it is conserved in other ascomycetes, a distinct Prp4 ortholog was absent in Saccharomycotina species except Yarrowia lipolytica (S1 Fig). Most of Saccharomycotina species, including S. cerevisiae and Candida albicans, may have lost the PRP4 ortholog during evolution after massive intron loss [17].
Unlike prp4 in S. pombe, the putative FgPrp4 mutant was viable in F. graminearum [18]. In this study we first confirmed the Fgprp4 mutant by Southern blot analysis (S2 Fig). Careful examinations showed that the Fgprp4 mutant had severe growth defects (Fig 1A) and rarely produced morphologically abnormal conidia (Fig 1B). The length of Fgprp4 conidia (28.3 ± 7.1 μm) was approximately 45% shorter than that of wild-type conidia (51.2 ± 8.9 μm). Deletion of FgPRP4 also reduced conidiation. Whereas the Fgprp4 mutant produced 2.4 ± 1.7x104 conidia/ml in 5-day-old CMC cultures, the wild type strain produced over 106 conidia/ml under the same conditions. In addition, the Fgprp4 mutant failed to produce perithecia on mating plates (Fig 1C) and was non-pathogenic in infection assays with flowering wheat heads (Fig 1D). To confirm its function, we re-introduced the full-length FgPRP4 allele into the Fgprp4 mutant strain FP1. The resulting Fgprp4/FgPRP4 transformant FPC1 (Table 1) was similar to the wild type in growth rate, conidiation, sexual reproduction, and virulence (Fig 1). Therefore, deletion of FgPRP4 is responsible for all the phenotypes observed in the Fgprp4 mutant.
To determine its subcellular localization, we fused GFP to the carboxyl-terminus of FgPRP4 and transformed the FgPRP4-GFP construct into the Fgprp4 mutant FP1. The resulting FgPRP4-GFP transformant FPN1 (Table 1) was normal in growth (S3 Fig), reproduction, and pathogenesis. When examined by epifluorescence microscopy, GFP signals of similar strength were observed in the nucleus in conidia, germlings, and hyphae (Fig 2A). When assayed by qRT-PCR, FgPRP4 had similar expression levels in conidia, germlings, perithecia, and infected wheat heads (Fig 2B). These results indicate that FgPRP4 is constitutively expressed in F. graminearum and its localization to the nucleus may be associated with its functions in the spliceosome.
Like hPrp4, FgPrp4 has a long N-terminal region that is rich in serine and arginine (SR-rich) and contains one putative nuclear localization signal (NLS). This N-terminal SR-rich domain of FgPrp4 is absent in its orthologs from S. pombe (S4 Fig). To determine its function, we generated the FgPRP4ΔN310-GFP construct deleted of the N-terminal 310 aa and transformed it into the Fgprp4 mutant. The resulting FgPRP4ΔN310-GFP transformant had similar phenotypes with the original mutant (Fig 2C) and GFP signals in the cytoplasm (Fig 2D). These results indicate that the N-terminal region of FgPrp4 is essential for its localization and function in F. graminearum. Interestingly, FgPRP4 has two isoforms based on our RNA-seq data (S5A Fig) [19]. Isoform A encodes the full-length FgPrp4 kinase as predicted by automated annotation. Isoform B has the retention of the forth intron and encodes a protein with the predicted C’-terminal 73 aa region replaced with 66 aa encoded by the retained intron (S5B Fig). The protein encoded by isoform B should have no kinase function because the protein kinase domain was disrupted (S5B). Nevertheless, isoform A accounted for over 85% of the FgPRP4 transcripts in RNA-seq data of hyphae, conidia, and perithecia (S5A Fig). This observation was verified by qRT-PCR analysis (S5C Fig), indicating that isoform A is the predominant transcript of FgPRP4.
To determine the defects of Fgprp4 in intron splicing, RNA samples were isolated from aerial hyphae of 9-day-old PDA cultures for RNA-seq analysis. RNA-seq data from two independent experimental replicates were obtained and analyzed. Among the total of 13,321 genes in the Fusarium graminearum genome [20], 10,268 have at least one intron and the average intron size is 83 bp. In our RNA-seq data, the expression of 8,028 genes (CPM≥10) was detected in both replicates and 6,359 of them have introns. Although deletion of FgPRP4 did not completely block intron splicing, the level of retained introns (un-spliced introns) was significantly higher in the mutant than in the wild type (P<0.0001, t-test) (Fig 3A). In comparison with the wild type, over 38% of the introns in 47% of the genes with detectable transcripts were increased in intron retention over 2-fold in Fgprp4 (Fig 3B). Approximately 76% of them (7,837) were identified in both RNA-seq data (Fig 3C), confirming that retention of these introns was related to FgPRP4 deletion. A third of these introns had over 4-fold reduction in splicing efficiency in both replicates. Nevertheless, splicing of approximately 60% of the predicted introns was not significantly affected (<2-fold) by FgPRP4 deletion (Fig 3B). Therefore, FgPRP4 is not essential for intron splicing but it affects splicing efficiency.
Based on GO analysis, genes with over 4-fold reduction in splicing efficiency in the mutant belong to various functional categories, which may contribute to its pleiotropic phenotype. A number of them are known to be functionally related to DNA recombination and repair (S1 Table) based on the functions of their yeast orthologs, including the FgPHR1 (FGSG_00797), FgNHP6A (FGSG_00385), and FgEAF1 (FGSG_05512) genes that were confirmed to be reduced in splicing efficiency in the Fgprp4 mutant by RT-PCR analysis (Fig 3D). Therefore, the Fgprp4 mutant may be compromised in DNA repair.
We then compared sequences of the introns that were not affected by FgPRP4 deletion with those with over 4-fold reduction in splicing efficiency in the mutant. No differences were identified in the sequences of the branch point (BP), 5’ss, and 3’ss (S6 Fig). However, introns with reduced splicing efficiency in the Fgprp4 mutant tend to be longer (p<0.001) than introns unaffected by FgPRP4 deletion (S7A Fig), mainly due to longer distance between the BP and 5’ss sequences (S7B Fig). We also noticed that genes with intron splicing efficiency affected by FgPRP4 deletion tend to have fewer introns that those unaffected in the Fgprp4 mutant (S7C Fig). Because it is not directly involved in the recognition of 5’ss, BP, and 3’ss sequences, Prp4 likely affects intron splicing by interacting with other spliceosome proteins such as Prp8 [21] or phosphorylation of its substrates in F. graminearum.
The Fgprp4 mutant was not stable. Approximately 10% of Fgprp4 cultures produced fast-growing sectors after incubation for 2 weeks (Fig 4A). We randomly collected 49 subcultures of spontaneous sectors and categorized them into two types based on their growth rate and colony morphology (Fig 4B). Thirty two type I suppressor strains (>65%) had similar growth rate and colony morphology with the wild type. The other 17 type II suppressors grew slower than the wild type but faster than Fgprp4 (Fig 4B).
For the 32 type I suppressors, we also assayed their defects in conidiation, sexual reproduction, and plant infection (S2 Table). Twenty four of them were still defective in plant infection (Fig 4C). The other 8 were pathogenic on wheat heads but still impaired in sexual reproduction (Fig 4D) or conidiation. (S2 Table). These results indicate that none of these suppressor strains were fully rescued in the defects of Fgprp4.
We selected two type I suppressor strains, S2 and S47, for RNA-seq analysis. In comparison with the original Fgprp4 mutant, only 74.3% and 34.7% of the introns with over 8-fold splicing deficiency were recovered in splicing efficiency in S2 and S47 (S8 Fig), respectively. Therefore, these spontaneous suppressor strains may be not fully recovered in splicing efficiency for all the introns that were affected in the Fgprp4 mutant. FgPRP4 must be important for proper regulation of intron splicing and expression of various genes involved in different biological processes.
To identify suppressor mutations, we sequenced 10 genes orthologous to the known components of the U4/U6 and U4/U6.U5 tri-snRNPs [2,3] amplified from 18 type I and 2 type II suppressor strains (Table 2). Whereas 11 of them had no mutations in these candidate genes, 9 type I suppressor strains had mutations in the FgPRP6 (FGSG_10242), FgPRP31 (FGSG_01299), FgPRP8 (FGSG_02536), and FgBRR2 (FGSG_01210) genes (Table 2). However, we failed to identify mutations in the rest 11 suppressor strains, suggesting that suppressor mutations may occur in other FgPrp4-targets or tri-snRNP components.
In suppressor S30, the G308E mutation was identified in the FgBRR2 gene (FGSG_01210). G308 is located in the long N-terminal region of Brr2 that has no known motifs but is required for the in vitro helicase activity [22]. Sequence alignment showed that G308 of FgBrr2 is at the same position with A311 of Spp41 (Fig 5A). In S. pombe, the A311E mutation in spp41 is known to suppress the temperature sensitive prp4-73 mutant [3]. Therefore, the G308E and A311E mutations that changed a neutral amino acid residue (G or A) to a charged one (E) must have similar effects on the structure and function of the Brr2 helicase.
Two suppressor mutations, D1153G and E1429K (Table 2) were identified in FGSG_02536 that is orthologous to S. cerevisiae PRP8 and S. pombe spp42. Sequence alignment revealed that both D1153 and E1429 are well conserved in Prp8 orthologs (Fig 5B). D1153 of FgPrp8 is at the same position with D1192 of yeast Prp8, which is in the RT fingers/palm domain. In S. cerevisiae, the D1192G mutation is a suppressor of the U4-cs1 (cold sensitive) mutant that is defective in U4/U6 unwinding due to a mutation in the U4 RNA [23]. In F. graminearum, the same D to G mutation in FgPRP8 suppressed the growth defects of Fgprp4, further indicating the role of FgPrp4 in the activation of B-complex and U4-U6 unwinding.
The E1429K mutation occurs in the linker region (Fig 5B). Structural modeling based on yeast Prp8 showed that E1429 and E1412 (= E1450 of yeast Prp8) of FgPrp8 are in the same α-helix that is involved in the formation of the catalytic cavity binding to pre-mRNA (boxed in Fig 5C). E1429K mutation in FgPrp8 may have similar effects with E1450K mutation in yeast on the interaction of Prp8 with the RNA catalytic core.
Four suppressor strains had mutations in the ortholog of S. cerevisiae PRP6 (= Prp1 of S. pombe). The FgPrp6 protein has an N-terminal PRP6_N domain and 19 tetratricopeptide repeats (TPRs). Whereas strains S39 and S46 had the same △E308 mutation, suppressor strains S47 and S22 had R230 changed to H and C, respectively (Fig 6A). In humans, five hPrp4-phosphorylation sites have been identified in the linker region of hPrp6 between the PRP6_N domain and TPR repeats [11]. Sequence alignment showed that two of them, T252 and T261, are conserved in FgPrp6 and its orthologs from other filamentous fungi (Fig 6A). Whereas R230 is in the linker region, E308 is in the first TPR repeat and not far away from the conserved Prp4-phosphorylation sites (Fig 6A). The R230C/H and △E308 mutations may have similar effects on FgPrp6 functions as phosphorylation by FgPrp4 in F. graminearum.
Arginine methylation is known to affect the nucleocytoplasmic localization of the hnRNP protein A2 [24] and the RNA helicase A [25]. The suppressor mutation in site R230H/C of FgPrp6 is located in a putative non-GAR methylarginine motif GXXR [26,27] that is conserved between FgPrp6 orthologs from filamentous fungi, humans, and S. pombe (Fig 6A). This non-GAR methylarginine motif is not conserved in Prp6 of S. cerevisiae (Fig 6A), which lacks Prp4 kinase. To determine whether mutations at R230 will interfere with its subcellular localization, we generated the FgPRP6- and FgPRP6R230H-GFP fusion constructs and transformed them into the wild-type strain. In the resulting transformants, GFP signals were mainly observed in the nucleus (Fig 6B). No obvious difference was observed in the strength or localization of GFP signals between the FgPRP6- and FgPRP6R230H-GFP transformants (Fig 6B). Therefore, R230H mutation had no effect on the localization of FgPrp6.
In suppressor mutant S17, the L532P mutation was identified in FgPRP31 (FGSG_01299). Interestingly, the non-sense mutation at R464 in suppressor S2 resulted in the truncation of the C-terminal 130 aa residues of FgPrp31, including L532 (Fig 7A). In RNA-seq data with suppressor strain S2, the FgPRP31 transcripts also had the G1392A mutation that caused the change of R464 (CGA) to a stop codon (UGA). Whereas the NOSIC and NOP domains (spanning the 93–368 aa region) are well-conserved and known to interact with Prp6 and the U4 RNA, the R464* and L532P suppressor mutations occurred in or after the less-conserved PRP31_C (Fig 7A).
Although the exact phosphorylation site or function is not clear, nine hPrp4-phosphorylation sites have been identified in hPrp31 by phosphoproteomics analysis [11]. Five of them, S485, S486, S520, S521, and T525 are conserved in FgPrp31 (Fig 7A). The nonsense mutation at R464 eliminated all of these putative Prp4-phosphorylation sites in FgPrp31. These data suggest that the C-terminal region of FgPrp31 likely plays a negative role in B-complex activation, possibly by inhibitory binding to its own N-terminal region or other Prp31-interacting proteins. Phosphorylation by FgPrp4 in the phosphorylation or modulation region may result in conformational changes and release the inhibitory self-binding.
We selected FgPRP31 for further characterization because of interesting features of the R464* truncation mutation. The geneticin resistant FgPRP31R464* and FgPRP31L532P gene replacement constructs were generated and co-transformed with the hygromycin-resistant FgPRP4 knockout cassette [18] into protoplasts of PH-1. Transformants resistant to both hygromycin and geneticin were screened by PCR for deletion of FgPRP4. In the resulting Fgprp4 mutants, the replacement of endogenous FgPRP31 with the FgPRP31R464* or FgPRP31L532P mutant allele was confirmed by PCR amplification and sequencing analysis. Similar to suppressor strains S2 and S17, the Fgprp4/FgPRP31R464* and Fgprp4/FgPRP31L532P transformants were normal in growth rate and sexual reproduction (Fig 7B) but still defective in plant infection (Fig 7C). Therefore, the R464* and L532P mutations are directly responsible for the recovery of growth rate in suppressor strains S2 and S17.
Because mutations were not identified in 11 type I suppressors that were analyzed, we selected suppressor S3 for whole genome sequencing analysis. After aligning the sequences of S3 (approximately 50 coverage) generated by Illumina Hi-seq with the genome sequence of PH-1, the C to A mutation at 305 was identified in FGSG_11793, which is orthologous to yeast MSL1, a U2B component of the U2 SNP [28]. The resulting Q to K mutation occurred at the Q86 residue that is conserved in MSL1 orthologs from filamentous fungi (S9 Fig). The Q86K mutation is in the predicted RNA recognition motif (RRM) domain (S9 Fig) and will likely affect its interaction with pre-mRNA or other components of sn-RNP during B-complex activation.
To determine whether FgPrp4 kinase itself is activated by phosphorylation, we generated the FgPRP4-3xFLAG construct and transformed it into PH-1. The resulting transformant FPF1 (Table 1) had the expected 88-KD Prp4-3xFLAG fusion protein band on western blots detected with the anti-FLAG antibody (Fig 8A). To assay FgPrp4 phosphorylation, total proteins isolated from the FgPRP4-3xFLAG transformants were incubated with anti-FLAG beads. Proteins eluted from anti-FLAG beads were treated with trypsin and enriched for phosphopeptides with the PolyMac approach as described [29]. The resulting peptides were analyzed by MALDI-TOF/TOF MS analysis. In three independent biological replicates, phosphorylation of S289 was detected in the peptide AAS289PASTLP of FgPrp4.
Because S289 was the only phosphorylation site identified in FgPRP4, we generated the FgPRP4S289A-GFP mutant allele and transformed it into the Fgprp4 mutant. The resulting Fgprp4/FgPRP4S289A transformant FPA2 (Table 1) had GFP signals in the nucleus (Fig 8B) but, like the original mutant, displayed severe growth and conidiation defects (Fig 8C), indicating that FgPRP4S289A failed to complement the Fgprp4 mutant in growth and conidiation. Therefore, phosphorylation at S289 is essential for FgPrp4 functions. It is possible that FgPrp4 is activated by phosphorylation at S289 by itself or other protein kinases for spliceosome activation in F. graminearum.
Among all the spliceosome components, Prp4 is the only protein kinase and it is conserved in humans, plants, and S. pombe [21,30]. Interestingly, all the sequenced Saccharomycotina species except Y. lipolytica lack a distinct Prp4 ortholog (S1 Fig). Whereas S. cerevisiae has only 376 introns, Y. lipolytica, a dimorphic yeast, has over 1,500 introns [31]. Because lower fungi such as Rhizopus oryzae and Batrachochytrium dendrobatidis have this kinase gene, Saccharomycotina species may have lost the PRP4 ortholog after massive intron loss during evolution [17,32].
In F. graminearum, the Fgprp4 mutant was viable although it had severe growth defects. To our knowledge, null mutants of the Prp4 kinase have not been reported in any other eukaryotic organisms except in N. crassa, in which the putative stk-57 mutant deleted of the PRP4 ortholog (NCU10853) generated in a large-scale protein kinase gene knockout study had no defects in hyphal growth, asexual reproduction, and sexual development but could not be purified by isolation of ascospores [33]. Because of its striking difference from the Fgprp4 mutant, we obtained the putative stk-57 mutant (stock number FGSC17970) from Fungal Genetics Stock Center (www.fgsc.net) and conducted PCR analyses. Both the STK-57 kinase gene and the hygromycin-resistant marker could be amplified in this putative knockout mutant (S10 Fig). Furthermore, we failed to amplify any PCR products with the anchor primers that were designed to amplify the upstream and downstream fragments resulted from gene replacement events (S10 Fig). These results indicate that this putative stk-57 knockout mutant was not a true mutant but likely an ectopic transformant.
Considering the fact that many essential genes have introns in F. graminearum, the viability of Fgprp4 mutant suggests that deletion of FgPRP4 does not block spliceosome activation and intron splicing. This hypothesis was confirmed by RNA-seq data. FgPrp4 kinase is not essential for RNA splicing but it regulates splicing efficiency. Consistent with its pleiotropic defects, splicing efficiency of introns in over 39% of the F. graminearum genes involved in various physiological and developmental processes were reduced significantly in the Fgprp4 mutant. Although no unique 5’ss, BP, and 3’ss sequences were identified in introns affected in the mutant, we noticed that splicing of larger introns with longer distance between the BP and 5’ss sequences is more sensitive to FgPRP4 deletion. In addition, intron splicing efficiency in the Fgprp4 mutant was not related to predicted gene functions. In fact, it is often that the splicing efficiency was only affected by FgPRP4 deletion for some but not all the introns in the genes with multiple introns in F. graminearum. Furthermore, we noticed that the position of introns in mRNA has no effects on intron splicing affected by FgPRP4 deletion. The budding yeast has approximately 300 genes with small introns although it lacks the Prp4 ortholog. Among 136 of them with orthologs in F. graminearum, only two of them had normal intron splicing efficiency in the Fgprp4 mutant. Therefore, the function and evolutional relationship of genes have no effect on whether intron splicing was affected or not by deletion of FgPRP4 in F. graminearum.
The Fgprp4 mutant was unstable and produced fast growing sectors. Our RNA-seq and RT-PCR results showed that deletion of FgPRP4 resulted in splicing defects in a number of genes important for DNA recombination and repairing, which may be responsible for the production of spontaneous suppressors. Among the 49 sectors we isolated, over 60% were fully recovered in the growth rate and colony morphology, the others grew faster than the original mutant but still slower than the wild type, and may had additional defects in aerial hyphal growth or colony pigmentation, indicating that suppressor mutations may occur in different genes. Even for spontaneous suppressors with the wild-type growth rate and colony morphology, none of them were normal in all the other phenotypes assayed, including virulence, conidiation, and sexual reproduction. Therefore, although suppressor mutations suppressed the defects of Fgprp4 mutant in vegetative growth, they failed to rescue all the other defects associated with FgPRP4 deletion. This observation may explain why F. graminearum still keep the FgPRP4 gene although suppressor mutations occur at such a high frequency in its deletion mutant.
Prp6, Prp8, Prp31, and Brr2 are key components of the U4/U6-U5 tri-snRNP [34,35] (Fig 9). Suppressor mutations identified in these genes may have similar effects with phosphorylation by Prp4 on the interactions among these tri-snRNP components. In S. pombe, suppressor mutations of the prp4-73ts mutant have been identified in the Brr2 (Spp41) and Prp8 (Spp42) orthologs [3,36]. The G308E mutation of FgBrr2 is the same to A311E of Spp41, changing from a neutral, non-polar residue (G or A) to an acidic, polar one (E). G308 is in the N-terminal region of Brr2 required for the in vitro helicase activity [22]. The N-terminal region, RecA-1, and RecA-2 of hBrr2 also may be involved in interacting with hPrp6 [9]. Therefore, G308E and A311E mutations may have similar effects on Brr2 helicase activity or its interaction with Prp6 to suppress prp4 mutant.
For the G2248D suppressor mutation characterized in spp42 [36], we failed to identify mutations at the same residue in FgPRP8. However, D1153G, one of the two suppressor mutations identified in FgPRP8, is the same to D1192G of PRP8 that could suppress the yeast U4-cs1 mutant [23]. When modeled after the crystal structure of yeast Prp8885-2413, D1153 is at the tip of the exposed loop following the RTα12. Interestingly, this region of Prp8 also contains other suppressor mutations of U4-cs1 and suppressor mutations of brr2-1 [37,38], suggesting its involvement in interaction with Brr2 and other proteins or RNA. The D to E mutation may affect the interaction of Prp8 with other tri-snRNP components. Nevertheless, the D1153E mutation in FgPRP8 suppressed the Fgprp4 mutant, indicating that FgPrp4 may play a critical role in U4/U6 unwinding by affecting the interactions between different tri-snRNP components.
Like D1153G, E1429K did not change the overall structure of FgPrp8. In yeast Prp8, the E1450K mutation suppresses the 3’ss mutation [38,39], indicating its involvement in RNA binding. In FgPrp8, E1429 and E1412 (= E1450 of yeast Prp8) are in the same α-helix that is involved in the formation of the catalytic cavity [38]. Because they are in the same cleft and have the same E to K change, the E1429K and E1450K mutations may have similar effects on the catalytic cavity of tri-snRNP and B-complex activation.
In S. pombe, suppressor mutations of prp4-73ts mutant have not been reported in Prp6. In humans, phosphorylation of Prp6 that occurs after the tri-snRNP being integrated into the B-complex may release the inhibition of Brr2 by Prp8 and is important for spliceosomal B-complex activation [3,11]. In this study, we identified four suppressor strains with mutations in the PRP6 ortholog. In RNA-seq data with suppressor strains, the transcripts of FgPRP6 had the C690U mutation in suppressor strain S47, which is consistent with the FgPRP6R230H mutation detected by DNA sequencing analysis. Because both R230 and E308 are in the proximity of T252 and T261, two conserved Prp4-phosphorylation sites, it is likely that mutations at these two residues have similar effects with phosphorylation by FgPrp4 on FgPrp6 functions.
Among 20 suppressor mutants that were sequenced, we only identified suppressor mutations in nine of them. For the other 11 suppressor strains, none of them had mutations in the candidate tri-snRNP components that were selected for sequencing analysis. Suppressor mutations likely occur in other tri-snRNP components, such as orthologs of Snu13, Snu66, Snu114, Cpr1, Sad1, and Dib1 [40,41]. However, in suppressor S3, the Q86K mutation in FgMSL1 was identified by whole genome sequencing. To our knowledge, suppressor mutations in MSL1 orthologs have not been reported in other organisms. In S. cerevisiae, MSL1 is an nonessential gene that encodes a U2 snRNP-specific protein [42]. In Drosophila, the U2B protein is part of a protein network that is important for splicing accuracy and efficiency [43]. In F. graminearum, the Q86K mutation suppressed the defects of the FgPrp4 mutant in growth. It is possible that this mutation in FgMSL1 may affect the U2-U6 coupling and complex B activation.
Unlike Prp4 in S. pombe, FgPrp4 has a long N-terminal SR-rich region/domain that is conserved in metazoan Prp4 kinases [44]. Expression of the FgPRP4ΔN310-GFP allele failed to complement the Fgprp4 mutant and GFP signals became localized to the cytoplasm instead of the nucleus, indicating that this N-terminal region is important for the function and subcellular localization of FgPrp4 in F. graminearum. This region contains two putative NLS sequences conserved among the FgPrp4 orthologs. To our knowledge, the NLS sequence responsible for the localization of Prp4 kinases to the nucleus has not been characterized. It will be important to further characterize the function of these two NLS sequences in the N-terminal region of FgPrp4. In humans, a number of other splicing factors, such as SRSF1 and ASF/SF2, also have the N-terminal RS-rich region that may be phosphorylated by SR protein kinases such as CLK and SRPK [45]. In this study, we showed that S289 is a phosphorylation site important for FgPrp4 functions. Because auto-phosphorylation of Prp4 has been reported in humans [44], it will be important to determine whether phosphorylation of S289 is catalyzed by FgPrp4 itself or other protein kinases.
The wild-type strain PH-1, Fgprp4 mutants, and all the transformants generated in this study were routinely cultured on potato dextrose agar (PDA) [46] or complete medium (CM) at 25°C and preserved in 20% glycerol at -80°C [47]. Growth rate, conidiation, and sexual reproduction were assayed as described [18]. Protoplasts prepared from 12 h germlings were used for PEG-mediated transformation [48]. For infection assays, flowering wheat heads of cultivar XiaoYan 22 were drop-inoculated with 10 μl of conidium suspensions (2.0×105 conidia/ml) as described [49]. Scab symptoms were examined 14 days post-inoculation (dpi).
RNA was isolated with the TRIzol reagent (Invitrogen) from conidia, 12 h germlings, perithecia at 10 days post-fertilization, and infected wheat heads collected at 7 dpi as described [50,51]. For qRT-PCR analysis, first-strand cDNA was synthesized with the Fermentas 1st cDNA synthesis kit (Hanover) following the instructions provided by the manufacturer. The β-tubulin gene FGSG_06611 of F. graminearum was used as the internal control [52]. The mean and standard deviation were calculated with data from three biological replicates.
For complementation assays, the FgPPR4 gene was cloned into pFL2 [48] by gap repair [53]. The resulting FgPRP4 construct carrying the geneticin-resistant marker was transformed into the Fgprp4 mutant FP1. The same gap repair approach was used to generate the FgPRP4-GFP, Fgprp4/FgPRP4S289A, and FgPRP4ΔN310-GFP construct with primers showed in S3 Table. The resulting constructs were confirmed by sequencing analysis and transformed into protoplasts of FP1 to generate the complemented transformants.
Fast-growing sectors of the Fgprp4 mutant were transferred with sterile toothpicks to fresh PDA plates. After single spore isolation, each sub-cultures of spontaneous suppressors were assayed for defects in growth, differentiation, and plant infection [18]. To identify suppressor mutations in the candidate tri-snRNP components, PCR products amplified with primers listed in S3 Table were sequenced at BGI-Beijing. Mutation sites were identified by sequence alignment and confirmed by re-sequencing analysis.
Vegetative hyphae of PH-1, Fgprp4 mutant FP1, S2, and S47 were harvested from 9-day-old PDA colonies formed over sterile dialysis membrane and used for RNA isolation with the TRIzol Reagent (Life technologies, US). Poly(A) mRNA was isolated with the Oligotex mRNA mini kit (Qiagen, Germany). Library construction and sequencing with an Illumina Hiseq 2000 sequencer were performed at Shanghai Biotechnology Corporation (Shanghai, China). For each sample, at least 25 Mb high-quality reads were obtained. The resulting RNA-seq reads were mapped onto the reference genome of F. graminearum strain PH-1 with the Tophat2 program (ccb.jhu.edu/software/tophat/index.shtml). To filter out weakly expressed genes, only genes with a minimum expression level of 1 count per million were included in the analysis. The intron retention level was defined as the number of reads that aligned to the predicted intron divided by the number of reads aligned to the corresponding transcript.
RNA was isolated with the TRIzol Reagent (Life technologies) from vegetative hyphae of PH-1 and the Fgprp4 mutant. The Fermentas 1st cDNA synthesis kit (Hanover, MD, USA) was used to synthesize the first-strand cDNA following the instruction provided by the manufacturer. The primers used for PCR amplification of the FgPHR1 (FGSG_00797), FgNHP6A (FGSG_00385), and FgEAF1 (FGSG_05512) genes were listed in S3 Table.
The FgPRP4-3xFLAG fusion construct was generated by the gap repair approach by co-transformation of the full-length FgPRP4 fragment and XhoI-digested pFL7 into yeast strain XK1-25 [48]. The resulting fusion construct rescued from Trp+yeast transformants was confirmed by sequence analysis and transformed into the wide-type strain PH-1. Geneticin-resistant transformants expressing the fusion constructs were identified by PCR and confirmed by western blot analysis with the anti-FLAG antibody (Sigma). Total proteins isolated from the resulting transformant were incubated with the anti-FLAG M2 beads (Sigma) as described [54]. Proteins eluted from anti-FLAG beads were digested with proteomics grade trypsin (Sigma) and enriched for phosphopeptides with the polymer-based metal ion affinity capture (PolyMAC) as described [29]. Phosphopeptides enriched by PolyMac were analyzed with an ABI 4800 MALDI-TOF/TOF mass spectrometer. Proteome Discoverer (version 1.0; Thermo Fisher Scientific) was used to identify peptide sequences and phosphorylation sites as described [29].
Multiple alignments of protein sequences were constructed with COBALT (www.ncbi.nlm.nih.gov/tools/cobalt) and manually modified. The analysis of type I and type II functional divergence was performed with the Diverge 3.0 software [55]. Maximum likelihood (ML) phylogenies were estimated with PhyML3.0 assuming 8 categories of γ-distributed substitution rate and SPRs algorithms. For phylogeny of protein sequences, the bestfit model for each datasets selected by ProtTest2.4 [56] was used. The reliability of internal branches was evaluated based on SH-aLRT supports. The 3D-structural model of FgPrp8 was modeled after that of Prp8 in S. cerevisiae (PDB ID: 3SBT and 2OG4) and displayed with Chimera 1.8.1 [57].
To identify mutations in suppressor S3, DNA isolated from 12 h germlings were sequenced by Illumina platform at Shanghai Biotechnology Corporation (Shanghai, China) to 50x coverage with pair-end libraries. The sequence reads were mapped onto reference genome of strain PH-1 by using Bowtie 2.23 [58]. Mutation sites were called by SAMtools with the default parameters. Annotation of the mutation sites was performed with Variant Effect Predictor (VEP) [59].
RNA-seq data generated in this study were deposited in the NCBI Sequence Read Archive database under the accession code of SRP062439.
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10.1371/journal.pcbi.1004091 | Protein Sectors: Statistical Coupling Analysis versus Conservation | Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.
| Statistical analyses of alignments of evolutionarily related protein sequences have been proposed as a method for obtaining information about protein structure and function. One such method, called statistical coupling analysis, identifies patterns of correlated mutations and uses them to find groups of coevolving residues. These groups, called protein sectors, have been reported to be relevant for various functional aspects, such as enzymatic efficiency, protein stability, or allostery. Here, we reanalyze existing data in order to assess the relative importance of two factors contributing to statistical coupling analysis, namely single-site amino acid frequencies and pairwise correlations. Although correlations have been shown to be informative in other studies, we point out that in existing large-scale data that has been analyzed with statistical coupling analysis, single-site statistics seems to be a dominating factor.
| A fundamental question in biology is the relation between the amino acid sequence of a protein and its function and three-dimensional structure. Given the rapid growth in the sequence data available from many organisms, it has become possible to use statistical sequence analysis to approach this question. Based on sequence similarity, protein sequences can be grouped into families thought to share common ancestry; the proteins in such a family typically perform related functions and fold into similar structures [1, 2]. It has been shown in many studies that a statistical analysis of a multiple sequence alignment (MSA) corresponding to a given protein family can be used to find amino acids that control different aspects of a protein’s function or structure.
A basic statistical quantity that can be calculated for a multiple sequence alignment is the distribution of amino acids at each site. In particular, the level of sequence conservation at each site is of biological relevance, since it is expected that conservation is low in the absence of selective pressures. For this reason, conservation has long been used to predict which parts of a protein are most likely to be functionally significant [3–7].
More recently, the availability of large sets of protein sequences has made it possible to also estimate higher-order statistics, such as the correlations between the amino acids found at each pair of sequence positions. In a number of examples, these statistics have been shown to contain information about the structure and function of proteins [8–12]. One way in which pairwise correlations might arise is for a deleterious mutation at a given position to be compensated by a mutation at a different position. This can yield a scenario in which the two individual mutations are relatively rare, but the combination of both is common in natural proteins.
Statistical coupling analysis (SCA) was introduced by Lockless and Ranganathan in 1999 as a way to infer energetic interactions within a protein from a statistical analysis of a multiple sequence alignment [13]. The authors compared the statistics of an alignment of PDZ domain sequences to measurements of the binding affinity between a particular member of the alignment (PSD95pdz3) and its cognate ligand. The statistical analysis assumed that the frequencies of mutations obey a Boltzmann distribution as a function of binding free energy, allowing estimation of the binding affinity by ΔGi ∼ log fi, where fi is the frequency of an amino acid type at a given site in the alignment. By conditioning on amino acid type at a second site, they calculated the amount by which the effect of a mutation at one site changed depending on the amino acid present at the second site: ΔΔGi∣j = ΔGi∣j − ΔGi ≡ ΔΔGstat. This gave an estimate for the effective coupling between the sites.
The assumptions behind the original formulation of SCA are likely to be violated, since the selective pressures acting on a protein are more complex than simply maximizing binding to a ligand. Despite this, the method seemed to be effective. In the original paper [13], mutant cycle analysis was used to measure ΔΔGbinding, the amount by which the effect of a given mutation on ligand binding affinity of PSD95pdz3 changes when the mutation occurs on a background containing a second mutation. This can be written as ΔΔGbinding = ΔGi∣j − ΔGi, where now ΔG represents a change in the physical free energy as opposed to a statistical construct. The quantity ΔΔGbinding was observed to be well correlated with the statistically-calculated ΔΔGstat. The set of residues identified by SCA to be coupled with a particular site known to be important for binding specificity of the PDZ domain was found to physically connect distal functional sites of the protein [13]. This led to the suggestion that these residues may mediate an allosteric response. Experimental evidence later showed that indeed some of the residues identified by SCA participate in allostery [14–17]. Moreover, in a different study, a large fraction of the artificial WW domains built by conserving the pattern of statistical couplings calculated by SCA were observed to be functional, while sequences built to conserve single-site statistics alone were not [8, 9].
Motivated by these observations, Halabi et al. reformulated SCA in purely statistical terms, avoiding the assumptions related to energetics [18]. The reformulation amounted to a particular way of combining correlations with conservation. The basic idea was to multiply each element of the covariance matrix Cij by a product ϕiϕj, yielding the “SCA matrix” C ˜ ij = ϕ i ϕ j C ij. The “positional weights” ϕi were a function of the frequency fi of the most prevalent amino acid at each position, and were roughly given by ϕi ∼ log[fi/(1 − fi)]. This particular form was chosen to reproduce the results from the original formulation of SCA [18, 19]. In subsequent work regarding SCA, several variations on this basic idea were used; all of these yield similar though not identical results and are described more precisely in Methods and S1 Text.
Running either the original or the reformulated analysis on several examples [8–9, 13, 16–18, 20], it was noticed that the resulting SCA matrix had an approximate block structure. In analogy to previous work in finance, Halabi et al. analyzed this structure by looking at the top eigenvectors of the SCA matrix [18, 21]. The corresponding groups of residues were called “protein sectors” because similar clusters observed in the correlations of stock prices were found to correspond to financial sectors. Experiments found that mutating residues in distinct sectors specifically affected different phenotypes of the protein [18], leading to the suggestion that each SCA sector might comprise a group of amino acids that control a particular phenotype.
It is important to note that there are several subtly different meanings that have been attributed to protein sectors (see Table 1). The description outlined above defines protein sectors as the results of a statistical analysis of a multiple sequence alignment. This definition depends on the statistical method employed; it would, for example, depend on the choice of positional weights in the case of SCA, or on the precise thresholds and methods used for clustering. To distinguish this from other meanings, we will call these statistical sectors (or SCA sectors when the statistical method is SCA).
The sectors identified by SCA have also been given an evolutionary interpretation [18, 20, 22], based on the fact that they are defined as groups of residues whose mutations are correlated in an alignment, and the sequences in the alignment are likely to be evolutionarily related. However, this argument is insufficient to prove the evolutionary nature of the statistical sectors, given that their precise composition is dependent on the statistical method employed [23]. Thus it is difficult to assess to what extent the sector’s composition is actually related to the evolutionary process itself, as opposed to the choice of the statistical method. Strikingly, Halabi et al. showed that for an alignment of serine proteases, one of the sectors can be used to distinguish between vertebrates and invertebrates, suggesting that indeed an evolutionary interpretation may be appropriate [18]. However, before concluding that in general SCA sectors have an evolutionary interpretation, it would be important to extend these studies to different alignments. An alternative, more direct, approach would be to perform artificial evolution experiments to check whether the SCA sectors maintain their integrity under strong selection, or whether new sectors can be created in this way. In addition, such experiments would provide data on the evolutionary dynamics of proteins, and thus help to define more precisely the notion of evolutionary sectors.
Another surprising property of the groups of residues identified by SCA is that they usually form contiguous structures in the folded protein, although they are not contiguous in sequence [18, 20, 22, 24, 25]. This suggests the notion of structural sectors, groups of residues having different physical properties compared to their surroundings. An experimental test for such inhomogeneities inside proteins could employ NMR spectroscopy to follow the dynamics of specific atoms while the protein is undergoing conformational change [14, 26]. In addition, analyzing room-temperature X-ray diffraction data could shed light on residues with coupled mobility or increased fluctuations in an ensemble of structures [27, 28] (Doeke Hekstra, personal communication). Alternatively, this kind of experiments could be done in silico using for example molecular dynamics simulations to identify correlated motions in the protein [29] (Olivier Rivoire, personal communication).
Finally, as mentioned above, a number of mutational studies have suggested yet another interpretation of the sectors identified by SCA as functional sectors, groups of amino acids that cooperate to control certain phenotypic traits of a protein, such as binding affinity [13, 18, 20, 25], denaturation temperature [18], or allosteric behavior [14–17, 24]. It is this aspect of the sectors that has been most emphasized in the literature.
In the language we just introduced, we can say that there is some data suggesting that SCA can identify groups of residues that act as evolutionary, structural, and functional sectors in a protein. It is important to note that these aspects can exist independently of one another. As an example, the existence of a physical inhomogeneity overlapping the statistical sector positions would support the idea that SCA can identify structural sectors, but would provide no guarantee that these also have an associated phenotype. For this reason, independent experimental verification is needed to support each of these claims.
We focus here on the experimental evidence supporting the hypothesis that SCA sectors act as functional sectors of proteins [8–9, 18, 20, 24, 25]. We note that with the exception of Halabi et al. [18], this data refers to proteins in which a single SCA sector was identified, and we show that in this case, within statistical uncertainties, a method based on sequence conservation can identify functional residues as well as SCA. We also give a simple mathematical argument describing why this might happen.
Given that conservation information is explicitly used in calculating the SCA matrix, it is not surprising that SCA sectors are related to conservation. However, what we show here is that conservation dominates the SCA calculations in the single-sector case; thus, in order to establish whether the functional significance of SCA sectors is more than what is expected from single-site statistics alone, experiments need to focus on the examples where SCA identifies several sectors. The analysis of serine proteases described above provides such a study [18], but it is essential to have more data for different protein families to assess the robustness and generality of these observations.
We analyze existing experimental datasets to compare the functional significance of SCA residues to that of conserved residues. Some of these datasets (PDZ, DHFR, and the voltage-sensing domains of potassium channels) have already been analyzed using SCA; one of them (lacI) has not. We show that in all these cases, conservation identifies functional positions just as effectively as SCA. This holds true for a wide range of choices of thresholds used to define conserved and functional residues, respectively.
There have been several versions of the SCA approach that have been used in the literature. Indeed, each of the three datasets mentioned above for which SCA has been applied was analyzed using a different variation of the method. To avoid ambiguities, here we use a uniform method for all the alignments (see Methods for details). While Halabi et al. explicitly ignored the top eigenvector based on an analogy to finance [18], here we focus only on the top eigenvector. The reason for this is that, besides Halabi et al., all other published studies related to SCA have included this mode in their analysis [20, 24, 25]. The case of DHFR is somewhat special: Reynolds et al. define the single sector using not only the top eigenvector, but the top five [24]. As we discuss below, the results from that paper are, however, not significantly changed if the sector is defined based only on the top eigenvector. The alignments used were generated using the HHblits software [30] with a consistent set of options (see Methods for details).
The definition of conservation we use is based on the relative entropy (Kullback-Leibler divergence) [18],
D i = ∑ a f i ( a ) log f i ( a ) q ( a ) , (1)
where fi(a) is the frequency at which amino acid a occurs in column i of the multiple sequence alignment and q(a) is the background frequency for amino acid a. We use the same background frequencies as employed in SCA, which were calculated by Lockless and Ranganathan by averaging over a large protein database [13]. Other common definitions for conservation, such as the frequency of the most prevalent amino acid at a given position, tend to be highly correlated with Di described above.
It is important to note that the qualitative results are unchanged regardless which version of SCA, which definition for conservation, or which alignments are used (see S1 Text for details). This work was motivated by the empirical observation that for many alignments the components of the top eigenvector correlate strongly with the diagonal elements of the SCA matrix (see Fig. 1A). The values of the diagonal elements can be calculated in terms of single-site statistics, raising the question whether correlations are needed to find the positions comprising the top sector. In fact, the components of the top eigenvector are also well-correlated with the conservation Di defined above (see Fig. 1B). Note that these observations are not particularly surprising, given that the SCA matrix is weighted by quantities related to conservation. However, they also raise the question whether the observed functional significance of SCA sectors [24, 25, 31] could be due to conservation instead of correlations.
The ability of SCA to identify residues that are important for protein function was recently tested in a high-throughput experiment involving a PDZ domain [25]. Each amino acid of the PSD95pdz3 domain was mutated to all 19 alternatives and the binding affinity of the resulting mutants to the PSD95pdz3 cognate ligand was measured. The measurement involved a bacterial two-hybrid system in which the PDZ domain was fused to the DNA-binding domain of the λ-cI repressor, while the ligand was fused to the α subunit of the E. coli RNA polymerase. This was used to control expression of GFP, which allowed the binding affinity between PSD95pdz3 and its ligand to be estimated using fluorescence-activated cell sorting (FACS). In order to quantify the sensitivity to mutations at a given site, the mutational effects on binding affinity were averaged over all 20 possible amino acids at that site. While mutations at most sites were found to have a negligible effect on ligand binding, 20 sites were identified where mutations had a significant deleterious effect [25].
The sector identified by SCA according to the methodology outlined above was found to indeed contain residues that are more likely to have functional significance than randomly chosen positions in the protein: 14 of the 21 sector residues are functionally significant, or 67%, compared to 25% for the entire protein (see Fig. 2A). This is statistically-significant according to a Fisher exact test (one-tailed p = 1×10−6), and this result is robust to changing the threshold used to define the sector. This mirrors the results from McLaughlin Jr. et al. [25], obtained there with a different alignment constructed using a structural alignment algorithm [13].
There is another way of assessing the functional relevance of the sector positions that avoids making a sharp distinction between functional and non-functional residues [25]. The functional effects of mutations at all the positions in the domain were used to define a background distribution showing how likely an effect of a given magnitude was. If the sector is able to identify functionally-relevant positions, then the distribution of functional effects restricted to the sector positions should differ from this background distribution. Fig. 3A shows the comparison for the PDZ experiment. A two-sample Mann-Whitney U test [32] finds that indeed sector positions have a statistically-significant distribution of functional effects compared to all residues.
We now test whether we could have obtained similar results by considering only sequence conservation. Indeed, although the 21 most conserved residues are different from the 21 residues identified by SCA (only about 60% are shared), the fraction of these residues that is functionally significant is the same (see Fig. 2B). The histogram of functional effects is also essentially the same between SCA sector residues and conserved residues (see Fig. 3B), and in fact a Mann-Whitney U test confirms that the difference is not statistically significant.
McLaughlin Jr. et al. performed a similar analysis and obtained similar histograms as our Fig. 3 (see Fig. 3a in their paper [25]). The reason why our results seem so different is that, due to an error, the top histogram in Fig. 3a in McLaughlin Jr. et al. is missing the data for the five sector residues that do not have a significant mutational effect. These five sector residues are mentioned and taken into account in other parts of the paper by McLaughlin Jr. et al [25], for example in Table S6a in the supplementary information, but they do not appear in the histogram. Had they been included, the histograms for conserved residues and that for SCA sector residues would look almost identical, in agreement with our results.
We stress again that these results do not imply that correlations in protein alignments are not informative. Indeed, as mentioned in the introduction, experimental data on the creation of artificial WW domains showed that ignoring correlations leads to non-functional proteins, while proteins designed based on conservation-weighted correlations can often be functional [8]. Moreover, correlation information was used to provide quite accurate predictions for contact maps and three-dimensional structures of a variety of proteins [10–12]. This is not possible using single-site statistics alone. The question we are asking, however, is whether the particular way in which alignment correlations are used in SCA is more useful for predicting functional information than conservation. The answer seems to be negative for the case of PDZ.
All the observations reported above are qualitatively the same when using different alignments, including the alignment employed by McLaughlin Jr. et al. [25] and a Pfam alignment. The observations are also robust to varying the threshold used for defining the sector: in Fig. 4 we show a statistical comparison between the SCA sector and conserved residues calculated for various sizes of the sector.
Note that there are some potential caveats for the statistical tests we used. One assumption of both the Mann-Whitney U test and the χ2 test employed above is that the samples analyzed are independent. In our case, the samples are the mutational effects at different residues in a protein domain, which are unlikely to be independent. Designing a statistical test that overcomes this difficulty would require a detailed model of evolutionary dynamics that accurately describes the relation between the binding affinity of PSD95pdz3 to its cognate ligand, and the evolutionary information contained in a multiple sequence alignment. To our knowledge, there is unfortunately no unambiguous way of constructing such a model. Despite these issues, the analysis presented here suggests that, for the top sector, SCA is not significantly better than conservation at predicting functionally-important sites.
The case of dihydrofolate reductase (DHFR) [24] exhibits some interesting differences from PDZ. The experimental assay in this case involved perturbing the DHFR protein by attaching a light-sensitive domain (LOV2) between the atoms of the peptide bond immediately preceding each surface residue. The experiment used a folate auxotroph mutant of E. coli whose growth was rescued by a plasmid containing DHFR and thymidylate synthetase genes. The growth rate of the bacteria, which was measured with a high-throughput sequencing method, was shown to be approximately proportional to the catalytic efficiency of DHFR. The functional effect of each insertion of the LOV2 domain was measured by the difference in growth rates between lit and dark conditions. Out of the 61 measured surface sites, 14 were found to have a significant functional effect [24].
The effects of the insertion of the LOV2 domain are not localized on a single residue of the protein, which makes the analysis of the functional significance of the SCA sector positions more complicated in the case of DHFR. We follow here the method employed in the original study by Reynolds et al., which is to define a range around the insertion point within which a residue could conceivably feel the influence of the inserted domain [24]. More specifically, 4 Å spheres were centered on each of the four atoms forming the peptide bond broken by the insertion of LOV2, and any residues having at least one atom centered within any of these spheres was counted as “touching” the light-sensitive residue. The exact size of the cutoff is not important: we repeated the analysis with the cutoff set to 3 Å and 5 Å and obtained the same qualitative results.
Using the methodology described above, the SCA sector identified from the top eigenvector of the SCA matrix is found to “touch” all 14 of the functionally-significant LOV2 insertion sites. A set of conserved residues of the same size as the SCA sector “touches” 12 of the functionally-significant sites, and the difference is not statistically significant (see Fig. 5).
The results we obtained for DHFR are somewhat less robust than those obtained for the other proteins. For the HHblits DHFR alignment, the qualitative result was the same for all sector sizes we tested (see Fig. 6), but when using the Pfam alignment, very small SCA sectors (less than 10 residues) “touched” many more functionally-significant sites than sets of conserved residues of the same size. It is hard to verify whether this is a chance occurrence or a real phenomenon, and it is unclear whether the notion of a sector still makes sense when it comprises such a small part of the protein. One complication arises from the fact that highly conserved residues tend to cluster closer to the core of the protein (see Fig. 7), and thus are less likely to “touch” its surface.
Another dataset on which some work related to SCA has already been performed [31] was collected by Li-Smerin et al. [33]. In their experiments, 127 residues of the drk1 K+ channel were analyzed. For each of the mutants, voltage-activation curves were measured and fit to a two-state model, from which the difference in free energy between open and closed states ΔG0 was estimated.
Following Lee et al. [31], we identified a set of functional sites using the condition ∣ Δ G 0 mut − Δ G 0 wt ∣ ≥ 1 kcal/mol and we compared this set to the SCA sector and to the most conserved residues. As with the other datasets, SCA and conservation turned out to be just as good at identifying functional positions in the voltage-sensing domains of potassium channels (see Fig. 8).
A similar dataset to the PDZ dataset described above is available for the lac repressor protein in E. coli [34]. The authors used amber mutations and nonsense suppressor tRNAs to perform a comprehensive mutagenesis study of lacI. In this study, each one of 328 positions was mutated to 12 or 13 alternative amino acids, and the ability of each mutant protein to repress expression of the lac genes was tested. We summarized this data by recording, for each position, how many of the tested mutations had a significant effect on the phenotype of the lac repressor. We further identified “functionally-significant” sites by considering all the positions for which at least 8 substitutions resulted in loss of function. This threshold can be varied in the whole range from 1 to 10 without significantly altering the results.
As before, we observed a significant association between SCA sector positions and functional positions in the lac repressor; see Figs. 9A and 10A. However, again, the set of most conserved positions was equally good at predicting functional sites—see Figs. 9B and 10B. The results were not significantly affected by changing the size of the sector (see Fig. 11).
In the previous sections, we showed that a significant fraction of the sector positions obtained from the top eigenvector of the SCA matrix can be predicted from single-site statistics. This can be attributed to a strong correlation between the components of the top eigenvector and the square root of the diagonal elements of the SCA matrix (see Fig. 1A). In Halabi et al., the top eigenvector of the SCA matrix was ignored by analogy to finance, where this mode is a consequence of global trends in the market that affect all the stocks in the same way [18]. For proteins, the analogy is suggested to be with parts of sequences that are conserved due to phylogenetic relationships between the sequences in the alignment. Here we show that there is a different mechanism that can generate a spurious top eigenmode of the SCA matrix even when there are no phylogenetic connections between the sequences in the alignment. The main ingredient in this mechanism is a positive bias for the components of the SCA matrix.
Suppose that the underlying evolutionary process has no correlations between positions. Due to sampling noise, empirical correlations will typically be non-zero, and will fluctuate in a certain range. We denote the size of these fluctuations by x. The off-diagonal elements of the covariance matrix will have mean zero and variances of order C i j 2 ∼ C i i C j j x 2. In this case, the reason for the positive bias for the components of the SCA matrix is the fact that typically SCA takes the absolute value of the covariances (or some norm that produces only non-negative values; see S1 Text) [18, 24, 25]. This implies that the off-diagonal entries of this matrix will have expectation values of order x C i i C j j. Note that the positional weights can be absorbed into the diagonal elements Cii, so we do not write them out explicitly.
Even when the absolute value is not used, the correlation between the components of the top eigenmode of the SCA matrix and the diagonal elements of this matrix may also occur; this happens for example for the alignment in Smock et al. [20]. Simulations involving random alignments show that this phenomenon occurs whenever there are weak, uniform correlations between all the positions in an alignment. This can be the result of phylogenetic bias, but could have a different origin. This situation could be distinguished from the one above by looking at how the magnitude x of the off-diagonal correlations scales with alignment size; it should scale roughly like the inverse of the number of sequences if it is due to sampling noise, and be approximately constant otherwise (we thank D. Hekstra for this observation).
To try to explain these empirical observations, let us consider a simplified version of the SCA matrix:
M = ( Δ 1 d 1 d 2 x ⋯ d 1 d n x d 2 d 1 x Δ 2 ⋯ d 2 d n x ⋯ ⋯ ⋱ ⋯ d n d 1 x d n d 2 x ⋯ Δ n ) . (2)
Writing out the eigenvalue equation and performing some simple algebraic manipulations reveals that the eigenvector components vi corresponding to eigenvalue λ are related to the diagonal elements Δi by
Δ i v i ∝ λ − Δ i 1 + x . (3)
When the top eigenvalue is much larger than the other ones, which is usually the case when applying SCA to protein alignments, the following approximation holds:
λ top ≈ x 1 + x ∑ i Δ i . (4)
Empirically, this is observed to roughly match the results of SCA on real protein alignments. Given that λtop ≫ Δi, we can also write
v i , top ≈ α λ top × Δ i , (5)
where α is a normalization constant. This is the observed linear relation between the top eigenvector and the square root of the diagonal elements of the SCA matrix (Fig. 1A). Note that the SCA matrix for an alignment does not really have the highly symmetric form (2); instead it shows fluctuations in the off-diagonal components. Because of this, we cannot expect to see all the eigenvectors obey eq. (3). Indeed, for SCA matrices obtained from protein alignments, eq. (3) seems to hold only for the top eigenvector. A treatment of this problem in the framework of random matrix theory might help to clear up the expectations one should have for the top eigenvector of the SCA matrix, but such an analysis goes beyond the scope of this paper.
The simple argument described above suggests that, under certain conditions that seem to hold in the cases where SCA has been applied, the top eigenvector of the SCA matrix is indeed related to conservation, and is largely independent of correlations between positions. This does not mean that there is no information contained in this top mode, but does imply that most of this information can be obtained by looking at single-site statistics alone.
Note again that in our derivation the origin of the off-diagonal entries is not specified. While we showed that they can be a simple artifact of sampling noise, they could also be partly due to a non-trivial phylogenetic structure of the alignment, as previously suggested [18].
It is perhaps not surprising that conservation is a good indicator of the functionally-important residues in a protein; indeed, this fact is one of the original motivations for using positional weights in SCA that grow with conservation levels [19]. However, as a consequence, for proteins with a single SCA sector, it is difficult to distinguish between the functional significance of sector residues and that of conserved residues. The natural solution to this problem is to focus on proteins with multiple sectors, such as the serine protease family analyzed by Halabi et al. [18].
In the serine protease case, three SCA sectors were identified by placing thresholds on certain linear combinations of eigenvectors of the SCA matrix. The top eigenvector was ignored based on an analogy to finance, and thus the issues outlined in the previous section do not apply here. The three sectors (called ‘blue’, ‘red’, and ‘green’) were found to have independent effects on various phenotypes of the protein: the blue sector affected denaturation temperature, the red one affected binding affinity, and the green sector contained the residues responsible for catalytic activity.
There are two attractive features of the serine protease data. One is that several different quantities were measured for each mutant, thus allowing for a test of the idea that the protein is split into groups each of which affects different phenotypes. Another important feature is that some double mutants were also measured, showing that mutations in different sectors act approximately independently from each other. Collecting more extensive data of this type for serine proteases and for other proteins should give more weight to the idea that SCA sectors act as functional sectors in proteins. To reduce the amount of work involved, we point out that from our observations, it seems that instead of a complete scan of all 19 alternative amino acids at each position, an alanine scan, involving only mutations to alanine, might be sufficient. Using only alanine replacements, even a complete double-mutant study of PSD95pdz3 would require about 3000 mutants, only a factor of two more than were already studied [25]. For proteins exhibiting multiple SCA sectors, this number could be lowered by focusing only on those double mutants that combine mutations in different sectors, thus testing the independence property.
Finding several relevant quantities to measure for each of the mutants might not be an easy task. An ideal system for this would be related to gene expression or signal transduction, allowing measurements to be made in realistic conditions. Furthermore, it would be convenient to have a low-dimensional quantitative description of the protein’s phenotype, so that one could check whether the sectors predicted by SCA correlate with the mutations that affect the parameters in this description.
One difficulty in the application of SCA is that the identification of sectors is non-trivial. Halabi et al. used visual inspection to identify linear combinations of eigenvectors to represent the sectors [18]. Independent component analysis (ICA) has also been invoked to find the linear combinations [19, 20, 22], but a mathematically rigorous motivation for the application of this procedure is missing. An approach that avoids these difficulties is to check whether a linear regression can approximate the measured quantities for the different mutants with linear combinations of the eigenvectors of the SCA matrix. This seems to work for the case of serine protease (see S1 Text and S1 Fig.), though the small number of data points prevents a statistically rigorous analysis. A similar approach does not work for the PDZ data from McLaughlin Jr. et al., in which binding to both the cognate (CRIPT) ligand and to a mutated T−2F ligand was measured [25] (see S1 Text and S2 Fig.). It also does not work for the potassium channels dataset, in which both the activation voltage V50 and the equivalent charge z were measured for each mutant [33] (see S1 Text and S3 Fig.). This is consistent with the idea that these proteins exhibit a single sector.
Conservation alone cannot in general be used to find several distinct groups of residues that have distinct functions. For this reason, finding evidence for functionally significant and independent SCA sectors would automatically favor SCA over a simple conservation analysis. However, it is important to point out that SCA, with the particular set of weights as defined by Halabi et al. [18], is only one possible procedure for analyzing correlations in sequence alignments. Once more data is available for proteins containing multiple sectors, it will be important to compare different sets of positional weights, or different models altogether, to identify the best approach for analyzing MSAs [23].
We analyzed the available evidence regarding the hypothesis that the residues comprising the sectors identified by statistical coupling analysis are functionally significant. We looked at a number of studies, some directly related to SCA [18, 24, 25], and some unrelated [33, 34], and we showed that while the sector positions identified by SCA do tend to be functionally relevant, in the case of single-sector proteins, conserved positions provide a statistically equivalent match to the experimental data. This observation was traced to a property of the SCA matrix that makes the components of its top eigenvector correlate strongly with its diagonal entries. We presented a mathematical model that might explain this correlation. This model suggests that, as a generic property of statistical coupling analysis, the top eigenvector of the SCA matrix does not contain information beyond that provided by single-site statistics.
The observation that conservation is an important determinant of the SCA sectors is of course not very surprising, since one of the principles of SCA is to upweight the correlation information for conserved residues compared to poorly-conserved ones. However, this does pose a problem for the interpretation of the large-scale experiments that have been performed in relation to SCA [24, 25], given that these provide most of the available evidence for the functional significance of SCA sectors. Our analysis shows that this functional significance might be due to conservation alone. Since function is not the only reason for which protein residues may be conserved [35], it is not surprising that the overlap with functional residues is not perfect.
Once again, it is important to note that our findings do not imply that correlations within MSAs are uninformative; the contrary seems to be supported by experimental data [8, 10–12]. However, in order to test whether the particular way in which these correlations are used within the SCA framework is useful for making functional predictions about proteins, it will be necessary to go beyond single-sector proteins and measure several different phenotypes. Such data exists [18], but is too limited at this point to be conclusive. A thorough investigation of the idea that SCA sectors act as functional sectors requires more of this type of data, for a wider class of proteins.
Whether small groups of residues inside proteins act as independent “knobs” controlling the various phenotypes is a question that can be asked independently of any statistical analysis of alignments. Such functional sectors could be found by mutagenesis work, as described above. Alternatively, one could look for structural sectors using NMR or X-ray data to search for correlated motions. This has the advantage of not requiring the modification of proteins through mutations. Finally, evolutionary sectors could be searched for by using artificial evolution experiments. If the existence of these functional, structural, or evolutionary sectors is verified with sufficient precision, one could then more easily approach the question of whether a statistical method is capable of inferring their composition from an MSA, and in this case, which method is the most efficient and accurate.
Statistical coupling analysis requires an alignment of protein sequence homologs as input data. This may contain both orthologs and paralogs, and at least moderate sequence diversity within the alignment is necessary, because an alignment of identical sequences will not contain any information about amino acid covariance. The alignments we used were generated using HHblits, with an E-value of E = 10−10. States with 40% or more gaps were considered insert states, and were later removed from the calculations. The Uniprot IDs of the seed sequences used with HHblits are as follows: DLG4_RAT (PDZ), DYR_ECOLI (DHFR), KCNB1_RAT (K+ channels), and LACI_ECOLI (lacI). To check the robustness of the results, we also ran our analysis on Pfam alignments when available, and on the alignments from McLaughlin Jr. et al. [25], Reynolds et al. [24], and from Lee et al. [31] for the PDZ, DHFR, and potassium channels datasets, respectively.
The statistical coupling analysis was performed in accordance with the projection method [19, 25], which is the default in the newest version of the SCA framework from the Ranganathan lab. The code we used for the analysis can be accessed at https://bitbucket.org/ttesileanu/multicov.
Consider a multiple sequence alignment represented as an N×n matrix A in which aki is the amino acid at position i in the kth sequence. We first construct a numeric matrix X ˜ by
x ˜ ki = { ϕ i ( a ki ) f i ( a ki ) ∑ b≠gap ϕ i 2( b )f i 2 ( b ) if a k i ≠gap, 0 if a k i = gap, (6)
where ϕi(a) is a positional weight, and fi(a) the frequency with which amino acid a occurs in column i of the alignment. The positional weights are given by
ϕ i ( a ) = log [ f i ( a ) 1 − f i ( a ) 1 − q ( a ) q ( a ) ] , (7)
where q(a) is the background frequency with which amino acid a occurs in a large protein database. The SCA matrix is, up to an absolute value, the covariance matrix associated with X ˜,
C ˜ i j = | 1 N ∑ k x ˜ k i x ˜ k j − 1 N 2 ∑ k , l x ˜ k i x ˜ l j | . (8)
Finally, the sector was identified by finding the positions where the components of the top eigenvector of C ˜ ij went above a given threshold. The threshold was chosen so that the sector comprised about 25% of the number n of residues contained in each alignment sequence.
More details about this method and descriptions of the other variants of SCA found in the literature can be found in the S1 Text.
The conservation level of a position in the alignment is calculated using the relative entropy (Kullback-Leibler divergence), as described in eq. (1). A different definition, as the frequency of the most prevalent amino acid at a position, is highly correlated with Di and gives similar results.
Note that the calculation of the relative entropy as defined in eq. (1) requires that ∑a fi(a) = 1 and ∑a q(a) = 1. For the first of these relations to hold, we need the sum over a to include the gap, but this requires a value for the background frequency of gaps q(gap). This is not straightforward to estimate or even to define. There are several solutions possible: one is to assume that the background frequency for gaps is equal to the gap frequency in the alignment averaged over all positions. Another approach is to simply ignore the gaps by focusing only on the sequences that do not contain a gap at position i. We chose the former solution, as it is the default one in the SCA framework, but the results are very similar when using the latter choice.
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10.1371/journal.pmed.1002717 | Small for gestational age and risk of childhood mortality: A Swedish population study | Small for gestational age (SGA) has been associated with increased risks of stillbirth and neonatal mortality, but data on long-term childhood mortality are scarce. Maternal antenatal care, including globally reducing the risk of SGA birth, may be key to achieving the Millennium Development Goal of reducing under-5 mortality. We therefore aimed to examine the association between SGA and mortality from 28 days to <18 years using a population-based and a sibling control design.
In a Swedish population study, we identified 3,795,603 non-malformed singleton live births and 2,781,464 full siblings born from January 1, 1973, to December 31, 2012. We examined the associations of severe (<3rd percentile) and moderate (3rd to <10th percentile) SGA with risks of death from 28 days to <18 years after birth. Children born SGA were first compared to non-SGA children from the population, and then to non-SGA siblings. The sibling-based analysis, by design, features a better control for unmeasured factors that are shared between siblings (e.g., socioeconomic status, lifestyle, and genetic factors). Hazard ratios (HRs) were calculated using Cox proportional hazards and flexible parametric survival models. During follow-up (1973–2013), there were 10,838 deaths in the population-based analysis and 1,572 deaths in sibling pairs with discordant SGA and mortality status. The crude mortality rate per 10,000 person-years was 5.32 in children born with severe SGA, 2.76 in children born with moderate SGA, and 1.93 in non-SGA children. Compared with non-SGA children, children born with severe SGA had an increased risk of death in both the population-based (HR = 2.58, 95% CI = 2.38–2.80) and sibling-based (HR = 2.61, 95% CI = 2.19–3.10) analyses. Similar but weaker associations were found for moderate SGA in the population-based (HR = 1.37, 95% CI = 1.28–1.47) and sibling-based (HR = 1.38, 95% CI = 1.22–1.56) analyses. The excess risk was most pronounced between 28 days and <1 year of age but remained throughout childhood. The greatest risk increase associated with severe SGA was noted for deaths due to infection and neurologic disease. Although we have, to our knowledge, the largest study sample so far addressing the research question, some subgroup analyses, especially the analysis of cause-specific mortality, had limited statistical power using the sibling-based approach.
We found that SGA, especially severe SGA, was associated with an increased risk of childhood death beyond the neonatal period, with the highest risk estimates for death from infection and neurologic disease. The similar results obtained between the population- and sibling-based analyses argue against strong confounding by factors shared within families.
| Small for gestational age (SGA) occurs in more than 30 million infants every year.
SGA has been associated with increased risks of stillbirth and neonatal mortality, but data on long-term childhood mortality are scarce.
We evaluated the association between SGA and mortality from 28 days after birth to 18 years of age, using both general population and sibling comparators in more than 3.7 million Swedish children over a period of 40 years.
Compared with non-SGA children, children born with severe SGA had an increased risk of death throughout childhood in both the population- and sibling-based analyses.
The greatest risk increase associated with severe SGA was noted for deaths due to infection and neurologic disease.
Similar but weaker associations were found for moderate SGA.
Our findings provide insights on associations between SGA and causes of death in childhood. SGA cannot be reversed, and hence primary preventive strategies are needed to decrease the long-term risks of intrauterine growth restriction.
Even if absolute risks are low, an opportunity to reduce the harms of SGA may exist in infectious disease prevention and treatment.
| The Barker hypothesis proposes that intrauterine growth restriction may cause cardiovascular disease in middle and old age [1–3]. Some data indicate that fetal growth may also influence other diseases in adulthood [4,5], although confounding remains an issue [6].
Small for gestational age (SGA) refers to newborns with a low birth weight for gestational age, according to the reference curve for normal fetal growth or birth weight for gestational age. SGA, defined as either less than the 3rd percentile or less than the 10th percentile, has been associated with stillbirth and neonatal [7,8] and postneonatal mortality (death at 0–27 and 28–364 days of life, respectively) [8,9]. Despite the fact that SGA yearly occurs in more than 30 million infants worldwide [10], there are limited data on the association of SGA with mortality beyond the perinatal period. If such an association is confirmed, it may provide additional motivation for achieving the Millennium Development Goal of lowering under-5 mortality [11] and even mortality throughout childhood by globally reducing SGA births.
We are aware of 5 studies that examine the relationship between SGA and mortality beyond the first year of age. Two studies followed up children to the age of 5 years but not after that [12,13]. They found increased mortality, but did not report cause-specific mortality. Another study found that poor fetal growth (per quartile) was associated with an increased risk of death from cardiovascular disease, but did not look at overall mortality [14]. A fourth study reported cumulative mortality up to 14 years of age in children born SGA but did not calculate relative risks [15]. Finally, a Danish study reported an increased risk of death in children born SGA [16]. However, that study included children with malformations, which may substantially contribute to SGA-related mortality, and did not discriminate between severe (<3rd percentile) and overall (<10th percentile) SGA. Recent data have suggested that using a cutoff at the 3rd percentile (severe SGA), rather than the 10th percentile, may better reflect poor fetal growth [17]. Finally, a study using within-sibling comparisons—which, by design, feature a better control for factors that are unmeasured but potentially shared between siblings (e.g., socioeconomic status, lifestyle, and genetic factors)—is warranted.
In this study we evaluated the association between SGA and mortality from 28 days after birth to <18 years, using both general population and sibling comparators in more than 3.7 million Swedish children over a period of 40 years. We hypothesized that severe SGA (<3rd percentile), and to a smaller degree moderate SGA (3rd to <10th percentile), would be associated with increased mortality throughout childhood. We also hypothesized that such associations could not be entirely explained by genetic and early life risk factors.
Swedish registers include information on a personal identity number (PIN), uniquely assigned to all Swedish residents [18]. We used the PIN to link data on pregnancy outcomes in the Swedish Medical Birth Register (MBR) [19] and deaths in the Swedish Cause of Death Register [20]. The MBR started in 1973, and includes information on pregnant women and their offspring. The register covers more than 98% of all births in Sweden. The data collection starts at the first antenatal visit (around 12 gestational weeks). Since 1982, all data have been registered using standardized charts. The Cause of Death Register has recorded the time and cause of all deaths in Sweden since 1952. Causes of death are coded in accordance with the International Classification of Diseases (ICD) system.
Information on the highest level of maternal education was obtained from the yearly updated Swedish Register of Education. Data were also obtained from the Swedish National Patient Register [21], a register that includes information on inpatient discharge records from 1964 onward (nationwide since 1987) and hospital-based outpatient specialist care from 2001 onward. The positive predictive value for most diagnoses in the National Patient Register is 90% [21]. Follow-up was determined through the Total Population Register, which records data on family relationships and life events, including birth, death, name change, marriage and divorce, and migration to and from Sweden [22]. A portion of this register goes under the name of the Multi-Generation Register [23], which allows the identification of parents for all live births included in the present analyses.
In Sweden, between January 1, 1973, and December 31, 2012, there were 3,997,744 singleton live births recorded in the MBR. We excluded 10,889 neonatal deaths (death of a live birth from 0 to 27 days of life), as we intended to study mortality from 28 days after birth and onwards as the primary outcome. We further excluded births with missing or invalid PINs (n = 20,003) and births with missing information on gestational age (n = 8,977), maternal age (n = 4,376), date of birth (n = 219), or infant sex (n = 2), or missing or implausible values of birth weight (n = 15,086). Finally, to exclude the contribution of congenital malformations to the association between SGA and mortality, we excluded 124,589 births diagnosed with major malformations during the first year of life. Information on malformations was ascertained through the MBR and the Swedish National Patient Register. The specific ICD codes (ICD-8 during 1973–1986, ICD-9 during 1987–1996, and ICD-10 during 1997–2012) for malformations are provided in S1 Table. As a result, we constructed a population cohort of 3,795,603 singleton live births surviving the neonatal period, and followed the cohort to death, emigration, the day before the 18th birthday, or December 31, 2013, whichever came first.
An association between SGA and childhood mortality might be attributable to unmeasured genetic or non-genetic confounders. Using information on mothers (identified from the MBR) and fathers (identified from the Multi-Generation Register), we constructed a cohort of 2,781,464 full siblings (73% of the population cohort). Within this cohort, we used a sibling control design in which children born SGA were compared with their non-SGA full siblings. The purpose of this design is to eliminate (genetic and environmental) confounders shared by siblings.
SGA was defined as having a birth weight for gestational age less than the 10th percentile, according to the ultrasound-based sex-specific Swedish reference curve for normal fetal growth [24]. Gestational age was defined according to ultrasound measurements early in the second trimester or by gestational age estimated from information of the last menstrual period. Early second trimester measurements of fetal dimensions have been offered to all pregnant women in Sweden since 1990 and are performed in some 95% of pregnancies [25]. If no ultrasound data were available, we calculated gestational age based on last menstrual period. SGA was further divided into severe SGA (<3rd percentile) and moderate SGA (3rd to <10th percentile).
The primary outcome measure was all-cause mortality between 28 days and <18 years of age. The secondary outcome measure was cause-specific mortality, focusing on the most common underlying causes of death during childhood: infection, injury, cancer, and neurologic disease [26]. About 96% of deceased individuals have a recorded cause of death in the Cause of Death Register [20]. The specific ICD codes used to classify the underlying causes of death are presented in S1 Table. In an evaluation of deaths occurring among individuals at the age of 0–44 years, the underlying cause of death recorded in the Cause of Death Register (used for this study) was confirmed in 98% of the cases, using medical information from patient charts [27].
Information about maternal education (<10 years, 10–11 years, 12 years, 13–14 years, ≥15 years, or unknown) was derived from the Register of Education, and information about maternal country of birth (Nordic versus non-Nordic country) from the Total Population Register. Other information was gained from the MBR [28]. Preterm births were defined as <37 (and term births as ≥37) completed gestational weeks at the time of delivery. We also obtained information on maternal age, maternal parity (1, 2–3, or ≥4), sex of child, calendar period of birth (1973–1976, 5-year intervals from 1977 to 2006, or 2007–2012), and mode of delivery (vaginal or cesarean delivery). For children born during 1992–2012 (n = 1,986,114), we obtained additional information on maternal smoking during pregnancy [29] and body mass index (BMI) in early pregnancy [30,31]. Information on maternal weight for calculating BMI is based on measured maternal weight, and 90% of maternal weight measurements were performed during the first trimester [30].
In the population cohort, we first calculated standardized mortality rates (SMRs, per 10,000 person-years) for all-cause and cause-specific mortality across different ages at follow-up (28 days to <1 year, 1 year to <5 years, 5 years to <10 years, and 10 years to <18 years) in severe SGA, moderate SGA, and non-SGA births separately. Because the mortality rates varied greatly during the 40-year study period, SMRs were estimated using the method of direct standardization by calendar period of birth. The accumulated person-time during the entire follow-up of the population cohort was used as the standard.
We examined the association between SGA and childhood mortality in the population and sibling cohorts. In the population-based analyses we compared mortality risk in children born SGA to that of children not born SGA (the reference group), whereas in the sibling-based analyses we compared mortality risk in children born SGA to that of their non-SGA siblings. In the sibling-based analyses only sibling pairs that were discordant on both exposure (i.e., SGA) and outcome (i.e., mortality hazard) contributed to the estimates. Using attained age at follow-up as the underlying time scale, we derived hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality from ordinary and stratified Cox proportional hazards models in the population- and sibling-based analyses, respectively. Given the observed different SMRs across age groups and for different underlying causes of death, we estimated the HRs for mortality by age at follow-up (28 days to <1 year, 1 year to <5 years, 5 years to <10 years, and 10 years to <18 years) and by underlying cause of death (infection, injury, cancer, and neurologic disease). We further calculated age-specific HRs for the 4 underlying causes of death.
In addition to calculating estimates of age-specific HRs, we used flexible parametric survival models to assess all-cause and cause-specific mortality in relation to severe and moderate SGA. Such models allow the HRs to change continuously over age at follow-up. A spline with 4 degrees of freedom (3 intermediate knots and 2 knots at each boundary, placed at the quintiles of the distribution of death events) was used for the baseline rate, and a spline with 2 degrees of freedom was used for the time-varying effect. We performed comparable analyses for cause-specific mortality. Because of the similar results obtained from the population- and sibling-based analyses, the flexible parametric models were only performed in the population cohort.
To provide additional detailed information on the change in risk of mortality with different birth weight percentiles, and to assess a potential nonlinear relationship, we performed secondary analyses using restricted cubic splines on birth weight percentile for gestational age, instead of categorization. We applied the spline with 4 knots placed at the 0.05, 0.35, 0.65, and 0.95 quantiles of the distribution of outcome events. HRs were estimated in both the population and within-sibling analyses using the 50th percentile as the reference.
In all population-based analyses we adjusted for maternal age, parity, education, and country of birth, and the child’s sex and calendar period of birth. In the sibling-based analyses we adjusted for maternal age and the child’s sex. We did not additionally adjust for parity in the sibling-based analyses as it is highly correlated with maternal age for siblings.
To assess the potential effect modification of gestational age, we performed an additional analysis of overall childhood mortality by adding an interaction term between SGA and gestational age. Without assuming a linear relationship, we applied restricted cubic splines on gestational age, and placed 4 knots at the 0.05, 0.35, 0.65, and 0.95 quantiles of the distribution of outcome events [32]. Age-varying HRs were predicted and visualized thereafter. With great interest in term SGA births (as fewer neonatal complications are expected), we further performed a subgroup analysis by restricting the analyses to 3,619,514 term live births in the population-based analysis and 2,597,959 term live births in the sibling-based analysis. Children born with severe SGA are more likely to have a cesarean delivery, and the mode of delivery might influence a child’s immune development through, for example, a differential microflora, and, consequently, its risk of diseases and mortality [33]. We therefore performed a subgroup analysis by mode of delivery for all-cause and cause-specific mortality. As the mortality rate varied greatly over the study period, we also stratified the analyses by calendar period of birth, roughly by decade (1973–1981, 1982–1991, 1992–2001, and 2002–2012).
Although the sibling-based analysis could control for shared familial (genetic as well as in utero and childhood environmental) factors, it could not control for factors that vary between pregnancies. In another analysis, we additionally adjusted for maternal smoking during pregnancy and BMI in early pregnancy among children born between 1992 and 2012. Smoking status was divided into no smoking, smoking, or unknown (4.4% with missing data). BMI was calculated based on the height and weight of the pregnant woman at the first antenatal check-up. To properly address the nonlinear relationship between maternal BMI and offspring mortality, we applied a restricted cubic spline (also 4 knots) to maternal BMI.
We defined statistical significance for HRs as 95% CIs that do not include 1.0. Data were processed by SAS 9.4 (SAS Institute) and analyzed in STATA 14.2 (StataCorp).
The study was approved by the Regional Ethical Review Board in Stockholm, Sweden (No. 2013/2192-32). Because of the strict register-based nature of the study, informed consent was waived [34].
Maternal and infant characteristics at the time of birth are shown in Table 1. Among all live births included in the population cohort, 80,924 were born with severe SGA (2.1%) and 216,037 with moderate SGA (5.7%). Preterm births accounted for 4.6% of all births, and 16.3% and 6.0% for severe and moderate SGA births, respectively.
During the follow-up (median 18 years), there were 10,838 deaths in the population cohort. Crude mortality rates per 10,000 person-years were 5.32 in children born with severe SGA, 2.76 in children born with moderate SGA, and 1.93 in non-SGA children. There were 1,572 deaths among the informative sibling pairs in the sibling cohort. More deaths were observed among SGA children who were born during earlier calendar periods compared to later calendar periods.
Overall and age-specific all-cause mortality rates were higher in children born with severe SGA than in children born with moderate SGA or non-SGA in both the population and sibling analyses (Table 2). Compared with non-SGA children, children born with severe SGA had an increased overall mortality in the population-based (HR = 2.58, 95% CI = 2.38–2.80) and sibling-based (HR = 2.61, 95% CI = 2.19–3.10) analyses. Such associations were largely confirmed when using birth weight percentile for gestational age as a continuous variable (S4 Fig). The associations were strongest between 28 days and <1 year of age (HR = 4.46, 95% CI = 3.98–5.00, in the population-based analysis and HR = 3.41, 95% CI = 2.67–4.36, in the sibling-based analysis; Table 2). Severe SGA was associated with an increased mortality rate in all age groups, but the magnitude declined with increasing age. Similar but weaker overall associations were found for moderate SGA in both the population-based (HR = 1.37, 95% CI = 1.28–1.47) and sibling-based (HR = 1.38, 95% CI = 1.22–1.56) analyses. Similar to severe SGA, these associations were also strongest between 28 days and <1 year of age and declined thereafter. Moderate SGA was not associated with increased mortality beyond 10 years of age. The age-varying HRs for mortality in severe and moderate SGA were further confirmed in flexible parametric modeling (Fig 1).
The most common cause of death during follow-up was injury (Table 3). However, the strongest association of severe SGA with cause-specific mortality was noted for death due to infection (HR = 3.19 in the population-based analysis and HR = 4.24 in the sibling-based analysis), followed by neurologic disease (HR = 2.22 in the population-based analysis and HR = 1.97 in the sibling-based analysis) and injury (HR = 1.32 in the population-based analysis and HR = 1.43 in the sibling-based analysis), although some associations were of borderline significance in the sibling-based analyses. In both the population and sibling analyses, moderate SGA was associated with a higher risk of death caused by infection (HR = 1.63 in the population-based analysis and HR = 1.49 in the sibling-based analysis) and neurologic disease (HR = 1.55 in the population-based analysis and HR = 2.14 in the sibling-based analysis). Severe and moderate SGA did not influence risk of cancer-related mortality.
Overall and cause-specific mortality rates across age at follow-up are displayed in Fig 2. The associations between severe SGA and risk of death due to infection and neurologic disease were strongest during the first year of life, but risk of death due to infection also remained high later in childhood (S1 Fig). No clear association was noted for severe SGA and death from cancer. Largely similar patterns, but weaker associations, were noted for moderate SGA (S1 and S2 Figs). Similar patterns were observed when separately calculating the age-specific associations for different causes of death in the population-based analysis (S2 Table).
Slightly stronger HRs for mortality were suggested for preterm SGA, particularly for severe SGA, despite wide confidence intervals (S3 Fig). Subgroup analysis restricting the age- and cause-specific mortality analyses to term live births (≥37 gestational weeks) rendered largely unchanged results (S3 and S4 Tables). We also stratified analyses by mode (S5 Table) and year of delivery (S6 Table). Overall and infection-related mortality were generally higher in children with severe and moderate SGA delivered by cesarean section than in those with vaginal delivery (S5 Table). The associations of severe and moderate SGA with all-cause mortality from 28 days to <1 year of age were stronger in the most recent birth cohorts compared with earlier birth cohorts (S6 Table). Further adjustment for maternal smoking during pregnancy and BMI in early pregnancy only marginally influenced the results (S7 Table).
In this population study of more than 3.7 million children we found that severe SGA (<3rd percentile) was associated with increased mortality from 28 days to <18 years of life. Moderate SGA (3rd to <10th percentile) was associated with more modestly increased mortality within the first 10 years of life. The similar results between the population- and sibling-based analyses argue against strong confounding by familial factors. Given the high prevalence of SGA, our findings have important public health implications and underline the need to tackle long-term consequences of SGA.
Expectedly, severe SGA was associated with an increased risk of postneonatal mortality [8,9]. Our study confirms previous findings [8,9,16] and contributes additional evidence in several important ways. First, the large sample size allowed us to calculate precise relative risk estimates (HR = 2.58, 95% CI = 2.38–2.80, in the population-based analysis). Second, we were able to differentiate between severe and moderate SGA. Third, we excluded children with malformations from our analyses. Fourth, we used a sibling control design, which, by design, controls for genetic and environmental factors shared by siblings. Thus, access to data on siblings allowed us to eliminate the influence of certain intrafamilial characteristics, including maternal traits that may otherwise be difficult to measure. We found similar HRs for childhood mortality beyond age 28 days in the population and sibling analyses, suggesting that the association of SGA with childhood mortality is unlikely to be dependent on familial factors. It is also well recognized that preterm birth, often linked to SGA, contributes to long-term health consequences, including mortality [35,36]. However, in a sensitivity analysis we restricted the comparisons to term births and found largely unchanged results, alleviating the potential concern that the excess mortality in children born SGA is due to underlying prematurity alone.
We were able to examine mortality at different ages during childhood, where some distinct patterns emerged. With age, the relative risk of mortality for severe and moderate SGA decreased: for severe SGA, the decrease went from a HR of 4.46 up until age 1 year to a HR of 1.49 at ages between 10 and <18 years in the population-based analysis. This finding is consistent with data from Denmark, where SGA less than the 10th percentile was associated with increased mortality that decreased over time (HR = 3.47 for <2 years including the neonatal period, 1.70 for 2–5 years, 1.42 for 6–13 years, and 1.34 for 14–19 years) [16]. As shown in the flexible parametric modeling, our findings further highlight the greater mortality rate under 4 years. While childhood mortality is decreasing, a recent UNICEF report warns that the Millennium Development Goal to reduce the under-5 mortality rate by two-thirds may not be met until 2026 [11]. Maternal antenatal care, in order to globally reduce the risk of SGA birth, is key to achieving that goal [11].
The large sample size also allowed us to examine causes of death and to speculate on possible mechanisms for the noted positive association between SGA and childhood mortality. We focused on infection, injury, cancer, and neurologic disease, which are the most common underlying causes of death during childhood [26] and reflect different mechanisms of action. For severe SGA, we noted an excess mortality for all causes except cancer in early childhood, but only for infection and neurologic disease between 10 and <18 years of age. Wennerström et al. also reported an increased risk of death from infection throughout childhood (HR = 1.52); however, they did not report risk estimates for neurologic disease [16]. We found largely a null association between SGA and death from cancer, except for at age 10 to <18 years. This result was expected given the null results of the Wennerström et al. study, which also reported a positive association between large for gestational age and cancer risk [16].
Our study showed consistently higher HRs for the association of SGA with mortality in the latest calendar period, although risk of death at 10 to <18 years could not be calculated for children born in 2002–2012 because of the unavailable data. For instance, children born with severe SGA in 1973–1981 were shown to have a 1.92-fold increased risk of death between 5 and 10 years of age (95% CI = 1.38–2.68), whereas children born with severe SGA in 2002–2012 had a 2.55-fold increased risk (95% CI = 1.04–6.27), compared with non-SGA children born during the respective time periods (S6 Table). Similarly, stronger associations were seen for other age groups in children born after 2002. The mechanisms for this are not clear. We speculate that this may partly be explained by a greater improvement of long-term survival in children born non-SGA than in those born with severe SGA during the study period. Another possible explanation may be that children born SGA today are more likely to survive the neonatal period compared with children born SGA in the past.
The free and standardized medical care in Sweden [37] minimizes selection bias. We therefore believe that our data are representative of the whole population and that our findings can be generalized to similar populations. We used nationwide registers to define both exposures and outcomes. These registers contain prospectively collected, high-quality data on healthcare, which minimize the risk of differential measurement biases.
We adjusted for a number of potentially important confounders (e.g., maternal age [38], maternal education level [39], maternal country of birth [39], parity, infant sex, and calendar year of birth [39]). We used a sibling control design to further minimize the influence of unmeasured confounding (such as by feto-maternal factors) and found similar results in the population- and sibling-based analyses. In a subset of the sibling-based analyses, we further adjusted for maternal smoking during pregnancy and BMI in early pregnancy, 2 exposures that might differ between pregnancies, and found comparable results. The similar results obtained from this analysis further alleviated concerns of residual confounding. Furthermore, because the origin of term SGA and preterm SGA may differ, we also performed sensitivity analyses restricted to term live births and again found similar results.
We acknowledge a number of limitations. Although we have to our knowledge the largest study sample so far addressing the research question, some subgroup analyses, especially the analysis of cause-specific mortality, had limited statistical power in the sibling-based analysis. The proportion of children born SGA was 7.8% in our study, which is lower than the expected 10%. This is probably due to population changes over time [40]. The Swedish reference curve for normal fetal growth is based on pregnancies until the early 1990s [24]. A Swedish study reported an increase in birth weight from 1992 to 2001, and that the increase in large for gestational age births was explained by concurrent increases in maternal BMI and decreases in maternal smoking [41]. Since 2001, maternal obesity has continued to increase and maternal smoking to decrease in Sweden [42]. Other limitations include potential misclassification, missing data on some covariates, and that not all ICD codes used in our study have been validated. Finally, we did not have data on the precise indication for cesarean section and could therefore not assess whether the indication for cesarean section or the surgery itself modified the association of SGA with childhood mortality.
While a host of different causes of death were overrepresented in SGA children, deaths from infection may be particularly important as these represent an important cause of death in absolute numbers and because children with severe SGA had a 3–4 times higher risk of death due to infection, compared both with population controls and with their siblings. This finding was also seen when restricting the analysis to term live births, suggesting that the association was not driven by preterm birth.
Fetal growth restriction may potentially influence organ development and maturation of the immune system, leading to an altered risk of infections [43,44]. For instance, it has been shown that the risk of perinatal sepsis increases with growth restriction [45]. Few studies, however, have specifically examined the associations of severe and moderate SGA with the risk of fatal infection beyond the first year of life. SGA may be linked to lower levels of certain cytokines (e.g., interleukin 1 beta) [46], which are involved in the inflammatory response in infection. Finally, children born SGA are more likely to receive neonatal intensive care [47], and it has been shown that early exposure to intensive care, with frequent use of antibiotics, may have an impact on immune development [48].
We also found a positive association between SGA and death from neurologic disease, which confirms a Danish study reporting that the incidence of epilepsy in the first 5 years of life was correlated with birth weight by gestational age [49]. In children with severe SGA we also noted a small excess risk of death from injury. We speculate that this may potentially be due to impaired neuropsychological development and thereby deficient cognitive function [50]. Although the absolute risk increase associated with SGA was smaller, compared to deaths due to infection and neurologic disease, the fact that injury is the leading cause of childhood mortality worldwide makes this finding both relevant and important.
SGA cannot be reversed, and hence primary preventive strategies are needed to decrease risk of intrauterine growth restriction. Such measures include smoking cessation and nutritional support in pregnant woman. Antenatal detection of SGA using symphysis-fundal height measurement and ultrasound (and where available Doppler velocimetry), followed by targeted interventions [51], may reduce the risk of not only perinatal death but also death throughout childhood. Furthermore, fetal growth in SGA should be carefully monitored through repeated ultrasound of fetal dimensions. Optimal obstetrical and neonatal care is necessary to reduce risks of asphyxia-related neonatal complications.
Our findings provide important insights on associations between SGA and causes of death in childhood. Along with the global call, active follow-up on SGA children up to 4 years of age might further reduce the mortality rate under 5 years, even in high-income countries. Finally, we want to highlight the excess risk of death due to infection in children born SGA. Even if the absolute risk is low, death caused by infectious disease can often be prevented through timely detection and the early use of appropriate antibiotics. More research is however needed to identify subgroups for which interventions may be both clinically effective and cost-effective.
In conclusion, population- and sibling-based analyses show that SGA and fetal growth restriction are associated with increased mortality throughout childhood.
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10.1371/journal.pcbi.1004841 | Modeling the Excess Cell Surface Stored in a Complex Morphology of Bleb-Like Protrusions | Cells transition from spread to rounded morphologies in diverse physiological contexts including mitosis and mesenchymal-to-amoeboid transitions. When these drastic shape changes occur rapidly, cell volume and surface area are approximately conserved. Consequently, the rounded cells are suddenly presented with a several-fold excess of cell surface whose area far exceeds that of a smooth sphere enclosing the cell volume. This excess is stored in a population of bleb-like protrusions (BLiPs), whose size distribution is shown by electron micrographs to be skewed. We introduce three complementary models of rounded cell morphologies with a prescribed excess surface area. A 2D Hamiltonian model provides a mechanistic description of how discrete attachment points between the cell surface and cortex together with surface bending energy can generate a morphology that satisfies a prescribed excess area and BLiP number density. A 3D random seed-and-growth model simulates efficient packing of BLiPs over a primary rounded shape, demonstrating a pathway for skewed BLiP size distributions that recapitulate 3D morphologies. Finally, a phase field model (2D and 3D) posits energy-based constitutive laws for the cell membrane, nematic F-actin cortex, interior cytosol, and external aqueous medium. The cell surface is equipped with a spontaneous curvature function, a proxy for the cell surface-cortex couple, that is a priori unknown, which the model “learns” from the thin section transmission electron micrograph image (2D) or the “seed and growth” model image (3D). Converged phase field simulations predict self-consistent amplitudes and spatial localization of pressure and stress throughout the cell for any posited stationary morphology target and cell compartment constitutive properties. The models form a general framework for future studies of cell morphological dynamics in a variety of biological contexts.
| Individual cells must have the capability for rapid morphological transformations under various physiological conditions. One of the most drastic shape transformations occurs during the transition from spread to rounded morphologies. When this transition occurs rapidly, there is insufficient time for significant changes in surface area to occur, although the final size of the rounded cell indicates a significant reduction in apparent cell surface area at light microscope resolution. By contrast, high-resolution scanning electron micrographs of rapidly rounded cells reveal that a large amount of surface area is stored in a highly convoluted surface morphology consisting of bleb-like protrusions (BLiPs) and other small structures that are unrecognizable at lower resolution. This surface reserve is an important part of the mechanism that allows rapid and efficient large scale transformations of cell shape. Remarkably, although this convoluted morphology has been observed for decades, there has been very little effort recognizing and including this surface surplus in mathematical modeling of cell morphology and physiology. In this paper, we develop three complementary models to fill this void and lay the foundation for future investigations of the mechanisms that drive cellular morphological dynamics.
| Cells maintain their structural integrity while being flexible enough to adopt a variety of shapes. In general, it is the cytoskeleton of eukaryotic cells that drives shape transformation leading to cell movement and provides the structural support to the cytoplasm and the means to resist external forces. The periphery of cells, consisting of the plasma membrane (PM) and the acto-myosin cortex, is highly dynamic to accommodate shape change. The plasma membrane (PM) consists of a high density of proteins [1] embedded in a phospholipid bilayer of 5–10 nm thickness, with a very limited ability to extend without rupture [2,3] but highly amenable to bending [4,5,6]. The thin (50–500 nm) layer of cytoskeleton structure immediately subjacent to the plasma membrane, known as the cell cortex, consists of a dense F-actin network that is cross-linked by actin binding proteins and is amenable to contractility mediated by myosin motors. Interposed between the cortex and the PM is a thin spectrin-actin network, forming a ‘fishnet’ with a mesh size of ~100 nm [7,8]. This structure is anchored both to the PM and cortex by adaptor proteins. In the following, we term the plasma membrane and spectrin-actin network as the “cell surface”.
Previously we [9] suggested that most dynamical shape changes exhibited by non-spread (rounded) cells originate from a membrane-cortex folding-unfolding process and an excess of cell surface area is a necessary requirement for such changes. We investigated the dynamics of periodically protruding cells and hypothesized that the plasma membrane and thin cortical layer remain coupled during all stages of shape transformation. We also assumed that densely compressed cell surface folds and small protrusions could be kept intact by the underlying actin-myosin network residing in the cortex proper. While this notion may be applicable to many shape transitions occurring in non-spread cells, in this paper we reconsider this hypothesis in context of one of the most drastic changes of cell shape: the transformation from a fully spread to a rounded state.
If a cell transitions from a spread to rounded state while maintaining a constant volume, it will experience an excess of surface area over the minimum needed to cover the enclosed volume. Because this process typically happens rapidly (~30s-), there is insufficient time for excess membrane to be internalized by endocytosis. Thus, another mechanism for storing surface area at the plasma membrane must exist. Indeed, there is significant evidence from both electron and fluorescence microscopy that during the rounding process the cell surface adopts a tightly folded morphology [9,10,11,12].
While there are a number of models for cell shape, most of them treat the cell surface as smooth [13,14,15,16,17] and do not take into account the possibility that rounded cells store excess surface area in a dense distribution of bleb-like protrusions (BLiPs). Thus, new modeling approaches are needed to understand the dynamics of cell shape changes that involve active use of this surface storage. We introduce three complementary modeling approaches, each incorporating the concept of excess surface area. The first approach is a 2D model based on a thin cell surface structure that is coupled to a thicker, contractile actomyosin layer. This model allows us to investigate the folding of the excess surface and to estimate the bending energy in different configurations. The second approach is a random “seed and growth” model that produces 3D morphologies consistent with the distributions of BLiP size and number estimated from scanning electron micrographs. This model yields insight into how large numbers of BLiPs are efficiently packed on the cell periphery.
The third approach is a multi-compartment phase field model. By faithfully capturing the physical properties of the cortex, cytosol, and cell surface, the model predicts the stress and pressure distributions associated with a highly folded 2D morphology and a dense distribution of 3D BLIPs. Phase field models have been widely used to study complex systems comprised of distinct material phases and their adjacent interfaces. When the separate material phases are immiscible, the phase field approach is to prescribe a finite thickness of a “diffuse” interface within which there is a mixture of the two materials [18,19]. The phase field method is an alternative to sharp interface methods; in both methods the shape and evolution of the sharp versus diffuse interface are part of the solution. For every pair of adjacent material components, a phase field variable is introduced that interpolates from one material phase to the other through the finite thickness boundary. Phase field models have been employed to describe shapes of lipid bilayer vesicles in which the surface tension and Helfrich bending energy are approximated using a bulk energy defined within the diffuse interfacial layer [19]. Phase field models have been applied to many interfacial problems including liquid drops, multiphase complex fluids [20], and fractures in solid-state materials [21].
The phase field model simulations achieve separate goals. From either a 2D transmission electron micrograph or a 3D image reconstruction of the cell morphology, the model “learns” the spontaneous curvature functional of the rounded, BLiP-rich, morphology. Since the phase field model faithfully captures material properties of each cellular compartment, the model converges to the cell target morphology while constructing self-consistent stress and isotropic pressure distributions for the cell surface, cortex and cytoplasm, as well as estimating the nematic orientation within the cortex.
Storage of excess cell surface in folds or bleb-like protrusions at the periphery is likely to be important for a variety of rapid cell shape changes, taking place over a time scale of a few minutes or less, such as those that occur in forms of amoeboid migration or either within or in the transitions between the phases of mitosis. It seems likely that rapid cell shape changes can be accomplished more quickly by calling upon a reserve of excess membrane stored in the BLiP distributions rather than relying on extensive membrane-cortex remodeling and exocytosis. Thus, the theoretical approaches presented here should be applicable in a number of different biological contexts.
When spread cells (Fig 1A) are chemically detached from an underlying substrate, they rapidly transition to a rounded state on a characteristic time scale of ~30-60s (Fig 1B). Numerous studies suggest that in media with constant osmolarity, cell volume is stable [3,22]. We estimated cell volume by reconstructing 3D geometries from Z-stacks of spinning disc fluorescence images of cells undergoing rounding. The mean volume for Chinese hamster ovary (CHO) cells in the spread state is 6.5±2.82*103 μm3, while the mean volume in the rounded state is 5.7±2.30*103 μm3, indicating a slight decrease in cell volume after rounding. Because this slight decrease in cell volume would only increase excess surface area, in all our models, we assume that cell volume remains constant during rounding.
The surface area of a spread cell is estimated as twice the area measured from images to account for dorsal and ventral surfaces. In reality cells are not completely flat and have more surface area due to finite thickness, particularly around the nucleus. Therefore, we are underestimating the surface area of a spread cell. In the rounded state, the minimal surface area needed to enclose the measured cell volume can be found by assuming the cell is spherical and calculating the radius. For example, the cell 1 in Fig 1A has a surface area of ~ 31000 μm2 while the surface area needed to enclose the rounded state is only ~ 2200 μm2. Therefore after rounding, this cell has ~ 14 times more surface area than is required to enclose its volume. This image presents an extreme case of surface area excess. For cell 2 in Fig 1A, which is less spread before detachment, an excess surface area of about five times the required amount is accumulated following rounding. It is important to note that the amount of excess surface area that is accumulated during rounding depends on cell type and characteristics of the spreading and detachment for individual cells. Using DIC and fluorescence microscopy, we studied populations of cells before and after detachment, and individual cells rounding during trypsinization. The histogram in Fig 1C presents the distribution of surface areas for spread CHO cells (blue bars; population mean = 4310±3600 μm2, N = 199) and cells immediately after rounding (red bars; population mean = 892±284 μm2, N = 1646). The distribution of rounded cell sizes is narrow with majority of cell radii (Fig 1E) being between 7 and 9.5 μm (mean = 8.36±1.24 μm; N = 1646). Separate experiments, where we followed the change in morphology of individual cells during rounding, demonstrated that for CHO cells the average excess surface, defined as the ratio of spread cell area to that required to smoothly cover a sphere with radius corresponding to that of the rounded cell, accumulated due to detachment and rounding is 3.8± 2.06 with maximum value of 12 (N = 99).
To gain insight into how much excess surface area can be stored in BLiPs, we first consider the case of a rounded cell uniformly covered with equally sized sphererical BLiPs.
It is easy to show (S1 Appendix) that as spherical BLiPs become smaller, more excess surface area can be accommodated. The maximum possible surface excess that can be stored in the equally sized sphererical BLiPs is 5 (in the limit of BliP radius r→0). The fact that we observed rounded cells with the surface excess as high as 14, means that cells utilize a more efficient packing strategy. Also no limits on the surface excess ratio would be imposed if we did not require BLiPs to be spherical, but rather allow for an arbitrarily high curvature of the surface, as occurs, for example, in tubules. Yet, the majority of BLiPs appear to be rounded immediately after detachment. These considerations suggest that the actual morphology of the folded cell surface is dictated by a balance between the necessity to pack tightly a very large number of BLiPs and the necessity to generate, regulate, and maintain significant surface curvature.
To better understand the process of packing cell surface excess into a convoluted surface morphology, we constructed a 2D geometric model designed to produce BLiPs. We hypothesize that the cell surface and underlying contractile cortex form a two-layer structure that is coupled at certain fixed points. The first layer, which we term the cell surface, is passive and consists of the plasma membrane and membrane associated cytoskeleton. This layer is assumed to be similar to the spectrin-actin network that is coupled to the plasma membrane of red cells [23]. Such structures have been shown by Kusumi and co-workers to exist in many other cell types [8]. The membrane associated cytoskeleton has been termed the membrane skeleton fence [8]. It is basically a very thin filamentous meshwork that provides a “fishnet” with a mesh size of approximately 100 nm immediately underlying the PM. This layer is coupled to the plasma membrane via adaptor proteins including the ankyrin and ERM families as well as by interactions of the membrane skeleton fence with lipids in the inner monolayer of the PM. The layer is thought to be passive undergoing only thermal motions, serving to anchor some transmembrane proteins and restrict the free diffusion of others. We assume that this thin layer is coupled via adaptor proteins to a thicker, active contractile layer containing actin and myosin. This view of the cortex-cell surface couple is consistent with that advanced by Charras et al (2006) [24] in the context of spontaneously blebbing cells.
Additional evidence for this structure comes by imaging employing confocal and electron microscopy. Fig 1F shows a confocal image of the actin-myosin cortex in rounded cells as visualized with GFP-lifeact and RFP-myosin merged with a DIC image of the same cell. The green signal for Lifeact marks F-actin filaments associated with the folded morphology of the cell periphery and this fluorescent signal originates from both the thin layer immediately subjacent to the membrane and the thicker contractile layer. The fluorescent signal from myosin (red) shows that this protein is localized mainly to a thin circle located below the BLiPs and more toward the cell interior. S1 Fig presents Z-stack images of the same cell. From this image, it is clearly visible that the convoluted morphology covers the whole cell. Fig 1G and 1H shows immunogold TEM images of GFP-lifeact where F-actin is seen underlying BLiPs (arrow) and also in a layer closer toward the center of the cell (arrowhead).
To construct the 2D geometric model introduced qualitatively above, we implement a two-layer architecture in a 2D bead-spring model of the cell membrane and cortex (model description in Methods). The bead-spring model consists of two-layers (Fig 2A and S2 Fig), where one layer (outer layer) represents the membrane and underlying actin mesh (i.e. the cell surface) and the other layer (inner layer) represents the actomyosin-rich contractile cortex. In each layer, beads are connected pairwise by springs and contact points serve to connect the two layers. By minimizing the bending energy we explored the steady-state shapes of BLiPs generated during cell rounding when the cell is rapidly presented with a substantial excess surface. For simplicity we define excess surface ratio (ER) as a ratio of perimeters of the surface layer and contracted cortex. Here we define a normalized total bending energy (E) as for
E=∮sκ2ds≈LN∑i=1Nκi2
(1)
where κ is the local curvature measured between two neighboring beads, L is the perimeter, and N is the number of beads in the outer layer [25,26,27,28,29].
The number of contact points determines the number of folds (M). In the simulation the total Hamiltonian of the two-layer system is minimized with the result that a folded configuration of outer layer is produced. Fig 2B shows the resulting shapes as a function of both M and ER. Fig 2C shows a portion of a model cell with BLiPs at steady state where the gold line represents the contractile part of the cortex with contact points. While the appearance of folds is expected, the shape of folds and the bending energy stored in each configuration is of particular interest.
Fig 2D gives a comparison of the fold configuration for several different sets of parameters with the calculated bending energy for each shape. Inspection of the fold shapes shows that in order to accommodate more surface, the folds tend to develop long necks. (Note that in case where there is heterogeneity in the size of folds, this effect would allow small folds to grow under larger ones, an effect that permits accommodation of more excess surface.) The smallest possible bending energy will be achieved when ER = 1 and M = 0 (no surface excess and no BLiPs). For a given value of ER, the energy increases with the number of BLiPs (Fig 2D and S2 Fig). However, the bending energy is decreasing while the excess surface ratio is increasing. Although this result looks counterintuitive, it can be explained. The local curvature is the inverse of local radius. Folds with a longer perimeter have bigger inner radii which substantially decreases bending energy with the square of local radius (Fig 2D and S3 Fig). Thus, morphologies with longer perimeters corresponding to larger ERs will have lower energy compared to the shapes with the same number of folds but with shorter perimeters (i.e. smaller ERs). The analysis of the area which is stored inside the folds (i.e., volume in 3D) shows that for the same surface excess, more area is stored in folds when the number of folds used for accommodation of this surface surplus is smaller (S3D Fig).
At the resolution achievable by standard fluorescence microscopy, the convoluted cell surface often appears as a thickening of membrane and cortical stains (Fig 3A and 3B). However, surface morphology can be imaged at higher resolution using both scanning (SEM) and thin-section transmission (TEM) electron micrographs (Figs 3C and 1H, respectively). At this scale, bleb-like protrusions (BLiPs) and other cell surface protuberances are clearly visible. Note that only fully spread cells have a smooth surface essentially devoid of protrusions (S4 Fig).
To determine length, area and volume metrics of BLiPs, we manually segmented SEM images of cells that were fixed after rounding (S5 Fig). Each protrusion was approximated as a sphere and the area of the protrusion visible on the image was interpreted as a two dimensional projection of that sphere. We calculated the radius that corresponds to a projection of that size, and consider it as the radius of the BLiP. The distributions of BLiP radii derived from 10 SEM images (25 cells) that include 7096 BLiPs is presented in Fig 3D. We find that the distribution of radii is skewed with a preponderance of small BLiPs and a decreasing frequency of larger BLiPs. The mean BLiP radius is R = 0.25 μm with a median of 0.22 μm and mode of 0.19 μm.
It is important to mention that during the processes of detachment and rounding some part of the cell surface can be lost due to incomplete detachment from the substrate or because it remains in retraction fibers. However, the area remaining in retraction fibers is quite small. Using SEM images from cells that spread for 24 h and rounded 5 minutes before fixation, we estimated that the surface area that might be stored in retraction fibers represents between ~0.5–5% of the cell surface area in the spread state.
To investigate how the large number of BLiPs required to accommodate the excess cell surface are packed on the cell periphery, we constructed two models. As a plausible starting point, we employed a Voronoi approach, in which a spherical ball of radius R=S/4π contains the surface area, S of the spread cell before detachment and rounding; n seed point locations are randomly sampled from a uniform spatial distribution on the ball. The ball is then partitioned by a Voronoi tessellation according to the n seed points (Fig 4A) such that any point in each Voronoi cell is closer to the parent seed point than any other seed point. The area of each Voronoi cell is then determined. In this configuration, we assumed that, upon the cell rounding to its final state with the “BLiPed” morphology, each Voronoi cell of area v morphs into a spherical BLiP with radius r=ν/4π. Fig 4D demonstrates that, constructed in this way, the distribution of BLiP radii has a well-defined length scale with the bell-shape distribution, which is not consistent with the skewed distribution of experimental data. This result arises from the fact that Voronoi cells corresponding to two very closely positioned seeds are not necessarily small themselves, as might be expected from two closely positioned BLiPs.
In order to mitigate this effect, we introduce an alternative 3D “seed and growth” model (Fig 4B and 4C), in which BLiP radii are proportional to spacing between randomly distributed seeds. In this model, spheres are generated from each seed point by increasing their radii at a uniform rate. Simultaneously, the locations of the seed points are moved outward radially at the same rate, so that the spheres always remain tangent to the cell. When one sphere encounters another, it stops growing. When all spheres have stopped growth, a spherical cell coated by different sized BLiPs is produced (Fig 4B). The resulting BLiP radius distribution in Fig 4D is more consistent with the experimental distribution than that produced by the Voronoi model. The generated structure is also consistent with SEM image data, which show approximately spherical BLiPs largely covering the cell but with some areas devoid of BLiPs. We reproduced 2D cross-sectional views from the simulated 3D geometries (Fig 4F); these show similarity to the thin section TEM images of rounded cells (Fig 4G) where some of the BLiPs appear to be detached from the cell body because BLiPs are not always sectioned through their centers.
In the “seed and growth” model, a larger number of BLiPs results in a higher surface area excess ratio (Fig 4E) and a smaller percentage of the cell volume stored within the BLiPs, which is consistent with our simplified estimations based on an 2D equal-sized BLiP distribution. In principle, BLiPs that are not of equal size could allow a more efficient packing of excessive cell surface (with smaller BLiPs filling the space between larger ones), which is important for accommodating very high excess ratios (>5). However, in this model the packing is still inefficient because it always generates areas devoid of BLiPs. A potential improvement to our model might be to incorporate stochastic seeding of new BLiPs and occasional “shrinking” BLiPs that are in contact, so that BLiPs are dynamic and continue to adjust themselves toward the most efficient filling of the available space. Such a “seed, growth, and shrinking” model would be consistent with the BLiP dynamics observed in our experiments, but is beyond the scope of the current paper. Another potential improvement would be to make final BLiP size proportional to the rate of expansion of the BLiP; this has been found to be the case in an earlier study of blebbing cells [30].
The preceding models help build mechanistic intuition, yet, while predictive, they do not capture all of the essential physics of the rounded phenotype. In order to approach this goal, we formulated 2D and 3D phase field models for a cell immersed in the aqueous extracellular environment. The model is formulated in 3 space dimensions (3D), but it also restricts to 2D for purposes of modeling a cell cross-section. In our case, we have three phases (exterior aqueous medium, cortex, interior cytosol) and two diffuse interfaces. The external aqueous medium and interior cytosol are modeled as viscous fluids with specified viscosities and the cortex is modeled as a nematic (liquid crystal) gel [31]. The first diffuse (i.e., finite thickness) interface is what we have termed the cell surface, consisting of the plasma membrane and very thin underlying filamentous “fishnet”, that separates the aqueous medium and the cortex proper. As described below in Methods, a particular level set function in the phase field formulation will afford our definition of the “cell surface”. The second diffuse interface is the cortex-cytosol transition layer. Fig 5 is a 2D schematic of a 3D cell cross-section with individual components and diffuse interfaces labeled, along with the phase variables defined below. (We do not explicitly model the nucleus within the cytoplasm for this paper since we are primarily concerned with the stationary rounded morphology.) A more complete mechanical formulation giving the total system free energy in terms of its components is found in Methods.
We summarize the key numerical results of the phase field modeling of a 2D cell surface morphology due to an imposed excess arc length enclosing the 2D area. We require a 2D image of the membrane morphology, taken from 2D transmission electron micrographs. From the image file, we posit an initial smooth membrane boundary, and then evolve the phase field model while adjusting the spontaneous curvature function C1 until the model converges to the image dataset. We first illustrate the ability of the phase field model to match an arbitrary specified 2D boundary by “learning” the spontaneous curvature function; the results are shown in Fig 6A for an illustrative benchmark in which the cell perimeter contains 25 regularly spaced, uniform “BLiPs”. (In 2D, this is achieved by superimposing the appropriate Fourier mode on a circle.) Next, we used as input the actual periphery of a rounded cell from a 2D transmission electron micrograph (TEM) image. The results in Fig 6B show the convergence of the phase field membrane morphology to the TEM image, where the nematic phase ordering (representing F-actin orientation) in the cortex is depicted. This result assumes tangential anchoring condition of F-actin at both cortical diffuse interfaces.
Fig 6C–6E shows the phase field predictions of the pressure distribution (C) and the first invariant of the dominant stored stress, the Ericksen stress, for the converged stationary morphology (D,E) shown in Fig 6B. These results reveal the orders of magnitude as well as spatial localization of pressure and stored stress for the target 2D morphology.
A 3D simulation is depicted in Fig 7 to demonstrate the capability of our phase field model to converge to a target 3D cell surface morphology. The excess surface area ratio for this illustration is s0 = 3. Because it is impossible to reproduce a full 3D morphology of a rounded cell from a single scanning electron micrograph, we use the “seed and growth” model to simulate a 3D cell surface target. This model (Fig 4) provides a 3D surface morphology consistent with the measured BLiP size distribution data; therefore, we posit the output image from this model as the target morphology for the 3D phase field simulation. As shown earlier in 2D and here in Fig 7 in 3D, the phase field model converges to the target 3D morphology from an initial posited surface, while satisfying the volume and excess surface area constraints. The model does so by iterating the spontaneous curvature function until all constraints are satisfied; once converged, the model then yields the pressure and stresses within the cell surface and cortex that are self-consistent with the 3D surface morphology and constitutive properties of the exterior and cell compartments.
In Fig 7A, the target cell morphology is shown. The evolution of cell morphology, from an initial rounded cell guess to the target cell shape, is provided in (Fig 7B and 7C). Fig 7D depicts 2D projections in three mutually orthogonal planes of the cell surface morphology as well as the cortical layer and interior cytosol domains, displaying the values of the phase field variables for each domain. In Fig 7E and 7F the model predictions for hydrostatic pressure distributions and stored stress in the same orthogonal planar sections for the stationary morphology are given correspondingly. The pressure values are not unreasonable (e.g. a 1 mm depth of water at atmospheric pressure exerts a hydrostatic pressure of 9.8 Pa). The pressure is low and positive in the external aqueous medium and cytosol; therefore, an inward pressure is exerted from the external medium to the cell surface and an outward pressure from the cell interior (cytoplasm) to the cortex. The pressure is negative in the plasma membrane and cortical layer meaning that this layer experiences an inward compressive pressure from the external aqueous medium and cytosol. The highest (compressive) pressures arise in the cell surface “interphase” nearby high curvature BLiPs, with about an order of magnitude lower values within the cortical layer itself. These stationary pressure gradients suggest a propensity for fluid absorption from the exterior aqueous medium into the cortex phase and cell surface interface. It is important to note that our simulations assume a stationary morphology, and the pressure and stress distributions are a consequence of the stationary assumption. In reality, these morphologies are non-stationary, and in particular there is flow of the cytoplasm that fills the BLiPs. The new balance of pressure and stress will then dictate the directional flux and flow of the external aqueous medium and cytosol through the various cell compartments.
The 3D orientational distributions of cortical actin-filaments, comprising the nematic cortical phase, are given in Fig 8A; planar 2D slices are shown at the specified positions in Fig 8B–8D. These figures convey the degree and direction of order within the F-actin filaments of the cortex, and their strong correlation with the cell surface morphology. Note that cortical F-actin is assumed to prefer parallel alignment at both cortical interfaces for this illustration of the 3D phase field model. The anchoring energy at the interfaces together with the presumed strength of the nematic potential are responsible for the relatively high degree of alignment of the F-actin; these parameters are tunable to match experimental data on nematic order within the cortex.
The nematic order was changed by varying Flory order parameter from 1 to values approaching 0. The results of these simulations are shown in S6 Fig which shows the nematic order superimposed on the 3D morphology for order parameters that range from 1.0 to 0.01. Cross-sections taken at three orthogonal planes are shown in S7 Fig. Note that the values assumed for K (the Franck elastic constant), and h1 and h2 will not significantly affect the pressure or the Ericksen stress of the stationary morphology; this is because the dominant contribution to the Ericksen stress is from spatial gradients of the level set function ϕ1 = 1/2 that defines the cell surface. As shown in S6 and S7 Figs, h1 and h2 only affect the nematic order of the stationary state and K does not affect any of the predictions for the stationary morphology. However, for dynamic processes, these parameter values will strongly dictate the results of the simulation.
Using comparative measurements of many individual cells in two distinct configurations, spread on a substrate versus in a rounded state detached from the substrate, we showed that the cell surface area in the rounded state is highly convoluted and far exceeds the surface area of a sphere that would enclose the volume of the cortex, cytosol and nucleus. We analyzed the size and distribution of bleb-like protrusions (BLiPs) on the cell periphery that served as storage for the excess of surface area. We then developed three complementary modeling approaches that incorporate the concept of excess surface area on rounded cells in different ways.
Employing a 2D discrete geometric model, we tested whether the two-layer composition of the cortex, with the outer layer termed the cell surface, giving the local shape of the BLiPs and the inner layer responsible for contraction, is sufficient to reproduce the highly folded surface observed after rounding. This model demonstrated that morphologies with longer perimeters corresponding to larger ERs will have lower energy compared to the shapes with the same number of folds but with shorter perimeters (i.e. smaller ERs). This result predicts that during cell rounding, larger folds, which are energetically more favorable, should appear early in the process. On the other hand, smaller folds will appear later because it can require time to build structures that will support the higher curvature folds. Indeed, several preliminary experiments in which cells are detached and imaged appear to support this notion. In the early stages of rounding, big folds often appear on the cell surface but, as time goes on, the cell breaks the large folds into the smaller ones. This is supported by the fact that we see a prevalence of small BLiPs in the distribution of fold sizes from the SEM and fluorescence imaging. It is also possible that smaller BLiPs are required as the cell approaches a rounded, steady state because the large folds stored too much of the cell volume (S3D Fig) which could disrupt normal cell functioning. In addition, the ability to create smaller folds would be advantageous in storing large surface excesses in that small BLiPs could form under larger ones for more efficient packing.
Our 3D random “seed and growth” model of BLiPs approximated the BLiP number density and size distribution from 3D scanning electron micrographs. The model provided insight into the SEM image analysis that revealed skewed size distributions of the BLiPs, with a preponderance of small-scale features and successively fewer large-scale protuberances. Moreover, this model demonstrates that efficient packing of BLiPs requires that heterogeneity of BLiP sizes is needed to recapitulate 3D morphologies.
To begin to capture the physical properties underlying the convoluted morphology of the rounded cell, we introduced a generalized phase field formulation. The model accounted for the cell surface as a diffuse interface between the exterior aqueous phase and the interior cortical phase and cytoplasm. In the model, the cell surface is equipped with a Helfrich bending elastic energy that includes a spontaneous curvature function that encodes the bending energy associated with the BLiPs. The spontaneous curvature function is a consequence of molecular components (the spectrin-actin “fishnet”) that mediate the attachment between the cell surface and cortex. However, this molecular information is implicit at this stage of the phase field model, with future extensions aimed at coupling these molecular origins of the spontaneous curvature. At this juncture, our multi-compartment, phase field model accepts 2D or 3D images of the cell morphology as input targets and “learns” the membrane curvature of that target morphology. Since each cell compartment is endowed with constitutive properties, the phase field model predicts physical consequences of the target morphology throughout the cell compartments, restricted for this study to input stationary morphologies. In particular, the model predicts pressure and stress distributions that are concentrated within the cell surface diffuse interface and highly correlated with membrane-cortex interface gradients associated with BLiPs. In future model developments, when the dynamics of the rounded phenotype are introduced, the pressure-stress distributions will evolve in time, and the consequences of constitutive properties of each compartment will dominate the evolution, unlike the stationary predictions where viscous and nematic stresses relax to zero.
How do these three distinct approaches relate to one another? We postulate that cell surface regions rich in adaptor proteins bind the cell surface to the cortex, inheriting the mean curvature of the cortex. We have termed these regions attachment or contact points. Although the species composing these regions have not been identified, presumably they would belong to groups such as the ERM family of cytoskeletal-membrane adaptors as previously suggested in [24]. Moreover, one would expect that these contact points would be transient and regulatable leading to more dynamic behavior than we capture in the current models. Domains with less binding proteins allow the cell surface to detach from the contractile part of cortex forming BLiPs. We assume in our models that the distribution of binding protein species dictates the surface morphology, which in turn dictates the spontaneous curvature function. In the discrete geometric Hamiltonian model, the binding sites forming the attachments between the cell surface and the cortex are explicitly modeled, leading to an induced cell surface morphology.
In the phase field model, we choose the level set ϕ1 = 0.5 to define and match the surface morphology captured in 2D micrographs or reconstructed in 3D using the seed-and-growth model. Thus discrete Hamiltonian and phase field modeling approaches are complementary: the discrete Hamiltonian model is based on postulated attachment points that determine morphology, whereas the phase field model, in which specific molecular features are coarse-grained, is based on a spontaneous curvature function specific to and constructed from the morphology itself. In 3D, micrographs are not sufficient to provide a 3D image file due to significant occluded cell surface. Thus, we used images produced by seed-and-growth model that yields 3D images of the surface morphology that are statistically consistent with the measured 3D BLiP distribution data from scanning electron microscopy images. The phase field model then uses the 3D surface construction as an imposed target morphology, and the model evolution adapts the spontaneous curvature function until the target morphology is reached. The phase field model then predicts the stationary stresses and energies within the cell surface and cortex self-consistent with that surface morphology. It is important to note the stationary aspect of the model predictions which identify stress contributions that are due to spatial gradients of the fitted membrane morphology. Indeed, since the model simulation converges to the input stationary morphology, the stored stresses due to nematic elasticity all relax and are negligible. I.e., the stress components are insensitive to the nematic parameters, and are dominated by the gradients of the level set function ϕ1 = 0.5 learned from the morphology. The power of the model will be further revealed when we investigate the dynamics of the highly convoluted morphology, where nematic parameters and constitutive properties of all compartments will then have significance. It will be important from a biological standpoint to learn the bounds on these parameters.
An additional caveat is that the models presented are purely mechanical or steric in nature. It is certainly possible that active processes other than cortical contraction, giving rise to cortical tension [32] during rounding could play a role even in the short time span of cell rounding from a spread state. For example, in our model, we did not include the actin polymerization process explicitly although but it is true that smaller BLiPs, which are the majority of the population, have higher bending energy so that they require stronger cortical support perhaps requiring additional actin nucleation and polymerization [33] on short time scales.
Highly convoluted surface morphologies are often apparent in three-dimensional tissue contexts and in cells that are not fully spread on a two-dimensional surface. The storage of the cell surface in folds or bleb-like protrusions at the cell periphery is likely to be crucial to a variety of rapid cell shape changes such as those that occur in cell migration. It seems more feasible and energetically favorable that rapid cell shape changes can be accomplished quickly by calling upon and pulling out the excess surface stored in the BLiP distributions as an alternative to large scale endo- and exocytosis accompanied by membrane-cortex remodeling. Although the results presented here are derived for stationary BLiP-laden morphologies, these models form the foundation for future studies of cell surface dynamics regulated by coupling to reaction-diffusion kinetics of various molecular species. These kinetics can be expected to be controlled by signal transduction in many cases. The theoretical approaches presented here should find application in a number of different biological contexts.
The bead-spring model consists of two-layers (Fig 2A), where one layer (outer layer) represents the membrane and underlying actin mesh (i.e. the cell surface) and the other layer (inner layer) represents the myosin-rich contractile cortex. Within each layer, beads are connected pairwise by springs. Special contact points serve to connect the two layers via springs. At the beginning of simulation both layers have the same perimeter. During the simulation the inner layer (cortex) shrinks in order to reach the target enclosed area with smaller perimeter, imitating cortex contraction. The presence of contact points between two layers enforces outer layer bending (S2 Fig). Although the more correct definition of the excess surface ratio is ε2D=L¯2πR¯=L¯/2πAtotal, where L¯ is the perimeter of the surface layer and R¯ is the radius of the circle that would enclose the area inside this surface layer (Atotal), for the simplicity we define excess surface as a ratio between the perimeters of surface layer and contracted cortex.
Let the surface layer with the perimeter L be represented by N beads (S2 Fig), with the notational convention that bead 0 corresponds to bead N (representing a closed contour). Then the Hamiltonian for this outer layer of beads and springs (i.e. the cell surface) is:
Hout=c1Σi=1Nκi2+c2Σi=1N(li−L¯/N)2,
(2)
where ki is the local curvature of the surface at bead i; li is the length of the spring between beads i and i+1; and c1 and c2 are free parameters that define relative contributions of the energy terms. The first term in Eq 2 is the energy cost for bending the surface layer. The second term ensures that the outer layer does not significantly stretch or contract during the simulated process. c1 and c2 are chosen with c2 ≫ c1 so that as the system approaches a steady (minimum energy) state, the first term tends to a configuration that minimizes curvature and the second term tends to zero.
The Hamiltonian of the inner, contractile layer (i.e. the cortex) is:
Hinn=c3Σj=1Mpj2+c4(A−A¯)2,
(3)
where M is the number of beads in the inner cortex (M<N); pj is the length of the spring between inner beads j and j+1; A is the area of the polygon formed by the inner beads with perimeter P; A¯ is the target area; and c3 and c4 are scaling parameters that define relative contributions of the energy terms. At the steady state this layer approaches the circular shape with A→A¯ and lj→2πA¯/M.
The total Hamiltonian of the two-layer system contains three additional terms:
Htot=Hout+Hinn+Hcontact+Hcross+Hself.
(4)
Hcontact is the energy stored in the springs between inner and outer cortex contact points:
Hcontact=c5Σi=1P∥ti−τi∥2,
(5)
where ti denote the contact points on the outer cortex and τi denote the corresponding contact points on the inner cortex. This term ensures that these contact points remain close. Hcross penalizes crossing of outer cortex beads into the inner cortex polygon:
Hcross=c6Σi=1M∥ti−l¯∥1pi∈Inn,
(6)
where Inn denotes the interior of the polygon formed by the points of the inner cortex, and the indicator function 1pi∈Inn = 1 if the outer cortex point pi is in Inn and 0 if outside. ti denote the points on the outer cortex and l¯ denotes the segment closest to each point. We calculate this function by computing the point’s winding number. Lastly, Hself penalizes self-crossing of the outer polygon. Let l¯ denote the line segment connecting beads i and i+1.
Hself=c7Σi≠jcross(li¯,lj¯),
(7)
where the function cross(li¯,lj¯)=1 if li¯ and lj¯ cross and 0 if they do not. We let c7 = ∞ with the convention 0∙∞ = 0, effectively preventing any self-crossings of the outer cortex. In practice, this condition is enforced by considering all other energy terms and keeping bead i fixed if li¯ crosses any lj¯ for any j ≠ i in the next iteration. As the system approaches steady state each of these additional terms tends to zero.
While the target area (A¯) constraint is more aptly applied to the outer layer, it is numerically more feasible to apply the target area constraint to the inner layer Hamiltonian and to scale the final simulated result by multiplying the coordinates of each point by a multiplicative factor to match the target area. With this scaling the overall shapes of the “cell surface” and cortex do not change but all simulated shapes get the same area inside their surface layer which includes cortex and folds.
We introduce phase variables ϕi,i = 1,2,3 (Fig 5) that denote the volume fractions of phase 1 (the exterior aqueous medium surrounding the rounded cell), phase 2 (cortex) and phase 3 (interior cytosol), respectively. Clearly, in any pure phase i, the respective ϕi = 1, whereas in diffuse interfaces between phases i and j, ϕi+ϕj = 1, with ϕk = 0,k ≠ i,j, and everywhere the total volume fraction is 1. Thus in the external aqueous medium, ϕ1 = 1; in the F-actin rich, cell cortical layer, ϕ2 = 1; and in the interior cytoplasm, ϕ3 = 1. The phase boundaries are: the cell surface, as defined above, that separates the external aqueous medium and cortical layer, where 0<ϕ1,ϕ2<1; and, the transition layer between the cortex and interior cytosol where 0<ϕ2,ϕ3<1. For graphical purposes and for matching 2D TEM and 3D simulated topology images from the seed and growth model, the cell surface is defined by the level sets ϕ1 = ϕ2 = 0.5, while the cortex-cytosol interface is defined by ϕ2 = ϕ3 = 0.5. We do not allow all three phases to come into contact in this model, achieved by an energy penalty term. Therefore the level set ϕ1 = 0.5, in domains where ϕ3 = 0, determines the cell surface. Below, we illustrate how to constrain this level set function to match the experimentally measured cell surface, in both shape and surface area.
We note that for this paper the external aqueous medium and interior cytosol are modeled as viscous fluids with specified viscosities and the cortex is modeled as a nematic (liquid crystal) gel [31]. Viscoelasticity of the interior cytoplasm is easily incorporated into our phase field formulation [34], but for the purposes of the stationary morphology any stored elastic stress in the cell interior relaxes to zero. Thus we simplify to a viscous cytosol for this paper.
The governing equations for the three phases and two diffuse interfaces are presented next. The phase field method is an energy-based variational theory, comprised of free energy functionals for each phase and diffusive interface.
Swiss 3T3 cells (obtained from Tissue culture facility UNC Chapel Hill) were cultured in DMED (Gibco) with 10% FBS(Gibco). CHO-wt cells (from ATTC) were grown in medium DMEM/F12 (Gibco) containing 10% FBS. CHO cells stably expressing Lifeact-GFP (the small 17-amino-acid peptide,Lifeact, fused to green fluorescent protein, GFP) were obtained from the James Bear laboratory (UNC-Chapel Hill). CHO-wt cells were transiently transfected by GFP-PH-delta domain (gift from Con Beckers, UNC-CH) using Lipofectamine Plus reagent (Invitrogen) and images were taken 24–48 hours after transfection.
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10.1371/journal.ppat.1002677 | Reversing the Resistance Phenotype of the Biomphalaria glabrata Snail Host Schistosoma mansoni Infection by Temperature Modulation | Biomphalaria glabrata snails that display either resistant or susceptible phenotypes to the parasitic trematode, Schistosoma mansoni provide an invaluable resource towards elucidating the molecular basis of the snail-host/schistosome relationship. Previously, we showed that induction of stress genes either after heat-shock or parasite infection was a major feature distinguishing juvenile susceptible snails from their resistant counterparts. In order to examine this apparent association between heat stress and snail susceptibility, we investigated the effect of temperature modulation in the resistant snail stock, BS-90. Here, we show that, incubated for up to 4 hrs at 32°C prior to infection, these resistant snails became susceptible to infection, i.e. shedding cercariae at 5 weeks post exposure (PE) while unstressed resistant snails, as expected, remained resistant. This suggests that susceptibility to infection by this resistant snail phenotype is temperature-sensitive (ts). Additionally, resistant snails treated with the Hsp 90 specific inhibitor, geldanamycin (GA) after heat stress, were no longer susceptible to infection, retaining their resistant phenotype. Consistently, susceptible snail phenotypes treated with 100 mM GA before parasite exposure also remained uninfected. These results provide direct evidence for the induction of stress genes (heat shock proteins; Hsp 70, Hsp 90 and the reverse transcriptase [RT] domain of the nimbus non-LTR retrotransposon) in B. glabrata susceptibility to S. mansoni infection and characterize the resistant BS-90 snails as a temperature-sensitive phenotype. This study of reversing snail susceptibility phenotypes to S. mansoni provides an opportunity to directly track molecular pathway(s) that underlie the B. glabrata snail's ability to either sustain or destroy the S. mansoni parasite.
| Biomphalaria glabrata snails that are either resistant or susceptible to the parasite, Schistosoma mansoni, have been an invaluable resource in studies aimed at understanding the molecular basis of the snail/schistosome interaction. Schistosomes cause the chronic debilitating disease schistosomiasis. Thus, it is hoped that dissecting pathways that underlie the snail/schistosome relationship might translate into alternative control strategies that will include blocking transmission of the parasite at the snail-stage of its development. Induction of stress genes is a feature distinguishing early exposed juvenile susceptible NMRI snails from resistant BS-90 snail stocks. To further analyze this apparent involvement of stress induction and snail susceptibility, here we applied heat stress to the resistant BS-90 snail, enhancing induction of stress genes (Hsp 70, Hsp 90 and RT) prior to infection. Results showed these resistant snails became susceptible, indicating resistance as being a temperature sensitive phenotype in these snails. Stressed resistant snails treated with the Hsp 90 specific inhibitor, geldanamycin, prior to exposure, were, however, shown to maintain their refractory phenotype. Interestingly, inhibitor treated susceptible snails also became non-susceptible. Collectively, these data point to stress induction as an important early step in the ability of S. mansoni to infect juvenile B. glabrata snails.
| Schistosomes are parasitic trematodes that cause the chronic debilitating disease schistosomiasis, a neglected tropical disease that persists in over 70 countries of the developing world. It is estimated that at least 200 million people are chronically infected with the parasite with another 800 million remaining at risk for exposure. The disease burden is estimated at over 70 million disability-adjusted life years (DALYs) and there is increasing awareness that schistosomiasis can impact the epidemiology of other infectious diseases such as HIV (especially in female patients with genital schistosomiasis). A concerted effort is, therefore, being made to develop novel intervention tools that include blocking transmission of the parasite at the snail stage of its life cycle [1]–[3].
Freshwater snails serve as obligatory intermediate hosts for the development of parasitic trematodes. Throughout South America and the Caribbean Islands the snail, Biomphalaria glabrata plays an important role in the transmission of Schistosoma mansoni. The relative ease of maintaining B. glabrata in the laboratory has enabled it to become the host/pathogen model system of choice in which studies aimed at elucidating the molecular basis of snail/schistosome interactions are being conducted. Thus far, studies using representative snail stocks that are either resistant or susceptible to the parasite provide an invaluable resource towards unraveling the complex biology of the snail/schistosome encounter. For example, using pedigree snail stocks with varying susceptibility phenotypes, a strong genetic basis was shown to exist for the susceptibility of B. glabrata to S. mansoni [4]. In adult B. glabrata, resistance to S. mansoni has been shown to be a dominant single-gene trait that is inherited by simple Mendelian genetics. In juvenile snails, however, genetics of resistance has been shown to be a complex trait, involving 5 to 6 genes each with multiple alleles. Similarly, genetics of susceptibility to the parasite either in juvenile or adult snails has been shown to be multi-genic [5].
Using snail stocks that represent these different susceptibility phenotypes, the genetic locus/loci governing these traits have been assessed by a variety of DNA genotyping tools. These studies have led to the identification of heritable markers that underscore the adult snail parasite resistant phenotype [6]. Advances have also been made towards the identification of genes associated with snail susceptibility phenotypes by examining differences in gene expression profiles between snails that are either resistant or susceptible in response to parasite infection [6]–[10]. Accordingly, several genes involved in the snail's innate defense system are now known to play a significant role in the balance of whether the snail becomes infected or not [11], [12]. For example, in a resistant snail, such as the well-known representative BS-90 stock, the anti-parasite response in this snail has been shown to culminate in the encapsulation of the invading miracidia by a cell-mediated response involving hemocytes that, with plasma (hemolymph) factors, destroys the miracidium within a few days after it penetrates the snail. In a typical susceptible snail, such as the NMRI stock, however, there is no such active innate defense response against the invading miracidium and, therefore, the parasite survives, differentiates into sporocyts, producing cercariae that when released into freshwater can infect a human host, and go on to complete the life cycle.
Aside from the well-recognized genetic basis of the snail-schistosome relationship, shared molecular determinants of both organisms (snail and parasite) are also thought to play a role in the snail host compatibility to S. mansoni. Thus, interactions of snail diversified fibrinogen-related proteins (FREPs) and polymorphic mucins of schistosomes have been identified as some of the target molecules of snail and parasite, respectively that either by interacting, or not, with each other define compatibility/incompatibility of the snail/schistosome encounter [13]–[15]. This concept of shared, or molecular mimicry, at the snail - parasite interphase, underlying mechanisms of schistosome-snail compatibility/incompatibility is referred to as the matched- mismatched hypothesis [16].
Variations in susceptibility of B. glabrata to S. mansoni have been well documented [17], [18]. Furthermore, age- related variations in susceptibility have also been described. For example, Minchella and Richards showed that a snail that is susceptible as a juvenile can become resistant once it reaches adulthood, to the same strain of S. mansoni [19]. Given these variations, compounded with the fact that younger snails are, in general, more vulnerable to infection than adults [20], we felt that to identify the mechanism(s) governing susceptibility to S. mansoni, in juvenile, rather than adult snails, might be more beneficial in the long run towards our eventual goal of blocking disease transmission in the snail host. For this reason, therefore, the present study was performed entirely with juvenile and not adult snails.
To date, very few studies have investigated the modulation of stress genes and B. glabrata susceptibility to S. mansoni. However, Lockyer et al. (2004), while examining differential gene expression between resistant and susceptible adult snails, in response to S. mansoni, detected upregulation of the transcript encoding the stress response gene, heat shock factor (Hsp) 70 in resistant but not susceptible snails after S. mansoni infection [21]. These results are in contrast to those we obtained [22], showing instead upregulation of this transcript in early parasite exposed juvenile susceptible, but not resistant snails. Additionally, unlike the Lockyer et al. study where constitutive expression of Hsp 70 transcript was not observed in either resistant or susceptible adult snails, we showed that the expression of Hsp 70 occurs at similar levels in both normal resistant and susceptible juvenile snails. Furthermore, another study done using hemocytes collected from parasite exposed adult resistant and susceptible snails, showed down regulation of the Hsp 70 protein occurs in hemocytes of both these snails following infection, with more suppression of the transcript in susceptible than in resistant adult snails [23]. Thus, from the above studies, it is clear that there are major discrepancies concerning the expression of Hsp 70 between juvenile and adult resistant and susceptible snails, either with, or without infection, and also from hemocytes removed from infected adult resistant and susceptible snails. These discrepancies notwithstanding, as early as 1954 it was shown that raising the water temperature for maintaining B. glabrata shortened the length of the pre-patent period in S. mansoni infected snails, and also helped to maintain snail infectivity [24]. Additionally, this early study showed that some snails lost their infections when they were maintained at low temperature. In 1991, Lefcort and Bayne showed that S. mansoni infected resistant snails (13–16-R1 stock) displayed a preference for lower temperature compared to similarly exposed susceptible snails. No molecular explanations were, however, provided for these earlier observations [25].
While examining changes in gene expression profiles between juvenile resistant and susceptible snails soon after parasite exposure, we showed that the stress gene, Hsp 70 was induced early in susceptible but not resistant juvenile snails [9], [22]. Subsequently, we showed that the Hsp 70 transcript was co-expressed with the transcript corresponding to the reverse transcriptase (RT) domain of the B. glabrata non LTR-retrotransposon, nimbus, after exposure of susceptible juvenile snails to normal but not to irradiated miracidia. Similar gene profiling studies done in B. glabrata after exposure to another trematode, Echinostoma paraensi, also reported the upregulation of Hsp 70 in response to this parasite infection in the snail [26].
Because of this apparent association of an early stress induction and juvenile snail susceptibility, in this study we tested the hypothesis that enhancing stress prior to infection of a representative resistant snail, such as the BS-90 stock, by non-lethal temperature modulation, reverses the resistance phenotype.
The BS-90 snail originally isolated in the 1960s by Paraense and Correa in Salvador (Brazil) is a wild type snail that is resistant at any age (either as juveniles or adults) to both new and old world S. mansoni [17]. For this reason, most investigators have, since 1990, used this stock for studies aimed at identifying genes that underlie the aforementioned active innate defense response seen in these snails against S. mansoni. At ambient temperature (25°C) susceptible snails, such as the NMRI stock, reliably shed cercariae (varying between 85–95%) within 4 to 6 weeks after miracidia exposure, whereas exposed BS-90 snails destroy the parasite soon after infection, and thereby remain negative. Since arriving in our laboratory in 1990, BS-90 snails have never been known to become susceptible. Furthermore, in an early series of experiments conducted by Paraense and Correa (1963) where these snails were exposed to S. mansoni under different temperature conditions during the coldest (19.5–22.7°C) and warmest (24.9–27.6°C) months in the laboratory, no effect of temperature was detected. Indeed, in this same study, the snails derived from the original stock (collected in a lake near a beach at Amaralina district in Salvador), exposed to up to 100 miracidia remained negative.
Here, we show that by subjecting the resistant BS-90 stock to non-lethal heat shock treatment at 32°C, herein referred to as heat-pulse, prior to exposure, the snails consistently reversed their phenotype, shedding cercariae 5 weeks after infection. In contrast, similarly exposed, but unstressed BS-90 snails, remained uninfected. Additionally, if the stressed BS-90 snails were immediately treated with the Hsp 90 inhibitor, geldanamycin (GA) before exposure, they remained resistant. Interestingly, treatment of the highly susceptible NMRI snails with 100 mM of the same inhibitor before exposure to S. mansoni also prevented infection. These findings are consistent with an apparent association of the induction of stress genes (Hsp 70, Hsp 90 and RT) and B. glabrata susceptibility to S. mansoni.
Furthermore, the temperature sensitive switching of the resistant phenotype of B. glabrata to S. mansoni susceptibility provides an important means of directly tracking mechanisms that underscore the parasite's survival or destruction in the B. glabrata intermediate snail host.
Female SW mice were purchased from Taconic (Germantown, NY) and maintained in the Biomedical Research Institute's (BRI) animal facility, which is accredited by Lewis et al. [27], [28] the American Association for Accreditation of Laboratory Animal Care (AAALAC; #000779), is a USDA registered animal facility (51-R-0050), and has an Animal Welfare Assurance on file with the National Institutes of Health, Office of Laboratory Animal Welfare (OLAW), A3080-01. Maintenance of the mice, exposure to S. mansoni cercariae, and subsequent harvesting of the adult worms were approved by the BRI Institutional Animal Care and Use Committee (IACUC protocol approval number 09-03). All procedures employed were consistent with the Guide for the Care and Use of Laboratory Animals.
Juvenile snails were subjected to heat-pulse by incubation in pre-warmed (32°C) sterile water for 1–4 hrs as previously described [22]. After the heat-pulse treatment, the snails were immediately exposed to S. mansoni miracidia (10 miracidia/snail) at ambient temperature (25°C) in fresh aerated tap water for at least 3 hrs, individually, as previously described [9]. The heat-pulse treatment of juvenile resistant snails was performed on the basis of our previous data that showed optimal induction of Hsp 70 and RT transcripts occurred in these snails only after prolonged (2 to 4 hrs) heat stress [22]. Infected snails were maintained as described above, but in the dark. After 4 weeks, snails were screened individually for cercarial shedding by placing each snail in the well of a 12 well -plate (∼3 ml aerated water in each well) under a light source at room temperature for 1 hr as previously described [22], [31]. Given that the objective of this study was to examine the effect of temperature modulation on juvenile resistant snails, we scored snails as susceptible if they released any cercariae at all after infection, and not by how many parasites were shed per snail. This method of scoring was chosen to reflect the real life situation that it takes only a few viable parasites to transmit schistosomiasis in endemic regions. The water in individual wells was subsequently examined for the presence, or absence of cercariae under a dissecting microscope. The snails that were not shedding cercariae at the 9th week post-exposure, however, were kept and examined every week until the 12th week thereafter the snails were monitored on a monthly basis for cercarial shedding.
After heat-pulse treatment as described above, BS-90 snails were transferred immediately into a beaker containing a solution of 100 mM geldanamycin (GA) (Sigma Aldrich) overnight at room temperature. Snails were removed from the drug solution, washed twice (5 min intervals) in ∼30 ml of fresh aerated water at room temperature to rinse off residual contaminating drug before exposing to miracidia individually in 2 ml of fresh aerated water (in a 12 well plate). Each snail was exposed in fresh water at room temperature while monitoring under a dissecting microscope for miracidia penetration. All snails were exposed to twice the number of miracidia (10 miracidia/snail) we normally use for exposing juvenile NMRI snails.
Different types of experimental conditions (12 snails for each cohort) were set-up as follows: 1) normal (unstressed and unexposed) BS-90 snails, 2) parasite exposed-normal BS-90 snails, 3) heat-pulsed only (unexposed) BS-90 snails, 4) exposed then heat-pulsed BS-90 snails, 5) heat-pulsed/GA treated-exposed BS-90 snails, 6) GA treated normal BS-90 snails. Water changes were done on a weekly basis on all the snails. Snails were examined for evidence of parasite infection (cercarial shedding) as described above. In this study, aerated fresh water collected from the same container (30 gallons capacity barrel) was used for all snail husbandry and miracidia exposures.
To examine the effect of GA treatment on B. glabrata NMRI susceptible snails and parasite exposure, juvenile snails (4–6 mm in diameter) were used for the study. Twelve snails in a cohort were treated with GA at final concentrations, 0, 0.1, 1.0, 10 and 100 mM. Snails were treated as described above by incubating overnight in a 50 ml beaker containing the drug solution. Treated snails were removed from the drug solution, washed as described above before being transferred into 2 ml of aerated fresh water (in a 12 well plate) for individual miracidia exposure. Each snail was exposed in freshwater at room temperature (10 miracidia/snail) and monitored under a dissecting microscope for miracidia penetration. After exposure, snails were transferred into fresh water for the remainder of the study. Experiments were repeated, 5 times, for the highest dose (100 mM) of GA, and 3 times for the lower drug concentrations, representing 5 and 3 biological replicates, respectively. Twelve size-matched snails were used for each drug concentration. As a control, miracidia were treated with the same high dose of GA for 5 hrs then used for snail exposures as described above. Data were pooled (12 snails/dose of drug) from 5 independent experiments for the high (100 mM) concentration (N = 60) and 3 independent experiments from the low (0.1–10 mM) concentration (N = 36) and standard error (SE) determined. In addition, three other controls (12 snails for each cohort) were used as follows: 1) normal NMRI snails 2) exposed normal NMRI snails and 3) unexposed GA treated-NMRI snails. Water changes were done on a weekly basis on all the snails, and exposed snails were examined for evidence of parasite infection (cercarial shedding) as described above. From the 3 and 5 biological replicates (N = 36, and N = 60, respectively), one-way ANOVA was used to determine if differences in cercarial shedding (%) between low and high doses of GA treatment of NMRI snails were significant.
To investigate the expression of stress genes Hsp 70, Hsp 90 and RT, snails from the same cohort, subjected to heat-pulse, and exposed individually to miracidia (10 miracidia/snail) (as described above) were snap frozen in liquid nitrogen and kept at −70°C until required for RNA isolation. Of the three cellular stress genes examined in the present study, the differential induction of Hsp 70 and RT between susceptible NMRI and resistant BS-90 snails after parasite exposure have previously been reported [22]. To determine differences in the expression of Hsp 90 between juvenile BS-90 and NMRI snails they were exposed, individually as described above for 0, 15, 30, 45, 60 and 120 min before being frozen for RNA isolation.
Total RNA was extracted from the whole snail by single-step simultaneous RNA isolation using RNAzol RT (Molecular Research Center, Inc., OH) [32]. RNA from individual snails was utilized for all qPCR analysis. The quantity and quality of RNA was determined by UV absorbance (A260) and an A260/A280 ratio (∼1.9–2.0) obtained by using the NanoDrop 1000 (Thermo Scientific). Eighty nanograms of total RNA was analyzed by real time qPCR using Brilliant II SYBR Green QRT-PCR Master mix according to the manufacturer's instructions (Stratagene, CA). Real time qPCR reactions were done using the 7300 real - time PCR system from ABI (Applied Biosystem). At the beginning of the assay, a validation protocol (done according to the manufacturer's instructions) was performed to test amplification efficiencies of stress genes (Hsp 70, Hsp 90 and RT) and myoglobin (constitutively expressed house keeping gene) [9], [22], [33], [34], [35]. The validation experiment was done using four different RNA templates dilutions to confirm that amplification efficiencies were equal between our genes of interest and myoglobin genes (data not shown). The 25 µl final reaction volume contained either 200 nM of gene specific primers for Hsp 70, Hsp 90 and RT or 50 nM of primers for the house keeping myoglobin gene that amplifies a 349 bp fragment [33]. Nucleotide sequences of the gene specific primers for Hsp 70 and RT have previously been described [22]. The Hsp 90 specific primer pair (forward primer; 5′-tgtgcgcagagtgttcatcatgg-3′ and reverse primer; 5′-ctcctgtgaggcttcaatgagtc-3′) was designed by Primer-Blast software (http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome) from publically available Expressed Sequence Tags (ESTs of B. glabrata 5′end clone; accession number, AA547771 that has homology to heat shock protein 90). The Hsp90 gene specific primers amplify a 199 bp fragment from B. glabrata BS-90 and NMRI stocks. All primers were checked for non-cross amplification with S. mansoni cDNA template as well as with other schistosome parasite species (data not shown). Reactions were performed in triplicate (technical replicates) for each individual snail RNA sample in a one-step format with the first strand cDNA synthesis and real time PCR amplification done as described previously [35]. Each reaction contained a no template negative control to rule out non-specific amplification from contamination in the primers and buffers. The gene transcript levels were normalized relative to myoglobin expression. In our study, myoglobin showed stable expression in BS-90 and NMRI snails under normal and stress conditions (data not shown). After five biological replicates using 10 snails/time point (50 snails in total) the fold change in transcription of corresponding genes of interest were calculated by using the formula below [36].
P-values were calculated by comparing the delta-Ct value as previously described for each group by Student's t-test to determine if the differential expression of the transcripts between experimental and control groups was significant (9, 22, 32, 33). Fold-changes in transcripts corresponding to stress genes of interest, normalized relative to myoglobin expression from individual snail RNA (10 snails/time point, assayed in triplicate), were determined from five independent experiments. These data were pooled (N = 50) and standard error (SE) determined.
Each reaction contained a no template negative control to rule out non-specific amplification from contamination in the primers and buffers. To monitor the differential transcription of Hsp 90 between NMRI and BS-90 snails, RNAs were isolated at different time points following exposure, as described above, and were normalized relative to myoglobin expression.
To determine constitutive expression of Hsp 90 in both BS-90 and NMRI snails, we performed qualitative RT-PCR as previously described [35] using first strand cDNA synthesized from total RNA using a cDNA synthesis kit (Promega) with M-MLV reverse transcriptase isolated from two same-sized (4–6 mm in diameter) individual snails of either the resistant (BS-90) or the susceptible (NMRI) stock. Using the Hsp 90 gene specific primers described above, second strand PCR was performed with cDNA template that was prepared either in the presence or absence of M-MLV RT under the following conditions: denaturation step at 95°C for 30 sec, annealing step at 65°C for 30 sec, extension at 72°C for 1 min, repeated for 29 cycles. The PCR products were resolved by agarose gel electrophoresis (1.2%) and amplicons were visualized by ethidium bromide staining under UV trans-illumination using the Quantity One software (Gel Doc XR imaging system, BioRad). A 100 bp ladder from a commercial source (Invitrogen Laboratories, CA) was utilized as standard for the qualitative analysis. Parallel PCR-amplification using equal amounts of the same cDNA template was done by using gene specific primers corresponding to the house keeping myoglobin gene as previously described [33]. PCR amplifications were also done without cDNA template, as negative control, to monitor the reagents utilized in the assay for possible contamination.
Snails were screened as described above for cercarial shedding and those found to be positive at 7 weeks PE were allowed to shed into an appropriate container for 30–60 min under a light source at room temperature. After shedding, the snails were removed from the container and the cercariae suspension was allowed to stand (10 min at room temperature) to allow any non-specific sediment (mucoid material and feces) from the snail to settle at the bottom of the container. The sediment-free top part of the cercariae suspension was transferred to another container and was used for the mouse infection.
Two female Swiss-Webster (SW) mice (2–3 months old weighing18 grams) were used for the cercariae infection (50 cercariae/mouse) by tail exposure, allowing the cercariae to penetrate individually for ∼30 min. Forty-nine days after infection, feces from the mice were examined for first appearance of parasite eggs. The positive mice were euthanized for recovery of adult worms by the perfusion technique using 0.1 M citrate-0.15 M sodium chloride solution [28].
Mice infected with cercariae that were shed from the ‘resistant’ heat- pulsed -exposed BS-90 snails were perfused at 6 weeks post infection as described by Lewis et al. [28]. Harvested adult worms were frozen individually at −70°C until required. Genomic DNA was isolated from frozen individual worms as described by Simpson et al. [37], and screened by RAPD-PCR using random primers OPR-14 and OPA-O6 (EurofinsMWG/Operon, AL) as previously described [6]. Amplified fragments were separated by agarose gel electrophoresis, and the presence/absence of bands visualized by ethidium bromide staining under UV Trans-illumination (ChemiDoc imaging system, BioRad CA). For comparison, an equal amount of DNA from adult S. mansoni harvested from the susceptible snail was analyzed in parallel, using the same random primers mentioned above.
To investigate if the expression of Hsp 90 in the resistant (BS-90) and susceptible (NMRI) snail would be similar as was shown previously for the levels of Hsp 70 and the nimbus RT domain, qualitative RT-PCR was conducted with template cDNA prepared with and without M-MLV-RT from RNA isolated from uninfected individual snails. As shown in Figure 1A, qualitative RT-PCR revealed that basal constitutive expression of Hsp 90 in both snail stocks was remarkably similar. Thus, the PCR amplification of cDNA from two individual BS-90 snails (Fig. 1A, lanes 1 and 2), using the Hsp 90 gene specific primers described in Materials and Methods, produced a 199 bp expected-size band that was similar in size and intensity (Fig. 1A, lanes 1 to 4) to the PCR amplification product produced from cDNA templates from two individual NMRI susceptible snails (Fig. 1A, lanes 3 and 4). To rule out any possibility that the amplicons detected in lanes 1 to 4 might have originated from genomic DNA contamination in RNA preparations utilized for first strand cDNA synthesis, control reactions performed without reverse transcriptase (−M-MLVRT) in the first stand reaction were also utilized for PCR amplifications (Fig. 1A, lanes 5 to 8). Thus, using the Hsp 90 gene-specific primers for amplification reactions with this control (minus M-MLV RT) template produced no bands in either the BS-90 (Lanes 5 and 6) or NMRI (Lanes 6 and 7) samples, indicating that there was no genomic DNA contamination in the RNA preparations utilized for this assay. In parallel RT-PCR was performed with primers corresponding to the housekeeping myoglobin gene using equal amounts of the cDNA templates utilized in lanes 1 to 4. In this case, (also in lanes 10 to 13) an expected 350 bp size PCR product was obtained, indicating similar constitutive expression of myoglobin in both these two snail stocks. As another negative control, PCR was also conducted without cDNA template. The absence of a product (lanes 9 and 18) showed there was no contamination in the reagents utilized for the assay.
Since previous studies showed dramatic differences in the temporal modulation and degree of induction of the Hsp 70 and RT transcripts following S. mansoni infection between resistant and susceptible snails, in the present study we chose to examine whether parasite exposure mediates the differential expression of another major cellular stress gene, Hsp 90. The induction of the Hsp 90 transcript in susceptible NMRI and resistant BS-90 juvenile snails exposed for different time points (0, 15, 30, 45, 60 and 120 min) to miracidia is shown in Figure 1B. RNA isolated from the snails was analyzed by real time qPCR as described in Materials and Methods using the uniform constitutive expression of the myoglobin transcript in both snail stocks as internal standard. From 5 independent (biological replicates) assays done by using 10 individual snails per time point, with each RNA sample run in triplicate, results showed that as early as 15 min post- exposure almost 10-fold induction of the Hsp 90 transcript was obtained in the susceptible NMRI snail. Furthermore, the transcript remained upregulated in the susceptible snail throughout the 120 min PE time period examined. In contrast, a smaller difference in the upregulation of this transcript (1.54 fold change) occurred in the resistant snail at the early 15 min time point after infection. Although variations in the induction level (of Hsp 90 transcript) were observed in the infected susceptible snail between the early 15–120 min PE time period, none of the levels we detected in the infected resistant snail (1.45 to 1.54 fold change) exceeded those detected in the infected susceptible snail.
Previously we ascertained, as mentioned above, that induction of stress genes occurs in the resistant BS-90 snail after a long 2–4 hrs time period either in response to parasite exposure, or heat shock. Accordingly, here we kept the BS-90 snails at 32°C for 3–4 hrs before exposing them immediately to miracidia as described in Materials and Methods. Using real time qPCR, modulations in expression of the three cellular stress genes (Hsp 70, Hsp 90 and RT) were assessed. The fold changes in expression of transcripts corresponding to Hsp 70 (Fig. 2a), Hsp 90 (Fig. 2b) and RT (Fig. 2c) in resistant BS-90 juvenile snails either following heat-pulse treatment, or subsequently subjecting the heat- pulsed snails to S. mansoni exposure for 2 hrs were determined (Fig. 2). As shown in Figure 2a, in comparison to snails exposed without prior heat-pulse treatment, minor induction (1.2 fold) of the Hsp 70 transcript was detected in these resistant snails after parasite exposure. In contrast, however, the heat-pulse for either 3 or 4 hrs promoted a more significant induction (6.1 and 7.9 fold, respectively) of this transcript. Interestingly, exposing the snails to miracidia immediately after 3 and 4 hrs heat-pulse kept the induced Hsp 70 transcript at a level that was higher (4 and 6.2 fold induction, respectively) than the 1.2 fold induction we observed in snails that were exposed without being heat pulsed.
In Figure 2b, while a 2.5 fold induction of the Hsp 90 transcript was observed in the unstressed 2 hrs parasite-exposed BS-90 snail, we detected a 7.9 and 7.8 fold increase in this transcript in snails subjected to 3 and 4 hrs of heat-pulse, respectively. The fold change in the Hsp-90 transcript remained relatively higher in snails that were heat-pulsed and immediately exposed to miracidia (3.3 and 2.9 fold increase) compared to the 2.5 fold increase observed in the normal (minus heat-pulse) parasite-exposed snail.
In Figure 2c, in unstressed BS-90 snails exposed to miracidia, a 3.5 fold increase in the RT transcript was observed compared to a 27.3 and 26.3 fold induction of this transcript when snails were kept for 3 and 4 hrs at 32°C prior to infection. Thus, in this case, the dramatic increase in the RT transcript remained elevated in snails that were heat -pulsed for 3 to 4 hrs and then immediately exposed to miracidia. Thus, a strong 20.5 and almost 10 fold increase was detected in these snails (heat-pulse plus exposure) compared to snails that were exposed without heat-pulse treatment where only a 3.3 fold increase in the RT transcript was observed.
The above results showed that compared to snails that were only exposed to miracidia, the induction of all the stress transcripts examined (Hsp70, Hsp90 and RT) was more elevated in BS-90 snails that were responding to parasite exposure after heat - pulse treatment.
Results in Figure 3a shows the effect of combining heat -pulse treatment with immediate exposure of the stressed resistant snails to miracidia. Parasite-exposed unstressed snails were monitored as control. As shown in Figure 3a, as expected, juvenile resistant snails that were exposed to miracidia without heat -pulse treatment released no cercariae for the entire 7 weeks duration of the experiment. In contrast, a significant number, more than half (60.7% and 53.9%), of resistant snails that were heat -pulsed either for 3 or 4 hrs prior to exposure were found to shed cercariae starting at 5 weeks PE. Interestingly, by 7 weeks PE, all such prior heat -pulsed, and parasite -exposed resistant snails were shedding cercariae.
To determine if cercariae released from these positive resistant snails were biologically viable i.e. capable of infecting the experimental mouse host and developing successfully into adult worms in this host, cercariae released from ‘resistant’ snails were utilized for mouse infections as described in Materials and Methods. Since we were able to perfuse adult worms from the mice infected with ‘resistant’-snail derived parasites, we can conclude that larval parasites released from these ‘resistant’ snails were indeed infectious, developing normally within the expected 6 weeks time-frame into adult worms in the infected mouse. To determine whether these adult worms were in fact genotypically identical to parasites harvested from mice exposed to cercariae released from our representative susceptible snails (NMRI stock), we analyzed genomic DNA from worms harvested from mice that were infected by using either resistant, or susceptible snail derived parasites, with the multi-locus RAPD-PCR genotyping tool. As shown in Figure 3b, the DNA profile, using random primer OPR-14, from 7 individual worms (lanes 1 to 7) was comparable to that of an adult worm recovered from a mouse (by perfusion) that was exposed to cercariae shed from the representative susceptible snail (lanes 8 to 10). Likewise, DNA profiling of the same samples as utilized in lanes 1 and 7 but amplified, this time, with random primer OPA-06 showed no polymorphisms existed between either resistant (lanes 1 to 7) or susceptible (lanes 8 to 10) snail derived parasites. The absence of bands in lane 11, using either of the two random primers but without template DNA shows there was no contamination in either the buffer or enzyme employed for the RAPD assay.
As shown above, heat -pulse treatment followed immediately by miracidia exposure of the resistant snail, resulted in the robust resistant BS-90 snail becoming susceptible. On the basis of the above results, we, therefore, felt it was necessary to determine the outcome of exposing the resistant snails to miracidia before subjecting them to the heat -pulse treatment. Thus, juvenile BS-90 snails were initially exposed to miracidia, and immediately heat -pulsed at 32°C for 3 hrs. Figure 4a shows the real time qPCR results of the fold difference in expression of transcripts encoding Hsp 70, Hsp 90 and RT at different time points (0, 15, 30, 60 and 120 min) after maintaining the infected snails at 32°C. Interestingly, unlike results shown in Figure 2 where enhanced induction of all three transcripts was detected in snails that were heat pulsed before exposure, in this experiment, induction of all the stress transcripts examined remained relatively unchanged.
Accordingly, we monitored these snails (parasite- exposed then heat -pulsed) for cercarial shedding as described above. Results (Fig. 4b) showed that all (100%) the resistant snails that were exposed to the parasite before being subjected to heat -pulse treatment remained negative. From these results, it is clear that exposing these resistant snails to the parasite before they are stressed, maintains the refractory status of these snails, further demonstrating the temperature sensitivity of BS-90 resistant phenotype to S. mansoni infection.
To determine, more precisely, if significant upregulation of stress -related transcripts is indeed a factor in rendering the heat -pulsed resistant snails susceptible to infection, we investigated the effect of blocking the action of one of the stress proteins, Hsp 90, by using a specific inhibitor drug geldanamycin (GA) that prevents this protein from performing its role as an essential chaperone in the cell [38]–[40]. In Figure 5, resistant snails that were heat -pulsed for 3 hrs were exposed either immediately to miracidia as described above, or were treated with GA (100 mM) before being exposed to miracidia. Results showed that without inhibitor treatment of stressed snails, the majority (70%) of heat -pulsed and exposed snails were found to shed cercariae 9 weeks after exposure. In contrast, snails that were heat –pulsed, and immediately treated with GA before exposure failed to shed cercariae, remaining negative for the entire 9 weeks duration of the experiment. These data clearly show that blocking the action of Hsp 90 by GA treatment in the stressed resistant snails before exposure maintained their refractory phenotype, thereby indicating that a sustained significant stress induction of Hsp 90 was involved in the mechanism(s) of B. glabrata susceptibility to S. mansoni.
To further demonstrate the link between stress induction in juvenile B. glabrata snails and their susceptibility to S. mansoni, here the effect of pre -treating the susceptible NMRI snail with the aforementioned Hsp 90 inhibitor (GA) on the outcome of infection was examined. Figure 6a shows the survival of snails after either infection alone (minus GA inhibitor) or after treatment with 100 mM of GA. Results (Fig. 6a) showed that snails tolerated the drug at this dose and survived at the same rate (100%) as those that were exposed without drug treatment. Since all the snails tolerated this relatively high dose of the inhibitor, we proceeded to examine whether pre -treating the susceptible snail with various doses of GA would affect their susceptibility phenotype.
As shown in Figure 6b, susceptible snails that were infected without prior drug treatment, as expected, were found to be shedding cercariae at 5 weeks PE (18.3%), with all untreated-exposed susceptible snails shedding cercariae at 9 weeks after infection. In contrast, susceptible snails that were treated with 100 mM GA prior to exposure failed to shed cercariae at the same 9 weeks PE time period while only a small percentage (3.7 to 4.8%) of snails pre-treated with lower doses of GA (0.1 to 10 mM) were found to be shedding cercariae. The longest surviving miracidial exposed, drug treated- (100 mM) snails remained negative (no cercarial shedding) for up to 9 months after infection. All snails not shedding cercariae by the 9th week after exposure remained negative at week 12 and continued to be negative for at least 9 months PE.
The reduction of shedding (in percentages) from infected snails treated with the higher dose of GA compared to the lower doses was significant as determined by one-way ANOVA (P-value<0.05) [N = 60 and N = 36].
To rule out the possibility that results presented above might simply reflect the effect of the drug inhibiting the parasite's Hsp 90 homolog, thereby impairing the ability of miracidia to either penetrate the snail, or transform successfully into sporocysts, we treated miracidia directly with the highest dose of GA (100 mM) that was utilized in this study. These drug -treated miracidia were then utilized for snail exposures in comparison to exposures done with untreated miracidia, as control. In these experiments, more than 75% of miracidiae (with or without GA treatment) penetrated the snails within 5 min, and all (100%) successfully penetrated the snail within 1 hr (data not shown). These data were similar to penetration behavior we previously observed where we used either normal or irradiated miracidiae for snail exposures [22]. As shown in Figure 7, 41.7% and 87.5% of NMRI susceptible snails exposed to GA treated miracidia shed cercariae at 4–5 weeks. At 4 to 5 weeks PE, results analyzed by student's t-test showed that the percentage of cercarial shedding between NMRI snails exposed to either GA treated or normal miracidia was statistically significant (P –value<0.05). However, after week 5 PE, results showed that there was no statistically significant difference between the percentage of cercarial shedding between NMRI exposed to either GA-treated or normal miracidia. NMRI snails that were exposed to the untreated miracidia, likewise released cercariae after week 4 PE as we have routinely come to expect for snail infections performed by using this parasite strain and snail-host combination.
To date, very little information exists on molecular mechanisms that determine the outcome of the snail/schistosome interaction. Here, we have shown that upregulation of stress-related transcripts, such as those examined in this study, Hsp 70, Hsp 90 and RT in the B. glabrata snail host, soon after infection, plays an important role in their susceptibility to S. mansoni. These results are consistent with our previous data that showed a differential induction of stress genes occurs between juvenile susceptible and resistant B. glabrata snails after exposure to S. mansoni miracidia. Thus, upregulation of transcripts corresponding to Hsp 70 and RT was detected sooner, and more dramatically in susceptible compared to resistant snails following either heat shock or parasite infection [22]. Also, in this previous study, we showed that the stimulus/stimuli for this stress induction may be present in normal but not in irradiated attenuated miracidia.
Although we are yet to discover the nature of the parasite stress elicitor(s), it is most likely released from the incoming parasite as excretory secretory products (ESP). Interestingly, several studies have described using miracidial ESP to induce changes in either snail hemocytes, or the B. glabrata embryonic cell line, Bge [41]–[43]. Despite our limitation of not knowing what triggers the induction of stress in the snail (soon after exposure to miracidia) it is clear from this study that by using the heat -pulse regimen described to enhance the induction of stress to levels not typically seen under normal circumstances in resistant snails after exposure, it is possible to successfully reverse the resistant phenotype of juvenile resistant BS-90 snails i.e. render them susceptible to S. mansoni. Therefore, even though our results showed that parasite infection of the heat –pulsed snails caused a reduction in the induction of the stress transcripts, a more enhanced induction of all the three transcripts was still observed in heat-pulsed- infected snails than in snails that were exposed to miracidia alone without heat -pulse, helping to switch the phenotype of juvenile BS-90 snails from being resistant to susceptible. Thus, from these results, it is clear that resistance to S. mansoni in the juvenile BS-90 snail is a temperature-sensitive (ts) phenotype. Furthermore, cercariae released from these ts snails were infectious, developing fully into adult worms in the infected mouse, and with no change detected in DNA profiles of adult worms harvested from either these ‘resistant’ snails or our representative susceptible NMRI snail stock.
Historically, studies regarding B. glabrata susceptibility to S. mansoni have emphasized either the role of genetics, or innate defense in the snail/parasite association. While these areas of study have been pivotal in explaining some of the complex dynamics behind why schistosomes are either destroyed or survive in the snail host, it is clear from our results that temperature sensitivity of the stress gene loci reported is a contributing factor in the outcome of this host/pathogen interaction. Accordingly, we can only speculate that the ts resistant BS-90 snail's phenotype must involve changes in the activation of the three stress response genes to account for their altered kinetics. In a recent study, we showed that an unknown external stimulus/stimuli from the parasite was indeed able to mediate the non-random repositioning of gene loci of interphase chromosomes in the snail embryonic cell line, Bge [44]. These gene loci repositioning studies have since been reproduced in intact snails responding to S. mansoni (Arican, unpublished), and we are currently investigating if the stress genes are repositioned upon induction.
Previous studies have shown that the induction of stress is important for successful outcomes of other host-pathogen relationships as well. For example, in baculovirus infected Sf-9 cells, an increase in the expression of Hsp 70 was found to correlate with active virus replication. Thus, in this study, Lyupina et al. showed that inhibiting Hsp70 expression by the drug KNK437 suppressed virus replication [45]. Additionally, Hsp 90 has been shown to be essential for the growth of the malaria-causing agent, P. falciparum, in human erythrocytes [46]. Consequently, derivatives of GA, the Hsp 90 inhibitor used in the present study, has also been used to inhibit the growth of P. falciparum and another protozoan, Trypanosoma evansi [46], [47]. GA is a benzoquinone ansamycin antibiotic that binds to the N-terminal ATPase site of Hsp 90 to inhibit its chaperone activity. Although the inhibitor and its derivatives have previously been used to inhibit cell proliferation in cancer [48] and the growth of other parasites, as mentioned above it has never been shown, until now to treat any mollusk in the context of examining host-pathogen interactions. Interestingly, our results showed that GA treatment neither impaired the penetration behavior of the miracidia nor their ability to remain infectious. More studies, especially performed in vivo for longer time points will be needed to further examine this apparent lack of GA toxicity on the larval parasites. A similar lack of GA toxicity, which might be due to non-binding of GA to Hsp 90 homologues of free-living nematode larval stages, has previously been reported [49].
Heat shock proteins are highly conserved proteins that have been shown to play a critical role in maintaining protein integrity, preventing the aggregation of misfolded proteins in the cell, thereby maintaining normal cell function in the face of cellular injury from physical or physiological stress [50]. Very few studies have examined stress induction in relation to mollusk/pathogen interactions. Our results showing the very early (within 15 min) induction of Hsp 90 in susceptible snails (but not resistant snails) after infection was surprising and underscores the need for more studies on this stress protein, especially in relation to snail-schistosome interactions. Hsp 90 is expressed abundantly even in the absence of stress and constitutes a large portion of constitutively expressed protein in cells. The protein is regarded as being essential to cell viability because of its central role in forming complexes with a wide variety of co-chaperones and client proteins that are involved in major cellular pathways, such as signal transduction and cell-cycle control [51].
How Hsp 90 interacts directly, or indirectly with either the B. glabrata Hsp 70 or nimbus RT has yet to be investigated. Particularly, whether (or not) key molecules of the snail's innate defense system, such as FREPs are client proteins of Hsp 90, remain to be investigated. Previously, we showed that co-induction of Hsp 70 and nimbus RT transcripts occur soon after S. mansoni infection of juvenile susceptible snails [22]. Mobile Genetic Elements (MGEs), such as nimbus are responsive to cellular stress [52], [53]. However, the role of the nimbus non-LTR retrotransposon in the stress pathway of B. glabrata remains unknown. Further studies are, therefore, required to elucidate the relationship between all these stress genes (Hsp 90, Hsp 70 and RT) in the snail's behavior towards S. mansoni.
In other mollusks, such as the clam, Mercenaria mercenaria, the upregulation of Hsp 70 was observed in this mollusk in response to the opportunistic parasite, known as Quahog Parasite Unknown, QPX [54]. Also, in another clam, Meretrix meretrix, it has recently been shown that expression of Hsp 70 was upregulated soon after Vibrio parahaemolyticus infection [55]. Additionally, in the disk abalone, Haliotis discus, a recent molecular characterization showed that Hsp 90 is induced within 4 hrs after treatment with lipopolysaccharide, LPS [56]. In another B. glabrata susceptible snail, the M-line stock, Hanington et al. [26] showed upregulation of stress related transcripts following infection of these snails with trematodes, either S. mansoni or Echinostoma paraensei. The modulation (down regulation) of Hsp 70 in hemocytes isolated from exposed B. glabrata resistant and susceptible snails (the cells most intimately associated with the active destruction of schistosomes in the snail host) has also been reported, suggesting an involvement of this stress protein in the snail host's defense system [7], [23].
Indeed, an immunological role for stress proteins has been widely documented [57]. Thus, it might be reasonable to assume that in the snail/schistosome system, cellular stress triggered against parasite proteins that are recognized in the snail as non-self, by maintaining the homeostasis of the host, paradoxically protects the parasite as well. Larval schistosomes have been shown to express RNA transcripts for heat shock proteins. Presumably, such heat shock proteins (released from the parasite) might induce stress genes in the snail host, providing the cytoprotection that the parasite needs for its own successful invasion. While a strong anti-schistosome Hsp 70 humoral response has been reported in several infected (S. mansoni and S. hematobium) mammalian (murine, human and baboon) hosts [58], [59] nothing is known about the role of schistosome heat shock proteins and the snail's innate defense system. Thus far, we have evidence showing that an active defense system plays an important role in the BS-90 resistant snail's ability to ward off the parasite infection. By showing in this study that the deliberate use of stress in the form of non-lethal heat-pulse (boosting the level of inducible stress in the resistant BS-90 snail before infection) was a necessary step in rendering these normally robust resistant snails susceptible, we can suggest that the stress induced dampened the anti-schistosome response that is typically seen in these snails. Previously, it was shown that the resistance phenotype can be interfered with in resistant snails (10-R2 and 13–16-R1 stocks) if snails were first infected with other trematodes, such as E. paraensei and E. lindoense before being exposed to S. mansoni [60]. While no molecular explanation was given for this apparent suppression of the defense system by the dual infection protocol first described by Lie and Heyneman et al. [61], these early results showed that susceptibility to S. mansoni in these resistant stocks developed shortly (within 1 hr) after they had been exposed to E. paraensei. In light of our current results, we can assume that in this previous study, the primary echinostome infection, by triggering a stress response, dampening the innate defense system allowed the secondary S. mansoni infection to survive and develop. In other host-pathogen systems, there is clear evidence that expression of stress proteins, in particular Hsp 70 is an important feature in modulating the host innate immune response [62].
Another plausible explanation for the results presented here might be that the initial heat -pulse could have destroyed the resistant snail's hemocytes, thereby rendering these cells incapable of killing the incoming miracidia. It is also possible that the induction of stress might be a reflection of the successful establishment of the parasite in the snail. As mentioned above several factors, and not hemocytes alone govern compatibility/incompatibility issues between B. glabrata and S. mansoni. The heat pulse regimen utilized to enhance the induction of stress genes in the resistant snails was not lethal. All the snails survived at this elevated temperature, and a colony of BS-90 snails that we maintain (now in their eighth month) at 32°C continue to thrive. Interestingly, all (100%) of progeny snails (F1, exposed to miracidia and kept at 32°C after exposure) bred from BS-90 snails maintained at 32°C were found to shed cercariae at 3 to 4 weeks PE (Ittiprasert, Miller and Knight unpublished).
While we have no data supporting the notion that higher prevailing environmental temperatures might facilitate snail susceptibility, our results show that it is possible that climate change might impact resistant snail susceptibility to schistosomes. Indeed, a recent study showed that global warming might result in an increase in cercarial output of infected snails [63].
By using a recently developed gene silencing method based on soaking snails in siRNA coupled to the inert cationic carrier, polyethylene imines (PEI) [44], we are currently working to systematically knock-down transcription of Hsp 70, Hsp 90 and nimbus RT in the snail by the PEI delivery tool. Thus far, preliminary results indicate that knocking down these transcripts to levels comparable to those we routinely obtain for suppressing the expression of low copy RNA transcripts will be more challenging. Despite these initial challenges, we hope to elucidate the role of these stress proteins in the snail host schistosome relationship by knocking down their corresponding transcripts.
In addition, we have used the Hsp 90 inhibitor drug used in this study, GA, for treating pre-patent (2 week exposed snails) and results show consistently that once established, the drug has no effect on the infection and all treated pre-patent snails go on to shed cercariae.
In conclusion, we have shown in this study that by applying stress in the form of mild heat pulse to resistant BS-90 snails before they are exposed to S. mansoni, renders these snails susceptible. In contrast, infecting these snails before stressing them does not reverse their resistance phenotype, suggesting that the stress induction is an early necessary step in the sequence of molecular events that contribute towards making a snail susceptible. In addition, use of the stress inhibitor to treat susceptible snails before exposure was able to prevent them from shedding cercariae, again confirming that the stress pathway is indeed required for snail's to succumb to the parasite infection. These data open up a new opportunity to delve into unraveling the mechanism(s) that helps snails to either overcome or sustain the S. mansoni parasite infection, investigations that should lead to developing novel tools to interfere with schistosome-snail infections and thus reduce transmission of schistosomiasis.
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10.1371/journal.pntd.0007203 | Efficacy of novel indoor residual spraying methods targeting pyrethroid-resistant Aedes aegypti within experimental houses | Challenges in maintaining high effectiveness of classic vector control in urban areas has renewed the interest in indoor residual spraying (IRS) as a promising approach for Aedes-borne disease prevention. While IRS has many benefits, application time and intrusive indoor applications make its scalability in urban areas difficult. Modifying IRS to account for Ae. aegypti resting behavior, named targeted IRS (TIRS, spraying walls below 1.5 m and under furniture) can reduce application time; however, an untested assumption is that modifications to IRS will not negatively impact entomological efficacy. We conducted a comparative experimental study evaluating the residual efficacy of classically-applied IRS (as developed for malaria control) compared to two TIRS application methods using a carbamate insecticide against a pyrethroid-resistant, field-derived Ae. aegypti strain. We performed our study within a novel experimental house setting (n = 9 houses) located in Merida (Mexico), with similar layouts and standardized contents. Classic IRS application (insecticide applied to full walls and under furniture) was compared to: a) TIRS: insecticide applied to walls below 1.5 m and under furniture, and b) Resting Site TIRS (RS-TIRS): insecticide applied only under furniture. Mosquito mortality was measured eight times post-application (out to six months post-application) by releasing 100 Ae. aegypti females /house and collecting live and dead individuals after 24 hrs exposure. Compared to Classic IRS, TIRS and RS-TIRS took less time to apply (31% and 82% reduction, respectively) and used less insecticide (38% and 85% reduction, respectively). Mortality of pyrethroid-resistant Ae. aegypti did not significantly differ among the three IRS application methods up to two months post application, and did not significantly differ between Classic IRS and TIRS up to four months post application. These data illustrate that optimizing IRS to more efficiently target Ae. aegypti can both reduce application time and insecticide volume with no apparent reduction in entomological efficacy.
| Vector control is the primary strategy for managing Aedes aegypti and reducing transmission of Aedes-borne diseases; however, the indoor resting behavior of Ae. aegypti and the evolution of insecticide resistance reduces the effectiveness of many vector control tactics. Indoor residual spraying (IRS) is effective against Ae. aegypti, but lengthy application time makes IRS difficult to scale within urban environments. We compared the application and entomological efficacy of Classic IRS against two novel Aedes-targeting IRS application methods (Targeted IRS [TIRS]- insecticide applied to walls below 1.5 m and under furniture and Resting Site TIRS [RS-TIRS]- insecticide applied only under furniture) within experimental houses using a carbamate insecticide. Both TIRS and RS-TIRS took less time to apply and used less insecticide compared to Classic IRS. Mortality of pyrethroid-resistant Ae. aegypti did not differ among treatments out to two months post-application, and there was no difference in mortality between Classic IRS and TIRS out to four months post-application. These data provide evidence that IRS application methods can be improved to take less time and insecticide yet not lose entomological efficacy, making TIRS more scalable within urban environments. However, larger field studies with epidemiologic endpoints are needed to further assess the efficacy of these modified TIRS techniques.
| Vector control is the principal approach for managing Aedes aegypti and reducing transmission of Aedes-borne diseases (ABD; e.g., dengue, chikungunya, Zika). Implementation of vector control targeting ABDs has primarily been in response to reports of virus transmission, using methods such as truck-mounted ultra-low volume spraying (ULV)/thermal fogging, source reduction and larviciding [1, 2]. Recent assessments of the public health value of these reactive interventions, triggered by the need to contain Zika transmission and prevent the devastating congenital malformations attributed to infection of pregnant woman, has highlighted the dearth of data supporting the role of vector control tactics in preventing ABDs [3–5]. Multiple factors challenge the efficacy and coverage of existing vector control tactics, including rapid urbanization leading to widespread Ae. aegypti distribution [6], the occurrence of cryptic larval habitats [7, 8], the rapid rise of insecticide resistance [9] and the multiplicity of virus transmission locations generated by fine-scale human mobility patterns [10, 11]. Given these challenges, management of Ae. aegypti requires highly effective, innovative approaches that can be implemented across epidemiological settings and within integrated vector management strategies [4].
Adult Ae. aegypti in urban settings typically rest indoors, where they feed frequently and almost exclusively on human blood [12–14]. This endophilic and anthropophilic behavior partially explains why outdoor space spraying (e.g., truck-mounted ultra-low volume spraying) has very limited efficacy against Ae. aegypti and ABD transmission [15]. Vector control methods that deliver insecticides indoors are more promising because they can exert a direct impact on resting adult mosquitoes [5]. The principal methods of applying insecticides indoors are indoor space spraying (ISS; application of insecticides with a droplet size of < 50 μm that kill adult vectors upon contact [5]) and indoor residual spraying (IRS; the application of aqueous formulations of insecticides with longer term residual efficacy on the walls and ceilings of houses that kill the adult vectors landing on these surfaces [16]). In terms of application and performance, ISS and IRS are very different. Indoor space spraying can be deployed rapidly, particularly during epidemics, because it can be applied quickly (< 10 min), but ISS can require up to three application cycles to achieve maximum efficacy and has a short-lived insecticidal effect, as it only targets flying mosquitoes making contact with the transient insecticidal cloud. Indoor residual spraying can provide longer-term protection after a single application; however, application time can be lengthy if all furniture and belongings need to be removed from the spray area. Despite field evidence pointing to significant epidemiological impacts of IRS in preventing dengue [5, 10, 17], and recent modeling work forecasting significant long-term reductions in disease burden after its implementation [18], the perceived labor-intensive nature of IRS (in comparison to ISS) and issues of community acceptance [19] have hindered its adoption for urban vector control targeting Ae. aegypti.
To overcome the time-consuming aspects of IRS and account for Ae. aegypti-specific behaviors, several modifications to the ‘classic’ IRS strategy intended to control vectors of malaria or Chagas disease (i.e., full house spraying, movement of furniture and treatment of all walls and ceiling) have been proposed. In Cairns, Australia, IRS is performed targeting Ae. aegypti resting sites, and insecticide is applied to exposed low walls (below 1.5 m), under furniture, inside closets and on any dark and moist surface where Ae. aegypti may be found resting [10]. This modified IRS was implemented in Cairns after the detection of local dengue transmission and dramatically reduced IRS application time and resulted in the successful containment of multiple outbreaks [10, 17, 20].
One of the untested assumptions of the modifications introduced to the classically-applied IRS is that there is no negative impact on entomological efficacy. Using a novel experimental house setting, we conducted a comparative study to evaluate the residual efficacy of classically-applied IRS against two novel IRS application methods using a non-pyrethroid insecticide against a locally-derived, pyrethroid-resistant strain of Ae. aegypti. For each IRS application method, the application time and volume of insecticide used were measured. Entomological impact over time was compared among the IRS application methods. We hypothesized that the two novel IRS application methods would provide similar levels of entomological efficacy as classically-applied IRS, but would be applied faster and use less insecticide. Furthermore, we hypothesized that the efficacy of a non-pyrethroid insecticide, specifically a carbamate insecticide (bendiocarb), would be similar between the two novel IRS application methods and classically-applied IRS.
Within a replicated system of nine experimental houses, we tested the residual efficacy of three IRS application methods on free flying, field-derived Ae. aegypti. The experimental houses were located in Caucel, a neighborhood at the periphery of the subtropical city of Mérida, México, and were rented long-term by the Universidad Autónoma de Yucatán (UADY) after explaining the purpose and extent of the study to the owners. Mérida is the capital of the state of Yucatán, has a population of roughly one million and experiences a rainy season from May through October. Dengue is endemic and transmission occurs throughout the year, although peak transmission occurs between July and November and corresponds with the rainy season [18, 21, 22]. Average dengue sero-prevalence rate in the population is 73.6% [23]. Since 2016, Chikungunya and Zika viruses also circulate within Merida, impacting the public health system and vector control operations [22]. Local management tactics for Ae. aegypti include ISS with either pyrethroids (e.g., deltamethrin) or organophosphates (e.g., malathion) and ULV with organophosphate insecticides (e.g., chlorpyrifos and malathion) [24]. Resistance to pyrethroids (both type I and type II) occurs in local Ae. aegypti populations, however these populations are still presently susceptible to carbamates [24–26].
Distance between experimental houses ranged from 0.3 to 2 km. The houses were similar in floor plan and design; all were concrete, single-story and had one or two living rooms, two bedrooms, one bathroom and one kitchen (Fig 1). Houses were on average 57.8 ± 2.8 m2 (mean ± SEM) and uniformly had walls 2.5 m in height. Construction characteristics were that of subsidized middle to low-income housing in Mérida, typical of areas with high ABD transmission [22].
To prevent any mosquitoes used in the experiments from escaping from the houses, all windows and doors were screened on both the outside and inside of each house before the study began. Additionally, a double screened-door vestibule was built into the main entrance of each house to allow personnel to enter and exit while preventing mosquitoes from escaping (Fig 1). Sinks, drains and toilets were also sealed with window screening. Existing furniture within houses was removed, and where furniture could not be removed (e.g., built-in kitchen or closet cabinets) it was sealed with window screening. Houses were then refurnished with standardized furniture and clothing that represented typical elements found within houses (Fig 1). Furniture within in the living room (or split between two living rooms) included two black plastic tables and four plastic chairs. Within each bedroom was a bed made out of PVC tubing and black cloth, a black plastic night stand and six articles of clothing (3 black and 3 white) hung within the closet. Additionally, four plastic buckets (1 L) were half filled with water and a dark cloth and placed throughout each house to provide moisture into the environment and reduce mosquito mortality due to desiccation. Ant baits (Antex Gel, Allister de México) were placed next to each door or any other location where ants were observed to enter the experimental houses. The house layout was carefully designed to mirror elements and surface materials found in regular homes, but making sure that they were standardized in a way that allowed replication and comparability between replicates.
Insecticide was applied within experimental houses on 3 July 2017. A manual compression sprayer (Hudson 93793 X-Pert) fitted with flat nozzles and a flow control valve (model CFV.R11/16SYV.ST, CFValue, Gate LLC) was used to spray houses at a flow rate of 550 mL / min. Bendiocarb (Ficam 80% WP, Bayer CropScience; 125 g sachet / 7.5 L water), a carbamate insecticide, was applied at a dosage of 0.375 g active ingredient / m2 as recommended by the WHO [16]. Bendiocarb was used because of the known susceptibility of local Ae. aegypti populations that were resistant to synthetic pyrethroids [24]. Additionally, a previous RCT in Mérida found high community acceptance of bendiocarb, with no reported adverse reactions, when it had been applied within homes [24]. The same individual applied insecticide for each of the nine experimental houses.
Houses were randomly assigned to one of three different IRS application methods: 1) Classic IRS- insecticide applied to walls and under furniture (n = 3 houses), 2) Targeted IRS (TIRS)- insecticide applied to walls below 1.5 m and under furniture (n = 3 houses) or 3) Resting Site TIRS (RS-TIRS)- insecticide only applied under furniture (n = 3 houses). Furniture was not removed from experimental houses during the insecticide application and insecticide was not applied to clothing or the plastic buckets with water. Duration of application was measured for each house, starting when the applicator entered the house and ending when the applicator exited. To estimate the volume of insecticide applied within each house, the insecticide within the sprayer was measured using a graduated cylinder before and after each application.
To test the residual efficacy of each IRS application method, a total of 100 Ae. aegypti females were released within each experimental house. The strain used (San Lorenzo strain) was locally derived, had a high level of resistance to pyrethroids and full susceptibility to carbamates [24, 26]. The San Lorenzo strain was reared and maintained at the insectaries of the Unidad Colaborativa para Bioensayos Entomológicos, UADY, Mérida, México. Mosquitoes released into houses were three to seven days old from the F4 generation, before release had only been provided sugar solution and were non-bloodfed.
Post-insecticide application, mosquitoes were released into the experimental houses eight times over a six month period; 1) +1 day, 2) +14 days, 3) +1 month, 4) +2 months, 5) +3 months, 6) +4 months, 7) +5 months and 8) +6 months. To facilitate mosquito recovery, all experimental houses were vacuumed and swept clean of any debris on the floor one day prior to mosquito release. After 24 hrs exposure, a team of four field technicians entered each house and searched for live mosquitoes using a Prokopack aspirator [27] and searched by hand for dead mosquitoes. Searching for Ae. aegypti ceased when either 100 mosquitoes were collected or > 20 minutes elapsed after the last mosquito was collected (circa 30–40 min / house). Natural mortality within experimental houses was measured by placing three unsprayed control cups (250 mL) within each house, with each cup containing 10 San Lorenzo strain females. Control cups were placed within experimental houses simultaneously during the main release of mosquitoes during the +4, +5 and +6 months post-application evaluations. After searching for released Ae. aegypti ceased, the number of live and dead Ae. aegypti within control cups were counted.
For each sampling period, mortality was calculated per house by dividing the number of dead individuals by the number of individuals released. Missing individuals were assumed to be dead. Mortality was compared between IRS application methods using mixed-model analysis of variance (ANOVA) in R 3.2 statistical software (https://www.r-project.org/). Sampling date, IRS application method, and their interaction were classified as fixed effects and experimental house was classified as a random effect. When significant differences were detected, pairwise comparisons were made using LSMEAN package and alpha levels were adjusted for multiple comparisons using the Tukey correction. Additionally, regression analysis was used to assess the relationship between application time and volume of insecticide applied among the three IRS application methods.
This was an experimental study, and because mosquitoes were released into uninhabited houses rented on long-term contracts, we did not require an Institutional Review Board.
Compared to Classic IRS, TIRS reduced application time on average by 5.8 min / house (31.3% reduction), whereas RS-TIRS reduced application time on average by 15.2 min / house (82.0% reduction) (Table 1). Similarly, compared to Classic IRS, TIRS used on average 2.02 L / house less insecticide (37.9% reduction), while RS-TIRS saved on average 4.53 L / house (84.8% reduction) (Table 1). Compared to TIRS, RS-TIRS reduced both application time by 9.40 min / house (73.8% reduction) and insecticide volume by 2.50 L / house (75.5% reduction) (Table 1). Reductions in both application time and insecticide volume were significantly linear (F = 140.1; df = 1, 7; P < 0.0001), indicating consistent insecticide application among IRS application methods.
A total of 7,200 Ae. aegypti females were released within the experimental houses throughout the trial. Mosquito recovery averaged 96.9 ± 0.82% (Mean ± SEM; n = 72 releases). Based on pilot data, we attribute high recovery to pre-cleaning the floors of experimental houses the day before mosquitoes were released and to effective management of ants using baits.
Mortality within control cups average 4.4 ± 1.3%, 1.5 ± 0.7% and 5.0 ± 1.7% (Mean ± SEM) for evaluations from +4, +5 and +6 months post-application, respectively, indicating high Ae. aegypti survival within the experimental house environments.
There was a significant interaction between IRS application method and sampling time post application (F = 6.3; df = 14, 42; P < 0.0001) (Fig 2). Almost complete mortality of all released mosquitoes was observed up to two months post-application (ranging from 97.3 to 100%); there were no significant differences in mortality among the three IRS treatments within the first 4 sampling periods. At three months post-application, mortality of Ae. aegypti dropped significantly in houses treated with RS-TIRS (from 97.3% at +2 months to 48.1% at +3 months) compared to Classic IRS and TIRS houses, where mortality remained high (99.7% and 94.5%, respectively). At four months post-application, mortality of Ae. aegypti from Classic IRS and TIRS treated houses dropped to 79.8% and 74.2%, respectively, but were both significantly greater compared to mortality of Ae. aegypti from RS-TIRS houses, which dropped to 19.7%. Mortality in experimental houses with Classic IRS remained high five months post-application (78.4%) and was significantly greater compared to both TIRS (25.5%) and RS-TIRS (10.8%), which did not differ from each other. Efficacy of all three treatments was greatly reduced six months post-application (one month beyond the expected residual duration of bendiocarb). Mortality in Classic IRS treated houses was reduced to 39.2%, yet was significantly greater compared to RS-TIRS (10.4%), although neither treatment differed significantly from TIRS (16.6%) (Fig 2).
We compared the residual efficacy of Classic IRS against two novel IRS application methods, TIRS and RS-TIRS, in experimental houses, and hypothesized that the two novel IRS application methods would be as efficacious as Classic IRS. Furthermore, we hypothesized that the efficacy of a non-pyrethroid insecticide, bendiocarb, would be similar among the two novel IRS application methods and Classic IRS. Although both TIRS and RS-TIRS took less time to apply and used less insecticide compared to Classis IRS (Table 1), these data support our hypotheses, as pyrethroid-resistant Ae. aegypti mortality did not differ among the three IRS application methods up to two months post-application and did not differ between Classic IRS and TIRS up to four months post-application (Fig 2).
Using bioassays within experimental houses that closely simulate typical living conditions, this study provides important information that can help improve the mode of IRS application and cost-effectiveness within the urban context of ABD transmission. Improvements in IRS efficiency and application are key for increasing scalability and adoption of this management tactic [28]. Recent and rapid scaling-up of IRS for malaria control illustrate the potential public health benefits of this approach [29], but also point to the difficulties of reaching and sustaining high coverage levels due to IRS’s labor-intensive nature [30]. If IRS were to be widely adopted for urban Ae. aegypti management, lessons from IRS scale-up for malaria vector control should be taken into consideration to better frame the operational conditions and approaches for intervention delivery.
Field observational studies from Central and South America have found that Ae. aegypti primarily rest indoors and below 1.5 m, particularly on or near dark places such as behind or under furniture, under beds, on clothing and on lower parts of walls [13, 27, 31]. This low-resting behavior has also been observed in experimental hut studies using an Ae. aegypti strain from Thailand [32]. Modifying IRS to account for key Ae. aegypti resting behaviors resulted in important reductions in application time and insecticide volume (Table 1) without sacrificing entomological efficacy for two to four months post application (Fig 2). The fact that we detected high mortality with no statistical difference between Classic IRS and TIRS methods show that Ae. aegypti are not avoiding treated locations by shifting resting behaviors above 1.5 m. Additionally, RS-TIRS was applied only to common resting sites (beds, chairs and other furniture) and resulted in to up to 2 months of full protection, providing further evidence of the remarkable preference of Ae. aegypti for specific resting locations.
Duration of protection differed between TIRS and RS-TIRS applications. Although RS-TIRS could be completed on average in 3.3 min / house (Table 1), the protection provided (using > 80% mortality as a threshold) by this approach lasted two months, or half the duration of Classic IRS or TIRS (Fig 2). One of the challenges of RS-TIRS when applied in real households (which would likely be more cluttered and full of personal items than our experimental houses) is that it may entail the treatment of personal belongings that are preferentially used by Ae. aegypti as resting sites (e.g., suitcases, clothes, etc.). Applying insecticide to personal belongings could potentially lead to community disapproval of the methodology, as well as potentially result in unanticipated exposure to insecticides [19]. As such, while there are significant reductions in application time and insecticide volume, performing RS-TIRS may be more challenging than performing TIRS. Given that TIRS provides longer-term protection (up to 4 months) compared to RS-TIRS, we see the former as a methodology highly suitable for implementation within the context of urban Ae. aegypti management.
A randomized controlled trial evaluating the entomological impact of Classic IRS using bendiocarb against pyrethroid-resistant populations of Ae. aegypti in Mérida, México, demonstrated a 65–75% reduction in adult Ae. aegypti abundance in treatment clusters, compared to controls, up to three months post-application [24]. Furthermore, the application time of Classic IRS from this trial averaged approximately 30 min / house [24]. Our experimental study demonstrated that an application of TIRS required roughly 12 min to complete but resulted in a 4-month protection of treated houses. The residual effects observed were driven by the insecticide used (bendiocarb residuality is expected to last between 3 and 5 months), and its interaction with treated substrates (in our case, painted walls, cloth, wood and plastic). Given the recent development of new residual insecticide formulations for malaria, which extend residual duration out to 6–8 months and are effective against pyrethroid-resistant mosquitoes [33, 34], there is potential for extending residual power of TIRS beyond the 4-month mark.
Despite the higher cost of novel insecticide formulations, applying novel insecticides via TIRS would not only reduce application time but also potentially increase cost-effectiveness. Furthermore, extending residual duration can provide a longer window of protection and shift IRS application from reactive (in response to reported clinical cases, as in [10]) to pro-active (performed prior to the transmission season [18]). A recent analysis of historical dengue, chikungunya and Zika cases geocoded to the household level found a significant level of spatial overlap of the three pathogens within specific geographic units that accumulated more than half of all cases [22]. The pro-active (pre-season) deployment of high-quality interventions such as TIRS within hot-spot areas could offer additional protection to areas that consistently report high rates of ABD transmission [22, 35]. An insecticide with residual duration that lasts more than 5 months could protect a household for an entire transmission season (which lasts 5 to 6 months) using a single TIRS application. Additionally, using insecticides pro-actively should be coupled with insecticide-resistance monitoring and insecticides used for TIRS changed when resistance is first detected. Previous studies have demonstrated that fitness costs associated with pyrethroid resistance in Aedes aegypti do exist and that susceptibility can be regained in the absence of selection [36]. While the efficacy of such pro-active TIRS implementation in preventing ABD will require further evaluations with proper epidemiologic endpoints [37], the findings presented here provide clear evidence for how IRS applications could be optimized for urban Aedes management. However, larger field studies with epidemiologic endpoints are needed to further assess the efficacy of these modified TIRS techniques.
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10.1371/journal.pgen.1002284 | A Barcode Screen for Epigenetic Regulators Reveals a Role for the NuB4/HAT-B Histone Acetyltransferase Complex in Histone Turnover | Dynamic modification of histone proteins plays a key role in regulating gene expression. However, histones themselves can also be dynamic, which potentially affects the stability of histone modifications. To determine the molecular mechanisms of histone turnover, we developed a parallel screening method for epigenetic regulators by analyzing chromatin states on DNA barcodes. Histone turnover was quantified by employing a genetic pulse-chase technique called RITE, which was combined with chromatin immunoprecipitation and high-throughput sequencing. In this screen, the NuB4/HAT-B complex, containing the conserved type B histone acetyltransferase Hat1, was found to promote histone turnover. Unexpectedly, the three members of this complex could be functionally separated from each other as well as from the known interacting factor and histone chaperone Asf1. Thus, systematic and direct interrogation of chromatin structure on DNA barcodes can lead to the discovery of genes and pathways involved in chromatin modification and dynamics.
| Packaging of eukaryotic genomes by the histone proteins influences many processes that use the DNA, such as transcription, repair, and replication. One well-known mechanism of regulation of histone function is the covalent modification of histone proteins. Replacement of modified histones by new histones has recently emerged as an additional layer of regulation (hereafter referred to as histone turnover). Although histone replacement can affect substantial parts of eukaryotic genomes, the mechanisms that control histone exchange are largely unknown. Here, we report a screening method for epigenetic regulators that we applied to search for histone exchange factors. The screening method is based on our finding that global chromatin changes in mutant cells can be inferred from chromatin states on short DNA barcodes. By analyzing the chromatin status of DNA barcodes of many yeast mutants in parallel, we identified positive and negative regulators of histone exchange. In particular, we find that the HAT-B complex promotes histone turnover. HAT-B is known to acetylate the tails of newly synthesized histones, but its role in chromatin assembly has been unclear. Hif1, the nuclear binding partner of HAT-B in the NuB4 complex, also promotes histone exchange but by non-overlapping mechanisms. These results provide a new perspective on pathways of histone exchange.
| The epigenetic landscape in the cell is dynamic and shaped by histone modifying and demodifying enzymes. In addition, histones themselves can also be dynamic; they can be moved along the DNA through the action of ATP-dependent nucleosome remodeling enzymes or can be evicted and replaced by new histones. Many histone modifying and remodeling enzymes have been identified and several factors have been found to be involved in changing nucleosome occupancy during gene activation and repression [1]–[3]. Recent studies indicate that histones can also be replaced by replication-independent mechanisms that do not involve obvious changes in nucleosome occupancy [3]–[9]. The replacement of existing chromatin-bound histones by newly synthesized histones most likely affects the stability of chromatin marks and thereby epigenetic mechanisms of gene regulation.
Histone replacement or turnover requires assembly and disassembly of nucleosomes, processes that most likely involve the action of histone chaperones. Chaperones are acidic proteins that bind the highly basic soluble histone proteins and thereby prevent non-specific interactions of histones with other proteins and DNA [10]–[12]. The HAT-B complex is one of the factors that binds newly synthesized histones H3 and H4 in the cytoplasm [13]. This evolutionary conserved complex, composed of the chaperone Hat2 and the acetyltransferase Hat1 (also known as Kat1), acetylates newly synthesized soluble histone H4 on lysine 12 (H4K12) and lysine 5 (H4K5) [14]–[17]. Hat1 specifically acts on soluble histones because it is inactive towards chromatin-bound nucleosomal histones [13]. Hat1 is the founding (and still only known) member of the family of type B HATs, which are cytoplasmic and specific for free histones [13], [14]. Whether the HAT-B complex or its acetyltransferase activity towards the H4 tail has a role in subsequent steps of histone trafficking or chromatin assembly is not well understood [14]. Cells lacking the HAT-B complex show no growth defect, indicating that acetylation of newly synthesized histones by Hat1 is not essential for replication-dependent histone deposition [14]. In addition, the acetylation marks introduced by HAT-B are removed upon deposition of new histones in chromatin [14]. However, several studies have indicated connections between Hat1 and chromatin [15], [18]–[24]. In addition, recent biochemical studies suggest that HAT-B guides newly synthesized histones from the cytoplasm to the nucleus, where it binds to the histone chaperone Hif1 to form the NuB4 complex and hand over the histones to other chaperones such as Asf1 [25], [26]. Asf1 is involved in the stimulation of H3K56 acetylation on soluble histones prior to their deposition [11], [12]. By binding to the chromatin assembly factor complex (CAF1) and chaperone Rtt106, Asf1 can subsequently deliver histones for deposition at the replication fork [27]–[30]. In addition, Asf1 can bind to the HIR complex and thereby deliver histones for replication-independent histone deposition [11], [12], [27]–[29], [31], [32]. How chaperones affect histone assembly and disassembly is still largely unknown but recent studies are starting to reveal some of the underlying mechanisms [30], [33]–[36].
We recently developed Recombination-Induced Tag Exchange (RITE) as an assay to measure histone turnover under physiological conditions [7]. RITE is a genetic pulse-chase method in which replacement of old by new histones can be examined by immunoblots or chromatin immunoprecipitation (ChIP). To unravel the significance of the high rate of histone turnover that we and others observed in yeast [4]–[9], [37], the underlying mechanisms will need to be identified. However, identification of genes involved in histone turnover is not straightforward. Screening for mutants that affect epigenetic processes is usually carried out using indirect read-outs such as activity of reporter genes or developmental phenotypes. Mutants that affect histone post-translational modifications have also been identified by global proteome analysis [38]. However, it is not clear whether and how histone turnover affects gene expression, reporter genes, or developmental phenotypes. As a consequence, no indirect reporter assays are available to screen for histone turnover genes by mutant hunts. The alternative, direct assessment of chromatin changes in mutant clones is typically laborious (involving ChIP-sequencing or ChIP-on-chip) and is usually not suitable for genetic screening. To speed up the discovery of histone turnover pathways, we directly interrogated chromatin structure using RITE combined with methods that have been developed for parallel analysis of fitness phenotypes in yeast [39], [40]. Using this strategy we identified mutants that either positively or negatively affected histone turnover and we provide the first in vivo evidence for a function of the NuB4 complex in histone exchange.
The collection of gene-deletion mutants in Saccharomyces cerevisiae enables the systematic analysis of gene function. A pair of unique DNA barcodes (UpTag and DownTag) is present in each yeast deletion strain, flanking a common selectable marker gene used to knock out the respective genes (Figure 1). Molecular counting of the barcodes by DNA microarrays or digital counting by next-generation sequencing allows parallel analysis of the relative abundance of yeast clones in pooled cultures [40], [41]. The fitness of each yeast deletion mutant can be inferred from the changes in the relative abundance of the barcodes after exposure to the condition of interest. Using these same principles, we reasoned that in a pool of yeast deletion mutants the relative abundance of each barcode in a ChIP experiment might report on the abundance of a particular chromatin mark in that region in each mutant. Here we refer to the identification of epigenetic regulators by a barcode-ChIP-Seq approach as Epi-ID (Figure 1).
To explore the possibilities of Epi-ID and to search for genes involved in histone turnover we used the genetic pulse-chase method RITE to allow the detection of old and new histone H3 proteins in yeast [7] (Figure 1). Briefly, following deletion of one histone H3 gene copy, the sole remaining H3 gene was tagged with an HA tag flanked by LoxP sites, and a downstream orphan T7 tag. Initially all H3 proteins are tagged with an HA tag. Upon induction of a hormone-dependent Cre recombinase by the addition of estradiol, the HA tag in the genome is replaced by the T7 tag and from then on all newly synthesized H3 will be T7 tagged. Histone turnover results in replacement of H3-HA by H3-T7, which can be detected and quantified by immunoblot and ChIP (Figure 2). We note that histone turnover measurements obtained using RITE correlate well with measurements obtained using the previously used inducible pGAL-system to ectopically overexpress a tagged copy of histone H3 [42]. One of the advantages of RITE is that the tagged histone gene is expressed from its endogenous promoter, and old and newly synthesized histone H3 can be simultaneously detected and followed under any (physiological) condition of interest, independent of changes in nutrients to induce ectopic promoters [7], [43].
We introduced the RITE elements into 92 clones of the yeast deletion collection using Synthetic Genetic Array (SGA) analysis [44] (Figure 1). The deletions in this library represented genes known or suspected to be involved in epigenetic processes and a set of non-chromatin genes (Table S1). The clones of this new library of RITE deletion mutants were first grown separately in liquid cultures, then pooled, and subsequently arrested by starvation (Figure 3A and Figure S1). Recombination was induced to switch the epitope tags and chromatin samples were taken before and one and three days after induction of the tag switch. We previously found that yeast cells arrested by starvation (which we here refer to as G0) undergo replication-independent turnover of chromatin-bound histones [7]. In addition, we found a substantial amount of new bulk histone synthesis during three days of starvation by immunoblot analysis and ChIP (Figure 2C-2D and Figure S2). Arresting cells by starvation allows for efficient switching of the epitope tags by the induced Cre recombinase. Moreover, replication-dependent histone deposition and cell-cycle or growth rate differences between different mutants are eliminated. To measure histone turnover ChIP was performed on old (H3-HA) and new (H3-T7) histone H3. The barcode regions in the bound DNA were amplified using common primer sequences and adapters to allow parallel sequencing on the Illumina platform. Four base pair index tags were introduced in each sample to allow multiplex analysis (Figure S1). After digital barcode counting (see Materials and Methods) the relative ratio of new/old H3 was calculated as a value for replication-independent histone turnover in the pool of gene deletion mutants for each UpTag and DownTag barcode and for each of two time points after induction of the tag-switch (Figure 1, Figure 3A).
We performed three analyses to test the validity of the concept of Epi-ID. First, we verified that the independent measurements of the two time points (day 1 and 3) showed similar trends (Figure 3B–3C). Second, we compared UpTags with DownTags (U and D). The overall correlation between UpTag and DownTag barcodes suggests that position effects are not a major confounder in this assay (Figure 3D–3E; but also see Discussion). The few clones that did not correlate well between different time points or between UpTag and DownTag barcodes were eliminated from further analysis (see below). Third, the barcodes of the SIR3 and SIR4 deletion mutants (which do not mate and cannot be used for genetic crosses such as SGA), were integrated in the genome of strains constitutively expressing only H3–HA or only H3-T7. These clones were combined with the RITE library pool as internal negative and positive controls, respectively. The two control strains could be separated from each other at all three time points, both at the UpTag and DownTag barcodes. They also provided an indication of the dynamic range of the turnover measurements in this assay. For further analysis, clones for which severe growth defects were observed after the tag switch (and after release of the arrest by re-feeding) were excluded to eliminate mutants in which the new H3-T7 tagged histone may not be fully functional or causes tag-specific rather than true turnover effects (see Materials and Methods). Only those clones were included that showed low variation between the two time points and between UpTag and DownTag. The two control strains are shown as a reference (Figure 3F).
Of the resulting set of deletion mutants that passed the selection criteria, two clones with the lowest and two clones with the highest turnover signal were selected to examine whether the mutants affected turnover at loci independent of the barcode sequences. Each clone was grown individually and arrested by starvation. After induction of the epitope tag switch histone turnover was examined by ChIP-qPCR at four independent loci unrelated to the barcoded region analyzed in the parallel screen (promoter regions of IMD1, ADH2, HHT2, and ADH1) (Figure 3G). The changes in histone turnover at these four loci was similar to the changes measured at the barcodes, confirming that the chromatin changes of the barcodes reflected overall changes in the genome (Figure 3G). Nhp10 and Gis1 were found to be negative regulators of histone turnover. Hat1 positively regulated histone turnover. For every turnover experiment, the efficiency of the tag switch (percent of cells that had undergone a Cre-mediated recombination event) was determined (Table S2). By a colony plating assay we noticed that cells lacking HAP2 showed very poor Cre-mediated recombination, which was most likely the cause of the low ratio of new/old H3 in this clone (Figure S3). This clone was excluded from further analysis. Given the high conservation of Hat1 and its known activity towards new histones, we focused our further studies on Hat1.
The histone acetyltransferase Hat1 together with the histone chaperone Hat2 forms the evolutionary conserved HAT-B complex that acetylates soluble histones. The functional consequences of Hat1's activity are not well understood. Hat1 plays a role in gene silencing [19] and DNA repair [14], suggesting that it affects chromatin structure. However, many of these phenotypes require additional mutations in the N-terminal tail of histone H3 and how chromatin is affected by Hat1 is not known. Our findings provide direct evidence that the Hat1 protein is important for efficient histone turnover in vivo (Figure 3F-3G). We first examined the role of Hat1's enzymatic activity. A strain containing a catalytically compromised (but not completely inactive) Hat1 protein (HAT1-E255Q) [19] showed a decrease in histone turnover similar to a hat1Δ strain (Figure 4A), suggesting that the acetyltransferase activity is important for efficient histone turnover. Hat1's primary known targets are lysines 5 and 12 of histone H4 (H4K5 and H4K12). Mutating the target lysines to arginine (H4K5,12R) did not substantially affect histone H3 turnover, whereas alanine or glutamine mutants (H4K5,12A and H4K5,12Q) showed enhanced turnover of histone H3 at most loci tested (Figure 4B and Figure S4). Arginine and lysine both contain a long hydrophobic side chain and a positive charge. Therefore, arginine might mimic the constitutively unacetylated (positively charged) state of lysine. Our results suggest that loss of the positive charge of H4K5,12 by acetylation is not sufficient to explain the role of Hat1 in histone turnover. However, changing the positively charged residues to neutral amino acids enhanced turnover. These results suggest that H4K5/K12 play a role in histone turnover but that loss of acetylation of these sites is not sufficient to cause a histone turnover defect.
Several lines of evidence suggest that H4K5 and H4K12 have evolutionary conserved roles in replication-dependent chromatin assembly [13], [26], [45]–[49] or nuclear import of histone H4 [50], [51]. However, in yeast, mutation of these lysines does not lead to growth defects and no changes in global chromatin organization have been observed [51]–[53]. To investigate the role of H4K5,12 in replicating cells we performed the tag switch in starved cells, released the switched population into fresh media, and then measured histone turnover in cells arrested in G2/M after one round of replication (monitored by FACS analysis). Cells expressing H4K5,12Q showed increased turnover at two of the three promoter regions analyzed, whereas cells expressing H4K5,12R showed decreased histone turnover (Figure 4C). In contrast, in two long coding sequences, the two H4K5,12 mutants affected turnover in a similar manner. In both H4K5,12 mutants turnover at the 3′ end was reduced relative to turnover at 5′ regions (Figure 4C). This is consistent with results we obtained previously with the H4K5,12R mutant in replicating cells and may indicate a role of these residues in 3′ to 5′ retrograde movement of old histones by passage of RNA Polymerase II [54].
Hat1 in yeast and other organisms was initially identified as a cytoplasmic histone acetyltransferase [13], [14]. More recently, Hat1 was also found to be (predominantly) localized in the nucleus [14], [19], [26], [49], [55]. To investigate whether the role of Hat1 in histone turnover is mediated by a cytoplasmic or nuclear activity, we next examined the consequences of fusion of Hat1 to a nuclear export signal (Hat1-NES), which excludes Hat1 from the nucleus [19]. The NES fusion resulted in a modest decrease of histone turnover (Figure 5A), indicating that the cytoplasmic activity of Hat1 is not sufficient for Hat1's function in histone turnover and that at least part of Hat1's effect on histone turnover is mediated by a nuclear activity. To further investigate whether Hat1's role in histone turnover is indeed linked to its nuclear location, we analyzed the nuclear binding partners of Hat1. In the nucleus the members of the HAT-B complex, Hat1 and Hat2, interact with Hif1 (Hat1 Interacting Factor-1) and form the nuclear NuB4 complex [49], [55]. Hif1 belongs to the evolutionary conserved family of SHNi-TPR family of histone chaperones, which also includes Hs_NASP, Xl_N1/N2 and Sp_Sim3 [26], [56]. To examine the role of the NuB4 complex in histone turnover, we deleted Hif1 and compared this to independent deletions of Hat1 and Hat2. In this strain background, deletion of Hat1 by homologous recombination (which was confirmed by standard PCR analysis and by microarray analysis [e.g. see Figure S9]) did not affect histone turnover as much as in the mutant derived from the genetic cross with the yeast deletion collection or the catalytic mutant. The cause of this difference is unknown, but may involve differences in the genetic strain backgrounds.. However, cells lacking Hat2 or Hif1 showed reduced histone turnover, supporting the idea that the nuclear NuB4 complex plays a role in histone turnover (Figure 5B). To genetically test whether Hif1 and Hat-B affect turnover by means of a common pathway or protein complex (NuB4), we generated double mutant strains for epistasis analysis. Previous studies have shown that Hat2 is a central component of the NuB4 complex; deletion of HAT2 disrupts the nuclear localization of Hat1 and interactions between Hif1, Hat1, and histones [19], [49], [55]. Unexpectedly, deleting either HAT1 or HAT2 in combination with HIF1 resulted in a more severe decrease in histone turnover than in either one of the single mutants, suggesting that Hif1 and Hat1/Hat2 act at least in part by independent mechanisms (Figure 5C).
Histone turnover is strongly correlated with and induced by transcription by RNA Polymerase II [4]–[7] (and Figure S2). To investigate whether the observed G0 histone turnover defects in mutants of the NuB4 complex were caused by transcription defects we performed expression profiling and measured RNA Polymerase II occupancy. In mutant cells arrested in G0, no significant changes were found in the expression of the target genes analyzed in the turnover experiments when compared to wild-type cells (Figure 6A–6B). In addition, no significant changes (fold change >1.7, p<0.01) were found in the expression of the single H3 and H4 genes, with the exception of the H4K5,12 mutants, which showed a slight upregulation of the histone H3 gene. Thus, in G0 cells, reduced histone H3 turnover was not caused by reduced expression of (new) histones or by reduced expression of the loci at which histone turnover was measured. To compare transcriptional changes in the NuB4 mutants to other mutants, we also performed microarray analyses of NuB4 mutants made in the genetic background of the yeast deletion collection. These mutants were grown under standard mid-log conditions [57], [58]. We note that under these conditions the members of the NuB4 complex play the same role in histone turnover as in G0 (Figure S5). In general, no significant transcriptional changes were found in any of the NuB4 mutants compared to WT (fold change >1.7, p<0.01) in mid-log cultures. However, when examined in more detail, the expression profiles of mutants that contain a deletion of HIF1 and to a lesser extent HAT2, showed upregulation of the genes encoding histone H3 and H4 in mid-log cultures (Figure 6C). Regulation of histone gene expression seems to be a common property of nucleosome assembly factors [27], [59], providing further support for a link between the NuB4 complex and histone turnover.
Biochemical studies suggest that the NuB4 complex interacts with Asf1, which led to the suggestion that NuB4 might hand over newly synthesized histones to Asf1 for subsequent transfer to nucleosome assembly factors [25], [26], [60]. However, the histone genes clearly respond differently to deletion of ASF1 than to deletion of genes encoding members of the NuB4 complex [27], [59] (Figure 6C), suggesting a more complex relationship. Unfortunately, we could not test the genetic relationship between Asf1 and Hat1 because deletion of Asf1 in the strain background used for the RITE assay is lethal, similar to what has been reported previously [61]. To investigate the connection between Hat1 and Asf1 by alternative means, we used RITE as a genetic pulse-chase tool to examine the nature of the histone molecules bound to each protein. Rather than indirectly inferring the origin of the histones (new or chromatin derived) from the pattern of post-translational modifications, the epitope tag-switch pulse-chase allows for a direct distinction between resident and newly synthesized histones. In cells that had recently undergone a tag switch on H3 and therefore contained a mix of new and old histone H3, affinity purified Hat1 bound both new and old histones with a preference for new histones (Figure 7A–7B and Figure S6). Asf1 also bound both new and old histones but without a preference for new histones. (Figure 7A–7B and Figure S6). The binding of Hat1 and Asf1 to a different subset of the pool of soluble histones suggests that they affect different steps of chromatin assembly and disassembly.
Using RITE as a biochemical-genetic pulse-chase tool, we previously observed rapid exchange of histone H3 in chromatin in yeast cells outside S-phase [7]. Similar results have been reported using an inducible pGAL-system to overexpress an ectopic tagged histone H3 copy [4]–[6], [37], [62], [63]. By using RITE, in contrast to the pGAL system, the tagged old and new histone H3 species are expressed by the endogenous H3 promoter from the endogenous chromosomal location [43]. Therefore, the high levels of histone exchange observed with RITE were not caused by misregulation of histone H3 expression. Indeed, qRT-PCR and microarray analyses showed that RITE strains containing tagged H3 express very similar H3 mRNA levels as wild-type cells containing untagged H3 at different phases of the cell cycle [7] (Figure S7). Interestingly, although histone mRNAs are cell cycle regulated and peak in S-phase when the demand for new histones is highest [64], [65], histone H3 mRNA expression is still relatively high outside S-phase, providing an explanation for the abundant synthesis of new histones outside S-phase [7]. To investigate the biological function of histone turnover and its consequences for chromatin structure and function, we developed the Epi-ID barcode screen for chromatin regulators and combined it with RITE. In this screen we found that Hat1 and subsequently also the other members of the NuB4 complex positively regulate histone turnover. To our knowledge, our data provide the first evidence that a Type B histone acetyltransferase complex regulates histone assembly in vivo. Hat1 was the first histone acetyltransferase identified [13], [66]. It is part of a multi-subunit complex that interacts with histone chaperones and acetylates free histones but is inactive towards nucleosomal histones [14], [26]. The biological significance of these biochemical activities of the Hat1 complexes remained elusive [14] although in genetic tests Hat1 was found to play a role in gene silencing and DNA damage response [14]. However, manifestation of these phenotypes required additional mutations in the N-terminal tail of histone H3 and whether these chromatin-related phenotypes are related to histone deposition defects remained unknown.
The known and conserved substrates of HAT-B/NuB4 are lysines 5 and 12 of histone H4 [14]. Mutation of these residues has revealed functions in histone H4 nuclear import and chromatin assembly [48], [50], [51]. However, H4K5,12 mutants generally show no major growth phenotypes or global changes in chromatin organization [24], [48], [52], [53]. Here we found a positive effect of H4K5,12A and H4K5,12Q mutants on histone turnover in promoters, suggesting that NuB4 may exert its turnover function via H4K5/K12 acetylation. However, H4K5,12R, mimicking the hypo-acetylated state of these lysines, did not cause a turnover defect (Figure 4). One possible explanation of these results is that NuB4 has additional substrates that contribute to its role in histone turnover [67]. We do not know whether other substrate lysines on histones or perhaps non-histone proteins are also involved and play roles redundant with the acetylated histone H4 tail.
The nuclear function for HAT-B in histone turnover (Figure 5) indicates that HAT-B's role in histone metabolism may be more complex than previously anticipated and extends beyond the acetylation of newly synthesized histones. This is in line with observations that Hat1 can be recruited to chromatin at origins of replication and DNA double strand breaks [20], [21] and with the role of members of the NuB4 complex in depositing histones following repair of a DNA double strand break [18]. Unexpectedly, our studies revealed that Hat1 and Hat2 act in parallel with Hif1, and that Hat1 and Asf1 bind a different subset of the soluble histone pool. In previous studies Hat1/Hat2, Hif1, and Asf1 have been shown to bind to each other [26], which led to the suggestion that Asf1 acts downstream of Hat1/Hat2/Hif1 and passes on new histones acetylated on H4K5/K12 (and H3K56) to chromatin assembly factors CAF-I, HIR, and Rtt106 [11], [12]. Our results suggest that Hat1/Hat2, Hif1 and Asf1 act, at least in part, via distinct pathways of chromatin assembly and/or disassembly (Figure 6C and Figure 7). The equal binding of Asf1 to new and old histones suggests that Asf1 may be involved in depositing as well as escorting histones evicted from chromatin (Figure 7C), which is in concordance with the finding that H3K56 acetylation (mediated by Rtt109/Asf1) is a mark of new histones, yet is important for histone eviction and nucleosome destabilization [11], [33]. Indeed, histone chaperones may not exclusively function in chromatin assembly [68]. For example Nap1, which can escort H3/H4 and H2A/H2B and assemble histone octamers into nucleosomes, but may orchestrate this by promoting nucleosome disassembly [33]. Another example is CAF1, which is involved in replication-coupled assembly of new histones into chromatin, yet histone H3 bound to this complex (or to Rtt106 or Asf1) contains methylated H3K79 [30], which is a mark of chromatin-bound histones [69], [70].
What are the functional consequences of altering histone turnover? Histone turnover might affect several aspects of the epigenome, such as nucleosome occupancy, DNA accessibility, or dynamics of histone modifications. No changes in growth or cell cycle progression were observed for single, double, or triple hat1Δ, hat2Δ, hif1Δ mutants (Figure S8) and no significant transcriptional changes were observed (see Figure 6 and Material and Methods). Apparently, slowing down turnover of histone H3 by loss of the NuB4 complex has no profound consequences under these conditions. Deletion of HAT2 or HIF1 resulted in a moderate increase in expression of the genes encoding histone H3 and H4 in mid-log cultures (Figure 6C). We expect that this may be a response to the histone turnover defects caused by deletion of Hat2 and Hif1, since deletion of Hat1, which overall has a lower impact on histone turnover, did not affect histone gene expression. It is possible that the phenotypes of the hat1Δ strain are relatively weak because of compensation of Hat1's activity by other HATs, such as Gcn5, which acetylates newly synthesized histone H3 [71]. In our microarray analyses, we did not observe significant upregulation of mRNA levels of other (putative) HATs in G0 cultures (Figure S9). Overall, our results indicate that loss of NuB4 function alone has no major consequences for global chromatin organization.
What is the function of histone turnover? Histone turnover leads to turnover of histone modifications and can thereby affect the pattern as well as dynamics of the epigenome. When a chromatin state is controlled by two opposing activities (e.g. modification and demodification by turnover) this could lead to a more rapid establishment a new equilibrium after perturbation of the epigenome, such as during DNA replication or after exposure to stress (e.g. see [72]). Based on models proposed for histone acetylation one could also envision that dynamic turnover (cycles of modification and demodification) rather than the steady state may be relevant for chromatin function [73]. Alternatively, histone turnover could counteract the accumulation of histone modifications that are less susceptible to demodification. For example, methylation of histone H3K79, which accumulates in a non-processive manner on aging histones [72] is enriched in genomic regions that show low histone turnover and retain old histone H3 molecules, suggesting that histone inheritance and dynamics help shape the epigenome [54]. The identification of additional mutants in future screens will help to further deconstruct the pathways of histone turnover and to discover their biological significance.
In the Epi-ID screen we also identified Gis1 and Nhp10 as negative regulators of histone turnover. Gis1 is a zinc-finger transcription factor involved in regulation of stress genes [74] and contains a Jumonji domain, which has been associated with histone demethylase activity [75]. Gis1 has also been reported to bind to several factors involved in DNA metabolism [76]. It will be interesting to test whether any of these Gis1-binding proteins or its putative demethylase activity is involved in this novel function of Gis1. Nhp10 is a non-essential subunit of the essential INO80 chromatin remodeling complex that can move or mobilize nucleosomes. Two recent studies suggest a role for INO80 in redeposition of histones during induced transcription [77], [78]. That Nhp10 slows down histone turnover provides further support for the idea that the INO80 complex can help to preserve the chromatin architecture during transcription. In an Epi-ID screen using 1536 chromosome biology mutants in which the old and new tags on histone H3 were swapped (old-T7 and new-HA), NHP10 and GIS1 mutants also showed more histone turnover (data not shown), indicating that the phenotypes observed were not caused by tag-specific effects and that Epi-ID can be scaled up.
The application of Epi-ID is not restricted to histone turnover. In fact, in screens for other epigenetic marks such as histone modifications or nucleosome occupancy Epi-ID, can be applied without the elaborate genetic crosses and genetic switches that are required for screens based on the RITE pulse chase assay. Future applications in yeast may benefit from other barcoded mutant collections that are being developed [79]–[81]. Although our study suggests that position effects of the barcoded marker are not major confounders in Epi-ID and can be (at least in part) excluded by comparing UpTag with DownTag barcodes, DNA barcodes at a common genetic locus separated from the gene deletion would be preferable for epigenetic screens. The recently developed Yeast Barcoders Library represents such a collection in which barcoded markers are integrated at the common HO locus thereby providing opportunities to further expand and improve the application of Epi-ID in yeast [82]. Finally, the basic principles of this approach should also be applicable to barcoded mutant libraries in other organisms, such as barcoded episomes, or transposon or virus insertion libraries.
Yeast strains used in this study are listed in Table S3. Yeast media were described previously [7]. The pilot set of mutants (see Table S1) was manually made from the MATa haploid gene knockout library (Open Biosystems). H3-RITE (strain NKI4114) was crossed in duplicate with 92 mutants by Synthetic Genetic Array analysis [44] with the following modifications. After mating, diploids were selected and kept on Hygromycin, G418 and CloNat triple selection on rich media for one night. After 13 days on sporulation media a series of selections followed to select for the proper MAT haploids: twice on haploid MATa selection (YC-His+Can+SAEC), twice on triple resistance selection (YC-His+Can+SAEC+MSG+ Hygromycin, G418 and CloNat), and then twice on YC-His-Leu to select for H3-RITE strains in which the second, untagged, copy of H3 was deleted by insertion of LEU2. NKI2178 and NKI4179 are derivatives of BY4733. Plasmid pTW087, which was used to make strain NKI2178, was made by inserting a 6xHis tag behind the HA tag into pFvL118 [7] by PCR mutagenesis. Plasmid pTW088, which was used to make strain NKI4197, was made by replacing the HA tag in pTW081 [7] by a HA-6xHIS tag generated by PCR amplification from pTW087. NKI4128 was derived from a cross between Y7092 and NKI4004 [7], [44]. NKI8013 and NKI4140 were derived from NKI4179 and NKI4128 after elimination of the first tag and HphMX marker by induction of recombination. BAR1 was deleted using pMPY-ZAP. NKI2176 was derived from BY4733 using reagents described previously [7]. NKI2215 and NKI2216 were derived from NKI2176 by targeting the RITE cassettes from pFvL118 and pTW081 to the HHT2 locus [7]. NKI2300 and NKI2301 were derived from NKI2215 and NKI2216, respectively, after elimination of the first tag and HphMX marker by induction of recombination.
The tag switch assay was performed as described previously [7] with a few adjustments. Briefly, all strains were grown in 600 µl YPD containing Hygromycin (200 µg/ml, Invitrogen) in 96-well format for three nights at 30°C. Cells were then pooled in 50 ml of saturated media without Hygromycin containing 1 µM β-estradiol (E-8875, Sigma-Aldrich). Approximately 1x109 cells were fixed with 1% formaldehyde for 15 minutes before addition of β-estradiol (t = 0), after 16 hours (t = 1) and after 3 additional days (t = 3) for chromatin immunoprecipitation. In the follow-up analysis of candidate turnover mutants, we identified several possible confounders in our specific turnover screen. In certain mutants low turnover measurements were caused by lack of the Cre-recombinase mediated tag-switch. These mutants were excluded from the follow-up studies. Some mutants showed severe loss of viability after the tag-switch and when released into fresh media. These clones were also excluded from the follow-up studies to avoid clones in which the new H3-T7 tagged histone may not be fully functional and possibly causes tag-specific rather than a physiological turnover effects.
ChIP was performed as described previously [7]. One tenth of each sample was taken as input. After DNA isolation all samples were amplified using different SeqiXU1 primers in combination with P7U2 for the amplification of the UpTag and SeqiXD1 primers with P7D2 for amplification of the DownTag (primers are listed in Table S4). PCR amplification was conducted in 50 µl reactions using Phusion DNA polymerase (Finnzymes) with the following conditions: 10 cycles of 98°C/15 s, 56°C/15 s, 72°C/20 s; 20 cycles of 98°C/15 s, 72°C/15 s, 72°C/20 s. The amplicons of different conditions were pooled per tag, size separated on a 2% gel and the correct sized amplicons were excised and extracted using a Qiagen gel purification column. In a subsequent PCR reaction equal amounts of DNA of the UpTag and DownTag were amplified with primers P5seq and either P7U2 or P7D2 to attach the adapter fragments necessary for cluster formation and sequencing on the Illumina genome analyzer. PCR amplification was conducted in 50 µl reactions using Phusion® DNA polymerase with the following conditions: 10 cycles of 98°C/15 s, 56°C/15 s, 72°C/20 s; 20 cycles of 98°C/15 s, 72°C/25 s. The indexed barcode libraries were analyzed on an Illumina GAII genome analyzer and processed as described below.
The indexed barcode libraries were analyzed on an Illumina GAII. Sequence reads were expected to have the following composition: 4 bp index (i), 18 bp common UpTag (U1) or 17 bp common DownTag (D1) primer sequence, up to 20 bp unique UpTag or DownTag barcode sequence. A database of expected sequence reads was generated by combining the barcode sequences originally designed (http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html) with corrected sequences based on re-sequencing of the barcodes of the yeast diploid heterozygous deletion collection [40], [83]. Multiplex indexed barcodes were identified at position 1 to 6 allowing no mismatches. Barcodes were identified starting at position 22, 21, or 23, respectively, initially allowing no mismatches over a length of 11 nt. Unidentified reads were further analyzed in a second round by FASTA using the optimal alignment of gene tags. FASTA alignments were only considered with a minimal alignment length of 10 bases and a minimal identity of 90%. Only alignments that start within 2 bases from position 22 were allowed and alignments were not allowed to stop more than 5 bases from the end of the barcode. A set of unused barcodes [39], [41] was used to verify that allowing mismatches did not lead to a high false discovery rate and to determine cut-offs for P-values (see below). Out of a total number of 7446311 reads, 6249225 could be assigned to an indexed barcode amplicon. The mapped sequence reads were binned in UpTag and DownTag barcode fractions, further binned in sample fractions using the 4 bp indexes, and then the relative abundance of each barcode within each specific bin was determined using reads per million counts for each bin. Based on the behavior of the unused barcodes, to avoid false positive assignments clones with outlying up / down ratio counts (P-value <0.01) in any of the indexed samples were excluded from further analysis. Histone turnover was determined by calculating the ratio of T7 ChIP over HA ChIP for t = 1 and t = 3 days (t = 1, t = 3) and for the UpTag and DownTag barcodes. Only clones with a low variation between these four samples (SD <0.17; and thereby only clones for which both the UpTag and DownTag barcode were identified) were included for further analysis. Cut offs for variation were set such that all false positive identifications of the unused barcode set were excluded. Of the 92 clones in the screen, 53 were included in the final dataset. Drop-outs were caused by the genetic crossing or by the stringent selection criteria.
Strains were grown individually to saturation in 50 ml of YPD; ChIP was performed only on samples after three days of saturation. ChIP DNA was quantified in real-time PCR using the SYBR Green PCR Master Mix (Applied Biosystems) and the ABI PRISM 7500 as described previously. An input sample was used to make a standard curve, which was then used to calculate the IP samples, all performed in the 7500 fast system software. As a measurement for turnover, the amount DNA of the T7-IP was divided over the HA-IP. The antibodies used for ChIP and immunoblots are HA (12CA5), T7 (A190-117A, Bethyl or 69522-3, Novagen), H3 C-terminus [7], RITE-spacer+LoxP [7], RNA PolII/Rpb1 (8WG16). Primers used for qPCR are listed in Table S5.
The equivalent of 1x109 cells was washed with cold TBS, resuspended in 1ml cold TBS with a protease inhibitor cocktail. All steps were performed cold at 4°C unless otherwise stated. Cells were briefly spun and the pellet was frozen at −80°C. The pellet was dissolved in 400 µl lysis buffer (25 mM Hepes pH 7.9, 50 mM NaCl, 0.1% NP-40, 1 mM EDTA, 10% glycerol) containing a protease inhibitor cocktail. Cells were lysed by the addition of 400 µl glass beads and vortexing for 15 min on a multivortex. The total lysate was spun at maximum speed for 5 min, the soluble fraction was transferred to a new tube and 1 ml of lysis buffer was added. The lysate was then spun for 5 min 14K, transferred to a new tube, then spun for 15 min 14K and again transferred to a new tube. Of this fraction 50 µl was used as input, the rest was incubated with 30 µl IgG beads (Invitrogen) for 2 hrs. The beads were washed three times with cold lysis buffer for 5 min and once with TEV buffer (50 mM Tris pH 8, 0.5 mM EDTA, 50 mM NaCl, and 1 mM DTT). The beads were resuspended in 100 µl TEV buffer to which 175 µg recombinant TEV protease is added and kept overnight. The soluble fraction contains the immunoprecipitated fraction and was analyzed by quantitative immunoblotting. Lysates were separated on a 16% polyacrylamide gel and blotted onto 0.45 µm nitrocellulose membrane. Membranes were blocked with 2% Nutrilon (Nutricia) in PBS. Primary antibody incubations were performed overnight in Tris-buffered saline-Tween with 2% Nutrilon, anti-HA (mouse 12CA5), anti-T7 (Novagen, 1∶1000) or a polyclonal antibody obtained against the LoxP peptide (1∶2500) [7]. Secondary antibody incubations were performed for 45 minutes using LI-COR Odyssey IRDye 800CW (1∶12.000). Immunoblots were subsequently scanned on a LI-COR Odyssey IR Imager (Biosciences) using the 800 channel. Signal intensities were determined using Odyssey LI-COR software version 3.0.
To monitor cell cycle progression and cell cycle arrests the DNA content of the cells was measured by flow cytometry as described previously [7], using SYTOX Green and a 530/30 filter (Becton-Dickinson). Analysis was performed using FCSexpress2.
Each mutant strain was profiled four times from two independently inoculated cultures and harvested in early mid-log phase in synthetic complete medium with 2% glucose or harvested in starvation conditions in rich media as described above for the turnover experiments. Sets of mutants were grown alongside corresponding WT cultures and processed in parallel. Dual-channel 70-mer oligonucleotide arrays were employed with a common reference WT RNA. All steps after RNA isolation were automated using robotic liquid handlers. These procedures were first optimized for accuracy (correct FC) and precision (reproducible result), using spiked-in RNA for calibration [84]. After quality control, normalization, and dye-bias correction [85], statistical analysis for mid-log cultures was performed for each mutant versus the collection of 200 WT cultures as described by Lenstra et al [57]. The reported FC is an average of the four replicate mutant profiles versus the average of all WTs. HAT1, HAT2, and HIF1 single, double, and triple mutants in the BY4742 background were not different from wild type (less than three genes changed p<0.01, FC >1.7 after removal of WT variable genes). Mutants in G0 were compared to replicates of the corresponding wild-type RITE strain. Due to variability under conditions of starvation [86] we did not perform genome-wide statistical analyses of expression changes in G0 cultures.
Microarray data have been deposited in ArrayExpress under accession numbers E-TABM-1175 (mutants) and E-TABM-773/E-TABM-984 (200 WT replicates), as well as in GEO under accession number GSE30168.
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10.1371/journal.pbio.2001104 | Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species | Current climate change may be a major threat to global biodiversity, but the extent of species loss will depend on the details of how species respond to changing climates. For example, if most species can undergo rapid change in their climatic niches, then extinctions may be limited. Numerous studies have now documented shifts in the geographic ranges of species that were inferred to be related to climate change, especially shifts towards higher mean elevations and latitudes. Many of these studies contain valuable data on extinctions of local populations that have not yet been thoroughly explored. Specifically, overall range shifts can include range contractions at the “warm edges” of species’ ranges (i.e., lower latitudes and elevations), contractions which occur through local extinctions. Here, data on climate-related range shifts were used to test the frequency of local extinctions related to recent climate change. The results show that climate-related local extinctions have already occurred in hundreds of species, including 47% of the 976 species surveyed. This frequency of local extinctions was broadly similar across climatic zones, clades, and habitats but was significantly higher in tropical species than in temperate species (55% versus 39%), in animals than in plants (50% versus 39%), and in freshwater habitats relative to terrestrial and marine habitats (74% versus 46% versus 51%). Overall, these results suggest that local extinctions related to climate change are already widespread, even though levels of climate change so far are modest relative to those predicted in the next 100 years. These extinctions will presumably become much more prevalent as global warming increases further by roughly 2-fold to 5-fold over the coming decades.
| Climate change is an important threat to the world’s plant and animal species, including species on which humans depend. However, predicting how species will respond to future climate change is very difficult. In this study, I analyze the extinctions caused by the climate change that has already occurred. Numerous studies find that species are shifting their geographic ranges in response to climate change, typically moving to higher elevations and latitudes. These studies also contain valuable data on local extinctions, as they document the loss of populations at the “warm edge” of species’ ranges (lower elevations and latitudes). Here, I use these data to show that recent local extinctions related to climate change have already occurred in hundreds of species around the world. Specifically, among 976 species surveyed, local extinctions occurred in 47%. These extinctions are common across climatic zones, habitats, and groups of organisms but are especially common in tropical regions (which contain most of Earth’s species), in animals (relative to plants), and in freshwater habitats. In summary, this study reveals local extinctions in hundreds of species related to the limited global warming that has already occurred. These extinctions will almost certainly increase as global climate continues to warm in the coming decades.
| Anthropogenic climate change may be a major driver of biodiversity loss in the next 100 years, but the possible impacts of climate change on species survival remain highly uncertain [1–3]. Global mean annual temperatures increased by ~0.85°C between 1880 and 2012 and are likely to rise by an additional 1°C to 4°C by 2100 [4]. Modeling studies have predicted that various levels of species loss will result from this future climate change, ranging from 0% to >50% of all species currently known [3]. This uncertainty has many sources (e.g., different climate models and different hypotheses about species dispersal). One of the most important sources of uncertainty hinges on the details of how species respond to climate change. For example, if species can evolve rapidly enough in response to changing climate, then species extinctions due to climate change might actually be limited [5,6].
Species can potentially respond to climate change in several ways. The most important case to consider may be that when the species’ present-day (realized) climatic niche no longer occurs within the species’ current geographic range (because of the potential for global extinction of the species under these conditions). In this case, the possible responses of the species include the following: (i) undergoing niche shifts, such that the species’ realized niche changes to incorporate these new climatic conditions (e.g., through plastic changes and/or by evolutionary adaptation to the modified abiotic and/or biotic conditions), (ii) dispersing to track the original climatic conditions over space (e.g., moving to higher latitudes or elevations), and (iii) going extinct [5–8]. While each of these responses has been shown in some cases (at least in local populations), the relative frequency of each is still unclear [7,8]. However, changes in species’ geographic ranges have been especially well documented [9–11].
These data on geographic range shifts contain important but underutilized information on how species respond to climate change. Range shifts observed under climate change typically involve an overall shift towards higher latitudes and higher elevations [9–11]. These shifts can be composed of one (or both) of two types of changes (Fig 1): (i) range expansions at the cool edge of the species range (higher latitudes and elevations) and (ii) range contractions at the warm edge (lower latitudes and elevations). The presence of warm-edge contractions is critically important. A warm-edge contraction occurs when populations from one or more localities at the lowest latitudes or elevations of a species’ regional distribution disappear (i.e., are inferred to no longer occur at those localities), leading to an overall shift in the species range towards higher latitudes or elevations. These contractions indicate that species are failing to shift their niches sufficiently to tolerate these new conditions and that these populations are instead going extinct (referred to as “local extinction” hereafter). This must be true regardless of the specific mechanism of local extinction (e.g., elevated death rates, increased emigration, or declining recruitment). The many papers that have assessed range shifts and that have included surveys of warm-edge populations can therefore provide a wealth of data about which species have (and have not) undergone local extinctions potentially related to climate change. These data are particularly useful because published papers on range shifts need not be strongly biased towards documenting warm-edge contractions, given that many studies that included data on warm edges also surveyed the cool edge. Thus, even though studies that failed to find any range shifts might go unpublished (a potential source of bias), studies that documented an overall range shift need not show a warm-edge contraction.
Here, I analyze the extensive data on range shifts to examine the prevalence of local extinctions related to modern climate change. I also provide a synthesis of inferred local extinction across habitats, climatic zones, and taxonomic groups. I systematically searched the literature for studies that examined shifts in species’ ranges at their warm edges, shifts that were considered (in the original studies) to be related to current climate change. Hundreds of examples of local extinctions were found across diverse climatic zones, habitats, and taxonomic groups. Not all species exhibiting range shifts showed warm-edge contractions, but ~50% of the species surveyed had local extinctions inferred to be related to climate change. These results suggest that even the relatively small changes in climate that have already occurred are sufficient to cause widespread local extinctions and that many species may be unable respond to climate change fast enough to avoid extinction as global climate warms even further.
The Web of Science was searched repeatedly between December 2014 and March 2016 using keywords related to climate change, range shifts, and local extinctions (see Materials and Methods). All studies that monitored the warm edge of at least one species’ range and that tied their results to climate change with explicit statistical analyses were included. Importantly, studies can document overall range shifts but need not find that the warm-edge populations that they examined had local extinctions.
A total of 27 studies (Table 1; [13–39]) met all the necessary criteria to address potential climate-associated warm-edge range shifts (see Materials and Methods). The sampled species were broadly distributed across clades (e.g., animals = 716; plants = 260) and regions (e.g., Asia = 332; Europe = 268; Madagascar = 30; Oceania = 58; North America = 233; South America = 55). Among the 976 unique species surveyed, 460 species had warm-edge contractions, and 516 did not (S1 Appendix). Therefore, local extinctions related to climate change are already very common (47.1% of species examined), even given the relatively modest rise in global temperatures that has occurred so far (less than 1°C increase in global mean annual temperature; [4]).
These 976 species spanned many clades, habitats, and regions (Table 1; S1 Appendix). Comparison between those species that showed warm-edge contractions and those that did not provides potential insights into which species may be most sensitive to climate change, in terms of the climatic zones and habitats that they occur in and the clades that they belong to. Furthermore, there is no evidence that there were more species with local extinctions in studies that ended more recently, were of longer duration, or began earlier (based on midpoints for ranges of values; Table 1). Specifically, regression analyses of the proportion of species with local extinctions against (i) the study end date, (ii) the duration of the study, and (iii) the study start date all yielded nonsignificant results (end date: r2 = 0.001, p = 0.8910; duration: r2 = 0.045, p = 0.2896; start date r2 = 0.047, p = 0.2788; after removing nine studies with four or fewer species: end date: r2 = 0.146, p = 0.1181; duration: r2 = 0.132, p = 0.1376, but unexpectedly trending towards fewer extinctions in studies with longer durations; start date r2 = 0.177, p = 0.0821, with more extinctions in studies beginning more recently, not earlier). Therefore, the frequency of local extinctions was initially compared across species in different studies, regardless of differences in the duration, beginning, or end date of the study in which they were surveyed.
Overall, the frequency of local extinctions was similar (close to 50%) across most climatic zones, habitats, gradients, and clades. Nevertheless, there were some significant differences. First, local extinctions were significantly more common in species from tropical and subtropical regions (combined and referred to as tropical hereafter for brevity) than in those from temperate regions (p < 0.0001; Chi-squared test, testing the assumption of equal frequencies of local extinction among species between regions; subsequent p-values are also from Chi-squared tests). Specifically, 54.6% of the 504 included tropical species had local extinctions, whereas only 39.2% of the 472 temperate species did (Fig 2A). The pattern was even stronger when only considering terrestrial species on elevational gradients (54.6% of 504 tropical species versus 28.2% of 301 temperate species), which applied to all plants and most animals. In part, this pattern of more frequent tropical extinction arose from a much lower frequency of extinctions for temperate plants (59.4% of 155 tropical species versus 8.6% of 105 temperate species; p < 0.0001). The very low frequency of temperate extinctions in plants was based on a single study from very high latitudes [19]. Nevertheless, there were also significantly more local extinctions in tropical animals (52.4% of 349 tropical species versus 38.8% of 196 temperate species; p = 0.0022), if one compares terrestrial species on elevational gradients. This restriction also made them more comparable to the sampled plants (all from terrestrial, elevational gradients) and still encompassed most sampled animal species (76.1%; 545 of 716 species). Across all animals, the difference was not significant (p = 0.2309), possibly because of the influence of temperate marine and freshwater species (see below). Among the most well-sampled groups of animals, tropical extinction was significantly more common in birds (51.4% of 109 tropical species versus 37.1% of 124 temperate species; p = 0.0284), but not in insects (local extinctions in 55.2% of 210 tropical species versus 59.0% of 61 temperate species; p = 0.6007). For other animal groups, the species sampled here were either predominantly temperate (mammals, fish, and marine invertebrates) or tropical (squamate reptiles and amphibians), and so did not allow for similar within-clade comparisons.
Overall, the frequency of climate-related local extinctions (Fig 2B) was similar in terrestrial (45.6% of 835 species) and marine environments (50.9% of 110; p = 0.2964). In contrast, the frequency in freshwater species was substantially higher (74.2% of 31; p = 0.0053 across all three habitats). However, the estimate for freshwater species was based on a single study of European fishes [17]. Comparing fish only (all temperate) also supported a significantly higher frequency of extinction in freshwater environments relative to marine environments (p = 0.0240; local extinctions in 47.4% of 38 marine species versus 74.2% of 31 freshwater species). All marine species included here were temperate animals, but there was no significant difference in extinction frequencies between marine and terrestrial environments when only temperate animals were compared (p = 0.1676; marine: 50.9% of 110 species, terrestrial: 42.9% of 226 species). Terrestrial and freshwater species remained significantly different in this more restricted comparison (p = 0.0011).
The frequency of local extinctions (Fig 2C) was somewhat lower for species surveyed along elevational gradients relative to those on latitudinal gradients (elevational: 45.8% of 836 species; latitudinal: 55.0% of 140 species; p = 0.0439). Most (78.6%) species measured along latitudinal gradients were marine (and all marine studies focused on latitudinal gradients), and all were temperate. Again, most species included here were based on studies of elevational gradients in terrestrial environments.
Local extinctions were also broadly similar in frequency across taxonomic groups (Fig 3). Nevertheless, local extinctions were significantly more common (p = 0.0018) in animals (50.1% of 716) than plants (38.8% of 260). This difference was reduced when comparing only animals and plants on terrestrial, elevational gradients (47.3% of 556 animal species versus 38.8% of 260 plant species; p = 0.0236). Among these latter species, the plant–animal difference was nonsignificant for tropical species (and was actually reversed: local extinctions in 52.4% of 349 tropical animal species versus 59.4% of 155 tropical plants; p = 0.1500) but was strong for temperate species (38.6% of 207 temperate animal species versus 8.6% of 105 temperate plants; p < 0.0001).
The frequencies of local extinctions across different animal groups (Fig 3) were broadly similar to the overall value for animals (50.1%), but with higher values in insects (56.1% of 271 species; based on six studies; Table 1) and fish (59.4% of 69 species; three studies) relative to mammals (35.0% of 40 species; four studies), birds (43.8% of 233 species; five studies), amphibians (36.8% of 19 species; one study), and squamate reptiles (lizards and snakes; 41.7% of 12 species; two studies). Local extinctions were also broadly similar in frequency in various groups of marine invertebrates, including crustaceans (46.7% of 15; one study), annelids (64.5% of 31; one study), and molluscs (45.4% of 22; two studies). The frequency in echinoderms was lower (25.0%; one study) but was based on a very small sample size (4 species).
Results were generally similar using both general linear models (GLMs; see below) and general linear mixed models (GLMMs; see next paragraph). GLM results are given in full in S2 Appendix and are summarized here. Simultaneously including all 976 species and most variables (habitat [terrestrial versus freshwater versus marine], climatic regions [tropical versus temperate], taxonomic group [plants versus animals], survey type [latitudinal versus elevational], and study dates [start date, end date, and duration in between]) showed that most variables had significant effects on the frequency of extinction, except for the study dates. There were strong effects of habitat and climate (p < 0.00001) but weaker effects of taxonomic group (p = 0.0246). Results were similar when excluding study dates and taxonomic group. Including geographic regions showed that most regions had no significant effect (except for Madagascar and South America). Given that Madagascar and South America were represented by one study each, these region effects were not considered further. Furthermore, the effects of climatic region, habitat, taxonomic group, and survey type remained significant when geographic regions were included. Comparing species only on terrestrial elevational gradients (805 species in total) further confirmed the significant effects of climate and taxonomic group. Similarly, considering plants only (260 species) also confirmed the significant effects of climatic region. Considering only terrestrial animals on elevational gradients (545 species) showed a significant effect of climate (p = 0.0023) after removing study dates, which had no significant effect. Considering birds alone (233 species) and including climatic region, survey type, and study dates showed that climatic region, survey type, start date, and end date had significant effects. For insects (271 species), when including climatic region, study dates, and survey type, no variables were significant. For fish (69 species), a model including habitat (freshwater versus marine), study dates, and survey type showed that no variables were significant. However, habitat was significant if other variables were removed. Similarly, for temperate animals (367 species), a model including habitat, survey type, and study dates showed that only habitat and survey type were significant. Comparison of plants and animals on terrestrial elevational gradients (including study dates) showed that extinction is significantly different between temperate plants and animals (more common in animals), but not between tropical ones. Across animals, the effects of taxonomic group were limited and depended on the other variables included. If only taxonomic groups and study dates were included, then annelids, fish, and insects showed significantly more extinction (p = 0.03–0.05). Including habitat and survey type (and removing study dates) showed stronger effects in fish and annelids (as well as in crustaceans and molluscs), but not in insects.
Results were also broadly similar using GLMMs, with study identity included as a random effect. Results are summarized below and given in full in S3 Appendix. The impacts of study dates were somewhat counterintuitive (and rarely significant), and analyses including them sometimes failed. When most variables were included (habitat, climatic region, taxonomic group [plant versus animal], survey type, and study dates), all variables were significant except for study dates and taxonomic group, with strong effects of habitat, climatic region, and survey type. When study dates were removed, only habitat and survey type were significant. When geographic regions were included (and study dates excluded), only South America had a significant effect, and habitat, taxonomic group, and climatic region were significant or marginally significant. Comparing tropical and temperate species on terrestrial, elevational gradients showed significant effects of climatic region (p = 0.0017) and taxonomic group (p = 0.0119), but not of study dates. When study dates were removed, no variables were significant. Plants alone showed a significant effect of climatic region (p < 0.0001), but analyses failed if study dates were included. Animals on terrestrial, elevational gradients showed no significant effect of climatic region (again, study dates had to be excluded). Considering birds alone showed no significant effect of climate but a significant effect of survey type (excluding study dates). Insects showed no significant effects of climate or survey type, regardless of whether study dates were included. Analyses of fish failed unless study dates and survey type were excluded, but habitat alone (marine versus freshwater) had a significant effect (p = 0.0265). Analyses of temperate animals (367 species) including habitat, survey type, and study dates showed only habitat type as significant (p = 0.0307), but excluding study dates showed significant effects of habitat and survey type. Comparing only temperate plants and animals showed a significant effect of taxonomic group, when study dates were included (p = 0.0116) or excluded (p = 0.0005; study dates had no significant effect). In contrast, there was no significant effect of taxonomic group when comparing tropical plants and animals (504 species total; excluding study dates). Analyses of animals alone showed no significant effect of taxonomic group.
In summary, several patterns emerged as significant across all (or most) analyses. First, there were significant effects of climatic region overall, with extinction more common in tropical regions. This was present in plants across all analyses and generally present in animals. Animals showed significantly more extinction than plants overall and when comparing temperate, but not tropical, species. There were significant effects of habitat on animals overall (higher extinction in freshwater), even when considering fish alone. Finally, GLM analyses showed some effects of taxonomic groups across animals (with higher extinction in fish and annelids) and possibly in insects, molluscs, and crustaceans. The GLMM analyses did not show these group effects, possibly because many animal groups are included based on a single study.
The results of this study show that local extinctions (inferred to be related to climate change) are already widespread and have occurred in hundreds of species. Roughly half of the 976 species that were surveyed for range shifts showed evidence of local extinctions (47%). This proportion was surprisingly similar across diverse climatic regions, habitats, and taxonomic groups. The results here suggest that even the modest changes in climate that have occurred so far are enough to drive local populations in many species to extinction. The results here also suggest that local populations in many species cannot shift their climatic niches rapidly enough to prevent extinction. This pattern of widespread local extinction seems likely to become even more prevalent as the global climate warms further (by roughly 2 to 5-fold [4]) in the next several decades.
The results here showed generally similar patterns of local extinction across climatic zones, habitats, and clades. Nevertheless, most analyses showed that local extinctions were significantly more common in tropical species (Fig 2A), in freshwater species (Fig 2B), and in animals. A greater impact of climate change on tropical species has been predicted by several authors (e.g., [40–42]). This prediction is related to the narrower climatic niche widths for temperature-related variables in tropical species that are associated with reduced temperature seasonality in the tropics (e.g., [43,44]) and lower rates of temperature-related climatic niche change in tropical species (e.g., [42]). The results here provide support for this prediction based on documented local extinctions that have already occurred: species in tropical regions had local extinctions more frequently than those in temperate regions (54.6% versus 39.2%), especially when species were compared on terrestrial, elevational gradients (54.6% versus 28.2%). This pattern was strongest in plants and when animals were compared on terrestrial elevational gradients. Overall, these results further support the idea that the negative impacts of climate change on biodiversity are more frequent (per species) in tropical regions [40–42], where biodiversity is highest.
Climate-related local extinctions were also similar in frequency in marine and terrestrial species (Fig 2B) but were more common in freshwater species (although freshwater habitats were represented by a single study). Freshwater species may be especially susceptible to changes in precipitation patterns (e.g., drought), which can substantially alter or eliminate their habitats (e.g., [45]), quickly resulting in local extinction. In contrast, marine species may experience less impact from changes in precipitation. Furthermore, they may be buffered from temperature changes because they can potentially adjust the temperatures that they experience by movement within the water column (more so than is possible for most freshwater species; [46,47]).
The frequency of local extinctions was also broadly similar across diverse taxonomic groups (~35%–60%; Fig 3), including plants, insects, fish, amphibians, squamate reptiles, endothermic vertebrates (birds and mammals), and many marine invertebrates (annelids, crustaceans, and molluscs). However, local extinctions were significantly more common in animals than plants (and animals are far more species-rich than plants). They were also relatively common in insects (the most species-rich group of animals) and fish (the most species-rich group of vertebrates). Local extinctions were not particularly common in amphibians (36.7%) or squamate reptiles (41.7%), although both groups were included here based primarily on one study [32]. Nevertheless, both groups appear to have been strongly impacted by climate change overall. For example, many amphibian species have undergone sharp declines and global extinctions, many of which are thought to be caused by an interaction between climate change and an infectious disease (chytrid fungus; [48]). However, these chytrid studies were not included here because they were not focused on surveying warm-edge populations over time. Similarly, local extinctions related to climate change have been documented in many lizard species [49]. Again, these were not included here because they were not based on a systematic survey of warm-edge populations. Nevertheless, if the species studied by Sinervo et al. [49] were included here, the frequency of local extinctions in squamates would go from 41.7% (of 12 species) to 77.4% (of 124 species), but with the caveat that their study focused on documenting local extinctions and so might overestimate this frequency. It should also be noted that the well-publicized declines in amphibian populations globally are not necessarily inconsistent with the frequency of local extinction observed here. For example, a global assessment of amphibian populations [50] noted declines in 43% of amphibian species (compare to the 47% of all species here with local extinctions and the 37% for amphibians), but these declines also included those unrelated to climate change (e.g., habitat destruction and overexploitation). Thus, the frequency of climate-related declines here is not necessarily an underestimation relative to the declines documented by the global amphibian assessment [50].
A major conclusion of this study is that populations of many species are already unable to undergo niche shifts that are fast enough to prevent local extinction from climate change. The rate is emphasized here because even if the absolute amount of niche change needed to avoid extinction might be attainable, it might require more time to achieve than is allowed by the rapid pace of anthropogenic climate change. Given this result, and that climate is predicted to change even further in the near future, the persistence of many species might depend largely on their ability to successfully shift their geographic ranges to higher latitudes or elevations and remain within their original climatic niche. Indeed, the summary here shows numerous instances of cool-edge expansions (in 367 of 904 species, with cool edges that were stable in 371 others and contracted in 166 others).
Unfortunately, these movements may be impeded for many species by one or more factors. First, human impacts may prevent species from successfully dispersing (including agriculture, roads, and urbanization), or these human impacts may simply leave them no habitat to disperse to (e.g., [51,52]). Second, many species are already confined to islands, peninsulas, and mountaintops, where dispersal to higher latitudes or elevations may not be possible (e.g., [53]). Third, even if dispersal is unimpeded by human or natural barriers, it may simply occur too slowly to allow species to remain within their climatic niche (e.g., [54,55]).
The combination of these potential limits to dispersal and the widespread local extinctions documented here is troubling. However, the results here do not rule out the possibility that rapid niche shifts will occur in some populations of many species in the future, preventing global extinctions. Indeed, roughly half of the species surveyed showed no local extinctions, and most species had some populations that persisted locally (but again, this is under the limited climate change that has already occurred). The future persistence of species will depend on many factors [6,8], including rates and patterns of climate change at each location, dispersal, niche shifts, local climatic microrefugia [56], and the contribution of population-level niche width to species-level niche width (e.g., whether species are broadly tolerant or locally specialized to different climatic conditions across their ranges [44]). Most importantly, I suggest that the patterns of present-day local extinctions obtained from range-shift studies should be part of the evidence used to predict species persistence in the future.
There are several potential sources of bias that may have influenced some aspects of these results but should not overturn the major conclusions. First, “local extinction” means that individuals of a given species are entirely absent from a location that they previously occupied. However, it can be difficult to distinguish between extinction and a substantial decline in abundance that causes the species to go undetected at a given location (e.g., [57]), and studies did not necessarily provide statistical evidence for the absence of a species at a site. Here, the estimates of previous researchers were used, and it was assumed that they adequately documented local absences (otherwise, their estimates of range shifts would also be erroneous). Furthermore, strong declines that make a species undetectable at a given site might soon lead to local extinction. Second, there may be a bias in terms of unpublished results. Specifically, some researchers who monitored the warm edge of a population but failed to find any changes associated with climate change may not have published their negative results. Such a reporting bias would lead to overestimating the proportion of species experiencing local extinction in this study. Nevertheless, local extinctions were still documented in hundreds of species across regions and clades, even if there are hundreds of additional species in which these local extinctions did not occur. Additionally, numerous species (n = 171) showed evidence of a cool-edge expansion without a corresponding contraction in the warm edge. Thus, a species can undergo a range shift but without local extinction, which should limit this source of publication bias. Third, it was assumed that previous researchers correctly associated the patterns that they observed with climate change. In theory, other factors such as overharvesting or habitat destruction may have contributed to the observed local extinctions in some cases (e.g., [21]). Again, the analyses here primarily assume that the main conclusions of these previous studies were not erroneous.
Finally, despite the widespread pattern of warm-edge contractions and local extinctions, 521 species showed no local extinctions at the warm edge, indicating that they have successfully persisted in the face of the climate change that has occurred so far. However, even these species might still go globally extinct when global climate changes further. Additionally, contrary to the overall trend, 54 species were documented here as having expansions at both their warm edge and their cool edge (6.0% of 904 species with data on both cool and warm edges). One scenario by which this may occur is if cool-edge limits are set by colder temperatures (allowing expansion as global climate warms) and warm-edge limits are set by low precipitation (allowing warm-edge expansion), given that precipitation may increase in some areas because of climate change [4]. Indeed, some studies have found evidence for warm-edge expansions through this mechanism [58]. It is also important to note that local extinctions related to climate change need not be confined to the warm edge of the species range and so might actually be underestimated here. For example, there could be climate-related local extinctions far from the warm edge that are associated with certain microclimates (e.g., equatorially facing slopes at the cool edge of a species range; [59]).
In summary, the results here show that widespread local extinctions (seemingly related to climate change) have already occurred in hundreds of species, with broadly similar patterns of extinction across diverse clades, habitats, and climatic regions. Importantly, levels of climate change so far are limited relative to those generally predicted for the next 100 years [4]. The results here suggest that many species are unable to shift their niches rapidly enough to prevent local extinction. This inference of climate change outpacing niche change supports predictions from other sources, including transplant experiments in plants [60], phylogenetic analyses of rates of niche change in plants and animals [42,61,62], and projections based on selection, heritability, and temperature tolerances in lizards [49]. Local extinctions from climate change might also impact species that many human populations depend on for food, such as grasses (e.g., wheat, rice, and corn [62]). More generally, this study demonstrates that analyses of range shifts can provide extensive data on local extinctions related to climate change that have already occurred. These local extinctions offer a potentially important but underutilized source of information for the challenging task of predicting patterns of species survival and extinction in the future.
Web of Science searches were initially conducted from December 2014 to April 2015 using the Boolean search terms Topic = (global warming OR climate change) AND Topic = (local extinction OR range contraction OR range shift). A second Web of Science search was conducted between April 2015 and May 2015 to identify additional studies potentially missed by the first set of keywords, using the search terms TS = (global warm* OR climate change) AND TS = (extinction* OR contraction* OR range shift*), excluding results from TS = (global warming OR climate change) AND TS = (local extinction OR range contraction OR range shift). Each set of Web of Science results was sorted by relevance and then binned into subsets of 50. Searching was ceased when less than 1 in 50 studies per subset was relevant (see below for criteria). Finally, a third Web of Science search was performed on 1 March 2016 to find more recently published studies. This third search used the keywords TS = (global warm* OR climate change) AND TS = (extinction* OR contraction* OR range shift*). A total of 1,530 results were found in this third search. Results were sorted by relevance, and the first 300 (~20%) were examined. The last 40 of these 300 included no relevant studies.
Some additional studies were also found that were listed as references in the papers identified by these initial Web of Science searches. The reference list was also checked against a recent review study [11], which also conducted thorough searches of the literature on climate-related range shifts. Three studies were added from that survey which were not initially included here. Finally, several relevant studies were also found in the survey of Gibson-Renemer et al. [63], which had similar rules for inclusion of studies. Although those authors did not conduct a systematic search of the literature (as done here), they nevertheless included five studies not found in the searches described above. These were also added here.
In theory, the fact that “extinction” and “contraction” were included as keywords might have biased the results to include more papers documenting local extinctions and range contractions than would be obtained from a search of range-shift studies that excluded these as keywords (possibly leading to overestimation of the frequency of local extinctions). However, this seems unlikely in practice. First, these were included as “or” keywords, along with “range shifts.” Examining the keywords and titles of the 27 selected papers showed that most were focused on overall range shifts, with no mention of local extinction (extinction or extirpation are mentioned in the titles of only 4 of 27 studies and as keywords in only 4 of the 21 studies with keywords; “contraction” is mentioned in only 1). Furthermore, the fact that the survey results here were checked against another recent review on range shifts [11], and that three missing studies were added, also makes this potential bias seem unlikely. In other words, if many range-shift studies were missed because of this bias, they should have been added at that point.
Overall, these searches were extensive but may not be truly exhaustive. Regardless, many studies were found that documented local extinctions, and finding more studies that did so would not overturn this main conclusion.
Studies were included that monitored one or more populations at the warm edge of a species’ range (the edge that is lower in elevation or closer to the equator) over a relatively long time span. Studies were only included that spanned an interval of at least 10 years. The mean study duration was ~50 years (range = 14 to 159; Table 1). Studies were included that related their findings on range shifts to climate change through an explicit statistical analysis (but noting that these inferences could still be incorrect, for example, if other factors instead of climate change caused local extinctions of a particular species). The included studies all documented populations along elevational or latitudinal transects at two or more discrete time points.
Some recent studies have inferred climate-related range shifts based on overall trends in latitudinal and elevational distributions across a large number of localities over time, rather than systematically resurveying specific localities at different time points (e.g., [64]). These studies are valuable for documenting range shifts in general but were excluded here, since they do not unambiguously represent local extinctions (because the overall patterns described might be driven solely by range expansions instead).
Studies that documented warm-edge range contractions (and that were linked to climate change by the authors of the original studies) were considered evidence of climate-associated local extinction, regardless of changes at the cool edge. Studies differed in whether they reported changes at the population level (e.g., [28,37]) or species level (e.g., [33]). The analysis here was conducted at the species level. Therefore, if populations of the same species differed in the pattern of their range shifts, the species was categorized as showing evidence of local extinction if at least one population did so.
Most species were included in only one study. However, the plant species Anthoxanthum odoratum was included by both Angelo and Daehler [13] (in Hawaii) and Felde et al. [19] (in Europe). However, since this species is not native to Hawaii, it was excluded from the dataset of Angelo and Daehler [13], along with all other nonnative species in that study.
For each study, it was noted whether the range shifts were elevational or latitudinal, as well as the general habitat of the organisms (i.e., terrestrial, freshwater, or marine), the higher taxa to which they belonged, the specific geographic location of the study, and whether the species occurred in a tropical or subtropical region (arbitrarily defined as within 35° of the equator) or in a temperate region (>35°). Species were assigned to these climatic regions based solely on the location where they were surveyed, rather than on their overall geographic range. Species were also assigned to taxonomic categories, including plants, insects, fish, amphibians, birds, mammals, and squamate reptiles (i.e., lizards and snakes), as well as marine annelids, crustaceans, echinoderms, and molluscs. The beginning and end dates of the study were also noted (e.g., the date of the initial survey and the subsequent resurvey) and were used to estimate the duration of the study. Some studies provided a range of dates for the start and/or end date. In these cases, the midpoint of each range of dates was used to estimate the start, end, and duration (Table 1). Data for all species are provided in S1 Appendix.
The studies included (Table 1) spanned many geographic regions (e.g., North America, South America, Europe, Asia, and Oceania). Many studies were conducted in North America (n = 13; here extending to Central America) and Europe (n = 8), but the actual number of species sampled was more broadly distributed among regions (e.g., Asia = 332; Europe = 268; Madagascar = 30; Oceania = 58; North America = 233; and South America = 55). Africa and Australia were not represented, although nearby Madagascar and New Guinea were. The numbers of temperate and tropical species included were nearly equal. Further, there was no clear hypothesis for why particular continents alone should be an important factor influencing the frequency of local extinctions (e.g., separate from temperate versus tropical effects).
Chi-squared analyses were initially used to compare the proportion of climate-associated local extinctions across some categories (i.e., tropical versus temperate; freshwater versus marine versus terrestrial; and latitudinal versus elevational gradients), testing the null hypothesis that frequencies of local extinction were equal between these categories. A series of analyses were conducted to assess whether frequencies of local extinction were higher in tropical regions relative to temperate regions, after accounting for the potential influence of different habitats, gradients, and clades (see Results). Similar analyses were conducted to assess the impacts of different habitats and clades (i.e., plants versus animals). However, potential analyses were restricted by the available data. For example, it was not possible to compare the effect of tropical versus temperate climates on marine or freshwater organisms, since only temperate marine and freshwater species were included here. For this reason, different sets of analyses were conducted for each question.
These analyses were then repeated using GLMs and GLMMs, both in R. These analyses were implemented treating the presence of warm-edge local extinction in a species as the binomial, dependent variable. GLMM analyses were conducted using the R package lme4 [65]. GLMM analyses treated the study (from which the species data were obtained) as the random variable and the other variables as the fixed variables. GLM and GLMM analyses initially included all species and all or most variables and were then restricted to smaller sets of species (and variables) to test additional hypotheses and reduce potentially confounding effects (as in the Chi-squared analyses).
Phylogenetic information was not incorporated here, since phylogenies and comparable branch lengths spanning all the included species were not available (especially species-level phylogenies for fish, insects, plants, and marine invertebrates). Nevertheless, some analyses were conducted to assess patterns within and between clades (see Results).
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10.1371/journal.pbio.1002582 | A Low-Correlation Resting State of the Striatum during Cortical Avalanches and Its Role in Movement Suppression | During quiet resting behavior, involuntary movements are suppressed. Such movement control is attributed to cortico-basal ganglia loops, yet population dynamics within these loops during resting and their relation to involuntary movements are not well characterized. Here, we show by recording cortical and striatal ongoing population activity in awake rats during quiet resting that intrastriatal inhibition maintains a low-correlation striatal resting state in the presence of cortical neuronal avalanches. Involuntary movements arise from disturbed striatal resting activity through two different population dynamics. Nonselectively reducing intrastriatal γ-aminobutyric acid (GABA) receptor-A inhibition synchronizes striatal dynamics, leading to involuntary movements at low rate. In contrast, reducing striatal interneuron (IN)-mediated inhibition maintains decorrelation and induces intermittent involuntary movements at high rate. This latter scenario was highly effective in modulating cortical dynamics at a subsecond timescale. To distinguish intrastriatal processing from loop dynamics, cortex-striatum-midbrain cultures, which lack feedback to cortex, were used. Cortical avalanches in vitro were accompanied by low-correlated resting activity in the striatum and nonselective reduction in striatal inhibition synchronized striatal neurons similar to in vivo. Importantly, reduction of inhibition from striatal INs maintained low correlations in the striatum while reorganizing functional connectivities among striatal neurons. Our results demonstrate the importance of two major striatal microcircuits in distinctly regulating striatal and cortical resting state dynamics. These findings suggest that specific functional connectivities of the striatum that are maintained by local inhibition are important in movement control.
| Even in the absence of apparent motor output, the brain produces a rich repertoire of neuronal activity patterns known as “resting state” activity. In the outer layer of the cortex, resting state patterns emerge as neuronal avalanches, precisely scale-invariant spatiotemporal bursts that often engage large populations of neurons. Little is known about how the brain suppresses involuntary movements during such activity. Here, we show that the striatum, which is part of the cortex-basal ganglia loop, maintains a low-correlation state during resting activity. By using a combination of in vivo and in vitro approaches with pharmacological manipulations, we demonstrate that the precise configuration of this low-correlation state effectively contributes to involuntary movements. Nonselective blockade of intra-striatal inhibition abolished the low-correlation striatal resting state, barely affected cortical avalanches, and led to involuntary movements at low rate. In contrast, selectively reducing striatal interneuron inhibition strongly affected cortical avalanches and triggered involuntary movements at high rate while maintaining a relatively decorrelated striatal resting state. Our results demonstrate the importance of different inhibitory striatal circuits in the suppression of involuntary movements and suggest that the precise spatiotemporal configuration of striatal activity plays an active role in the regulation of cortical resting state activity and motor control.
| In the absence of specific sensory input or motor output, the brain nevertheless is highly active. In the cortex, such resting activity exhibits long-range spatial and temporal correlations [1–3], with intermittent neuronal bursts described by power laws and defined as neuronal avalanches [4]. Neuronal avalanches have been identified in spontaneous activity in vitro in isolated cortex preparations [4–6] as well as in vivo in rodents [7–9], nonhuman primates [10–13] and humans [2,14,15], suggesting that, during resting, the cortex resides close to a critical state [16,17] at which numerous aspects of information processing are optimized [18].
The scale-free nature of cortical avalanches implies maximal variability in size and synchrony of neuronal events [19,20]. When monitored in motor cortical areas, avalanches unfold without the presence of apparent movements [10,13], raising the question why even large avalanches during resting do not translate into sporadic or involuntary motor outputs. Here, we study this question in the context of forebrain loops that encompass cortex and basal ganglia and that are considered crucial for the initiation of voluntary as well as suppression of involuntary movements [21–25].
The main entry point from cortex to the basal ganglia is the striatum, which consists of more than 95% of γ-aminobutyric acid (GABA)-releasing spiny projection neurons (SPNs) and a small percentage of GABAergic interneurons (INs), particularly parvalbumin-positive, fast-spiking INs [26,27]. Although changes in intrastriatal inhibition have long been identified to lie at the core of many movement disorders (e.g., [23,25]), the distinct roles of SPNs and INs remain unclear. SPNs form a sparse network of inhibitory recurrent connections with each other [28–30], which theory and simulations suggest support competitive dynamics [31,32] that decorrelate networks [33]. In contrast, striatal fast-spiking INs provide a dense network of perisomatic inhibitory connections on SPNs, typically interpreted as cortical feedforward inhibition of SPNs [34–37]. Reducing striatal fast-spiking neuron activity induces involuntary movements in rodents [38], in line with a reduced number of those neurons in humans suffering from Tourette syndrome [39,40]. However, how inhibition in striatal microcircuits relates to cortical avalanche dynamics at rest and suppresses involuntary movements is unclear.
Here, we demonstrate in awake rats during quiet resting that cortical activity organizes as neuronal avalanches, whereas the striatum actively maintains a low-correlation state. Involuntary movements emerge from this dynamical profile through two distinct mechanisms. During nonselective reduction of inhibition in the striatum, movements emerged at low rate with little change in cortical avalanches but large increase in striatal synchrony. In contrast, when reducing inhibition from striatal INs only, movements emerged at high rate with corresponding large changes in cortical avalanches yet small change in relative striatal synchrony. In both scenarios, involuntary movements correlated with striatal and cortical bursts. To distinguish intrastriatal processing from loop dynamics, cortex-striatum-midbrain cultures, which lack feedback to cortex, were employed. Cortical avalanches in vitro were accompanied by low-correlated resting activity in the striatum and nonselective reduction in striatal inhibition synchronized striatal neurons, similar to in vivo. Importantly, reduction of inhibition from striatal INs maintained low correlations in the striatum while reorganizing functional connectivities among striatal neurons. Our findings demonstrate the importance of two major striatal microcircuits in distinctly regulating striatal and cortical resting state dynamics. We suggest that specific functional connectivities of the striatum that are maintained by local inhibition are important in movement control.
To study striatal resting activity during cortical avalanches and its change during involuntary movements, we exploited two dyskinesia models in rat. Involuntary movements in vivo have been induced either by nonselective reduction of intrastriatal inhibition using the GABAA-antagonist picrotoxin (PTX) [41–45] or selective reduction of striatal IN-mediated inhibition [38] using IEM-1460 [46,47]. Accordingly, we chronically implanted a cannula guide for local drug injection combined with a 16-channel microwire array (MWA) into the dorsal striatum (Fig 1A and 1B; S1 Fig). Ongoing striatal local field potentials (LFPs) and multi-unit activity (MUA) were recorded before, during, and after local drug infusion in unrestrained awake rats not involved in any particular task. PTX (1 mM; n = 8 rats) induced stereotypical movements at low rate (0.58 ± 0.06 s-1) in the contralateral front paw and/or neck region (Fig 1C and 1D, left; S1 Movie). In contrast, IEM-1460 (5 mM) induced more variable, intermittent movements at ~6 times higher rate (3.45 ± 0.44 s-1; S2 Movie; n = 7 rats; paired t test, t(10) = –7.9, p < 0.001; PTX versus IEM) in the contralateral front paw (Fig 1C and 1D, left; S2 and S3 Figs). Involuntary movements correlated with positive LFP (pLFP) deflections in the striatum (Fig 1C and 1D, right), which mirrored the significant increase in rate for IEM-1460- over PTX-induced movements (Fig 1E and 1F, middle; rANOVA, F(2,12) = 51.84, p < 0.001). Importantly, the increased pLFP rate under IEM-1460 was not dose dependent, as a ten times lower concentration of IEM-1460 induced pLFPs at a similar rate as 5 mM (S3 Fig; pLFP rate at 0.5 mM: 3.96 ± 0.76 s-1; 5 mM: 4.38 ± 0.53 s-1).
The change in pLFP rate was not paralleled by a corresponding change in cross-correlation (CC) between pLFPs, which was found to be relatively high at baseline and increased only weakly under PTX or IEM-1460 (Fig 1F, right; 4 ms bin size; rANOVA, F(2,12) = 10.13, p = 0.003; 1.16-fold increase). Because pLFPs could largely reflect synaptic input to the striatum, we additionally analyzed striatal MUA, which more directly reflects intrastriatal processing. Indeed, striatal MUA showed an increase in rate as well as an order of magnitude increase in spatial correlations for dyskinetic conditions. At baseline, spatial MUA correlations were low (r = 0.02 ± 0.01; n = 5 rats, 20 ms bin size) and increased 8-fold for IEM-1460 and even 11-fold for PTX (Fig 1G, right; IEM-1460: r = 0.16 ± 0.05; n = 4 rats; PTX: r = 0.22 ± 0.03, n = 4 rats; rANOVA, F(2,6) = 10.23, p = 0.012; baseline versus PTX: p = 0.013, baseline versus IEM-1460: p = 0.069, Bonferroni-corrected). Similarly, the temporal correlation between MUA was also wider for PTX than IEM (Fig 1G, right; PTX: 60.0 ± 6.63 ms; IEM-1460: 34.3 ± 9.5 ms; half-width in the CC function). The dissociation between pLFP- and MUA-based measures is supported by the weak correlation between striatal MUA and the LFP under baseline conditions (Fig 1G, left; S4 Fig). Our findings so far suggest that striatal activity changes from a weakly correlated state during resting to a more correlated state under PTX- and IEM-1460-induced dyskinesia, with IN-mediated disinhibition causing involuntary movements at higher rate compared to nonselective striatal disinhibition.
Changes in striatal MUA correlation could still reflect changes to striatal input rather than differences in local striatal processing. Specifically, the recruitment of cortico-basal ganglia loops during involuntary movements is supported by early reports on interrupting involuntary movements through cortical cooling in rodents and the emergence of synchronized cortical and striatal LFP deflections before movement onset [48].
Indeed, when recording ongoing LFP and MUA activity in cortex from up to 32 electrodes while repeating our local infusion of PTX or IEM-1460 into the striatum (Fig 2A and 2B), the cortical LFP was found to change similar to the striatal LFP (Fig 2C). In particular, the rate of negative LFP (nLFP) deflections in cortex was significantly higher during IEM-1460 than PTX (Fig 2D, middle; see also Fig 1F). In contrast, spatial correlations in cortex were markedly increased for IEM-1460 but not for PTX compared to baseline (Fig 2D, right; 4 ms bin size; rANOVA, F(2,6) = 12.0, p = 0.008; baseline versus IEM-1460: p = 0.014; PTX versus IEM-1460: p = 0.019, Bonferroni-corrected), which differed from what we found for the striatum (see also Fig 1F, right). Given that cortical MUA strongly correlated with cortical nLFPs during all conditions (S5 Fig), the CC for cortical MUA was also found to be the largest for IEM-1460 (baseline: 0.02 ± 0.0, n = 5; PTX: 0.07 ± 0.03, n = 5; IEM-1460: 0.12 ± 0.05, n = 2).
This increase in cortical synchronization for IEM-1460 compared to PTX suggests that synchronization of striatal activity under IEM-1460 might be largely explained by changes in cortical activity. To compare the striatal change in synchrony relative to that in cortex, we normalized the average spatial correlation in the striatum by that found in cortex. Indeed, PTX-induced movements revealed a strong increase in relative striatal synchrony, whereas IEM-1460–induced movements emerged from relatively decorrelated striatal conditions (Fig 2E). This decorrelated striatal state under normal resting conditions and during IEM-1460–induced movements was confirmed whether using cortical LFP or cortical MUA, which strongly correlated with cortical LFP during all conditions (S5 Fig). Taken together, these results establish two vastly different population scenarios for striatal induction of involuntary movements—a nonselective disinhibition, which induces movements at low rate in face of large relative striatal synchrony, and a selective reduction of IN-mediated inhibition, which induces movements at high rate with modest changes in relative striatal synchrony.
We next demonstrated that the observed changes in striatal and cortical activity indeed arise from a resting state in cortex that organizes in the form of neuronal avalanches, and that, compared to PTX, IEM-1460–induced involuntary movements are more effective in introducing deviations from avalanche dynamics. Neuronal avalanches reflect spatiotemporal clusters of activity, which, besides pairwise correlations, also contain significant higher-order correlations that establish precise scale-invariant dynamics in space and time [12,13]. Cortical avalanches have been described in local populations of pyramidal neurons [7,9] and at the mesoscopic scale using nLFPs [4,8] as well as in humans using magnetoencephalography and functional magnetic resonance imaging [1,2,14,15]. In cortex, nLFPs are associated with increased firing in local synchronized neuronal populations [8,10,49]. We therefore used the nLFP (S5 Fig) to measure spatiotemporal activity clusters and quantify cortical dynamics. Fig 2F illustrates the definition of spatiotemporal avalanches using a given threshold for detection of nLFPs (black dots) and bin size, Δt, for concatenation of successive nLFPs into spatiotemporal clusters (adjacent dark gray time bins). In line with previous reports on ongoing activity in vivo [8], spatiotemporal clusters of cortical nLFPs during baseline distributed in size according to a power law with exponent α = –1.45 ± 0.08 and cut off at array size close to 32, the defining characteristics of avalanche dynamics ([50]; Fig 2G, baseline; threshold: −2.5 standard deviation (SD), Δt = 4 ms; n = 5 rats; power law versus exponential: log-likelihood ratio (LLR) = 169.1 − 2738.1, all p < 0.001 in favor of power law, see Materials and Methods). The power law barely changed during PTX (Fig 2G, middle; same threshold as for baseline), whereas IEM-1460 increased the probability of large cortical clusters significantly compared to baseline and PTX (Fig 2G, right), as measured by the Kolmogorov–Smirnov (KS) distance (DKS), which here quantifies the deviation from a power law with exponent α = −1.5 (Fig 2H, rANOVA, F(2,6) = 16.92, p = 0.003; baseline versus IEM-1460: p = 0.004, PTX versus IEM-1460: p = 0.019, Bonferroni-corrected). In line with the observed increase in nLFP frequency, the rate of spatiotemporal clusters increased during both drug conditions and was highest under IEM-1460 (baseline: 1.93 ± 0.75 s-1, PTX: 7.27 ± 4.72 s-1, IEM-1460: 12.3 ± 3.66 s-1). Importantly, the average duration of spatiotemporal clusters was less than 10 ms under all conditions and thus approximately one order of magnitude shorter than the time between clusters, indicating that the increased probability of larger clusters under IEM-1460 did not result from coalescing clusters due to the chosen bin time, Δt.
In summary, a striatal resting state, in which IN-mediated inhibition is reduced, is highly effective in entraining cortical dynamics away from neuronal avalanches.
In order to dissociate changes in striatal dynamics due to intrastriatal processing versus cortico-basal ganglia-thalamic loops, we next studied striatal responses to cortical avalanches in organotypic cortex-striatum-midbrain cultures, which lack striatal feedback to cortex [51,52]. Cultures were grown on custom planar microelectrode arrays (MEAs) with two electrode fields, allowing for simultaneous recording from cortex (8×4 electrodes) and striatum (6×5 electrodes) (Fig 3A). Recordings were performed between 13 to 28 days in vitro (DIV) when the striatum was innervated by corticostriatal projection neurons [53] and densely innervated by tyrosine-hydroxylase (TH)-positive fibers (Fig 3B, left) originating from substantia nigra neurons of the midbrain culture (Fig 3B, right; 175 ± 33 TH-positive neurons, range: 37–385; n = 11 cultures; [51]). During that period, cortical and striatal population activities were highest (Fig 3C) [54], showed stable activity profiles (S6 Fig), and electrophysiological properties of striatal neurons had matured appropriately (Figs 3E and 4B).
This open-loop in vitro system confirmed our in vivo finding that cortical neuronal avalanches are accompanied by low-correlated periods in striatal activity. First, nLFP amplitudes, which correlate with MUA activity (Fig 3D), as well as spatial correlations between nLFP or MUA activity were smaller in the striatum compared to cortex (Fig 3F and 3G). Second, spatiotemporal nLFP clusters in cortex revealed avalanche signatures, i.e., a power law in cluster size distribution with exponent α close to −1.5 (Fig 3H, black, discrete: n = 8, power law versus exponential: LLR = 1,564–27,090, all p < 0.001 in favor of power law; α = −1.47 ± 0.02, [4]). In contrast, striatal nLFP cluster size distributions, although consistent with a power law distribution (Fig 3H, red; n = 8, LLR = 123–3,225, all p < 0.01; α = −3.04 ± 0.27), showed a more negative exponent (Fig 3I; paired t test, t(7) = −5.9, p < 0.001); that is, the probability of large nLFP clusters was lower in striatum compared to cortex, in line with our finding of low spatial correlations in the striatum in vivo during resting activity. A similar relationship was observed when defining cluster size as the absolute sum of nLFP amplitudes (Fig 3H, continuous). The difference between cortical and striatal cluster size distributions was of dynamical nature because it was significantly reduced by bath application of PTX (4 μM; DKS between cortical and striatal cluster size distributions, n = 8, rANOVA, F(2,14) = 11.67, p = 0.001, S7 Fig).
Our open-loop in vitro model confirms our in vivo finding that resting state activity in the form of cortical avalanches is associated with a low-correlation resting state in the striatum.
To further study the differential effects of PTX and IEM-1460 on striatal dynamics observed in vivo, we first confirmed that IEM-1460 selectively suppressed firing in striatal INs in our in vitro system. Whole-cell current-clamp recordings (Fig 4A) of electrophysiologically identified INs (Fig 4B and 4C) showed that spontaneous action potential firing was significantly reduced in response to local application of 500 μM IEM-1460 (Fig 4D, t test, t(12) = 5.9, p < 0.001). To confirm that IEM-1460 did not affect AMPA-mediated excitatory postsynaptic currents in SPNs, we recorded spontaneous up-state currents in putative SPNs in the presence of QX-314 (5 μM, intracellular) and AP5 (100 μM, bath application) to block active sodium currents and N-methyl-D-aspartate (NMDA) receptors, respectively. To minimize inhibitory postsynaptic currents, voltage-clamp recordings were performed at the estimated GABA reversal potential, Vh = −59 mV. As expected, local ejection of the selective AMPA receptor antagonist DNQX significantly reduced up-state currents in all putative SPNs (Fig 4E, t test, t(12) = 4.6, p < 0.001). In contrast, local ejection of IEM-1460 did not significantly change the average peak amplitude of spontaneous compound postsynaptic currents in putative SPNs (Fig 4F, t test, t(12) = 0.3, p = 0.75). Taken together, these results show that IEM-1460 selectively reduces spontaneous firing in striatal INs without altering AMPA-mediated inputs to SPNs, in line with a previous study [38].
Although the wire arrays used in vivo allowed us to study interactions between striatal neurons, they do not allow for a more detailed analysis of local clusters of neighboring striatal neurons in relation to cortical avalanche dynamics. We therefore recorded intracellular, spontaneous calcium transients in local populations of striatal neurons in these cultures during cortical avalanche activity (12–100 neurons, average: 45.7 ± 1.3, n = 11 cultures). Neurons were loaded with the calcium indicator Oregon Green 488 BAPTA-1 (OGB; Fig 5A), and background-corrected calcium transients of spontaneous activity were converted to changes in fluorescence over baseline fluorescence, ΔF/F (see Materials and Methods). Simultaneous loose-patch recordings and calcium imaging (Fig 5B) demonstrated a linear relationship between the number of striatal spikes and corresponding peak ΔF/F amplitudes (Fig 5C), as reported previously [56,57].
Under normal conditions, spontaneous striatal population activity was characterized by irregularly occurring, near-simultaneous episodes in which most neurons participated with largely varying peak amplitudes (Fig 5D, baseline). Amplitude heterogeneity was seen both within episodes and within neurons. Within <30 s of local striatal PTX application (100 μM), peak amplitudes increased (Fig 5F and 5G; n = 8, rANOVA, F(2,14) = 36.74, p < 0.001) and became highly similar across neurons for each episode (Fig 5D, PTX). This effect was largely reversed after 5 min of drug washout (Fig 5D–5G). Accordingly, the nonselective reduction of fast intrastriatal synaptic inhibition strongly increased the CC between striatal neurons (Fig 5H, top; Fig 5I, rANOVA, F(2,14) = 57.23, p < 0.001) in line with our in vivo finding. The unchanged rate of striatal activity episodes during PTX supports intrastriatal location of PTX action (baseline: 0.15 ± 0.02 s−1, PTX: 0.14 ± 0.02 s−1, washout: 0.15 ± 0.02 s−1, n = 8, rANOVA, F(2,14) = 0.63, p = 0.55; S8 Fig), given that cortical disinhibition would have induced prolonged activity periods at much lower rate in this system [58]. As further control, striatal changes to intrastriatal PTX application did not depend on midbrain inputs, further supporting exclusive intrastriatal PTX action (S9 Fig).
In contrast, when locally applying IEM-1460 to the striatum, average ΔF/F peak amplitudes in the local striatal population did not change (Fig 5E and 5F bottom; Fig 5G, n = 11, rANOVA, F(2,20) = 1.77, p = 0.195), and the average CC between neurons did not increase (Fig 5H, bottom; Fig 5I, rANOVA, F(2,20) = 6.88, p = 0.005; CCbaseline > CCIEM-1460 > CCwashout). These in vitro results demonstrate that, in the presence of cortical avalanches, striatal neurons show low CCs that depend on local GABAA-mediated inhibition and were not abolished after reduction of striatal IN-mediated inhibition. It confirms our initial results in vivo that nonselective intrastriatal disinhibition increases striatal synchrony, whereas a decorrelated striatal resting state is maintained after disruption of IN-mediated inhibition.
The previous analysis provides a picture of average changes but, in general, does not capture individual alterations in ΔF/F amplitude of single neurons or pairwise correlations (i.e., CCs) between neurons [59]. That is, different constellations of amplitudes or correlations could result in the same average. Indeed, the inability of IEM-1460 to change the average CC in the striatum was contrasted by its ability to significantly change individual CCs between neurons, that is, to reorganize the functional connectivity of the striatum while maintaining a low-correlation resting state. This is illustrated in more detail in Fig 6A, in which CC was quantified for consecutive segments of ΔF/F of each ~2-min duration. CC values from consecutive segments were plotted, and the coefficient of determination, RCC2, was calculated using linear regression. A value of RCC2 close to one indicates little change of individual CCs between segments, whereas RCC2 towards zero indicates a strong change. The value of RCC2= 0.65 in Fig 6A provides a reference value for the expected change of CCs within a few minutes for a single culture. In this example, the comparison baseline versus IEM-1460 yielded a reduced value of RCC2= 0.19 (Fig 6A, middle), demonstrating that individual CCs changed upon local application of IEM-1460, as can be seen in the corresponding scatterplots. Fig 6B shows density plots of CCs for all consecutive segments and cultures (PTX: n = 8; IEM-1460: n = 11). The corresponding RCC2 values are summarized in Fig 6C, demonstrating that, similar to PTX, IEM-1460 led to a highly significant change in CCs (rANOVA; PTX: F(4,28) = 16.21, p < 0.001; IEM-1460: F(4,40) = 15.72, p < 0.001). The analysis of the change in individual ΔF/F peak amplitude averages revealed a similar picture. That is, although IEM-1460 did not lead to changes in the grand average ΔF/F (Fig 5G), it changed the ΔF/F responses in individual striatal neurons significantly (Fig 6D, rANOVA, PTX: F(4,28) = 21.79, p < 0.001; IEM-1460: F(4,40) = 5.38, p = 0.0015). That changes in individual CCs under IEM-1460 as quantified by R2 were of similar magnitude compared to PTX (Fig 6C and 6D) further suggests that the lack of synchronization under IEM-1460 cannot be explained by insufficient blockade of IN inhibition.
In summary, these results strongly suggest that, under normal conditions, the low-correlation state among striatal neurons requires local GABAA-mediated inhibition and that reduction of spontaneous IN firing changes the pairwise correlation state while maintaining a low average correlation (Fig 6E).
Movement disorders, in which the basal ganglia play a pivotal role, remain a significant public health burden [25]. Although our study confirms that suppression of involuntary movements requires intact striatal inhibition [25,38,41,48], here, we demonstrate two vastly different mechanisms of the emergence of involuntary movements when manipulating striatal inhibition. The particular dynamics that gave rise to involuntary movements involved cortico-basal ganglia loops, which, due to their recurrent feedback nature, made the analysis of the population dynamics particularly challenging from a systems’ point of view. A combined in vivo and in vitro approach using microelectrode array recordings and cellular resolution population imaging enabled us to study striatal population dynamics under closed- and open-loop conditions. Through this combination of techniques, we identified a low-correlation resting state of the striatum stabilized by striatal INs from which, after disruption of IN inhibition, involuntary movements can emerge at high rate involving cortico-basal ganglia loops. These pathological dynamics might be of particular clinical relevance for humans suffering from Tourette syndrome who reveal a reduction in striatal IN number [39,40]. This scenario was distinguished from a nonselective disinhibition in the striatum, which significantly increased striatal synchrony and gave rise to dyskinetic movements at much lower rate, a condition that might be encountered in early stages of Huntington’s disease when striatal neurons degenerate [22].
In the current study, we used two pharmacological agents that affect striatal inhibition. The first, PTX, blocks GABAA receptors expressed in SPNs and INs. In the past, GABAA-antagonists have been extensively used to study the effect of striatal disinhibition on neuronal firing and motor behavior [41–45]. The second drug, IEM-1460, influences striatal inhibition indirectly by blocking AMPA-mediated inputs to striatal inhibitory INs, thus leading to the disinhibition of SPNs by reducing IN-to-SPN activity. Although PTX also affected the latter connection (i.e., IN-to-SPN synaptic transmission), we observed in our experiments distinct activity and behavioral phenotypes. In vivo, both substances increased striatal synchrony and induced involuntary movements. However, local striatal PTX increased striatal synchrony even in the absence of a closed corticostriatal loop (via globus pallidus/substantia nigra/thalamus), leading to highly synchronized striatal events in our in vivo and in vitro preparation. In contrast, local striatal IEM-1460 showed increased synchrony only in vivo, most likely due to increased synchronous input from cortex (and thalamus). That is, local striatal IEM-1460 application deviated cortical activity away from avalanches into a highly synchronized state not observed under PTX. In addition, PTX and IEM-1460 showed neuronal bursts and involuntary movements at very different frequencies. That even low doses of IEM-1460 (0.5 mM versus 5 mM) induced striatal bursts at high rate further suggests that, indeed, PTX and IEM-1460 influence cortico-basal ganglia loop activity in very different ways. Thus, although both substances induced involuntary movements, our findings suggest distinct mechanisms underlying the emergence of these movements. We also note that the relatively high frequency of involuntary movement components under IEM-1460 could suggest tremor-like spontaneous movements. However, due to their variability (S2 Movie) and intermittency (S2 Fig), these movements do not resemble a continuous tremor. These mechanisms and behavioral phenotypes need to be further explored in future studies to improve the understanding of normal and pathological conditions in the basal ganglia.
Our combined in vitro and in vivo findings identified a low-correlation resting state of the striatum that is maintained in the presence of cortical neuronal avalanches and depends on intrastriatal inhibition. Avalanche dynamics in cortex are characterized by long-range spatial and temporal correlations and are described by a power law in burst size distribution with exponent close to –1.5 [4]. The low-correlation striatal resting state dynamics qualitatively differed from cortical neuronal avalanche activity as measured by a more negative power-law exponent in vitro, indicating a spatially more confined activation of striatal neuronal populations compared to cortex. Our results further show a differential participation of two major striatal microcircuit components in maintaining and regulating ongoing striatal and cortical avalanche activity through cortico-basal ganglia-thalamic loops during resting. This finding relied on a precise quantification of the cortical resting state, that is, the measurement of cortical avalanches and the quantification of deviations from avalanche dynamics. Avalanche dynamics are robustly identified using the LFP at the mesoscopic level, although recent advances with single-cell resolution have been obtained for cortex [7]. We confirmed that cortical LFPs are related to local neuronal activity and that they organize as neuronal avalanches both in vivo and in vitro.
The low rate of neuronal population bursts and corresponding involuntary movements induced by nonspecific reduction of intrastriatal inhibition is due to a refractoriness of cortex, with an absolute refractory period >300 ms [45]. Disfacilitation of inhibitory striatal INs using IEM-1460, which left the feedback inhibition between SPNs intact, quickly abolished cortical neuronal avalanche dynamics and induced corticostriatal population bursts, often less than 300 ms apart [60]. This suggests that the striatum might require lateral inhibition between SPNs to efficiently entrain cortical activity at a subsecond time scale. This finding leads us to propose that it is not the change in average correlation or activity in striatal output but rather the specific functional connectivity of the striatum, supported by lateral SPN inhibition, that influences ongoing cortical avalanche dynamics, presumably via substantia nigra/globus pallidus and thalamus by promoting certain avalanche patterns in cortex.
The striatum receives excitatory and inhibitory input from various sources that are part of intricate feedback loops, such as cortex [61,62], thalamus [63], globus pallidus [64], and substantia nigra [65]. Using an open-loop in vitro system that exhibits the same resting state dynamics as in vivo, i.e., cortical neuronal avalanches, allowed us to isolate those aspects of striatal dynamics and corresponding microcircuits that underlie the observed dynamical changes in vivo. We confirmed in vitro that nonselective reduction of intrastriatal inhibition using PTX synchronizes striatal action potential firing, in line with the striatal changes at the multi-unit and LFP levels in vivo. Importantly, we could demonstrate in vitro that avalanche-induced striatal firing remains decorrelated when reducing IN firing with IEM-1460, in line with a low increase in relative striatal synchrony observed in vivo under those conditions (Fig 2E). Taking advantage of monitoring local clusters of striatal neurons at high spatial resolution using intracellular calcium imaging, our in vitro approach allowed us to dissect the apparent discrepancy between the strong effect of IN manipulation in the absence of major changes in striatal synchrony. We could demonstrate in vitro that IEM-1460 strongly affected which neurons were (co-) active with little or no influence on the average CC between striatal neurons and average single neuron response amplitudes, respectively. We believe that this finding introduces the specific functional connectivity maintained dynamically by local striatal inhibition as a major factor in the regulation of activity in cortico-basal ganglia loops.
We note that changes in single neuron activity under IEM-1460, quantified by the coefficient of determination, were comparable to PTX (Fig 6C and 6D). In addition, local IEM-1460 injections in vivo induced strong changes in striatal neuron activity at high as well as ten times lower concentrations, with corresponding strong changes in cortical avalanche dynamics. This suggests that even small reductions in IN inhibition induce strong dynamical changes in cortico-basal ganglia loops.
In addition to the reduction of AMPA-mediated currents for receptors lacking the glutamate receptor 2 (GluR2) subunit, IEM-1460 has been shown to reduce NMDA receptor currents at high concentrations, thereby possibly influencing SPN input. However, the effectiveness of blocking NMDA currents is two orders of magnitude smaller compared to the effect on AMPA currents [66]. Importantly, such NMDA receptor blockade would have been expected to decrease SPN neuron firing. However, our in vitro results showed no change in average response amplitude in SPNs, and a subset of SPNs even increased their responses to IEM-1460 application (see also [38]). IEM-1460 has been shown to target AMPA receptors in cholinergic INs, and cholinergic neurons via inhibitory neuropeptide Y-positive neurogliaform neurons can influence SPN firing [67–69]. However, the absence of correlations between cholinergic INs and SPNs in nonhuman primate recordings [70] suggests that this pathway might not dominate striatal resting activity in vivo or in general during cortical neuronal avalanches. Accordingly, even after blockade of cholinergic transmission, selective reduction of IN firing using IEM-1460 can induce hyperkinesia [38].
Our pharmacological approach in vitro allowed for the manipulation of intrastriatal circuits in the absence of cortico-basal ganglia feedback loops and inhibitory inputs originating from globus pallidus or midbrain. However, our approach is not able to exclude other possible sources that might contribute to the observed decorrelation effect, such as inputs from diverse striatal IN classes or from a newly described corticostriatal inhibitory pathway [71]. While cell–specific manipulations can be achieved in striatal SPNs and INs using optogenetic techniques in transgenic mice [67,72], changing SPN firing to precisely test whether inhibition between SPN maintains an intrastriatal low-correlation resting state currently faces two caveats. First, manipulation of SPN firing does not allow differentiating between intrastriatal (i.e., SPN-to-SPN) and loop (i.e., striatopallidal/-nigral) connectivity. Second, SPN firing was used as a readout of the network state to calculate response amplitudes and CCs. Dissecting the functional role of feedback inhibition between SPNs would require an opto- or pharmacogenetic approach that directly manipulates SPN-to-SPN synapses, for which techniques are currently being developed [73,74].
In our in vivo experiments, MUA did not allow us to differentiate striatal cell types involved, and even single-unit analysis in the striatum is limited in mapping waveforms to identifiable cell types [75,76]. Although our multi-unit and LFP analyses identified the differential effect of PTX and IEM-1460 on cortico-basal ganglia loops, we were unable to demonstrate the corresponding specific changes in striatal IN firing. Naïvely, one might assume that the net effect of IEM-1460 in vivo reduces IN firing; however, as shown by the dramatic changes in loop activity, loop reverberation does not permit interpretations of changes in IN activity based on direct drug action alone. Therefore, we extended our in vivo approach to in vitro, in which we recorded up to a hundred striatal neurons at single-cell resolution in an open-loop configuration to further quantify the network changes in the striatum. These in vitro results showed reduced IN firing during spontaneous avalanche dynamics under IEM-1460 as well as a decorrelated striatal state. The open-loop findings in vitro are in line with a reduced glutamatergic drive of INs and the limited role of INs in decorrelating striatal activity.
The average low correlation in the striatum was maintained when manipulating INs, supporting the view that lateral (i.e., feedback) inhibition between SPNs [28–30] might be responsible for the low-correlated striatal resting state, in line with prediction from theory and simulations [31,32]. That feedback inhibition can affect the population of striatal neurons was indeed shown in acute slices through antidromic electrical activation [77] (with potential contributions from pallidal-striatal projections [64]). The increase in striatal synchrony upon nonselective reduction of intrastriatal inhibition using PTX is also in line with another study [78], in which acute slices were activated by electrical cortical stimulation or NMDA receptor activation.
Computational studies suggest that networks of inhibitory neurons with realistic connectivity regimes for lateral inhibition reduce the level of activity, increase the contrast of responses, i.e., decorrelate striatal input [79,80], and can cause transitioning between striatal cell assemblies [32,81]. Although lateral inhibition is not the only mechanism by which neuronal network activity can be decorrelated [82], it can greatly enhance pattern decorrelation, as recently shown in a computational network model of neurons with threshold nonlinearities [33]. Thus, threshold nonlinearities [83] and corticostriatal connectivity [62] are likely to contribute to the observed decorrelation in striatal activities.
The above models have in common that they require the collective inhibitory influence of cell groups. We propose that the large number of SPNs and wide distribution of measured synaptic strengths [35,36,84] provide the basis for lateral inhibition to affect striatal output and, consequently, future cortical activity [85]. Striatal inhibitory INs, on the other hand, might influence the functional connectivity of SPNs, thus promoting changes between different states of low correlation in the striatum that might encode specific motor programs. This idea is supported by a recent study [86], which found that striatal fast-spiking INs increase their firing particularly during the choice execution period in a choice task.
In the context of our study, we propose that a change in IN activity promotes switching between low-correlation states in the striatum, which entrain cortico-basal ganglia loops supported by lateral inhibition between striatal projection neurons. In summary, our results uncover different dynamical influences of two major intrinsic striatal microcircuits in regulating cortico-basal ganglia resting activity important for the suppression of involuntary movements in normal behavior.
All animal procedures were in accordance with National Institutes of Health guidelines. Animal procedures (protocol numbers LSN-01 and LSN-12) were approved by the National Institute of Mental Health Animal Care and Use Committee.
Male Sprague-Dawley rats (5–8 wk old) were used for behavioral assessment and/or chronic recording of LFPs and MUA in the cortex or striatum. To study the influence of the striatal inhibitory mechanisms, two different substances were microinjected into the dorsal striatum (AP: 0.9–1.5 mm, ML: 2.2–2.6 mm, 4.2–5.5 mm from cortical surface) through a chronic cannula (26 gauge, 1–2 mm projection; Plastics One, Roanoke, VA, United States): (1) PTX (Sigma-Aldrich), a GABAA-receptor antagonist, and (2) IEM-1460 (Tocris Bioscience), an antagonist of GluR2-lacking AMPA receptors selectively expressed in striatal INs [46,47,87]. Implantation of the cannula guide and the recording array was done under isoflurane anesthesia (1.5%–4%, 100% oxygen) and presence of the analgesic ketoprofen (5 mg/kg, subcutaneous). All cannula guides, cannulas, and recording arrays were sterilized. During the implantation surgery, care was taken to avoid blood vessels. To prevent unnecessary brain injury, the dura was carefully ruptured and the cannula guide and/or recording array was slowly lowered at a rate of ~150–200 μm per min. Ketoprofen was given for up to 2 d post surgery, and animals were allowed to recover for 2–5 d before recordings. Spontaneous activity for LFP and MUA analyses (see below) was recorded up to 2 wk post surgery from superficial layers of the primary motor cortex and/or the somatosensory forelimb region (AP: 0.5–2.2 mm, ML: 3.2–3.5 mm, and 0.2–1.1 mm from cortical surface) using 8×4-MEAs (8 shanks with 4 electrodes each, plus additional reference electrode implanted along the anterior-posterior axis; 28–32 working electrodes; 200 μm inter-electrode spacing; 23 μm electrode diameter; Neuronexus, Ann Arbor, MI, US), or the dorsolateral striatum (AP: 0.7–2.1 mm, ML: 3.2–3.5 mm, 3.2–3.5 mm from cortical surface) using 16-channel MWAs (8×2 electrodes plus additional reference wire implanted along the anterior-posterior axis; 14–16 working electrodes; 150 μm inter-wire distance; 0.6–0.9 MΩ impedance; Microprobes, Gaithersburg, MD, US). The ground wire was connected to a scull screw located ~1 mm posterior to lambda. Data were recorded for at least 30–60 min at 30 kHz using a Cerebus data acquisition system (Blackrock Microsystems). After baseline recordings, 0.8–1.5 μl sterile drug solution (PTX, 1 mM; IEM-1460, 5 mM) was injected at a rate of 0.3 μl/min for 3–5 min. The internal cannula was left in place for 1–2 min post injection, after which recordings were performed. Animals were allowed to recover for 1 d before the next recording session. In n = 3 rats, we tested a ten times lower IEM-1460 concentration (0.5 mM) and found that two out of three rats showed involuntary movements that were of similar nature as under higher IEM-1460 concentration, that is, intermittent movements at high rate in the contralateral front paw.
Animal behavior was video-recorded with a Logitech c920 camera (10–30 frames per s, fps) for behavior-only recordings, or simultaneously with LFP and MUA using a triggered CMOS camera (40 fps; Thorlabs). Involuntary movements were analyzed using custom scripts in Matlab (Mathworks, MA, US). We defined a “movement” signal as 1 minus the frame-to-frame correlation for a region of interest (i.e., contralateral front paw or neck), and involuntary movements were extracted by applying a threshold of 2–3 SDs (Fig 1C and 1D). Only periods during which animals were resting (i.e., no locomotion, cage exploration, or grooming) were included in the video analysis. After recordings were finished, brains were dissected and the locations of cannula and electrode placements were confirmed in a subset of animals.
In total, data from 17 rats were analyzed in this study. All but one rat were chronically implanted with a cannula guide for local drug infusion in the striatum. A subset of rats was implanted with an MWA in the striatum (n = 8 rats) or an MEA in the superficial layers of cortex (n = 5 rats). A list of all rats and the recordings and observations is given in S1 Table.
Coronal slices from rat cortex (350 μm thick, postnatal days 0–2; Sprague Dawley), striatum (500 μm thick), and midbrain (substantia nigra pars compacta; 500 μm thick) were cut on a vibratome (VT1000 S, Leica, Wetzlar, Germany) in ice-cold, sterile Gey’s balanced salt solution (0.4% D-glucose) and cultured on poly-D-lysine coated and plasma-/thrombin-treated carriers to allow proper tissue adhesion [52]. After tissue adhesion to the carrier, standard culture medium was added (600 μl of 50% basal medium, 25% HBSS, 25% horse serum, 0.5% glucose, and 0.5% of 200 mM L-glutamine; Sigma-Aldrich) and changed every 3–4 DIV. At 1, 8, and 20 DIV, 10 μl mitosis inhibitor (0.3 mM uridine, 0.3 mM ARA-C cytosine-β-D-arabinofuranoside, and 0.3 mM 5-fluoro-2′-deoxyuridine) was added for 24 h to prevent excess glia cell formation. Cultures were incubated at 35.5 ± 0.5°C. Carriers were either coverslips for calcium imaging and patch recording experiments or 60-channel, planar MEAs for the recording of LFPs and MUA (see below). Cultures on coverslips were incubated in a roller tube incubator at 0.6 rotations/min, and MEA cultures were incubated on a rocking storage tray at ±75°, 0.25 cycles/min (±25°, 0.6 cycles/min during the recording sessions except for developmental data, Fig 3C).
Planar titanium nitride MEAs with 60 channels (59 recording electrodes plus one reference electrode; 200 μm inter-electrode distance, 30 μm electrode diameter) were obtained from Multichannel Systems (Reutlingen, Germany). For the developmental recordings, a standard 8×8 layout was used. For all other MEA recordings, a custom layout with two sub-arrays for cortex (8×4, 31 electrodes) and striatum (6×5, 28 electrodes) was used. Both sub-arrays were separated by 1,200 μm (Fig 3A). Data were recorded at 25 kHz for MUA or 1 kHz for LFP using an MEA1060 amplifier and the MC Rack software (Multichannel Systems). Spontaneous activity for the developmental data (20 min) and the experiments with PTX bath application (4 μM, 60 min) was recorded in culture medium under sterile conditions. Washout recordings were done 24–48 h after the culture medium was replaced with conditioned medium collected 3–4 d before the experiment. All recordings were performed at 35 ± 0.5°C after 2 wk in vitro if not stated otherwise.
Calcium imaging was performed on coverslip cultures loaded with 50 μM OGB (Life Technologies, NY, US) dissolved in 10 μl pluronic F-127 (20% in DMSO; Life Technologies, NY, US) and 790 μl freshly prepared artificial cerebrospinal fluid (ACSF) [88]. ACSF was bubbled with 95% O2 and 5% CO2 and contained (in mM): 124 NaCl, 3.5 KCl, 10 D-(+)-glucose, 26.2 NaHCO3, 0.3 NaH2PO4, 1.2 CaCl2, and 1 MgSO4. Cultures were incubated for 60–90 min in a roller tube incubator and perfused with ACSF (flow rate ~100 ml/h) for 20–30 min before imaging. After baseline recordings (5 min), PTX (100 μM) or IEM-1460 (500 μM) was ejected locally in the striatum at a rate of 12 μl/min for 5 min using glass pipettes with a tip diameter of ~80–100 μm, while intracellular calcium was simultaneously imaged. Washout conditions were recorded 10–20 min after the drug application ended. Drug spillover to the cortex was prevented by using a two-compartment chamber in which a glass coverslip separated the bath between the cortical and striatal tissue. The glass coverslip was positioned ~300 μm above the tissue and sealed with agar pieces around the recording chamber. ACSF and drug flow was directed away from cortex. This approach was highly efficient in avoiding any drug spillover to cortex, as shown in S8 Fig. All recordings were performed between 13–28 DIV.
Image sequences (12 bit, 2×2 binning, 320×240 pixels) were acquired with a Peltier-cooled CCD camera (Imago from TILL Photonics, Gräfelfing, Germany) on an inverted microscope (Olympus IX70) with a 20× water-immersion objective (numerical aperture 0.7). Excitation wavelength was set to 492 nm using a monochromator (Polychrome II, TILL Photonics). Excitation, dichroic, and emission filters from Omega Optical (Brattleboro, VT, US) were XF1087 (445–495 nm band-pass), XF2077 (reflection <500 nm), and XF3105 (508–583 nm band-pass), respectively. Image sequences of up to 320 s (7,000 frames) were obtained at a rate of 21.7 frames/s (cycle time 46 ms, exposure 28 ms) using the TILLvisION 4.0 software (TILL Photonics). Image sequences were converted into TIF file format after acquisition and analyzed in Matlab using custom scripts.
Regions of interest (ROIs) were manually selected by identifying typical cell bodies (Fig 5A), and background subtraction was performed by automatically subtracting the fluorescence signal from a dark background region within the area of two cell body diameters. All fluorescence values are expressed as relative change in fluorescence from baseline, denoted by ΔF/F, and measured as percentage. Formally, ΔF/F is defined as the percentage change in fluorescence over baseline, that is, ΔF/F = 100 (FROI−F0)/F0, where FROI and F0 denote the background-corrected fluorescence intensities in the ROI and the baseline, respectively. For the spike-triggered detection of fluorescence changes (Fig 5B and 5C), the baseline was calculated as the average fluorescence 50 ms before the spike. For all other analyses, the baseline was calculated from a 30-s sliding window as the average of the 50% smallest values (i.e., excluding transients that correspond to neuronal activity). To allow for a more robust detection of calcium transients, successive increases in fluorescence (ΔF/F > 0) were summated, and the threshold detection was performed on this summated signal. The percentage of spuriously detected ΔF/F-peaks was lower than 0.5% (n = 8 neurons).
For the estimation of the rate of up-state events in striatal neurons, we used the summed widefield (bulk) fluorescence signal within the field of imaging. Because up-states among striatal neurons are correlated [51,89] and driven by cortical input, this approach gave a good approximation of the input rate arriving from cortex.
Patch pipettes were pulled from borosilicate glass using a P-97 micropipette puller (Sutter Instrument, CA, US), and had a resistance of 5–10 MΩ. For all recordings, pipette resistance and capacitance were compensated for. Loose-patch and cell-attached recordings were performed in voltage-clamp mode using patch pipettes that were filled with regular ACSF. Whole-cell patch-clamp recordings were done in current-clamp mode with an intracellular solution containing (in mM) 132 K-gluconate, 6 KCl, 8 NaCl, 10 HEPES, 2 Mg-ATP, and 0.39 Na-GTP, or voltage-clamp mode with an intracellular solution containing (in mM) 132 CsMeSO3, 1 CsCl, 10 HEPES, 2 Mg-ATP, 0.39 Na-GTP, and 5 QX-314. The intracellular solution was kept on ice during the experiment. For the local application of PTX (100 μM) or IEM-1460 (500 μM), a second patch pipette was placed in close vicinity (<60 μm; Fig 4A) of the patched soma and drugs were ejected at 50–55 mmHg. As expected, all recorded neurons (5/5) responded to ejection of DNQX (50 μM, Fig 4E). Significance was calculated by comparing 100 s baseline plus 100 s washout with 80 s of drug data using Student’s t test.
A subset of cultures was used for post-hoc immunostaining of TH. Cultures were rinsed in phosphate buffered saline (PBS), fixed in 4% paraformaldehyde for 40–60 min, and incubated for 2 h at room temperature in blocking solution (10% normal goat serum and 0.5% Triton X-100 in PBS). For all subsequent steps, a carrier solution consisting of 1% normal goat serum and 0.3% Triton X-100 in PBS was used. Cultures were incubated for ~12 h at 4°C in a TH-antibody solution (1:1000, antimouse, Immunostar, WI, US), washed three times for 10 min each, incubated 1–2 h at room temperature in secondary antibody solution (1:1000, Alexa 555 anti-mouse, Invitrogen, NY, US), and washed again three times for 10 min each at room temperature. Before the confocal imaging, cultures were rinsed in PBS and mounted on coverslips using a fluorescence-preserving mounting medium (Vector Laboratories, CA, US).
Confocal images were obtained with a Zeiss LSM 510 using a 63× oil immersion objective (numerical aperture 1.4, 0.6 μm optical thickness). For the cell counting of TH-positive neurons in the substantia nigra pars compacta, images were obtained with a high-speed scanning confocal microscope (Leica TCS SP5 II, 10× objective) with tile scan function. Cell counts were obtained from maximum z-stack projections (42 μm thick, 6 μm optical thickness). For TH-positive neurons that were organized in dense clusters, only well-distinguishable somata or somata with distinct (dark) nucleus were counted (Fig 3B, right panel), thus likely underestimating the actual number of dopaminergic neurons.
For in vitro recordings, MUA was detected by band-pass filtering at 300–4000 Hz and subsequent thresholding (–5 SD of each trace). LFPs were band-pass filtered at 1–100 Hz for the developmental data, which contained dominant frequency components above 50 Hz, and 1–50 Hz for all other analyses. nLFP deflections were detected by finding the minimum value of the LFP signal that crossed a given threshold z (measured in SDs). Previous studies showed that cortical nLFPs are associated with increased activity and synchrony in local firing [8,10,49]. That cortical nLFP are correlated with cortical multi-unit firing was confirmed in this study (S5 Fig). We furthermore found this relationship in the striatum in vitro (Fig 3D) despite small amplitudes of striatal nLFPs. The SD was determined for each channel individually and estimated from 2–3 s of baseline activity (z = –4.5). The threshold value z was varied to confirm the robustness of the reported power-law exponents (see also [10,50]). For the power spectral analysis of the developmental data, ±500 ms around nLFP threshold crossings were analyzed. The power spectrum was calculated by using the fast Fourier transform with a Hann window function. Averages for individual cultures were calculated across all channels and subsequently normalized (integral over the entire frequency range normalized to unity) before calculating the average over all cultures. All in vitro data were analyzed using the phase-neutral filter implementation filtfilt in Matlab and the Neuroshare library (http://neuroshare.sourceforge.net) for data import.
For in vivo recordings, presumable multi-unit spikes were extracted from the high-pass filtered signal (>250 Hz) by applying a threshold at –6 times the root mean square of the signal using the Cerebus Central software (Blackrock Microsystems). Because movements could cause artifacts in the high-pass filtered signal, thresholded waveforms were subsequently offline-sorted using the Offline Spike Sorter (Plexon Inc., Dallas, TX, US). Only electrodes were used for analysis for which MUA could be isolated from movement artifacts based on the typical biphasic waveform of multi-unit spikes (Fig 1G, inset). Calculation of LFPs was performed as described for in vitro using the entire signal for each electrode for the estimation of SD (z = –2.5). For the avalanche analysis, z was varied to confirm the robustness of the estimated power-law exponents (see above). In the striatum, MUA was associated with pLFP deflections (S4 Fig; Fig 1G, left). We therefore extracted pLFP deflections (z = 2.5–3) from in vivo striatal recordings.
CCs were calculated from binned time series (rasters) of p/nLFPs, multi-unit spikes, or from continuous ΔF/F traces. Values for p/nLFP and MUA rasters were discrete and corresponded to the number of p/nLFP or spike events per bin, respectively. The raw CC between two time series, xt and yt, was defined as
CC(τ)= E[(xt−μx)(yt+τ−μy)]σxσy
where E[·] denotes the expected value operator, τ the time lag, and μ and σ denote mean and SD, respectively. CC for n/pLFP or MUA rasters were shuffle-corrected by subtracting CCshuffle (average of ten repetitions) from CC. The calculation of CC for calcium imaging data was performed on the ΔF/F traces. The average CC was reported as the average value across all electrode or neuronal pairs for time lag τ = 0 if not stated otherwise. In Fig 6, individual CCs were analyzed.
Rasters of nLFP events that crossed a predefined threshold, z, were created by binning the nLFP times with bin size Δt = 2–4 ms [4,11,50]. Previous studies showed that cortical nLFPs can be used as a readout of cortical synchronized population activity (see also S5 Fig) [8,10,49] to measure the propagation of spatiotemporal activity clusters. Due to the predominantly local propagation of activity [11], compact 8×4 MEAs (Fig 2A in vivo, Fig 3A in vitro) were used as described above. From the recorded nLFP rasters, spatiotemporal clusters were extracted by finding cascades of nLFP events that were separated by at least one bin width (Fig 2F). The size of a cluster was defined as the number of nLFPs within the cluster (“discrete,” Figs 2G and 3H, left). Alternatively, cluster sizes can be defined as the sum of absolute nLFP amplitudes (“continuous,” measured in μV; Fig 3H, right), resulting in a continuous distribution [4]. Neuronal avalanches are defined by a distribution of cluster sizes that follows a power law with exponent –1.5 [4] up to the number of electrodes in the recording array. Importantly, the power law is invariant to the number of electrodes used in the recording array up to the so-called “cut-off,” which is given by the number of electrodes in the recording array. This property allows for a robust estimation of the power law exponent [11,50], as described below.
Power-law exponents were estimated using a maximum-likelihood approach [50,90]:
α^=arg maxα l(α|s)
where
l(α|s)= ∑i=1nln pα(si)
denotes the log-likelihood of observing the vector of given cluster sizes s = (s1,…,sn) assuming a power law with exponent α, that is,
pα(s)=sα∑x=1Nxα
In cortical networks, the cut-off is typically at the system size, N, which is given by the number of electrodes in the cortical array (see [4,11,50]). Thus, cortical event size distributions were fitted on the range from one to the number of electrodes in the cortical array. Correspondingly, exponents for striatal distributions are reported for a model that ranged from one to the number of electrodes in the striatal array.
For the comparison of power law versus exponential distribution (the expected distribution for independent neuronal activity), we used the LLR test [50,90]:
LLR(s) =l(α|s) −l(λ|s)
where l(α|s) denotes the log-likelihood for a power law with exponent α, and l(λ|s) the log-likelihood for an exponential distribution with parameter λ
pλ(s)= e−λs∑x=1Ne−λx
For the comparison of distributions to a power law with exponent –1.5, or across different experimental conditions, we used the KS statistic [50]
DKS=maxx|Pdata(x)−Pcompare(x)|
where Pdata denotes the cumulative distribution of the data and Pcompare the cumulative distribution of the reference power-law model [i.e., Pcompare(x)=∑s=1xpα(s)] or data from a different experimental condition.
For paired comparisons of two or more means, we used the paired Student’s t test and repeated-measures ANOVA with Bonferroni correction, respectively. Values are expressed as mean±standard error of the mean if not stated otherwise.
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10.1371/journal.pcbi.1006315 | Irrelevance by inhibition: Learning, computation, and implications for schizophrenia | Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment (“relevance”), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to “gate” both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.
| Individuals with schizophrenia have difficulty ignoring ideas and experiences that most people would treat as unimportant. There is evidence that this may be due to changes in neuronal inhibition, suggesting that inhibitory neurons may be involved in learning to ignore irrelevant inputs. By developing a computational model that learns relevance and irrelevance through changes in the strength of feedforward inhibition, we are able to simulate many specific effects of inhibitory neuron dysfunction on behavior. We also show two computational advantages to this mechanism: (1) if relevance is signaled by the level of excitatory activity, then downstream circuits can easily avoid learning from irrelevant stimuli, (2) relevance learning can occur simultaneously with other types of learning. The model therefore offers insight into the relationships between neural inhibition and behavior, including symptoms of schizophrenia.
| Many symptoms of schizophrenia can be understood as an inability of the brain to appropriately assign relevance to environmental stimuli and internal representations. Schizophrenic patients exhibit difficulties filtering-out, or gating, irrelevant external stimuli [1, 2, 3, 4, 5, 6, 7], and delusions may also be the product of misattributing relevance (or “salience”) to certain types of internally-generated representations [8]. While many neural explanations have been proposed, convergent evidence points to dysfunction in inhibitory processes within the neocortex. This idea dates back at least to Johnson (1985) [9], who hypothesized that schizophrenia symptoms arise from a failure of feedforward inhibition—i.e. activation of inhibition by a system’s inputs. Circuits for cortical feedforward inhibition are now relatively well defined, and may principally involve fast-spiking, parvalbumin-expressing (PV+) inhibitory interneurons [10, 11, 12, 13]. It is also now well established that PV+ interneurons are compromised in schizophrenia (reviewed by [14, 15, 16, 17, 18]).
Computational models have helped to articulate the link between inhibitory dysfunction and schizophrenia [19, 20, 21]. An important example is work by Vogels & Abott (2007, 2009) [19, 20], which demonstrated how inhibition may serve to selectively gate some representations but not others. A theme of these models is the importance of balanced excitation and inhibition (EI balance) within the network. EI balance has been extensively studied across a range of cortical regions (e.g., auditory cortex [22, 23, 24], somatosensory cortex [25, 26, 27, 28], olfactory cortex [29], visual cortex [30, 31], and frontal cortex [32]). Importantly, EI balance can fluctuate dynamically, and can reflect the expectation of rewards or punishments [33, 34, 35, 36, 37]. Therefore, a better understanding of the relationship between cortical inhibition, reinforcement signals, and relevance coding may be critical to understand schizophrenia.
The goal of the present study is to improve our understanding of how disruptions in neural inhibition could compromise the brain’s ability to ignore irrelevant inputs, as observed in schizophrenia. Three main questions are addressed. First, how might inhibitory neurons learn the relevance of specific input patterns, as defined by the patterns’ ability to predict reward or punishment? Second, how might this learning, and corresponding fluctuations in EI balance, help explain experimentally observed relationships between cortical inhibition and behavior? Third, how might relevance learning in inhibitory neurons fit with other learning mechanisms in cortex, such as category learning? Answering these questions will help explain how inhibitory neurons contribute to the “gating” of inputs, potentially lending insight into how neural dysfunction may result in some symptoms found in schizophrenia.
To answer the three questions above, we have developed a neural network model that can learn to ignore specific inputs, but not if inhibition is disrupted. The fundamental proposal in the model is that the overall level of excitation in a cortical circuit signals the temporally discounted expectation of rewards and/or punishments (Fig 1A; [38]). According to this formulation, deviations in EI balance come to represent the network’s estimate of the magnitude of the value signal used in reinforcement learning [39]. By representing relevance using the magnitude of excitatory activity across the population, it is easy for a downstream circuit with a threshold to ignore irrelevant stimuli. Furthermore, this formulation also enables a “multiplexed” code, where the population-level activity represents relevance, while the specific pattern of activity can represent other pieces of information (e.g. stimulus category).
Three sets of simulations are used to demonstrate the explanatory power of the model. The first set of simulations demonstrate the model’s capacity to learn about input relevance/irrelevance, and that, paralleling symptoms of schizophrenia (e.g., [40, 41, 42]), relevance processing is disrupted by impaired inhibition. The second set of simulations use an extended model to show how the proposed relevance code can be used by a downstream circuit to prevent behavioral adaptation to irrelevant stimuli, which we use to reproduce the effects of manipulating inhibition in rodent frontal cortex [43, 44]. The final set of simulations show how relevance learning could occur concurrently with other types of learning, e.g. categorization of input patterns, thereby providing a mechanism to multiplex information about stimulus-relevance and stimulus-identity. Importantly, this model is not meant to provide a comprehensive theory of relevance learning, nor the etiology of schizophrenia, but to offer a computational proof-of-concept for how circuit dysfunction may result in certain, observed behavioral pathologies.
Our first goal was to develop a simplified neural network model in which feedforward inhibitory processes are involved in learning to ignore a stimulus. We take as an assumption that cortical brain networks, as a default, are relatively more responsive to novel input patterns. We therefore define “learning to ignore” as the process by which a network learns to be less responsive to those stimuli that are not predictive of rewards/punishments. Behaviorally, repeated presentations of a stimulus lead to subjects taking longer to associate that stimulus with a second, valued stimulus—a phenomenon known as latent inhibition [45]. Latent inhibition is known to be impaired in schizophrenia [46, 47, 48, 49]. While we ultimately develop the model into one that exhibits latent inhibition (see “Effect of relevance learning on downstream circuitry”, below), the first step was to build a network that could maintain a high level of responding to a stimulus that predicts the arrival of an unconditioned stimulus (US), while responding less to stimuli that do not make predictions about an US.
The basic structure of the model is illustrated in Fig 1B and described in detail in Methods. Briefly, the input, ‘Sensory’ layer of the network, x(t) = [x1(t), …, xn(t)] (n = 1000), drives activity in the ‘Cortex’ layer excitatory units, E(t) = [e1(t), …, em(t)] (m = 800), through a set of positive connection weights, Wx→E. (For notation purposes, we use bold symbols for all vectors and matrices). The Sensory layer also drives activity in the Cortex inhibitory population unit, I(t), through positive connection weights Wx→I. The inhibitory unit divides Cortex excitatory activity through the weight matrix WI→E. The inhibitory unit is intended to loosely model the population of cortical fast-spiking inhibitory interneurons, which evidence suggests provide a divisive “blanket” of feedforward inhibition that is synchronized by gap-junctions [50, 51, 52, 53]. Any US (positive or negative) is represented by the variable u(t) ∈ {0, 1}, which is set to 0 if no reinforcement is present, and 1 if reinforcement is present. Hence, u(t) is an unsigned reinforcement signal, which simply indicates the presence or absence of a US. Fig 1B also shows Cortical excitatory units acting on a layer of ‘Output’ units. The Output layer was not necessary for the initial simulations of relevance learning, but became essential for recapitulating empirical data and demonstrating multiplexing, as described below.
In order to derive analytical results, we initially relied on a deterministic, rate-based model, i.e. we treated x(t), E(t), and I(t) as rates-of-fire (see Methods). However, in our simulations, we sampled the number of spikes generated by each neuron at each time-step from a Poisson distribution, which introduced stochasticity and, given the short time-steps used, meant that neurons fired only zero or one spike per bin, effectively introducing a threshold non-linearity. Empirically, we found that the behavior which our analytical derivations predicted still applied when Poisson spiking was used in the simulations.
At its core, the ability of the model to learn stimulus relevance or irrelevance depends on feedback from a signaling pathway depicted on the right side (gray boxes) of Fig 1B. The total level of Cortex excitatory unit activity (measured by the norm of E(t)) is compared against a baseline, homeostatic level (H) to compute relevance, or the ‘Salience’ signal (S(t)):
S ( t ) = ∥E ( t )∥ 2 - H (1)
The goal of learning in our model is to have S(t) accurately represent the expected future magnitude of unconditioned stimuli, as predicted by current sensory inputs. This would mean that S(t) would be high for stimuli that predict reward/punishment, and close to zero for stimuli that do not. Put another way, the goal of learning in the model is to have S(t) come to represent the variable U(t), which is an unsigned version of the value function from reinforcement learning [39]:
U ( t ) = ⟨ ∑ i = 1 ∞ γ i - 1 u ( t + i ) ⟩ (2)
where 0 < γ < 1 is a temporal discounting term and 〈⋅〉 indicates the expected value. The formal goal of relevance learning in our model is to have S(t) be equal to a scaled version of U(t), i.e. to have S(t) = AU(t), where A is a scaling variable set to achieve physiologically realistic levels of cortical activity (see Methods). If we can achieve this goal, then the overall level of excitation in the Cortical layer encodes an estimate of how relevant a set of sensory inputs are for predicting reward/punishment. In such a case, stimuli that are predictive of an US will drive higher overall levels of excitatory activity than stimuli that are uninformative regarding an US. A downstream circuit could then use this S(t) value implicitly or explicitly to drive learning or gate behavioral reactions (we touch on this more below). We note, though, that any downstream circuit that utilized the explicit value of S(t) itself would require some form of non-linear calculation to compute the vector norm.
From a practical perspective, one way to ensure that S(t) = AU(t) is to perform stochastic gradient descent on the squared difference between S(t) and AU(t). More precisely, we can update the synaptic weight, W j x → I, from unit j in the Sensory layer onto the inhibitory unit using the following learning rule:
W j x → I ← W j x → I + α Δ W j x → I Δ W j x → I = - ∂ ( S ( t ) - A U ( t ) ) 2 ∂ W j x → I (3)
where α is the learning rate. Based on the equations given in the Methods, we derive the following:
∂ ( S ( t ) - A U ( t ) ) 2 ∂ W j x → I ∝ β ( t ) x j ( t ) (4)
where β(t) is a prediction error term:
β ( t ) = A u ( t ) + γ S ( t ) - S ( t - 1 ) (5)
This prediction error term corresponds to an unsigned version of the δ prediction error term that is common in reinforcement learning [39]. Indeed, this learning update is equivalent to an unsigned version of the temporal difference learning algorithm [39]. It can be shown that the learning algorithm defined by Eq 3 converges when the following condition holds:
∥E ( t )∥ 2 = H + A U ( t ) (6)
When taken together with the definition of S(t) given in Eq 1, we know that if Eq 6 is true, then the goal of having S(t) = AU(t) is met.
For most simulations, we updated the Sensory-to-Inhibitory synapses (Wx→I), as specified in Eq 3. However, the same method of stochastic gradient descent can be applied to any synapses in the network. Therefore, to explore other possible mechanisms for relevance learning, in two other sets of simulations (see Relevance Learning in Methods and Learning to ignore and blocking below) we examined how relevance learning operates when a similar gradient descent rule is applied to Sensory-to-Excitatory (Wx→E) or Inhibitory-to-Excitatory (WI→E) synapses. The equations for these learning updates are provided in Methods.
It should be noted that the model is highly abstract, and makes a number of simplifications for the sake of mathematical tractability. For example, we omit feedback connections between excitatory units in the Cortex layer to focus the present investigation on the hypothesis that plasticity in feedforward inhibition can support relevance learning (discussed in more detail in Discussion). Additionally, we generally steer away from being overly specific in identifying brain regions (or networks of regions) and neurotransmitters with the specific computational processes that are modeled. For readability, and general conceptualization, we offer the following approximate mapping between modules in the model and the brain, and discuss the implications of this in more detail in Discussion: “Cortex” is inspired by work in anterior cingulate cortex (in rodents, the medial prefrontal cortex, or mPFC); “Sensory” therefore represents afferents to the anterior cingulate/mPFC; “Output” is modeled in some simulations as the amygdala (detailed below), and in another simulation represents a downstream region of cortex that categorizes stimuli presented to the “Sensory” layer; finally, we think of the salience signal and prediction error as a combination of neuromodulatory inputs and intrinsic homeostatic processes that could, in principle, also engage loops between cortex and sub-cortical systems. A model at this level of abstraction captures only a minor set of the physiological features present in these brain regions, so these interpretations should be judged as semi-agnostic.
Given this framework, and with the ultimate goal of simulating function and dysfunction of behavioral phenomena like latent inhibition, our first goal was to demonstrate whether the model could indeed learn to use S(t) to represent the relevance of the Sensory inputs for predicting a US.
The first set of simulations tested whether the model was capable of learning to ignore specific stimuli after repeated presentations. The principle idea is that all novel stimuli are treated as intrinsically salient (high S(t)), but if a stimulus is not predictive of other valued experiences then the network will learn to reduce its estimate of salience to the level that would be observed if no stimulus were present. The simulation was run using a time step (dt) of 20 ms, which was chosen because it approximates the estimated cortical pyramidal neuron membrane time constant [54, 55, 56] and the inter-spike-intervals of fast-spiking basket neurons (and, relatedly, the period of the gamma oscillation). This timestep is also still large enough to prevent Poisson noise from having an undue effect on the gradient calculations. Each unit of the Sensory layer was assigned a baseline activity level to simulate the layer’s response to contextual variables. At the beginning of the simulations, a 60 s adaptation period without stimulus presentations was run, which allowed the synaptic weights to adjust to this baseline.
Following the adaptation period, two different 200 ms long stimuli were presented to the network using independent, inter-trial-intervals of US presentation between 20 and 30 seconds (based on classical conditioning protocols, as in [57, 58]). The stimuli were simulated as increases in the firing rates (20 Hz) of a pre-determined set of Sensory units (10% of the total population). One of the two stimuli, CS+, was consistently paired with a US (by setting u(t) = 1). The onset of the CS+ preceded the onset of the US by 100 ms, though learning could proceed with different delays between the CS+ and US, if the hyperparameters in the simulation were altered (S1 Fig). In general, the goal of the model was not to capture temporal delay effects, so we did not focus on selecting hyperparameters that reproduced experimental findings on CS-US delay periods. Moreover, as a model with no recurrent dynamics, any ability to account for more interesting temporal phenomena is limited. The other stimulus, CS0, was random in time with respect to the US. The set of Sensory units representing the CS+ was non-overlapping with the set of units representing the irrelevant stimulus, CS0 (Fig 2A). Explanations for the parameters used for connection weights and firing rates are provided in Methods. In general, all firing rate parameters were based on observations made from Ref [38].
During the initial presentations of the CS+ and CS0, the network responded with increased levels of Cortex inhibitory unit activity (Fig 2B, top left panels above colored boxes) and excitatory unit activity (Fig 2B, bottom-left panels). This was due to the increased input from the Sensory layer, x(t), associated with presentation of either stimulus. As the number of presentations accumulated, there was a selective reduction in the degree to which excitatory units responded to the CS0, to the point that the CS0 was treated as being equivalent to an absence of a stimulus, from the perspective of overall levels of excitation. But, there was no reduction in the degree to which the network responded to the CS+ (Fig 2B, right). Thus, the network learned to “ignore” the CS0 (treat it like an absence of stimuli) and not the CS+. Fig 2C illustrates the gradual decrease in excitatory unit population responses to the CS0 (left) and the corresponding increases in the Cortex inhibitory unit (I(t)) response (right). S2 Fig shows the distribution of excitatory unit firing-rates across the simulation and the final distribution of the trained synaptic weights.
The increased responsiveness of the Cortex inhibitory unit to the CS0 over presentations was due to the gradually increased connection weights between the units of the Sensory layer and the Cortex-inhibitory unit (Wx→I), caused by the learning rule. We next examined whether the same patterns could be observed using other model versions, in which synapses either between Sensory and Cortex-excitatory units (Wx→E), or between Cortex-inhibitory and excitatory units (WI→E) were modified. This comparison allowed us to assess how each model responds to disrupted inhibition (see Methods): if current theories of impaired inhibition in schizophrenia are correct [17], then disrupting inhibition in our model should produce impairments in the ability to learn to ignore irrelevant stimuli, as is observed in schizophrenic patients [46, 47, 48, 49].
The results of these tests are described in Fig 2D. Both Wx→I plasticity and Wx→E plasticity models exhibited much better learning of relevant versus irrelevant stimuli, indicated by the salience signal (S(t)) during the CS+ relative to CS0, compared with the WI→E plasticity model. Disrupted inhibition only eliminated the ability to learn to ignore in the Wx→I plasticity model. Differences in how the model types responded to disrupted inhibition could be assessed statistically: even ten repetitions of the simulation was more than sufficient to demonstrate an interaction effect between model type and inhibitory manipulation (two-way ANOVA, type × manipulation: F(2, 54) = 23.97, p = 3.6 × 10−8; one-way ANOVA comparing the disrupted inhibition conditions: F(2, 27) = 14.87, p = 4.4 × 10−5, multiple comparisons between all groups significantly different using a Bonferroni correction).
The use of a single unit to simulate all feedforward inhibition is obviously not biologically realistic, and evidence suggests that models with a single inhibitory input cannot capture the true complexity of disruptions to EI balance that occur in some neurological disorders [59]. Moreover, the effects of manipulating inhibition may depend on detailed excitatory-inhibitory interactions [60]. Hence, one potential concern is that our results would not be reproduced with a more realistic inhibitory network, or even with multiple inhibitory interneurons. However, because our model does not include recurrent excitation and feedback inhibition, we effectively have a built-in level of excitatory stability, so the use of a single inhibitory unit may be inconsequential for our specific study. Indeed, analytically, we find that similar results hold when I(t) is treated as a population (I(t) = [i1(t), …, ik(t)], k = 500). To confirm this, we also ran simulations with a more realistic population of inhibitory neurons, rather than a single unit, and we found the same pattern of learning to ignore as occurred with a single inhibitory unit (S3 Fig). Thus, for our particular study, the use of a single inhibitory unit did not affect the results. More detailed models are likely to be very important for understanding cortical dynamics and EI balance [59, 60], but they are not required to understand or examine the basic relevance learning mechanism that we propose here.
These simulations on “learning to ignore” offer a first step toward a more complete model that links behavioral symptoms in schizophrenia, such as latent inhibition, with inhibitory neuron dysfunction. Given the model’s specific set of assumptions and simplifications, a Wx→I plasticity model offers the best fit to make this link. This is not to suggest that plasticity of synapses onto inhibitory neurons is impaired in schizophrenia. Rather, it suggests that if real cortical networks rely on plasticity in feedforward inhibitory synapses for learning to ignore stimuli, then the causal link between inhibitory neuron dysfunction and irrelevance learning impairments in schizophrenia can be explained. Hence, if we take inhibitory impairment to be a part of schizophrenia, then our results predict that relevance learning in the cortex may be mediated by plasticity of synapses onto inhibitory neurons. The next step was to examine whether other known impairments in relevance learning in schizophrenia could be captured by our model.
Another well-established relevance learning phenomenon is “blocking”, in which one stimulus that has been previously reinforced can occlude learning for another reinforced stimulus [61]. Blocking is also known to be affected in schizophrenia [40, 41, 42]. To examine whether the model exhibited blocking, a standard blocking protocol was simulated, as illustrated in Fig 3A. Two different conditioned stimuli (CS) were presented to the network, CS-A (non-blocked) and CS-B (blocked). As with the previous simulation, each stimulus was simulated as an increase in firing rate of a non-overlapping set of Sensory units (10% of the population). The difference between the non-blocked, CS-A, stimulus and the blocked, CS-B, stimulus is that CS-A was conditioned alone with the US (following habituation pre-exposures) while CS-B was conditioned only when paired with CS-A (following pre-exposures and CS-A conditioning). When this type of protocol is used in either rodent (e.g., [61, 62]) or human (reviewed by [63]) experiments, it leads to CS-A being recognized as relevant for reward/punishment, but CS-B being judged irrelevant. The blocking effect was measured in the model by comparing the response of the excitatory unit population to CS-A and CS-B during the final test sessions (Fig 3B, also Fig 3C inset). As predicted, the model exhibited the basic blocking effect seen in people and animals, with CS-A generating a large excitatory response and CS-B generating a small one (Fig 3C, left inset). Because learning was supported by inhibitory neuron plasticity (in this case, the Wx→I plasticity model), changes in population responses to both CS-A and CS-B over stimulus presentations paralleled changes in inhibitory neuron responses (Fig 3C, right inset). Notably, a strong increase in inhibitory neuron activity was observed during the “blocking” phase of conditioning, reflecting feedforward inhibition compensating for both stimuli being presented simultaneously (Fig 3C, presentations 0-50). We note that this is also consistent with observations of increased fast-spiking neuron activity during stimulus presentations and movement [38].
As with the “learning to ignore” simulation, blocking and the effect of inhibitory disruptions were tested in different versions of the models, defined by which synapses (Wx→I, Wx→E, WE→I) were plastic. Consistent with our predictions, the blocking effect was eliminated in the Wx→I plasticity model, following even a 10% disruption of inhibition (Fig 3D, left bars). Blocking was also observed in the Wx→E model; however, in this model version an unexpected “reverse blocking” effect was observed following inhibitory disruptions (Fig 3D, middle bars). This was likely due to the learning mechanism becoming over-active following inhibitory disruptions, leading to a reduction in synaptic weights corresponding with CS-A presentations (regardless of it being paired with the US). No blocking effects could be obtained in the WI→E model (Fig 3D, right bars). Once again, these results could be judged statistically, with 10 repetitions more than sufficient to reveal an interaction between model type and manipulation (two-way ANOVA, F(2, 54) = 44.08, p = 10−12, one-way ANOVA of disrupted inhibition condition: F(2, 27) = 12.3, p = 2.0 × 10−4, with multiple comparisons test with Bonferroni correction showing a significant difference between Wx→I and Wx→E models).
Similar to the results from the “learning to ignore” simulations, these simulations demonstrate that there is potentially a relatively straightforward link between impaired blocking effects in schizophrenia and inhibitory neuron dysfunction when relevance is mediated by plasticity of synapses onto inhibitory interneurons. Both sets of simulations were built on the empirically-based assumption that “relevance” is coded by increased excitatory neuron activity in the network. The next step was to expand the model to examine whether this code for relevance could be used by downstream circuits, to recapitulate experimental effects of manipulating inhibition in cortex.
In order to simulate behavior, it was necessary to demonstrate how the output of the Cortex layer, and in particular the relevance signal, S(t), might be used by an efferent region that directly controls behavioral output. A downstream circuit should be able to use S(t) to differentially respond to relevant versus irrelevant stimuli, in that relevant stimuli should drive more learning and be associated with increased behavioral responses. A simple way to implement this is by use of a threshold mechanism, such that only activity patterns with sufficiently high levels of excitatory activity can drive a behavioral output. Based on our interest in simulating phenomena like latent inhibition, in which relevance impacts not only behavior, but also learning, we hypothesized that a threshold could be used not only to drive activity in an efferent network, but also to drive learning. Our next step was to provide a proof-of-concept for this idea. Since many of the rodent studies in learned irrelevance and latent inhibition use fear conditioning, our efferent Output layer was designed to loosely represent the mammalian amygdala, and the levels of ‘Amygdala’ unit activity were equated with fear expression.
The Amygdala output layer activities, y(t) = [y1(t), …, yℓ(t)] (Fig 1B, top layer), were modeled as a competitive network [64] with ℓ = 10 units receiving inputs z(t) = [z1(t), …, zℓ(t)] that were driven by Cortex excitatory activity via an ℓ × m synaptic weight matrix, WE→y:
z i ( t ) = ∑ j W i j E → y e j ( t ) - θ y i ( t ) = { z i ( t ) + 0 . 5 u ( t ) if z i ( t ) > z j ( t ) , ∀ j ≠ i and ( z i ( t ) ≥ 0 or u ( t ) > 0 ) 0 otherwise (7)
where θ = H/4 is a threshold variable.
What Eq 7 says is: (i) the Amygdala layer is silent unless at least one neuron’s input passes the threshold defined by θ or an US is present, and (ii) only one unit in the Amygdala layer can be active at any point in time, i.e. it is a “winner-takes-all” circuit. We use this “winner-takes-all” formulation due to experimental evidence for competitive coding in the Amygdala [65, 66], and because it allows us to derive an analytical guarantee regarding the behavior of the Amygdala layer (see below).
In-line with standard competitive learning methods [64], we update the synapses onto the Amygdala units with the following update rule:
W i j E → y ← W i j E → y + α y u ( t ) y i ( t ) ∥E ( t )∥ 2 Δ W i j E → y Δ W i j E → y = e j ( t ) ∥E ( t )∥ 2 - W i j E → y (8)
where αy is the learning rate. Note also that the weights W i j E → y are rescaled after every update such that ∑ j W i j E → y = 1 (see Methods).
Importantly, we can use the formulation of Δ W i j E → y to analytically demonstrate that there is a θ for which the Amygdala will only respond to a given sensory input if that input is, or has been, paired with an US. First, we note that according to Eq 8, the Amygdala does not learn if there are no units that pass threshold (i.e. if yi(t) = 0 ∀i) and no US (i.e. u(t) = 0). Second, the strength of input to a given Amygdala neuron, i, is determined by the dot product W i E → y · E ( t ), where W i E → y is the set of synapses onto Amygdala neuron i. Finally, when a given neuron in the Amygdala, i, always “wins” (zi(t) > zj(t) ∀j ≠ i) in response to excitatory population vectors sampled from the set E i ( t ) = [ e 1 i ( t ) , . . . , e m i ( t ) ] ∈ E i, then the update rule in Eq 8 will push the synaptic weights for i to meet the following condition:
W i j E → y = ⟨ e j i ( t ) ∥ E i ( t ) ∥ 2 ⟩E i (9)
where 〈 · 〉 E i denotes expectation over elements of E i. In other words, competitive learning in the Amygdala will encourage the synaptic weight vector for unit i, W i E → y, to be a normalized version of the mean of the set of excitatory activity vectors that it “wins”, E i. As the unit’s synapses are pushed in this direction, the dot product W i E → y · E i ( t ) will generally increase. Hence, we can assume that inputs to the Amygdala units are initially small, but increase over learning.
Moreover, thanks to the relevance learning that is occurring in the Cortical excitatory population, we can make a more explicit guarantee about Amygdala responses. Consider the case where unit i “wins” for a given excitatory input pattern E ′ = [ e 1 ′ , . . . , e m ′ ] ∈ E i. After Amygdala learning has converged, Eqs (7) and (9) tell us that in the absence of an US (u(t) = 0), the input to unit i in response to E′ is given by:
zi(t)=∑j〈 eji∥Ei(t)∥2 〉Eiej′−θ≤∑jej′2∥E′∥2−θ=∥E′∥2−θ(10)
Eq 10 tells us that when no US is present, then zi(t) is bounded by ∥E′∥2 − θ. When we consider that relevance learning in the Cortex layer will scale ∥E′∥2 to be close to H for irrelevant sensory inputs, and close to H + A for relevant sensory inputs, we know that:
z i ( t ) ≤ { H - θ if irrelevant ( H + A ) - θ if relevant (11)
thus, we know there exists a threshold H < θ < (H + A) for which the Amygdala can respond only to relevant stimuli. In practice, we find that the zi(t) are much lower than H for most stimuli, including relevant stimuli, since the weights rarely converge to perfect alignment with a given stimulus pattern. From searching the hyperparameter space we found that a threshold of θ = H/4 was best for distinguishing relevant and irrelevant stimuli, and this value was used in our simulations.
To summarize the importance of this result: if no US is present and no training has occurred, then it will be likely that yi(t) = 0 ∀i, and learning will not occur (Fig 4A). If an US is paired with E′, then learning will occur (Fig 4B), particularly if the inputs are novel or already learned to be relevant, because the competitive learning algorithm will make W i E → y more similar to E′. If inputs are not novel or learned to be relevant, then fear learning will take place more slowly, with the competitive learning algorithm taking hold as relevance learning increases the norm ∥E′∥2 to be closer to H + A. The increased norm in one layer, and competitive learning changes taking place in the next, increase the dot product W i E → y · E ′, making it more likely that ∃i such that yi(t) > 0, even when no US is present (Fig 4C). In this way, we can guarantee that the Amygdala layer only learns and responds to stimuli that are currently being paired with an US or were previously paired with an US.
To put this result in more general terms, we have provided a proof-of-concept for the claim that if stimulus relevance is encoded using the overall level of excitatory activity in a population, then it is possible for an efferent region to react and learn only in response to relevant stimuli. We demonstrated this using a simulation implementing a “learned irrelevance” paradigm. This showed that associative learning is slower if a stimulus has previously been learned as being irrelevant than if it has not (S5 Fig).
Although we haven’t explored the use of alternatives to the competitive learning algorithm implemented here, the same principle should apply to any mechanism that uses a threshold and some form of learning that aligns input vectors and synaptic weight vectors. As such, we consider this to be a general, novel insight from the model: not only can relevance learning be implemented using feedforward inhibition to control the overall level of excitatory activity, such an implementation makes it natural for downstream circuits to ignore irrelevant stimuli. In this way, we can gain new insight as to why manipulations of inhibition in cortical afferent regions to the Amygdala can alter animal behavior in fear learning tasks.
To determine how relevance learning and our Amygdala circuit interact to produce behavior we simulated experimental studies that link EI balance in cortex to relevance learning and fear conditioning [43, 44]. Our first set of simulations with the Amygdala layer examined the findings of Piantadosi & Floresco (2014) [43]. Their study showed that a GABA-A receptor antagonist, applied to the medial prefrontal cortex (mPFC), can have different effects on latent inhibition when applied at different phases of the learning protocol. As stated previously: latent inhibition refers to the phenomenon wherein it is harder to associate a stimulus with a reinforcer if a subject has previously been exposed to that stimulus. In the study by Piantadosi & Floresco (2014), animals were separated into two groups: those that received pre-exposures to a CS and those that had no pre-exposure. When the CS was subsequently paired with a footshock, the pre-exposure group was less likely to learn the fear association compared with the no pre-exposure group (i.e. the animals exhibited latent inhibition). Importantly, the authors found that blocking GABA-A receptors had different effects if done during the conditioning period or during the test: GABA-A antagonists infused during conditioning amplified latent inhibition, whereas infusions during testing disrupted latent inhibition (Fig 5A). We examined whether our model would exhibit a similar pattern of responses. To determine this, the experiments were simulated using a 20% reduction in inhibition to mimic blockade of GABA-A receptors (see Methods).
As with previous simulations, stimulus presentation was modeled as an increase in baseline firing rate to 20 Hz across a pre-determined set of Sensory units (10% of all units; i.e., 100 units), the timestep used was 20 ms, and 1000 Sensory, 800 Cortex, and 10 Amygdala units were used.
The model showed a similar pattern of results as observed by Piantadosi & Floresco (2014), with simulated GABA-A blockade increasing the latent inhibition effect if applied during conditioning, and eliminating latent inhibition if applied during testing [43] (Fig 5B). The link between the salience signal (S(t), determined by levels of excitatory unit activity) and Amygdala activity can be better understood by examining how each changed from one trial to the next (Fig 5C and 5D). As illustrated by the downward slope of activity among Cortex excitatory units over pre-exposure trials, the network receiving pre-exposures learned to treat the CS as irrelevant (Fig 5C). As a result, during the conditioning period (gray shaded area in Fig 5C and 5D), Cortex activity was too low to push the Amygdala past threshold, making it less likely for learning to occur in the Amygdala in the pre-exposure condition (gray dotted line in Fig 5C and 5D). In contrast, the excitatory activity during conditioning for the non pre-exposure condition was high due to relevance learning, which resulted in a sufficiently strong Amygdala response to the CS to induce fear association learning (gray solid line in Fig 5C and 5D). When inhibitory signaling was experimentally reduced during conditioning (red lines in Fig 5C and 5D), both Cortex activity and Amygdala learning were amplified. However, this learning led to greater than normal inhibition during the test phase, so that in the pre-exposure condition the network remained relatively inactive during testing (red dotted line in Fig 5C and 5D; compare to control pre-exposure condition, grey dotted line). In contrast, when inhibitory signaling was reduced during testing, many Cortex units become active in both the pre-exposure and no pre-exposure conditions (blue lines in Fig 5C). The key to our competitive learning algorithm, though, is that the Amygdala weights align to the excitatory inputs (Fig 4). Thus, the over-activity of the Cortex units actually made it slightly harder for the Amygdala to pass threshold during testing (blue lines in Fig 5D). Hence, our model qualitatively recapitulated the results of Piantadosi & Floresco (2014) thanks to the interaction between relevance learning and the threshold effects in our Amygdala output layer. These data provide a new interpretation of the Piantadosi & Floresco (2014) experiments. Specifically, they suggest that by manipulating the inhibition in cortex, Piantadosi & Floresco (2014) may have been altering the encoding of stimulus relevance, and thereby, affecting the behavior of a downstream circuit, such as the amygdala, that may respond/learn from relevant stimuli using a threshold mechanism.
The second simulation of experimental results we conducted addressed work by Courtin et al. (2014) [44], which examined how the activity of PV+ interneurons in the mPFC controls fear expression. As mentioned above, PV+ interneurons are the cells that we intended to model using the Cortex inhibitory unit, I(t). We simulated the experiments of Courtin et al. (2014) using the same network and parameters as used to simulate latent inhibition above (see Methods). The original study by Courtin et al. (2014) demonstrated that optogenetic stimulation of PV+ interneurons in the mPFC results in increased fear responses in mice, both before conditioning and, even more prominently, when stimulation was paired with a CS following extinction [44] (Fig 6A, left). When we applied the same protocol to our model, using a reduction in inhibitory inputs to mimic optogenetic silencing (see Methods), the same pattern of activity was observed in the Amygdala layer (Fig 6B, left). The original experiments also showed that activation of mPFC PV+ interneurons decreased freezing to a conditioned CS (Fig 6A, right). This was consistent with activity patterns in the model in a subsequent set of simulations (Fig 6B, right). As with the latent inhibition tests above, our results here provide a novel interpretation for the Courtin et al. (2014) study. Specifically, our data suggest that the effects of silencing or activating PV+ inhibitory interneurons in the mPFC may be explained by the interaction between a relevance code mediated by feedforward, divisive inhibition, and a threshold mechanism in the amygdala. They also offer evidence that the present model, in spite of its simplicity, may capture an essential relationship between the role of inhibition in the mPFC region and the competitive network in the amygdala [65, 66].
A final set of simulations was used to investigate a key computational advantage to using the overall level of excitation for signaling relevance. If the overall level of excitatory activity encodes relevance (via S(t)), and this is controlled by feedforward inhibition, then the excitatory synapses in the network should still be free to control the specific pattern of E(t) to encode other information. This can be described mathematically by viewing the excitatory Cortex activity patterns E(t) as vectors, where the norm (length) of the vector is a signal of relevance (S(t)), but the position that the vector points in encodes other aspects of a stimulus, such as orientation, frequency, or category.
To test this idea, the network was trained to categorize 10 different stimulus classes, with only one of these paired with a reward. The prediction was that the network could learn information about relevance and also learn to respond with output patterns specific to the correct stimulus category. To train the network to categorize stimuli, we employed a softmax Output layer (see Methods) and trained the excitatory pathway in the network with backpropagation-of-error [67] (Fig 7A). It is worth noting that although backpropagation-of-error is not a biologically realistic learning algorithm, there is evidence that it could be approximated with biologically realistic mechanisms [68, 69, 70, 71]. Furthermore, independent of the specific algorithm used, the goal of the simulation was simply to offer a proof of the multiplexing concept.
Training of the excitatory pathway with backpropagation was done concurrently with training of the feedforward inhibition pathway using the relevance learning algorithm (as described in Methods). Over the course of training, the network learned to dissociate the rewarded stimulus category from the unrewarded ones, via the relevance signal, S(t) (Fig 7B). Importantly, S(t) did not differentiate between the unrewarded categories (Fig 7B, orange lines), demonstrating that it was not encoding the categories, per se, but only their relevance for predicting reward. At the same time, the set of ‘Category’ output units did learn to differentiate all 10 categories of stimuli. A cross-entropy loss function was used to evaluate the success of categorization, with lower values indicating a higher degree of separation between the categories. Over the course of 20 simulated seconds of training, this measure dropped to almost zero and the output layer was achieving roughly 95% accuracy on average (Fig 7C). We found similar results when we rewarded three of the stimuli, rather than only one (S4 Fig). Importantly, relevance learning and category learning were operating simultaneously in these simulations. The results demonstrate the potential for multiplexing relevance signals with other stimulus information by using the overall level of excitatory activity as a code for value.
The simulations presented here explored how disruptions in feed-forward, neural inhibition could compromise the brain’s ability to ignore irrelevant inputs, as observed in schizophrenia. The model was structured as simply as was necessary to examine this connection, incorporating three core mechanisms. First, relevance was coded as the overall excitatory activity in the ‘Cortex’ layer. Specifically, the norm (length) of the excitatory units’ activity vector was treated as a reinforcement learning value function, though “unsigned” in that it treated positive (reward) and negative (punishment) values equivalently (Fig 1). Second, the model used feedforward inhibition—i.e., the connections from the ‘Sensory’ input layer to Cortex inhibitory units—to control the overall level of Cortex excitatory activity. When paired with the first mechanism, the result was that disruptions to inhibition led to failures in normal relevance attribution (Figs 2 & 3). Third, the model used a form of an established reinforcement learning algorithm, temporal difference learning, to train the feedforward inhibitory connections and thereby learn to differentiate relevant versus irrelevant stimuli. When these mechanisms were further connected in sequence with an output (the ‘Amygdala’) that used a threshold and a competitive learning mechanism (Fig 4), they offered specific predictions about how disruptions to inhibition alter fear behavior (Figs 5 and 6).
These three mechanisms are highly consistent with previous empirical work. The idea that overall levels of excitation in “Cortex” may provide a code for unsigned value was inspired by work on the medial prefrontal cortex (mPFC), a region that has been implicated in schizophrenia and many other disorders [72]. Recent data has demonstrated the importance of mPFC disinhibition for coding relevant situations [33, 44, 73, 74, 75], including the observation that net levels of activity in putative pyramidal neurons increase near reward sites [38]. The second mechanism, assigning control of this relevance code to feedforward inhibition, matches empirical findings on the importance of inhibition for behaviors like latent inhibition (e.g. [43]). It also matches decades of work linking relevance impairments in schizophrenia [1, 2, 3, 4, 5, 8, 9] with evidence that inhibitory neurons, and in particular, classes of inhibitory neurons supporting feedforward inhibition, may be differentially compromised in the disease [14, 15, 16, 17, 18, 76]. Finally, the third mechanism, wherein inhibitory interneuron plasticity is the means for learning to differentiate relevant versus irrelevant stimuli, is consistent with findings that the neural connections supporting feedforward inhibition are plastic [77, 78, 79], in some cases requiring NMDA receptors with a well established importance for associative plasticity [80, 81, 82]. These three mechanisms together comprise a more general theory of cortical coding: plasticity involving inhibitory neurons may act in parallel with excitatory neuron plasticity to accomplish different learning functions. While excitatory plasticity may provide a mechanism for carrying information about stimulus specifics, plasticity involving inhibitory neurons may be important for relevance learning (Fig 7). This suggests a multiplexing of learning functions in the neocortex, and links a large literature on inhibitory plasticity with theories about the importance of these neurons for maintaining EI balance.
As this was an abstract neural network model, many features of the real brain were absent. The most notable was the absence of feedback connections within Cortex. By excluding these connections, the mathematical complexity of calculating synaptic balances and their experience-dependent changes could be minimized, and it became possible to isolate the learning algorithms that explain the behavioral phenomena of interest. The results demonstrate that plasticity in the synapses connecting inputs to inhibitory neurons is sufficient to support relevance learning. Such a mechanism also causes relevance learning to be dysfunctional following disrupted inhibition. In contrast with our model, which lacks feedback excitatory connections, Murray and colleagues used a more detailed circuit model that included these connections to show how inhibition helps maintain intact memory representations, and how this could be disrupted in schizophrenia [21]. The aim of this previous study was very different from the present investigation; the findings, however, are not inconsistent. It would be beneficial in the future to examine the interrelationships between functions and algorithms of feedforward versus feedback excitation, including the dependencies that may exist between working memory and stimulus gating.
Another feature missing from the present model was the absence of different types of inhibitory neurons. Recent work by Yang et al. [83] tackled the question of how the inhibitory system regulates signal propagation (“gating”) using functionally distinct types of inhibitory neurons. They were able to show how signal propagation may require parallel signaling between disinhibitory and excitatory inputs onto the same neurons. This model highlights some key features that distinguish the present work from other research in the area ([20, 83]). Most obviously, our model does not adhere to the requirement that EI balance must be strictly maintained: regulation of signal propagation takes place at the population level by allowing for dynamic EI balance (discussed above). Relatedly, no signals are completely gated within the cortex: “relevant” versus “irrelevant” information is differentially represented with relatively subtle, average firing rate differences across the population of all neurons. It is only when Cortex signals reach an efferent region (in our case, the competitive-learning Amygdala) that information related to particular input patterns is prevented from propagating forward. Indeed, in our multiplexing experiments the category information of irrelevant stimuli was maintained (Fig 7). These two distinctions, the population-level regulation of signal propagation and the graded way in which it is implemented, provide the basis for multiplexing of learning functions, by allowing inhibitory and excitatory plasticity processes to follow independent learning rules (discussed below). The population-level approach is also intuitively consistent with the necessarily high-dimensionality of single-neuron coding in regions like prefrontal cortex (e.g., [84]). Moreover, recent computational work suggests that ensemble activity in cortical pyramidal neurons can itself multiplex feedforward and feedback signals [85]. Such a mechanism, paired with our results, could provide a means of simultaneously encoding relevance, stimulus identity, and top-down information (e.g. feedback or attention) in the same cortical microcircuits.
While the abstract nature of the model offers only a proof-of-concept for certain elements of true cortical computation, it also raises potentially fundamental questions about how certain processes may be implemented in the brain. Particularly compelling is the question of where the computed salience signal (S(t)) and corresponding prediction error (β(t)) come from. We consider two non-exclusive possibilities. In one scenario, the salience signal is explicitly read-out by cells in neuromodulatory nuclei, as has been described within the dopaminergic system [86], which is then used to compute a prediction error signal that feeds back to the cortical afferent, modifying local plasticity accordingly [87]. In the second scenario, the prediction error calculation responsible for maintaining EI balance is carried out by circuits that are local to the cortex, and may take place, for example, by intrinsic signaling processes within the inhibitory neurons (see also [88, 89]). In this case, either the salience signal itself (the excitatory input onto inhibitory neurons) or a set of intrinsic, cellular processes that compute the difference between inputs and “desired” output levels (the prediction error signals), are modified by neuromodulatory signals carrying information about current rewards/punishments.
However the prediction error signal is implemented, the plasticity processes involved must be intertwined with local mechanisms for maintaining EI balance; otherwise, EI balance maintenance would be constantly working to compensate for changes associated with relevance learning. As described in Introduction, the ability of local cortical circuits to maintain EI balance is well established [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90]. The process by which networks maintain this balance, while not fully known, has been proposed to be supported by plasticity at inhibitory synapses in response to feedback excitatory signals [88, 89]. Recent work has demonstrated local changes in synaptic scaling at both inhibitory and excitatory synapses following local changes in excitation [91]. One possibility is that while the local network is capable of maintaining EI balance, its set-point can be adjusted by signals from extrinsic neuromodulators. Dopamine has received much attention in signaling salience, but acetylcholine has also come under the spotlight (e.g. [36, 92, 93, 94]). Disinhibition may also involve a class of inhibitory neurons that contain vasoactive intestinal polypeptide (VIP+ interneurons [33]). To accurately capture the mechanisms of the supervisory process, it will likely be necessary to increase the complexity of the model by including feedback connections between excitatory neurons, and connections from excitatory to inhibitory neurons.
One detail that was critical in the present model, and a primary prediction of the present work, is that relevance learning not only involves inhibitory changes, but specifically may involve plasticity on the feedforward, input-to-inhibitory neuron synapses in cortical circuits. This prediction is consistent with recent data showing that the number of excitatory synapses onto parvalbumin-expressing inhibitory neurons is reduced in schizophrenia [95]. It also adds to a growing literature on the functions of inhibitory neuron plasticity [96, 97, 98]. One recently proposed idea is that during memory encoding, patterns of inhibitory modifications mirror excitatory modifications. This would ensure that EI balance can be appropriately maintained and thereby reduce inappropriate recall [96]. The present model fits with this proposal. Inhibitory synapses in the model learned to match any increases in excitatory synapses in order to keep the “Cortex” output at the homeostatic set-point. Only when the prediction error signal indicated that an input was relevant was this mirrored inhibition relaxed to allow the excitatory activity to increase.
The simulations also addressed how the proposed code for relevance might impact learning and activity in a post-synaptic region. With the aim of simulating experimental fear-learning data, the post-synaptic region chosen was the amygdala, which was modeled as a competitive learning network [64]. Use of a competitive network is consistent with known properties of the mammalian amygdala [65, 99]. Additionally, projections to the amygdala can be found throughout the mPFC (e.g. [100]) and these projections are known to excite principle neurons in that region (e.g. [101]). By combining our proposed relevance code at one layer of the model with a competitive learning rule with a threshold at the next, the model was capable of responding selectively to only those specific stimuli that had been paired with reinforcement signals in the past (Fig 4). The model thereby became capable of replicating behavioral patterns from both pharmacological [43] and optogenetic [44] manipulations to mPFC inhibitory neurons (Figs 5 and 6).
Our model shows how changes in inhibitory gain control can determine how excitatory activity is involved in representing distinct stimuli and, in the Amygdala simulations, how this can drive behavior and learning. It is important to emphasize that this idea in itself is not novel. Another notable model in which plasticity is modulated by stimulus-regulated gain control has been described by Harris and Livesey [102, 103]. This particular example uses a very different structure from our own network, and is aimed at a very different question: how associative learning can take place for stimulus combinations, even when the representations of such stimuli are “elemental” (see also [104]). It is also capable of replicating many classical conditioning phenomena that our own simulations do not address, and further work would be required to identify which features of the two models are compatible. What is perhaps most interesting, however, is not so much the differences between models, but the apparent utility and versatility of gain control regulation for network computation. The present work specifically focuses on how inhibitory plasticity may support some of these functions.
One novel contribution of the model is the mechanism it proposes for the multiplexing of different learning functions in cortical networks. By allowing inhibitory plasticity to rely on an entirely independent learning rule from excitatory neuron plasticity, we allow the network to perform associative learning on both relevant and irrelevant input patterns (Fig 7). The ability of the network to learn even in the absence of novelty or relevance could be thought of as a kind of implicit learning, in which knowledge of the environment—including its statistical structure—is extracted from experience in the absence of reinforcement, attention or consciousness [105, 106]. This differs from many other models, including those cited above, in which inhibitory plasticity is closely tied to excitatory plasticity (e.g., [83]). In the future it will be useful to examine the specific roles of inhibitory neuron plasticity in more detail, and to see whether the differences in approaches may be reconciled through different inhibitory neuron types, cortical layers, or other factors.
There are a number of behavioral phenomena, well reported in the classical conditioning literature, that fall outside of the scope of our simulations. One that has been observed since some of the earliest experiments by Pavlov and Konorski is the phenomenon where a stimulus not associated with an US can become a “conditioned inhibitor”—i.e., it can become salient in its own right, inhibiting responses normally associated with the US [107]. An example of this is if the pre-exposed CS in the latent inhibition paradigm comes to be perceived as a salient “safety cue”. The extent to which behaviors like latent inhibition are determined by the CS becoming a conditioned inhibitor is unclear, and likely depends on the specific protocol used. An interesting possibility is that different cortical areas make use of a similar scalar code for relevance, but apply them to different—and sometimes opposing—functions. Within the mPFC the infralimbic cortex seems to be involved in signaling safety, important for fear extinction, while the slightly more dorsal, ventral prelimbic cortex may instead signal danger, important for fear learning (e.g., [108, 109]). If a punisher is assumed by default given past history and context, then the absence of the punisher may effectively act as an US, suppressing inhibitory neuron activity. In the mPFC this may engage the infralimbic cortex to promote the signaling and learning of safety. If a punisher is not assumed, then, according to our framework, the only effect of its absence following a CS0 would be plasticity processes on inhibitory neurons to maintain excitatory-inhibitory balance, resulting in loss of attention to the CS0 and retardation of learning a subsequent CS-US pairing (see S5 Fig). Explorations of the differences between learning the relevance of a stimulus for safety, punishment, or reward in different circuits were outside of the scope of the current study, but should be explored in future work.
Another area that was not explored in the current set of experiments was the increasingly apparent link between pathologies of EI balance and deficits in social behavior and motivation [110, 111, 112]. It seems likely, however, that some of the more basic results from the present investigations could offer understanding for why some individuals have more difficulty filtering or dynamically processing social information. Tackling these complex problems will require a convergence of multiple experimental and theoretical approaches, and mathematically tractable network models that include excitatory-inhibitory interactions will be an essential tool.
Altogether, our theoretical investigations provide a potential explanation for why behaviors such as gating and relevance learning could depend on feedforward inhibition, and therefore, how pathologies of inhibition may underlie neuropsychiatric conditions such as schizophrenia. In many ways the ideas reformulate a long existing hypothesis that schizophrenia is a disruption of feedforward inhibition [9]. But the model offers a computational description of the process with defined links between several functional elements. Furthermore, it offers valuable predictions about the importance of plasticity in both excitatory and inhibitory neurons, lending insights into the normally functioning brain.
The core of the model is a two-layer feedforward neural network composed of different types of units. Stimuli are encoded by a set of input units, x(t) = [x1(t), …, xn(t)], the ‘Sensory’ layer. In our analysis of the network, we treat x(t) as a vector of firing rates. In our simulations, this layer is a set of excitatory Poisson units with firing rates ϕ x ( t ) = [ ϕ 1 x ( t ) , … , ϕ n x ( t ) ]. Changes in the Sensory layer take place when stimuli are presented, as described in more detail below. Sensory units feed into a middle layer, ‘Cortex’, that is comprised of two populations: an excitatory population, E(t) = [e1(t), …, em(t)], and an inhibitory population modeled as a single unit I(t) that acts divisively on the excitatory units. As with the sensory inputs, we treat E(t) as a vector of firing-rates when conducting our analyses, but simulate it as a vector of Poisson units with rates ϕ E ( t ) = [ ϕ 1 E ( t ) , … , ϕ m E ( t ) ]. The receptive fields of Cortex units have no temporal dimension, so the activity at any point only reflects the current inputs to the network.
The connections from input units to the excitatory cortex units are contained in an m × n synaptic weight matrix, Wx→E, the connections from input units to the inhibitory Cortex unit are contained in the n-dimensional vector of synaptic weights, Wx→I, and the connections from the inhibitory unit to the excitatory units are contained in an m-dimensional vector of synaptic weights, WI→E.
Altogether, this set-up gives the following equations which describe the activity of the model in the simulations:
x ( t ) ∼ P o i s s o n ( d t ϕ x ( t ) ) I ( t ) = W x → I · x ( t ) + b I ϕ E ( t ) = ( W x → E · x ( t ) + b E ) ⊘ ( W I → E I ( t ) + I f l o o r ) E ( t ) ∼ P o i s s o n ( d t ϕ E ( t ) ) (12)
where ⊘ represents element-wise division of a vector/matrix, dt is the time-step, which is 20 ms for most simulations, bE and bI are bias terms, and Ifloor = 0.1 prevents division by zero. We note that here we have indicated I(t) as a scalar, since it was in most simulations, but it can be formulated as a vector with no change to the results (see S3 Fig). As well, we note that for our mathematical analyses and gradient calculations we simply set x(t) = ϕx(t) and E(t) = ϕE(t).
One additional component that is not included in the above equations, but which contributes to relevance learning (see Relevance Learning, below), is a signal communicating the unsigned magnitude of reward or punishment, i.e. the unconditioned stimulus (US). In the present simulations the value of the US at a given time (u(t)) is either 1 or 0, though in principle it could as easily be a graded value.
In some simulations, we add an additional output layer of units with activity y(t) = [y1(t), …, yℓ(t)] that receives inputs from the excitatory cortical units via an ℓ × m synaptic weight matrix, WE→y. In those simulations which address previous experimental findings (Figs 5 and 6), the output layer is intended to represent an amygdala (‘Amygdala’) and implements a competitive learning algorithm (according to the framework of [64]). In the competitive learning module, a maximum of only one unit may be active at any given time (it is possible for no units to be active). Whether a unit, i, is active depends on two conditions: (1) the unit is receiving stronger input than any of the other units, (2) the unit’s input, W i E → y · E ( t ), is greater than a threshold, θ. Amygdala units also receive signals from the US, such that u(t) can help to increase output, yi(t). Based on all of this, the activities of the Amygdala units are governed by the following equations:
z i ( t ) = ∑ j W i j E → y e j ( t ) - θ y i ( t ) = { z i ( t ) + 0 . 5 u ( t ) if z i ( t ) > z j ( t ) , ∀ j ≠ i and ( z i ( t ) ≥ 0 or u ( t ) > 0 ) 0 otherwise (13)
Note that the activities yi(t) are rescaled after every weight update (see below) such that y i ( t ) ← y i ( t ) ∑ i z i ( t ) and ∑ j W i j E → y = 1. This rescaling provided an important normalization of the Amygdala activity, keeping it in a reasonable range without impacting learning.
The threshold, θ, determines when the Amygdala layer can have any active units. An explanation for how θ was selected is given in the results in ‘Effect of relevance learning on downstream circuitry’.
Although having a single neuron firing is undoubtedly not what occurs in the mammalian amygdala, there is evidence for a competitive “winner-takes-all” mechanism [65, 66], such that a single ensemble of neurons is active and all others are silent. Therefore, the active unit in our model ‘Amygdala’ could be taken to represent an ensemble of “winning” neurons. Since individual units in this case were representative of larger ensembles, the winning unit’s firing rate was kept as a continuous “activation level” value rather than Poisson-distributed spike counts.
The output layer takes on a different form in those simulations that demonstrate how our model can multiplex relevance signals and stimulus identity. In this case, the output units represent some efferent, such as a second area of cortex, that is responsible for categorizing input activity. For simplicity, we refer to this layer in the simulations as the ‘Category’ layer. The Category layer is a set of softmax, linear-non-linear-Poisson units with rates ϕy(t) governed by:
ϕ i y ( t ) = κ ∑ j W i j E → y e j ( t ) ∑ k ∑ j W k j E → y e j ( t ) y i ( t ) ∼ P o i s s o n ( d t ϕ i y ( t ) ) (14)
where κ = 20 Hz scales the firing-rates such that the activity of the units is proportional to the probability of each of the ℓ possible categories for the current stimulus, with a rate-of-fire of 20 Hz corresponds to a probability of 1.
Sensory (input) units are divided into sets of stimulus-coding and non-coding units. Each stimulus is capable of activating one tenth of the Sensory units. In the case of the learning to ignore simulations, for example (see Learning to ignore and blocking, below), one tenth of the units are activated by the stimulus, “CS+” that is paired in time with the US, one tenth of the units are activated by another stimulus, “CS0”, that are random in time relative to the US, and the rest are activated by neither CS+ nor CS0. Importantly, Sensory units fire both when activated by a stimulus and not, just at different rates. An active sensory unit generates spikes with a Poisson process at a rate of ϕon = 20 Hz, while an inactive sensory unit generates spikes with a Poisson process at a rate selected randomly based on a gamma distribution that peaks at 0.6 Hz and has a variance of 3 Hz2. These rates were selected based on baseline firing characteristics among putative excitatory neurons recorded from the rat medial prefrontal cortex [38]. Hence, for example, if the CS+ is presented to the network, then the ten percent of the units activated by CS+ will be firing at a rate of 20 Hz, while other units will continue firing at their typically low (0-2 Hz) but occasionally high (10 or 20 Hz) baseline rate. In those simulations that use the Category output layer, Sensory units are divided into ten sets, with each set activated differentially by a particular category (in these simulations, baseline rates were also simplified to be homogeneously 2 Hz, as variance was found to not impact the results).
Initialization of the three sets of connection weights in the first layers—Sensory to Cortex excitatory units (Wx→E), Sensory to Cortex inhibitory unit (Wx→I), and Cortex inhibitory to excitatory units (WI→E)—took into account three issues. First, when novel stimuli were first presented to the network, the evoked activity in Cortex excitatory units needed to be higher than baseline levels, but ideally not much higher than levels associated with “relevant” stimuli (described below in Relevance learning). Second, baseline activity of the cortex inhibitory unit had to be high enough that reducing Wx→I had an impact on Cortex excitatory units. Third, that Wx→I were balanced with Wx→E, such that small changes in Wx→I could not dramatically alter population activity. These three constraints were additionally considered alongside the targeted, average firing rates associated with “relevant” and “irrelevant” input vectors. Based on data from Insel and Barnes [38], these corresponded to average firing rates in regular-firing, wide-waveform neurons of 3.9 (at reward sites) and 2.6 Hz (during quiet waking) respectively (see also Relevance Learning, below). With these constraints and target firing rates, a grid search was used to search for a combination of 4 parameters to set the starting weights of the network: 1) fixed starting weights for Wx→I (ax→I), 2) fixed starting weights for WI→E (aI→E), 3) center of Gaussian for Wx→E (μx→E), 4) variance of the Gaussian for Wx→E (σx→E). Once the target firing-rates had been met by the weight parameters, the grid search was ended. This produced values of ax→I = 0.5, aI→E = 0.4, μx→E = 0.3, and σx→E = 0.4. It is important to note that this initialization grid search did not make learning any easier, because nothing about the initialization contained stimulus information. All that the initialization search did was provide physiologically realistic firing rates. It would likely be possible to satisfy the same constraints and firing rate patterns using different initialization parameter sets, but this was not explored.
To summarize, starting weights for the Cortex for most simulations were set as follows: W ix → I = 0 . 5∀i, W i I → E = 0 . 4∀i, and Wijx→E∼N(μx→E,σx→E)∀i,j. The one exception to this is the latent inhibition simulations shown in (Fig 5). In these simulations we maintained the stimulus information in the initial condition to a greater degree by setting the weights Wx→E to be a smoothed diagonal matrix.
All weights from the Cortex to the output units were initialized using a uniform random distribution: W i j E → y ∼ U ( 0 , 1 ).
Finally, we note also that in all our simulations we respected “Dale’s” law by clipping any negative connections weights at zero. (Clipping at zero did not prevent later increases to the weights). This was done both for initialization and during learning. All of the specific implementations of these initialization procedures can be found by downloading our code (see the repository link below).
The principal learning mechanism used in this paper is a modification of the temporal difference learning algorithm [39]. Specifically, a population-based relevance (or “salience” signal), S(t), reflects the deviations in excitatory activity from an established baseline. The baseline level can be thought of as the EI balance set-point maintained by the cortical network. The level of Cortex excitatory unit activity was measured as the vector norm of the population of excitatory units, ∥E ( t )∥ 2 = ∑ i e i ( t ) 2 (the reasons for using the norm become clear in Eq 10). S(t) is therefore determined by the difference between ∥E(t)∥2 and the homeostatic set-point for the population, H (Fig 1B):
S ( t ) = ∥E ( t )∥ 2 - H (15)
(Note: this equation is identical to Eq 1 in the Results). The goal of relevance learning in the network is to have S(t) come to represent expected relevance, which we interpret as “unsigned value”, U(t):
U ( t ) = ⟨ ∑ i = 1 ∞ γ i - 1 u ( t + i ) ⟩ (16)
where u(t) is the unsigned reward/punishment signal, US, described above, γ is a temporal discounting factor, and 〈⋅〉 indicates expected value. U(t) is akin to the value function used in temporal difference learning [39]. Similar to temporal difference learning, the goal of learning in our model is, in part, to ensure that S(t) is a good estimate of U(t). This is accomplished using a prediction error signal, β(t):
β ( t ) = A u ( t ) + γ S ( t ) - S ( t - 1 ) (17)
where A is a salience scaling factor that determines how much cortical activity levels should deviate from the set point in response to relevant stimuli. We use β to represent our prediction error signal, rather than the usual δ, to distinguish it from prediction error signals that measure differences in signed (as opposed to unsigned) value estimates [39]. (The notation also deviates slightly from convention by using t rather than t + 1, to avoid questions about whether the model has future information. This is just a re-indexing, though, and does not affect the results in any meaningful way).
To put it another way, the system learns to ensure that fluctuations in ∥E(t)∥2 away from the set-point, H, reflect experience with rewards/punishments (the US). The scale of the fluctuations is determined by A. Training the salience signal S(t) involves updating the synaptic weights in Cortex to achieve β(t) = 0. It can be seen that this is achieved when:
∥E ( t )∥ 2 = H + A U ( t ) ⇒ S ( t ) = A U ( t ) (18)
since U(t − 1) = 〈u(t)〉 + γU(t). Therefore, β is generally close to zero when the following conditions have been achieved: (1) for stimuli that do not predict any reward or punishment, the norm of the spike count in the excitatory cortical population is equal to the homeostatic constant, H and (2) for stimuli that do predict reward or punishment S(t) is a linear function of U(t) with a slope of A.
To learn this, we perform stochastic gradient descent on the squared difference between S(t) and AU(t) (see (3)). If we treat x(t) and E(t) as rates of fire, it can be shown that:
∂ ( S ( t ) - A U ( t ) ) 2 ∂ W j x → I= β ( t ) ∥E ( t )∥ 2 [ ( W I → E ) T · ( ( E ∘ E ) ⊘ ( W I → E I ( t ) ) ) ] x j ( t ) (19)
where ∘ indicates element-wise multiplication. Because we followed Dale’s law in our simulations, and firing rates can only be positive, none of the terms in Eq 19 can be negative except for β(t). Moreover, the only element of the equation that helps to differentiate Sensory inputs is xj(t). Thus, all of the other terms in Eq 19 can be treated as scaling terms. What this means is that the gradient direction in weight space is specified solely by β(t) and xj(t), while the other terms merely indicate the magnitude of the gradient in these directions. In practice, gradient descent can still occur when following the gradient direction, even if the magnitude of the gradient is ignored. Thus, this allowed us to simplify this expression and use only β(t) and xj(t) as in Eq 4, while still achieving the same results as would be obtained from following the true gradient defined by Eq 19.
In some simulations (Figs 2 and 3), the performance of this learning rule is compared against rules in which we perform gradient descent on either the Wx→E or WI→E synapses. The partial derivatives for the squared difference between S(t) and AU(t) with respect to these weights are:
∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i j x → E = - β ( t ) ∥E ( t )∥ 2 e i ( t ) x j ( t ) ∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i I → E = β ( t ) ∥E ( t )∥ 2 [ e i ( t ) 2 W i I → E I ( t ) ] I ( t ) (20)
which we can simplify again thanks to Dale’s law and positive firing rates, giving us approximations of the gradients:
∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i j x → E ∝ - β ( t ) x j ( t ) ∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i I → E ∝ β ( t ) I ( t ) (21)
which we then use for the weight updates:
W i j x → E ← W i j x → E + α Δ W i j x → E Δ W i j x → E = - ∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i j x → E W i j x → E ← W i I → E + α Δ W i I → E Δ W i I → E = - ∂ ( S ( t ) - A U ( t ) ) 2 ∂ W i I → E (22)
Using these weight updates for relevance learning can theoretically provide the same coding for relevance in S(t). However, they make different predictions regarding the effects of impaired inhibition (Figs 2 and 3).
As already noted in the previous section, the specific H and A used corresponded to empirical data measuring the average firing rates in the rat medial prefrontal cortex, with H = 6.5 Hz and A = 1.4 Hz, as observed by Insel and Barnes [38].
In simulations with output units, y(t), such as the Amygdala, synapses between Cortex excitatory units and the Output units were also trained. In simulations using an Amygdala output layer, the Cortex-to-Amygdala weights, WE→y, were trained with a competitive learning algorithm as defined in Eq 8. As outlined in Effect of relevance learning on downstream circuitry, a suitable threshold, θ, can be found to ensure that in the absence of a US the Amygdala only responds to stimuli that have been paired with reward or punishment in the past. In the simulations presented here, the value of θ was set by grid search so that the probability of any neuron crossing threshold would be very low if no learning had occurred, and very high if an US was present or learning had converged and the network was presented with a relevant stimulus. The final value that was used in our simulations was θ = H/4.
In simulations where we trained the output units to categorize input stimuli, we used backpropagation-of-error [67] to train the weight matrices Wx→E and WE→y. More precisely, target vectors, o(x(t)) are defined, where each stimulus provided to Sensory units has a corresponding target vector for the output Category units. The cross-entropy [67] between the Category activity and target vectors was used as the loss function to train the network:
L ( x ( t ) )= ∑ i = 1 ℓ o i ( x ( t ) ) ln ( y i ( t ) ) (23)
where oi(x(t)) is the “target” response to input x(t) for output unit i, i.e. oi(x(t)) = 1 if i is the correct category for x(t), and it is zero otherwise.
For any weight Wij in Wx→E or WE→y, the weight update is determined by the partial derivative of this loss function with respect to the weight:
Δ W i j = - α y ∂ L ( x ( t ) ) ∂ W i j (24)
where αy is the learning rate. This ensures that the network learns to correctly categorize the stimuli (i.e., the pattern of Sensory unit activity, x(t)) using the output, Category units y(t). As with Amygdala learning, the categorization learning proceeded in tandem with the relevance learning.
The first set of simulations tested the network’s ability to learn to ignore irrelevant stimuli and engage in blocking. These were both run using dt = 20 ms, which was selected to be just long enough prevent Poisson noise from affecting learning. At the beginning of the simulations, a 60 s adaptation period without stimulus allowed weights to adjust to the randomly-selected baseline input activity levels. All pharmacological simulations were implemented after adaptation.
Piantadosi and Floresco (2014) [43] demonstrate the effect of GABA-A antagonists on latent inhibition. Latent inhibition refers to the classic behavioral phenomenon whereby it is harder to associate a familiar stimulus (one that a subject has been pre-exposed to) with a reinforcer [45]. Latent inhibition is also known to be disrupted in schizophrenia [46, 47, 48, 49]. As shown in Fig 5A–5D, a protocol was created that matched the one used in rats (see also [115, 116]). For processing time purposes, the stimulus and inter-stimulus times used in the original were reduced by a factor of 5. The protocol began with a 60 s adaptation period, followed by three phases: 1) a pre-exposure phase, in which the network was presented with the conditioning stimulus (CS) 30 times (10% of input units, 6 s long, inter-stimulus interval of 6 s), 2) a conditioning phase, in which the CS was presented simultaneously with foot shock (u(t) = 1), and 3) a test phase, in which the CS was presented by itself 4 times. The protocol was performed on three pairs of network models, with each pair including one network given pre-exposures and one that was not given pre-exposures. The three pairs simulated the treatment groups used in the original study: animals treated with saline were simulated without any modification to the network, treatment with GABA-A antagonist during conditioning were simulated using a 20% reduction in inhibition, according to Eq 25 during the conditioning phase, and treatment with antagonist during testing were simulated with the same disruption during the testing phase. Conditioned fear responses were measured as the maximal response of amygdala units, averaged across all timesteps during CS presentation.
Recent work by Courtin et al. [44] found that inhibition of PV+, fast-spiking neurons in the mouse mPFC can evoke fear responses, while excitation of the same neurons can decrease fear responses. The protocol used in that study was presently simulated as precisely as possible (Fig 6A and 6B), using all of the same parameters as used in the latent inhibition design. To simulate optical inhibition of PV+ cells, a 20% reduction in inhibition were implemented, similar to Eq 25. During the pre-conditioning phase, this reduction in the inputs was applied for 250 ms intervals separated by 860 ms (equivalent to 0.9 Hz stimulations, as in the original study). This was followed by a conditioning phase, in which a 6 s CS+ was paired with footshock (i.e., the firing rate of input units coding for the CS was set to ϕon and u(t) = 1). As in the previous protocol, all stimulus and inter-stimulus times were decreased from the original study by a factor of 5. One change from the original protocol is that the 1 s US presentation used in the original study was lengthened to the entire CS period. We justify this change based on an assumed difference between real brains and the model: whereas in the brain, activity and plasticity are likely regulated by change, such as the onset or offset of a stimulus, the model treats each time point equivalently. Thus, the period during which the CS is on but US is off will extinguish the associations learned during their concurrence. The CS–US pairings were presented 12 times with an inter-trial interval of 4-30 s. The conditioning phase was followed by an extinction phase, in which the CS was again presented 12 times with the same inter-trial interval, followed in turn by a series of CS presentations accompanying the 40% reduction in Wx→I values. To test the effect of inhibitory activation during a conditioned CS, the same conditioning protocol was used, but was followed by presentations of the CS accompanying increases to inhibitory unit activity. We found that only a 10% increase in WI→E was necessary to elicit changes approximating those observed in the original study.
To examine the ability of the network to carry both the salience signal and the other information simultaneously (i.e. to multiplex the salience signal with other signals) simulations were run wherein the feedforward excitatory weights (Wx→E and WE→y) were trained to perform categorization of the inputs, x(t), while the excitatory weights onto the inhibitory unit, Wx→I, were trained according to the relevance algorithm described in Eqs (3) and (4) (Fig 7 & S4 Fig). To do this, each of the ten stimuli (described in Stimuli) was presented in a fixed order for 200 ms, and this 2 s sequence (considered 1 epoch) was repeated 50 times, leading to a total simulation time of 100 s. (Note that not all epochs are presented in Fig 7 & S4 Fig, as the learning converged quickly).
All code was written in Matlab (Mathworks Inc.), using the Statistics Toolbox. The code can be downloaded for free from https://github.com/jordan-g/Irrelevance-by-Inhibition and used to generate all of the data presented in the paper.
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10.1371/journal.pgen.1002851 | F-Box Protein Specificity for G1 Cyclins Is Dictated by Subcellular Localization | Levels of G1 cyclins fluctuate in response to environmental cues and couple mitotic signaling to cell cycle entry. The G1 cyclin Cln3 is a key regulator of cell size and cell cycle entry in budding yeast. Cln3 degradation is essential for proper cell cycle control; however, the mechanisms that control Cln3 degradation are largely unknown. Here we show that two SCF ubiquitin ligases, SCFCdc4 and SCFGrr1, redundantly target Cln3 for degradation. While the F-box proteins (FBPs) Cdc4 and Grr1 were previously thought to target non-overlapping sets of substrates, we find that Cdc4 and Grr1 each bind to all 3 G1 cyclins in cell extracts, yet only Cln3 is redundantly targeted in vivo, due in part to its nuclear localization. The related cyclin Cln2 is cytoplasmic and exclusively targeted by Grr1. However, Cdc4 can interact with Cdk-phosphorylated Cln2 and target it for degradation when cytoplasmic Cdc4 localization is forced in vivo. These findings suggest that Cdc4 and Grr1 may share additional redundant targets and, consistent with this possibility, grr1Δ cdc4-1 cells demonstrate a CLN3-independent synergistic growth defect. Our findings demonstrate that structurally distinct FBPs are capable of interacting with some of the same substrates; however, in vivo specificity is achieved in part by subcellular localization. Additionally, the FBPs Cdc4 and Grr1 are partially redundant for proliferation and viability, likely sharing additional redundant substrates whose degradation is important for cell cycle progression.
| Most cells only divide when they receive the proper cues. When a cell receives a signal to divide, levels of G1 cyclin proteins increase and drive entry into the cell division cycle. Overexpression of G1 cyclins can drive cells into the cell cycle inappropriately and thus may contribute to the hyperproliferation of cancer cells. Despite the importance of controlling G1 cyclin levels, the mechanisms regulating the degradation of these proteins are not well understood. We have now elucidated the mechanism of degradation of the yeast G1 cyclin Cln3. In contrast to related cyclins in yeast, Cln3 is targeted for degradation by two redundant pathways, which act to keep Cln3 levels extremely low. This finding may have implications for understanding how G1 cyclins are degraded in human cells and how expression of G1 cyclins may be misregulated during cancer development.
| The ubiquitin-proteasome system plays an essential role in controlling passage through the eukaryotic cell cycle [1]. A significant fraction of cell cycle-regulated ubiquitination is carried out by SCF (Skp1-Cullin-F-box protein) family ubiquitin ligases, which target numerous cell cycle regulators for proteasomal degradation. All SCF ligases consist of three core subunits: a structural cullin subunit (Cdc53 in yeast, Cul1 in mammals), an adaptor protein (Skp1) and a RING finger protein (Rbx1), plus one of a family of modular substrate-specificity subunits called F-box proteins (FBPs) [2]–[7]. There are large numbers of FBPs in all eukaryotes, and each is believed to target the SCF to a specific set of substrates by interacting with distinct epitopes in those proteins. In almost all instances, FBPs recognize proteins that have been post-translationally modified, usually by phosphorylation, which enables ubiquitination to be regulated by substrate modification [8].
In budding yeast, the FBPs Cdc4 and Grr1 have well-established cell cycle-regulatory roles [1]. Both FBPs recognize phosphorylated epitopes in their substrates, however they bind to these epitopes through distinct phosphorecognition domains: a WD40 repeat domain in Cdc4 and a leucine rich repeat domain in Grr1 [8]. Interestingly, although Grr1 and Cdc4 are thought to have entirely non-overlapping sets of substrates, each is capable of interacting with targets that have been phosphorylated by cyclin dependent kinase (Cdk). This group of substrates includes several proteins that regulate entry into S phase including the Grr1 substrates Cln1 and Cln2 [9], as well as the Cdc4 substrates Sic1 [10] and Cdc6 [11]. In addition to this group of defined SCF targets, Cdk phosphorylates hundreds of yeast proteins [12], [13], and many of these are rapidly degraded [14], suggesting that there is a widespread connection between Cdk phosphorylation and protein degradation. However, the majority of these proteins have not been identified in genome-wide screens for Cdc4 or Grr1 targets [15], [16], suggesting that they may be targeted for degradation by alternate ubiquitin ligases.
One such Cdk-phosphorylated protein is the G1 cyclin Cln3. Similar to cyclin D1 in mammals, Cln3 is the furthest upstream cyclin, which senses growth cues and triggers entry into the cell cycle. Cells become committed to progress through the cell cycle upon phosphorylation of the transcriptional repressor protein Whi5 by Cln3/Cdc28, which leads to Whi5 inactivation and increased expression of downstream genes including the related cyclins Cln1 and Cln2 [17], [18]. Consistent with Cln3 having a critical role in cell cycle entry, its levels are very tightly controlled. In addition to being regulated by transcription [19], [20] and subcellular localization [21]–[23], Cln3 is rapidly degraded. This proteolytic degradation is critical to restrain Cln3 activity, since expression of a truncated and stable form of the Cln3 protein drives cells through G1 phase prematurely, resulting in a significant reduction in cell size [24]–[26]. Despite the physiological importance of Cln3 degradation, the ubiquitin ligase that targets Cln3 for degradation has not been identified.
Previous studies have implicated an SCF ligase in Cln3 degradation [26], [27], however no FBP has been identified that recognizes Cln3. Here, we show that Cdc4 and Grr1 redundantly target Cdk-phosphorylated Cln3 for degradation. Mutation of either FBP alone has no detectable effect on Cln3 levels or stability, yet Cln3 is completely stable in double mutant cells. Surprisingly, we find that both Cdc4 and Grr1 interact with all 3 G1 cyclins (Cln1, Cln2 and Cln3) in cell extracts, however only Cln3 is redundantly targeted in vivo, because it is the only G1 cyclin that localizes primarily to the nucleus. Cln2 is cytoplasmic and exclusively targeted by Grr1 [9], [28]. However, we show that Cdc4 can target Cln2 for degradation when cytoplasmic Cdc4 localization is forced in vivo. Finally, we observed a synthetic growth defect in cdc4 grr1 double mutant cells that is not suppressed by deletion of CLN3. In sum, these data demonstrate that the binding specificities of FBPs do not necessarily dictate which proteins are in vivo targets, and suggest that Cdc4 and Grr1 have additional redundant targets whose regulated degradation is necessary for normal cell cycle control.
To better understand the regulation of Cln3 degradation, we examined Cln3 protein levels throughout the cell cycle and found that they paralleled the reported transcriptional expression profiles [19], [20], [29], rising in mitosis, shortly after the mitotic cyclin Clb2, and persisting through G1 phase (Figure 1A; Figure S1A). Cln3 was rapidly degraded in cells arrested in either G1 or mitosis, demonstrating that it is degraded throughout the cell cycle (Figure 1B; Figure S1B). Previous data suggested that an SCF ubiquitin ligase is responsible for targeting Cln3 [26], [27]. Consistent with these observations, we found that phosphorylated Cln3 is stable and accumulates in cells expressing a temperature-sensitive allele of CDC53 (cdc53-1) upon shift to the restrictive temperature (Figure 1C; Figure S1C). Similarly, transcriptional shut-off of the genes encoding Cdc53 or Cdc34 (the E2 enzyme that functions with SCF ligases) stabilized Cln3 (Figure S1D).
We next attempted to identify the specific FBP that targets Cln3 for degradation. Cln3 levels were compared among strains carrying single deletions of every non-essential yeast F-box protein (Figure 2A; data not shown), however no single deletion caused a significant increase in Cln3 protein levels. Furthermore, inactivation of the essential FBP Cdc4 did not stabilize Cln3 (Figure 2B; Figure S1D). Together, these data suggested that Cln3 might be redundantly targeted by two or more FBPs. The most likely candidate FBPs to redundantly regulate Cln3 were Grr1 and Cdc4, since each of these proteins is known to target Cdk-phosphorylated substrates [9]–[11], [30], [31]. Indeed, we found that simultaneous inactivation of Grr1 and Cdc4 led to complete stabilization of Cln3, whereas deletion of GRR1 or inactivation of Cdc4 alone had no significant effect on Cln3 levels or stability (Figure 2B). Cln3 protein was almost undetectable in both single mutant strains after only 2 minutes of cycloheximide treatment (Figure 2C), and reintroduction of either Cdc4 or Grr1 into grr1Δ cdc4-1 cells led to an equivalent reduction of Cln3 levels (Figure S2A). Consistent with these observations, overexpression of Cln3 in either grr1Δ cells, or cells with limiting amounts of Cdc4 (cdc4-1 grown at the permissive temperature) was lethal (Figure 2D), indicating that both FBPs are required for the cell to tolerate high levels of Cln3. Together, these data demonstrate that Cln3 is redundantly regulated by Cdc4 and Grr1.
In order to demonstrate that Cdc4 and Grr1 directly target Cln3, we examined the binding of Cln3 to each FBP. Because complexes between ubiquitin ligases and substrates are unstable and often difficult to detect, we assayed binding of Cln3 to GST-tagged Grr1 or Cdc4 proteins that lack the F-box domain and therefore cannot interact with the remainder of the SCF ubiquitin ligase complex (Cdc4ΔF and Grr1ΔF) [15], [32]. In contrast to the full-length proteins, expression of GST-Cdc4ΔF or GST-Grr1ΔF had no effect on levels of Cln3 (Figure S2A), confirming that they were not incorporated into active SCF complexes. Upon pull-down of GST-tagged proteins from cellular lysates, Cln3 associated with both Cdc4ΔF and Grr1ΔF proteins but not with GST (Figure 3A, lane 2), supporting the model Cdc4 and Grr1 directly target Cln3 in vivo.
Previous studies suggested that the C-terminal tail of Cln3 is required for its degradation [26] . To verify this result and narrow down the region required for Cln3 degradation we constructed a series of C-terminal truncation mutants (Figure S2B) and examined their half-lives in vivo. With the exception of the largest deletion of 177 amino acids (equivalent to the previously characterized allele Cln3-1; [26]), all mutants were still turned over at significant rates (Figure 3B). Although each of these mutants was slightly more stable than wild-type Cln3, cells expressing these mutants did not show any obvious changes in cell cycle progression (Figure S2E–S2F), suggesting that partial stabilization does not impact the cell cycle in vivo. However, since each truncated protein was more stable than wild-type Cln3 in either grr1Δ or cdc4-1 cells (Figure 2B), this indicates that these mutations must partially interfere with degradation by both FBPs.
The large size of the C-terminal tail makes it possible that each FBP recognizes a distinct epitope within this domain. Therefore, to further narrow down the requirements for binding to each FBP, we analyzed the binding of the C-terminal Cln3 truncation mutants to Cdc4 and Grr1. Interestingly, we found that all truncations disrupted binding to Cdc4 (Figure 3A, middle panels), including the smallest deletion of just 22 amino acids. In contrast, all truncations except for the largest deletion (Cln3-1) bound well to Grr1, albeit at reduced levels (Figure 3A, bottom panels). These data suggest that while Grr1 and Cdc4 both require the Cln3 C-terminus for binding, they do not bind identical epitopes.
Cdk-phosphorylation of the Cln3 C-terminus is also required for its degradation [27], [33]. Since both Cdc4 and Grr1 bind Cdk-phosphorylated epitopes, this suggested that both FBPs might bind to the Cln3 C-terminus in a phospho-dependent manner. To test this, we first constructed a stable, Cdk-deficient allele of Cln3. The Cln3 C-terminus includes 10 serine/threonine-proline motifs, constituting the minimal Cdk-consensus site. A previous report demonstrated that mutation of the single full Cdk consensus site (S/TPxK) in the C-terminus of Cln3, serine 468, partially stabilized a Cln3 C-terminus-β-Gal fusion protein [27]. Notably, mutation of this site in the context of the full-length protein only had a minor effect on Cln3 stability and did not affect cell cycle position (Figure S2C–S2D). We then changed all of the remaining C-terminal Cdk consensus sites to alanine residues within the endogenous CLN3 locus and assayed the stability of the mutated Cln3 proteins. Mutation of the 9 most C-terminal sites completely stabilized Cln3 (Figure 4A, Cln3-9A), and led to a cell cycle profile consistent with a significantly shortened G1 phase (Figure 4B). Next, we tested whether Cln3-9A could interact with Cdc4 and Grr1. Consistent with our prediction, significantly less Cln3-9A bound to each FBP, in comparison to wild-type Cln3 (Figure 4C, compare lanes 6 & 8, 10 & 12). These data strongly suggest that phosphorylation of the Cln3 C-terminus contributes to its interaction with both Cdc4 and Grr1.
Although Cdk phosphorylation of the C-terminus is required for Cln3 degradation, our binding data suggested that each FBP recognizes a distinct epitope within this domain, so we further analyzed the requirements for these C-terminal phosphosites in degradation by each FBP. Both Cdc4 and Grr1 are thought to target regions of their substrates that include multiple Cdk-phosphorylated residues [9]–[11], [30], [31], so we mutated two clusters of Cdk phosphosites in attempt to specifically interfere with binding to one or the other FBP (Figure S2B). First, we mutated five Cdk sites in the N-terminal half of the C-terminal domain, spanning residues 447–468 (designated Cln3-5A). Interestingly, we found that Cln3-5A was partially stabilized in wild-type cells (Figure 4A), and Cln3-5A-expressing cultures contained a slightly reduced fraction of cells in G1 phase (Figure 4B). Since degradation of wild-type Cln3 is unaffected in either cdc4-1 or grr1Δ cells, the partial stabilization of the Cln3-5A protein suggested that these mutations partially interfered with targeting by both Cdc4 and Grr1. Consistent with this possibility, Cln3-5A was further stabilized in both cdc4-1 and grr1Δ single mutant cells (Figure 4D, top panels).
Next, we mutated three sites at the extreme C-terminus (designated Cln3-3A). Notably, these sites are adjacent to the region required for Cdc4 interaction (Figure 3A), and are separated by two amino acids each, which matches the doubly phosphorylated degron motif that is preferred by Cdc4 [34], [35]. In wild-type cells Cln3-3A was rapidly degraded and its expression had no effect on cell cycle progression (Figure 4A–4B). However, Cln3-3A was degraded differently in cells lacking GRR1 and CDC4: Cln3-3A turnover was unaffected in cdc4-1 cells, but almost completely blocked in grr1Δ cells (Figure 4D, bottom panels). This demonstrates that Cln3-3A cannot be targeted by Cdc4 and is consistent with the prediction that degradation by Cdc4, but not Grr1, requires phosphorylation of these three C-terminal sites.
Together, these data suggest that although the Cdk-phosphorylated C-terminus of Cln3 is required for its targeting by Cdc4 and Grr1, the two FBPs recognize different epitopes within this domain. However, since Cln3 is thought to be constitutively bound to Cdk, it is possible that these sites are all constitutively phosphorylated in cis and that this phosphorylation allows Cln3 to be degraded by both FBPs throughout the cell cycle. A second possibility is that Cln3 is phosphorylated in trans by an alternate cyclin/Cdk complex in order to be targeted for degradation. Phosphorylation in trans could lead to cell cycle-regulated Cln3 degradation. To test whether cis phosphorylation of Cln3 by Cdk is required for its turnover, we constructed an N-terminal truncation mutant of Cln3 that removes the cyclin box and therefore can not bind to Cdk (Cln3ΔN). Interestingly, blocking cis phosphorylation in this manner led to almost complete stabilization of Cln3 protein (Figure 4E). The subtle degradation that was observed was dependent upon Cdk-phosphorylation, since mutation of the 9 C-terminal Cdk sites further stabilized the protein, suggesting that this protein could be phosphorylated by alternative cyclin/Cdk complexes. However, since Cln3ΔN is considerably more stable that full length Cln3, we conclude that the primary mode of Cln3 degradation depends upon phosphorylation in cis.
Although Cdc4 and Grr1 have both been shown to bind and ubiquitinate Cdk-phosphorylated proteins, they are thought to recognize non-overlapping sets of targets [36]. This raises the question of what is unique about Cln3 that enables it to be recognized by both F-box proteins. To address this issue, we compared Cln3 and the related cyclin Cln2, which is targeted for degradation exclusively by Grr1 [9]. Like Cln3, Cln2 has an N-terminal cyclin box and a C-terminus that includes a PEST domain and Cdk phosphorylation sites (Figure 5A; [37]). Moreover, the 169 C-terminal amino acids of Cln2 constitute a transferrable degron, which can direct Grr1-mediated degradation of a heterologous protein [38]. Together, this suggests that all of the FBP specificity resides within the C-terminal domains of both cyclins.
To test this possibility, chimeric proteins were created by exchanging the C-terminal degron domains of Cln2 and Cln3 (Figure 5A) and the stability of these chimeric proteins was assayed in strains lacking Grr1, Cdc4, or both FBPs. As expected for a Grr1 target, Cln2 was degraded rapidly in wild-type cells, but completely stable in grr1Δ cells (Figure 5B, top panels). Moreover, Cln2 was degraded in cdc4-1 cells, although the protein level was higher overall (which was expected because cdc4-1 cells arrest in G1/S phase when CLN2 transcription peaks). In contrast to Cln2, Cln3 was not affected by loss of either Grr1 or Cdc4, but was completely stabilized in grr1Δ cdc4-1 cells (Figure 2B). However, when the C-terminus of Cln3 was replaced with the C-terminus of Cln2 (Cln3-2C), the Cln3 degradation profile was nearly identical to that of Cln2 (Figure 5B, middle panels). In wild-type cells, the half-life of Cln3-2C was longer than Cln3 (compare Figure 2B to Figure 4B) and, importantly, Cln3-2C was completely stable in grr1Δ cells, demonstrating that it could no longer be targeted by Cdc4. When the C-terminus of Cln2 was replaced with the C-terminus of Cln3 (Cln2-3C), the opposite result was observed (Figure 5B, bottom panels). In wild-type cells, Cln2-3C was considerably less stable than Cln2. In addition, unlike Cln2, Cln2-3C was degraded in grr1Δ cells (although it was slightly more stable than in wild-type cells). However, Cln2-3C was stable in grr1Δ cdc4-1 cells. Together, these data demonstrate the C-termini of Cln2 and Cln3 confer their FBP specificity, and indicate that there is a unique feature in the Cln3 C-terminus that promotes Cdc4-mediated turnover.
Our analysis of Cln3 truncation mutants demonstrated that the last 22 amino acids of Cln3 are important for interaction with Cdc4 but not Grr1 (Figure 3A). Interestingly, this 22 amino acid sequence in Cln3 includes a bipartite nuclear localization signal (NLS), which is important for Cln3 nuclear localization (Figure S3; [21], [22]). In contrast, Cln2 does not have an NLS motif and it is predominantly cytoplasmic [22], [28]. This raises the possibility that the localization of G1 cyclins may also contribute to FBP specificity in vivo. The localization of the FBPs supports this model: Grr1 is both cytoplasmic and nuclear, whereas Cdc4 is exclusively nuclear [39]. Therefore, since Cln2 is primarily cytoplasmic, it may only be accessible to Grr1. In contrast, since Cln3 is primarily nuclear, this may enable targeting by both Grr1 and Cdc4. Consistent with this model, the Cln3Δ22 mutant, which lacks the NLS sequence and is primarily cytoplasmic (Figure S3; [22]), is almost completely stable in grr1Δ cells (Figure 5C). Notably, we find that Cln3Δ22 is considerably more stable than full-length Cln3 expressed in cells lacking Cdc4 (compare Figure 5C and Figure 2B), despite that fact that it can bind to Grr1 as well as the full-length protein in whole cell extracts (Figure 3A). This suggests that nuclear Cln3 is more susceptible to degradation than cytoplasmic Cln3, perhaps because most cytoplasmic Cln3 is tethered to the ER and not fully active [23].
Another prediction from our data is that Cdc4 may be capable of targeting Cln2 for degradation, but does not do so in vivo only because Cln2 is predominantly localized to the cytoplasm. To test this idea, we first tested whether Cln2 could interact with Cdc4. Importantly, Cln2 associated with both Cdc4ΔF and Grr1ΔF proteins in extracts (Figure 6A, lanes 6 & 10). Moreover, Cln2-4T3S, a stable Cln2 protein that has mutations in 7 Cdk-consensus sites [37], was unable to bind to Grr1 or Cdc4 (Figure 6A, lanes 8 &12), suggesting that Cdc4 and Grr1 bind to similar Cdk-phosphorylated epitopes. In addition, we found that the third G1 cyclin, Cln1, also interacted with both Cdc4 and Grr1 (Figure S4). Thus, like Cln2, Cln1 is a potential target of both Cdc4 and Grr1, but may be regulated exclusively by Grr1 in vivo by virtue of its cytoplasmic localization.
Since Cdc4 interacts with Cln1 and Cln2 in extracts, this suggests that Cdc4 might be capable of targeting all three G1 cyclins in vivo, if it were localized in the same subcellular compartment as all three cyclins. To test this, we utilized a previously characterized Cdc4 protein (NES-Cdc4) that is fused to a nuclear export signal and localizes to the cytoplasm [39]. Expression of NES-Cdc4 in grr1Δ cells led to decreased Cln2 protein levels, whereas expression of wild-type Cdc4 had no effect (Figure 6B). In addition, this downregulation was dependent upon Cdk-phosphorylation of Cln2, since levels of Cln2-4T3S were unaffected by NES-Cdc4 expression (Figure 6B). Together, these data show that localization of yeast G1 cyclins determines which of the FBPs can target each in vivo, and that the binding specificities of the FBPs do not dictate which proteins are in vivo targets.
The finding that Cdc4 and Grr1 can bind to some of the same Cdk-phosphorylated proteins, and that they redundantly target Cln3 for degradation in vivo, suggests that these two FBPs may share a number of overlapping targets that are important for cell cycle progression. In support of this possibility, we found that cells carrying a GRR1 deletion and having compromised Cdc4 function (grr1Δ cdc4-1, grown at the permissive temperature for cdc4-1) demonstrate a synergistic growth defect well beyond that predicted for a combination of the individual (minor) growth phenotypes (Figure 7). This type of negative genetic interaction is consistent with Cdc4 and Grr1 having partially redundant roles in one or more common cellular functions [40]. Importantly, deletion of CLN3 did not reverse this growth defect (Figure 7), supporting the idea that additional proteins are redundantly targeted by Cdc4 and Grr1. Alternatively, it is possible that Cdc4 and Grr1 have specific substrates that cause a synergistic growth defect when their levels are elevated. However, the large number of unstable, Cdk-phosphorylated proteins in the cell [13], [14], [41], combined with the ability of Grr1 and Cdc4 to bind some targets in common, raises the intriguing possibility that these two FBPs target a significant fraction of the cell cycle proteome for degradation.
Cln3 turnover is essential for accurate entry into the cell cycle and for proper cell size control [24]–[26], however the identity of the ubiquitin ligase that targets Cln3 for destruction has remained a long-standing question. Here, we show that Cln3 is redundantly targeted by two F-box proteins with key cell cycle-regulatory roles, Cdc4 and Grr1. Interestingly, inactivation of either FBP alone has no detectable effect on Cln3 expression or half-life, yet Cln3 is completely stable in double mutant cells (Figure 2B–2C). This result is quite surprising because, although redundant regulation of degradation has been previously found for other proteasome substrates in both yeast and mammals [42]–[44], in most cases elimination of one ubiquitin ligase or the other leads to a partial stabilization of the substrate, which we do not observe with Cln3. This may be because targeting of these other substrates by two ligases is only partially redundant in the sense that the ligases may recognize different epitopes on the substrate, or respond to different physiological cues.
In the case of Cln3, our data suggests that both Cdc4 and Grr1 recognize the Cdk-phosphorylated C-terminus of Cln3 (Figure 4). However, the two FBPs do not recognize identical epitopes. Cdc4 targeting requires three phosphosites at the extreme C-terminus of Cln3, whereas mutation of these sites have no effect on targeting by Grr1 (Cln3-3A, Figure 4D, bottom panels). Interestingly, mutation of a second cluster of five phosphosites partially interferes with targeting by both Cdc4 and Grr1 (Cln3-5A, Figure 4D, top panels). This suggests that the two FBPs may also interact with some overlapping residues. Alternatively, these five phosphosites may be required as priming sites that promote the phosphorylation of the three C-terminal sites that Cdc4 requires, in a mechanism similar to what has recently been demonstrated for the Cdc4-specific target Sic1 [35]. Further work will be required to dissect the mechanism of targeting by each FBP. However, since the Cln3 C-terminus is likely to be constitutively phosphorylated by Cdk in cis (Figure 4E), and we have not been able to identify any condition when only one FBP targets Cln3, this suggests that Cdc4 and Grr1 are truly redundant for Cln3 degradation.
The fact that there is no significant change in Cln3 levels in either single mutant, along with the observed genetic interaction between CDC4 and GRR1 (Figure 7), raises the possibility that there are additional redundant targets of Cdc4 and Grr1 that have not yet been identified. A likely possibility is that other Cdk-phosphorylated, nuclear proteins are dual-regulated targets. Given the large number of Cdk-phosphorylated proteins that are expressed in a cell cycle-dependent pattern [12], [13], [19], [20], it is possible that Cdc4 and Grr1 recognize a much larger fraction of all cell cycle-regulated proteolysis than was previously recognized. However, Cdk-phosphorylation cannot be the only factor that determines whether a protein can be an in vivo substrate of both Grr1 and Cdc4, since established Cdk-phosphorylated Cdc4 targets, such as Sic1 and Far1, cannot be targeted by Grr1 in vivo or in vitro [4], [39]. In addition, we find that deletion of SIC1 does not reverse the synthetic sickness of grr1Δ cdc4-1 strains (data not shown). Whether the synthetic growth defect observed for cdc4 and grr1 is due to upregulation of a single common target protein that is crucial for proliferation, or multiple targets (either shared or specific) that each play a modest role in the cell cycle is a key question requiring further study.
In contrast to Cln3, which is targeted by both Cdc4 and Grr1, the related cyclins Cln1 and Cln2 are targeted exclusively by Grr1 in vivo [9]. Interestingly, we find that Cdc4 can interact with both Cln1 and Cln2 in extracts (Figure S4; Figure 6). Moreover, previous studies with recombinant proteins reported a weak association between Cln2 and Cdc4 [4] and demonstrated that Cdc4 can ubiquitinate Cln2 in vitro [39]. Interestingly, since Cln2 has been shown to phosphorylate Whi5 in the nucleus [45], this suggests that Cdc4 may have an unappreciated role in targeting a small but important nuclear fraction of Cln1/2. Alternatively, the small fraction of nuclear Cln2 may be protected from degradation in some way. In addition, these data indicate that FBPs do not necessarily have the exquisite binding specificity for substrates that has been proposed, and that co-localization of FBPs and substrates also contributes to substrate specificity in vivo. In the future it will be of interest to investigate whether any other Grr1 substrates can interact with Cdc4, or be targeted by Cdc4 upon co-localization.
Importantly, the finding that FBP specificity for G1 cyclins depends upon the localization of cyclins may aid in our understanding of the mechanism of degradation of the mammalian cyclin D1 protein. Several FBPs have been implicated in cyclin D1 degradation [46]–[51], and it is possible that it is regulated by similar redundant mechanisms. It is interesting to note that degradation of the furthest upstream G1 cyclins appears to be quite complex and include redundancy. Since both Cln3 and cyclin D1 are crucial regulators that act as sensors of extracellular growth signals and trigger entry into the cell cycle, cells may have evolved redundant mechanisms to degrade these cyclins in order to buffer cells against dramatic fluctuations in cyclin synthesis and inappropriate cell cycle entry.
A complete list of strains, including the specific experiments each was used in, is provided in Table S1. Unless otherwise indicated, all strains are in the S288c background and have a 13Myc-HIS3MX C-terminal tagging cassette integrated at the CLN3 or CLN2 genomic locus. Strains carrying point mutations in phosphorylation sites were generated by integrating a PCR product containing the desired mutations, the 13Myc tag, and the HIS3MX marker at the CLN3 or CLN2 genomic locus. The integration of each mutation was then confirmed by sequencing.
Plasmids expressing GST and GST-tagged Grr1 have been described previously [32]. To construct pYES2-GST-CDC4, the CDC4 sequence was amplified from genomic DNA by PCR and cloned into the pYES2-GST vector. pYES2-CDC4ΔF was constructed similarly, except that the sequence corresponding to amino acids 323–475 was amplified and cloned into pYES2-GST. To construct the GST-NES-CDC4 plasmid, a DNA fragment including the NES and the CDC4 N-terminal sequences was subcloned from pBM138 (provided by Matthias Peter, [39]) into pYES2-GST-CDC4.
All strains were grown in YM-1 complete medium with 2% dextrose, with the exception of strains carrying GST plasmids, which were grown in C medium lacking uracil with 2% dextrose or raffinose [15]. To arrest cells in G1, 20 µg/ml alpha-factor (United Biochemical Research, Inc.) was added for 2–3 hours. To arrest cells in mitosis, 10 µg/ml nocodazole (US Biological) was added to cells for 2 hours. Strains carrying temperature-sensitive alleles of CDC53 or CDC4 were grown at 23°C and shifted to 37°C for 2 hours to inactivate the respective proteins. Strains carrying tetracycline-regulated alleles of CDC53, CDC4 and CDC34 were treated with 10 µg/ml doxycycline (EMD Biosciences) for 8 hours to shut off transcription from the tetracycline-regulated promoters (Figure S1D). For GST-pulldown assays to analyze Cln3 binding (Figure 3A; Figure 4C), grr1Δ cdc4-1 strains carrying GST plasmids were grown in 2% raffinose and then induced by the addition of 2% galactose for 20–22 hours. For GST-pulldown assays to analyze Cln1 and Cln2 binding (Figure 6A; Figure S4), grr1Δ strains carrying GST plasmids were grown in 2% raffinose and then induced by the addition of 2% galactose for 8 hours. To examine levels of Cln2 and Cln2-4T3S following expression of GST, GST-CDC4, and GST-NES-CDC4 (Figure 6B), grr1Δ cells carrying GST plasmids were grown in 2% raffinose and expression was induced by the addition of 0.05% galactose for the indicated number of hours.
Equivalent optical densities of cells were pelleted, lysed in pre-heated SDS sample buffer (50 mM Tris pH 7.5, 5 mM EDTA, 5% SDS, 10% glycerol, 0.5% β-mercaptoethanol, bromophenol blue, 1 µg/ml leupeptin, 1 µg/ml bestatin, 1 mM benzamidine, 1 µg/ml pepstatin A, 17 µg/ml PMSF, 5 mM sodium fluoride, 80 mM β-glycerophosphate and 1 mM sodium orthovanadate) and heated to 95°C for 5 minutes. Glass beads were then added and samples were bead-beat for 3 minutes in a Mini-BeadBeater-96 (Biospec), followed by centrifugation. For cycloheximide assays with two minute time points, cell pellets were lysed in cold TCA buffer (10 mM Tris pH 8.0, 10% trichloroacetic acid, 25 mM ammonium acetate, 1 mM EDTA) and incubated on ice. Samples were then centrifuged and the pellets resuspended in Resuspension Solution (0.1 M Tris pH 11.0, 3% SDS). Samples were heated to 95°C for 5 minutes, allowed to cool to room temperature, and clarified by centrifugation. Supernatants were added to 4× SDS-PAGE Sample Buffer (0.25 M Tris pH 6.8, 8% SDS, 40% glycerol, 20% β-mercaptoethanol) and heated to 95°C for 5 minutes. Extracts were then subjected to SDS–polyacrylamide gel electrophoresis (SDS–PAGE), followed by transfer to nitrocellulose membranes, and Western blotting with antibodies against Myc (Clone 9E10, Covance) Cdc28 (sc-6709, Santa Cruz Biotechnology), Clb2 (sc-9071, Santa Cruz Biotechnology), Cdc53 (sc-6717, Santa Cruz Biotechnology), Flag (Clone M2, Sigma) and GST (Clone 4C10, Covance).
Cells were grown to mid-log phase, or arrested as indicated, then treated with 50 µg/ml cycloheximide. Samples were fixed for flow cytometry, and cell pellets from equivalent optical densities of cells were collected for Western blotting at indicated time points.
Cells were fixed with 70% ethanol and stored at 4°C overnight. Cells were then sonicated, treated with 0.25 mg/ml RNase A for 1 hour at 50°C, followed by digestion with 0.125 mg/ml Proteinase K for 1 hour at 50°C and labeling with 1 µM Sytox Green (Invitrogen). Data was collected using a FACScan (Becton Dickinson) and analyzed with FlowJo (Tree Star, Inc.) software.
Cell pellets containing 20–30 optical densities of cells were lysed in HEPES lysis buffer (25 mM HEPES pH 7.6, 400 mM NaCl, 0.2% Triton X-100, 1 mM EDTA, 10% glycerol, 1 µg/ml leupeptin, 1 µg/ml bestatin, 1 mM benzamidine, 1 µg/ml pepstatin A, 17 µg/ml PMSF, 5 mM sodium fluoride, 80 mM β-glycerophosphate and 1 mM sodium orthovanadate) by bead beating in a cold block for 3 minutes and clarified by centrifugation at 4°C. Extracts were incubated with 20 µl of a 50% slurry of glutathione-sepharose 4B in lysis buffer (GE Healthcare), while rotating at 4°C for 2 hours. Beads were collected by centrifugation and washed seven times with 1 ml lysis buffer. Proteins were eluted by boiling in 2× SDS-PAGE sample buffer and analyzed by Western blotting against the GST and Myc tags. 2% input of each extract is also shown.
Cells expressing Myc-tagged Cln3 proteins were fixed in 3.7% formaldehyde for 1 hour at 23°C followed by two washes in potassium phosphate buffer (83 mM K2HPO4, 17 mM KH2PO4, pH 7.5) and one wash in sorbitol phosphate buffer (1.2 M sorbitol, 83 mM K2HPO4, 17 mM KH2PO4, pH 7.5). Cells were then spheroplasted with zymolyase and adhered to poly-L-lysine coated slides. Cells on slides were permeabilized with methanol and acetone, then blocked in PBS-BSA (10 mg/ml bovine serum albumin, 0.04 M K2HPO4, 0.01 M KH2PO4, 0.15 M NaCl, 0.1%NaN3). Cells were then incubated with rabbit anti-Myc antibody (sc-789, Santa Cruz Biotechnology) overnight, followed by 5 washes in PBS-BSA, and incubation with Alexafluor 488-conjugated goat anti-mouse secondary antibody (Invitrogen). Cells were again washed 5 times with PBS-BSA, then stained with 1 µg/ml DAPI and mounted with ProLong Gold Antifade reagent (Invitrogen). Microscopy was carried out using a Zeiss Axioskop 2 fluorescence microscope with an 100× 1.3NA Plan-NEOFLAUR objective. Images were taken with a RT Monochrome SPOT camera (Diagnostic Instruments, Inc.) and accompanying software. Image analysis was done with Adobe Photoshop software. All images were captured for the same exposure times and adjustments to contrast and brightness were performed equally on all panels.
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10.1371/journal.pgen.1005943 | Swi1Timeless Prevents Repeat Instability at Fission Yeast Telomeres | Genomic instability associated with DNA replication stress is linked to cancer and genetic pathologies in humans. If not properly regulated, replication stress, such as fork stalling and collapse, can be induced at natural replication impediments present throughout the genome. The fork protection complex (FPC) is thought to play a critical role in stabilizing stalled replication forks at several known replication barriers including eukaryotic rDNA genes and the fission yeast mating-type locus. However, little is known about the role of the FPC at other natural impediments including telomeres. Telomeres are considered to be difficult to replicate due to the presence of repetitive GT-rich sequences and telomere-binding proteins. However, the regulatory mechanism that ensures telomere replication is not fully understood. Here, we report the role of the fission yeast Swi1Timeless, a subunit of the FPC, in telomere replication. Loss of Swi1 causes telomere shortening in a telomerase-independent manner. Our epistasis analyses suggest that heterochromatin and telomere-binding proteins are not major impediments for telomere replication in the absence of Swi1. Instead, repetitive DNA sequences impair telomere integrity in swi1Δ mutant cells, leading to the loss of repeat DNA. In the absence of Swi1, telomere shortening is accompanied with an increased recruitment of Rad52 recombinase and more frequent amplification of telomere/subtelomeres, reminiscent of tumor cells that utilize the alternative lengthening of telomeres pathway (ALT) to maintain telomeres. These results suggest that Swi1 ensures telomere replication by suppressing recombination and repeat instability at telomeres. Our studies may also be relevant in understanding the potential role of Swi1Timeless in regulation of telomere stability in cancer cells.
| In every round of the cell cycle, cells must accurately replicate their full genetic information. This process is highly regulated, as defects during DNA replication cause genomic instability, leading to various genetic disorders including cancers. To thwart these problems, cells carry an array of complex mechanisms to deal with various obstacles found across the genome that can hamper DNA replication and cause DNA damage. Understanding how these mechanisms are regulated and orchestrated is of paramount importance in the field. In this report, we describe how Swi1, a Timeless-related protein in fission yeast, regulates efficient replication of telomeres, which are considered to be difficult to replicate due to the presence of repetitive DNA and telomere-binding proteins. We show that Swi1 prevents telomere damage and maintains telomere length by protecting integrity of telomeric repeats. Swi1-mediated telomere maintenance is independent of telomerase activity, and loss of Swi1 causes hyper-activation of recombination-based telomere maintenance, which generates heterogeneous telomeres. Similar telomerase-independent and recombination-dependent mechanism is utilized by approximately 15% of human cancers, linking telomere replication defects with cancer development. Thus, our study may be relevant in understanding the role of telomere replication defects in the development of cancers in humans.
| Eukaryotic cells must accurately replicate their genetic information every cell cycle. However, this process is challenged by the presence of natural impediments throughout the genome that can halt replisome progression, potentially causing genomic instability, a hallmark of cancer and other hereditary disorders [1–4]. These natural impediments are termed replication fork barriers (RFBs) and are typically classified into two groups. The first group arises from non-histone DNA-binding proteins, such as fork-blocking proteins found at eukaryotic rDNA genes, the fission yeast mating-type locus, and long terminal repeats (LTRs) [5–9]. The second group includes DNA secondary structures such as G quadruplexes, hairpins, and triplex DNA, which are often found at repetitive or palindromic DNA sequences [10–15]. Although these RFBs present obstacles for DNA replication, the nature of these barriers and the mechanisms by which the cell ensures the smooth passage of the replisome through each RFB are not fully understood.
Key to the replisome regulation at RFBs is the fork protection complex (FPC), which is composed of the Timeless and Tipin proteins in mammalian cells. The functions of the FPC are conserved throughout evolution, reflecting its fundamental role in genome stability [16, 17]. The FPC travels with the replisome during DNA replication, stabilizes stalled forks, and promotes full activation of the DNA replication checkpoint [18–25]. The FPC also plays a critical role at several RFBs. In the fission yeast Schizosaccharomyces pombe, Swi1 (Timeless orthologue) and Swi3 (Tipin orthologue) are necessary for programmed fork termination and pausing at RTS1 (replication termination site 1) and MPS1 (mat1 pausing site 1) at the mating-type locus (mat1), respectively, leading to an imprinting event required for mating-type switching [5, 26, 27]. Swi1 and Swi3 are also required for site-specific replication fork arrest at the Ter1-3 sites within the intergenic spacer of the rDNA repeats [28]. Furthermore, Swi1 prevents recombinogenic lesions at RFBs localized at tRNA genes in fission yeast [29].
In addition to the RFBs described above, studies in yeast and human cells have shown that telomeres form specialized chromatin structures that cause replication fork pausing during telomere replication [30–32]. Telomeres are nucleoprotein structures at the ends of linear chromosomes in eukaryotes. Repetitive G-rich sequences at telomeres can form G-quadruplex structures that may hinder DNA replication. Telomeres also recruit heterochromatin proteins and telomere-specific proteins that form the shelterin complex [33–35]. Such structures may cause genomic instability during DNA replication if this process is not properly regulated.
In fission yeast, the RNA interference (RNAi) pathway initiates heterochromatin formation, leading to Clr4-dependent histone H3 methylation and subsequent recruitment of Swi6, the orthologue of heterochromatin protein 1 (HP1) [36, 37]. At telomeres, heterochromatin establishment can occur by an additional pathway that uses Taz1, a component of the shelterin complex, which regulates telomerase activity and inhibits DNA damage response at telomeres [38]. Taz1 contains a Myb DNA-binding domain, associates with telomeric repeats, and plays a role in efficient replication of telomeres [39]. In mammalian cells, deletion of TRF1, a Taz1 orthologue, results in inefficient telomere replication when monitored by DNA combing, while overexpression of TRF1 can also lead to fork stalling at telomere repeats [40, 41]. These results suggest that the shelterin complexes including Taz1/TRF1 play a critical role in regulation of telomere replication. Previously, we have found that Timeless (Swi1 orthologue) interacts with TRF1, a component of shelterin, and prevents telomere shortening and fork collapse at telomeres in human cells [42]. In S. cerevisiae, loss of Tof1 (Swi1 orthologue) or its partner Csm3 (Swi3 orthologue) causes an increase in telomere-size heterogeneity but not significant telomere shortening [43]. These findings suggested a role of the FPC in telomere replication. However, whether the function of the FPC at telomere is conserved throughout evolution is not known, nor is it known what features of telomeres actually present the barrier to the DNA replication process.
In this study, we investigated the role of Swi1 in fission yeast telomere maintenance. We show that Swi1 deletion results in telomere shortening, accumulation of DNA damage, and increased recombination at telomeres. Our results suggest that the primary obstacle for unstable replisomes lacking Swi1 may lie in the repetitive nature of the telomeric DNA and not in the presence of heterochromatin or non-histone DNA-binding proteins. We also show that the telomere shortening in swi1Δ cells is likely caused by replication problems and not by defects in telomerase recruitment. Furthermore, we show that swi1Δ cells more frequently utilize alternative lengthening of telomeres (ALT)-like mechanisms than wild-type cells to amplify telomeric/subtelomeric regions in the absence of telomerase to maintain telomeres. Taken together, our analyses reveal novel and conserved roles of the FPC in telomere maintenance and in replication fork stabilization at repetitive DNA regions. Considering that approximately 10–15% of all human cancers activate ALT pathways to maintain telomeres [44, 45] and that ALT was recently shown to occur even in normal mammalian somatic cells in vivo [46], our results may be relevant in understanding how Swi1Timeless ensures telomere stability and prevents ALT activation in subsets of human cancers.
We previously found that swi1Δ cells accumulate spontaneous Rad52 DNA repair foci during DNA replication [47]. Rad52 recombinase binds single-stranded DNAs (ssDNAs) at sites of DNA damage and is required for DNA repair [48, 49], indicating that swi1Δ cells accumulate DNA lesions. However, it was unknown whether these Rad52 foci are localized randomly throughout the genome or at specific chromosome loci in swi1Δ cells. Since human Timeless, the ortholog of S. pombe Swi1, is involved in preventing telomere damage [42], we hypothesized that Rad52 is preferentially recruited to telomeres in swi1Δ cells. To address this hypothesis, we performed ChIP-seq analysis of Rad52 using wild-type and swi1Δ cells endogenously expressing Rad52 fused to 12 tandem copies of the Pk epitope (Rad52-12Pk). We previously reported that Rad52-12Pk is functional as rad52-12Pk cells showed no significant change in DNA damage sensitivity, and Rad52-12Pk was recruited to the mat1 locus in wild-type cells [50].
Interestingly, Rad52-12Pk ChIP-seq analysis showed that subtelomeric regions have increased Rad52 binding in swi1Δ cells compared to wild-type cells (Fig 1A). In order to confirm the specific enrichment of Rad52 at subtelomeric regions, we compared Rad52 enrichment between subtelomeres and other chromosome regions (Fig 1B). For this purpose, we divided the entire genome into non-overlapping 2-kb windows and compared Rad52 enrichment at each 2-kb window between swi1Δ and wild-type samples. We accounted 20-kb from each chromosome end as a subtelomere region (ten 2-kb windows from a chromosome end). We excluded rDNA regions from the analysis because Rad52 is enriched at rDNAs in swi1Δ cells (described later). The box plots for log2 ratios of Rad52 enrichment showed a significant increase of Rad52 association at subtelomeres over other chromosome regions in swi1Δ cells (Fig 1B).
To address the role of Swi1 in telomere maintenance, we also performed PCR-based ChIP analysis of Rad52 at telomeres. We found that in wild-type cells, Rad52 enrichment at subtelomeres (the TAS1 region) was approximately 3 times more than that at a control locus (Fig 1C, see Fig 2A for location of TAS1 subtelomeric region), indicating that telomeres are inherently difficult to replicate and are prone to generating ssDNA. These results are in agreement with previous reports showing differential arrival of the leading- and lagging-strand DNA polymerases and an S phase-specific increase in RPA recruitment at telomeres [51, 52]. Importantly, Rad52 binding at the TAS1 region was further increased in swi1Δ cell (Fig 1C), suggesting the role of Swi1 in limiting Rad52 accumulation at telomeric regions. To further substantiate this result, we conducted a telomere-dysfunction induced foci (TIF) assay that examines co-localization of Rad52 and a telomere maker, Taz1. For this purpose, Rad52 and Taz1 were fused to yellow fluorescent protein (YFP) and mCherry, respectively, and expressed from their own promoters at their chromosomal loci. We counted over 100 nuclei and determined the ratio of TIF-positive nuclei in each cell culture. This analysis revealed that swi1Δ cells present a higher amount of TIF-positive nuclei than wild-type cells (Fig 1D and 1F). swi1Δ cells did not show any significant difference in the number of Taz1 foci; however, they displayed more Rad52 foci overall than wild-type cells (Fig 1E and 1F). Therefore, it was possible that the elevated TIF-positive nuclei seen in the swi1Δ culture was a consequence of increased stochastic DNA damage events occurring throughout the genome. To exclude this possibility, we next calculated the ratio of TIF events to total Rad52 foci. TIF occurred in 5.19% and 11.08% of Rad52 foci in wild-type and swi1Δ cells, respectively (see S1A Fig), indicating that telomeres undergo increased DNA damage in the absence of Swi1. In addition, we observed the occurrence of Rad52 foci lateral to Taz1 foci in swi1Δ cells (S1B Fig). This may reflect the Rad52 enrichment found at subtelomeres in our Rad52 ChIP-seq experiments (Fig 1A), and subtelomeric regions are also susceptible to DNA damage. However, these Rad52 foci lateral to Taz1 foci were not considered as TIFs in our analysis. Thus, increase in DNA damage occurring at telomeric/subtelomeric regions is likely to be even more severe than suggested by our analysis of TIF-positive cells. Taken together, our results established the critical role of Swi1 in preventing DNA damage at telomeres in fission yeast.
Although the role of the FPC in telomere maintenance has been suggested [16, 42, 53], mechanistically how Swi1 regulates telomere length is unknown. In order to understand the role of the FPC in telomere replication, we first investigated the role of the Swi1-Swi3 FPC in telomere length maintenance. Cells were consecutively streaked at least 8 times on YES plates at 3-day intervals (~33 doublings per streak) in order to stabilize telomere lengths after deletion mutants were generated. To detect telomere fragments, the genomic DNA from several independent isolates of FPC mutants was digested with ApaI (Fig 2A) and analyzed by Southern blot (Fig 2B). Consistent with previous reports [42, 53], swi1Δ cells had much shorter telomeres when compared to wild-type cells (Fig 2B). In addition, we found that swi3Δ cells carry short telomeres similar to swi1Δ (Fig 2B). Telomere lengths in swi1Δ and swi3Δ cells were similar to those in rad3Δ and rad26Δ mutants, already known to harbor significantly short telomeres [54–56] (Fig 2B). Telomere shortening in swi1Δ cells was rescued by transforming swi1Δ cells with a plasmid vector containing the swi1+ gene but not with a control vector (Fig 2C), indicating that the loss of telomeric repeats is directly caused by loss of Swi1 and can be reversed by reintroducing a fully functional FPC. Furthermore, a cell cycle-ChIP analysis found that Swi1 is specifically recruited at telomeres during S phase and that addition of HU, which have been previously established to inhibit late-S phase replication of telomeres [57], eliminated Swi1 recruitment (Fig 2D). These observations suggested that the Swi1-Swi3 FPC is important for proper replication of telomeres to ensure telomere length maintenance in fission yeast.
The FPC stabilizes replication forks and mediates activation of the replication checkpoint [18, 21, 47]. In the latter function, Swi1 is required for proficient activation of the checkpoint kinase Cds1. However, previous reports indicated that Cds1 has no role in telomere length maintenance [54, 56, 58]. Consistently, we also observed that cds1Δ cells do not show telomere shortening (Fig 2E). In the absence of Swi1, cells accumulate replication stress, leading to Chk1-dependent G2/M delay, and swi1Δ chk1Δ cells shows growth defects and are much more sensitive to genotoxic agents than either single mutant [47]. Therefore, we tested whether telomere length in swi1Δ cells is affected by loss of Chk1. However, there was no further shortening of telomere length in swi1Δ chk1Δ cells compared to swi1Δ cells (Fig 2E). Thus, we concluded that Swi1-mediated telomere length maintenance does not rely on Swi1’s role in regulation of checkpoint kinases Chk1 and Cds1.
Several telomeric features can hamper the passage of the replication machinery and cause telomere damage and shortening. G-quadruplexes, repetitive DNA, heterochromatin, and the potential to form t-loop structures have been suggested as possible obstacles for the replication machinery; however the concrete nature of the telomere barrier remains elusive. We hypothesize that swi1 deletion results in an unstable replisome that will be more vulnerable to these obstacles. Since previous studies have found that lagging-strand synthesis at fission yeast telomeres is substantially delayed compared to leading-strand synthesis [52, 59, 60], the FPC could be especially important for protecting integrity of replisome at telomeres until lagging-strand synthesis is successfully completed.
To determine whether Swi1 generally functions in maintaining integrity of repetitive DNA sequences, we first tested the role of Swi1 during replication in maintaining stability of E. coli LacO array derived from the pSV2-DHFR-8.32 vector [61]. This array contains 32 direct repeats of a 317-bp DNA fragment. Each fragment includes 8 direct repeats of a 36-bp DNA sequence containing the LacO operator and a 29-bp linker sequence; thus, this LacO array has 256 repeats of the 36-bp DNA sequence [61]. In order to investigate whether the LacO repeats interfere with the replication process, we used S. pombe strains that carry the LacO array at the ade3+ or ade1+ locus [62, 63] as these loci are not associated with known repeat DNA sequences [64, 65]. These strains were initially designed to express the LacI-GFP fusion protein that binds the LacO repeats inserted at ade3 or ade1 loci. Since LacI-GFP may affect replication of the LacO array [66], we removed the LacI-GFP transgene from these strains by genetic crossing before the swi1+ gene was deleted. Absence of LacI-GFP expression was confirmed by fluorescent microscopy.
Immediately after the swi1 deletion was introduced into the strains that carry the LacO array, segregants were passaged by restreaking multiple times on YES plates in order to stabilize repeat length. Southern blot analyses using a LacO repeat-specific probe show that wild-type cells maintained the LacO repeat length (~8 kb and ~6 kb in strains carrying the LacO array at ade3 and ade1 loci, respectively) even after cells were passaged multiple times (Fig 3A). In contrast, swi1Δ cells displayed dramatic shortening of LacO repeats integrated at the ade3 locus indicative of repeat instability. The effect of swi1 depletion on LacO repeat instability was clear at the earliest passage and became more prominent with the consecutive streaks. We also observed considerable repeat instability that resulted in both longer and shorter LacO repeats when the construct was integrated at the ade1 locus, suggesting that Swi1 is involved in replication and/or maintenance of repeat DNA sequences independently of the repeat location (Fig 3A). Such instability appears to cause DNA damage as swi1 deletion resulted in a significant enrichment of Rad52 at the LacO repeats while no difference was found at a non-repetitive locus such as rec8 (Fig 3B). We also found similar results at rDNA repeat regions, where loss of swi1 caused increased DNA damage as represented by elevated recruitment of Rad52 at rDNA repeats (Fig 3C). This is consistent with the fact that swi1Δ cells have a shorter chromosome III [67–70], which contains rDNA repeats in S. pombe. These results suggest that Swi1 is required for preventing DNA damage at repeat DNA regions. In particular, our findings indicate that Swi1 plays a critical role in proper replication of LacO as well as rDNA repeats.
The aforementioned results suggest that repetitive DNA presents a replicative obstacle in swi1Δ cells. Since telomeres are composed of highly repetitive DNA, we hypothesized that telomere shortening observed in swi1Δ cells could be attributable to the repetitive nature of telomere DNA. The presence of DNA ends at telomeres could complicate interpretation of our analysis because the end-replication problem, telomerase recruitment, and recombination among chromosome ends can all affect telomere length. Thus, to specifically investigate contribution of Swi1 in maintaining telomere repeat stability, we studied the stability of a single synthetic internal telomere tract (~300 bp) within an episomal plasmid (Fig 4A).
We generated pAL-SK plasmids carrying a single 300-bp telomere tract (leu1::telomere) inserted in either forward (+) or reverse (-) orientation with respect to the position of the replication origin in the plasmid. These plasmids represent the natural (+) and inverse (-) orientation of the telomeric repeats in reference to the replisome movement (Fig 4A). To allow for plasmid replication in the presence or absence of Swi1, wild-type and swi1Δ cells were transformed with either the empty plasmid or the plasmid carrying the telomere tract in either orientation. Recovered plasmid was amplified in bacteria, and 10 bacterial colonies per treatment were analyzed by restriction digestion with PvuII.
As shown in Fig 4B, both wild-type and swi1Δ mutant strains were able to amplify the empty plasmid as all colonies analyzed displayed the expected restriction fragment length. Interestingly, the restriction pattern of the pAL-SK-telo(-) plasmid obtained form swi1Δ cells was significantly altered in six out of the 10 colonies analyzed, while the same plasmid obtained from wild-type cells showed the expected band size in all cases. Restriction analysis of the recovered pAL-SK-telo(+) plasmids showed the expected fragment lengths in most of the colonies from both wild-type and swi1Δ mutants strains (Fig 4B). Although these results might seem contradictory as the pALSK-telo(+) plasmid carries the native repeat orientation, it is important to note that we obtained a very low number of colonies for swi1Δ cells transformed with the pALSK-telo(+) plasmid (<10 E. coli colonies per transformation) in comparison to all other treatments (50–100 colonies per transformation). One possible explanation of this observation is that replication of the pAL-SK-telo(+) plasmid could generate toxic intermediates that are unfavorable for growth of swi1Δ cells. It is likely that, as a result of this toxicity, the colonies obtained represent a small subset of yeast cells that carried an unaltered pAL-SK-telo(+) plasmid. Nevertheless, these results are consistent with a notion that Swi1 is critical for replication of repetitive DNA sequences, independently of location and DNA sequence. Furthermore, our data suggests that telomeric repeats, in either orientation, are a significant replication obstacle for Swi1-depleted cells, but their presence in native conformation might be most detrimental for cells. Our data are in agreement with experiments done with trinucleotide repeats where it was shown that the stability of trinucleotide repeats in bacterial, yeast, and cultured mammalian cells depend on their orientation with regard to replication origins [71–74]. Thus, our results indicate that repetitive DNA at telomeres is an important source of instability in the absence of Swi1.
Similar to the endogenous telomere ends, the telomere tract in the pAL-SK-telo plasmids can recruit telomere-binding proteins such as shelterin components and heterochromatin [75]. Therefore, it was possible that replisome progression through telomeric repeats might be blocked by telomere-binding factors such as shelterin components and heterochromatin related proteins in swi1Δ cells. If so, removal of these features from telomeres would rescue the telomere shortening phenotype of swi1Δ cells.
To disrupt heterochromatin, we deleted genes required for heterochromatin formation. These genes include swi6+ and clr4+, which encode proteins homologues to human HP1 [76, 77] and Suv39 family of histone methyltransferases [78–80], respectively. swi6 or clr4 deletion failed to shorten telomere length in southern blots of ApaI fragments (Fig 5A and 5B), in agreement with related studies [81, 82]. When Swi1 was removed from swi6Δ or clr4Δ cells, telomeres were shortened to the extent of the telomere length in swi1Δ cells (Fig 5A and 5B). Thus, disruption of these HP1-related heterochromatin proteins failed to alleviate telomere shortening of swi1Δ cells. These results suggest that heterochromatin does not cause observable telomere damage and thus does not present a severe obstacle to the DNA replication process.
The shelterin complex, which directly associates with telomeres, has been shown to facilitate replication of telomeric repeats in fission yeast cells [39, 60]. However, previous studies in budding yeast have reported that telomere-binding proteins can become a barrier to DNA replication process [31, 83]. Because the replisome is unstable in swi1Δ cells, we tested whether shelterin could act as a replication obstacle in the absence of Swi1. Loss of shelterin components including Taz1, Poz1, and Rif1 resulted in telomere lengthening when compared to wild-type cells as seen in ApaI-telomere length analysis (Fig 5C). This is consistent with previous findings showing that shelterin can act as a negative regulator of telomerase [84–86], as well as with a protective role of Taz1 against activation of homologous recombination (HR)-based telomere lengthening [87, 88]. We then determined telomere length of swi1Δ taz1Δ, swi1Δ poz1Δ, and swi1Δ rif1Δ strains. swi1 deletion led to considerable telomere shortening in poz1Δ and rif1Δ cells and less dramatic but still significant telomere shortening in taz1Δ (Fig 5C), indicating that Swi1 is still important for telomere length maintenance even in the absence of shelterin subunits that inhibit telomere extension.
Our analysis of telomere-repeat stability within episomal plasmids in swi1Δ cells (Fig 4) suggested that inability of swi1Δ cells to maintain stable repetitive telomere repeats may contribute to telomere shortening in swi1Δ cells. However, since telomerase recruitment is coupled to replication of telomeres by replicative DNA polymerases, it was possible that loss of FPC might prevent efficient recruitment of telomerase, leading to telomere shortening. Thus, we decided to examine the recruitment of telomerase to telomeres in swi1Δ cells. As shown in Fig 6A, ChIP assays revealed that Trt1 recruitment was significantly elevated in swi1Δ cells when compared to wild-type cells. This is consistent with previous studies showing that telomerase is selectively recruited at shorter telomeres in fission yeast [60].
Interpretation of epistasis analysis between swi1Δ and shelterin subunit mutant (taz1Δ, poz1Δ and rif1Δ) is complicated due to the fact that telomerase can indeed still act on telomeres to extend telomeres in the absence of Swi1. Thus, to evaluate whether the Swi1 indeed plays a telomerase-independent role in maintaining telomere length, we then sought to evaluate effect of eliminating Swi1 in cells that lack telomerase. Since telomerase (trt1+) deletion frequently leads to telomere loss and generation of survivor cells that eliminate telomeres by circularizing chromosomes [87], we utilized a taz1Δ trt1Δ double mutant strain, which stably maintains heterogeneous telomeres via recombination-dependent, ALT-like telomere maintenance mechanism [87, 88].
Telomeres in taz1Δ trt1Δ cells were much longer than those in wild-type cells, which is consistent with the ALT-like phenotype of taz1Δ trt1Δ cells (Fig 6B). Importantly, swi1Δ taz1Δ trt1Δ triple mutant cells carried shorter telomeres than taz1Δ trt1Δ cells, suggesting that telomere shortening due to swi1 deletion is not caused by a defect in telomerase activity. Interestingly, swi1Δ cells rapidly undergo telomere shortening within the first passage after swi1 deletion, and the telomere length becomes stable afterwards. In striking contrast, trt1Δ cells displayed a much slower and progressive telomere shortening (S2 Fig) [89]. Taken together, we conclude that Swi1 is involved in telomere length maintenance independently of telomerase activity.
Rad3-Rad26 and Tel1-MRN (Mre11-Rad50-Nbs1) kinase complexes are redundantly required for telomerase recruitment at telomeres by promoting phosphorylation of the threonine 93 residue of Ccq1 [90, 91]. Thus, in the absence of Tel1, Rad3-Rad26 becomes essential for telomerase recruitment as rad3Δ tel1Δ and rad26Δ tel1Δ double mutants lose telomeres [56, 58]. To understand the relationship between Swi1 and Rad3-Rad26, we constructed swi1Δ tel1Δ double mutants cells. If Swi1 contributes to Rad3-Rad26-dependent telomerase recruitment, we expect that swi1Δ tel1Δ cells would have shorter telomeres than swi1Δ cells. However, tel1 deletion had no effect on the telomere length of swi1Δ cells (Fig 6C), suggesting that Swi1 is unlikely to be required for Rad3-Rad26’s ability to promote telomerase recruitment. Such conclusion is entirely consistent with our earlier ChIP analysis, which suggested that Swi1 is not necessary for telomerase recruitment (Fig 6A).
On the other hand, we found that telomere length of swi1Δ rad26Δ cells is similar to that of either single mutant (Fig 6C), suggesting that the telomere length maintenance defect observed in rad26Δ is epistatic to swi1Δ. Since previous genetic analysis indicated that Rad3-Rad26 contributes to telomere protection function that appears to be distinct from Rad3-Rad26’s role in telomerase recruitment [92], we suggest that Rad3-Rad26’s ability to protect telomeres is mediated by Swi1.
Although telomeres rapidly shorten after the loss of Swi1, swi1Δ cells do not lose all telomeric repeats. Instead, they stably maintain short telomeres even after extensive passages. Because Rad52 recruitment is increased in swi1Δ cells, it was possible that recombinational telomere maintenance pathways might maintain telomeres in the absence of Swi1. Therefore, we inactivated homologous recombination (HR) by deleting rad51+ or rad52+ and determined the effect of these deletions on telomere length. As shown in Fig 7, telomere length of rad51Δ and rad52Δ cells was similar to that of wild-type cells. Importantly, when we deleted rad51+ or rad52+ from swi1Δ or swi3Δ cells, cells still showed short telomeres similar to those in swi1Δ cells (Fig 7). These results indicate that HR is not the major pathway used to maintain short telomeres in swi1Δ cells and further suggest that telomerase is functional in swi1Δ cells.
The elevated recruitment of telomerase in swi1Δ cells and the ability of these cells to maintain telomeres in HR-deficient backgrounds suggest that short but stable telomeres in swi1Δ cells are maintained by telomerase. This notion prompted us to investigate the physiological consequences of telomerase loss in swi1Δ cells. To inactivate telomerase, we first utilized an est1-deletion background because the telomerase regulatory subunit Est1 is essential for telomerase-dependent telomere maintenance in fission yeast [93]. We deleted swi1+ from est1+/est1Δ diploid cells and generated a swi1+/swi1Δ est1+/est1Δ diploid strain, from which we obtained haploid est1Δ, swi1Δ and est1Δ swi1Δ strains. As expected from previous studies [93], all est1Δ strains underwent extreme telomere shortening (Fig 8A and 8B). When telomere length of multiple swi1Δ est1Δ strains was monitored, some strains also had extremely short telomeres. However, a strikingly higher proportion of swi1Δ est1Δ strains developed long and heterogeneous telomeres (Fig 8A and 8B), a feature reminiscent of ALT activation in humans [94, 95] and type-II survivors in S. cerevisiae and Kluyveromyces lactis [96–98].
To further confirm this phenomenon, we also deleted telomerase catalytic subunit trt1+. Because we were unable to obtain viable swi1Δ trt1Δ cells when we attempted to obtain double mutant cells by gene deletion or genetic cross of trt1Δ and swi1Δ strains, we chose to cross swi1Δ cells with trt1Δ taz1Δ cells to obtain viable swi1Δ trt1Δ cells. Wild-type, swi1Δ, taz1Δ and swi1Δ taz1Δ trt1Δ segregants obtained from the tetrad dissection displayed the expected telomere phenotypes (S3 Fig). Interestingly, again, many of swi1Δ trt1Δ double mutant strains showed long and heterogeneous telomeres whereas most of trt1Δ mutants had extremely short telomeres or lost telomeres (Fig 8B and S3 Fig). The number of ALT-like telomeres observed in the trt1Δ strains was higher than expected from trt1-deficient strains (Fig 8B). This is probably due to the presence of long telomeres in the taz1Δ trt1Δ strain that was used to generate trt1Δ segregants. Taken together, these results suggest that, in the absence of telomerase, short telomeres caused by replication fork instability become templates for recombination-dependent (ALT) maintenance of telomeres. Our findings also suggest that Swi1 protects telomeres from hyper-recombination when telomerase is absent.
In this work, we identified a novel role of Swi1 in preventing repeat instability during DNA replication in S. pombe. More specifically, our investigation revealed that loss of FPC causes telomere instability during replication, which may contribute to telomere shortening in swi1Δ cells. Telomeric repeats rather than other telomeric features present a significant obstacle for the replisome and thus render telomeres difficult to replicate. In addition, our results also suggest that Swi1Timeless prevents recombination-dependent ALT-like telomere amplification in the absence of telomerase. Based on these results, we propose that the role of the FPC could be further exploited in the context of mammalian models as a potential link between genomic instability and ALT-dependent tumorigenesis.
Swi1 deletion renders S. pombe cells highly sensitive to fork-stalling agents and causes extensive accumulation of spontaneous Rad52 DNA repair foci during S phase, indicating that Swi1 stabilizes stalled replication forks [47]. Rad52 foci may represent DNA damage at specific chromosome loci that are more difficult to replicate when Swi1 is depleted. These loci include rDNA repeats and telomeres as they are enriched with Rad52 (Fig 1C, Fig 3C). In addition, swi1Δ cells accumulate broken forks at rDNA loci and show elevated levels of TIFs (Fig 1D and 1F) [47]. We therefore suggest that these genomic regions are among the hot spots of DNA damage in swi1Δ cells.
It is possible that stalled or slowed forks at rDNA- or telomere-repeat regions need to be stabilized by Swi1, in order to prevent fork collapse. Importantly, Swi1 coordinates leading- and lagging-strand synthesis at the replication fork [47, 70]. Furthermore, the recruitment of the lagging-strand polymerase (Pol δ) at telomeres is significantly delayed when compared to the leading-strand polymerase (Pol ε) in wild-type cells [52, 59, 60]. Therefore, we propose that loss of Swi1 leads to extensive uncoupling of leading- and lagging-strand synthesis, causing telomeres to adopt unusual open configurations that are prone to fork collapse and DNA damage.
Telomere damage was linked to the occurrence of telomere shortening in swi1Δ cells. Telomere length in swi1Δ strains was comparable with that in rad3Δ and rad26Δ cells. In fission yeast, Rad3ATR and Rad26ATRIP form a complex that is essential for cell cycle arrest by activation of both Cds1Chk2 and Chk1Chk1 kinases responsible for the DNA replication and damage checkpoints, respectively [99]. Swi1 is also involved in full activation of the Cds1 checkpoint kinase in S. pombe [47]. However, our epistasis analysis indicates that both Swi1-Swi3 and Rad3-Rad26 complexes maintain telomere length independently from their role in the activation of downstream checkpoint kinases Cds1 and Chk1 (Fig 9). Our genetic data also suggest that the FPC and Rad3-Rad26 are in the same pathway for telomere length maintenance (Fig 9). Although Rad3-Rad26 phosphorylates the threonine 93 residue of Ccq1 to facilitate telomerase recruitment at telomeres [90, 91], Swi1 appears to play no role in telomerase recruitment as swi1Δ cells showed an increased level of telomerase at telomeres (Fig 6A). Since our previous genetic analysis indicated that Rad3-Rad26 contributes to telomere protection [92], and this protective function appears to be distinct from it’s role in telomerase recruitment, we suggest that telomere protection function of Rad3-Rad26 is medicated by Swi1 (Fig 9).
swi1 deletion causes contraction of rDNA repeats [68, 70]. In this study, we demonstrated that swi1Δ cells harbor short telomeres and undergoes repeat loss and instability associated with the LacO array, indicating that the primary replicative obstruct in swi1Δ cells is repeat DNA sequence.
In E. coli, LacO arrays bound by the LacI protein block replication by forming a site-specific protein–DNA complex that serves as an RFB [100, 101]. In mammalian cells, insertion of LacO repeats generates fragile sites in metaphase chromosomes [102]. This also appears to be dependent on LacI repressor proteins that form RFBs by binding to the LacO repeats [103]. Furthermore, studies in S. pombe showed that the LacI-LacO system leads to replication fork block and DSBs [66], further suggesting the role of protein-DNA complexes in replication interference. Consistently, in our present study, LacO arrays induced only a mild increase in Rad52 recruitment when LacI was not present in wild-type cells. However, Rad52 was further accumulated at LacO repeats in swi1Δ cells even in the absence of LacI (Fig 3B), suggesting that Swi1 is required for replication of repeat DNA. Considering that the induction of DNA damage response at the LacO arrays bound by LacI occurs as a consequence of DNA replication [103], the repetitive nature of repeat sequences poses an initial layer of replication problems that is counteracted by Swi1. As in the case of the LacI-LacO RFB, protein-DNA RFBs may provide another layer of replication stress that further complicates replisome passage even in wild-type cells.
Although our studies demonstrated that repetitive DNA is the primary replication problem in the absence of Swi1, it is important to note that several other characteristics of the telomeres may further complicate telomere replication. In budding yeast, the replication fork pauses at telomeres, and this pausing is intensified in the absence of Rrm3 helicase [31]. Furthermore, the presence of Rap1 bound to the telomeric sequences has been suggested to be a component of the telomere replication barrier [30]. In fission yeast, Taz1 is required for efficient telomere replication, and Poz1 and Rap1 also contribute to the completion of telomere replication by promoting timely accumulation of the lagging-strand DNA polymerase and the Stn1-Ten1 complex at telomeres [30, 31, 39]. In addition, the presence of heterochromatin and the potential to form t-loop structures may provide an additional barrier to the passage of the fork [104]. Therefore, further research is necessary to fully understand the composition of the telomeric barrier in fission yeast as well as the replication factors involved in overcoming this barrier. Future studies should focus on the mechanisms by which these telomere features add additional layers of difficulty to the passage of the replication fork.
Approximately 10–15% of cancer cells including glioblastomas and osteosarcomas [44] are able to escape replicative senescence by activating the recombination-dependent ALT pathway in the absence of any detectable telomerase activity [105]. Although the prevalence of ALT telomeres is well established, the factors or events that trigger certain cancer cells to activate ALT pathways over telomerase reactivation are not well understood. Accordingly, there are no treatments targeting ALT specifically in cancer cells and, in addition, these tumors are predicted to be resistant to anti-telomerase therapies [106].
Our results show that Swi1-deficient cells display increased association of Rad52 recombinase, and that loss of Swi1 promotes recombination-based survival in cells lacking telomerase. Therefore, telomere damage during DNA replication may enhance DNA repair processes using the homologous recombination machinery. It is possible that this pathway provides a more robust and efficient response to drastic telomere shortening than telomerase reactivation, in order to maintain telomere length after a rapid and significant telomere repeat loss.
A recent paper showed that cancer cells bearing ALT telomeres are sensitive to ATR inhibitors. This is because ALT telomeres have elevated levels of RPA-coated ssDNA, which is known to recruit ATR [106]. Our findings in S. pombe are in agreement with this work, as loss of Swi1 generates long stretches of ssDNA at the stalled forks [21, 70]. Consistently, Chk1, an effector kinase downstream of Rad3ATR is activated in swi1Δ cells [47]. It is therefore conceivable that Rad3ATR activation in Swi1-deficient cells mediates the formation ALT telomeres observed in swi1Δ est1Δ and swi1Δ trt1Δ cells. Future investigations are warranted to test this possibility and further address the role of Swi1Timeless in preventing ALT-dependent cancers.
The methods used for genetic and biochemical analyses of fission yeast have been previously described [107, 108]. For telomere length assays, yeast cells were grown at 32°C on solid YES (yeast extract with supplements) plates. To ensure stabilization of the telomere length, cells were streaked on YES plates every 3 days at least 8 consecutive times prior to genomic DNA preparation. Microscopic analyses of fluorescent proteins, Western blotting, and drug sensitivity assays were performed as described in our earlier studies [21, 47, 68, 109].
The S. pombe strains used in this study were constructed using standard techniques [107, 108], and their genotypes and sources are listed in S1 Table. To construct episomal plasmids that carry a telomere tract, a 300 bp S. pombe telomere fragment was subcloned from pTELO plasmid [60] into the PvuII site of the pAL-SK plasmid [110], which carries the LEU2 gene, resulting in pAL-SK-Telo (+) and pAL-SK-Telo (-). To express Rad52-YFP (originally called Rad22, but recently renamed as Rad52 in S. pombe) as a sole source of Rad52 from the native promoter, pJK210-Rad52CT-YFP [68] was linearized at the AflII site, which is localized within the DNA sequence that encodes the C-terminal (CT) domain of Rad52, and inserted at the rad52 locus of S. pombe strains. rad52Δ (rad52::hphMX6) was generated by a two-step PCR method [111], to replace the rad52 open reading frame with the hphMX6 gene. cds1Δ (cds1::hphMX6) was generated by a one-step marker switch method [112] using cds1::kanMX6 [67]. taz1-mCherry (taz1-mCherry::hphMX6) was also generated by the two-step PCR method, to construct a mCherry tag at the C-terminus of taz1. Plasmids used as template for the PCR-based gene disruption and gene tagging have been previously described for pFA6a-hphMX6 [113] and pFA6a-5FLAG-kanMX6 [114]. pFA6a-mCherry-hphMX6 was constructed by replacing the BglII-EcoRI kanMX6 fragment in pFA6a-mCherry-kanMX6 [115] with hphMX6 fragment. pJK148-Swi1 was constructed by inserting the 3.6 kb genomic fragment containing the swi1+ gene into the SacI/BamHI site of pJK148 [116].
Mutations and epitope-tagged genes have been previously described for swi1Δ (swi1::kanMX6, swi1::natMX6) [47, 69], rad3Δ (rad3::ura4+, rad3::kanMX6) [67, 117], rad26Δ (rad26::ura4+) [118], tel1Δ (tel1::ura4+, tel1::LEU2) [56], cds1Δ (cds1::ura4+) [119], chk1Δ (chk1::ura4+, chk1::kanMX6) [67, 118], clr4Δ (clr4::kanMX6) [79], bdf2Δ (bdf2::hphMX6) [120], taz1Δ (taz1::ura4+, taz1::LEU2) [85], trt1Δ (trt1::his3+) [89], rad52Δ (rad52::LEU2) [121], swi1-5FLAG (swi1-5FLAG::kanMX6), swi1-13Myc (swi1-13Myc::kanMX6), rad52-YFP (rad52-YFP::ura4+) [21], rad52-12PK, trt1-G11-9FLAG [50], ade3::[kanr-ura4+-lacO], ade1::[kanr-ura4+-lacO] [62, 63]. Strains containing the following gene deletions were obtained from National BioResource Project Japan and used to generate various strains used in this study: swi6Δ (swi6::kanr, FY13724), est1Δ (est1::kanMX, FY14265), poz1Δ (poz1::hyg, FY18508), and rif1Δ (rif1::ura4+, FY14160).
Genomic DNA was digested overnight with the indicated restriction enzymes, and separated on an agarose gel using 1x Tris-Acetate-EDTA buffer. DNA from the agarose gel was transferred to a Hybond-XL membrane (GE Healthcare, Little Chalfont, UK) using buffer containing 0.5 M NaOH and 1.5 M NaCl. The membrane was then UV cross-linked using an XL-1000 UV Crosslinker (Spectronics, Westbury, NY) and incubated with a DNA probe labeled with [α-32P] dCTP. Hybridization was carried out overnight in Church buffer at 65°C as described [122]. Membranes were exposed for 2 days to a Phosphorimager screen, and detection of telomere or LacO repeats was done using a Storm 840 Phosphorimager (GE Healthcare). The DNA probe for the detection of telomere repeats by Southern blotting has been described previously [56]. For detecting LacO repeats, the 316-bp XbaI fragments that contain LacO repeats were excised from the pSV2-DHFR-8.32 plasmid [61] and used as a probe. The 448-bp PvuII fragment from the pJK148 backbone [116] was used as a probe to detect the DNA fragment containing the internal telomere tract.
ChIP assay and its quantification in all figures except for Fig 2D were carried out as described previously [123–125] with modifications. Briefly, exponentially growing cells were fixed in 3% paraformaldehyde, and chromatin was sheared into 500 to 700-bp fragments using a Misonix Sonicator 3000 (Qsonica, Newtown, CT). Chromatin-associated proteins were then immunoprecipitated using mouse monoclonal anti-V5/Pk SV5-Pk1 (AbD Serotec, Kidlington, UK) or anti-FLAG M2 (Sigma-Aldrich, St. Louis, MO) antibodies in combination with Protein G-coupled Dynabeads (Life Technologies, Carlsbad, CA). DNA extracted from the immunoprecipitates was subjected to PCR analysis, and the PCR products were separated on a 4% polyacrylamide gel. The gel was stained with SYBR Green I (Life Technologies) and analyzed with Storm 840 Phosphorimager (GE Healthcare). Relative enrichment of the target sequences was calculated by multiplex PCR including primers that amplify a gene-free region (GFR) [126] as internal control as described previously [123–125]. ChIP assay described in Fig 2D was performed using the anti-myc 9E10 monoclonal antibodies (Cell Signaling Technology, Danvers, MA) as previously described [90, 92, 127]. PCR primers used in our ChIP studies are listed in S2 Table.
ChIP sequencing was performed as described [124] with some modifications. ChIP samples from non-tag control (SP1173), rad52-12PK (Y4250) and rad52-12PK swi1Δ (Y4256) strains were obtained from asynchronous cultures as [128, 129]. Quality of the ChIP samples was assessed by PCR using primers that are designed to amplify tel3s (10 μM), rDNA (2.5 μM, 10 μM), and rec8 (10 μM) loci. DNA amount was quantified using Qubit dsDNA HS assay kit (Invitrogen) and a Qubit 2.0 Fluorometer (Invitrogen). For each sample, 3.1 ng of DNA were used for library preparation. The library preparation for Illumina sequencing was done using NEBnext ChIP-seq Library Prep Master Mix Set for Illumina (New England Biolabs) following their protocol. Final DNA samples were quantified using Qubit dsDNA HS assay kit (Invitrogen) and a Qubit 2.0 Fluorometer (Invitrogen), and 120 ng of library DNA for each sample was submitted to the sequencing facility for analysis using Illumina HiSeq2000 (Illumina). Sequences were processed by the Illumina analysis pipeline version 1.6.1, and aligned to the fission yeast sequence (version ASM294v1.18). Data was visualized with Integrated Genome Browser [130]
Cells expressing Rad52-YFP and Taz1-mCherry from their own promoters were grown at 25°C in Edinburgh minimal medium (EMM) with necessary supplements until mid-log phase. Cells grown at 25°C have more stable fluorescence with lower background signals. Live-cell imaging analysis of Rad52-YFP and Taz1-mCherry localization was performed using an Olympus PROVIS AX70 microscope equipped with a Retiga EXi camera (QImaging, Surrey, BC, Canada). Images were acquired with iVision software (BioVision Technologies, Exton, PA) and analyzed with ImageJ software (National Institutes of Health, Bethesda, MD). At least 100 cells were counted for each experiment.
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10.1371/journal.pntd.0001374 | Comparison of Two Multilocus Sequence Based Genotyping Schemes for Leptospira Species | Several sequence based genotyping schemes have been developed for Leptospira spp. The objective of this study was to genotype a collection of clinical and reference isolates using the two most commonly used schemes and compare and contrast the results.
A total of 48 isolates consisting of L. interrogans (n = 40) and L. kirschneri (n = 8) were typed by the 7 locus MLST scheme described by Thaipadungpanit et al., and the 6 locus genotyping scheme described by Ahmed et al., (termed 7L and 6L, respectively). Two L. interrogans isolates were not typed using 6L because of a deletion of three nucleotides in lipL32. The remaining 46 isolates were resolved into 21 sequence types (STs) by 7L, and 30 genotypes by 6L. Overall nucleotide diversity (based on concatenated sequence) was 3.6% and 2.3% for 7L and 6L, respectively. The D value (discriminatory ability) of 7L and 6L were comparable, i.e. 92.0 (95% CI 87.5–96.5) vs. 93.5 (95% CI 88.6–98.4). The dN/dS ratios calculated for each locus indicated that none were under positive selection. Neighbor joining trees were reconstructed based on the concatenated sequences for each scheme. Both trees showed two distinct groups corresponding to L. interrogans and L. kirschneri, and both identified two clones containing 10 and 7 clinical isolates, respectively. There were six instances in which 6L split single STs as defined by 7L into closely related clusters. We noted two discrepancies between the trees in which the genetic relatedness between two pairs of strains were more closely related by 7L than by 6L.
This genetic analysis indicates that the two schemes are comparable. We discuss their practical advantages and disadvantages.
| Two independent multilocus sequence based genotyping schemes (denoted here as 7L and 6L for schemes with 7 and 6 loci, respectively) are in use for Leptospira spp., which has led to uncertainty as to which should be adopted by the scientific community. The purpose of this study was to apply the two schemes to a single collection of pathogenic Leptospira, evaluate their performance, and describe the practical advantages and disadvantages of each scheme. We used a variety of phylogenetic approaches to compare the output data and found that the two schemes gave very similar results. 7L has the advantage that it is a conventional multi-locus sequencing typing (MLST) scheme based on housekeeping genes and is supported by a publically accessible database by which genotypes can be readily assigned as known or new sequence types by any investigator, but is currently only applicable to L. interrogans and L. kirschneri. Conversely, 6L can be applied to all pathogenic Leptospira spp., but is not a conventional MLST scheme by design and is not available online. 6L sequences from 271 strains have been released into the public domain, and phylogenetic analysis of new sequences using this scheme requires their download and offline analysis.
| Leptospirosis is a common zoonotic disease worldwide, with a particularly high prevalence in warm humid countries [1]–[4]. About 350,000 severe cases of leptospirosis are estimated to occur annually, with case fatality reports up to 50% [5]–[7]. Reported cases are likely to be a gross under-estimate of global incidence rates, the result of a combination of factors including lack of surveillance, diagnostics and notification in those countries with the highest disease burden. Leptospirosis is currently considered a globally re-emerging disease, with frequent outbreaks in South East Asia (including Thailand, India, The Philippines and Sri Lanka) as well as in Latin America [3], [8]–[14]. International travel also leads to presentation of leptospirosis cases in settings where incidence is low and clinicians are unfamiliar with its clinical manifestations [7],[15].
Identification and typing of Leptospira species plays an important role in understanding disease epidemiology and pathogenicity, together with the development of diagnostic tools, effective vaccines and preventive strategies. During the last three decades many molecular typing methods have been proposed for Leptospira spp. These include DNA-DNA hybridization analysis [16]–[19], randomly amplified polymorphic DNA (RAPD) fingerprinting [20], arbitrarily primed PCR (AP-PCR) [21], [22], pulsed field gel electrophoresis (PFGE) [23], [24], restriction fragment length polymorphism (RFLP) analysis [25], [26], bacterial typing methods based on insertion sequences (IS) [27], detection of variable number of tandem repeats (VNTR) [28], [29], rrs sequencing [30]–[32], and sequencing of specific genes or gene fragments including rpoB, gyrB, secY and ligB [33]–[37].
Multilocus sequencing typing (MLST) has been widely adopted for the study of bacterial evolution and population biology of a large number of microbial species [38], and represents the leading molecular method for bacterial genotyping. MLST based on 7 housekeeping loci has been developed for Leptospira [39], and is supported by a publically accessible database by which genotypes can be readily assigned as known or new sequence types. An alternative sequence based genotyping scheme of 6 loci including housekeeping genes, a 16S rRNA gene and genes encoding surface-expressed proteins has also been developed and used by several groups. This has led to uncertainty as to which scheme should be adopted. The aim of the current study was to compare the two schemes in terms of their discriminatory ability, both within and between species, by generating data using both schemes for a single set of isolates. We also discuss the practical aspects relating to each scheme.
The Leptospira isolates used in this study and their providers are shown in Table 1. Genomic DNA was extracted from laboratory bacterial cultures as described previously [39], [40].
All isolates were evaluated using both genotyping schemes [39],[40]. The MLST scheme described by Thaipadungpanit et al. (2007), is based on pntA, sucA, pfkB, tpiA, mreA, glmU and fadD [39], and the scheme described by Ahmed et al. (2006) is based on adk, icdA, secY, rrs2, lipL41, and lipL32 [40]. The terms 7L and 6L have been adopted throughout to refer to the 7 and 6 gene schemes, respectively. No modifications were made to the published primers or cycling conditions of 7L. Table 2 lists the primer pairs used for 6L. Four of the 12 primers (adk-F, adk-R, secY-R and icdA-R) were modified compared with the published 6L scheme, and used in a repeat PCR reaction in the event that the original primers failed to generate an amplicon. Cycling conditions were as described previously for 6L, with the exception that reactions using the four new 6L primers had a reduced annealing temperature of 54°C. Sequence data were edited using SeqMan software contained within the DNASTAR package (DNASTAR Inc., Wisconsin, USA). The region of sequence used to define each locus of 7L was as described previously [39], but the region used to define each locus of 6L was altered as follows. Three loci (secY, lipL32 and lipL41) were changed because the published PCR product and the region of sequence used to define the locus were either identical (secY and lipL32) or different by just two bp [40]. This meant that we were unable to obtain high quality sequence traces for the first 10–20 bases of the amplicon, and so trimmed the sequence in frame by approximately 20 bp at either end for all three genes. The other 3 published loci of 6L (adk, icdA and rrs2), were trimmed by one or two bases to put them in frame, which simplifies the analysis of synonymous and non-synonymous substitutions. The sequence start and end points for the 6 loci of 6L are shown in Table 2.
The alleles at each of the 7L loci were assigned and the sequence type (ST) defined using the publically accessible Leptospira MLST website (http://leptospira.mlst.net/). Allelic numbers, profiles and STs were not generated for the 6L data.
Sequence alignment, nucleotide diversity and reconstruction of phylogenetic trees were performed using Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0 [41]. Mean pairwise distances (p distance) were calculated using the Kimura Two Parameter nucleotide substitution model. Synonymous (dS) and non-synonymous (dN) nucleotide substitutions were calculated based on the Modified Nei-Gojobori method with Jukes Cantor correction using MEGA 4. Neighbor joining trees were reconstructed based on concatenated sequences of each scheme using the Kimura Two-Parameter substitution model. Gene order of the concatenated sequences were glmU, pntA, sucA, fadD, tpiA, pfkB, and mreA for 7L, and adk, icdA, lipL32, lipL41, rrs2, and secY for 6L. Discriminatory ability (D value) and 95% confidence intervals (CI) were estimated as described previously [42], [43]. These values were verified using the LIAN web tool housed on pubmlst.org [44]. A sliding window analysis of within- and between-species variation was carried out using DNAsp v. 5.0 [45]. An initial “window” of 400-bp was selected, as this is roughly equivalent to a single allele. The first window was thus from base 1 to base 400 of the concatenated sequence. From this we took each species in turn and calculated the average number of nucleotide differences per site over all pairwise comparisons (π), to give the within species polymorphism. Similarly, we calculated the number of fixed differences between species (substitutions) per site to gauge the divergence between L. interrogans and L. kirschneri. The window region was then moved along 50-bp and these parameters recalculated. GenBank accession numbers of 6L generated sequences are JF509178–JF509357.
A total of 48 strains and isolates belonging to L. interrogans (n = 40) and L. kirschneri (n = 8) were included in this study, of which 17 were reference strains and 31 were clinical isolates – further referred to as strains (Table 1). Nine strains had been evaluated previously by both schemes [39], [40], and 39 strains typed previously by only one of the two schemes were typed by the other scheme during this study. Two strains (a Thai clinical isolate strain L1207 of unknown serovar and a reference strain of serovar Kuwait strain 136/2/2) could not be typed using 6L as both had a deletion of three nucleotides in the lipL32 sequence. These two strains were excluded from further analysis.
7L resolved the 46 strains into 21 STs, shown in Table 1. 6L data were analysed off line, and the alleles at the six loci given arbitrary allelic numbers to construct an allelic profile and determine the number of genotypes. This demonstrated a total of 30 genotypes (data not shown). Overall levels of diversity (D) were comparable for the 7L and 6L schemes (92.0 (95% CI 87.5–96.5) and 93.5 (95% CI 88.6–98.4), respectively). The discriminatory ability per locus ranged from 59% (sucA) to 87% (glmU and mreA) for 7L and 66% (rrs2) to 92% (secY) for 6L (Table 3). All D values were verified using the LIAN web tool housed at pubmlst.org and found to be identical to the values shown. The majority of alleles of both schemes were species specific (that is, found in either L. interrogans or L. kirscheri but not both). There were three exceptions where alleles were found in both species, as follows: 7L, allele 1 of sucA; 6L, one allele of lipL32 and one allele of rrs2.
Overall nucleotide diversity (based on concatenated sequences) for the 46 isolates was 3.6% and 2.3% for 7L and 6L, respectively (Table 3). The diversity within L. interrogans was lower than that within L. kirschneri (0.5% and 1.1% for 7L, and 0.4% and 0.8% for 6L, respectively). Table 3 also details the nucleotide diversity by locus. This ranged from 3.6% to 6.1% for 7L, and 0.5% to 6.7% for 6L. The lowest diversity was observed for lipL32 and rrs2 of 6L. The dN/dS ratios calculated for each locus indicated that none were under positive selection (that is, all values were lower than 1) (Table 3).
A sliding window analysis of the concatenated sequences was performed to provide a visual comparison of the degree of polymorphism within both species, and the level of divergence between them. This revealed a generally higher level of variation within L. kirschneri compared to L. interrogans, particularly at sucA (7L) and to a lesser extent lipL41 (6L), although the sample size for the former species was very small (n = 8) (Figure 1). This analysis confirmed that the degree of within species polymorphism showed very little difference between the 7L and 6L scheme. However, 7L tended to provide better resolution between species, which was largely accounted for by the low level of divergence for lipL32 and rrs2 of 6L.
Neighbor joining trees were reconstructed for 7L and 6L based on the concatenated sequences of their respective loci (Figure 2). Both trees showed two distinct groups corresponding to L. interrogans and L. kirschneri. There were also several obvious similarities within L. interrogans between the two trees. For example, the clonal structure of ST34 and ST46 as defined by 7L was maintained by 6L. A common finding, however, was that 6L had a tendency to split single STs as defined by 7L into closely related clusters. For example, the three isolates designed as ST49 by 7L were split into three different genotypes by 6L. Further examples of splitting of a clone by the 6L scheme were 7L ST42, ST37, ST68 and ST17. A number of discrepancies were noted between the two trees. Two strains of L. kirschneri (strains Moskva V and Kipod 179) were designated by 7L as ST110, but these were resolved into different genotypes by 6L. These two strains differed by 9 nucleotides over 3 loci, with secY accounting for 7 of these. A difference was also noted for L. interrogans strain 654 (a Thai clinical isolate), which was closely related to L. interrogans strain Hardjoprajitno by 6L (differing by only 1 nucleotide), but was more distantly related by 7L (differing by 11 nucleotides over 6 loci).
The authors of this paper include representatives of the scientific groups that reported two independent genotyping schemes for Leptospira spp. Here, we provide the scientific community with the findings of a study that compared and contrasted the two schemes, together with a discussion of the practical aspects related to undertaking each.
The two schemes are unrelated and different by design. 7L was founded on a conventional strategy for MLST of selecting 7 housekeeping genes that were distributed around the genome and were not under positive selection. The design of 6L varied from this in that 6 loci were selected from different functional categories. For example, lipL41 and lipL32 encode surface expressed proteins that would be expected to be under positive selection as a result of being immunogenic and a target for the host response. At the other end of the spectrum, rrs2 is one of two 16S rRNA genes that would be predicted to be highly conserved.
Contrary to our expectations, we did not find that any of the 6L genes were under positive selection. More genotypes were resolved by 6L than by 7L, in part a function of the high number of alleles for secY. Analysis of genetic diversity indicated that there was little difference in within-species variation difference between the two schemes, both pointing to generally higher levels of variation within L. kirschneri than L. interrogans. The conserved nature of two loci used in 6L (lipL32 and rrs2), resulted in the finding on sliding window analysis that 7L provided better between-species resolution. Interestingly we noted that rrs2 of 6L showed a higher D value than the housekeeping gene sucA of 7L. Although this is an exception to the general rule that housekeeping metabolic genes provide more discrimination than conserved genes such as those encoding ribosomal RNA, such an observation is not unprecedented [46].
6L has been applied to six pathogenic Leptospira spp. [40], which compares favorably with 7L which was designed for the two closely related species L. interrogans and L. kirschneri. However, this disadvantage of 7L will be resolved within the next 12 months; the scheme has already been extended to L. borpetersenii (manuscript in preparation), and the laboratory work to extend this to all pathogenic species is now completed. These improvements will be made publicly available by the end of 2011.
Conversely, the 6L scheme does not conform to the original concept of MLST as it includes a non-housekeeping gene (rrs2), and genes that encode cell surface proteins. Furthermore, the sequence start and stop sites used to define the allele for each locus were not provided in the original description of 6L scheme and so could not be performed based on the published methodology alone, although these have been detailed in this study. Minor changes were necessary to the start and stop sites, but we think it unlikely that this led to a change in the performance of the scheme.
The 6L scheme is not associated with a publically accessible website that allows an investigator to compare new data with existing sequence data. 6L has recently been applied to an extended set of strains and isolates (n = 271) encompassing a wide diversity of hosts and geographic regions [47], providing a rich source of sequence data that has been released into the public domain (GenBank). Comparative phylogenetic analysis by individual investigators will require downloading and storage of these data. In contrast, a website for 7L was launched at the time of publication and is regularly maintained and curated. At least one representative of each ST is recorded in a downloadable spreadsheet, providing a mechanism by which a picture of global bacterial diversity can be developed over time. This is easy to use, provides tools for comparison of a given strain with all of the other strains in the database, is more suited to investigators with limited phylogenetic training and experience, and so has the power to reach a wider audience.
In conclusion, we have provided detailed comparisons of two major genotyping schemes for Leptospira spp., and have described their advantages and disadvantages. 7L complies with the philosophy of MLST (housekeeping genes only supported by website), but will not be ready for use for the study of all pathogenic Leptospira spp. until the end of 2011. In the meantime, a bioinformatics analysis of the discriminatory power of 4 genes (three of which are not present in either scheme) as well as a new scheme with 7 loci both limited to L. interrogans and L. kirschneri have been reported [48], [49], adding further diversity to the tools available for the phylogenetic study of Leptospira spp. There is a pressing need for consensus within the leptospirosis community as to the preferred genotyping scheme, an essential step if the wealth of knowledge gathered for other bacterial species based on detailed analysis within a single scheme is to be replicated for Leptospira spp. Both schemes contain highly discriminative and less discriminative loci. While it is feasible to formulate a consensus MLST combining the most discriminative housekeeping genes from both schemes, we have resisted the temptation of presenting an interim scheme that has not been extensively validated. Instead, we aim to expedite the release of the 7L MLST scheme for all the major pathogenic species, and recommend its use for the study of the global epidemiology of pathogenic Leptospira spp.
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10.1371/journal.ppat.1003156 | Dual Short Upstream Open Reading Frames Control Translation of a Herpesviral Polycistronic mRNA | The Kaposi's sarcoma-associated herpesvirus (KSHV) protein kinase, encoded by ORF36, functions to phosphorylate cellular and viral targets important in the KSHV lifecycle and to activate the anti-viral prodrug ganciclovir. Unlike the vast majority of mapped KSHV genes, no viral transcript has been identified with ORF36 positioned as the 5′-proximal gene. Here we report that ORF36 is robustly translated as a downstream cistron from the ORF35–37 polycistronic transcript in a cap-dependent manner. We identified two short, upstream open reading frames (uORFs) within the 5′ UTR of the polycistronic mRNA. While both uORFs function as negative regulators of ORF35, unexpectedly, the second allows for the translation of the downstream ORF36 gene by a termination-reinitiation mechanism. Positional conservation of uORFs within a number of related viruses suggests that this may be a common γ-herpesviral adaptation of a host translational regulatory mechanism.
| Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiologic agent of multicentric Castleman's disease, primary effusion lymphoma and Kaposi's sarcoma. KSHV expresses a number of transcripts with the potential to generate multiple proteins, yet relies on the cellular translation machinery that is primed to synthesize only one protein per mRNA. Here we report that the viral transcript encompassing ORF35–37 is able to direct synthesis of two proteins and that the translational switch is regulated by two short upstream open reading frames (uORFs) in the native 5′ untranslated region. uORFs are elements commonly found upstream of mammalian genes that function to interfere with unrestrained ribosomal scanning and thus repress translation of the major ORF. The sequence of the viral uORF appears unimportant, and instead functions to position the translation machinery in a location that favors translation of the downstream major ORF, via a reinitiation mechanism. Thus, KSHV uses a host strategy generally reserved to repress translation to instead allow for the expression of an internal gene.
| Translation initiation of eukaryotic mRNAs is dependent on the 5′ mRNA cap and proceeds by ribosomal scanning until recognition of an AUG codon in a favorable context [1], [2]. As a consequence of the translation machinery not engaging start codons at internal positions within the mRNA, eukaryotic transcripts generally encode only one functional protein. For the majority of mRNAs the most 5′-proximal AUG is selected, however strategies exist to bypass upstream start codons to enable downstream initiation. For example, leaky scanning can occur if the nucleotides flanking the 5′-proximal AUG are not in the Kozak consensus sequence (ccRccAUGG), allowing the 40S ribosomal subunit to instead engage a downstream methionine codon [2], [3]. Alternatively, when an upstream AUG is followed shortly thereafter by an in-frame termination codon, ribosomes can reinitiate translation, albeit with reduced efficiency, at a downstream AUG. These upstream open reading frames (uORFs) presumably permit translation of a downstream gene because factors necessary for initiation have not yet dissociated during the short elongation period. Notably, uORFs are common regulatory elements in eukaryotic transcripts, and generally function to reduce translation of the major ORF [3], [4]. Additional, although rare, examples of internal ORF translation also exist, for example after ribosome shunting over a highly structured upstream sequence [5]–[8], or upon direct 40S recruitment via internal ribosome entry sites (IRESs) [9]–[13].
Viruses do not encode translation machinery and thus operate under the constraints of host protein synthesis. However, the compact nature of viral genomes has resulted in the evolution of specialized strategies to maximize their coding capacity. Examples of such mechanisms include translation of a large polyprotein that is cleaved into multiple proteins, ribosomal frameshifting and non-canonical translation mechanisms such as those described above [14]. Accordingly, many viral mRNAs do not conform to the one protein per mRNA cellular paradigm and require specialized mechanisms to subvert the translational constraints of the host.
Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiologic agent of several human cancers including multicentric Castleman's disease, primary effusion lymphoma and Kaposi's sarcoma (KS), one of the early AIDS-defining illnesses [15]–[17]. KSHV is a double-stranded DNA virus of the γ-herpesvirus subfamily, possessing a ∼165-kb genome and encoding an estimated 80 viral proteins [17], [18]. The viral genes closely resemble those of their cellular counterparts in that they have canonical transcriptional promoters, consensus pre-mRNA splice sites and 3′-end formation signals. However, one notable departure from the cellular paradigm is the scarcity of poly(A) sites distributed throughout the genome, with a single signal often allocated to several consecutive ORFs. These gene clusters give rise to viral transcripts with polycistronic coding potential, although in general only the 5′-proximal gene is translated on each mRNA [19]–[21]. Most genes are positioned as a 5′ cistron by the use of multiple transcriptional start sites upstream of common poly(A) signals and/or alternative splicing [21], [22]. To date, the only described KSHV mechanism to enable translation of a 3′-proximal ORF is an IRES identified in the coding region of vCyclin (ORF72), which allows for expression of vFLIP (ORF71) [23]–[25].
A previously described tricistronic KSHV mRNA encompasses three partially overlapping open reading frames that are expressed with lytic kinetics (ORF35, 36, and 37). However, the mechanism of translation initiation of the 5′-distal ORF36 and ORF37 proteins has remained unresolved [26], [27]. The function of the protein product of the 5′-proximal ORF35 is ill defined, although it shares limited sequence similarity with the α-herpesvirus UL14 gene product, which has described heat shock protein-like properties and functions to inhibit apoptosis during host cell infection [28], [29]. The second gene, ORF36, encodes a serine/threonine kinase that activates the cellular c-Jun N-terminal kinase (JNK) signaling pathway and phosphorylates the viral transcriptional transactivator K-bZIP, two processes involved in the progression from early to late viral gene expression [27], [30], [31]. Moreover, ORF36 sensitizes KSHV-infected cells to ganciclovir, an anti-viral drug shown to reduce KSHV replication in cultured cells and in patients [32]–[35]. The 3′-proximal ORF37 expresses SOX (shutoff and exonuclease), a viral protein responsible for promoting widespread degradation of host mRNAs and also thought to assist in viral DNA replication and packaging [36]–[38].
Here, we demonstrate that the ORF35–37 transcript is functionally bicistronic, supporting translation of both ORF35 and ORF36, whereas ORF37 is expressed from a previously uncharacterized monocistronic transcript. The polycistronic locus lacks IRES activity, and both proteins are expressed in a cap-dependent manner. Interestingly, translation of ORF36 occurs via a reinitiation mechanism after engagement of one of two overlapping short uORFs located in the 5′-untranslated region (UTR), which also regulate the relative expression levels of these proteins. Thus, KSHV uses a host strategy normally reserved to repress translation of the major ORF to instead permit expression of a 3′-proximal cistron on a viral polycistronic mRNA. Analysis of homologous genetic loci from additional γ-herpesviruses similarly revealed the presence of dual short upstream ORFs (uORFs), suggesting this may be a conserved mechanism of translation initiation among these viruses.
Two potential functionally polycistronic mRNAs are transcribed from the KSHV ORF34–37 genetic locus during lytic replication: a minor transcript encompassing ORFs 34, 35, 36, and 37 (ORF34–37) and a major transcript encompassing ORFs 35, 36 and 37 (ORF35–37) (Figure 1A) [26], [27]. Although both ORF36 and ORF37 proteins play important roles in the viral lifecycle, no transcripts were reported in which these ORFs were present as the 5′-proximal cistron [26], [27]. To confirm this observation, we searched for transcripts produced from this locus in a B cell line (TREx BCBL1-RTA) that harbors KSHV in a latent state but can be stimulated to engage in lytic replication. RNA isolated from cells infected latently or lytically for 8–36 h was Northern blotted with riboprobes specific for ORF36 or ORF37. In infected cells, the ORF36 probe recognized transcripts co-migrating with or larger than the polycistronic ORF35–37 mRNA but did not reveal any smaller, potentially monocistronic species (Figure 1B). Results from ORF36 5′ rapid amplification of cDNA ends (RACE) experiments were in agreement with its transcript initiating upstream of ORF35 at nucleotide position 55567 as previously reported by Haque et al. (Figure 1A, data not shown) [18]. In contrast, the ORF37 probe reacted with transcripts ≥3.4 kb and an additional ∼1.7 kb transcript that co-migrated with the control ORF37 monocistronic mRNA (Figure 1C). Analysis of transcription start sites by 5′ RACE (data not shown), as well as similar observations in a related γ-herpesvirus further supported the presence of an ORF37 monocistronic transcript [39]. Thus, ORF37 is most likely translated by the canonical cap-dependent scanning mechanism and is present as a silent cistron on the ORF35–37 polycistronic mRNA.
We next sought to evaluate directly whether the ORF35–37 transcript could support translation of ORF36 as a downstream gene. 293T cells were first transfected with a plasmid expressing the coding sequence of ORF35–37 downstream of the native viral 72-nt 5′ UTR, and lysates were Western blotted using polyclonal antisera specific for ORF36 or, as a control, ORF37. The ORF36 protein was readily translated from this polycistronic construct, whereas the ORF37 protein was detected only in cells transfected with the monocistronic ORF37 plasmid (Figure 1D, 1E). In these and all subsequent experiments, Northern blotting of the mRNAs produced from each transfection confirmed that the transcripts were of the expected size and of equivalent abundance across experiments (Figure 1D, 1E).
ORF35 is conserved between the α, β, and γ-herpesvirus subfamilies but its function remains unknown and antibodies are not available to detect it in KSHV-infected cells [40]. ORF35 is predicted to encode a 151-amino acid protein, and its start site resides in a favorable Kozak context. Nonetheless, we considered the possibility that ORF35 is not translated, instead serving as a portion of the 5′ UTR for ORF36. In order to directly compare the levels of ORF35 and ORF36 protein produced from the bicistronic construct, we engineered in-frame HA tags at the 5′ or 3′ end of each respective gene, maintaining the native viral 5′ UTR (5′ UTR HA-ORF35-ORF36-HA). Monocistronic versions of each HA-tagged gene were also generated as controls (5′ UTR HA-ORF35, ORF36-HA). Importantly, Western blotting with HA antibodies revealed that the ORF35 protein is produced from both the monocistronic and bicistronic constructs (Figure 2A).
Although our data indicated that the ORF35–37 transcript is functionally bicistronic, it was still formally possible that ORF36 translation occurred from a low-abundance monocistronic transcript generated by a cryptic internal promoter or splice site(s) in the DNA plasmid. To address this possibility, we transfected cells directly with in vitro transcribed monocistronic or bicistronic mRNAs, and performed anti-HA Western blots to detect each protein (Figure 2B). Again, both ORF35 and ORF36 protein were produced from the bicistronic 5′ UTR HA-ORF35-ORF36-HA mRNA, as well as from the appropriate control monocistronic mRNA, confirming that this locus is functionally polycistronic.
The only other known example in KSHV of translation of a downstream ORF from a polycistronic mRNA occurs via an IRES [23]–[25]. We therefore used an established dual luciferase assay to determine whether an IRES similarly resides upstream of ORF36. The dual luciferase construct consists of a 5′-proximal Renilla luciferase gene that can be constitutively translated via a cap dependent mechanism, followed by a 3′-distal firefly luciferase gene, which is not normally translated. The two genes are separated by a defective encephalomyocarditis virus (ΔEMCV) to prevent translational read-through [11], [41]. Sequences of interest are then inserted between the ΔEMCV and the firefly luciferase gene, and IRES activity leads to the translation of firefly luciferase. Sequences encompassing ORF35, ORF35–36 or ORF34–36 as well as two known IRES elements (EMCV and KSHV ORF72) were cloned into the dual luciferase construct. The capped and polyadenylated in vitro transcribed mRNA was electroporated into lytically infected TREx BCBL1-RTA cells (Figure 3A). The integrity of the mRNAs was verified by Northern blotting (data not shown). After 4 h, the ratio of firefly/Renilla luciferase activity was measured to determine whether IRES activity was detectable in the context of lytic infection. Although both the EMCV and ORF72 control IRES elements supported translation of firefly luciferase, none of the sequences upstream of ORF36 possessed detectable IRES activity (Figure 3B).
We next sought to determine whether ORF36 translation was instead initiated via a cap-dependent mechanism by inserting a strong 40 nucleotide hairpin (Hp7; ΔG = −61 kcal/mol) after nucleotide 32 within the 72 nucleotide native 5′ UTR of the 5′ UTR HA-ORF35-ORF36-HA construct (Figure 3C) [42]. Stable hairpin structures (ΔG<−30 kcal/mol) present near the 5′ cap dramatically reduce translation initiation by stalling the pre-initiation complex [42]. Translation of both ORF35 and ORF36 was markedly reduced in the presence of Hp7 following either DNA or RNA transfection (Figure 3D, S1A). Thus, recognition of the 5′ cap and subsequent 40S scanning are critical for translation of both ORF35 and ORF36.
It is notable that ORF36 protein production is robust given that its translation requires the pre-initiation complex to bypass the relatively strong Kozak context surrounding the ORF35 start codon (AgaAUGG) and to scan through 424 nucleotides of upstream sequence. To determine whether the context of the ORF35 start codon influences the expression of ORF36, we mutated the preferred nucleotide (A) at position −3 to the least preferred nucleotide (U) (35 KCS wkn; Figure 3E). As expected, ORF35 expression was reduced; however, surprisingly, this mutation this did not significantly alter ORF36 expression, arguing against a pure leaky scanning mechanism to explain ribosomal access to the ORF36 start site (Figure 3F). Direct transfection with in vitro transcribed mRNAs confirmed that this result was not due to induction of an alternative promoter (Figure S1B). Thus, the relative strength of the ORF35 start site does not dramatically influence ORF36 translation, suggesting that there is an alternative mechanism in place that disfavors initiation at the 5′ gene.
We searched for features of the ORF35–37 sequence that might contribute to translational start site selection. Within the 5′ UTR we noticed two short upstream ORFs (uORFs). The first nine codon uORF, dubbed uORF1, spans KSHV nucleotides 55603 to 55629 and has an AUG residing in a relatively weak Kozak context (CguAUGA) [18]. The second 11 codon uORF (uORF2) spans KSHV nucleotides 55626–55658 and overlaps with both the 3′ end of uORF1 and the ORF35 start codon (Figure 4A). To determine the contribution of uORF1 towards ORF35 and ORF36 translation, we mutated the uORF1 start site (Δ1) (Figure 4A). ORF35 expression was elevated in the Δ1 mutant (Figure 4B). We confirmed that the HA tag at the 5′ end of ORF35 did not alter this translational regulation by showing similar results upon repositioning of the HA tag internally within ORF35 (Figure S2). Thus, ORF35 expression undergoes modest negative regulation by ribosomal engagement at the uORF1 start codon, although this does not appear to influence ORF36 expression.
The uORF2 start codon is in a more favorable Kozak context than that of uORF1, and disruption of the uORF2 AUG (AUG→UUG; Δ2) or weakening the Kozak context of its start codon (KCS2 wkn) increased ORF35 translation and severely decreased translation of ORF36 in both DNA and RNA transfection experiments (Figure 4C–D, S3). Notably, the Δ2 mutant was designed to ensure the uORF1 stop codon remains intact, permitting the independent analysis of uORF1 and uORF2. Unlike uORF1, uORF2 therefore plays a key role in regulating expression of both genes in this polycistronic mRNA, likely due to the strong context flanking the uORF2 AUG as compared to the uORF1 start codon.
Although a few rare uORFs have been found to function in a sequence-dependent manner [43]–[47], for most characterized uORFs it is the act of translation rather than the peptide sequence that mediates their function. The fact that 45% of the uORF2 amino acid sequence is altered in the construct bearing the HA tag at the 5′ end of ORF35 is in agreement with the amino acid sequence of uORF2 not being the primary determinant of its activity. Indeed, rebuilding the uORF2 mutants into a construct in which the HA tag was moved to an internal position in ORF35 yielded indistinguishable results (Figure S4).
The above findings suggested that engagement of the translation machinery at either uORF1 or uORF2 rather than the sequence of the uORF-encoded peptide mediates their regulatory function. We therefore sought to confirm that these uORFs were indeed recognized by the translation machinery. Due to their small size, uORF-generated peptides tend to be highly unstable and are very difficult to detect. To circumvent this problem, we made a single nucleotide change in each uORF to place them in frame with ORF35 lacking its AUG (Δ35), thereby generating uORF-ORF35 fusions (Figure 4E). Thus, restoration of ORF35 expression is a direct readout translation initiation from the uORF start codon. In both cases, the uORF fusions restored ORF35 expression to levels corresponding to the relative strength of the Kozak consensus sequence of each uORF (Figure 4F). As expected, only the uORF2 fusion abrogated expression of ORF36 (Figure 4F).
Finally, to determine whether additional cis-acting elements within ORF36 are required for its translation after uORF2 engagement, we replaced the ORF36 gene with a GFP reporter (Figure 4G). GFP protein was expressed robustly as a downstream gene from this construct, arguing against a requirement for an element within ORF36 for its translation (Figure 4H). Similar to our results with ORF36, disruption of uORF2 compromised expression of GFP (Figure 4H), supporting a uORF2-dependent mechanism as the primary pathway enabling translation of a downstream gene from this locus.
Translation of a major ORF following engagement at a uORF generally occurs via a termination-reinitiation event. The length of a uORF is important for reinitiation, as it is thought that some of the translation initiation accessory factors have not yet dissociated prior to termination at the uORF stop codon [4]. In this regard, translation of the downstream ORF decreases dramatically if the time required to complete translation of the uORF is increased, for example by increasing the ORF length or inserting secondary structure to stall the ribosome [48], [49]. Therefore, we reasoned that if ORF36 translation initiates using the same 40S ribosomal subunit involved in translation of uORF2, then artificially elongating uORF2 should inhibit ORF36 expression. This experiment was performed on the construct backbone with the ORF35 HA tag located internally to mimic the wild type length of uORF2. Indeed, extension of uORF2 from 11 to 64 codons (uORF2-long) resulted in a dramatic drop in ORF36 expression (Figure 5A–B).
The rate-limiting step of reinitiation is postulated to be the re-acquisition of the pre-initiation complex (eIF2-GTP-Met-tRNAi) during ribosomal scanning, and thus a sequence of sufficient length must be present downstream of the uORF for this to occur [3], [4]. We therefore evaluated how the distance between the uORF2 stop codon and the subsequent start codon influences reinitiation within the viral mRNA. Start codons in a favorable Kozak context were inserted at two positions between the uORF2 stop codon and the ORF36 start site. We hypothesized that start codons located close to uORF2 would not be as efficiently recognized, and therefore they would not inhibit ORF36 expression. However, more distally located start codons should better engage the initiation machinery, thereby preventing translation from occurring at the authentic ORF36 start site. In agreement with this prediction, a start codon positioned 16 nucleotides downstream of uORF2 did not strongly inhibit ORF36 expression, whereas a methionine positioned 246 nucleotides after termination of uORF2 severely compromised ORF36 expression (Figure 5C–D). These data support the conclusion that engagement of the ORF36 start codon is dependent on the reacquisition of the pre-initiation complex after termination of uORF2 translation.
Translation reinitiation at the internal ORF36 start codon could occur either after linear scanning of the 40S complex through the 332-nucleotide intercistronic region between uORF2 and ORF36 or through shunting of the complex past this sequence and its subsequent positioning proximal to ORF36. To distinguish between these possibilities, two strong hairpins (Hp7) that impede scanning were inserted within the 5′-proximal or 3′-proximal coding region of ORF35 (Figure 5E). If the 40S ribosomal subunit were shunted past these internal sequences, one or both of the hairpins (depending on the location of the shunting sites) should not compromise ORF36 translation [5], [50]. However, we observed a significant reduction in ORF36 expression in the presence of either hairpin, arguing that the 40S complex scans in a linear fashion through ORF35 (Figure 5F).
One potential caveat is that the insertion of the hairpins might dramatically alter the RNA folding landscape, disrupting a secondary structure required for shunting. To exclude this possibility, the single natural methionine codon present within the coding region of ORF35, was mutated to an arginine (MidMut; Figure 5G). If this internal sequence were bypassed via shunting after uORF2 termination, the natural start codon should not be able to compete with the ORF36 AUG for the pre-initiation complex. However, we found that ORF36 expression was increased from the MidMut construct, arguing against a shunting mechanism and further suggesting that this methionine normally engages a fraction of the scanning ribosomes before they can reach the ORF36 start codon (Figure 5H). Translation of the peptide generated cannot be directly monitored due to the fact that it is only eight amino acids. Collectively, these data support a model in which the preferential recognition of uORF2 diverts ribosomes past the ORF35 start codon, whereupon they scan in a linear fashion and reacquire the pre-initiation complex before reinitiating translation at a downstream start codon.
To confirm that uORF2 regulates ORF36 expression during lytic KSHV infection, we engineered a uORF2 point mutant (BAC16-Δ2; ATG→TTG) and a revertant mutant rescue (BAC16-Δ2-MR; TTG→ATG) within the recently described KSHV BAC16 (Figure S5) [51]. BAC16-WT, BAC16-Δ2 and BAC16-Δ2-MR were transfected into iSLK-PURO cells bearing a doxycycline-inducible RTA expression system to enable lytic reactivation [52]. Immunoblot analysis using polyclonal anti-sera specific for ORF36 revealed that while ORF36 was readily detectable at 48 h post-lytic reactivation in cells infected with WT or the mutant rescue virus, deletion of the uORF2 start codon severely compromised ORF36 expression (Figure 6). In contrast, the uORF2 mutation had no effect on the levels of the KSHV latent protein LANA or the lytic protein ORF57, confirming its specificity for ORF36 (Figure 6). Thus, uORF2 plays a critical role in enabling expression of the ORF36-encoded viral protein kinase during lytic KSHV infection.
We examined whether the loci analogous to KSHV ORF35–37 in several additional γ-herpesviruses also possessed uORFs within their 5′ UTRs (Table S1). Indeed, we identified two 6–12 codon uORFs within the predicted 5′ UTR of the locus in Epstein Barr virus (EBV), herpesvirus saimiri (HaSV-2) and ateline herpesvirus 3 (AtHV-3) and one 11 codon uORF in good context within the 5′ UTR of the rhesus rhadinovirus (RRV) locus (Figure 7A, 7B). The fact that the uORF positioning but not the coding sequence is conserved supports the hypothesis that their regulatory contribution relies on their ability to engage translation complexes, rather than the actual peptide produced. Furthermore, eight of the nine ORF35 homologs examined contain ≤2 internal methionine codons, as would be predicted if a termination-reinitiation mechanism was used to translate the downstream gene (Table S1). Interestingly, in all cases where two uORFs are present, the first uORF is within a weaker Kozak context than the second uORF, which overlaps the start codon of each ORF35 homolog (EBV BGLF3.5, SaHV-2 ORF35, AtHV-3 ORF35 and RRV ORF35). Thus, the conservation of uORFs at this genetic locus suggests that using uORFs to enable expression of a 3′-proximal gene may be a conserved strategy for translational control among these viruses. However, whether these loci indeed encode a functional polycistronic mRNA and are regulated by a similar uORF-based mechanism remains to be experimentally verified.
In this study, we describe a novel functionally bicistronic viral mRNA that is translated via a unique adaption of ribosomal reinitiation. In other characterized examples of viral translation via a reinitiation mechanism, expression of the downstream gene is significantly tempered as a consequence of ribosomal engagement at an upstream start codon [43], [53]–[56]. Aside from being bicistronic, translation from the KSHV ORF35–37 transcript is unusual in that the protein product of ORF36 is at least as robustly expressed as the 5′ ORF35 despite the fact that the ORF35 start codon is in a favorable sequence context. We reveal that a key mechanism underlying this phenotype involves the position of a short uORF overlapping the start codon of ORF35, which enables translation of ORF36 (Figure 8). These findings provide the first example of cap-dependent non-canonical translation in KSHV and illustrate a novel strategy to translate polycistronic mRNA.
Several lines of evidence support the notion that ORF36 is expressed in a cap-dependent manner as a 3′-proximal cistron. No transcript of an appropriate size with ORF36 as the 5′-proximal cistron was detected in KSHV-infected cells, in agreement with the results of 5′ RACE that indicated its transcription starts upstream of ORF35 [26]. In addition, ORF36 protein expression was detected after transfection of an in vitro transcribed bicistronic RNA transcript. Finally, interfering with scanning from the 5′ mRNA cap via insertion of a hairpin blocked ORF36 translation, consistent with our failure to detect IRES activity within the locus. This is in contrast with the sole functionally bicistronic KSHV mRNA described to date, where an IRES is present within the coding region of ORF72 allows for ORF71 expression in a cap-independent manner [23]–[25].
Our results indicate that the ORF36 start codon is accessed via a termination-reinitiation event after translation of uORF2. The most 5′ uORF (uORF1) resides in a weaker context than uORF2, which overlaps the ORF35 start codon. Importantly, because the stop codon of uORF1 overlaps with the start site of uORF2, engagement of these uORFs is mutually exclusive. Therefore, preferential initiation at uORF2 likely drives the enhanced translation of ORF36 by causing ribosomes to bypass the favorable ORF35 start codon. After translating uORF2, ribosomes continue to scan through the following 332 nucleotides to reinitiate at ORF36. In support of this model, lengthening uORF2 to decrease the efficiency of reinitiation abrogated ORF36 expression. Furthermore, weakening the context surrounding the uORF2 start codon enhanced ORF35 expression, suggesting that the ORF35 start site is primarily reached by ribosomes that have bypassed the AUG of uORF2, likely by leaky scanning. This provides a rare example of a uORF enhancing translation of a downstream major ORF.
To date, the only described short uORF that enables access to the start codon of a downstream gene in a polycistronic transcript was identified in hepatitis B virus (HBV). The HBV uORF, dubbed C0, weakly inhibits the 5′-proximal C ORF while stimulating translation of the 3′-proximal J and P proteins [6], [57]. However, the termination-reinitiation event described for HBV may be facilitated by a shunting mechanism, as non-linear scanning was found to occur in the homologous region in the related duck hepatitis B virus [58]. This appears not to be the case for ORF36 because insertion of strong hairpins within the coding region upstream strongly compromises ORF36 expression, suggesting that the ribosomes are scanning continuously from the 5′ mRNA cap to the ORF36 start codon.
uORFs are common features found in the 5′ UTRs of many mammalian mRNAs [59]. They are widely recognized as cis-regulatory elements and their presence generally correlates with reduced translation of the major ORF by causing initiation to instead occur by leaky scanning or a low-efficiency reinitiation event, which is agreement with the function of uORF1 as a negative regulator of ORF35 [4], [59], [60]. A few cases have been described in which the ability of the uORF to repress downstream translation is dependent on the amino acid sequence of the encoded peptide [43]–[47]. For example, a uORF present in the 5′ UTR of the human cytomegalovirus gp48 gene attenuates downstream translation in a sequence-dependent fashion, likely by delaying normal termination and preventing leaky scanning by the 40S ribosomal subunit to reach the downstream AUG [43]. However, in general, engagement of the translation apparatus rather than the translated product itself represses translation of the major ORF. Indeed, regulation of the ORF35–37 transcript appears independent of the uORF peptide sequence because the 5′ HA-tagged construct had two amino acids mutated within uORF2 yet still functioned to permit translation of ORF36. Moreover, uORFs in homologous regions of the genome in related γ-herpesviruses lacked amino acid conservation. However, individual amino acid substitutions in all of the uORF1 and uORF2 codons would be required to formally rule out a role for the encoded peptides in the translational control of this mRNA.
Factors that influence the ability of a terminating ribosome to resume scanning remain poorly understood. It has been shown using chimeric preproinsulin mRNAs that efficient reinitiation progressively improves upon lengthening the intercistronic sequence up to 79 nucleotides [61]. Sufficient intercistronic sequence length is thought to be necessary to allow time for the scanning 40S ribosomal subunit to reacquire eIF2-GTP-Met-tRNAi prior to encountering the downstream start codon, although at what point the sequence length becomes inhibitory is not known [4], [49]. In the context of the viral ORF35–37 transcript, the ribosome is able to reinitiate translation with a high frequency despite scanning 332 nucleotides after terminating translation of uORF2, indicating that intergenic regions significantly longer than 79 nucleotides still enable reinitiation.
Interestingly, a prior report identified a translational enhancer element within the tricistronic S1 mRNA of avian reovirus that functions to increase expression of a downstream cistron. This occurs as a consequence of sequence complementarity to 18S rRNA, which is reminiscent of the prokaryotic Shine-Dalgarno sequence [62], [63]. A similar strategy of having 18S rRNA complementarity within a bicistronic mRNA was also found to enhance the ability of the minor calicivirus capsid protein VP2 to be translated by reinitiation [56], [64]. Whether enhancer elements exist in the KSHV uORF-ORF36 intercistronic region to facilitate translation at the downstream cistron remains to be determined. However, no critical reinitiation element exists downstream of the ORF36 start codon, as replacement of these sequences with GFP does not block its translation. This is distinct from the termination-reinitiation mechanism described for certain retrotransposons, which require complex downstream secondary structures [65].
The question arises as to what benefit is conferred by this finely tuned strategy of translational control for both ORF35 and ORF36. One possibility is that ORF35 and ORF36 are required at different points during lytic infection and that during the course of viral replication, conditions arise that favor translation of one protein versus the other. This type of regulation occurs in the well-characterized Saccharomyces cerevisiae GCN4 locus, where four short uORFs modulate reinitiation at the major ORF depending on the level of eIF2α phosphorylation [66]–[68]. Indeed, certain types of cell stress have also been shown to influence non-canonical translation of the cytomegalovirus UL138 gene [69]. Alternatively, the uORFs may confer a tight level of regulation to ensure that ORF36 is not synthesized at deleterious levels during infection. For example, an EBV mutant that over-produces BGLF4 (the ORF36 homolog) exhibited defects in viral replication [39]. Determining if and how this non-canonical mechanism of translational control influences the KSHV lifecycle will be an important future endeavor.
pcDNA3.1(+)-ORF35–37 was generated by PCR-amplifying the ORF35–37 genetic locus from the KSHV-BAC36 (kindly provided by G. Pari [70]) and cloning it into the EcoRI/NotI sites of pcDNA3.1(+) (Invitrogen). pcDNA3.1(+)-5′ UTR-HA-ORF35 was assembled in a two-step process starting with the addition of the N-terminal HA tag after the native start ATG (nucleotide sequence: GCTTACCCATACGATGTAC CTGACTATGCG) to the coding sequence amplified from the KSHV genome as above, followed by an overlap extension PCR to insert the 72 nucleotide (nt) native 5′ UTR. The final product was then inserted into the pcDNA3.1(+) EcoRI/NotI restriction sites. pcDNA3.1(+)-ORF36 was constructed by PCR-amplification of the ORF36 coding sequence or to add the in frame C-terminal HA tag (GCTTACCCATACGATGTACCTG ACTATGCGTGA) followed by insertion into EcoR1/Not1 restriction sites. pCDEF3-ORF37 is described elsewhere [37]. HA-ORF35-ORF36-HA was amplified from the KSHV-BAC36 using primers with additional HA tag sequences and inserted into the EcoR1/Not1 sites of pcDNA3.1(+). This was followed by scarless insertion of the native 5′ UTR via two-step sequential overlap extension PCR [70]. To construct 5′ UTR-ORF35iHA-ORF36-HA, a backbone construct consisting of 5′UTR ORF35-ORF36-HA was first generated by PCR-amplification from the KSHV-BAC36 with HA tag sequences solely for ORF36 and inserted into the EcoR1/Not1 sites of pcDNA3.1(+). This construct was then linearized by inverse PCR at nucleotide position 55795 followed by ligation-independent cloning using InFusion (Clonetech) with primers consisting of an HA tag flanked by 15 base pair regions of vector overlap. A stable hairpin structure (Hp7 sequence: GGGGCGCGTGGTGGCGGCTGCAGCCGCCACCACGCGCCCC, [42]) was inserted into the 5′ UTR at nucleotide position 55599, or within the ORF35 coding region at nucleotide position 55662 and at position 55862 [18]. For the 5′ UTR HA-ORF35Δ96-HA-GFP construct, HA-GFP was inserted between the NotI/XbaI restriction sites in pcDNA3.1(+), and the 5′ UTR-HA-ORF35 Δ96 fragment was then inserted between the EcoRI/NotI restriction sites upstream of HA-GFP. Two bicistronic, dual luciferase constructs, a negative control (ΔEMCV; mutated IRES sequence) and a positive control (ΔEMCV element+functional EMCV) were kindly provided by P. Sarnow (Stanford University) [11], [41]. ORF72, ORF34–36, ORF35–36 and ORF35 PCR amplicons were inserted into the EcoRI restriction site downstream of the ΔEMCV element and upstream of firefly luciferase. The primers used to generate these constructs are listed in Table S2.
Where specified, parental plasmids were subjected to site-directed mutagenesis using the QuikChange kit (Stratagene) as per the manufacturer's protocol. The context of the ORF35 start codon was weakened by mutating the wild type AgaAUGG to UgaAUGG (35 KCS wkn). uORF1 and uORF2 mutants (designated Δ1 and Δ2) were generated by substituting the AUG start codon with AGA or UGA, respectively. The uORF2 Kozak context was weakened by mutating the wild-type AccAUGA to UuuAUGA (KCS2 wkn). The ORF35 start codon was disrupted by mutating the wild type AUG to AGA (Δ35). The uORF1 fusion to Δ35 was generated by mutating the uORF1 stop codon UGA to UGG (uORF1-Δ35). The uORF2 fusion to Δ35 was generated by deleting one nucleotide (A) located immediately prior to the ORF35 start codon (uORF2-Δ35). Two codons within in the ORF35 coding region were converted to AUGs in a strong context: (1) AccAACU to AccAUGG and (2) AauUUUG to AauAUGG. The native AUG residing at location 55778-80 within the ORF35 coding region was mutated to an AGA (MidMut) [18]. uORF2 was lengthened from 11 to 64 codons by mutating the first UAA stop codon to AGA, the second UAA stop codon to CAA, the third UGA stop codon to CGA, and the fourth and fifth UAG stop codon to CAG, resulting in the use of the next downstream stop codon (uORF2-long).
The KSHV BAC16 was modified as described previously [51] use a two-step scarless Red recombination system [71]. Briefly, BAC16 was introduced in GS1783 E. coli strain by electroporation (0.1 cm cuvette, 1.8 kV, 200 Ω 25 µF). A linear DNA fragment encompassing a kanamycin resistance expression cassette, an I-SceI restriction site and flanking sequence derived from KSHV genomic DNA was generated by PCR and subsequently electroporated into GS1783 E. coli harboring BAC16 and transiently expressing gam, bet and exo. Integration of the KanR/I-SceI cassette was verified by PCR and restriction enzyme digestion of the purified BAC16 DNA. The second recombination event between the duplicated sequences resulted in the loss KanR/I-SceI cassette and the seamless recirculation of the BAC16 DNA, yielding kanamycin-sensitive colonies that were screened by replica plating. BAC16 DNA was purified from chloramphenicol-resistant colonies using the NucleoBond 100 (Machery-Nagel) as per the manufactures instructions.
Human embryonic kidney 293T cells were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) (Gibco). The iSLK-PURO KSHV-negative endothelial cell lines [51], [52] were maintained in DMEM supplemented with 10% FBS, penicillin (100 U/ml, Gibco) and streptomycin (100 µg/ml, Gibco). To induce lytic reactivation of KSHV, iSLK-PURO cells were treated with doxycycline (1 µg/ml, BD Biosciences) and sodium butyrate (1 mM, Sigma). TREx BCBL1-RTA [72] cells were maintained in RPMI supplemented with 10% FBS, L-glutamine (200 µM, Invitrogen), penicillin (100 U/ml), streptomycin (100 µg/ml) and hygromycin B (50 µg/ml, Omega Scientific). To induce lytic reactivation of KSHV, TREx BCBL1-RTA cells were split to 1×106 cells/ml and induced 24 h later with 2-O-tetradecanoylphorbol-13-acetate (TPA; 20 ng/ml, Sigma), doxycycline (1 µg/ml) and ionomycin (500 ng/ml, Fisher Scientific) [73].
For DNA transfections, constructs (1 µg/ml) were transfected into subconfluent 293T cells grown in 12-well plates, either alone or in combination with 0.1 µg/ml GFP as a co-transfection control using Effectene reagent (Qiagen) or Lipofectamine 2000 (Invitrogen) following the manufacturers protocols. For RNA transfections, 3 µg/ml of mRNA in vitro transcribed using the mMessage mMachine kit (Ambion) and polyadenylated with yeast poly(A) polymerase (Epicentre Technologies) was transfected into ∼90% confluent 293T cells grown in 12-well plates using Lipofectamine 2000. TREx BCBL1-RTA cells were transfected with 20 µg of DNA per 107 cells via electroporation (250 V, 960 µF) with a Gene Pulser II (Bio-Rad, Hercules, CA).
For BAC transfections and reconstitution, ∼70% confluent iSLK-PURO cells were grown in a 24-well plate followed by transfection with 500 ng of BAC DNA via FuGENE 6 (Promega), after 6 h, a further 500 ng BAC DNA was transfected with Effectene, following the manufacturers protocols and subsequently selected with 800 µg/ml hygromycin B to establish a pure population. iSLK-PURO-BAC16 cells were then induced with doxycycline (1 µg/mL) and sodium butyrate (1 mM) to enter the lytic cycle of KSHV replication.
Luciferase activities were determined using the dual-luciferase assay system (Promega) and a bench-top luminometer according to manufacturer's protocol. IRES activity was calculated by obtaining the firefly/Renilla activity ratios for each of constructs containing the putative IRES sequences or the positive controls and dividing them by the ratio obtained from the ΔEMCV negative control. The value of fold activation represents at least three independent experiments with triplicate samples in each electroporation. Error bars represent the standard deviation between replicates.
Protein lysates were prepared in RIPA buffer [50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 1% (v/v) Nonidet P-40, 0.5% (w/v) sodium deoxycholate, 0.1% (w/v) sodium dodecyl sulfate (SDS)] containing protease inhibitors (Roche), and quantified by Bradford assay. Equivalent quantities of each sample were resolved by SDS-PAGE, transferred to a polyvinylidene difluoride membrane and incubated with the following primary antibodies: mouse monoclonal GFP (1∶2000, BD Biosciences), mouse monoclonal HA (1∶2000, Invitrogen), rabbit polyclonal ORF36 (1∶5000, kindly provided by Y. Izumiya [27]), goat polyclonal horseradish peroxidase (HRP)-conjugated actin (1∶500, Santa Cruz Biotechnology), rabbit polyclonal SOX J5803 (1∶5000, [38]), rabbit polyclonal ORF57 (1∶5000, kindly provided by Z. Zheng [74], rabbit polyclonal LANA #6 (1∶1000) or mouse monoclonal S6RP (1∶1000, Cell Signaling) followed by incubation with HRP-conjugated goat anti-mouse or goat anti-rabbit secondary antibodies (1∶5000 dilution) (Southern Biotechnology Associates).
Total cellular RNA was isolated for Northern blotting using RNA-Bee (Tel-Test). The RNA was then resolved on 1.2–1.5% agarose-formaldehyde gels, transferred to Nytran nylon membranes (Whatman) and probed with 32P-labeled DNA probes made using either the RediPrime II random prime labeling kit (GE Healthcare) or the Decaprime II kit (Ambion). Strand-specific riboprobes specific for ORF36 and ORF37 were synthesized using the Maxiscript T7 kit (Ambion) with 32P-labelled UTP. The probes used for Northern blot analysis spanned the following regions according to the nucleotide positions described by Russo et al. [18]: ORF35 probe: 55639–56091, ORF36 full-length probe: 55976–57310: ORF36-specific probe: 56093–56805: and ORF37 probe: 57273–58733. Results in each figure are representative of at least three independent replicates of each experiment.
The uORF1 and uORF2 alignments were generated from data obtained from the NIAID Virus Pathogen Database and Analysis Resource (ViPR) online through the web site at http://www.viprbrc.org.
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10.1371/journal.pgen.1005121 | The Zinc-Finger Antiviral Protein ZAP Inhibits LINE and Alu Retrotransposition | Long INterspersed Element-1 (LINE-1 or L1) is the only active autonomous retrotransposon in the human genome. To investigate the interplay between the L1 retrotransposition machinery and the host cell, we used co-immunoprecipitation in conjunction with liquid chromatography and tandem mass spectrometry to identify cellular proteins that interact with the L1 first open reading frame-encoded protein, ORF1p. We identified 39 ORF1p-interacting candidate proteins including the zinc-finger antiviral protein (ZAP or ZC3HAV1). Here we show that the interaction between ZAP and ORF1p requires RNA and that ZAP overexpression in HeLa cells inhibits the retrotransposition of engineered human L1 and Alu elements, an engineered mouse L1, and an engineered zebrafish LINE-2 element. Consistently, siRNA-mediated depletion of endogenous ZAP in HeLa cells led to a ~2-fold increase in human L1 retrotransposition. Fluorescence microscopy in cultured human cells demonstrated that ZAP co-localizes with L1 RNA, ORF1p, and stress granule associated proteins in cytoplasmic foci. Finally, molecular genetic and biochemical analyses indicate that ZAP reduces the accumulation of full-length L1 RNA and the L1-encoded proteins, yielding mechanistic insight about how ZAP may inhibit L1 retrotransposition. Together, these data suggest that ZAP inhibits the retrotransposition of LINE and Alu elements.
| Long INterspersed Element-1 (LINE-1 or L1) is the only active autonomous retrotransposon in the human genome. L1s comprise ~17% of human DNA and it is estimated that an average human genome has ~80–100 active L1s. L1 moves throughout the genome via a “copy-and-paste” mechanism known as retrotransposition. L1 retrotransposition is known to cause mutations; thus, it stands to reason that the host cell has evolved mechanisms to protect the cell from unabated retrotransposition. Here, we demonstrate that the zinc-finger antiviral protein (ZAP) inhibits the retrotransposition of human L1 and Alu retrotransposons, as well as related retrotransposons from mice and zebrafish. Biochemical and genetic data suggest that ZAP interacts with L1 RNA. Fluorescent microscopy demonstrates that ZAP associates with L1 in cytoplasmic foci that co-localize with stress granule proteins. Mechanistic analyses suggest that ZAP reduces the expression of full-length L1 RNA and the L1-encoded proteins, thereby providing mechanistic insight for how ZAP may restricts retrotransposition. Importantly, these data suggest that ZAP initially may have evolved to combat endogenous retrotransposons and subsequently was co-opted as a viral restriction factor.
| Long INterspersed Element-1 (LINE-1, also known as L1) sequences comprise ~17% of human DNA and represent the only class of autonomously active retrotransposons in the genome [1]. L1s mobilize (i.e., retrotranspose) throughout the genome via an RNA intermediate by a copy-and-paste mechanism known as retrotransposition [reviewed in 2]. The overwhelming majority of human L1s are retrotransposition-deficient because they are 5' truncated, contain internal rearrangements (i.e., inversion/deletion events), or harbor point mutations that compromise the functions of the L1-encoded proteins (ORF1p and ORF2p) [1,3]. Despite these facts, it is estimated that the average diploid human genome contains ~80–100 L1 elements that are capable of retrotransposition [4–6]. It is estimated that a new L1 insertion occurs in approximately 1 out of 200 live human births [reviewed in 7]. On occasion, L1 retrotransposition events can disrupt gene expression, leading to diseases such as hemophilia A [8], Duchenne muscular dystrophy [9], and cancer [10,11]. Indeed, L1-mediated retrotransposition events are responsible for at least 96 disease-producing insertions in man [reviewed in 12].
A full-length human L1 is ~6 kb in length and encodes a 5' UTR that harbors an internal RNA polymerase II promoter that directs transcription from at or near the first base of the element [13–15]. The 5' UTR is followed by two open reading frames (ORFs) that are separated by a short 63 bp inter-ORF spacer, and a 3' UTR that ends in a variable length poly adenosine (poly(A)) tract [16,17]. The first L1 ORF encodes an ~40 kDa protein (ORF1p) that has nucleic acid binding [18–22] and nucleic acid chaperone activities [22,23]. The second L1 ORF encodes a much larger ~150 kDa protein (ORF2p) [24–26], which exhibits single-strand endonuclease (EN) [27] and reverse transcriptase (RT) [28,29] activities. Experiments in cultured cells have revealed that activities associated with both ORF1p and ORF2p are required for efficient L1 retrotransposition [27,30].
During a cycle of L1 retrotransposition, a full-length L1 is transcribed and the resultant bicistronic L1 mRNA is exported to the cytoplasm where it undergoes translation. Notably, L1 RNA is translated in a cap-dependent manner by an unconventional termination-reinitiation mechanism that facilitates translation of both L1 ORFs [31–34]. Following translation, ORF1p and ORF2p preferentially bind to their respective encoding L1 mRNA template (a phenomenon known as cis-preference [35,36]) to form an L1 ribonucleoprotein particle (RNP) [18,19,26,37,38]. Components of the L1 RNP gain access to the nucleus by a process that does not strictly require cell division [39], although L1 retrotransposition seems to be enhanced in dividing cells [40,41]. Once the L1 RNP has entered the nucleus, the L1 RNA is reverse transcribed and inserted into genomic DNA by a process known as target-site primed reverse transcription (TPRT) [27,42,43]. Briefly, the ORF2p endonuclease generates a single-strand endonucleolytic nick in genomic DNA at a thymidine rich consensus sequence (e.g., 5'-TTTT/A, 5'-TCTT/A, 5'-TTTA/A, etc.) [27,44,45]. The resulting 3' hydroxyl group then is used by the ORF2p reverse transcriptase as a primer to initiate (-) strand L1 cDNA synthesis from the L1 mRNA template [27,44]. The completion of L1 integration requires elucidation, but likely involves host proteins involved in DNA repair and/or replication [45–48]. Notably, the L1-encoded proteins also can work in trans to retrotranspose other cellular RNAs such as Short Interspersed Elements (SINEs) (e.g., Alu [49] and SINE-R/VNTR/Alu (SVA) elements [50–52]). L1 also can mobilize uracil-rich small nuclear RNAs (e.g., U6 snRNA [48,53,54], small nucleolar RNAs (e.g., U3 snoRNA [55]), and messenger RNAs, which results in the formation of processed pseudogenes [35,36]).
Since L1 retrotransposition can be mutagenic, it stands to reason that the host cell employs multiple mechanisms to restrict L1 mobilization [reviewed in 56]. For example, cytosine methylation of the L1 5' UTR suppresses L1 expression [57,58]. In addition, piwi-interacting RNAs (piRNAs) suppress L1 expression in germ line cells [reviewed in 56, 59, and 60]. Finally, emerging studies have demonstrated that several cellular proteins restrict L1 retrotransposition. These proteins include several APOBEC3 family members [61, reviewed in 62], TREX1 [63], MOV10 [64–66], hnRNPL [67], SAMHD1 [68], RNase L [69], and the melatonin receptor 1 (MT1) [70].
To gain a more complete understanding of the interplay between the L1 retrotransposition machinery and the host cell, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify proteins that co-immunoprecipitate with L1 ORF1p in HeLa cells, reasoning that some of these proteins may affect L1 retrotransposition. We next analyzed the effects of ORF1p-interacting proteins on L1 retrotransposition by overexpressing a subset of them in a cultured cell retrotransposition assay [30,71]. Here, we report that the zinc-finger antiviral protein ZAP [72] interacts with L1 RNPs and inhibits L1 retrotransposition in cultured cells. ZAP also inhibits human Alu retrotransposition and the retrotransposition of mouse and zebrafish LINE elements. Molecular genetic and biochemical analyses suggest that ZAP inhibits retrotransposition by suppressing the accumulation of full-length L1 RNA and L1-encoded proteins in the cell.
To identify proteins that interact with L1 ORF1p, we transfected HeLa cells with a human L1 construct, pJM101/L1.3FLAG, which expresses a version of ORF1p containing a FLAG epitope at its carboxyl-terminus (ORF1p-FLAG) (Fig 1A). The pJM101/L1.3FLAG construct exhibits robust retrotransposition activity in HeLa cells, albeit at a lower efficiency (~50%) than the untagged L1 construct, pJM101/L1.3 (S1A Fig).
Briefly, HeLa cells were transfected with pJM101/L1.3FLAG or pJM101/L1.3, a similar construct that lacks the FLAG epitope sequence (Fig 1A). Whole cell lysates from transfected cells then were incubated with anti-FLAG coated agarose beads to immunoprecipitate ORF1p-FLAG (see Methods). Immunoprecipitated fractions were analyzed by SDS-PAGE and proteins were visualized by silver staining (Fig 1B). The analysis of silver-stained gels revealed a prominent band of ~40 kDa (the theoretical molecular weight of ORF1p) in the pJM101/L1.3FLAG immunoprecipitation lane (Fig 1B; asterisk), which was not apparent in the pJM101/L1.3 lane. Western blot analysis with an antibody specific to L1.3 ORF1p (amino acids 31–49) confirmed the enrichment of ORF1p-FLAG in the pJM101/L1.3FLAG lane (Fig 1C and S1B Fig; bottom panel). We also observed a complex pattern of bands between ~25 kDa and ~150 kDa that was present in the pJM101/L1.3FLAG lane that was not evident in the pJM101/L1.3 lane (Fig 1B; black vertical bars). A similar pattern of protein bands was produced on silver-stained gels from pJM101/L1.3FLAG immunoprecipitation reactions using different wash and/or lysis conditions (S1B and S1C Fig, respectively).
To determine the identity of cellular proteins that associated with ORF1p-FLAG, the bands from the lanes corresponding to the pJM101/L1.3FLAG and pJM101/L1.3 immunoprecipitation experiments were excised from SDS-PAGE gels and submitted for LC-MS/MS (see Methods). An LC-MS/MS-identified protein was selected as an ORF1p-interacting candidate if it met the following criteria: 1) the protein was unique to the pJM101/L1.3FLAG immunoprecipitation, and 2) the protein was identified by two or more unique peptide sequences (peptide error rate ≤0.05; protein probability ≥0.95) (S1 Table and Methods). Thirty-nine ORF1p-interacting protein candidates were identified that met these criteria (S1 Table).
To confirm the interactions between LC-MS/MS-identified proteins and ORF1p-FLAG, we evaluated 13 of the 39 ORF1p-FLAG interacting proteins for which there were commercially available antibodies and/or cDNA expression clones. Western blot analyses confirmed that these proteins associated with ORF1p-FLAG (Fig 1D). The 13 ORF1p-interacting proteins are involved in a variety of cellular processes including antiviral defense (ZAP [72] and MOV10 [73]), nonsense-mediated decay (UPF1 [74]), RNA splicing (hnRNPL [75] and DHX9 [76,77]), and transcription (PURA [78], CDK9 [79], and ILF3 [80]). Notably, gene ontology [81] and global analyses of RNA binding proteins in human cell lines [82,83] revealed that the 13 validated ORF1p-FLAG interacting proteins are RNA binding proteins (RBPs). Consistently, immunoprecipitation experiments of ORF1p-FLAG conducted in the presence of RNaseA disrupted the association between ORF1p and each of the 13 ORF1p-interacting proteins (Fig 1D). Thus, the majority of ORF1p-interacting proteins associate with ORF1p by binding to L1 RNA and/or other RNAs present within the L1 RNP [84].
We next investigated whether overexpression of nine of the validated ORF1p-interacting proteins, as well as nine unvalidated ORF1p-interacting proteins, affects L1 retrotransposition [30,71]. Briefly, HeLa cells were co-transfected with a cDNA plasmid expressing one of the ORF1p-FLAG interacting proteins and an engineered human L1 construct (pJJ101/L1.3; [85]) marked with a blasticidin retrotransposition indicator cassette (mblastI) (Fig 2A and 2B; top panel). The mblastI cassette contains an antisense copy of the blasticidin deaminase gene, which is cloned into the L1 3' UTR. The blasticidin deaminase gene also is interrupted by an intron in the same transcriptional orientation as L1. This arrangement ensures that the blasticidin deaminase gene is expressed only when the L1 transcript is spliced, reverse transcribed, and inserted into genomic DNA. The resulting blasticidin-resistant foci then provide a visual, quantitative readout of retrotransposition activity [30,45].
To monitor potentially toxic side effects of cDNA overexpression, HeLa cells also were co-transfected in a parallel assay with a cDNA expression vector and a control plasmid (pcDNA6/TR) that expresses the blasticidin deaminase gene (Fig 2B; bottom panel). Following blasticidin selection, the resulting foci provide a visual, quantitative readout of the effect of cDNA overexpression on colony formation (Fig 2B; bottom panel). This control is essential to determine if a cDNA affects L1 retrotransposition or cell viability and/or growth.
We co-transfected HeLa cells with each of the 18 ORF1p-FLAG interacting candidates and pJJ101/L1.3 (Fig 2C). An empty pCEP4 vector that was co-transfected with pJJ101/L1.3 served as a normalization control (Fig 2B and 2C). As a negative control, we demonstrated that a plasmid that expresses the humanized renilla green fluorescence protein (pCEP/GFP) did not affect pJJ101/L1.3 retrotransposition. As a positive control, we demonstrated that a plasmid that expresses human APOBEC3A (pK_A3A) reduced pJJ101/L1.3 retrotransposition to ~18% of control levels (Fig 2C), which is in agreement with previous studies [61,86–88]. Four of the cDNA-expressing plasmids that we tested (ZAP-S (ZAP short isoform), hnRNPL, MOV10, and PURA) each reduced pJJ101/L1.3 retrotransposition to less than 50% of pCEP4 control levels. Notably, ZAP-S (~30% of control), hnRNPL (~30% of control), MOV10 (~13% of control), and PURA (~10% of control) inhibited retrotransposition to levels similar to that of pK_A3A (~18% of control) (Fig 2C). By comparison, the majority of the cDNA-expressing plasmids (14/18) did not significantly affect pJJ101/L1.3 retrotransposition levels (less than 50% inhibition when compared to pCEP4 control levels) (Fig 2C). Thus, the data suggest that ZAP-S, hnRNPL, MOV10, and PURA inhibit L1 retrotransposition in cultured cells.
The above data (Fig 2C) imply that ZAP, hnRNPL, MOV10, and PURA may function as host factors that restrict L1 retrotransposition. Notably, hnRNPL [67], MOV10 [64,65], and PURA [89] previously were shown to inhibit L1 retrotransposition. However, the effect of ZAP on L1 retrotransposition has not been studied; thus, we sought to determine how ZAP inhibits L1 retrotransposition.
ZAP is a poly (ADP-ribose) polymerase (PARP) family member [90] initially characterized as an antiviral protein that inhibits murine leukemia virus (MLV) replication in cultured rat cells [72]. Previous studies identified two human ZAP isoforms that resulted from alternative splicing [90] (Fig 3A; top panel). The long ZAP isoform (ZAP-L) is 902 amino acids in length and contains an amino-terminus CCCH zinc-finger domain and an inactive carboxyl-terminal PARP-like domain [90]. The short ZAP isoform (ZAP-S) is 699 amino acids in length and lacks the carboxyl-terminal PARP-like domain [90]. The HA-tagged human ZAP-L isoform restricted pJJ101/L1.3 retrotransposition to ~40% of control levels (Fig 3A; black bars) and the human ZAP-S isoform restricted pJJ101/L1.3 retrotransposition to ~30% of control levels (Figs 2C and 3A; black bars). Notably, overexpression of ZAP-L or ZAP-S did not dramatically affect the ability of HeLa cells to form blasticidin-resistant colonies in pcDNA6/TR control assays (Fig 3A, white bars). Western blot control experiments confirmed the overexpression of ectopic ZAP-L and ZAP-S compared to untransfected controls ~48 hours post-transfection (S2A and S2B Fig). Thus, ZAP inhibits L1 retrotransposition in cultured cells and the ZAP-L PARP-like domain is not required for L1 restriction.
Putative ZAP orthologs are present in several species [90]; thus, we tested whether a rat ZAP cDNA (rZAP) [72], that is orthologous to human ZAP-S [72,90] could restrict pJJ101/L1.3 retrotransposition. Overexpression of rZAP efficiently reduced retrotransposition to ~40% of control levels (Fig 3A; black bars). Thus, the ability to restrict L1 retrotransposition is not limited to human ZAP.
The ZAP zinc-finger domain binds to RNA and is required for antiviral activity [91,92]. To analyze the role of the ZAP zinc-finger domain in L1 restriction, we tested the effects of a truncated ZAP-S mutant that expresses the ZAP zinc-finger domain (ZAP-S/1-311; containing amino acids 1–311) as well as a ZAP-S mutant that lacks the zinc-finger domain (ZAP-S/Δ72–372; lacking amino acids 72–372) in pJJ101/L1.3 retrotransposition assays (Fig 3A; above graph). ZAP-S/1-311 restricted retrotransposition to ~10% of control levels (Fig 3A; black bars), whereas ZAP-S/Δ72–372 had little effect on retrotransposition (~80% of control levels) (Fig 3A; black bars). The overexpression of the wild type or mutant ZAP-S/Δ72–372 expression constructs did not adversely affect the ability of HeLa cells to form blasticidin-resistant colonies in pcDNA6/TR control assays (Fig 3A; white bars). Notably, transfection with ZAP-S/1-311 resulted in an ~50% decrease in the ability of HeLa cells to form blasticidin-resistant colonies; however, this effect has been accounted for through normalization (Fig 2B) and thus is independent of the ability of ZAP-S/1-311 to restrict L1 retrotransposition. Indeed, similar off-target effects have been reported for A3A cDNA expressing plasmids in HeLa cell-based L1 retrotransposition assays [61]. Western blot control experiments revealed that wild-type ZAP-S and the two mutant ZAP-S isoforms were expressed at similar levels ~48 hours post-transfection (S2B and S2C Fig). Thus, the ZAP zinc-finger domain is necessary and sufficient to inhibit L1 retrotransposition.
To determine if ZAP-S was able to restrict other non-long terminal repeat (non-LTR) retrotransposons, we tested whether ZAP-S expression affected human Alu retrotransposition. Unlike L1, Alu is a 7SL-derived non-autonomous retrotransposon that does not encode its own proteins [93]. Instead, Alu elements must parasitize L1 ORF2p in trans to mediate their retrotransposition [49]. Briefly, HeLa cells were co-transfected with a full-length L1 element (pJM101/L1.3Δneo), an Alu retrotransposition reporter plasmid (pAluneoTet), and a ZAP-S expression plasmid. Notably, ZAP-S potently reduced Alu retrotransposition to ~25% of control levels (Fig 3B). In contrast, the expression of the L1 restriction-deficient ZAP-S/Δ72–372 mutant did not negatively affect Alu retrotransposition (Fig 3B). Thus, ZAP-S is able to restrict the mobility of the two most prolific retrotransposons present in the human genome.
We next tested if human ZAP-S could restrict the retrotransposition of a natural mouse L1 (pGF21) [94], a zebrafish LINE-2 (pZfL2-2) [95], or a synthetic mouse L1 (pCEPsmL1) [96] that has been extensively mutagenized to alter 24% of the nucleic acid sequence without disrupting amino acid sequence. Human ZAP-S inhibited the retrotransposition of human L1 (pJM101/L1.3; ~43% of control levels), natural mouse L1 (pGF21; ~24% of control levels), zebrafish L2 (pZfL2-2; ~19% of control levels), and synthetic mouse L1 (pCEPsmL1; ~70% of control levels) (Fig 3C). The restriction-defective ZAP-S mutant, ZAP-S/Δ72–372, did not significantly affect the retrotransposition activity of these retrotransposons (Fig 3C). Notably, the milder inhibition of ZAP-S on pCEPsmL1 may be due to the elevated efficiency of pCEPsmL1 retrotransposition, the increased steady-state level of pCEPsmL1 mRNA and proteins, and/or the GC-rich nature of pCEPsmL1 [96]. Thus, ZAP-mediated restriction of retrotransposition is not specific to human non-LTR retrotransposons.
To test if endogenous ZAP restricts L1 retrotransposition, we used small interfering RNA (siRNA) to deplete endogenous ZAP from HeLa cells. Following siRNA treatment, cells were transfected with an L1 plasmid (pLRE3-mEGFPI) tagged with an EGFP indicator cassette (mEGFPI), which allows retrotransposition activity to be detected by EGFP fluorescence [97]. As a negative control, HeLa cells were transfected with the L1 retrotransposition-defective plasmid pJM111-LRE3-mEGFPI, which carries two missense mutations that adversely affect ORF1p RNA binding [22,30,98]. Treatment of HeLa cells with an siRNA pool against ZAP resulted in an ~80% and ~ 90% reduction of ZAP-L and ZAP-S protein levels, respectively, when compared to HeLa cells treated with a non-targeting control siRNA pool (Fig 3D; top left panel). ZAP siRNA treatment led to an approximately two-fold increase in pLRE3-mEGFPI retrotransposition activity when compared to assays conducted in the presence of a control siRNA (Fig 3D; bottom panel and S2D Fig). We further demonstrated that siRNA-mediated depletion of endogenous MOV10 (Fig 3D; top right panel) from HeLa cells resulted in an approximately two-fold increase in pLRE3-mEGFPI retrotransposition (Fig 3D; bottom panel and S2D Fig), which is in agreement with previous studies [64,65]. These data suggest that endogenous ZAP may restrict L1 retrotransposition.
To investigate how ZAP restricts L1 retrotransposition, we analyzed the effect of ZAP-S expression on the accumulation of the L1 RNA. HeLa cells were co-transfected with pJM101/L1.3Δneo and either ZAP-S or ZAP-S/Δ72–372. Polyadenylated RNA from whole cell extracts then was analyzed by northern blot using RNA probes complementary to sequences within the L1.3 5' UTR (5UTR99) and ORF2 (ORF2_5804) (Fig 4A). Co-transfection with ZAP-S resulted in a reduction of full-length polyadenylated L1 RNA levels (~13% of pCEP4 control) compared to cells co-transfected with either the restriction-defective ZAP-S/Δ72–372 (~47% compared to pCEP4 control) or an empty pCEP4 control vector (Fig 4B; black arrow in blot; black bars in graph). Interestingly, ZAP-S expression did not have a pronounced effect on the accumulation of smaller L1 RNA species, which may have resulted from cryptic splicing and/or premature polyadenylation (Fig 4B; top panel: blue and yellow arrows, bottom panel: blue and yellow bars) [99–101]. Finally, control experiments revealed that ectopic ZAP-S expression did not affect endogenous actin RNA levels (Fig 4B). Thus, ZAP-S expression reduces the accumulation of full-length L1 mRNA in cultured cells.
We next examined the effect of ZAP-S expression on the accumulation of ORF1p and ORF2p. We co-transfected HeLa cells with either ZAP-S or ZAP-S/Δ72–372 and the L1 plasmid, pJBM2TE1, which expresses an L1.3 element marked with a T7 gene10 epitope tag on the carboxyl-terminus of ORF1p and a TAP epitope-tag on the carboxyl-terminus of ORF2p (Fig 4C). Following co-transfection, HeLa cells were treated with puromycin to select for cells expressing pJBM2TE1. Both whole cell lysates (WCL) and RNP fractions were collected 5 days post-transfection and subjected to western blot analyses to monitor ORF1p and ORF2p expression levels.
Expression of ZAP-S led to a decrease in the level of ORF1p and ORF2p in both WCL and RNP fractions, whereas the expression of the restriction-defective ZAP-SΔ/72-372 mutant or an empty pcDNA3 vector did not dramatically affect ORF1p or ORF2p expression levels (Fig 4D). The reduction in ORF1p and ORF2p was most evident in the RNP fraction, likely because both ORF1p and ORF2p are enriched in RNPs [19,26,37,38,102]. Control experiments revealed that ZAP-S expression did not affect the level of eIF3 protein (Fig 4D) and that ZAP-S and ZAP-S/Δ72–372 are expressed at similar levels in whole cell lysates (Fig 4D: top WCL panel). By comparison, ZAP-SΔ/72-372 is present at much lower levels in the RNP fraction compared to wild-type ZAP-S (Fig 4D; bottom RNP panel), suggesting that the zinc-finger domain is responsible for ZAP-S localization to the RNP fraction.
To determine if ZAP-S affects the expression of non-L1 proteins, we examined the effect of ZAP-S on EGFP expression. We co-transfected ZAP-S with an L1 plasmid (pLRE3-EF1-mEGFPΔIntron) [103] that expresses the L1 element, LRE3 and an intact copy of the EGFP gene (S3A Fig). In this case, LRE3 and EGFP are under the control of convergent promoters, which allows the simultaneous expression of LRE3 and EGFP from pLRE3-EF1-mEGFPΔIntron. Thus, EGFP expression is not dependent on retrotransposition. Forty-eight hours post-transfection, flow cytometry was used to isolate EGFP-positive cells (i.e., cells expressing pLRE3-EF1-mEGFPΔIntron) (S3C Fig). Western blotting demonstrated a marked reduction in ORF1p when compared to EGFP levels in cells that were co-transfected with ZAP-S (S3B Fig). By comparison, ORF1p and EGFP were present at comparable levels in cells that were co-transfected with either an empty pCEP4 vector or the restriction-deficient ZAP-SΔ/72-372 mutant (S3B Fig). Control experiments revealed that ZAP-S did not affect endogenous tubulin protein levels (S3B Fig). Thus, ZAP-S expression appears to preferentially restrict the expression of L1 ORF1p.
ORF1p, ORF2p, and L1 RNA form RNP complexes that appear as discrete cytoplasmic foci when visualized by fluorescence microscopy [25,26,104]. Notably, previous studies have shown that ZAP predominantly is localized in the cytoplasm [105] and that ZAP antiviral activity also is localized to the cytoplasm [72]. To determine if ZAP co-localizes with ORF1p, we co-transfected HeLa cells with pJM101/L1.3Δneo and a plasmid that expresses a carboxyl-terminus turbo-GFP tagged ZAP-S protein (ZAP-S-tGFP). Control experiments showed that ZAP-S-tGFP restricted pJJ101/L1.3 retrotransposition to ~55% of control levels (S4A Fig). Confocal fluorescence microscopy revealed that ORF1p and ZAP-S-tGFP co-localized in discrete cytoplasmic foci in ~68% of cells that co-expressed both ORF1p and ZAP-S-tGFP (Fig 5A). To test if transfected ORF1p co-localizes with endogenous ZAP, we transfected HeLa cells with pAD2TE1, which expresses a human L1 (L1.3) containing a T7 gene10 epitope-tag on the carboxyl-terminus of ORF1p [26]. Confocal microscopy revealed that endogenous ZAP co-localized with ORF1p-T7 in cytoplasmic foci in ~91% of cells that contained ORF1p-T7 foci (Fig 5B). Next, to test if endogenous ORF1p co-localizes with transfected ZAP-S, we transfected PA-1 cells (a human embryonic carcinoma-derived cell line that expresses endogenous ORF1p [106,107]) with ZAP-S-tGFP. Confocal microscopy demonstrated that endogenous ORF1p co-localized with ZAP-S-tGFP in ~89% of PA-1 cells that expressed ZAP-S-tGFP foci (Fig 5C). Thus, ORF1p and ZAP generally localize to the same region of the cytoplasm.
To test if the ZAP-S zinc-finger domain is critical for the co-localization of ZAP-S with ORF1p, we co-transfected HeLa cells with pJM101/L1.3Δneo and a tGFP-tagged ZAP-S mutant that expresses the ZAP-S zinc-finger domain (ZAP-S/Δ310-645-tGFP; lacking amino acids 310–645), or a ZAP-S mutant that lacks the ZAP-S zinc-finger domain (ZAP-S/Δ72-372-tGFP; lacking amino acids 72–372) (S4A Fig). In control experiments, ZAP-S/Δ310-645-tGFP restricted pJJ101/L1.3 retrotransposition to ~32% of control levels whereas ZAP-S/Δ72-372-tGFP did not have a significant effect (~93% of control) on retrotransposition activity (S4A Fig). Confocal microscopy revealed that ORF1p and ZAP-S/Δ310-645-tGFP co-localized in cytoplasmic foci in ~74% of cells that co-expressed both ORF1p and ZAP-S/Δ310-645-tGFP (Fig 5D). In cells transfected with pJM101/L1.3Δneo and ZAP-S/Δ72-372-tGFP, ORF1p and ZAP-S/Δ72-372-tGFP co-localized in only ~14% of cells that co-expressed both ORF1p and ZAP-S/Δ72-372-tGFP (Fig 5C). Thus, the ZAP-S zinc-finger domain is necessary and sufficient for the co-localization of ZAP-S and ORF1p in cytoplasmic foci.
To determine if ZAP co-localizes with L1 RNA, we co-transfected HeLa cells with pJM101/L1.3 and either ZAP-S-tGFP, ZAP-S/Δ310-645-tGFP, or ZAP-S/Δ72-372-tGFP. To visualize L1 RNA, transfected cells were probed with fluorescently labeled oligonucleotide probes complementary to sequences within the L1 5' UTR. As a control, cells were co-transfected with pJM101/L1.3 and an empty pCEP4 vector. In pCEP4 control experiments, fluorescence microscopy revealed that ORF1p co-localized with L1 RNA in cytoplasmic foci in ~88% of cells that contained ORF1p cytoplasmic Foci (Fig 6A and S5A Fig). In HeLa cells co-transfected with pJM101/L1.3 and ZAP-S-tGFP, L1 RNA co-localized with ORF1p and ZAP-S-tGFP in cytoplasmic foci in ~23% of foci-containing cells (Fig 6B and S5A Fig). Thus, ZAP and L1 RNA co-localize in cytoplasmic foci.
Fluorescence microscopy further revealed that in cells co-transfected with pJM101/L1.3 and ZAP-S/Δ310-645-tGFP that L1 RNA was detected in ORF1p and ZAP-S/Δ310-645-tGFP foci in only ~18% of foci-containing cells (Fig 6C and S5A Fig). In contrast, in cells co-transfected with pJM101/L1.3 and ZAP-S/Δ72-372-tGFP, L1 RNA co-localized with ORF1p in ~77% of cells that expressed ZAP/Δ72-372-tGFP and contained ORF1p cytoplasmic Foci (Fig 6D and S5A Fig). Thus, the data suggest that ZAP prevents the accumulation of L1 RNA in cytoplasmic foci.
We next determined the effect of ZAP-S on ORF1p expression using confocal microscopy. HeLa cells were co-transfected with pJM101/L1.3Δneo and either ZAP-S-tGFP, ZAP-S/Δ310-645-tGFP, or ZAP-S/Δ72-372-tGFP. As a control, cells were co-transfected with pJM101/L1.3Δneo and an empty pCEP4 vector. In pCEP4 control experiments, confocal microscopy revealed that ~10.8% of cells expressed ORF1p after ~ 48 hours (S5B Fig). In contrast, only ~2.8% of cells that were co-transfected with ZAP-S-tGFP expressed ORF1p and ~2.8% of cells that were co-transfected with ZAP-S/Δ310-645-tGFP expressed ORF1p (S5B Fig). Approximately 8.0% of cells that were co-transfected with ZAP-S/Δ72-372-tGFP expressed ORF1p (S5B Fig). Thus, the data suggest that the ZAP-S zinc-finger domain is necessary and sufficient to inhibit the accumulation of ORF1p in HeLa cells.
L1 cytoplasmic foci also co-localize with an array of RNA binding proteins, including markers of cytoplasmic stress granules (SGs) [26,89,104]. Notably, ZAP also localizes to cytoplasmic SGs [108]. To determine whether ZAP-S/ORF1p foci co-localize with cytoplasmic SGs we transfected HeLa cells with pJM101/L1.3Δneo and ZAP-S-tGFP. Confocal microscopy revealed that ZAP-S-tGFP/ORF1p co-localized with the endogenous SG associated protein eIF3 (S4B Fig). Additionally, endogenous ZAP also co-localized with the SG marker, G3BP (S4D Fig). In contrast, ZAP-S-tGFP/ORF1p foci did not co-localize with endogenous tubulin (S4C Fig), and endogenous ZAP did not co-localize with the processing body associated protein, DCP1α (S4E Fig). Thus, L1 ORF1p, ZAP, and SG associated proteins partition to the same cytoplasmic compartment.
In this study, we identified 39 cellular proteins that interact with L1 ORF1p and validated 13 of these interactions in biochemical assays. Our data showed that the 13 validated ORF1p-interacting proteins associate with ORF1p via an RNA bridge (Fig 1D). Notably, 33 out of 39 of the ORF1p-interacting proteins also were detected in recent studies (S1 Table; [47,67,89]). Importantly, we discovered that ZAP restricts human L1 and Alu retrotransposition. We also showed that hnRNPL, MOV10, and PURA inhibit L1 retrotransposition, which is in agreement with previous studies [64–67,89]. Thus, our data both confirm and extend those previous analyses and will help guide future studies that endeavor to determine how L1 retrotransposition impacts the human genome.
ZAP inhibits the mobility of both human and non-human non-LTR retrotransposons. The overexpression of the human and rat orthologs of ZAP restricts human L1 retrotransposition (Fig 3A). Human ZAP-S overexpression restricts the retrotransposition of an engineered human SINE (Alu) (Fig 3B), an engineered mouse L1 (GF21), and an engineered zebrafish LINE-2 element (ZfL2-2) (Fig 3C). Although our studies primarily involved the overexpression of ZAP, we also demonstrated that the depletion of endogenous ZAP in HeLa cells led to an ~2-fold increase in L1 retrotransposition (Fig 3D). This observed increase in L1 retrotransposition activity is similar to increases in L1 activity that were observed upon depletion of MOV10 and hnRNPL proteins in other studies [64,65,67]. Thus, in principle, physiological levels of ZAP may be sufficient to influence retrotransposition in certain cell types.
The ZAP CCCH zinc-finger domain is required to both bind and mediate the degradation of viral RNA [92,109]. Our data indicate that ZAP binding to L1 RNA is critical for L1 restriction. We demonstrated that ORF1p-FLAG and ZAP interact via an RNA bridge (Fig 1D). Moreover, we showed that overexpression of the ZAP zinc-finger domain more potently inhibits L1 retrotransposition than overexpression of wild type ZAP-L or ZAP-S (Fig 3A), and that the ZAP-S zinc-finger domain is required to inhibit L1 retrotransposition (Fig 3A and S4A Fig). In addition to our genetic and biochemical data, fluorescence microscopy revealed that: 1) co-transfected ZAP-S, L1 RNA, and ORF1p co-localize in the cytoplasm of HeLa cells; 2) the ZAP-S zinc-finger domain is necessary and sufficient for the co-localization of ZAP-S, L1 RNA, and ORF1p; 3) endogenous ZAP co-localizes with transfected ORF1p in HeLa cells; and 4) endogenous ORF1p interacts with transfected ZAP-S in human PA-1 embryonic carcinoma cells (Figs 5A–5E and 6A–6D). Thus, the data suggest that ZAP interacts with L1 RNA in order to mediate L1 restriction.
Notably, the zebrafish ZfL2-2 retrotransposon lacks a homolog to ORF1 and only encodes a single ORF that contains an apurinic/apyrimidinic endonuclease-like (EN) and a reverse transcriptase (RT) domain [95]. The finding that ZAP-S efficiently restricts ZfL2-2 retrotransposition further indicates that ZAP-S likely restricts retrotransposition by interacting with LINE RNA. Although a ZAP consensus RNA target sequence/motif has not yet been identified, evidence suggests that ZAP recognizes long RNA stretches (>500 nucleotides) and/or specific RNA tertiary structure [91,92]. The ability of ZAP to inhibit non-human LINE elements suggests that ZAP may not recognize a particular LINE linear consensus RNA sequence, but instead may recognize an unidentified structural feature common to certain LINE RNAs [91,92].
Evidence suggests that ZAP prevents the accumulation of viral RNAs in the cytoplasm [72]. ZAP-S overexpression significantly reduced the amount of polyadenylated, full-length L1 RNA (Fig 4B), which would be expected to inhibit retrotransposition by limiting the supply of L1 mRNA available for translation and as a template for TPRT. Notably, while ZAP-S selectively inhibited the accumulation of full-length L1 transcripts, it did not dramatically affect the accumulation of shorter, spliced and/or polyadenylated L1 RNAs (Fig 4B) [99–101,110]. Thus, ZAP does not appear to affect L1 transcription per se, but instead likely affects the post-transcriptional processing of full-length L1 mRNA. In addition to biochemical data, fluorescence microscopy revealed that L1 RNA was depleted from L1 ORF1p cytoplasmic foci in the presence of ZAP (S5A Fig). The depletion of RNA from L1 cytoplasmic foci was dependent on the ZAP-S zinc-finger domain. Based on these data it is likely that ZAP prevents the accumulation of cytoplasmic L1 mRNA.
Previous studies have shown that ZAP also suppresses the expression of viral proteins [111–113]. Western blot experiments demonstrated that the overexpression of ZAP-S inhibited the accumulation of L1 ORF1p and L1 ORF2p in whole cell lysates and RNPs derived from transfected HeLa cells (Fig 4D and S3B Fig). In agreement with western blot experiments, confocal microscopy experiments showed that tGFP-tagged ZAP-S inhibited the expression of ORF1p in transfected HeLa cells and that the ZAP-S zinc-finger domain is critical for the inhibition of ORF1p expression (S5B Fig). ZAP-S expression also inhibited Alu retrotransposition (Fig 3B), which depends on ORF2p to be supplied in trans by L1 [49]. In contrast to these data, ZAP-S overexpression did not significantly affect the expression and/or accumulation of EGFP or other endogenous proteins (e.g., eiF3 and tubulin) (Fig 4D and S3B Fig). Thus, ZAP may preferentially limit the accumulation of ORF1p and ORF2p by interacting with L1 mRNA.
In sum, our data suggest that ZAP restricts L1 retrotransposition by preventing the accumulation of cytoplasmic L1 RNA. Notably, a recent study suggests that ZAP may interfere with translation of viral RNA, and that translation inhibition may precede viral RNA destruction [113]. Although a reduction in L1 RNA could explain the observed decrease in L1 protein expression, it also is conceivable that the interaction between ZAP and L1 RNA could interfere with L1 translation (Fig 7).
It remains unclear how ZAP might destabilize full-length L1 RNA to restrict retrotransposition. Evidence suggests that ZAP recruits exosome components [109] along with other proteins involved in RNA degradation [111,114] to destroy viral RNA. Interestingly, immunofluorescence microscopy experiments revealed that ORF1p and ZAP co-localize with components of cytoplasmic SGs (S4B and S4D Fig), which contain numerous RNA binding proteins involved in cytosolic RNA metabolism [reviewed in 115]. Indeed, ZAP previously has been shown to localize to SGs [108] and SGs have been suggested to play a role in regulating L1 retrotransposition [104] and viral pathogenesis [reviewed in 116]. The co-localization of ORF1p and ZAP with SGs also suggests that ZAP may possibly inhibit L1 translation, as SG assembly is stimulated by translational arrest [reviewed in 115]. Thus, we propose that ZAP interacts directly with L1 RNA in the cytoplasm, which likely results in the recruitment in SG components and/or other cellular factors involved in RNA metabolism to destabilize L1 RNA and/or block translation (Fig 7).
ZAP exhibits antiviral activity against a variety of viruses such as MLV [72], alphaviruses [117], filoviruses [118], HIV-1 [111], and hepatitis-B virus [119]. Interestingly, many putative L1 restriction factors also are involved in antiviral defense (i.e., a subset of APOBEC3 proteins, TREX1, MOV10, SAMHD1, and RNaseL). L1 elements have been active in mammalian genomes for ~160 million years [120–122]. Thus, it is tempting to speculate that some host factors, such as ZAP, may have first evolved to combat endogenous retrotransposons and subsequently were co-opted as viral restriction factors [90,123–125]. Indeed, identifying host factors that modulate L1 retrotransposition may prove to be an effective strategy to identify host antiviral factors.
HeLa-JVM cells were grown in high-glucose DMEM (Gibco) supplemented with 10% FBS (Gibco), 100 U/mL penicillin-streptomycin (Invitrogen), and 0.29 mg/mL L-glutamine (Gibco) [30]. HeLa-HA [126] and PA-1 [107] cells were grown in MEM (Gibco) with 10% FBS, 100 U/mL penicillin-streptomycin, 0.29 mg/mL L-glutamine, and 0.1 mM nonessential amino acids (Gibco). Cell lines were maintained at 37°C with 7% CO2 in humidified incubators (Thermo Scientific).
Oligonucleotide sequences and cloning strategies used in this study are available upon request. All human L1 plasmids contain the indicated fragments of L1.3 (accession no. L19088) [5] DNA cloned into pCEP4 (Invitrogen) unless otherwise indicated. A CMV promoter augments expression of all L1 and cDNA expressing plasmids unless noted otherwise. L1 plasmids also contain an SV40 polyadenylation signal that is located downstream of the native L1 polyadenylation signal. All plasmid DNA was prepared with a Midiprep Plasmid DNA Kit (Qiagen).
The following cDNA expression plasmids were obtained from OriGene: CDK9 (SC119344); DDX21 (SC108813); GNB2L1 (SC116322); hnRNPDL (SC107613); MOV10 (SC126015); MATR3 (SC113375); ZAP-S (ZC3HAV1 transcript variant 2) (SC101064); ZAP-S-tGFP (GFP-tagged ZC3HAV1 transcript variant 2) (RG208070); hnRNPA2B1 (SC313092); IGF2BP3 (SC111161); PURA (SC127792); UPF1 (SC118343).
The following cDNA expression plasmids were obtained from Open Biosystems: hnRNPL (6174088); LARP2 (5164712); LARP4 (5219803); SYNCRIP (5495201).
The following cDNA expression plasmids were obtained from Addgene: rZAP (pcDNA4-TO-Myc-rZAP; Addgene plasmid#: 17381, kindly provided by Dr. Stephen Goff) (Gao et al., 2002) and ZAP-L (pcDNA4 huZAP(L); Addgene plasmid#: 45907, kindly provided by Dr. Harmit Malik) [90].
pJM101/L1.3: is a pCEP4-based plasmid that expresses a human L1 (L1.3) equipped with an mneoI retrotransposition indicator cassette. L1 expression is augmented by a CMV promoter located upstream of the L1 5' UTR and an SV40 polyadenylation signal that is located downstream of the native L1 polyadenylation signal [5,30,127,128]
pJM101/L1.3FLAG: was derived from pJM101/L1.3 and contains a single FLAG epitope on the carboxyl-terminus of ORF1p. Dr. Huira Kopera (University of Michigan Medical School) constructed the plasmid.
pAluneoTet: expresses an Alu element cloned from intron 5 of the human NF1 gene [129] that is marked with the neoTet reporter gene. The reporter [130] was subcloned upstream of the Alu poly adenosine tract [49].
pCEP/GFP: is a pCEP4 based plasmid that expresses the humanized renilla green fluorescent protein (hrGFP) coding sequence from phrGFP-C (Stratagene), which is located downstream of the pCEP4 CMV promoter [33].
pJJ101/L1.3: is a pCEP4 based plasmid that contains an active human L1 (L1.3) equipped with an mblastI retrotransposition indicator cassette [85].
pJJ105/L1.3: is similar to pJJ101/L1.3, but contains a D702A missense mutation in the RT active site of L1.3 ORF2 [85].
pJM101/L1.3Δneo: is a pCEP4 based plasmid that contains an active human L1 (L1.3) [35].
pLRE3-EF1-mEGFPΔIntron: is a pBSKS-II+ based plasmid that expresses an active human L1 (LRE3) that is tagged with an EGFP cassette (mEGFPI) containing an antisense, intronless copy of the EGFP gene. A UbC promoter drives EGFP expression. An EF1α promoter drives L1 expression [103].
pAD2TE1: is similar to pJM101/L1.3 except that it was modified to contain a T7 gene10 epitope-tag on the carboxyl-terminus of ORF1p and a TAP epitope-tag on the carboxyl-terminus of ORF2p. The 3′-UTR contains the mneoI retrotransposition indicator cassette [26].
pJBM2TE1: is similar to pAD2TE1 except that the pCEP4 backbone was modified to contain the puromycin resistance (PURO) gene in place of the hygromycin resistance gene.
pLRE3-mEGFPI: is a pCEP4 based plasmid that contains an active human L1 (LRE3) equipped with an mEGFPI retrotransposition indicator cassette [97,106]. The pCEP4 backbone was modified to contain a puromycin resistance (PURO) gene in place of the hygromycin resistance gene. The CMV promoter also was deleted from the vector; thus, L1 expression is driven only by the native 5′ UTR [97].
pJM111-LRE3-mEGFPI: is identical to pLRE3-mEGFPI except that it contains two missense mutations in ORF1 (RR261-262AA), which render the L1 retrotransposition-defective [30]. Mr. William Giblin (University of Michigan Medical School) constructed the plasmid [69].
pGF21: contains an 8.8 kb fragment which includes a full length mouse GF21 L1 element that contains the mneoI indicator cassette [94].
pZfL2-2: is a pCEP4 based plasmid that contains the ZfL2-2 ORF (ZL15, accession no. AB211150) cloned upstream of the mneoI indicator cassette [95].
pCEP4smL1: contains a codon optimized full-length mouse element (derived from L1spa) containing the mneoI indicator cassette [96].
ZAP-S/1-311: encodes the ZAP-S amino acid sequence from 1–311 and the following sequence of non-templated amino acids (IIIYTGFLFCCGFFFFFFFLEGVSLCCPGWS).
ZAP-S/Δ72–372: was derived by deleting the SfoI-XhoI fragment from ZC3HAV1 transcript variant 2 (OriGene, SC101064), and expresses a ZAP-S mutant protein that lacks amino acid sequence from 72–372.
ZAP-S/Δ310-645-tGFP: expresses a ZAP-S mutant protein that lacks amino acid sequence from 310–645 and contains a carboxyl terminus tGFP epitope tag.
ZAP-S/Δ72-372-tGFP: expresses a ZAP-S mutant protein that lacks amino acid sequence from 72–372 and contains a carboxyl terminus tGFP epitope tag.
LARP5: was derived by cloning LARP5 cDNA (Open Biosystems, 40118844) into pcDNA3 (Invitrogen).
LARP1: was constructed by cloning the LARP1 cDNA (Open Biosystems, 3138935) into pcDNA3 (Invitrogen).
pK_A3A: expresses HA-tagged APOBEC3A and was a generous gift from Dr. Brian Cullen [131].
pDCP1α-GFP: expresses a GFP-tagged version of DCP1α and was a generous gift from Dr. Gregory Hannon [132].
pG3BP-GFP: expresses a GFP-tagged version of G3BP and was a generous gift from Dr. Jamal Tazi [133].
pcDNA6/TR: expresses the blasticidin resistance gene and was obtained from Invitrogen.
HeLa-JVM cells were seeded in T-175 flasks (BD Falcon) at ~6–8×106 cells/flask and transfected the next day with 20 μg of plasmid DNA using 60 μL of FuGENE HD (Promega). Approximately 48 hours post-transfection, hygromycin B (Gibco) (200 μg/mL) was added to the medium to select for transfected cells. After approximately one week of hygromycin selection, cells were washed 3 times with ice cold PBS and collected with a rubber policeman into 50 mL conical tubes (BD Falcon). Cells were then pelleted at 1,000×g and frozen at -80°C. To produce whole cell lysates (WCL), frozen cell pellets were rapidly thawed and then lysed in ~3 mL (1 mL lysis buffer per 100 mg of cell pellet) of lysis buffer (20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 10% glycerol, 1 mM EDTA, 0.1% IGEPAL CA-630 (Sigma), 1X complete EDTA-free protease inhibitor cocktail (Roche)) on ice for 30 minutes. WCLs were then centrifuged at 15,000×g for 15 minutes at 4°C. Supernatants were transferred to a clean tube and protein concentration was determined using the Bradford reagent assay (BioRad). For the IP, ~1 mL of the supernatant (~3 mg total protein) was pre-cleared with ~15 μL (packed gel volume) of mouse IgG-agarose beads (Sigma) for 4 hours at 4°C. Pre-cleared supernatants were then mixed with ~15 μL (packed gel volume) of EZview Red ANTI-FLAG M2 Affinity Gel (Sigma) and incubated overnight with rotation at 4°C. The beads then were rinsed 3x with 0.5 mL of lysis buffer, and then washed 3 times with 0.5 mL of lysis buffer for 10 minutes per wash on ice with gentle agitation. Protein complexes were eluted from the beads by adding ~70 μL of 2X NuPAGE LDS Sample Buffer (Novex), supplemented with NuPAGE Sample Reducing Agent (Novex), directly to the washed beads and incubating for 10 minutes at 70°C. Following incubation, the beads were pelleted and the sample was transferred to a fresh tube. For SDS-PAGE analysis, 20 μL of the IP were loaded onto a 4–15% gradient midi-gel (BioRad) and run under reducing conditions. Gels were silver stained using the SilverQuest Silver Staining Kit (Novex) to visualize proteins.
The Proteomics Facility at the Fred Hutchinson Cancer Research Center (Seattle, WA) conducted protein identification experiments. Excised silver-stained gel slices were destained and subjected to in-gel proteolytic digestion with trypsin as described [134]. Following gel-slice digestion, the digestion products were desalted using C18-micro ZipTips (Millipore) and were dried by vacuum centrifugation. The resultant peptide samples were resuspended in 7 μL of 0.1% formic acid and 5 μL were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). LC-MS/MS analysis was performed using an LTQ Orbitrap XL mass spectrometer (Thermo Scientific). The LC system, configured in a vented format [135], consisted of a fused-silica nanospray needle (PicoTip emitter, 50 μm ID) (New Objective) packed in-house with Magic C18 AQ 100A reverse-phase medium (25 cm) (Michrom Bioresources Inc.) and a trap (IntegraFrit Capillary, 100 μm ID) (New Objective) containing Magic C18 AQ 200A reverse-phase medium (2 cm) (Michrom Bioresources Inc.). The peptide samples were loaded onto the column and chromatographic separation was performed using a two mobile-phase solvent system consisting of 0.1% formic acid in water (A) and 0.1% acetic acid in acetonitrile (B) over 60 min from 5% B to 40% B at a flow rate of 400 nL/minutes. The mass spectrometer operated in a data-dependent MS/MS mode over the m/z range of 400–1800. For each cycle, the five most abundant ions from each MS scan were selected for MS/MS analysis using 35% normalized collision energy. Selected ions were dynamically excluded for 45 seconds.
For data analysis, raw MS/MS data were submitted to the Computational Proteomics Analysis System (CPAS), a web-based system built on the LabKey Server v11.2 [136] and searched using the X!Tandem search engine [137] against the International Protein Index (IPI) human protein database (v3.75), which included additional common contaminants such as BSA and trypsin. Search results were compared between the pJM101/L1.3FLAG lane and the pJM101/L1.3 lane to generate a list of candidate L1 ORF1p associated proteins unique to the pJM101/L1.3FLAG immunoprecipitation. The search output files were analyzed and validated by ProteinProphet [138]. Peptide hits were filtered with PeptideProphet [139] error rate ≤0.05, and proteins with probability scores of ≥0.95 were accepted. Suspected contaminants (e.g. keratin) were filtered from the final L1 RNP candidate list.
The cultured cell retrotransposition assay was carried out essentially as described [30,71]. For retrotransposition assays with L1 constructs tagged with mblastI, HeLa-JVM cells were seeded at ~1–2×104 cells/well in a 6-well plate (BD Falcon). Within 24 hours, each well was transfected with 1 μg of plasmid DNA (0.5 μg L1 plasmid + 0.5 μg cDNA plasmid or pCEP4) using 3 μL of FuGENE 6 transfection reagent (Promega). Four days post-transfection, blasticidin (EMD Millipore) containing medium (10 μg/mL) was added to cells to select for retrotransposition events. Medium was changed every two days. After ~8 days of selection, cells were washed with PBS, fixed, and then stained with crystal violet to visualize colonies. To control for transfection efficiency and off-target effects of cDNA plasmids, in parallel with retrotransposition assays, HeLa-JVM cells were plated in 6-well plates at 500–1,000 cells/well and transfected with 0.5 μg pcDNA6/TR (Invitrogen) plasmid + 0.5 μg cDNA plasmid using 3 μL of FuGENE 6 transfection reagent (Promega). The pcDNA6/TR control assays were treated with blasticidin in the same manner as for retrotransposition assays.
For retrotransposition assays with L1 constructs tagged with mneoI, HeLa-JVM cells were transfected as described above. Two days after transfection, cells were treated with medium supplemented with G418 (Gibco) (500 μg/mL) for ~10–12 days. As a control, HeLa cells were plated at ~2×104 cells/well in a 6-well plate and transfected with 0.5 μg pcDNA3 (Invitrogen) plasmid + 0.5 μg cDNA plasmid using 3 μL of FuGENE 6 transfection reagent (Promega). The pcDNA3 control assays were treated with G418 in the same manner as for retrotransposition assays.
For Alu retrotransposition assays [49], ~4×105 HeLa-HA cells were plated per well of a 6-well plate (BD Falcon) and transfected with 0.67 μg of pJM101/L1.3Δneo + 0.67 μg of pAluneoTet + 0.67 μg of cDNA plasmid using 6 μL FuGENE HD (Promega). Three days post-transfection, cells were grown in the presence of G418 (500μg/mL) to select for Alu retrotransposition events. As a control, HeLa-HA cells were plated at ~4×105 cells/well in a 6-well plate and transfected with 0.67 μg of pcDNA3 (Invitrogen) + 0.67 μg of pAluneoTet + 0.67 μg of cDNA plasmid using 6 μL of FuGENE HD (Promega). The pcDNA3 control assays were treated with G418 in the same manner as for Alu retrotransposition assays.
In experiments to study the effect of endogenous proteins on L1 retrotransposition, HeLa cells (~8×105 cells) were plated in 60 mm tissue culture dishes (BD Falcon). The next day, the cells were transfected with 50 nM of a control siRNA pool (D-001810-10, ON-TARGETplus Non-targeting Pool, Thermo Scientific) or siRNA against ZAP (L-017449-01-0005, ON-TARGETplus Human ZC3HAV1 (56829) siRNA—SMARTpool, Thermo Scientific) or MOV10 (L-014162-00-0005, ON-TARGETplus Human MOV10 (4343) siRNA—SMARTpool, Thermo Scientific) using the DharmaFECT 1 transfection reagent (Thermo Scientific). Twenty-four hours after siRNA treatment, cells were transfected with pLRE3-mEGFPI or pJM111-LRE3-mEGFPI (5 μg), using 15 μL of FuGENE HD transfection reagent (Roche). After 48 hours, cells were trypsinized and an aliquot of the cells (~2×106 cells) was used to monitor endogenous protein levels (72 hours after siRNA treatment) by western blot analysis (see below for list of primary antibodies). Blots were analyzed using an Odyssey CLx (LI-COR) with the following secondary antibodies: IRDye 800CW Donkey anti-Rabbit IgG (1:10,000) (LI-COR) and IRDye 680RD Donkey anti-Mouse IgG (1:10,000) (LI-COR). Knockdown efficiencies were calculated using LI-COR Image Studio Software (v3.1.4) and are the average of three independent experiments. Endogenous tubulin was used as the normalization control. The remaining cells were re-plated at ~2×105 cells/well of a 6-well plate and cultured in medium supplemented with puromycin (5 μg/ml, Gibco/Life Technologies) to select for cells transfected with pLRE3-mEGFPI. After 4 days of puromycin selection, the percentage of GFP positive cells was determined by flow cytometry using an Accuri C6 flow cytometer (BD Biosciences).
RNPs were isolated as previously described [37]. Briefly, HeLa-JVM cells were seeded onto 60 mm tissue culture dishes (BD Falcon) and 24 hours later cells were co-transfected with 2.5 μg of pJBM2TE1 and 2.5 μg of the indicated cDNA plasmid using 15 μL of FuGENE HD (Promega). Approximately two days after transfection, puromycin (5 μg/mL) was added to culture medium to select for cells transfected with pJBM2TE1. After ~3 days of puromycin selection (5 days after transfection), cells were lysed in RNP lysis buffer (150 mM NaCl, 5 mM MgCl2, 20 mM Tris-HCl (pH 7.5), 10% glycerol, 1mM DTT, 0.1% NP-40, and 1x complete EDTA-free protease inhibitor cocktail (Roche)). Following lysis, whole cell lysates were centrifuged at 12,000xg for 10 minutes at 4°C, and then the cleared lysate was layered onto a sucrose cushion (8.5% and 17% sucrose) and subjected to ultracentrifugation at 4°C for 2 hours at 178,000xg. The supernatant was discarded and the resulting pellet was resuspended in water supplemented with 1x complete EDTA-free protease inhibitor cocktail (Roche). Approximately 20 μg (total protein) of the RNP sample or ~30 μg (total protein) of the cleared whole cell lysate (supernatant post 12,000xg centrifugation) were then analyzed by western blot. Blots were analyzed using an Odyssey® CLx (LI-COR) with the following secondary antibodies: IRDye 800CW Donkey anti-Rabbit IgG (1:10,000) (LI-COR) and IRDye 680RD Donkey anti-Mouse IgG (1:10,000) (LI-COR).
To simultaneously analyze the effects of ZAP-S on ORF1p and EGFP protein expression, HeLa-JVM cells were seeded onto 10 cm dishes (~2.7×106 cells/dish) (BD Falcon) and transfected with 10 μg of plasmid DNA (5.0 μg pLRE3-EF1A-mEGFPΔIntron + 5.0 μg cDNA plasmid or pCEP4) using 30 μL of FuGENE HD. After 48 hours, cells were harvested with trypsin and then subjected to flow cytometry to isolate GFP expressing cells. Approximately 1.2–1.7×106 GFP positive cells were collected for each transfection condition using a MoFlo Astrios cell sorter (Beckman Coulter). The GFP gate was set using untransfected HeLa-JVM cells. The sorted cells were lysed as described in the IP procedure and lysates were then subjected to western blotting using standard procedures. For all other protein expression analyses, HeLa-JVM cells were seeded at ~4×105 cells/well in 6-well plates and transfected with 2 μg of plasmid DNA with 6 μL of FuGENE HD. Cells were collected 48 hours after transfection using a rubber policeman and lysates were prepared as described above. Western blots were visualized using either the SuperSignal West Femto Chemiluminescent Substrate (Pierce) or SuperSignal West Pico Chemiluminescent Substrate (Pierce) and Hyperfilm ECL (GE Healthcare).
HeLa-JVM cells were seeded in T-175 flasks (BD Falcon) and transfected with 20 μg of plasmid DNA (10 μg pJM101/L.13Δneo + 10 μg cDNA plasmid) using 60 μL FuGENE HD. Two days after transfection, cell pellets were collected and frozen at -80°C. Frozen cell pellets were then thawed and total RNA was extracted with TRIzol reagent (Ambion), and then poly(A)+ RNA was prepared from total RNA using an Oligotex mRNA kit (Qiagen). Each sample (~1.5 μg of poly(A)+ RNA) was subjected to glyoxal gel electrophoresis and northern blotting using the NorthernMax-Gly Kit (Ambion) according to the manufacturer’s protocol. Following electrophoresis, RNA was transferred to BrightStar Nylon membranes (Invitrogen) and then cross-linked using UV light. For northern blot detection, membranes were prehybridized for ~ 4 hours at 68°C in NorthernMax Prehybridization/Hybridization Buffer (Ambion), and then incubated with a strand specific RNA probe (final concentration of probe ~ 3×106 cpm ml-1) overnight at 68°C. For band quantification, northern blot films were analyzed using ImageJ software [140].
Strand-specific RNA probes were generated using the MAXIscript T3 system (Invitrogen). The 5UTR99 [100] probe corresponds to bases 7–99 of the L1.3 5' UTR and the ORF2_5804 probe corresponds to nucleotides 5560–5804 of the L1.3 sequence. RNA probe templates for T3 reactions were generated by PCR using pJM101/L1.3Δneo as a PCR template with the following primer pairs:
(5UTR99: 5'-GGAGCCAAGATGGCCGAATAGGAACAGCT-3' and 5'-AATTAACCCTCAAAGGGACCTCAGATGGAAATGCAG-3');
(ORF2_5804: 5'- GACACATGCACACGTATGTTTATT-3' and 5'- AATTAACCCTCACTAAAGGGTGAGTGAGAATATGCGGTGTTT-3').
The T3 promoter sequence (underlined) was added to the reverse primer of each primer pair. The pTRI-β-actin-125-Human Antisense Control Template (Applied Biosystems) was used in T3 reactions as a template to generate the β-actin RNA probe. Each northern blot experiment was independently repeated three times with similar results.
Immunofluorescence microscopy was performed essentially as described [26] with modifications. Briefly, cells were plated on round glass cover slips (Fisher) in a 12-well plate or into 4-well chambered glass slides (Fisher) and transfected ~24 hours later with 0.5 μg of plasmid DNA using 1.5 μL of FuGENE 6 transfection reagent. To visualize proteins, approximately 48 hours post-transfection cells were washed with 1x PBS, fixed with 4% paraformaldehyde for 10 minutes and then treated with ice-cold methanol for 1 minute. Next, cells were incubated for 30 minutes at 37°C in 1x PBS + 3% BSA. Cells then were incubated with primary antibodies in 1x PBS + 3% BSA for 1 hour at 37°C. Cells were washed three times with 1x PBS (10 minutes per wash) and then incubated with appropriate, fluorescently-labeled secondary antibodies diluted in 1x PBS for 30 minutes at 37°C. The following secondary antibodies were used for indirect immunofluorescence: Alexa Fluor 488 conjugated Goat anti-Mouse and Goat anti-Rabbit (Invitrogen) (1:1000), Alexa Fluor 546 conjugated Goat anti-Mouse and Goat anti-Rabbit IgG (Invitrogen) (1:1000), and Cy5 conjugated Donkey anti-Rabbit IgG (H+L) (Jackson ImmunoResearch) (1:100). To obtain images, a cover slip and/or slide was visually scanned and representative images were captured using a Leica SP5X confocal microscope (63x/1.4 objective; section thickness 1 μm).
Cells were plated on round glass cover slips (Fisher) in a 12-well plate and transfected ~24 hours later with 0.5 μg of plasmid DNA using 1.5 μL of FuGENE 6 transfection reagent. Approximately 48 hours after transfection, cells were fixed with 4% paraformaldehyde for 10 minutes and then permeabilized with 0.2% Triton X-100 in 1x PBS for 7 minutes. Following permeabilization, coverslips were incubated for 5 minutes in FISH (fluorescence in situ hybridization) wash buffer (2x SSC, 10% formamide) for 5 minutes. To visualize L1 RNA, coverslips were then incubated with 300 nM FISH probes (sequences below) in FISH hybridization buffer (2x SSC, 10% formamide, 1% dextran sulphate) for ~4 hours at 37°C. Following hybridization, cells were incubated for 30 minutes in FISH wash buffer at 37°C and then incubated with FISH wash buffer + 3% BSA for an additional 30 minutes at 37°C. To visualize L1 ORF1p by immunofluorescence, coverslips then were incubated with αORF1p antibodies (1:2000) in 1x PBS + 3% BSA for 1 hour at 37°C. Cells were washed three times with 1x PBS (10 minutes per wash). Cells were incubated with Alexa Fluor 546 conjugated Goat anti-Rabbit IgG (Invitrogen) (1:1000) in 1x PBS + DAPI (50 ng/mL) for 30 minutes at 37°C. Coverslips were mounted on slides with VECTASHIELD mounting media (Vector Laboratories). Combined RNA FISH/immunofluorescence samples were imaged with a Zeiss Axioplan2 microscope (63x objective; Axiovision 4.8 software). RNA FISH/immunofluorescence images (Fig 6A–6D) were globally processed using the Photoshop CS6 (version 13.0 x64) Levels tool to adjust input levels. The L1 RNA was labeled using 21 Quasar670-labelled anti-sense oligonucleotide probes complimentary to sequences within the L1.3 5' UTR (probes were designed and produced by Biosearch Technologies, Petaluma, CA). The sequences of the 21 L1 probes are as follows: 5'-aaatcaccgtcttctgcgtc-3', 5'-ggtacctcagatggaaatgc-3', 5'-cactccctagtgagatgaac-3', 5'-ccctttctttgactcagaaa-3', 5'-aatattcgggtgggagtgac-3', 5'-cttaagccggtctgaaaagc-3', 5'-caggtgtgggatatagtctc-3', 5'-tgctagcaatcagcgagatt-3', 5'-ttgcagtttgatctcagact-3', 5'-tttgtttacctaagcaagcc-3', 5'-cagaggtggagcctacagag-3', 5'-ctgtctttttgtttgtctgt-3', 5'-cacttaagtctgcagaggtt-3', 5'-ctctcttcaaagctgtcaga-3', 5'-ttgaggaggcagtctgtctg-3', 5'-ctgcaggtctgttggaatac-3', 5'-ttctaacagacaggaccctc-3', 5'-cctttctggttgttagtttt-3', 5'-gatgggttttcggtgtagat-3', 5'-gtctttgatgatggtgatgt-3', 5'-tttgtggttttatctacttt-3'.
Polyclonal antibodies against peptide sequences 31–49 of L1.3 ORF1p (αORF1p) were raised in rabbits and affinity-purified (Open Biosystems). αCDK9 (2316), αUPF1 (9435), and αGFP (2955) were obtained from Cell Signaling Technology. αhnRNPL (NBP1-67852), αILF3 (EPR3627), αLARP1 (NBP1-19128), αMATR3 (NB100-1761), αNCL (NB100-1920SS), and αDHX9 (NB110-40579) were obtained from Novus Biologicals. αFAM120A (ab83909), αPURA (ab79936), and αHA tag (ab9110) were obtained from Abcam. αMOV10 (SAB1100141), αZAP (Anti-ZC3HAV1 (HPA047818)), and αTubulin (T9026) were obtained from Sigma. αZC3HAV1 (16820-1-AP) was obtained from Proteintech. αeIF3 (p110) (sc-28858) was obtained from Santa Cruz Biotechnology. αT7-Tag mouse monoclonal (69522–3) was obtained from Novagen. αTAP rabbit polyclonal (CAB1001) was obtained from Thermo Scientific.
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10.1371/journal.pbio.1001183 | Regulation of Nucleotide Excision Repair by UV-DDB: Prioritization of Damage Recognition to Internucleosomal DNA | How tightly packed chromatin is thoroughly inspected for DNA damage is one of the fundamental unanswered questions in biology. In particular, the effective excision of carcinogenic lesions caused by the ultraviolet (UV) radiation of sunlight depends on UV-damaged DNA-binding protein (UV-DDB), but the mechanism by which this DDB1-DDB2 heterodimer stimulates DNA repair remained enigmatic. We hypothesized that a distinctive function of this unique sensor is to coordinate damage recognition in the nucleosome repeat landscape of chromatin. Therefore, the nucleosomes of human cells have been dissected by micrococcal nuclease, thus revealing, to our knowledge for the first time, that UV-DDB associates preferentially with lesions in hypersensitive, hence, highly accessible internucleosomal sites joining the core particles. Surprisingly, the accompanying CUL4A ubiquitin ligase activity is necessary to retain the xeroderma pigmentosum group C (XPC) partner at such internucleosomal repair hotspots that undergo very fast excision kinetics. This CUL4A complex thereby counteracts an unexpected affinity of XPC for core particles that are less permissive than hypersensitive sites to downstream repair subunits. That UV-DDB also adopts a ubiquitin-independent function is evidenced by domain mapping and in situ protein dynamics studies, revealing direct but transient interactions that promote a thermodynamically unfavorable β-hairpin insertion of XPC into substrate DNA. We conclude that the evolutionary advent of UV-DDB correlates with the need for a spatiotemporal organizer of XPC positioning in higher eukaryotic chromatin.
| Like all molecules in living organisms, DNA undergoes spontaneous decay and is constantly under attack by endogenous and environmental agents. Unlike other molecules, however, DNA—the blueprint of heredity—cannot be re-created de novo; it can only be copied. The original blueprint must therefore remain pristine. All kinds of DNA damage pose a health hazard. DNA lesions induced by the ultraviolet (UV) component of sunlight, for example, can lead to skin aging and skin cancer. A repair process known as nucleotide excision repair (NER) is dedicated to correcting this UV damage. Although the enzymatic steps of this repair process are known in detail, we still do not understand how it copes with the native situation in the cell, where the DNA is tightly wrapped around protein spools called nucleosomes. Our study has revealed the molecular mechanism by which an enigmatic component of NER called UV-DDB stimulates excision of UV-induced lesions in the landscape of nucleosome-packaged DNA in human skin cells. In particular, we describe how this accessory protein prioritizes, in space and time, which UV lesions in packaged DNA to target for repair by NER complexes, thus optimizing the repair process.
| Ultraviolet (UV) light generates mutagenic DNA lesions in the skin, primarily 6-4 pyrimidine-pyrimidone photoproducts (6-4PPs) and cyclobutane pyrimidine dimers (CPDs) [1] whose cytotoxic, inflammatory, and carcinogenic effects are mitigated by nucleotide excision repair (NER). Defects in this DNA repair system cause xeroderma pigmentosum (XP), a hereditary syndrome characterized by UV hypersensitivity and skin cancer [2],[3]. Although all principal biochemical steps are understood in detail [4]–[6], it is not yet known how NER is coordinated in the chromatin context, where the substrate is packed with histone proteins to generate arrays of nucleosome core particles joined by internucleosomal linkers [1],[7]. In the present study, we asked the question of how nucleosome arrays are inspected for DNA damage.
The UV-damaged DNA-binding (UV-DDB) and XPC-RAD23B complexes are the initial sensors of UV lesions in the global-genome repair branch of NER activity. XPC is essential for the recruitment of downstream NER factors including TFIIH, which comprises the XPB and XPD subunits, followed by XPA, replication protein A and the incision enzymes XPF-ERCC1 and XPG [8]. UV-DDB is a heterodimer: DDB1 associates with the CUL4A ubiquitin ligase [9]–[12], whereas DDB2 binds avidly to UV-irradiated DNA [13]–[18]. The absence of functional DDB2 in XP-E cells [19],[20] results in significantly delayed excision of 6-4PPs and overall reduced repair of CPDs [21],[22]. A widely accepted although unproven model is that UV-DDB recognizes these lesions and delivers the substrate to XPC, which is the actual NER initiator [22]–[26]. However, this putative handover remained elusive because it is not possible, for example in electrophoretic mobility shift assays, to detect stable intermediates where UV-DDB and XPC bind to the same damage simultaneously [23],[24],[27]. A general assumption was, therefore, that XPC is recruited only after the displacement of UV-DDB by CUL4A-mediated ubiquitylation and proteolysis [28]–[30]. The concomitant CUL4A-dependent ubiquitylation of XPC and histones is thought to potentiate the DNA-binding affinity of this repair initiator [25] and facilitate its access to chromatin [31],[32], but such models have been challenged by a more recent report where conditionally CUL4A-deleted mice show enhanced NER activity and resistance to UV-induced skin carcinogenesis [33]. Also, the known properties of UV-DDB have been difficult to reconcile with the manifestations of a DDB2 mutation in XP-E patients because UV-DDB binds with highest affinity to 6-4PPs [34],[35], although it is required mainly for an effective CPD removal [21],[22]. However, reconstitution assays showed that UV-DDB is not at all needed for CPD excision from naked DNA [36], thus pointing to an as yet unidentified function in chromatin. Finally, it was difficult to understand why, after UV irradiation, DDB2 is degraded before the DNA lesions are fully repaired [29].
The aim of this study was to elucidate the so far enigmatic link between UV-DDB, XPC, and CUL4A by analyzing their crosstalk in the chromatin of living cells. We found a completely novel ubiquitin-dependent regulatory principle whereby UV-DDB inspects the nucleosome arrays to probe damaged chromatin for accessibility. Unexpectedly, the associated CUL4A ubiquitin ligase is required to retain the XPC partner at internucleosomal sites that are more permissive than the corresponding core particles to the assembly of downstream NER complexes. As a back-up function that is independent of chromatin localization and ubiquitin, the DDB2 subunit of UV-DDB associates transiently with the DNA-binding domain of XPC to fine-tune its engagement with CPD lesions.
UV-DDB translocates to chromatin after UV irradiation [37]–[40], but this accessory sensor binds with highest affinity to 6-4PPs [35],[41] and earlier studies demonstrated that, in chromatin, 6-4PP lesions arise mainly in internucleosomal linker DNA between core particles [1],[42]. Prompted by these previous findings, we used a standard chromatin digestion assay to test the hypothesis that, in irradiated cells, UV-DDB accumulates preferentially at internucleosomal linker positions of nucleosome arrays. In particular, the localization of DDB2 (the DNA-binding subunit of UV-DDB) has been analyzed using the flow diagram of Figure S1A. First, free UV-DDB not bound to chromatin was removed by salt (0.3 M NaCl) extraction. Second, the resulting chromatin was dissected by a treatment with micrococcal nuclease (MNase). By cleaving internucleosomal linker regions (Figure S1B), this enzyme generates a solubilized supernatant representing digested internucleosomal sites (∼35% of cellular DNA), with traces of soluble core particles (∼5% of cellular DNA), and an insoluble fraction containing the vast majority of nuclease-resistant core particles (covering ∼60% of cellular DNA). This digestion pattern remained unchanged upon UV exposure as well as after siRNA-mediated DDB2 or XPC depletion and, in all cases, >80% of 6-4PPs appeared in MNase-sensitive internucleosomal regions whereas CPDs were evenly distributed across linker and core particle DNA (Figure S1C and S1D).
As shown in Figure 1A, treatment of the chromatin of UV-irradiated cells with a saturating MNase concentration (4 U/µl), which digests all linker DNA, released ∼70% of total DDB2 into the solubilized internucleosomal fraction (“S. inter.”) and only ∼20% of the cellular DDB2 pool remained associated with insoluble core particles (“I. cores”). In dose dependence experiments, even low MNase concentrations, which resulted in mild DNA digestions, liberated the same amount of DDB2 from chromatin (Figure S1E), thus confirming that UV-DDB binds predominantly to nuclease-hypersensitive and, hence, highly accessible internucleosomal DNA. These UV-DDB- and 6-4PP-enriched sites coincide with NER hotspots, as they were more permissive than insoluble core particles to the UV-dependent recruitment of downstream NER subunits like XPB (a TFIIH subunit), XPA, and XPG (Figure 1A). The accumulation of NER factors at these solubilizable internucleosomal sites led to faster kinetics of 6-4PP and CPD excision, measured by an immunoassay procedure, in comparison to the slow removal of these lesions from core particles (Figure 1B).
Unlike UV-DDB, XPC displayed a constitutive binding to both MNase fractions of chromatin even in the absence of UV lesions. However, in response to DNA damage, XPC moved by a large extent to the MNase-resistant and slowly repaired core particles (Figure 1A and 1C). Such a preferential XPC binding to core particles, accompanied by a UV-DDB translocation mainly to solubilizable internucleosomal sites, was also observed in p53-proficient U2OS fibroblasts (Figure 1D). The much higher amount of histone H3 as well as a co-localization of trimethylated H3 (H3K9m3), histone variant H1.0, and heterochromatin protein 1, which correlate with chromatin condensation [43],[44], support the conclusion that this insoluble fraction contains the bulk of nucleosome core particles. Importantly, the sequestration of XPC on these core particles reflects a specific binding to histone-assembled DNA, rather than the formation of insoluble protein aggregates, as the removal of core histones with 2.5 M NaCl [45] resulted in a nearly complete XPC release (Figure S1F).
Several parameters distinguish the just described MNase-solubilizable internucleosomal sites and MNase-resistant core particles. First, immunoblots against XPC revealed multiple higher molecular weight forms (>150 kDa), known to occur by polyubiquitylation [25],[46], that begin appearing within ∼5 min after UV irradiation (Figure 2A). It is important to note that, by increasing the polyacrylamide concentration, this typical ladder-like appearance of ubiquitylated XPC molecules was compressed to a more discrete signal in most immunoblots of this report. We consistently found that the proportion of ubiquitylated XPC, relative to unmodified protein, is markedly increased on internucleosomal DNA compared to the slowly repaired core particles (Figure 2B). Up to 40% of XPC bound to solubilizable internucleosomal sites but <10% in insoluble core particles are modified (Figure 2C). The substantial, although not complete, separation of ubiquitylated and non-ubiquitylated species achieved by MNase digestion suggested that this modifier plays a role in regulating the XPC partitioning within nucleosome repeats of chromatin (see siRNA-mediated depletion assays below).
A second difference was disclosed when the same samples were probed with antibodies against RAD23B. As observed in cell extracts, where XPC is mainly complexed with RAD23B [47], XPC carried this interaction partner to internucleosomal sites. However, the fraction of XPC that associated with the slowly repaired core particles is not accompanied by RAD23B (Figure 1A). For comparison, RAD23A (the second RAD23 homolog) is found only at internucleosomal sites independently of a UV stimulus. The third difference concerns the time course of XPC accumulation. In fact, XPC relocated to internucleosomal DNA immediately after UV irradiation (Figure 2B, 1-min time point) and, in this rapidly repaired microenvironment, returned to background levels corresponding to the constitutive XPC binding to chromatin within ∼3 h (Figure 2D). Instead, the UV-dependent XPC recruitment to insoluble core particles persisted further, thus reflecting a long-term DNA repair response. After an incubation of 6 h following irradiation, when DDB2 is reduced to ∼20% of its pre-irradiation level due to proteolytic degradation (Figure S2A) [29], the majority of chromatin-bound XPC was sequestered on these slowly repaired core particles (Figure 2D and 2E). Thus, time course experiments suggested that DDB2 is important to retain high levels of XPC on internucleosomal DNA (see siRNA-mediated depletion assays below).
As expected, the preferential appearance of DDB2 (the DNA-binding subunit of UV-DDB) on internucleosomal DNA was accompanied by an equivalent accumulation of DDB1 (its regulatory adaptor) in response to UV light. A DDB2 depletion by transfection with specific siRNA (Figure S2B) prevented this UV-induced DDB1 translocation to chromatin and, accordingly, suppressed the ubiquitylation of XPC (Figure S2C). As a consequence of this diminished ubiquitylation, the relocation of XPC to internucleosomal sites, but not to insoluble core particles, was reduced (Figure 3A). This and follow-up findings involving the role of protein ubiquitylation are confirmed by a quantitative assessment of immunoblots over 3–5 independent experiments (Figure 3B). In siRNA-mediated depletion experiments, DDB2 was down regulated incompletely to ∼10% of control cells (Figure S2B). However, a stronger aversion of XPC for internucleosomal DNA was observed in XP-E cells displaying no residual UV-DDB activity (Figure S2D). Finally, Figure S2E shows that the normal abundance of XPC at solubilizable internucleosomal sites was restored upon complementation of DDB2-depleted cells with DDB2 fused to green-fluorescent protein (DDB2-GFP).
CUL4A is primarily responsible for XPC ubiquitylation, while CUL4B (the other CUL4 family member) plays essentially no role in this process [33]. Therefore, to provide a direct proof for the function of ubiquitin modifiers in XPC positioning, four different strategies were used to dissociate UV-DDB from the CUL4A machinery. As expected, a siRNA-mediated CUL4A depletion (Figure S2B) suppressed XPC ubiquitylation (Figure S2C) and increased the DDB2 level in chromatin by preventing its UV-dependent proteolytic degradation (Figure 3C). Consistent with the just described effects of a DDB2 down regulation, the missing CUL4A activity reduced the presence of XPC at internucleosomal sites, but not in the insoluble core particle fraction, thus limiting the overall recruitment of downstream subunits like XPA to UV-irradiated chromatin (Figure 3C). Accompanying UV lesion excision assays demonstrated that this CUL4A depletion mimics the effect of a DDB2 deficiency by delaying substantially the removal of 6-4PPs and inhibiting the overall CPD repair (Figure 4A). However, in the corresponding core particles, this CUL4A depletion had no effect on 6-4PP excision and caused only a marginal, if any, further reduction of the slow rate of CPD removal (Figure 4B). As illustrated in Figure 4C, these functional assays therefore reveal that the CUL4A ubiquitin ligase is needed primarily for an effective DNA repair of internucleosomal sites, where its depletion slows down substantially the fast excision of 6-4PPs and strongly inhibits the processing of CPDs.
Next, we confirmed these effects of a DDB2 or CUL4A down regulation using small-molecule inhibitors. The E1 inhibitor PYR-41 suppressed XPC ubiquitylation following UV exposure (Figure S3A) and, as a consequence, inhibitor-treated cells were unable to retain XPC at internucleosomal sites upon UV irradiation. In contrast, the UV-dependent XPC accumulation in the core particle fraction was unchanged (Figure 3B and 3D). The proteasome inhibitor MG132 raised the DDB2 level in chromatin by inhibiting its UV-dependent proteolytic degradation. In addition, by depletion of the free ubiquitin pool, MG132 impedes the ubiquitylation of nuclear substrates [48] including XPC (Figure S3B). As a consequence of this MG132-inhibited ubiquitylation, XPC failed to persist at internucleosomal sites but was still able to bind to core particles (Figure 3B and 3E). Time course experiments with MG132 confirmed the finding of Figure 2B (1-min time point) demonstrating that the initial UV-dependent shuttling of XPC to internucleosomal sites is completely independent of ubiquitin. However, the subsequent ubiquitylation is required to retain XPC on these internucleosomal DNA locations (Figure S3C). As DDB2 and p53 regulate the synthesis of one another [21],[49], the MG132 inhibitor has also been used to confirm the key role of ubiquitylation in retaining XPC at internucleosomal sites in p53-proficient U2OS cells (Figure S3D).
Finally, this ubiquitin function was further established using mouse cells that harbor a temperature-sensitive ubiquitin-activating E1 enzyme [25],[46]. Due to their ubiquitylation defect when incubated at 39°C, these ts20 cells are unable to retain XPC at internucleosomal sites and, hence, respond to UV light with a nearly complete XPC translocation to the insoluble core particle fraction (Figure 3F). Instead, in control H38-5 cells corrected with wild-type E1, XPC was effectively retained at solubilizable internucleosomal sites at both 32°C and 39°C.
To search for direct UV-DDB actions, not mediated by ubiquitin, we exploited an XPC-GFP fusion that, unlike endogenous XPC, was poorly ubiquitylated (Figure 5A). Following 1 h after UV irradiation, a minor but detectable proportion of this construct remained at internucleosomal sites (Figure 5B) where it led to recruitment of downstream NER effectors like XPA, thus explaining its ability to correct the UV hypersensitivity of XP-C cells [47]. However, consistent with its poor susceptibility to ubiquitylation, most of these XPC-GFP constructs associated with the insoluble core particle fraction (Figure 5B) as noted before (Figure 3) for endogenous XPC in the background of a defective UV-DDB-CUL4A pathway.
To monitor DDB2-XPC interactions within chromatin rather than as free proteins in solution, this poorly ubiquitylated XPC-GFP fusion was expressed in Chinese hamster ovary (CHO) cells that lack endogenous DDB2 [50]. After local damage induction by irradiation through polycarbonate filters [39], during which only parts of each nucleus are exposed to UV light, we measured the increase of green fluorescence intensity in irradiated areas over the surrounding nuclear background. Figure 5C illustrates that the UV-dependent XPC-GFP accumulation was enhanced by co-expression of DDB2, which was tagged with red-fluorescent protein (DDB2-RFP). Time course experiments showed that the accumulation of XPC reaches a maximum around 15 min after irradiation (Figure S4A). Importantly, the stimulation of lesion recognition by DDB2 was insensitive to the E1 inhibitor PYR-41 (Figure 5D), thus confirming the notion that, by this approach, we measured a ubiquitin-independent UV-DDB function. Also, this stimulation of lesion recognition was maintained with an XPC truncate (XPC1–831) that, on its own, binds weakly to damaged sites (Figure 5E), indicating that a DNA-independent association between UV-DDB and XPC is involved in the substrate handover between these two factors.
Next, the filter irradiation assay was used to map UV-DDB-XPC interactions in chromatin using the constructs outlined in Figures 5F and S4B. Compared to full-length XPC, the truncate XPC1–741, like XPC1–831, showed a defective relocation to damaged sites but was still attracted to UV lesions when co-expressed with DDB2-RFP. Instead, the N-terminal fragment XPC1–495 was recruited to UV damage sites less efficiently than the full-length control or the much shorter C-terminal fragment XPC607–940 (Figure 5G). Collectively, this in situ mapping suggested that XPC residues 496–741, comprising a transglutaminase homology domain (TGD) and parts of the β-hairpin domains (BHDs), associate with DDB2. By eliminating the respective sequences, we tested the individual contribution of each of these motifs to DDB2-XPC interactions. TGD-deleted (ΔTGD) and BHD1-deleted (ΔBHD1) constructs display the same damage recognition capacity as the full-length control, but their accumulation in UV foci was not stimulated by co-expression of DDB2 (Figure 5H). In contrast, the BHD3 sequence is dispensable for DDB2-XPC interactions because the ΔBHD3 deletion construct was still efficiently recruited to UV lesions by DDB2 (Figure 5H).
We characterized the ubiquitin-independent UV-DDB-XPC associations by transfecting HEK293T cells with DDB2-FLAG and XPC-GFP fusions, followed by co-immunoprecipitation using anti-FLAG antibodies (Figure S4C). In the presence of full-length DDB21–427-FLAG, the isolated complexes comprised both endogenous DDB1 and XPC-GFP, demonstrating that there was sufficient free cellular DDB1 to probe its role in these interactions (Figure S4D). Additional co-immunoprecipitations showed that an N-terminal DDB2 truncate (DDB279–427-FLAG), which failed to associate with DDB1, still bound efficiently to XPC-GFP, demonstrating that DDB1 is not implicated in this binary DDB2-XPC crosstalk. The co-immunoprecipitations with fusion fragments XPC520–633-GFP and XPC607–831-GFP provided further support to the notion that DDB2 associates with both the TGD (Figure S4E) and BHD regions of XPC (Figure S4F).
In view of this preliminary domain mapping in HEK293T cells, polypeptides containing the TGD (XPC428–633), BHD1/2 (XPC607–741), or BHD2/3 (XPC679–831) sequences were tested as purified glutathione-S-transferase (GST) fusions, thus demonstrating that the TGD (Figure 6A) and BHD1/2 motifs (Figure 6B) make direct contacts with DDB2. In contrast, a polypeptide of similar length comprising the BHD2/3 sequence did not associate with DDB2, thus excluding this part of XPC as the interaction surface. We next found that DDB2-TGD associations are inhibited by the addition of either undamaged or damaged double-stranded DNA (Figure 6C). This latter finding provides a plausible explanation for the fact that it has never been possible to isolate and characterize a stable ternary complex with simultaneous binding of both UV-DDB and XPC to substrate DNA [23],[27].
In contrast to this interaction with the TGD motif, the association of DDB2 with the BHD1/2 fragment was stimulated by short DNA duplexes carrying a site-specific lesion. In line with the distinct affinity of UV-DDB for different types of UV damage, DNA duplexes with a 6-4PP promoted this interaction more efficiently than those carrying a CPD (Figure 6D). Taken together, these results indicate a dynamic process whereby the DDB2 subunit of UV-DDB first recruits XPC through a DNA-independent association with TGD and then positions XPC onto the lesion site by a DNA damage-stimulated interaction with BHD1.
The identification of an XPC domain, whose association with UV-DDB is stimulated by damaged DNA, demonstrated that the two factors are able to bind transiently to the same lesion. To understand how damaged DNA is transferred from UV-DDB to XPC during ongoing repair, we transfected CHO cells with XPC-GFP, alone or in combination with DDB2-RFP. Following the induction of local UV damage by irradiation through polycarbonate filters, the in situ stability of XPC-DNA interactions was tested by bleaching the green fluorescence signal at damaged sites, thus reducing its intensity to that of the surrounding nuclear background [51],[52]. The subsequent fluorescence recovery due to exchanges of bleached molecules with non-bleached counterparts was recorded over time, thus yielding distinct dissociation curves. In fact, this real-time analysis of nucleoprotein stability by fluorescence recovery after photobleaching on local damage (FRAP-LD) revealed that most XPC is only transiently immobilized at DNA lesions and that the expression of DDB2 doubles the half-life of these dynamic interactions between XPC and damaged DNA from ∼10 s to ∼20 s (Figure 6E). Conversely, the dissociation of DDB2, tested as a GFP fusion, from UV lesions was accelerated by XPC (Figure S5A).
Ultimately, damage recognition by XPC involves the insertion of a β-hairpin of BHD3 into the DNA double helix [53]. To test the role of this key rearrangement during the UV-DDB-XPC handover, we constructed an appropriate deletion by removing residues 789–815 from the human XPC sequence. The resulting β-hairpin-deleted mutant (ΔHairpin), although unable to detect DNA damage on its own, was very effectively recruited to UV lesions upon co-expression with DDB2 (see Figure 5H). Next, this ΔHairpin construct that relocates to damage in the presence of DDB2 has been subjected to FRAP-LD analyses to test again the in situ half-life of its interactions with substrate DNA. The resulting steep slope of fluorescence redistribution indicated, however, that UV-DDB fails to stabilize the ΔHairpin binding to damaged DNA (Figure 6F). Also, the dissociation of DDB2 from damaged DNA was not accelerated by this ΔHairpin deletion (Figure S5B). Thus, although UV-DDB attracts XPC to lesion sites, it only prolongs its residence time at damaged targets if XPC itself is able to insert the β-hairpin subdomain into the substrate double helix.
Since the identification of UV-DDB as an accessory DNA damage sensor, this heterodimer has been the subject of intense scrutiny, but its mechanism of action remained elusive. A consensus model is that UV-DDB helps to recruit the XPC partner to UV lesions [38],[39],[54]. However, experimental evidence for the suggested handover from UV-DDB to XPC is lacking because it has not been possible to isolate and characterize nucleoprotein intermediates where these two factors bind jointly to the same DNA substrate [23],[24],[27]. As to the associated CUL4A complex, it is generally thought that this ubiquitin ligase promotes the removal of UV-DDB from damaged sites [25],[29],[30], enhances the DNA-binding affinity of XPC [25] or opens chromatin to facilitate UV lesion recognition [31],[32]. After reexamining this long-standing issue in the nucleosome context of living cells, we now present an unexpected function that fully accommodates the role of UV-DDB and CUL4A in stimulating DNA excision repair. We found that UV-DDB inspects the chromatin to detect lesions preferentially, although not exclusively, in highly accessible internucleosomal sites distinguishable by their MNase hypersensitivity, and that the accompanying CUL4A-mediated ubiquitylation serves to retain the XPC partner at these particularly permissive DNA repair hotspots.
This newly identified UV-DDB and CUL4A function is critical for effective DNA repair because XPC, the initiator of NER activity, otherwise binds primarily to nucleosome core particles that represent a less permissive environment characterized by (i) poor recruitment of downstream NER subunits and (ii) slow excision of UV lesions (Figure 1). This property of XPC, i.e. its default-mode association with damaged core particles in the whole-chromatin context, challenges a long-held notion derived from biochemical reconstitution experiments [55],[56] that nucleosome repeats pose a barrier to recognition of UV lesions by XPC.
Interestingly, the characteristic XPC binding to damaged core particles is independent of UV-DDB- and CUL4A-mediated ubiquitylation (Figure 3). We even observed that, upon exposure to UV light, the initial XPC accumulation on internucleosomal DNA does not require the ubiquitylation reaction (Figures 2B and S3C). However, the following ubiquitin modification is essential to retain XPC at these highly accessible internucleosomal positions that allow for the fast excision of both 6-4PPs (half-life in internucleosomal DNA ∼1 h) and CPDs (half-life in internucleosomal DNA ∼2 h) (Figure 1B). It is important to point out that 6-4PPs are generated with ∼8-fold higher density in internucleosomal sites than in core particles [1],[42]. Thus, the fast CUL4A-dependent excision from internucleosomal DNA accounts for nearly all global repair of this lesion across the genome. As summarized in Figure 3B, the ubiquitin-dependent retention of XPC at internucleosomal sites is abolished by depletion of DDB2 or CUL4A, by inhibition of the E1 ubiquitin-activating enzyme (using a small-molecule inhibitor or a temperature-sensitive mutant), or by depletion of the ubiquitin pool (using a proteasome inhibitor).
That the chromatin location of XPC is determined by its own CUL4A-dependent modification can be inferred from an XPC-GFP fusion, which is poorly polyubiquitylated (although monoubiquitylation cannot be completely ruled out) and whose chromatin partitioning, characterized by a strong binding to damaged core particles, is similar to that observed with endogenous XPC after blocking the ubiquitylation pathway (Figure 5B). Despite such a negative effect exerted by the GFP tag on the CUL4A machinery, this construct complements the overt hypersensitivity of XP-C cells to killing by UV radiation [47] and, in our study, provides a helpful tool to demonstrate that it is the ubiquitylation of XPC itself that fine-tunes the nucleosome partitioning of this repair initiator. The resulting ubiquitin-dependent retention at internucleosomal sites may be a consequence of an increased affinity of polyubiquitylated XPC for naked DNA as reported by Sugasawa et al. (2005) [25]. Conversely, the lack of ubiquitin modifications may favor the release of RAD23B because we noted with two different antibodies that non-ubiquitylated XPC, which binds to core particles, is separated from RAD23B (Figure 1A). By mediating CUL4A activity, UV-DDB not only controls the spatial distribution of XPC but also the differential timing of its dissociation from chromatin. Indeed, the concomitant proteolysis of DDB2, induced by CUL4A, terminates the just described XPC retention at internucleosomal sites. With progressive DDB2 degradation after UV exposure, a growing proportion of chromatin-associated XPC evades ubiquitylation and, hence, disappears from internucleosomal DNA (Figure 2D and 2E).
The results discussed so far explain the delayed excision of UV lesions from internucleosomal sites in a DDB2- or CUL4A-deficient background (Figure 4C). Yet they do not accommodate the very slow removal of CPDs from nucleosome core particles following a DDB2 depletion, particularly considering that a comparable CUL4A depletion does not significantly affect the excision of these lesions from the same core particle substrate (Figure 4B). In support of a CUL4A-independent action, we found that, in addition to associating with the DDB1-CUL4A machinery, the DDB2 subunit makes direct contacts with a region of XPC that overlaps partly with its DNA-binding surface. The evidence underlying this conclusion is that DDB2 stimulates the recruitment of XPC-GFP fusions to UV lesions and that this recruitment is not affected by inhibition of the ubiquitylation pathway. Direct interactions are made between DDB2 and the TGD and BHD1 regions, two neighboring DNA-binding motifs of XPC (Figure 6). An association with TGD occurs regardless of DNA, whereas the binding to BHD1 is stimulated by damaged substrates, indicating that DDB2 and XPC alternate their contacts to hand over the DNA lesion from one recognition factor to the next.
The relevance of these direct interactions is demonstrated by ΔTGD and ΔBHD1 deletions whose recruitment to DNA damage is not stimulated by DDB2 (Figure 5H). In situ analyses of the role of these domains by protein dynamics show that damage-specific DDB2-XPC interactions take place transiently, that they stabilize the association of XPC with UV lesions, and that this stabilization additionally depends on a β-hairpin subdomain located in BHD3 (Figure 6). Because DDB2 does not make physical contacts with this BHD3 region of XPC, we conclude that the observed transient interactions involving the TGD and BHD1 motifs serve to guide the β-hairpin subdomain into the substrate double helix. Such an insertion occurs at a substantial energetic cost as it requires local disruption of base stacking and hydrogen bonds [53]. While 6-4PPs reduce the thermodynamic threshold of this conformational change by lowering the melting temperature of damaged DNA and, hence, allow for direct recognition by XPC, CPDs cause minimal DNA-destabilizing effects [57],[58]. Thus, the dependence on DDB2 for a β-hairpin insertion explains the exquisite defect of XP-E cells in repairing this more abundant type of UV lesion.
To summarize, UV-DDB exerts a bimodal action (Figure 7) to optimize the genome-wide NER reaction and ensure an initially fast (ubiquitin-dependent) removal of easily accessible lesions from internucleosomal DNA as well as the continued (ubiquitin-independent) excision of more intractable damage in nucleosome core particles. That an early (rapid) phase of repair takes place in internucleosomal DNA has already been shown by monitoring nucleotide incorporations into MNase-sensitive sites [59]. On the one hand, as illustrated in Figure 7, UV-DDB interrogates the chromatin to locate high-priority internucleosomal hotspots amenable to rapid excision. On the other hand, the DDB2 subunit of UV-DDB acts as a dynamic platform for the proper engagement of XPC with recalcitrant CPD lesions. Lower eukaryotes lack DDB2 [60], indicating that this subunit becomes critical in vertebrates, where larger and more compacted genomes necessitate a spatiotemporal coordinator of UV lesion recognition. The finding that CUL4A plays an accessory role by triggering a wave of fast DNA repair focused on only a fraction of chromatin, i.e. internucleosomal linkers, also reconciles the conflicting results as to the function of this ubiquitin ligase in stimulating [30]–[32] or inhibiting [33] UV responses. Because the same ligase also regulates the cellular level of DNA repair proteins and other transactions including the division cycle [33], it is conceivable that an interference with CUL4A activity may yield opposing effects depending on the organism, cellular context, or genetic background.
Additional experimental procedures are given in Text S1.
The 15-mer sequence 5′-ACAGCGGTTGCAGGT-3′, carrying a CPD, was synthesized from phosphoramidite precursors. The same 15-mer with a 6-4PP was produced by irradiation and liquid-chromatographic purification [61]. Control oligonucleotides (5′-ACAGCGGTTGCAGGT-3′) were synthesized by Microsynth. The siRNA directed to CUL4A (target sequence 5′-TTCGAAGGACATCATGGTTCA-3′), DDB2 (target sequence 5′-AGGGATCAAGCAGTTATTTGA-3′), and XPC (target sequence 5′-TAGCAAATGGCTTCTATCGAA-3′) were purchased from Qiagen. The siCTRL consists of a pool of scrambled siRNA with at least four mismatches for all sequences in the human genome. MG132 was obtained from Sigma-Aldrich and added to the culture medium 5 h before each assay, at a concentration of 10 µM. PYR-41 (Santa Cruz) was used at a concentration of 50 µM and added to the medium 5 h before the assays. Restriction enzymes and MNase were from New England Biolabs.
The human DDB2 sequence was obtained from plasmid DDB2-GFP-C1 (a gift from Dr. S. Linn, University of California, Berkeley, USA) by BamHI restriction and inserted into the expression vectors p3XFLAG-CMV-14 (Sigma-Aldrich) and pmRFP1-C3 (Dr. Elisa May, University of Konstanz, Germany). To construct the DDB279–427-FLAG fusion, NdeI sites were generated by mutagenesis of codons 1 and 78. Subsequently, codons 1–78 were removed by NdeI digestion. For the cloning of XPC truncations and deletions, NdeI restriction sites were generated at the appropriate positions of vector XPC-pEGFP-N3. XPC-RFP was cloned by insertion of the XPC sequence into vector pmRFP1-C3 using KpnI and SmaI sites. All plasmids were sequenced (Microsynth) to exclude accidental mutations.
HeLa, HEK293T, U2OS, and Chinese hamster ovary (CHO) cells V79 were grown in humidified incubators (37 °C, 5% CO2) using Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco), 100 U/ml penicillin, and 0.1 mg/ml streptomycin. For XP-E fibroblasts, the FBS concentration was 15% (v/v). Mouse embryonic fibroblasts (MEFs) ts-20 were grown at 32 °C in DMEM with 10% (v/v) FBS. Stably corrected H38-5 cells were cultured at 37 °C with hygromycin (50 µg/ml) to maintain expression of the complementing E1 enzyme. These MEFs were transferred to the restrictive temperature (39 °C) 18 h prior to the experiments.
After removal of medium, cells were rinsed with phosphate-buffered saline (PBS) and irradiated with the indicated doses of UV-C from a germicidal lamp (254 nm wavelength). The progressive excision of 6-4PPs and CPDs was monitored using commercial antibodies as described in Text S1. For local damage induction, a 5-µm polycarbonate filter (Millipore) presoaked in PBS was placed over the cells followed by irradiation with 100 J/m2. After removal of the filter, the cells were incubated in fresh medium before processing for chromatin dissection, immunocytochemistry, or FRAP-LD analyses.
A combined salt extraction and MNase treatment (Figure S1A) was applied to analyze the partitioning of NER proteins. On 10-cm culture dishes, 5×106 cells were grown to confluence and UV-irradiated for up to 10 s. After the indicated post-irradiation times (between 1 min and 24 h), the dishes were transferred onto ice, the cells were washed twice with 10 ml ice-cold PBS and scraped into a 1.5-ml tube with 0.3 ml of NP-40 lysis buffer [25 mM Tris-HCl (pH 8.0), 0.3 M NaCl, 1 mM EDTA, 10% (v/v) glycerol, 1% (v/v) NP-40, 0.25 mM phenylmethylsulfonyl fluoride, and EDTA-free protease inhibitor cocktail (Roche)] [25]. After a 30-min incubation on a turning wheel, free proteins not bound to chromatin (supernatant 1 in Figure S1A) were recovered by centrifugation (15,000 g, 4 °C, 10 min) and the volume was adjusted to 500 µl using NP-40 lysis buffer. The remaining insoluble chromatin was washed twice with 0.5 ml ice-cold CS buffer [31] consisting of 20 mM Tris-HCl, pH 7.5, 100 mM KCl, 2 mM MgCl2, 1 mM CaCl2, 0.3 M sucrose, and 0.1% (v/v) Triton X-100. Next, the chromatin was resuspended in 40 µl CS buffer and, after the addition of 5 µl 10× reaction buffer [500 mM Tris-HCl (pH 7.9), 50 mM CaCl2], 1 µl of bovine serum albumin (BSA; 1 mg/ml) and MNase (4 U/µl in a volume of 50 µl), incubated at 37 °C for 20 min. MNase digestions were stopped by the addition of EDTA (5 mM) and the solubilized proteins (supernatant 2 in Figure S1A) were separated from insoluble core particles by centrifugation at 15,000 g (10 min, 4 °C). This core particle fraction was dissolved in 80 µl denaturing buffer [20 mM Tris-HCl, pH 7.4, 50 mM NaCl, 1 mM EDTA, 0.5% (v/v) NP-40, 0.5% (v/v) deoxycholate, and 0.5% (w/v) sodium dodecyl sulfate (SDS)] [62] and sonicated (1×12 s). Alternatively, to generate the supernatant 3 of Figure S1F, the insoluble core particles were dissolved without sonication in 50 mM Tris-HCl, pH 8.0, 0.05% (v/v) NP-40 and 2.5 M NaCl as reported [63]. To obtain MNase dose dependences, chromatin pellets were digested with increasing enzyme concentrations. For the subsequent electrophoretic analysis, DNA fragments were extracted using the QIAamp Blood Kit (QIAGEN), resolved on 2% agarose gels, and stained with ethidium bromide.
Polypeptides of 135–204 residues fused to GST (GST-XPC607–741, GST-XPC607–766, GST-XPC679–832, and GST-XPC428–633) were cloned and expressed in E. coli as described [64]. These polypeptides (120 pmol) were incubated (1 h, 4 °C) with 25 µl glutathione-Sepharose beads in 500 µl washing buffer [50 mM Tris-HCl (pH 8.0), 1 mM EDTA, 1 mM dithriothreitol, 10% (v/v) glycerol, 0.5% (v/v) Nonidet P-40, 150 mM NaCl, and 200 g/ml bovine serum albumin] containing 0.5% (w/v) nonfat dry milk. In the experiments with DNA, UV-DDB (120 pmol) was pre-incubated (1 h, 4 °C) with the indicated amounts of undamaged or damaged duplexes in a separate tube containing 500 µl washing buffer. The bead suspension containing GST-tagged polypeptides were washed three times with 1 ml washing buffer and incubated with UV-DDB for 20 min at room temperature in a total volume of 500 µl. The beads were then washed 3 times with 1 ml washing buffer containing nonfat dry milk, twice with washing buffer without nonfat dry milk, resuspended in loading buffer, and resolved on 10% denaturing polyacrylamide gels.
FRAP-LD measurements were performed on a Leica TCS SP5 confocal microscope equipped with an Ar+ laser (488 nm) and 63× oil immersion lens. The assays were performed in a controlled environment at 37°C and a CO2 supply of 5%. Cells transfected with GFP or RFP constructs were UV-irradiated (254 nm, 100 J/m2) through 5-µm polycarbonate filters. After 15-min incubations in complete medium, regions of interest (ROIs) corresponding to sites of GFP accumulation were photobleached at 50% laser intensity to reduce their fluorescence to that of the surrounding nuclear background. Fluorescence recovery was monitored 10 times using 0.7 s intervals followed by 10 frames at 5 s and 6 frames at 20 s. The results were adjusted for overall bleaching by correction with a reference ROI of the same size monitored at each time point. The values were used to calculate ratios between the damaged area in the foci and the corresponding intensity before bleaching. In the data display, the first fluorescence measurement after photobleaching is set to 0, while all following data points are plotted as a function of time.
|
10.1371/journal.pcbi.1000378 | Differential Affinity and Catalytic Activity of CheZ in E.
coli Chemotaxis | Push–pull networks, in which two antagonistic enzymes control the
activity of a messenger protein, are ubiquitous in signal transduction pathways.
A classical example is the chemotaxis system of the bacterium
Escherichia coli, in which the kinase CheA and the
phosphatase CheZ regulate the phosphorylation level of the messenger protein
CheY. Recent experiments suggest that both the kinase and the phosphatase are
localized at the receptor cluster, and Vaknin and Berg recently demonstrated
that the spatial distribution of the phosphatase can markedly affect the
dose–response curves. We argue, using mathematical modeling, that the
canonical model of the chemotaxis network cannot explain the experimental
observations of Vaknin and Berg. We present a new model, in which a small
fraction of the phosphatase is localized at the receptor cluster, while the
remainder freely diffuses in the cytoplasm; moreover, the phosphatase at the
cluster has a higher binding affinity for the messenger protein and a higher
catalytic activity than the phosphatase in the cytoplasm. This model is
consistent with a large body of experimental data and can explain many of the
experimental observations of Vaknin and Berg. More generally, the combination of
differential affinity and catalytic activity provides a generic mechanism for
amplifying signals that could be exploited in other two-component signaling
systems. If this model is correct, then a number of recent modeling studies,
which aim to explain the chemotactic gain in terms of the activity of the
receptor cluster, should be reconsidered.
| In both prokaryotes and eukaryotes, extra- and intracellular signals are often
processed by biochemical networks in which two enzymes together control the
activity of a messenger protein via opposite modification reactions. A
well-known example is the chemotaxis network of Escherichia
coli that controls the swimming behavior of the bacterium in response
to chemical stimuli. Recent experiments suggest that the two counteracting
enzymes in this network are colocalized at the receptor cluster, while
experiments by Vaknin and Berg indicate that the spatial distribution of the
enzymes by itself can markedly affect the response of the network. We argue
using mathematical modeling that the most widely used model of the chemotaxis
network is inconsistent with these experimental observations. We then present an
alternative model in which part of one enzyme is colocalized with the other
enzyme at the receptor cluster, while the remainder freely diffuses in the
cytoplasm; moreover, the fraction at the cluster both binds more strongly to the
messenger protein and modifies it faster. This model is consistent with a large
number of experimental observations and provides a generic mechanism for
amplifying signals.
| The protein network that controls chemotaxis of Escherichia coli is
arguably the most-studied and best-characterized signal transduction pathway. Its
relative simplicity makes it an ideal model system for studying signal
amplification, integration, transduction, and adaptation. The network consists of
three parts: i) a cluster of receptors at the cell membrane, which detects the
extracellular ligand; ii) the intracellular signaling pathway, which transmits the
signal from the receptor cluster to the flagellar motors; iii) the network that
controls the response of the flagellar motors. The intracellular signaling pathway
is a push-pull network that consists of a kinase, CheA, that phosphorylates the
messenger protein CheY and a phosphatase, CheZ, that dephosphorylates the
phosphorylated messenger protein CheYp. In wild-type cells, CheA is
localized exclusively at the receptor cluster, and also CheZ is predominantly
localized at the receptor cluster [1]. Recently, however, Vaknin and Berg studied
mutants in which CheZ can no longer bind the receptor cluster, as a result of which
it is uniformly distributed in the cytoplasm [2]. They observed that the
response of the intracellular signaling pathway of these mutant cells differs
strongly from that of wild-type cells. Inspired by this observation, we recently
performed a mathematical modeling study of a canonical push-pull network, which
showed that the spatial distribution of the antagonistic enzymes by itself can have
a dramatic effect on the response [3]. Our study also
showed, however, that the effect depends upon the regime in which the network
operates. Here, we first address by detailed mathematical analysis of the canonical
model of the E. coli chemotaxis network whether the difference in
response between wild-type and CheZ mutant cells can be explained by the different
spatial distribution of CheZ in these cells. We find that this is not the case; also
realistic changes in parameters such as rate constants and protein concentrations do
not seem sufficient to explain the difference in response. We then consider two
refinements to the canonical model. First, we study the effect of cooperative
dephosphorylation of CheYp by CheZ [4]–[7].
Next, we consider a refined model of the intracellular chemotaxis network of
E. coli, in which a small fraction of CheZ is localized at the
receptor cluster, while the remainder is distributed in the cytoplasm. This model,
which is supported by a wealth of experimental data, can explain many of the
experimental observations of Vaknin and Berg [2], and it provides a novel
mechanism for signal amplification.
The canonical model of the intracellular chemotaxis network of E.
coli is described by the following set of chemical reactions:(1)(2)(3)
In this network, the phosphorylated form of the messenger, CheYp (), transmits the signal from the receptor cluster to the flagellar
motors. The phosphorylation level of CheY is regulated by a kinase CheA (A) and a
phosphatase CheZ (Z). CheYp also exhibits autophosphorylation and
autodephosphorylation, but these reactions are much slower than phosphorylation by
CheA and dephosphorylation by CheZ, respectively. The input to the signal
transduction pathway is , where is a parameter between zero and one that reflects the activity of
the receptor cluster and denotes the maximum rate of autophosphorylation of CheA. The value
of depends on the ligand concentration [L]: ; shifts to lower (higher) values upon the addition of attractant
(repellent). In order for E. coli to adapt to a changing ligand
concentration, the activity of the receptor cluster, , is also modulated by the methylation and demethylation enzymes
CheR and CheB, respectively.
In wild-type E. coli cells, not only CheA, but also CheZ is
localized at the receptor cluster [1]. In these cells, CheZ is anchored to the receptor
cluster by CheA [8],[9]. In a recent experiment, Vaknin and Berg compared
the response of wild-type cells to that of CheZ mutant cells, in which CheZ does not
bind to CheA, but diffuses in the cytoplasm [2]. They studied the
response of the chemotaxis network by measuring the interaction between CheZ and
CheYp using FRET imaging. While the input of the network was thus the
concentration of ligand, the measured output was proportional to the total,
integrated concentration of CheYp bound to CheZ, (see also Eq. 3).
Vaknin and Berg found that the colocalization of the antagonistic enzymes has a
marked effect on the dose-response curve [2]. In wild-type cells, in
which CheA and CheZ are colocalized at the receptor cluster, the response of to changes in the concentration of the attractant serine is more
sensitive than in mutant cells, in which CheZ is distributed in the cytoplasm.
Moreover, in cheRcheB cells, which lack the methylation and
demethylation enzymes, the response to the addition of serine is also sharper when
CheA and CheZ are colocalized at the receptor cluster [2].
In the next section, we show that the experiments of Vaknin and Berg [2] impose
strong constraints on any model that aims to describe the intracellular chemotaxis
network. In the subsequent section, we argue that the canonical model does not meet
these constraints: neither changes in the spatial distribution of CheZ, nor
realistic changes in the rate constants and protein concentrations seem sufficient
to explain the differences in the response curves of the mutant and wild-type cells.
Indeed, we argue that the experiments of Vaknin and Berg demonstrate that the
canonical model needs to be augmented.
In the subsequent sections, we present two refined models of the intracellular
chemotaxis network of E. coli, which both can explain the
difference in response between wild-type cells and CheZ mutant cells, as measured by
Vaknin and Berg [2]. The first model assumes that 1) in wild-type
cells, CheZ is localized at the cluster, while in the CheZ mutant cells, CheZ freely
diffuses in the cytoplasm; 2) CheZ in wild-type cells has a higher phosphatase
activity than CheZ in the CheZ mutant cells, as suggested by the observation of Wang
and Matsumura that interactions of CheZ with CheA enhance its phosphatase activity
[10]; 3)
CheZ in wild-type cells acts non-cooperatively, while CheZ in the mutant cells acts
cooperatively, as motivated by the experimental observations of [4],[6],[7].
While this model can describe the FRET response curves as measured by Vaknin and
Berg [2],
it assumes that in wild-type cells all CheZ proteins are bound at
the cluster. However, the experiments of Vaknin and Berg show that in wild-type
cells, only a small fraction of CheZ is bound at the receptor cluster; the remainder
freely diffuses in the cytoplasm [2].
In the next section, we therefore present an alternative model. The key ingredients
of this model are: 1) in wild-type cells, a small, yet significant, fraction of CheZ
is bound to the receptor cluster, while the remainder freely diffuses in the
cytoplasm [2]; 2) the fraction of CheZ at the cluster has a higher
binding affinity for the substrate CheY than that of cytosolic CheZ; 3) the
catalytic activity of CheZ bound to the cluster is higher than that of CheZ in the
cytoplasm. This model bears similarities to that recently proposed by Lipkow [11],
although our model neither requires oligomerization of CheZ at the receptor cluster
nor shuttling of CheZ between the cytoplasm and the receptor cluster. In the section
Differential affinity and catalytic activity we show using a
simplified model how the combination of differential binding affinity and
differential catalytic activity provides a novel mechanism for amplifying signals:
As the activity of the receptor cluster and hence that of the kinase CheA is
increased from zero and CheY becomes phosphorylated, CheYp first binds
CheZ at the receptor cluster; only when CheZ at the receptor cluster is saturated,
does CheYp bind CheZ in the cytoplasm; since CheZ at the cluster has a
higher catalytic activity than CheZ in the cytoplasm, the response of
CheYp is sigmoidal. Finally, we also incorporate cooperative binding of
CheYp to CheZ [5]–[7] into the model and
show that this model can explain the response of E. coli to changes
in serine concentration, as measured by Vaknin and Berg [2].
Vaknin and Berg performed experiments on four bacterial strains: wild-type cells,
cheRcheB cells lacking the methylation and demethylation
enzymes CheR and CheB, CheZ mutant cells, and CheZ mutant cells lacking CheR and
CheB [2]. Analysis of their dose-response curves (the concentration of CheYpCheZ—a
CheYp molecule bound to a CheZ dimer—as a function of
the ligand concentration L) is complicated by the fact that they are determined
by both the response of the receptor cluster, , to the change in the ligand concentration,
[L], and by the response of the intra-cellular signaling
pathway, , to changes in the activity of the receptor cluster, . However, these two networks can be viewed as two independent
modules connected in series, which can be analyzed separately, as we discuss
below. Moreover, this modularity means that the dose-response curves, , of the four strains can be obtained by multiplying the
response curves of the two modules.
The first module is the receptor cluster. Its activity, , depends upon the concentration of ligand,
[L], and upon the methylation states of the receptors, which
is controlled by the methylation and demethylation enzymes CheR and CheB,
respectively. However, the dynamics of receptor methylation and demethylation by
CheR and CheB are much slower than that of receptor-ligand (un)binding and
phosphorylation and dephosphorylation of CheY; in fact, this separation of time
scales allows E. coli to both respond and adapt to a changing
ligand concentration. This separation of time scales also makes it possible to
model the response to ligand at short time scales without explicitly taking into
account the (de)methylation dynamics; the absence of CheR and CheB in cells, will lead to different methylation states of the
receptors, yet can be modeled implicitly by taking different functional forms
for . For wild-type cells, the response of the cluster is thus
characterized by the response function , while for cheRcheB cells, the response is
described by .
The second module of the chemotaxis network, the intracellular signal
transduction pathway, is described by the set of reactions in Equations
1–3. The input of this network is , while the output is the concentration of CheYp, , or, as in the experiments of Vaknin and Berg, the total
concentration of CheYp bound to CheZ,
[2].
The response curve of this network, , depends upon the nature of CheZ, and will thus be different
for wild-type cells and CheZ mutant cells. Importantly, is independent of the methylation states of the receptors. We
assume that also does not depend upon the presence of CheB, although
phosphorylated CheA can phosphorylate not only CheY but also CheB, leading to
another form of adaptation on a time scale longer than that of the response; we
will come back to this in the Discussion section. Thus, we
assume that of cells is the same as that of wild-type cells; the absence of
CheR and CheB in cheRcheB cells only affects . Hence, the response of the intra-cellular signaling pathway
in wild-type cells is characterized by the response function , while the response of CheZ mutant cells is characterized by .
If the receptor cluster and the intracellular chemotaxis pathway indeed behave as
two independent modules connected in series, then the response function should be given by the composite function . Hence, the response function of the four strains in Ref.
[2] should be of the form: . As we show in Figure 1 of Text S1, the experiments of Vaknin and Berg
on the four different strains provide strong evidence for the hypothesis that
the receptor cluster and the intracellular network are indeed two independent
modules connected in series. Yet, these experiments do not uniquely prescribe
how the overall response is decomposed. This is illustrated in Figure 1, which show the
response curves of three different models, indicated by different colors, that
all can explain the dose-response curves of Figure 1A. Each model consists of the
functions and (Figure
1B), corresponding to wild-type and CheZ mutant cells respectively, and
the functions and (Figure
1C), corresponding to cells containing CheR and CheB and
cheRcheB cells lacking CheR and CheB, respectively. For each
model, the four composite functions exactly reproduce the four dose-response curves of Figure 1A. Model I (red lines
and points) relies on the assumption that is a straight line over the concentration range of interest
(see Figure 1B). This means
that and are proportional to of CheZ mutant cells lacking CheR and CheB and CheZ mutant
cells containing CheR and CheB, respectively; this can be verified by comparing
Figure 1A to Figure 1C. The experiments of
Vaknin and Berg [2] now fully determine the function , which can be constructed from and of the wild-type cells, and and of the cheRcheB cells (see Figure 1B); this function has
a strongly convex shape. Model II (blue lines and points) relies on the
assumption that is a linear function (see Figure 1B). In this case and are proportional to of wild-type and cheRcheB cells, respectively
(see Figure 1A and Figure 1C). The functional
form of of CheZ mutant cells now has a concave shape (see Figure 1B). These two models
are two extreme scenarios that both can explain the data shown in Figure 1A.
In the following sections we will also consider models that have less extreme
functional forms for ; these models lie in between model I and model II. We
construct such models, starting from models I and II, by defining functions as linear combinations , where is a parameter between zero and one; for the model reduces to model I, while for the model reduces to model II. Model III (black lines and
points) was constructed by putting equal to 0.5. For this model, of CheZ mutant cells is slightly concave, whereas of wild-type cells is slightly convex.
The model that can describe the response of to changes in ligand concentration should not only be able to
reproduce the dose-response curves of Figure 1, it should also satisfy other
important conditions. Most importantly, wild-type cells can chemotax, which
means that in their non-stimulated state they can respond to the addition as
well as to the removal of attractant. Bacteria lacking are able to chemotax towards attractants as well, although
less efficiently than wild-type bacteria [12]. These mutants
are probably similar to CheZ mutants in that the binding of CheZ to the receptor
cluster is hampered in both strains. The requirement that both strains can
chemotax means that the concentration of CheYp in the non-stimulated
state should be within the working range of the motor, i.e. between 1 and 5
µM [13],[14].
We now address the question whether the canonical model for the chemotaxis
pathway of E. coli, as given by Equations 1–3, can
describe the experimental results of Vaknin and Berg [2]. We first study the
effect of the spatial distribution of CheZ, thus leaving the other parameters
unchanged. As we will show, the spatial distribution of CheZ alone is not
sufficient to explain their experimental results. We will then also vary rate
constants and concentrations to see whether the canonical model can describe
these results.
To elucidate the effect of CheZ localization, we have computed the input-output
relations for a network in which CheA and CheZ are colocalized at the receptor
cluster (corresponding to wild-type cells) and for a network in which CheA is
localized at the receptor cluster, while CheZ is distributed in the cytoplasm
(corresponding to CheZ mutant cells); for both networks, the chemical reactions
are given by Equations 1–3. The steady-state input-output relations of
these networks were obtained numerically by discretizing the system on a 1D grid
and propagating the chemical rate equations, which are given in the
Methods
section, in space and time until steady state was reached.
As pointed out in the previous section, the input of the intracellular network is
not directly the ligand concentration [L], but rather (see Eq. 1), which implicitly depends upon
[L]. Importantly, we first assume that the functional
dependence of on the ligand concentration [L], as well as
the rate constants of all the reactions, is the same for wild-type and CheZ
mutant cells: this allows us to elucidate the effect of colocalization of the
antagonistic enzymes on the input-output relations. The model and the values of
its parameters were taken from Sourjik and Berg [14].
The principal results of our calculations are shown in Figure 2. This figure shows for wild-type and
CheZ mutant cells, the concentration of CheYpCheZ (a CheYp
molecule bound to a CheZ dimer) and the concentration of CheYp as a
function of (see Equation 1); the bullets correspond to the non-stimulated
state of the network [14]. Figure 2 shows that the model predicts that the spatial distribution
of CheZ affects the response to the addition of repellent or the removal of
attractant, which corresponds to an increase in . More importantly, the model predicts that the CheZ
distribution should not affect the response to the addition of attractant: When is lowered from its value in the non-stimulated state, both the change in and do not depend much on the spatial distribution of CheZ. This
result is thus in contrast with the drastic effect of enzyme localization on the
response found by Vaknin and Berg [2].
The network given by Equations 1–3 is very similar to a canonical
push-pull network, in which two enzymes covalently modify a substrate in an
antagonistic manner [15] (see Text S2 for how these networks can be mapped
onto each other). We have recently studied in detail the effect of enzyme
localization on the response of a push-pull network [3]. Our principal
finding is that enzyme localization can have a marked effect on
the gain and sensitivity of push-pull networks, seemingly consistent with the
experiments of Vaknin and Berg [2], but contradicting the numerical results shown
in Figure 2. The resolution
of this paradox is that both the quantitative and qualitative consequences of
enzyme localization depend upon the regime in which the push-pull network
operates. In particular, if the activation rate is independent of the substrate
concentration and if the deactivation rate is linear in the messenger
concentration, then phosphatase localization has no effect on the response curve
[3]. This is precisely the case for the chemotaxis
network studied here. For , CheZ is unsaturated [14] and the
dephosphorylation rate of CheYp is thus proportional to . The influx of CheYp is constant, i.e. independent of
[Y]. This is not because the phosphorylation reaction is in
the zero-order regime; this reaction is, in fact, in the linear regime [14].
The influx of CheYp at the cell pole is constant because a) in
steady state and b) in the weak activation regime CheA is predominantly
unphosphorylated (), which means that is fairly insensitive to the spatial distribution of CheZ.
Hence, according to the model of Equations 1–3, in this regime the
concentration of CheYp does not depend upon the spatial distribution
of CheZ, which is indeed what Figure 2 shows.
However, while the model of Equations 1–3 predicts that in wild-type
cells the response of [YpZ] to the addition of
attractant does not depend on the location of CheZ, the experiments by Vaknin
and Berg clearly demonstrate that it does [2]. What could be the
origin of the discrepancy between the model predictions and the experimental
results of Vaknin and Berg? As mentioned above, the response of
[YpZ] to the ligand concentration
[L] depends upon the response of
[Yp] to the activity of the receptor cluster, , and upon the response of to the ligand concentration [L]. If we keep
with the assumption that the functional dependence of on [L],
βk0([L]), is the same for
both wild type and CheZ mutant cells, the discrepancy between the predictions of
the canonical model and the experimental observations of Vaknin and Berg must
lie in the dependence of [YpZ] on . It is quite likely that the rate constants and/or
concentrations that are used in the calculations differ from those in
vivo. It is also possible that the topology of the canonical model
of the intracellular chemotactic pathway, Eqs. 1–3, is incorrect. In
order to discriminate between these two scenarios, we will, in the rest of this
section, first address the question whether it is possible to explain the
experimental observations with the canonical model by allowing for different
values of parameters such as rate constants and protein concentrations. We will
then argue that simply allowing for different parameter values is probably not
sufficient to explain the experiments of Vaknin and Berg, and that thus the
canonical model should be reconsidered.
Irrespective of the model parameters, it is always true that the rate of
phosphorylation equals the rate of dephosphorylation if the system is in steady
state. For the canonical model, i.e. Equations 1–3, this means that
for both the spatially uniform network in which CheA and CheZ are colocalized,
and the spatially non-uniform network in which CheZ is distributed in the
cytoplasm, the following relation holds in steady state:(4)Here, “FRET” denotes the FRET signal, which
is proportional to the total, integrated, concentration of CheYp
bound to CheZ, [YpZ]. For the regime of interest, , the concentration of unphosphorylated CheA,
[A], is essentially constant for the conventional model,
because only a small fraction of the total amount of CheA is phosphorylated;
below we discuss scenarios in which this relation might not hold. Equation 4
thus shows that if , the FRET signal only depends upon the activity of the
receptor cluster, , and upon the phosphatase activity, , but not upon other rate constants in the network, nor upon
the expression levels of, for instance, CheY and CheZ. Moreover, if , the FRET signal, in this model, is linear in
the activity of the receptor cluster: , where is the proportionality constant. Incidentally, this explains
the linear dependence of [YpZ] on for in Figure
2B.
The linear relation between [YpZ] and as predicted by the canonical model would mean that the
dose-response curves, i.e. FRET([L]), solely reflect the
response of the receptor cluster to the addition of ligand,
βk0([L]). Vaknin and
Berg report the renormalized FRET response: they normalize the
FRET signal at ligand concentration [L] to the FRET signal at
zero ligand concentration,
[2].
If the response of [YpZ] to would indeed be linear, then the renormalized FRET signal
would be given by . Hence, the proportionality factor would drop out. The renormalized FRET signal would thus be
given by the dependence of the activity of the receptor cluster on the ligand
concentration, βk0([L]).
While plotting the renormalized FRET signal may mask potentially useful
information, this observation does allow us to draw an important conclusion:
If βk0([L]) is
the same for wild type and CheZ mutant cells, and as long as
is linear, the canonical model cannot describe the experiments of Vaknin
and Berg, even if we allow for different parameter values for the rate
constants or protein concentrations.
The experiments of Wang and Matsumura illustrate the importance of this
conclusion [10]. Their experiments suggest that the phosphatase
activity is enhanced by its interaction with CheAs, which is
localized at the receptor cluster [10]. This would predict that in the CheZ mutant
cells (in which CheZ is distributed in the cytoplasm), the phosphatase activity
is lower. This could either be due to a decrease in the CheZ-CheYp
association rate , or to a decrease in the catalytic activity . Eq. 4 reveals that a change in the association rate has no effect on the FRET response curve, as long as . In contrast, a change in would change the dependence of
[YpZ] on (see Equation 4); in particular, decreasing would increase the slope. However, as long as the dependence
of [YpZ] on is linear, the renormalized FRET response would still be given
by βk0([L]): merely
changing the slope of [YpZ] as a function does not change the renormalized FRET response. More in
general, only allowing for different rate constants or protein concentrations
between the wild-type cells and mutant cells is not sufficient to explain the
data, if indeed βk0([L])
is the same for both cells and is linear.
The critical ingredient in the above analysis is that
[YpZ] varies linearly with , both for the wild-type and the CheZ mutant cells. We now
first address the question whether deviations from this linear relation could
explain the data, and then how these deviations might arise. The simplicity of
the canonical model, Equations 1–3, does not allow for a convex
dependence of [YpZ] on . Figure 1B
then immediately shows that any model that aims to describe the dose-response
curves of both the wild-type cells and the CheZ mutant cells, should exhibit a
linear relationship for wild-type cells and a concave function for CheZ mutant cells (blue data set). To generate a
non-linear response of [YpZ] as a function of over the concentration range of interest, the condition , which was the critical condition to generate a linear
relationship (see Equation 4), should be violated; this means that should increase significantly within the concentration range
of interest. An inspection of the canonical network, Equations 1–3,
reveals that increases more rapidly with , when , , or decrease ( and are very small, and can thus be neglected). The effect of
changing these parameters can be understood by considering the following
relations in steady state: . For example, as decreases, and [YpZ] tend to increase, and tends to decrease; the latter means that to obey the above
relations, should increase. Decreasing thus means that [YpZ] as a
function of not only has a higher initial slope, but also levels off more
rapidly because increases: the function becomes concave for lower values of . Similarly, it can be deduced that while a decrease of does not change the initial slope of (because for low , , and the slope is then independent of (see Equation 4)), it does lower the value of at which increases; again the function becomes concave for lower values
of .
Changes in the rate constants (, , , ) could thus potentially explain the dose-response curves
measured by Vaknin and Berg [2]. We have tested by extensive numerical
calculations, in which we did not only change these rate constants but also
protein concentrations, whether changing these parameters can indeed explain the
experiments. The results are shown in Text S2. The calculations reveal that
changing and does not have a large effect (see Figures 4 and 5 of Text S2); moreover, it does not seem likely
that changing CheZ affects the binding of CheY to CheA, although this cannot be
ruled out. Changing and has a stronger effect: assuming that in the CheZ mutant cells is a factor 10 lower than in the wild-type cells yields a reasonable fit to the FRET
data of Vaknin and Berg [2] (see Figure 7 of Text S2).
Do the CheZ mutant cells exhibit a tenfold lower phosphatase activity ()? The canonical model with the assumption that in the CheZ
mutant cells the phosphatase activity is ten times lower is an example of model
I discussed in the previous section (blue lines in Figure 1B). While this model could explain
the FRET data of Vaknin and Berg, it should be realized that according to this
model the CheZ mutant cells would be tumbling all the time: as Figure 7 of Text S2
shows, in the non-stimulated state the concentration of CheYp would
be at its maximal value, and the clockwise bias would be close to unity.
However, the experiments of Sanatinia et al.
[12] show that both the wild-type and the mutant
bacteria can chemotax, which suggests that not only in the wild-type cells, but
also in the CheZ mutant cells, is within the working range of the motor when the cells are in
their non-stimulated state. We therefore present two new models. In the next
section, we consider a model of type I, in which the FRET signal in wild type
cells is proportional to the activity of the receptor cluster , whereas the response curve for mutant cells is strongly concave. In the subsequent
section, we consider a model of type III that exhibits a weakly concave response
curve for the CheZ mutant cells, and, consequently, a convex
response curve for the wild-type cells.
Recent experiments strongly suggest that the intracellular chemotaxis network of
E. coli has a more complicated topology than that of the
canonical push-pull network discussed in the previous section. In particular, in
the canonical model discussed above the phosphatase reactions were described by
simple Michaelis-Menten reactions. However, experiments of Eisenbach and
coworkers [4],[6] and Silversmith et al.
[7] have shown that the activity of CheZ depends in a
cooperative manner on the CheYp concentration. It is clearly
important to understand how the response curve is affected by the cooperative dependence of phosphatase
activity on CheYp concentration. In this section, we present a simple
model for the cooperative dependence of the phosphatase activity on
CheYp concentration, which can be solved analytically. Furthermore,
we show that incorporation of cooperativity into the phosphatase reactions can
lead to a model of type I (see Figure 1) and therefore gives a possible explanation for the
experiments by Vaknin and Berg [2].
In vitro data [4],[6],[7] suggest that
the activity of CheZ depends in a cooperative manner on the CheYp
concentration. The experiments of Eisenbach and coworkers [4],[6] suggest that the
activity of CheZ also depends in a cooperative manner on the CheZ concentration,
suggesting that CheZ may oligomerize upon CheYp binding [4]–[6]. Other biochemical
in vitro experiments [16] and more
recent in vivo FRET experiments [9], however, do not
provide support for this idea. We therefore assume that the activity of CheZ in
the mutant cells only depends cooperatively on the CheYp
concentration.
The model for the cooperative dephosphorylation of CheYp by CheZ is
based upon the following assumptions: 1) a single CheZ dimer can bind up to two
CheYp molecules; 2) CheZ can dephosphorylate CheYp in
both CheYp-bound states, thus dephosphorylation can occur when only a
single CheYp molecule is bound or when two CheYp molecules
are bound. This model can be described by two coupled Michaelis-Menten
reactions, those of Eq. 3 in combination with(5)In steady state, the phosphatase activity is given by(6)where is the total concentration of CheZ and and are the Michaelis-Menten constants of Equation 3 and Equation
5, respectively (see Text S3 for a derivation). It can be seen
that if and if , the dephosphorylation rate is given by(7)This is a Hill function with a Hill coefficient of 2 and a
concentration at which the rate is half maximal (the inflection point) given by . Clearly, strong cooperativity arises when 3) the binding of
the first substrate molecule facilitates the binding of the second one, making and 4) the catalytic activity is higher when two substrate
molecules are bound than when one is bound, i.e. . In Text S3 we give an extended analysis of this
model, which shows that it can fit the in vitro data of Blat
and Eisenbach [6] not only qualitatively, but also
quantitatively; this fit satisfies criteria 3) and 4). Recently, Silversmith
et al. independently developed a similar model as that of
Eqs. 3 and 5 on the basis of their in vitro experiments [7], although they did not present the analytical
result of Eq. 6 [7]. Interestingly, their model also satisfies
criterion 3): binding of the first CheYp molecule facilitates the
binding of the second CheYp molecule. However, in their model binding
of the second CheYp molecule does not enhance the catalytic activity
of CheZ [7], in contrast to our model. We cannot obtain a
good fit to the in vitro data of Eisenbach and coworkers [6], nor,
as discussed below, to the in vivo data of Vaknin and Berg
[2], without relaxing criterion 4). Finally, we would
like to emphasize that the rate constants derived from fitting in
vivo data may differ from those obtained from fitting in
vitro data. In particular, diffusion-limited reaction rates will often
be lower in living cells due to a lower diffusion constant, and a detailed
analysis of this model (see Text S3) suggests that in this system this
might be the case.
In the model presented in this section, we assume that in wild-type cells all
CheZ proteins are localized at the receptor cluster, while in the CheZ mutant
cells all CheZ proteins freely diffusive in the cytoplasm. For both cells, the
chemical reactions are given by Eqs. 1–3 and Eq. 5. However, while the
rate constants of the phosphorylation reactions in Eqs. 1 and 2 are identical
for both cells, they differ for the dephosphorylation reactions of Eqs. 3 and 5.
In particular, in order to obtain a good fit to the FRET data [2], we
have to assume that in the CheZ mutant cells CheZ acts cooperatively, while in
the wild-type cells CheZ acts non-cooperatively. Specifically, while for the
wild-type cells, not only the two CheYp-CheZ association rates and , but also the two catalytic activities and can be assumed to be identical—; —, for the CheZ mutant cells it is required that and (see caption of Figure 3 for parameter values).
The results for this model are shown in Figure 3. The FRET response of wild-type
cells is similar to that in the canonical model discussed in the previous
section; it is essentially linear in over the relevant range of , because CheZ acts non-cooperatively. However, the FRET
response of wild-type cells is weaker than that of CheZ mutant cells over this
range. This is because the catalytic activity of CheZ with one CheYp
molecule bound, , is higher in wild-type cells than in CheZ mutant cells.
Indeed, this model would suggest that the interaction of CheA with CheZ enhances
the catalytic activity of CheZ when one CheYp molecule is bound to
CheZ. Another important point to note is that the FRET response of CheZ mutant
cells is strongly concave over the relevant range of . This model is indeed an example of type I, as discussed in
the section Decomposing the response. The concave FRET response
of CheZ mutant cells is a consequence of the cooperative dephosphorylation of
CheYp by CheZ: for small receptor activities , [Yp] is low, CheZ is mostly
singly occupied by CheYp, and since the catalytic activity of
CheYpCheZ, , is relatively small (as compared to that of , ), a given increase in must be balanced by a relatively large increase in and hence the FRET signal; for higher , increases, CheZ becomes doubly occupied with CheYp,
and since has a higher catalytic activity than CheYpCheZ, a
given increase in receptor activity is balanced by a relatively small increase in . Indeed, if would be similar to , as Silversmith et al. propose [7], the FRET response of the CheZ mutant cells would
not be concave, and no good fit to the data of Vaknin and Berg [2]
could be obtained.
While the model discussed in the previous section can describe the FRET response
as measured by Vaknin and Berg [2], it also assumes that in wild-type cells all
CheZ proteins are localized at the receptor cluster. However, the data of Vaknin
and Berg [2] suggest that only a small fraction of CheZ is
localized at the receptor cluster. We therefore present here an alternative
model, which, in our opinion, is consistent with the currently available
experimental data.
The experiments by Vaknin and Berg on the effect of CheZ localization on the
dose-response curves of E. coli
[2]
impose strong constraints on the design of a model of the intracellular
chemotaxis network. These experiments unambiguously demonstrate that the second
derivative of of CheZ wild-type cells is larger than that of CheZ mutant
cells (see Figure 1). The
topology of the intracellular chemotaxis network of the canonical model
(Equations 1–3) is such that the second derivative of must be equal to or smaller than zero: according to the
canonical model the response curve cannot be convex. One way to fit the data is
to assume that the response curve of CheZ wild-type cells is a straight line over the
concentration range of interest, while of CheZ mutant cells is concave. The canonical model can yield
such response curves. However, this scenario requires that in the CheZ mutant
cells, some of the rate constants, such as the phosphatase activity, differ
strongly from those in wild-type cells. Moreover, this would mean that CheZ
mutant cells would adapt to a state in which is outside the working range of the motor. This scenario thus
seems unlikely, although it cannot be ruled out.
Here, we have presented two different models that can explain the FRET data of
Vaknin and Berg [2]. In the first model, of CheZ wild-type cells is linear, while of CheZ mutant cells is strongly concave. The model is based
on the in vitro observation that CheZ dephosphorylates
CheYp in a cooperative manner [5]–[7]. The model leads over the relevant range of
interest to fairly similar response curves for wild-type and mutant cells, and the non-stimulated state
lies around 3 µM. This model, however, assumes that in wild-type cells
all CheZ proteins are localized at the receptor cluster, while the data of
Vaknin and Berg [2] suggest that in these cells only a fraction of
about 10–20% is localized at the receptor cluster.
We have therefore presented an alternative model that is consistent with most, if
not all, of the currently available data. In this model, of CheZ wild-type cells is sigmoidal, while of CheZ mutant cells is hyperbolic. The model relies on the
assumption that a small fraction of CheZ is localized at the receptor cluster,
while the remainder freely diffuses in the cytoplasm; moreover, it assumes that
CheZ localized at the receptor cluster has both a higher binding affinity for
CheYp and a higher catalytic activity than CheZ in the cytoplasm.
All these assumptions seem to be supported by experiment [2],[10].
In essence, the model that we propose consists of a push-pull network with one
activating enzyme, CheA, and two deactivating enzymes, CheZ bound to the cluster
and CheZ that freely diffuses in the cytoplasm. Our analysis shows that the
competition between these two deactivating enzymes for binding and deactivating
the substrate can yield an ultrasensitive response even when the push-pull
network does not operate in the zero-order regime. In fact, this mechanism of
differential-affinity-and-catalytic-activity is evocative of the
“branch point effect”, in which the interdependence of the
activities of two branch-point enzymes that compete for a common substrate can
yield an abrupt change in the flux through one of the enzymes [24].
In the model proposed here, the spatial dependence of both the substrate-binding
affinity and catalytic activity of CheZ only acts to create two types of
deactivating enzymes; the proposed scheme could also work in a well-stirred
model if one assumes that there exist two deactivating-enzyme species.
If the response function of wild-type cells is sigmoidal, as the
differential-affinity-and-catalytic-activity model predicts, then the large
number of recent studies on signal amplification by the receptor cluster has to
be reconsidered [25]–[33]. If the relation
between [YpZ] and would be linear, as predicted for wild-type cells in the
canonical and cooperative model, then the renormalized FRET response would be
given by the dependence of the activity of the receptor cluster, , on the ligand concentration [L]. This would
justify the studies that describe the ‘front end’
amplification of the chemotaxis network, namely the response of
[YpZ] to changes in [L], in
terms of the signal amplification properties of the receptor cluster [25]–[33]. However, if the
dependence of [YpZ] on the activity of the receptor
cluster, , would not be linear, then the front end amplification would
not be fully determined by the response of the receptor cluster to changes in
the ligand concentration. Indeed, to explain the front-end gain, the extent to
which the signal is amplified as it is transmitted from the receptor cluster to
CheYpCheZ would then also have to be taken into account.
Recently, Kim et al. experimentally addressed the question
whether CheZ contributes to the gain of the chemotaxis network [34]. To
this end, they compared the motor response of wild-type cells to that of mutant cells in which the activity of the receptor cluster was
adjusted by mutating the Tsr receptor to compensate for the change in
CheYp levels [34]. They observed that the change in the motor
bias upon a change in ligand concentration was similar for these cells, and
concluded that CheZ does not contribute to the gain. However, it should be noted
that the mutations in the Tsr receptor as made by Kim et al.
[34] may
affect the signal amplification by the receptor cluster, especially since it is
believed that interactions between receptors (and even between receptors of
different types) strongly affect the gain [25]–[33]. If
this would be the case, then the observation that in the “bias
adjusted” mutant cells the motor response is similar to that of
wild-type cells, would imply that CheZ does contribute to the gain. Our analysis
supports a scenario in which CheZ contributes to the gain, but cannot rule out
the alternative scenario. If CheZ does not contribute to the gain, then should be the same for wild-type cells and CheZ mutant cells
over the relevant range of the activity of the receptor cluster. In our
differential-affinity-and-catalytic-activity model, which is consistent with
most of the experimental data, the response curves are different (Figure 7), but in our
cooperative model they are, in fact, fairly similar (Figure 3). The problem is that while the data
of Vaknin and Berg [2] put strong constraints on any model that aims
to describe the response of the intracellular signaling pathway, they do not
uniquely prescribe it (Figure
1). To elucidate the response of the intracellular signaling pathway and
to discriminate between the models that we propose, we believe that FRET
measurements should be made of CheYp-CheZ and CheYp-FLiM
interactions [9], not only for wild-type cells, but also for mutants [34] and the CheZ F98S mutants studied by Vaknin
and Berg [2].
While the differential-affinity-and-catalytic-activity model can describe the
dose-response curves as reported by Vaknin and Berg [2], a number of issues
remain. The first is that in the full
differential-affinity-and-catalytic-activity model, which takes into account
CheZ cooperativity, the total concentration of
[Yp] in non-stimulated CheZ mutant cells is on
the border of the working range of the motor, while experiments on mutant cells
lacking CheAs, which plays a role in localizing CheZ to the receptor
cluster [12], suggest that CheZ mutant cells can chemotax.
This raises an interesting question, which to our knowledge has not been studied
yet: How strongly does the efficiency of chemotaxis depend upon the
concentration of CheYp in the adapted state? In particular, how well
must that be inside the working range of the motor? It is conceivable that cells
with [Yp] at the high end of the motor's
working range can chemotax, albeit less efficiently. Another possibility is that
CheZ mutant cells can chemotax, because [Yp] forms
spatial gradients inside CheZ mutant cells [2]: while
[Yp] at some motors will be outside the
motor's working range, [Yp] at other
motors might be inside the working range of the motor.
But perhaps the most likely explanation is that phosphorylation of CheB by
CheAp provides a negative feedback loop on the activity of the
receptor cluster that tends to keep the concentration of CheYp within
a certain range. The concentration of CheYp in the adapted state is
determined by the activity of the receptor cluster in the adapted state, which
is controlled by the activity of the methylation and demethylation enzymes CheR
and CheB, respectively. CheAp cannot only phosphorylate CheY, but
also CheB. Moreover, phosphorylated CheB has a higher demethylation activity
than unphosphorylated CheB. Since CheY and CheB compete with one another for
phosphorylation by CheAp, the concentration of phosphorylated CheB
increases as [Yp] increases and
[Y] decreases [35]. However, since
phosphorylated CheB has a higher demethylation activity, this tends to lower the
activity of the receptor cluster, which in turn tends to lower
[Yp]. In our model, the activity of the
receptor cluster is assumed to be the same for wild-type and CheZ mutant cells,
and it was chosen such that the concentration of CheYp in adapted
wild-type cells is within the working range of the motor. Yet, it is conceivable
that because of the negative feedback loop, the activity of the receptor cluster
in the adapted state is lower in CheZ mutant cells than in CheZ wild-type cells.
This would lower the concentration of CheYp in the CheZ mutant cells
and could bring it within the motor's range.
Vaknin and Berg measured not only the response to the addition to serine, but
also the response of [YpZ] to changes in aspartate
concentration [2]. They found differences in the response
between CheZ wild-type cells and CheZ mutant cells when -methylaspartate was used as an attractant with cells expressing only the aspartate receptor,
Tar. However, no differences were detected when these experiments were repeated
with either aspartate or -methylaspartate in wild-type cells. In our model, the overall
response of [YpZ] to changes in ligand
concentration [L] is determined by two independent modules
connected in series: . A different attractant only leads to a different response of
the receptor cluster,
βk0([L]): the response of to changes in the activity of the receptor cluster is assumed to be independent of the type of
attractant—while depends upon the nature of CheZ, it is the same for serine and
aspartate. Our model would therefore predict that the response to aspartate also
differs between CheZ wild-type cells and CheZ mutant cells, in contradiction
with the experimental results of Vaknin and Berg [2]. It is conceivable
that to explain these observations, the spatial organization of the receptor
cluster, in particular the spatial position of CheZ with respect to the
aspartate and serine receptors, has to be taken into account, and that a full
particle-based model [36],[37] is required to
explain the response to both aspartate and serine.
The canonical model of the intracellular chemotaxis network of E.
coli is given by the chemical reactions shown in Equations 1–3.
When CheA and CheZ are colocalized at the receptor cluster, the concentration
profiles of CheY and CheYp are uniform in space, and the concentrations
can be obtained by solving the following chemical rate equations:(10)(11)(12)(13)(14)(15)(16)Here, denotes the concentration of species X.
When CheZ cannot bind the receptor cluster and thus diffuses in the cytoplasm,
concentration gradients of CheY and CheYp will form. We will assume that
the cell is cylindrically symmetric, and we will integrate out the lateral
dimensions and . We thus consider a simplified 1-D model, with concentrations as a
function of . This leads to the following reaction-diffusion equations:(17)(18)(19)(20)(21)(22)(23)The components CheA, CheAp and CheApCheY are
localized at one end of the cell; the unit of their concentrations is the number of
molecules per area. The other components diffuse in the cell. Their concentrations,
which are in units of number of molecules per volume, depend upon the position in the cell, where measures the distance from the pole at which CheA,
CheAp and CheApCheY are localized; only in Equations 20 and 21
is the dependence explicitly indicated to emphasize that the
CheAp-CheY association rate depends on the concentration of CheY at
contact. Zero-flux boundary conditions are imposed at both cell ends. The
steady-state input-output relations of the network described by Equations
17–23 were obtained numerically by discretizing the system on a (1-D) grid
and propagating these equations in space and time until steady state was reached.
The reaction-diffusion equations for the other models described in the main text,
i.e. in section Differential affinity and catalytic activity of
CheZ and section Cooperativity, were derived and solved in
a similar manner.
|
10.1371/journal.pntd.0004484 | A Heterologous Multiepitope DNA Prime/Recombinant Protein Boost Immunisation Strategy for the Development of an Antiserum against Micrurus corallinus (Coral Snake) Venom | Envenoming by coral snakes (Elapidae: Micrurus), although not abundant, represent a serious health threat in the Americas, especially because antivenoms are scarce. The development of adequate amounts of antielapidic serum for the treatment of accidents caused by snakes like Micrurus corallinus is a challenging task due to characteristics such as low venom yield, fossorial habit, relatively small sizes and ophiophagous diet. These features make it difficult to capture and keep these snakes in captivity for venom collection. Furthermore, there are reports of antivenom scarcity in USA, leading to an increase in morbidity and mortality, with patients needing to be intubated and ventilated while the toxin wears off. The development of an alternative method for the production of an antielapidic serum, with no need for snake collection and maintenance in captivity, would be a plausible solution for the antielapidic serum shortage.
In this work we describe the mapping, by the SPOT-synthesis technique, of potential B-cell epitopes from five putative toxins from M. corallinus, which were used to design two multiepitope DNA strings for the genetic immunisation of female BALB/c mice. Results demonstrate that sera obtained from animals that were genetically immunised with these multiepitope constructs, followed by booster doses of recombinant proteins lead to a 60% survival in a lethal dose neutralisation assay.
Here we describe that the genetic immunisation with a synthetic multiepitope gene followed by booster doses with recombinant protein is a promising approach to develop an alternative antielapidic serum against M. corallinus venom without the need of collection and the very challenging maintenance of these snakes in captivity.
| Coral snakes are a group of deadly venomous snakes that exhibit a characteristic red, yellow/white, and black coloured banding pattern. Accidents involving these snakes tend to be very severe or even lethal, causing peripheral nervous system depression with muscle paralysis and vasomotor instability. The only acceptable medical treatment for snakebite accidents is the administration of an antivenom, generally produced by immunising horses with the snake venom. Nonetheless, for what concerns the antielapidic serum production in Brazil, the total amount of venom available for horse immunisations is insufficient. This is mainly due to the small size of coral snake glands, their underground life style, combined with its very low survival rates in captivity. Moreover, cases of patients being intubated and ventilated as a consequence of antivenom shortage in USA have also been registered. In this work, we present an alternative method for the development of antielapidic serum, which does not rely upon snake capture. This serum was produced by a heterologous DNA prime—with a multiepitope DNA string coding for the most reactive epitopes from the most abundant toxins of M. corallinus, a coral snake which occupy highly populated areas in Brazil—followed by recombinant multiepitope protein boost immunisation of mice.
| Envenomation by snakebite is a common and generally harmful, environmental and occupational neglected tropical disease that constitutes a highly relevant public health problem with worldwide mortality estimated to be around 50,000 deaths annually [1]. In the Americas, although most of the registered cases of snake envenomation are due to snakes from the Viperidae family [2], accidents caused by members of the Elapidae family can also be severe or even lethal [3]. Distributed throughout the tropical and subtropical regions around the world, the Elapidae family consists of 325 species divided into 61 genera of potentially deadly neurotoxic venomous snakes that exhibit a wide range of sizes and are characterised by hollow and proteroglyphous fixed fangs through which venom is injected.
The coral snakes are the only elapids found in the New World, being Micrurus the most diverse and abundant genus across Americas [4]. In Brazil, the envenomation accidents reported are mainly due to M. corallinus and M. frontalis, which occupy highly populated areas in central, south and southeast of the country [5]. For this reason, the immunisation of horses with equal amounts of M. corallinus and M. frontalis venoms is used at Butantan Institute for the production of the Brazilian coral snake antivenom [6], which is the only accepted medical treatment for coral snakebite envenomation [7].
Micrurus spp. coral snakes have an average dry venom yield of 13.87 mg [8], which results in the need of snake collections composed of numerous specimens in order to obtain sufficient amounts of venom for horse immunisation. On the other hand, due to characteristics such as fossorial habit, relatively small sizes and ophiophagous diet, it is very challenging to capture and keep these snakes in captivity, as survival rate rarely exceeds one year [9]. These limitations in maintenance, the small size of their venom glands and, consequently, the low yield of venom, have been the major factors jeopardising the production of the Brazilian antielapidic serum.
Additionally, being snakebite a health problem that mainly afflicts the poorest regions of the world [10], antivenom production holds very limited commercial value, which not only hinders its production by major pharmaceutical companies but also results in an increased shortage of antivenom. As a matter of fact, since 2003, Pfizer/Wyeth, discontinued the manufacture of ANTIVENIN®, the only FDA-approved coral snake antivenom used for the treatment of accidents caused by Micrurus fulvius, a coral snake found in the southeastern USA. Furthermore, since 2008, all of the 2003 antivenom lots have expired, culminating in critical situations of patients being intubated and ventilated while toxins wear off. Under these circumstances, there is an increase not only in the morbidity of these accidents, but also of registered cases of people dying as a result of antivenom shortage [11–13].
Another issue that should be addressed is that the venom glands of snakes produce a variety of proteins and biologically active peptides with only a small percentage of those molecules being actually responsible for the biological manifestations observed after envenomation. As a result, antivenoms contains antibodies against an extensive number of different proteins, irrespective of their toxicity or immunogenicity, leading to a reduction in the antivenom’s efficacy and to an increased probability of developing serum sickness reactions due to large volumes of equine proteins [14]. The development of an alternative, but still efficient immunising protocol for the generation of coral snake antivenom, with less reliance upon snake collection/maintenance and composed solely of toxin-specific antibodies, would, therefore, be ideal for the treatment of envenomation by coral snake bites.
The use of recombinant coral snake toxins as immunogens would be a reasonable way to accomplish both issues but, although these molecules did induce an immune response that indicated the recognition of the native proteins, very complicated steps were required for protein refolding [15]. On the other hand, however, the use of DNA immunisation to evoke IgG antibody titres and protective responses for the production of snake antisera have also been described [16–19]. Furthermore, researchers from the Liverpool School of Tropical Medicine, UK, demonstrated that the genetic immunisation of mice with a multiepitope DNA string coding for the most antigenic epitopes of metalloproteinases from Echis ocellatus (an African viper) could be used for the generation of an antiserum that neutralised the toxicity of different African snakes [20], similar to the responses observed when rabbits were immunised with recombinant toxins [21]. These observations not only indicate that the DNA immunisation is a plausible way of developing specific and neutralising antibodies against snake venoms with no need for recombinant protein expression and purification from heterologous organisms such as Escherichia coli, but alto indicates that these neutralising antisera could be developed by the genetic immunisation of animals with the most antigenic epitopes, only.
In a previous work, after the transcriptomic analysis of M. corallinus venom gland, the predominant proteins in the venom were identified and five toxins that could represent good antigenic candidates were chosen for DNA immunisations, providing an initial evidence of the feasibility of this approach for an antielapidic sera development [22]. Among the proposed candidates, there are four three-fingered toxins (3FTx) and one putative M. corallinus phospholipase A2, which were selected based on the abundance of each transcript.
The first antigen selected (Ag1) is a 3FTx similar to a previously characterised as neurotoxin homolog 8 (Nxh8), which differs from most 3FTx as it shows an extra disulphide bond in the first loop [23]. The second one (Ag2) refers to a more typical 3FTx and is homologous to the previously described Nxh7, Nxh3 and Nxh1 neurotoxins [24]. The other two 3FTx (Ag3 and Ag4) represent new identified proteins with similarity of no more than 50% to the sequences of 3FTx in the databanks. The fifth selected antigen candidate (Ag5) corresponds to the putative M. corallinus phospholipase A2 (PLA2).
In this work we describe the mapping, by the SPOT-synthesis technique [25], of potential B-cell epitopes from these five putative toxins. These epitopes were then analysed through different in silico methods and used for the design of two multiepitope DNA strings for the genetic immunisation of female BALB/c mice. By the end of the immunisation period, animals were bled and sera were subjected to further analysis concerning its neutralisation capabilities.
The identification of potential B-cell epitopes from the five most abundant toxins that constitute the venom of Micrurus corallinus [22] was performed by the SPOT-synthesis technique [25]. For this procedure, overlapping pentadecapeptides, frameshifted by three residues and spanning the whole sequences of all these toxins were adsorbed into a cellulose membrane according to the protocol of Laune et al. [26]. The cellulose membranes were obtained from Intavis (Koln, Germany); fluorenylmethyloxycarbonyl amino acids and N-Ethyl(hydroximino)cyanoacetate were from Novabiochem. A ResPep SL/AutoSpot SL Automatic Spot synthesiser (IntavisAG, Bioanalytical Instruments, Germany) was used for the automated peptide synthesis in the membrane. After assembling the peptide sequences, the side-chain protecting groups were removed by treatment with trifluoroacetic acid. A membrane map of epitopes can be found in Fig 1.
For the identification of immunoreactive peptides, after an overnight blocking step with 3% bovine serum albumin (BSA) diluted in phosphate buffered saline with 0.05% (v/v) Tween-20 (PBS-T), the SPOT membrane was probed with a 1:1000 dilution of a monospecific anti-M. corallinus horse antiserum (whole IgG, kindly provided by the antivenom facility of Butantan Institute, São Paulo, Brazil). Antibody binding was detected with an alkaline phosphatase-conjugated anti-horse IgG (Sigma Aldrich) and detection was performed with 60 μL of MTT 0.12M (methylthiazolyldiphenyl-tetrazolium bromide, Sigma Aldrich), 50 μL BCIP 0.16M (5-bromo-4-chloro-3-indolyl phosphate, Sigma Aldrich) and 40 μL MgCl2 1M diluted in 10mL of citrate buffered saline, pH 7.0 (137 mM NaCl, 3 mM KCl, 10mM citric acid). The membrane was digitalised using a colour scanner (ScanJet 3670, Hewlett-Packard) and subjected to densitometric intensity analysis with ImageJ—image processing software [27]. In order to eliminate unspecific binding of secondary antibody to spots, the membrane was also probed and detected with secondary antibody alone. A spot was considered immunoreactive when the relative density value obtained after incubation with both primary and secondary antibodies was higher than the value obtained by the incubation with the secondary antibody, alone.
Two multiepitope DNA strings were designed based on the results obtained with the SPOT-synthesis technique [25]. One of them codes for the reactive epitopes associated with the four selected 3FTx while the other one codes for the reactive epitopes associated with the putative phospholipase A2 (PLA2) toxin. All epitopes sequences were separated by a six amino acid residues linker and all cysteine codons were exchanged by serine codons in order to avoid the formation of disulphide bond-mediated protein multimerisation. The codon usage was optimised for both Mus musculus and Escherichia coli expression according to the Codon Usage Database (Kazusa DNA Research Institute) [28]. To facilitate further molecular cloning into expression vectors, a XhoI and a SfiI restriction sites were inserted at the 5’ region of each DNA string, while a PstI restriction site was inserted at the 3’ region of each DNA string. Theses DNA strings were synthesised by GeneArt® Gene Synthesis (Thermo Fischer Scientific).
The three-dimensional structure of all the five toxins described in this work have not been resolved yet. However, in order to obtain the approximated spatial localisation of reactive epitopes, we performed some protein structure homology modelling using the SWISS-MODEL workspace tool for molecular modelling [29–31]. Ramachandran plots [32] and QMEAN [33] scores were used for quality assessment and models were selected based on their Global Model Quality Estimation (GMQE) values [31]. Briefly, the Ag1 (Nxh8) homology model was obtained based on the crystal structure (PDB ID: 3nds) of Naja nigricollis toxin alpha (GMQE: 0.80 / Seq. identity: 54.10 / Seq. similarity: 0.48). The Ag2 (Nxh7/3/1) homology model was obtained based on the NMR structure (PDB ID: 1nor) of neurotoxin II from Naja naja oxiana (GMQE: 0.78 / Seq. identity: 40.35 / Seq. similarity: 0.48). The Ag3 (3FTx) homology model was obtained based on the crystal structure (PDB ID: 2h8u) of Bucain, a cardiotoxin from the Malayan Krait Bungarus candidus (GMQE: 0.89 / Seq. identity: 57.63 / Seq. similarity: 0.51). The Ag4 (3FTx) homology model was obtained based on the crystal structure (PDB ID: 4iye) of the green mamba, Dendroaspis angusticeps, ρ-Da1a toxin (GMQE: 0.75 / Seq. identity: 40.00 / Seq. similarity: 0.40). And, finally, the Ag5 (PLA2) homology model was obtained based on the crystal structure (PDB ID: 1yxh) of a phospholipase A2 from Naja naja sagittifera (GMQE: 0.82 / Seq. identity: 58.97 / Seq. similarity: 0.50). Protein images were generated using DeepView (Swiss PDB Viewer) [34].
We also decided to evaluate the hydropathic profile and antigenic index of all antigens so we could analyse if the reactive epitopes are localised in highly hydrophilic and antigenic regions of their respective proteins. This would corroborate and reinforce the empirical results obtained by the SPOT-synthesis technique. The hydropathic characters of all proteins were evaluated using the Kate & Doolittle algorithm [35] while the antigenic index were computed using the Jameson & Wolf algorithm [36]. All images were created with Protean computer program (DNASTAR Inc. Madison, Wisconsin, USA).
The complete coding sequences corresponding to the mature portions of the five selected antigens were PCR amplified from a previously constructed cDNA library [22]. Both KpnI and XhoI restriction sites were included in the 5’ end of the forward primer while a NotI restriction site was included in the 5’ end of the reverse primer. Each one of the amplicons were cloned into the KpnI and NotI endonucleases sites of individual pSECTAG2A mammalian expression vectors (Thermo Fischer Scientific). To avoid the expression of the c-myc epitope located at the 3’ region of the vector multi cloning site, a stop codon was introduced in the reverse primer. Alternatively, for the heterologous recombinant expression of toxins in Escherichia coli cells, the amplicons were also cloned into the XhoI and NotI restriction sites of a high-copy T7 promoter-based E. coli expression vector (pAE) [37].
Both multiepitope DNA strings were cloned either into the SfiI and PstI restriction sites of pSECTAG2A plasmids (for the genetic immunisations protocols) or into the XhoI and PstI restriction sites of pAE vectors (for the heterologous recombinant expression in Escherichia coli cells).
The correct transcription of toxins’ cDNA sequences by mammalian host cells was investigated by transiently transfecting COS-7 cells (ATCC CRL 1651), which were maintained in Dulbecco's Modified Eagle´s Medium (DMEM; Life Technologies, USA) supplemented with 2 mM L-glutamine, 100U⋅mL-1 penicillin, 100μg⋅mL-1 streptomycin, 0.25μg⋅mL-1 amphotericin B (Thermo Fischer Scientific, USA) and 10% foetal bovine serum (FBS; Cultilab, Campinas, SP, Brazil). Individual pSECTAG2A vectors, cloned with the complete cDNA sequences of each toxin [22], were used for the transient transfection of COS-7 cells using Lipofectamine 2000 (Thermo Fischer Scientific, USA) according to the manufacturer's instructions. Cells were washed three times with Phosphate Buffered Saline (PBS) 48 h after transfection and the medium was replaced with DMEM without FBS. After 24 h incubation, both medium and cells were collected—after centrifugation for 10 minutes at 3000 g—and stored at -20°C until use.
After transfection, cells had been treated with Trizol (Thermo Fischer Scientific, USA) for the isolation of total mRNA, which were reverse transcribed using an oligo(dT)20 primer (Thermo Fisher Scientific, USA). Total cDNA was than subjected to PCR amplifications for the detection of toxins’ cDNAs.
The heterologous toxin expressions by all COS-7 cells previously transfected were assessed by Western Blot analysis of cell extracts. For this, a SDS-PAGE with the cells extracts was performed and the proteins transferred to a nitrocellulose membrane. After an overnight blocking at 4°C with 10% (v/v) non-fat dry milk diluted in PBS-T, membrane was incubated with a 1:3000 dilution of monospecific anti-Micrurus corallinus horse antiserum (kindly provided by the antivenom facility of Butantan Institute) for 90 minutes at constant agitation at room temperature. Free, non-bound primary antibodies were removed with three 30 minutes’ washes in PBS-T. Goat anti-horse IgG-HRP antibodies (Sigma Aldrich) were used as secondary antibody at a dilution of 1:5000. Membranes were probed with ECL Prime detection reagent (GE Healthcare) according to manufacturer’s instruction.
Purified pSECTAG2A plasmid (vehicle control), pSECTAG2A-ag1, pSECTAG2A-ag2, pSECTAG2A-ag3, pSECTAG2A-ag4, pSECTAG2A-ag5, pSECTAG2A-3ftx-multiepitope, and pSECTAG2A-pla2-multiepitope were precipitated onto 1.6μm gold beads and coated on the inner surface of Tefzel ETFE Fluoropolymer resin tubing according to the manufacturer’s protocol (BioRad Laboratories, Inc.). The final quantity of DNA/gold beads for each shot was adjusted to 1 μg of DNA / 0.5 mg Au.
For the recombinant expression of toxins or multiepitope proteins, each one of the pAE plasmid constructions described before were introduced, by heat shock, into chemically competent Escherichia coli BL21 (DE3) cells (Thermo Fischer Scientific, USA), which were grown on Luria-Bertani (LB) medium and induced for three hours by the addition of 1mM isopropyl-1-thio-β-D-galactopyranoside (IPTG) when an OD600 (optical density at 600nm) of 0.6 was achieved. After the induction period, cells were collected by centrifugation and mechanically lysed by French Press (Thermo Fischer Scientific, USA). Recombinant proteins were expressed as inclusion bodies and were solubilised with 20 mL of solubilisation buffer (8 M urea, 50 mM Tris-Cl, 5 mM β-mercaptoethanol, pH 7.4).
After complete solubilisation, recombinant proteins were purified by immobilised metal ion affinity chromatography (IMAC). For this procedure, proteins were adsorbed on a 5mL column previously charged with Ni+2 and equilibrated with 5 column volumes (CV) of solubilisation buffer without β-mercaptoethanol. Washing procedures were performed with 5 CV of wash buffer (3M urea, 40mM imidazole, 150mM NaCl, 50mM Tris-Cl, pH 7.4). Protein elution were accomplished with 5 CV of elution buffer (3M urea, 1M imidazole, 150 mM NaCl, 50mM Tris-Cl, pH 7.4). Finally, before these expressed proteins could be used in immunisation regimens, imidazole was removed by simple dialysis against PBS buffer containing 3M urea (to avoid protein precipitation). The purity and concentration of recombinant protein were evaluated and quantified by SDS-PAGE densitometry analysis with ImageJ—Image processing software [27].
For the determination of the total IgG titre from the different antisera or from antielapidic antivenom produced by Butantan Institute, 96-well microtitre plates were coated with either 100 μL of purified recombinant antigens (10 μg⋅ml-1 in Carbonate-Bicarbonate Buffer, pH 9.6) or with 100 μL of Micrurus corallinus venom (10 μg⋅ml-1 in Carbonate-Bicarbonate Buffer, pH 9.6). After three washes with PBS-T, plates were blocked with 5% non-fat milk/PBS-T (m/V) at 37°C for 1 h. Serial dilutions of each serum in PBS-T were added to the wells and microtitre plates incubated for 1 h at 37°C. Bound antibodies were detected with a 1:5000 dilution of a commercial peroxidase-conjugated anti-mouse IgG (Sigma Aldrich). Detection was performed with 8 mg o-phenylelediamine (OPD) diluted in 20 mL of 0.2 M citrate-phosphate buffer, pH 5.0, in the presence of 10 μL of 30% H2O2. Reaction was stopped by adding 50 μL of 4M H2SO4 to each well. Absorbances were measured at 492 nm and titres were determined as the highest dilution, in which an absorbance value ≥ 0.1 was observed. ELISA experiments were performed in simultaneous duplicates with all detection reaction being stopped at the same time.
In order to evaluate the neutralisations capabilities of all experimental sera conceived during the immunisations protocols performed in this study, 3LD50 (21 μg) [38] of Micrurus corallinus venom were diluted in physiological saline to a final volume of 100 μL and mixed with the 100 μL of each serum. All venom/antiserum mixtures were, then incubated at 37°C for a total of 30 min before being intraperitoneally administered to groups of five female Balb/c mice weighting around 20 g. Animals were monitored every 6 hours with the number of deaths being recorded until 48 h after injection. For positive and negative controls groups, 3LD50 of venom were also incubated with either 100 μL of Butantan’s antielapidic antivenom, 100 μL of monospecific anti-M. corallinus horse antiserum or with 100 μL of serum from naïve mice, respectively.
All animal experimentation protocols were performed in conformity with the Ethical Principles on Animal Research of the Brazilian College of Animal Experimentation (COBEA) and were previously revised and approved by the Ethics Committee on Animal Research of Butantan Institute under identification number 657/09.
Two multiepitope DNA-strings were designed by identifying reactive B-cell epitopes in four major three-fingered toxins and one phospholipase A2 from M. corallinus venom. For this, synthetic pentadecapeptides covering the entire amino acid sequences of these toxins were adsorbed on a nitrocellulose membrane (Fig 1), which was incubated with a monospecific anti M. corallinus horse antiserum and revealed with an alkaline phosphatase-conjugated goat anti-horse IgG as secondary antibody. Unspecific spots were identified by incubating the SPOT membrane with only the secondary antibody. A spot was considered immunoreactive when its relative density value after incubation with both primary and secondary antibodies was higher than its relative density value obtained after incubation with only the secondary antibody (Fig 2).
Represented by spots 80/81 and 94/95/96/97, two linear epitopes with amino acid sequences KICNFKTCPTDELRHCAS (Epitope 1) and CAATCPTVKPGVNIICCKTDNSN (Epitope 2) were identified in the primary structure of Ag1 (Fig 2—Ag1). Likewise, the densitometric analysis of spots associated with the primary structure of Ag2 showed a single epitope (Fig 2—Ag2) represented by spots 68/69/70 and with amino acid sequence GCPQSSRGVKVDCCMRDKCNG (Epitope 3). In the same way, a single 27-mer epitope, represented by spots 116/117/118/119/120 and with amino acid sequence WKKGEKVSRGCAVTCPKPKKDETIQCC (Epitope 4) was detected on Ag3 (Fig 2—Ag3) and a single 24-mer epitope, represented by spots 139/140/141/142 and with amino acid sequence DDFTCVKKWEGGGRRVTQYCSHAC (Epitope 5) was detected on Ag4 (Fig 2—Ag4). In the case of the putative PLA2, two linear epitopes were detected (Fig 2—Ag5), represented by spots 21/22 and 44/45/46/47. The amino acid sequences of these epitopes are, respectively, GAGGSGTPVDELDRCCKV (Epitope 6) and AALCFGRAPYNKNNENINPNRCR (Epitope 7).
Considering that the hydrophilic regions of a protein are precisely those that, in theory, are more exposed to the immune system and consequently have a higher reactivity when in contact with an anti-M. corallinus serum, we decided to compare the position of these epitopes within an antigenic index and a hydrophilic profile of their respective antigens. The results clearly demonstrate that all epitopes are positioned within the antigenic and hydrophilic regions (Fig 2—Ags1–5). Additionally, when these epitopes were mapped into the three-dimensional models we created, we could observe that these epitopes are occupying large accessible surface areas (Fig 3), corroborating the empirical results obtained by this epitope mapping technique. Furthermore, concerning the PLA2 3D model, it is also worth noting that despite the two detected epitopes are located in opposite sides in the primary structure of the protein, they are situated in the same spatial region of the protein, which strongly suggests that these peptides are, indeed, important for an effective immune response (Fig 3E).
Having identified the most reactive peptides from all the five selected neurotoxins, two synthetic multiepitope DNA strings were designed, as previously described, based on the amino acid sequence of those epitopes. One of them, named 3ftx, codes for all five reactive epitopes associated with the four 3FTx (Fig 4). The other one, named pla2, codes for the two reactive epitopes associated with the PLA2 toxin (Fig 5). In both cases, cysteine codons were replaced by serine codons to avoid the formation of disulphide bond-mediated protein multimerisation. All epitopes were separated by a six residues linker and codons were optimised for both Mus musculus and Escherichia coli expression.
Although most of the registered cases of snakebite envenomation are due to snakes from the Viperidae family, accidents involving members of the Elapidae family do occur. Additionally, coral snakes, which are the only elapids found in the New World, possess one of the most potent venom found in snakes, which tend to have significant neurotoxicity, inducing peripheral nervous system depression in a way similar to curare poisoning, with muscle paralysis and vasomotor instability. Actually, accidents caused by coral snakes could be very severe or even lethal [3].
Since the early observations from Calmette and Vital Brazil [46], the only acceptable medical treatment for snakebite accidents is the administration of an antiserum generated by horse immunisation with snake venom. Here, for what concerns an antielapidic antivenom, due to a number of factors such as the small size of glands, fossorial habit and very low survival rates in captivity, the production of sufficient amounts of antivenom is jeopardised by the inadequate amount of venom available. In fact, there are already registered cases of patients being intubated and ventilated as a consequence of antivenom shortage in USA, leading to increased morbidity and mortality [11]. Under these circumstances, the development of a new and efficient procedure for coral snake antivenom development, with less reliance upon snake collection and maintenance, would be an important contribution for the treatment of coral snakebite accidents.
In a recent work, the B-cell epitope mapping of M. corallinus antigens was described and showed promising results when these epitopes were used as peptide antigens [47]. Here, on the other hand, we describe the design and synthesis of two multiepitope DNA strings through the identification of linear B-cell epitopes of five major toxins (four 3FTx and one PLA2 [22]) from the venom of M. corallinus. When these multiepitope DNA strings were used for the genetic immunisation (by GeneGun) of mice, detectable levels of specific antibodies with partial (40%) neutralisation capabilities in lethal dose assays were observed (Fig 6B-i). Furthermore, when these multiepitope DNA strings were used for the expression and purification of recombinant multiepitope proteins, which, in turn, were administered to those previously genetically immunised groups of mice, not only the IgG antibody titres increased but a 60% neutralisation capability was also observed in lethal dose assays (Fig 6B-ii), showing that both multiepitope DNA strings can be used for the generation of neutralising antibodies against M. corallinus toxins. These results also confirm that transcriptomic studies can provide potential targets for the development of neutralising antibodies and further studies concerning the characterisation of other B-cell epitopes, other formulations and immunisation protocols could help to improve venom neutralisation.
At last, but not least, the fact that a neutralisation of 100% could not be observed does not disqualifies this approach as a promising alternative method for the development of an antielapidic antiserum. As a matter of fact, it is worth noting that all the neutralisation capabilities observed in this work were, as expected, intimately related with the antibody titres. Unfortunately, however, the total volume of sera withdrawn from immunised animals was not sufficient to obtain reliable quantities of purified immunoglobulins, what would be, indeed, an interesting outcome of this work.
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10.1371/journal.ppat.1000660 | The Bacterial Defensin Resistance Protein MprF Consists of Separable Domains for Lipid Lysinylation and Antimicrobial Peptide Repulsion | Many bacterial pathogens achieve resistance to defensin-like cationic antimicrobial peptides (CAMPs) by the multiple peptide resistance factor (MprF) protein. MprF plays a crucial role in Staphylococcus aureus virulence and it is involved in resistance to the CAMP-like antibiotic daptomycin. MprF is a large membrane protein that modifies the anionic phospholipid phosphatidylglycerol with l-lysine, thereby diminishing the bacterial affinity for CAMPs. Its widespread occurrence recommends MprF as a target for novel antimicrobials, although the mode of action of MprF has remained incompletely understood. We demonstrate that the hydrophilic C-terminal domain and six of the fourteen proposed trans-membrane segments of MprF are sufficient for full-level lysyl-phosphatidylglycerol (Lys-PG) production and that several conserved amino acid positions in MprF are indispensable for Lys-PG production. Notably, Lys-PG production did not lead to efficient CAMP resistance and most of the Lys-PG remained in the inner leaflet of the cytoplasmic membrane when the large N-terminal hydrophobic domain of MprF was absent, indicating a crucial role of this protein part. The N-terminal domain alone did not confer CAMP resistance or repulsion of the cationic test protein cytochrome c. However, when the N-terminal domain was coexpressed with the Lys-PG synthase domain either in one protein or as two separate proteins, full-level CAMP resistance was achieved. Moreover, only coexpression of the two domains led to efficient Lys-PG translocation to the outer leaflet of the membrane and to full-level cytochrome c repulsion, indicating that the N-terminal domain facilitates the flipping of Lys-PG. Thus, MprF represents a new class of lipid-biosynthetic enzymes with two separable functional domains that synthesize Lys-PG and facilitate Lys-PG translocation. Our study unravels crucial details on the molecular basis of an important bacterial immune evasion mechanism and it may help to employ MprF as a target for new anti-virulence drugs.
| Certain bacterial immune-evasion factors such as the MprF protein are highly conserved in many bacterial pathogens and represent attractive targets for new ‘anti-virulence’ drugs. MprF, initially discovered in the major human pathogen Staphylococcus aureus, protects bacteria against ‘innate human antibiotics’ such as the defensin peptides. In addition, MprF has recently been implicated in resistance to the new defensin-like antibiotic daptomycin. MprF modifies bacterial membrane lipids with the amino acid l-lysine, which leads to electrostatic repulsion of the membrane-damaging peptides. The molecular mechanism of MprF has remained largely unknown. We demonstrate that MprF represents a novel bifunctional type of enzyme. The N- and C-terminal domains of MprF are both required for mediating antimicrobial peptide resistance but they can be expressed as two separate proteins without loss of function indicating that they represent distinct functional modules. While the C-terminal domain accomplishes lipid lysinylation the N-terminal membrane-embedded domain is required to expose the lysine lipid at the outer surface of the bacterial membrane where it is able to repulse the antimicrobial peptides. These findings unravel the molecular basis of an important bacterial immune evasion mechanism and they may help to employ MprF as a target for new anti-virulence drugs.
| In order to combat increasingly antibiotic-resistant bacteria such as Staphylococcus aureus, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and enterococci new antimicrobial strategies based on compounds with anti-virulence or anti-fitness properties are increasingly in the focus of research efforts [1],[2]. Bacterial immune evasion mechanisms such as the mprF or dltABCD-encoded pathways are conserved over a wide range of bacterial species thereby representing attractive targets for broadly active antimicrobial compounds that would not kill the bacteria directly but render them susceptible to endogenous host defense molecules [3],[4].
The occurrence of closely related immune evasion factors in many bacterial pathogens is reflected by the conserved nature of the most critical antimicrobial host defense molecules. Defensins, cathelicidins, kinocidins, and related cationic antimicrobial peptides (CAMPs) are essential components of the antimicrobial warfare arsenals in humans, vertebrate and invertebrate animals, and even plants [5],[6]. Although peptide structures vary, overall structural features (cationic, amphipathic properties; often with γ-core motif) and modes of action (damage of microbial membrane-associated processes) are shared by most of these peptides [7]. CAMPs appear to take advantage of the fact that bacterial membranes are formed mostly by anionic phospholipids [4]. Conversely, the MprF and DltABCD proteins protect many bacterial pathogens against CAMPs by reducing the negative net charge of bacterial cell envelopes [3],[8]. The dltABCD operon products neutralize polyanionic teichoic acid polymers by esterification with d-alanine in many Gram-positive bacteria [9]. Detailed investigations on this pathway have recently led to the development of specific DltA inhibitors, which proved to be very effective anti-virulence drugs for eradication of bacterial infections [10],[11].
Much less is known on the MprF protein, which represents a particularly interesting antimicrobial drug target because of its presence in both, Gram-positive and Gram-negative bacteria [3]. MprF is a large integral membrane protein catalyzing the modification of the negatively charged lipid phosphatidylglycerol (PG) with l-lysine thereby neutralizing the membrane surface and providing CAMP resistance [12]–[14]. The resulting lysyl-phosphatidylglycerol (Lys-PG), described in pioneering biochemical studies in the 1960es [15],[16], is produced by an unusual pathway that uses PG and Lys-tRNA as substrate molecules [17]–[19]. The Lys-PG-biosynthetic enzyme has been identified only recently in Staphylococcus aureus and named multiple peptide resistance factor (mprF) because mprF mutants lacking Lys-PG are highly susceptible to CAMPs [12],[13]. The loss of Lys-PG in mprF mutants also led to CAMP susceptibility in Listeria monocytogenes [20], Bacillus anthracis [21], and Rhizobium tropici [22] thereby demonstrating a general role of MprF in bacterial immune evasion.
Recently, mprF point mutations or alterations in Lys-PG content became notorious for spontaneous resistance of S. aureus to daptomycin [23],[24]. This antibiotic has recently been approved as an antibiotic of last resort for the treatment of methicillin-resistant S. aureus (MRSA), which are responsible for a large proportion of hospital and, increasingly, community-acquired bacterial infections [25]. Daptomycin has a negative net charge but it is believed to have CAMP-like properties and mode of action upon binding of calcium ions [26]. In addition, MprF has been implicated in S. aureus susceptibility to the cationic antibiotics vancomycin, gentamycin, and moenomycin [27].
mprF expression is upregulated in staphylococci upon contact with CAMPs by the sensor/regulator system ApsRS [28],[29], which has also been named GraRS [30],[31]. Deletion of mprF has led to profoundly reduced virulence of several bacterial pathogens in animal models, which underscores the pivotal role of Lys-PG in bacterial fitness during colonization and infection [12],[20],[32],[33]. Accordingly, it is tempting to elucidate the molecular functions of MprF as a prerequisite for the development of small inhibitory molecules that would block Lys-PG biosynthesis and render a large number of bacterial pathogens highly susceptible to innate host defenses and cationic antibiotics such as daptomycin, glycopeptides, or aminoglycosides.
Here we demonstrate that MprF is a bifunctional protein composed of distinct and separable domains. While the C-terminal part of MprF is sufficient to synthesize Lys-PG the N-terminal hydrophobic protein domain is essential for efficient translocation of Lys-PG from the inner to the outer leaflet of the cytoplasmic membrane to reduce the bacterial affinity for CAMPs such as α-defensins, LL-37, daptomycin, or gallidermin.
Most MprF-like proteins are composed of large N-terminal hydrophobic domains followed by hydrophilic C-terminal domains [34] (Fig. S1). The hydrophilic portions exhibit much higher degrees of sequence similarity between different members of the MprF protein family [12] suggesting that this domain may play the most crucial role in Lys-PG biosynthesis. The hydrophobic domain of S. aureus MprF ranging from amino acid 1 to 509 is predicted to consist of 14 TMSs (Fig. 1A). In order to study whether the hydrophobic domain plays a role in Lys-PG biosynthesis the protein was shortened from the N-terminus in a step-wise manner by removing two TMSs at a time (Fig. 1B). The shortened proteins were expressed as N-terminal His-tag fusions and evaluated for their capacity to mediate Lys-PG production in E. coli BL21(DE3).
Deletion of the first eight TMSs of MprF from the N-terminus did not affect the ability of the protein to mediate Lys-PG production (Fig. 2A). However, further truncations abrogated Lys-PG production indicating that at least 6 TMSs are required for maintaining a functional enzyme and that the N-terminal domain of MprF may have a separate function. The presence and stability of the proteins was verified by Western-blotting with a His-tag-specific antibody. The shorter versions of MprF with no, two, four, or six predicted TMSs were detectable as singular similarly pronounced bands indicating that these proteins are largely stable (Fig. 2B). Longer versions of MprF including the full-length protein could not be visualized by Coomassie Blue staining or Western blotting even upon extensive variation of expression, isolation, and detection methods (data not shown), possibly because of inaccessibility of the N-terminal His-tag in these proteins. However, since all proteins ranging from MprF to MprF(−8) yielded similar levels of Lys-PG production the protein amounts and activities are unlikely to exhibit major differences.
Taken together, our data indicate that the N-terminal eight TMSs are dispensable for full-level Lys-PG synthesis while any further shortening completely abrogates the functionality of MprF.
Alignment of C-terminal MprF domains from different bacterial species revealed several conserved sequence motives, which may represent essential amino acids for substrate binding, enzymatic reaction, or folding into a stable protein of the Lys-PG synthase domain (Fig. S2). In order to evaluate the essential nature of such positions, eight highly conserved amino acid residues were exchanged with alanine residues by site-directed mutagenesis of the pET28mprF(−8) plasmid. Exchange of K547, K621, E624, D731, R734, and K806 led to complete abrogation of Lys-PG production (Fig. 2C). In contrast, replacement of E685 or D546 with alanine resulted in strongly or only slightly reduced Lys-PG production, respectively. The same results were obtained when the mutations were introduced into the full-length MprF protein (Fig. S3A). All the MprF(−8)-derived mutant proteins were detectable in Western Blots as singular protein bands that corresponded to the MprF(−8) protein (Fig. S3B) indicating that even the inactive proteins were stably produced in E. coli. Taken together, these data demonstrate essential roles of K547, K621, E624, D731, R734, and K806 for the enzymatic activity of MprF and less critical but important roles of D546 and E685.
In order to investigate if MprF(−8) also mediates Lys-PG production in S. aureus, genes encoding the full-length and the MprF(−8) proteins were cloned in the E. coli/Staphylococcus shuttle expression vector pRB474 [35]. All the resulting plasmids led to Lys-PG production in S. aureus SA113 ΔmprF (Fig. 3A) thereby reflecting the E. coli results. However, the ΔmprF mutant with plasmid-encoded MprF or MprF(−8) did not reach the same level of Lys-PG as the wild-type strain. When the S. aureus strains were compared for susceptibility to CAMPs such as the α-defensins human neutrophil peptides 1–3 (HNP 1–3), the human cathelicidin LL-37, the bacteriocin gallidermin, or the antibiotic daptomycin, the mprF mutant was much more susceptible than the wild-type strain (Fig. 3B), which is in agreement with previous findings [12],[23]. The strain containing the pRB474mprF(−8) plasmid was as susceptible to daptomycin as the mprF deletion mutant or exhibited only slightly decreased susceptibilities as in the case of HNP1-3, LL-37, and gallidermin. However, only the full-length mprF gene led to full resistance to the four peptides. This result indicates that the N-terminal hydrophobic domain of MprF is necessary for mediating efficient CAMP resistance despite the fact that it is dispensable for Lys-PG biosynthesis.
The presence of a basic level of Lys-PG seemed to be sufficient for full-level CAMP resistance provided that the N-terminal hydrophobic domain of MprF was not absent, while the total amounts of Lys-PG did not correlate with the levels of CAMP susceptibility (compare Lys-PG amounts and MIC values for WT and ΔmprF containing plasmid pRBmprF). In order to verify this notion we cloned the minimal Lys-PG synthase domain MprF(−8) in the inducible staphylococcal expression vector pTX15, which has a higher copy number than pRB474 and permits xylose-inducible gene expression [35],[36]. S. aureus ΔmprF with the resulting plasmid pTX15mprF(−8) had a 2.5–3.5-fold increased Lys-PG content as with the above described pRB474mprF(−8) (Fig. 3C). However, the two strains were inhibited by similarly low concentrations of daptomycin thereby confirming that Lys-PG production per se does not necessarily cause CAMP resistance, irrespective of the produced amounts of Lys-PG.
In order to explore the role of the N-terminal domain of MprF in CAMP resistance the mprF(−C) gene encoding only the 14 TMSs without the hydrophilic C-terminal domain was expressed in S. aureus ΔmprF. Of note, the resulting strain did not show resistance to any of the tested CAMPs compared to the ΔmprF mutant (Fig. 3B) indicating that this protein domain alone cannot protect the bacteria from CAMPs and depends on the Lys-PG synthase. In order to evaluate if the two domains need to be fused or can be separated to achieve CAMP resistance, the mprF(−C) gene was cloned in pTX15, which is compatible with pRB474-derived plasmids. The resulting plasmid pTX15mprF(−C) or the empty control plasmid pTX16 were introduced into S. aureus ΔmprF bearing pRB474mprF(−8). The MIC values of daptomycin reached much lower levels in the presence of two plasmids compared to the experiments described above, which is probably due to increased stress imparted on the two plasmids-containing bacteria. Notably, when MprF(−C) was co-expressed with MprF(−8) in trans it conferred full CAMP resistance, which reached the same level as the unchanged MprF protein (Fig. 4B). Thus, the hydrophobic domain of MprF can only mediate CAMP resistance if the synthase domain is present but the two proteins can be separated and do not need to be covalently linked.
While Lys-PG is synthesized at the inner leaflet of the cytoplasmic membrane where the Lys-tRNA donor substrate is available, the lipid can only exert its role in CAMP resistance when present at the outer leaflet of the membrane, where the antimicrobial peptides are encountered. In order to evaluate the possibility that the N-terminal hydrophobic domain of MprF facilitates the translocation and exposure of Lys-PG at the outer leaflet of the membrane, we first investigated the impact of MprF(−C) on surface charge neutralization and concomitant repulsion of cationic peptides [12]. A previously described assay based on the bacterial binding capacity of the small red-coloured cationic protein cytochrome c was used for this approach [37]. As expected, the mprF mutant had a profoundly higher capacity to bind cytochrome c as the wild-type strain, which reflects the highly negatively charged membrane surface in the absence of Lys-PG (Fig. 5A). Likewise, expression of MprF(−C) or of the synthase domain MprF(−8) in S. aureus ΔmprF led to substantially reduced repulsion of cytochrome c compared to the unaltered MprF. However, when the two protein parts were simultaneously expressed in trans they led to the same level of cytochrome c repulsion as expression of the unaltered MprF protein (Fig. 5A). These results parallel the inability of MprF(−8) and MprF(−C) to confer CAMP resistance individually and they confirm that the two proteins have complementary functions that can be physically separated.
The ability of the N-terminal hydrophobic domain of MprF to facilitate the translocation of Lys-PG from the inner to the outer leaflet of the cytoplasmic membrane was verified by comparing the capacity of Lys-PG to be modified by the aminogroups-reactive, membrane-impermeable fluorescent dye fluorescamine in the absence or presence of MprF(−C). This assay has been developed to analyze the distribution of amino-phospholipids between inner or outer leaflets of membranes [38],[39] and has been successfully used to compare Lys-PG distribution in spontaneously CAMP-resistant S. aureus mutants [24],[40]. When only the synthase domain of MprF was expressed in S. aureus ΔmprF, only a small fraction of total Lys-PG was found in the outer leaflet (Fig. 5B). However, when MprF(−C) was coexpressed with the synthase domain, the amount of Lys-PG in the outer leaflet was strongly increased and reached a similar level as in the inner leaflet. Thus, the N-terminal hydrophobic domain of MprF is required for efficient translocation of Lys-PG.
While the anionic phospholipids PG and cardiolipin are produced by virtually any bacterial species, zwitterionic or cationic lipids such as PE or Lys-PG, respectively, are produced only by certain groups of bacteria [41]. Despite extensive research efforts the actual roles of the various phospholipids, their biosynthesis, turnover, and regulation, have remained incompletely understood. Of note, the same holds true for the identity, specificity, and mode of action of proposed bacterial translocator proteins required to flip the lipids, which are generated at the inner cytoplasmic membrane leaflet, to the outer leaflet. MprF represents the paradigm of a new class of bifunctional lipid-biosynthetic enzymes mediating the transfer of amino acids to anionic phospholipids. While the S. aureus MprF mediates exclusively the biosynthesis of Lys-PG, the MprF homolog from L. monocytogenes seems to confer both, Lys-PG and Lys-cardiolipin biosynthesis [20]. MprF homologs from C. perfringens and P. aeruginosa have been shown to mediate Ala-PG production [34],[42]. Our study represents a basis for investigating the determinants of substrate specificity of MprF.
Six of the 14 TMSs plus the hydrophilic C-terminal domain were sufficient to mediate Lys-PG production in E. coli or S. aureus. The levels of Lys-PG production varied between S. aureus strains with different plasmid vectors and promoters used to express MprF or MprF variants but the level of Lys-PG did not correlate with the level of CAMP resistance indicating that only a basic amount of Lys-PG is sufficient for repulsing antimicrobial peptides provided that the lipid is translocated to the outer leaflet of the membrane. It is amazing that the Lys-PG synthase whose active center is probably located in the hydrophilic domain of MprF with its many conserved amino acid positions requires so many TMSs to function since one or two such segments should be enough to anchor the hydrophilic C-terminus in the membrane. One might speculate that six TMSs are required to embrace a PG substrate molecule and fit it into a position that may allow its lysinylation. It should be noted that even the MprF homolog with the shortest integral membrane domain found in Mycobacterium tuberculosis is predicted to harbor six TMSs (data not shown), which suggests that the dependence on six TMSs is a general property of MprF-like enzymes.
Previous studies on in vitro Lys-PG biosynthesis with artificially altered aminoacyl tRNAs have demonstrated that the Lys-PG synthase recognizes features of both, the tRNA and of the bound amino acid [17],[19]. Accordingly, the lysyl group could not be transferred to PG when it was attached to a cysteinyl tRNA. However, it did not matter whether the tRNA came from S. aureus or from another bacterial species such as E. coli [18]. We identified six conserved amino acids in the C-terminal domain of MprF as essential for Lys-PG biosynthesis while exchange of two other amino acid positions led to reduced Lys-PG production. All these positions are also conserved in MprF homologs with Ala-PG synthase activity (Fig. S2), which suggests that they are not involved in specific recognition of the aminoacyl tRNA precursor and may rather play crucial roles in the enzymatic process or in non-specific binding of the substrate. Irrespective of the tRNA structure the substrate-binding domain of MprF may need basic properties to interact with the polyanionic ribonucleic acid. Accordingly, four of the six identified essential amino acid position represent cationic arginine or lysine residues that may participate in binding of tRNA phosphate groups.
A most intriguing finding of our study was the fact that Lys-PG production on its own did not lead to CAMP resistance but depended on the large N-terminal integral membrane domain of MprF. Lys-PG mediates CAMP resistance by repulsing the cationic peptides from the outer surface of the membrane, which is only possible upon translocation of the lipid to the outer leaflet (Fig. 6). Of note, Lys-PG could only alter the membrane surface charge considerably in the presence of the N-terminal integral membrane domain indicating that this part of MprF is required for this lipid to reach the outer leaflet of the membrane. Moreover, Lys-PG could only be labeled efficiently by the membrane-impermeable dye fluorescamine in the presence of the N-terminal hydrophobic domain of MprF, which confirms the critical role of this protein part in Lys-PG translocation. Thus, MprF does not only synthesize Lys-PG but also accomplishes translocation of Lys-PG from the inner to the outer surface of the membrane. These two functions are allocated in the C-terminal and N-terminal domains of MprF, respectively, and can be separated into two functional proteins (Fig. 6).
While lipid translocators have been investigated to some extent in eukaryotic cells [43], such proteins have been proposed but hardly described in bacteria. It is possible that the bacterial house-keeping translocator(s) are more specific for the standard anionic phospholipids PG and cardiolipin, while a cationic lipid such as Lys-PG may require a dedicated translocator. It remains unclear why a small fraction of Lys-PG was detectable in the outer leaflet of the cytoplasmic membrane even in the absence of the flippase domain of MprF. Phospholipids may be able to flip spontaneously with low efficiency as proposed recently [44] or one of the house-keeping flippases may have residual activity for Lys-PG. Lipid translocators have been classified into energy-dependent (flippases or floppases) and energy-independent (scramblases) transporters [43]. MprF does not contain conserved ATP-binding or other sequence motives indicative of energy consumption. Therefore, it remains unclear if MprF can accomplish an asymmetric distribution of Lys-PG. Nevertheless, recent studies suggest that Lys-PG can be asymmetrically distributed between the inner and outer leaflets of the membrane in S. aureus depending on the individual strain background [24].
The increasing resistance of major bacterial pathogens raises the specter of untreatable infections as in the pre-antibiotics era. MRSA are now more and more prevalent in the community and only a few antibiotics of last resort such as daptomycin have remained effective against such highly pathogenic S. aureus clones. As S. aureus can overcome even daptomycin by simple point mutations in mprF new strategies for antibacterial chemotherapy are urgently needed. Inhibitors for highly conserved immune evasion factors such as mprF that would render a wide range of bacteria susceptible to endogenous human defense mechanisms and cationic antibiotics such as daptomycin should be increasingly considered. Our study represents a basis for more detailed investigations on the structure and mode of action of MprF-like aminoacylphospholipid synthases and they should enable the systematic search for inhibitors for this class of enzymes.
The plasmids and strains constructed in this study are listed in Table S1 and primers are listed in Table S2. Construction of plasmids, growth conditions, alignment and prediction of MprF structure, Western-blot analysis, lipid extraction, and analysis of Lys-PG distribution are described in Text S1.
For detection of Lys-PG appropriate amounts of polar lipid extracts were spotted onto silica 60 F254 HPTLC plates (Merck, Darmstadt, Germany) using a Linomat 5 sample application unit (Camag, Berlin, Germany) and developed with chloroform/methanol/water (65∶25∶4, by vol.) in an automatic developing chamber ADC 2 (Camag, Berlin, Germany). Amino groups or phosphate groups-containing lipids were selectively stained with ninhydrin spray (0.3 g ninhydrin dissolved in 100 ml 1-butanol and 3 ml 100% acetic acid) or molybdenum blue spray (Sigma). Integrated lipid spot intensities of molybdenum blue-stained phospholipids were determined by ImageJ (http://rsbweb.nih.gov/ij/). MIC values of gallidermin, HNP1-3, and LL-37 were determined by diluting bacteria from overnight cultures to an OD600 nm of 0.05 −0.1 in fresh MHB medium (gallidermin) or half-concentrated MHB (HNP1-3 and LL-37) containing serial dilutions of antimicrobial peptides as described recently [45]. Gallidermin was kindly provided by Friedrich Götz. HNP1-3 was isolated from human neutrophils and purified by reversed-phase high-performance liquid chromatography (RP-HPLC) as described previously [37]. LL-37 was synthesized by solid-phase peptide synthesis and purified by RP-HPLC [46]. Susceptibility to daptomycin was determined by epsilometer test (E test) in the presence of CaCl2 according to the manufacturer's advise (AB Biodisk) [47]. Differences in bacterial capacity to repulse cationic proteins were determined by comparing binding of the red-coloured cationic protein cytochrome c as described recently [37],[48].
Q2G2M2: Staphylococcus aureus MprF; Q5HPI1: Staphylococcus epidermidis MprF homolog; C0H3X7: Bacillus subtilis MprF homolog; C0X347: Enterococcus faecalis MprF homolog; Q8DWT2: Streptococcus agalactiae MprF homolog; Q71YX2: Listeria monocytogenes MprF homolog; Q88YQ7: Lactobacillus plantarum MprF homolog; Q8FW76: Brucella suis MprF homolog; Q9I537: Pseudomonas aeruginosa MprF homolog; Q0SSM7 and Q0STHJ7: Clostridium perfringens MprF homologs.
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10.1371/journal.pbio.1001156 | ESRRA-C11orf20 Is a Recurrent Gene Fusion in Serous Ovarian Carcinoma | Every year, ovarian cancer kills approximately 14,000 women in the United States and more than 140,000 women worldwide. Most of these deaths are caused by tumors of the serous histological type, which is rarely diagnosed before it has disseminated. By deep paired-end sequencing of mRNA from serous ovarian cancers, followed by deep sequencing of the corresponding genomic region, we identified a recurrent fusion transcript. The fusion transcript joins the 5′ exons of ESRRA, encoding a ligand-independent member of the nuclear-hormone receptor superfamily, to the 3′ exons of C11orf20, a conserved but uncharacterized gene located immediately upstream of ESRRA in the reference genome. To estimate the prevalence of the fusion, we tested 67 cases of serous ovarian cancer by RT-PCR and sequencing and confirmed its presence in 10 of these. Targeted resequencing of the corresponding genomic region from two fusion-positive tumor samples identified a nearly clonal chromosomal rearrangement positioning ESRRA upstream of C11orf20 in one tumor, and evidence of local copy number variation in the ESRRA locus in the second tumor. We hypothesize that the recurrent novel fusion transcript may play a role in pathogenesis of a substantial fraction of serous ovarian cancers and could provide a molecular marker for detection of the cancer. Gene fusions involving adjacent or nearby genes can readily escape detection but may play important roles in the development and progression of cancer.
| Serous ovarian cancer, the most common form of ovarian cancer, is especially lethal because it is usually only detected at a late stage in its progression, after the cancer has spread to other tissues. We searched for molecular markers of this cancer that might provide a better way to detect tumors at a curable stage and that might provide targets for new treatments. Chromosomal rearrangements that fuse two genes to produce a recombinant gene that enhances growth or spread of the cancer are particularly specific biomarkers and have been found in many cancers. By “deep” sequencing of the RNA molecules that carry genetic information in serous ovarian cancers, we discovered a rearrangement that fuses the same two neighboring genes in at least 15% of these tumors. The two fused genes are ESRRA, which encodes a key regulator of gene expression, and an essentially uncharacterized gene, C11orf20, that is normally adjacent to the ESRRA gene. Chromosomal rearrangements that recombine parts of two nearby genes or even parts of a single gene may be a common, important feature of the cancer genome that eludes detection by most approaches to characterizing cancer genomes.
| Ovarian cancer is estimated to kill more than 140,000 women every year [1]. Like most cancers, ovarian cancer has a dismal prognosis once the disease has spread beyond the site of origin [2]. The histological subtypes of ovarian cancer differ substantially in their molecular features and natural history and can be considered distinct diseases. Ovarian carcinomas of the serous histological type are responsible for the majority of deaths from ovarian cancer; they typically progress to an advanced stage while the tumor is still much too small to be detected by any presently available screening method [3]. Discovery of truly tumor-specific molecular markers may be essential for effective early detection of these tumors.
Recurrent gene fusions are among the most tumor-specific molecular markers known. Investigations of oncogenic gene fusions, including BCR-ABL in chronic myelogenous leukemia, have provided critical insights into pathogenesis and led to important therapeutic advances [4].
With a few notable exceptions, however, recurrent gene fusions have rarely been identified in commonly occurring carcinomas, which often have multiple, complex chromosomal rearrangements that are difficult to analyze by traditional cytogenetic approaches. A recurrent gene fusion, TMPRSS2-ERG, with an estimated prevalence of ∼50% in prostate cancer was discovered by a targeted search for cancer-associated genes with anomalous expression patterns, in a large database of DNA microarray data [5]. An ex vivo functional screen of cDNA from a non-small cell lung carcinoma (NSCLC) led to identification of EML4-ALK as a recurrent gene fusion in ∼5% of NSCLCs [6],[7].
Ultra High Throughput Sequencing (UHTS) is a powerful method for discovery of novel RNA sequences, including cancer-specific gene fusions. Tumor-specific genomic rearrangements and fusion transcripts have been discovered in individual tumors by UHTS (see for example [8]–[10]), including in high-grade serous ovarian cancer [11], but none of those reported to date have been recurrent. For example, a UHTS survey of genomic aberrations in 24 breast cancers found more than 2,000 rearrangements; 29 of these were predicted to generate in-frame gene fusions, but none occurred in more than one individual [12]. Similarly, a UHTS analysis of RNA from 10 melanomas identified 11 gene fusions, none of which were recurrent either in the original set or 90 additional cases [10].
We combined deep, paired-end sequencing of tumor RNA with a statistical bioinformatic approach to search for gene fusions in a pool of mRNA isolated from 12 primary serous ovarian cancers. Our analysis identified a novel recurrent gene fusion, ESRRA-C11orf20, resulting from a chromosomal rearrangement. The methods we used have important differences from previous algorithms for identifying gene fusions and novel splice variants [8]–[10], mainly in the use of statistical models for fusion discovery, and may be useful for discovering gene fusions in other cancers. (Note: since the algorithm used to identify the ESRRA-C11orf20 fusion was built, other algorithms for detecting fusions with RNA-Seq have been published [11],[13] with methods related to but algorithmically distinct from ours.)
To search for recurrent or highly expressed fusion transcripts, we first prepared a cDNA library with an average insert size of 350 bp from a pool of 12 late-stage serous ovarian tumors. Using Illumina GA II instruments, we determined 30 million pairs of 76-nucleotide sequences and 80 million pairs of 38-nucleotide sequences from the ends of these cDNA segments, a total of 111 million purity filtered (PF) reads.
Our RNA sequence analysis pipeline is diagrammed in Figure S1. We began by identifying paired reads that mapped uniquely to any two distinct genes (call them genes A and B). However, most such paired reads are spurious, due to artifactual ligation during library preparation, sequencing errors, or paralogous sequences. We constructed a database of the sequences predicted for every possible exon-exon junction that might result from a fusion between such pairs of genes A and B in the RefSeq database. We then searched our sequence data for individual reads that failed to align to the RefSeq transcript database, but did align uniquely to a sequence in our database of hypothetical gene fusion exon-exon junctions (“junction reads”). To be considered further, we also required that any such sequence have at least 10 bp aligning to each side of the hypothetical junction and that its cognate paired-end read align to one of the corresponding fusion partners in an orientation consistent with the predicted A-B fusion (diagrammed in Figure S2). Rather than introduce more stringent filters to exclude potential artifacts, at the expense of discarding potentially important results, we used statistical models to estimate the distribution of falsely identified fusions and assess a false discovery rate (see Text S1).
A transcript composed of exons from the ESRRA and C11orf20 genes was one of two putative fusion transcripts supported by more than three junction reads. (The other appeared to be a read-through transcript and has subsequently been annotated as RefSeq gene LOC100630923. The full list of candidates is given in Table S2.) Two distinct splice variants of a hypothetical fusion between ESRRA and C11orf20, joining exon 2 of ESRRA to either exon 3 or exon 4 of C11orf20 (E2-C3 and E2-C4, Figure 1B), were represented, E2-C3 with a low estimated false-discovery rate. We confirmed both of these predicted fusion transcripts by using RT-PCR to amplify the diagnostic exon junction sequences from pool RNA, followed by Sanger sequencing (Figure S3).
ESRRA (Estrogen Receptor Related Alpha, also known as ERR1) encodes a well-studied orphan nuclear receptor related to the estrogen receptor, and implicated in regulation of energy metabolism and thermogenesis, its expression level has been positively correlated with breast cancer progression and angiogenesis ([14]–[18]; review in [19]). In ovarian cancer, ESRRA expression has also been associated with decreased survival [20], and kaempferol, which inhibits angiogenesis by ovarian cancer cell lines, acts at least partially by decreasing ESRRA expression [21]. Very recently, the ESRRA locus has been implicated in increased risk of ovarian cancer [22]. By contrast, C11orf20 is a mostly uncharacterized gene, though conserved in the mammalian lineage.
Using single read count data [23], we estimated the expression level of ESRRA to be roughly 2500th in abundance in our serous ovarian cancer pool, similar to the abundance, for example, of ESR1 (ranked ∼2700th) and TP53 (ranked ∼1700th). Based on a statistical model for mRNA isoforms in paired-end data [24], we estimated the relative abundance of the canonical ESRRA mRNA, the fusion transcript E2-C3, the fusion transcript E2-C4, and the canonical C11orf20 mRNA as 40∶10∶1∶0, respectively. The abundance of the fusion transcripts thus appeared to be comparable to or greater than that of the ESRRA transcript itself, in one or more tumors harboring the fusion. We found no evidence for expression of either the reciprocal fusion product or the predicted full-length C11orf20 transcript.
We evaluated the prevalence of the ESRRA-C11orf20 fusion in a set of 68 patients with serous ovarian cancer, by RT-PCR followed by Sanger sequencing. Nine of the 42 cases screened at the FHCRC and 1 of the 25 cases screened at the BCCA were fusion-positive (exemplary positive RT-PCRs in Figure 1C). This gives an estimated prevalence of ESRRA-C11orf20 fusion transcripts in serous ovarian cancer as 10 in 67, or 15% (exact binomial 95% confidence interval: 7% to 26%). It should be noted that, in order for a patient sample to be called fusion-positive, we required that the majority of technical PCR replicates be positive; some cases showed fusion products but less reproducibly and so our counts may be subject to false negatives; thus this prevalence may be an under-estimate.
Nearly all positive cases expressed one or both of the two ESRRA-C11orf20 fusion isoforms previously observed in our tumor pool (E2-C3, E2-C4). One patient expressed exclusively a third isoform (E2-C5) in which ESRRA exon 2 was spliced to exon 5 of C11orf20 (Patient 3, Figure 1C).
The ESRRA protein consists of an N-terminal regulatory domain (NTD), a DNA binding domain (DBD) comprising two zinc-fingers, and a putative ligand-binding domain (LBD) [19]. The fusion transcripts all encode the NTD and the first zinc-finger of the DBD, but lack both the second zinc-finger and the LBD. Two of the three fusion transcripts preserve reading frame across the junction (E2-C3 and E2-C5); both share sequences encoding the 35 C-terminal amino acids of the predicted C11orf20 protein, including a basic potential nuclear-localization signal (Figure 1D). The E2-C4 junction introduces a frameshift, resulting in a nonsense codon shortly after the junction (Figure 1B). All fusion-positive tumors we have identified expressed at least one of the in-frame isoforms.
In principle, the ESRRA-C11orf20 fusion transcripts could have resulted from: (1) an acquired or germline rearrangement of the C11orf20-ESRRA region of Chromosome 11 deviating from that in reported human reference genomes or reported variants (to our knowledge, no germline structural variant rearranging ESRRA and C11orf20's relative positions has been reported, including in the 1000 genomes project.), or (2) trans-splicing of ESRRA and C11orf20 transcripts from an unrearranged locus. To discriminate these possibilities, we used a hybridization-selection and UHTS strategy to deeply sequence the C11orf20-ESRRA genomic region in two tumors that were positive for the fusion transcripts E2-C3 and E2-C4. A matched normal PBMC sample was available for one tumor. We note in passing that all analyses presented here used original genomic DNA for library generation; initial attempts using phi29-amplified DNA gave apparently unreliable results.
Paired-end sequencing libraries were prepared from the three samples (a tumor/normal matched pair and one tumor lacking a matched normal). A 166 kb bacterial artificial chromosome (BAC) was used to enrich for reads in the ESRRA locus. The resulting enriched libraries were each sequenced in one lane of an Illumina GA IIx flowcell at an average sequencing depth of 8 million mapped 80 bp PE reads. The sequenced libraries all showed significant inhomogeneity in sequencing depth across the targeted interval (see Text S1); however, the inhomogeneous coverage was consistent between samples, allowing us to model copy number variation in the sequenced tumors by comparison to the normal PBMC sample.
Our genomic analysis strategy is summarized as follows and detailed in Text S1. Paired ends uniquely mapping to a 20 kb radius of ESRRA, whose joint chromosomal coordinates and orientations inconsistent with the reference genome were flagged. In Tumor 1, a statistically significant “pile” of PE reads predicted that ESRRA intron 2 had been rearranged upstream of C11orf20 exon 3. This hypothesis was tested using PCR to amplify the predicted rearrangement (PCR1, Figure 2A) and a computational method described below, both of which confirmed the expected breakpoint (sequence in Text S1).
Because the breakpoint in Tumor 1 is flanked by a SINE repeat both upstream in ESRRA and downstream in C11orf20, we performed additional PCRs using primers external to those in the first PCR, in parallel, on Tumor 1 DNA and negative control normal DNA, to rule out an in vitro PCR artifact. Each of these (PCR2, PCR3) produced a tumor-specific band of expected size, and the sequenced products showed the identical breakpoint.
In parallel with PCR confirmation, an unbiased computational approach using the de novo assembly program Velvet [25],[26] was used as a discovery tool (“orphan-end assembly”). Briefly, for each 200 bp window in the reference genome, all PE reads where one side aligned the reference in this window and the other side failed an alignment to the reference were flagged. The reads failing alignment were assembled using Velvet, and screened to determine if they supported a rearrangement placing ESRRA upstream of C11orf20. The breakpoint sequence discovered with PCR was also found using this computational method, and no other breakpoint providing a parsimonious explanation for an ESRRA-C11orf20 fusion was discovered in Tumor 1 or the other tumor (see Text S1). Furthermore, while Illumina library reads from Tumor 1 tiled the breakpoint, no Illumina sequence reads from any other library aligned to it.
Finally, copy number analysis of Tumor 1 (Figure 2B) shows a relative copy number increase precisely in the region between the reference coordinates defining the breakpoint (and nowhere else in the targeted region, analysis not shown). The simplest model to account for the junctional sequence and copy number data for Tumor 1 is that a tandem duplication of an interval between C11orf20 and ESRRA is present in one of two diploid copies of chromosome 1, as depicted in Figure 2A. Thus, sequence analysis provides strong evidence that the ESRRA-C11orf20 fusions in Tumor 1 are transcriptional products of a genomic rearrangement that positions ESRRA upstream of C11orf20 (rather than trans-splicing).
Tumor 2 shows significant copy number variation in the C11orf20 and ESRRA locus (Figure 2B), as well as a large degree of copy number variation throughout the region enriched by the BAC (analysis not shown). Although this is evidence for a genomic rearrangement in Tumor 2 in the critical region, we have not been able to pinpoint a breakpoint sequence with UHTS analysis for anomalously mapping read-pairs and orphan-end assembly, nor by long-range genomic PCR. Several types of rearrangements might not be detected by our short-read detection approach: for example, a complex rearrangement including ectopic sequence that does not hybridize to the BAC or a rearrangement within a region of ESRRA and C11orf20 that cannot be uniquely assigned to either gene. A substantial fraction of this region falls in a “blind spot” of this method: 10% of 80-mers in ESRRA (1,008 of 10,078) and 7% in C11orf20 (378 of 4,962) have more than 10 matches to the human genome (hg19 build).
We used UHTS analysis of RNA from a pool of tumor samples in a deliberate search for a recurrent gene fusion in serous ovarian cancer, a deadly cancer for which there is currently no early-detection screen and in which no recurrent gene fusions had been identified. Systematic analysis of the sequence data revealed novel fusion transcripts combining 5′ exons from ESRRA, a gene encoding a transcription factor of the nuclear hormone receptor family, and 3′ exons from C11orf20, an uncharacterized but phylogenetically conserved gene immediately upstream of ESRRA on Chromosome 11. In an RT-PCR/Sanger sequencing survey of serous ovarian cancers at two different institutions, we confirmed ESRRA-C11orf20 fusion transcripts in 10 of the 67 tumors, or 15% (95% confidence interval: 7% to 26%), suggesting that this fusion is present in a significant fraction of serous ovarian cancers.
To test the hypothesis that the ESRRA-C11orf20 fusion was the result of a genomic rearrangement, we combined hybridization selection of the C11orf20-ESRRA genomic region of Chromosome 11 with UHTS to analyze the structure of this interval in two tumors. The results provide strong evidence that a fusion transcript arose from a genomic rearrangement of the C11orf20-ESRRA region of Chromosome 11 in one tumor and copy-number variation evidence of rearrangement in the second tumor.
The ESSRA-C11orf20 fusion is, to our knowledge, the first recurrent gene fusion to be identified in serous ovarian cancer. This fusion gene and its components are now high-priority targets for further investigation of their potential roles in pathogenesis and as potential diagnostic or therapeutic targets. Our findings cast a spotlight on ESRRA as a candidate oncogene in serous ovarian cancer. ESRRA has been most studied in the context of breast cancer: it is a negative prognostic marker in ER(–) tumors [14],[15], and it induces VEGF mRNA expression and contributes to the malignant phenotype of a breast cancer cell line [16],[17]. It has been less studied in ovarian cancer, but has recently been associated with increased risk of ovarian cancer [22] and decreased patient survival [20].
Two of the three fusion isoforms we observed, E2-C3 and E2-C5, are in-frame and predicted to encode fusion proteins that contain the N-terminal portion of the ESRRA protein and the C-terminal portion of the predicted C11orf20 protein. Although one of the two zinc-finger domains and the putative ligand-binding domain of ESRRA are absent from the predicted fusion protein, important functional features of ESRRA are retained, including the first zinc-finger domain, critical for the DNA sequence specificity of ESRRA, as well as a phosphorylation site (Ser 19) and a phosphorylation-dependent sumoylation site (Lys 14) that have been shown to regulate transcriptional activation by ESRRA [19]. C11orf20 is a largely uncharacterized gene, with expression reportedly restricted to testis in mouse (RIKEN cDNA 1700019N12; [27]) and human (http://biogps.gnf.org). The predicted protein product of C11orf20 is conserved in mammals but uncharacterized; it lacks any known functional domains and has no apparent homology to any protein of known function.
Although any functional role for the ESSRA-C11orf20 fusion remains to be established, fusions to other nuclear hormone receptor transcription factors have been found in other cancers, including PAX8–PPARG in follicular thyroid tumors [28], EWSR1-NR4A3 in extraskeletal myxoid chondrosarcomas [29], and PML-RARA in acute promyelocytic leukemia [30]. In those fusions the nuclear receptor portion comprises the C-terminal component of the fusion protein and contains the entire DNA-binding and ligand-binding domains, whereas in the fusions reported here, the ESRRA component is N-terminal and contains only the first half of the DNA-binding domain (P-box zinc finger). Single zinc fingers, however, can mediate DNA-binding, for example in GATA-1 and SUPERMAN; in these known examples, adjacent basic regions are also required [31],[32]. It is therefore noteworthy that the in-frame fusions we identified join the ESRRA P-box zing finger to a basic sequence in the C11orf20 C-terminus (Figure 1).
We have presented evidence that a tumor-specific ESRRA-C11orf20 fusion transcript is present in a substantial fraction of serous ovarian cancers and that in one of two profiled tumors, Tumor 1, a genomic rearrangement that can account for the fusion transcript is present in a majority of tumor cells. Copy number variation at the ESRRA locus also suggests a structural rearrangement in Tumor 2. Although it remains possible that the ESRRA-C11orf20 fusion is an incidental consequence of another, functionally important, genetic event or that it is merely a “passenger,” the apparent frequency with which this rearrangement occurs in serous ovarian cancer and the lack of evidence that it accompanies large-scale structural variation (such as gene amplification) are more suggestive of a direct role.
Several characteristics of the ESRRA-C11orf20 rearrangement reinforce themes emerging from high-resolution studies of both normal human genetic variation [33],[34] and cancer-specific genomic alterations. Indeed, although none were found to be recurrent, 4 of the 11 gene fusions identified in a previous UHTS survey of RNA from a series of melanomas were strikingly similar to the ESRRA-C11orf20 fusion; adjacent genes in the same transcriptional orientation were rearranged to yield a fusion transcript in which the order of the two participating genes was reversed [10]. In a second study, using UHTS to profile genomic rearrangements in 24 breast cancers, the overwhelming majority of rearrangements identified were intrachromosomal; more than 90% of these involved breakpoints separated by 2 Mb or less [12]. These rearrangements, like the ESRRA-C11orf20 rearrangement described here, are consistent with a model in which double-strand breaks are preferentially repaired by joining sequences in physical proximity [35]–[37]. Most such fine-scale genomic rearrangements, including the ESRRA-C11orf20 rearrangement, cannot be detected by traditional cytogenetic methods, nor, unless they lead to extensive copy-number alterations, by array CGH. “Exome sequencing” will generally fail to detect any chromosomal rearrangement, except for the rare cases in which a breakpoint falls within an exon. A very recent large integrated genomics survey indeed found that high-grade serous ovarian carcinoma is characterized by a higher degree of somatic copy-number alterations and lower degree of somatic point mutations than the previously surveyed cancer glioblastoma [38]; however, the methods employed were unlikely to (and did not) identify the rearrangement presented here. We were able to detect the ESRRA-C11orf20 fusion, based on UHTS analysis of either RNA or genomic DNA, only by conducting a deliberate focused search for evidence of structural rearrangements. We suggest that chromosomal rearrangements involving nearby or adjacent genes may comprise a substantial fraction of oncogenic mutations that have heretofore escaped detection.
Ovarian cancer samples and matched controls were collected following procedures approved by the IRB at each institution: from the Pacific Ovarian Cancer Research Consortium (POCRC) and Fred Hutchinson Cancer Research Center (FHCRC), and from the British Columbia Cancer Agency (BCCA) Tumour Tissue Repository, Victoria, BC, a member of the Canadian Tumour Repository Network. Samples were (1) collected at initial debulking surgery using standardized protocols and (2) reviewed by a gynecological research pathologist to confirm the histological characteristics of the tissue; all tumor samples used in this article contained at least 70% malignant epithelium. Clinical data for RT-PCR screened samples are shown in Table S1.
RNA was pooled from 12 high grade serous stage III/IV carcinoma of the ovary samples together with doping control RNA (see Text S1). 10 micrograms total RNA was diluted with water to 50 microliters, heated to 70 °C for 5 min, and purified with DYNAL DynaBeads Oligo (dT)25 (Invitrogen, Carlsbad, CA, USA) per manufacturer protocol. RNA was fragmented to an average size of 350 bp by alkaline hydrolysis: RNA was added to preheated fragmentation buffer (50 mM sodium carbonate/bicarbonate, 1 mM EDTA, pH 9.2) and incubated at 95 °C for 6 min, then neutralized with 1/10 volume of 3 M sodium acetate pH 5.2, and precipitated with 3 volumes ice-cold EtOH. The pellet was washed with 75% EtOH, dried, and resuspended in water.
First and second strand cDNA synthesis, end repair, 3′-dA tail addition, and paired-end adaptor ligation were performed using standard protocols and reagents from the PAIRED-END Sample Prep Kit (part # 1001809, Illumina, San Diego, CA, USA). cDNA products were resolved by electrophoresis in 2% low-melting agarose gels, one sample per gel. The gels were stained with SYBR Gold (Invitrogen) and visualized on a blue light table (Dark Reader, Clare Chemical Research, Dolores, CO). The desired band was excised with sterile scalpels and purified with a QIAquick Gel Extraction kit with the modifications described in [39] to minimize GC-bias. Each sample was amplified with Phusion DNA Polymerase and Illumina primers PE 1.0 and PE 2.0 for 15 cycles, then purified with a QIAquick PCR purification kit per Illumina library preparation protocol.
The concentration of each sample was determined using an Agilent Bioanalyzer. Samples were then diluted to a concentration of 10 pM as specified by Illumina protocols. The sample derived from pooled tumor RNA was subjected to 76-base, paired-end sequencing in two lanes of an Illumina Genome Analyzer II and, in a separate run, 7 lanes of 38-base paired-end sequencing. Sequencing runs all used the Illumina Sequencing Kit v3-36 reagents. Sequencing data from this study are available on the SRA through dbGaP.
As seen in Figure S1, reads from two 76-base, paired-end lanes and seven 38-base, paired-end lanes were passed through the Illumina PF filter and aligned using Bowtie [40] to the hg19 RefSeq transcriptome as paired-end reads. Those paired ends that successfully aligned were put aside as they do not represent novel fusion events. The paired-end sequences that failed this alignment were then subjected to alignments of each end separately with a more stringent requirement for unique alignment within the RefSeq transcriptome. Paired reads, of which side 1 mapped uniquely to a RefSeq annotated gene (gene A) and side 2 mapped uniquely to a different RefSeq annotated gene (gene B), were taken as indirect evidence of a fusion between gene A and gene B. A FASTA file of all exon-exon junctions between gene A and gene B was generated; reads that failed to align to the reference transcriptome were aligned to this FASTA file of exon-exon junctions. 76-mer reads that aligned to a junction between genes A and B, including at least 10 bp overlap on each side of the junction, and whose mate mapped to either gene A or gene B, were subjected to further analysis.
cDNA was prepared with SuperScript III First-Strand Synthesis kit, PCR amplifications were performed with Platinum Taq DNA Polymerase, and products were cloned with TOPO TA Cloning kits, all from Invitrogen (Carlsbad, CA, USA).
For initial RT-PCR validation in the RNA pool, we used primers G1P1-FWD = 5′-GGCATTGAGCCTCTCTACATCA-3′ (ESRRA exon 2) and G2P1-REV = 5′-TCGATGTATCGCTGCAGCTCCTTA-3′ (C11orf20 exon 5). PCR was run for 40 cycles; each cycle was 94°C 15 s, 55°C 30 s, 70°C 60 s.
For screening of fusion transcript prevalence, we used a nested RT-PCR for increased specificity. For each sample, we performed up to 6 technical replicates, and only considered positive if a majority of replicates gave a fusion product. The outer primers were G1P1-FWD = 5′-GGCATTGAGCCTCTCTACATCA-3′ (ESRRA) and REV_pair3 = 5′-GGGTCAGGCTTGGGTCTG-3′ (C11orf20); the inner primers were G1P2-FWD = 5′-AAAGGGTTCCTCGGAGACAGAGA-3′ (ESRRA) and F1-REV = 5′-TAATTCACGTACAGCCTCTTGCTCCG-3′ (C11orf20). The outer PCR was run for 20 cycles, then diluted 1/200 into inner PCR mix, and run for 30 cycles; each cycle was 94°C 15 s, 55°C 30 s, 72°C 60 s.
Tissue samples were obtained from two FHCRC patients whose tumor samples expressed the ESRRA-C11ORF20 fusion transcript (one tumor lacked a matched normal). The samples were processed using TRIZOL (Invitrogen) to extract RNA and genomic DNA.
The DNA samples were sheared to an intended size of 400 bp in Covaris sample tubes (part # 500111; Covaris, Inc., Woburn, MA, USA) in a Covaris S2 controlled by SonoLab software, using settings of 10% duty cycle, intensity 4, 200 cycles per burst, for two 30-s periods.
We generally followed the Illumina protocol for hybridization enrichment libraries, using Herculase II Fusion Enzyme (Agilent, Santa Clara, CA, USA) for PCR amplification. Samples were purified between steps using Agencourt AMPure SPRI XP beads (Beckman Coulter, Brea, CA, USA).
Hybrid-selection of the Illumina genomic libraries was based on [41]–[43]. A fully sequenced BAC, RP11-783K16 (GenBank # AP001453) encompassing the C11orf20-ESRRA region, was obtained from BACPAC Resource Center (Oakland, CA). BAC DNA was biotinylated using a nick-translation kit (Roche Applied Science, Indianapolis, IN). Illumina library (0.8 micrograms) was hybridized at 65 °C for >24 h to biotinylated BAC DNA (0.2 micrograms) in a solution containing: Cot-1 DNA (4 micrograms), sheared E. coli DNA (1 microgram), sheared vector DNA (0.5 micrograms), four adaptor-blocking oligos ([43]; 600 pmoles each), in 5× SSPE, 5 mM EDTA, 5× Denhardt's, 0.1% SDS (total volume 24 microliters). The genomic library DNA that hybridized to the BAC probe was captured on streptavidin-magnetic beads (Dynal MyOne, Invitrogen), which were then washed and eluted with 0.1 M NaOH. The eluate was precipitated and resuspended in 60 microliters of water. 20 microliters of the resulting solution of hybridization-selected genomic library DNA was amplified with Illumina PCR primers for 18 cycles (within the exponential amplification range), yielding ∼1 microgram of product. Each hybridization-selected genomic DNA library was sequenced on a separate lane of an Illumina GAIIx flow cell.
We identified read-pairs in the selected region where the distance between the paired sequences in the reference genome was greater than 1 kb—inconsistent with library insert sizes (which were <0.8 kb). The C11ORF20-ESRRA genomic region was divided into bins. The counts of anomalous read-pairs were compiled in a 2-dimensional histogram where each axis represented the genomic coordinate (bin) of one end of the read-pair, with read 1 aligning in the (+) orientation and read2 aligning in the (−) orientation. This was done for each sample, both tumors and normals. Pile-ups were nominated for further consideration at a given false discovery rate using a Poisson model for PE reads that takes into account position-specific bias. This model and subsequent analysis is detailed in Text S1.
The following computational approach was implemented to discover highly represented sequences inconsistent with the reference. A 20 kb radius around the transcriptional start of ESRRA was discretized into 200 bp bins. For each bin, reads where one read aligned to the plus strand and the other read failed to align to the human genome hg19 build were flagged, and the unaligned reads were consolidated and input to the de novo assembler Velvet. The same procedure was followed for reads where one read aligned to the minus strand. Velvet outputs contigs: putative sequences assembled from input reads. These contigs were subjected to further analysis by computationally fragmenting each contig to tiling 80-mers and aligning these 80-mers to the genome. In order to narrow our search to tumor-specific rearrangements, only contigs with portions that failed to align to the reference genome were scrutinized. Contigs which had sample-specific representation in the sequencing data (i.e., present in one tumor, and none of the remaining samples, or present in the normal sample of one individual and none of the remaining samples) were further scrutinized. The only such sequence with the potential to directly explain a genomic configuration capable of generating the fusion transcript was found in Tumor 1 and confirmed by PCR (see Text S1). Sequencing data from this study are available on the SRA through dbGaP.
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10.1371/journal.pbio.1001460 | The Cell Cycle Timing of Centromeric Chromatin Assembly in Drosophila Meiosis Is Distinct from Mitosis Yet Requires CAL1 and CENP-C | CENP-A (CID in flies) is the histone H3 variant essential for centromere specification, kinetochore formation, and chromosome segregation during cell division. Recent studies have elucidated major cell cycle mechanisms and factors critical for CENP-A incorporation in mitosis, predominantly in cultured cells. However, we do not understand the roles, regulation, and cell cycle timing of CENP-A assembly in somatic tissues in multicellular organisms and in meiosis, the specialized cell division cycle that gives rise to haploid gametes. Here we investigate the timing and requirements for CID assembly in mitotic tissues and male and female meiosis in Drosophila melanogaster, using fixed and live imaging combined with genetic approaches. We find that CID assembly initiates at late telophase and continues during G1 phase in somatic tissues in the organism, later than the metaphase assembly observed in cultured cells. Furthermore, CID assembly occurs at two distinct cell cycle phases during male meiosis: prophase of meiosis I and after exit from meiosis II, in spermatids. CID assembly in prophase I is also conserved in female meiosis. Interestingly, we observe a novel decrease in CID levels after the end of meiosis I and before meiosis II, which correlates temporally with changes in kinetochore organization and orientation. We also demonstrate that CID is retained on mature sperm despite the gross chromatin remodeling that occurs during protamine exchange. Finally, we show that the centromere proteins CAL1 and CENP-C are both required for CID assembly in meiosis and normal progression through spermatogenesis. We conclude that the cell cycle timing of CID assembly in meiosis is different from mitosis and that the efficient propagation of CID through meiotic divisions and on sperm is likely to be important for centromere specification in the developing zygote.
| Centromeres are regions of eukaryotic chromosomes that recruit the kinetochores and are essential for faithful segregation of DNA during all cell divisions. The centromere-specific histone H3 variant CENP-A accumulates at the centromere, defining this region, and is maintained throughout cellular generations by epigenetic mechanisms in most eukaryotes. Previous studies have discovered many factors regulating both the maintenance and assembly of CENP-A at centromeres during mitosis in cultured cells, but the mode of regulation of CENP-A assembly during meiosis and mitosis in animal tissues is unknown. In this study, we use Drosophila melanogaster as an organismal model to investigate the timing and requirements for assembly of CID, the fly CENP-A homolog. We find that that CID is loaded at centromeres during telophase/G1 phase in brain stem and nonstem cells. In male meiosis, CID is loaded in two phases, during the first stages of meiosis I and after the second meiotic division. Meiosis I loading time is also conserved in females. We also report an unprecedented drop in CID levels after meiosis I and before meiosis II, which correlates with the timing of kinetochore reorientation. Additionally, we find that two essential centromere proteins (CAL1 and CENP-C) are necessary for CID assembly and chromosome segregation during meiosis. Our data demonstrate novel differential timing for CENP-A assembly during mitosis and meiosis in the whole organism.
| Centromeres are key regions of eukaryotic chromosomes that ensure proper chromosome segregation during cell divisions. In most eukaryotes, centromere identity is defined epigenetically by the presence of a centromere-specific histone H3 variant CENP-A (CID in flies, CENH3 in some organisms) [1]. Improper regulation of CENP-A assembly leads to aberrant segregation of chromosomes, aneuploidy, and cell death [2]–[5]. Relevance to human disease comes from observations that CENP-A is overexpressed and can misincorporate throughout chromatin in human cancers [6],[7], that most human cancers display severe aneuploidy [8], and that CID overexpression results in formation of ectopic centromeres and aneuploidy [3],[4].
Centromere propagation requires assembly of new chromatin components after they are diluted 2-fold by DNA replication and segregation of preexisting nucleosomes to sister centromeres. In recent years, great insight into how centromeres are reproducibly propagated during the mitotic cell cycle has emerged from studies investigating the cell cycle timing of CENP-A assembly [9]. A common theme has emerged for multicellular eukaryotes; unlike canonical histones, which are assembled concurrently with DNA replication, CENP-A nucleosome deposition occurs after centromeric DNA replication, during mitosis or G1 phase. In human tissue culture cells and Xenopus egg extracts, CENP-A assembly occurs during late telophase/early G1 phase [10]–[12]. In Drosophila, CID is assembled at metaphase in tissue culture cells [13] and anaphase in embryonic syncytial divisions [14]. Interestingly, anaphase loading was not observed in late embryonic stages in flies, and the exact timing of CID assembly during these or later developmental stages is unknown [14]. Thus, the timing of CENP-A assembly, and likely its regulation, differs between organisms, as well as developmental stages in the same organism. Indeed, aside from investigations in single cell eukaryotes, cells in culture, and unusual syncytial divisions (featuring rapid S and M phases with no gap phases), the cell cycle timing of CENP-A assembly in somatic mitotic tissues in animals has not yet been determined.
Additional biochemical and genetic approaches in single cell eukaryotes or cultured cells have identified many proteins critical for CENP-A assembly in mitosis. In humans, CENP-A deposition is mediated by its chaperone and assembly factor HJURP [15]–[18], while the HJURP homolog Scm3 performs these functions in yeasts [19]–[23]. In Drosophila tissue culture cells and embryos, the putative HJURP functional homolog CAL1 and the constitutive centromere component CENP-C are both required for CID localization at centromeres, and CAL1, CENP-C, and CID co-immunoprecipitate in vivo [13],[24]–[26]. Moreover, CAL1 has distinct binding domains for both CID and CENP-C, and its low levels prevent excess CID incorporation at mitotic centromeres [25]. There is also accumulating evidence that CENP-A assembly is tightly coupled to mitotic cell cycle activities, including activation of the Anaphase Promoting Complex/Cyclosome (APC/C), degradation of the mitotic regulator Cyclin A (CycA) in flies [13],[24], and inhibition of cyclin-dependent kinase (CDK) activities in mammalian cell lines [27]. However, the precise mechanisms and targets of cell cycle control of centromere assembly remain to be elucidated.
In contrast to mitosis, the functional requirements, regulation, and timing of CENP-A assembly in the specialized meiotic divisions that occur during gametogenesis are largely unknown. Meiosis produces haploid gametes (eggs and sperm) and encompasses two distinct types of chromosome segregation. In meiosis I, sister chromatids attach to a common kinetochore and mono-orient, segregating homologous chromosomes, while in meiosis II, sister chromatids bi-orient and segregate equationally, similar to mitosis. In C. elegans, normal levels of CENP-A are not required for meiosis, and CENP-A is removed from chromosomes during female meiosis II [28] and is also absent from mature sperm [29]. CENP-A is required for proper meiotic segregation in Arabidopsis, but meiosis-specific factors appear to facilitate CENP-A assembly [30],[31]. Thus, CENP-A assembly and propagation appear to be differentially regulated in mitosis and meiosis, both within an organism and between different species. Furthermore, in most eukaryotes, CENP-A is one of the few histones retained on mature sperm [32]–[35]. Presumably, marking the site of CENP-A assembly on paternal chromosomes is crucial for centromere inheritance and propagation in early embryonic divisions.
Here we investigate the cell cycle timing and regulation of CID assembly in animal tissues, specifically Drosophila melanogaster larval brains and male and female meiosis. We find that new CID is assembled at centromeres in late telophase and continues into early G1 phase in somatic mitoses, later than observed in early embryos (anaphase) and cultured cells (metaphase) [13],[14]. In meiosis, CID is assembled at two cell cycle phases: prophase of meiosis I and after exit from meiosis II, in spermatids. We also observe an unprecedented decrease in CID levels between the end of meiosis I and the beginning of meiosis II. Additionally, we show that CID assembly in meiosis requires CAL1 and CENP-C. We conclude that the cell cycle timing and dynamics of CID assembly in meiosis are different from mitosis and also differ between mitotic cells in culture and in the animal.
Current insights into the cell cycle timing of CENP-A assembly have come from experiments in tissue culture cells, single cell eukaryotes, or the unusual syncytial divisions in embryos (S and M phases with no gap phases). To elucidate the timing of CENP-A assembly in mitotic cells in animal somatic tissues, we stained dividing cells in larval brains with anti-CID antibody and measured total CID intensity at centromeres using custom software (see Materials and Methods). In brain nonstem cells, we found that levels of CID per cell are relatively constant throughout interphase, prophase, and metaphase; are reduced by half at anaphase; increase in intensity beginning at late telophase/early G1 phase (Figure 1A and 1B); and have doubled by early S phase (Figure S1A). Total CID intensity measured at early G1 phase was less than observed in interphase, implying that loading continues through G1, as previously reported in human cell lines [36],[37].
To exclude the possibility that changes in CID intensity were due to differences in antibody staining or penetration at different cell cycle phases, we analyzed CID assembly using live imaging of larval brains expressing GFP-CID and the chromatin marker H2Av-RFP (Figure 1C and Movie S1) [14]. Using custom software (see Materials and Methods), we determined that total centromeric GFP-CID fluorescence intensity increases in daughter nuclei at telophase (approximately 6 to 12 min after anaphase onset) and continues during early G1 phase (Figure 1D). Notably, GFP-CID intensity increases by approximately 20% at late telophase and by 50% at 36 min past anaphase. Together, the fixed and live analyses of brain nonstem cells demonstrate that CID assembly initiates in telophase and continues in G1 phase, until centromeric CID levels double, replenishing the 2-fold CID dilution that occurs during DNA replication in S phase.
We also analyzed CID assembly in larval brain neuroblasts, large stem cells that undergo asymmetric divisions within a morphologically distinct circular niche (Figure S1B and Movie S2). Similar to brain nonstem cells, we observed that CID assembly occurs at telophase/early G1 phase in both the self-renewing mother stem cell and the daughter cell that later differentiates into a neuron. Interestingly, in five out of five movies analyzed, the initiation of CID assembly in the stem cell (3 min after anaphase onset, approximately 6 min earlier than in brain nonstem cells) precedes CID assembly in the daughter cell (9 min after anaphase onset, approximately the same time as in brain nonstem cells) (Figure S1B and Movie S2). Differential CID loading in neuroblasts was confirmed in fixed larval brains immunostained for CID (Figure S1C), where the mother and daughter cells in telophase displayed different CID levels.
We conclude that CID assembly in larval brain nonstem and stem cells begins during telophase and continues in G1 phase. This cell cycle assembly timing is similar to that reported for human tissue culture cells [10] and in Xenopus egg extracts [11],[12] but occurs later than observed in fly tissue culture cells (metaphase) and in embryos (anaphase) [13],[14].
The cell cycle timing of CID assembly in meiosis is currently unknown and may differ from mitosis. The stages of male spermatogenesis encompass meiosis I, II, and subsequent differentiation steps that give rise to mature sperm (Figure 2A) [38]. We stained wild-type fixed late larval/prepupal testes with anti-CID antibody and quantified total centromeric CID fluorescence intensity per nucleus during these meiotic cell cycle stages using custom software (see Materials and Methods). We first focused our analysis on primary spermatocytes in 16 cell cysts that enter prophase of meiosis I; this is a developmentally specialized G2 phase that lasts for up to 90 hours, and is accompanied by a substantial increase in nuclear volume, followed by chromatin condensation at prometaphase I [38]. We observed a gradual increase in CID intensity from S1, S4, S5, and S6 stages up until late prophase/early prometaphase of meiosis I (M1a–b) (Figure 2B and 2C), indicating that CID assembly occurs over an extended period during prophase I. Surprisingly, we noted an approximate 4-fold increase in CID intensity during prophase I, larger than the predicted 2-fold increase expected to offset CID dilution during premeiotic S phase. We confirmed the gradual assembly of CID in prophase I by live imaging and quantification of GFP-CID intensity in primary spermatocytes expressing H2Av-RFP (Figure 2D and 2E). Consistent with results in fixed cells, we observed a gradual, greater than 2-fold increase in GFP-CID intensity at centromeres between stage S1 and early prometaphase of meiosis I (M1b) (Figure 2E). From time lapse imaging of cells in early prometaphase I, we observed one of the final CID assembly events (∼10% increase in GFP-CID intensity) in meiosis I, occurring in a relatively short, 10-min time window, approximately 40 min before condensed bivalents congress to the metaphase plate at prometaphase (Figure 2F and Movie S3).
Importantly, we did not detect any CID assembly after completion of meiosis I in fixed cells (compare stages M1a–M1b and M4a–M4b, Figure 2C) and further confirmed this result with live imaging (Figure 2D, Figure S2, and Movie S4). Surprisingly, in fixed and live cells we observe that CID intensity at stages M4a–M4b dropped by more than half of the amount present at stages M1a–M1b, indicating loss of centromeric CID after completion of meiosis I (Figure 2C and 2E). This decrease in CID levels in the absence of DNA replication is novel; CENP-A levels at centromeres have only been observed to decrease in response to replication and nucleosome segregation in S phase [10],[39]. At stages M4a–M4b, we were unable to detect distinct cell populations with high CID levels in either fixed or live cells, suggesting that the additional loss of CID after the first meiotic division occurs quickly after telophase.
Finally, we investigated CID assembly dynamics in female meiosis in ovaries fixed and stained for CID, using the synaptonemal complex marker C(3)G to identify the oocyte nucleus (Figure 3A) [40]. Quantification of total centromeric CID intensity in oocyte nuclei revealed a 2-fold increase in CID intensity from cystoblasts to stage 8/9 of egg chamber development (Figure 3B). Thus, CID assembly occurs during the pachytene and diplotene stages of prophase I, which last approximately 3 days [41]. Due to reduced antibody penetration at later stages of oocyte development, we were unable to assess whether CID loading continues during later stages of prophase I and beyond.
We conclude that CID assembly in Drosophila male and female meiosis I occurs during prophase and, surprisingly, that loading is gradual and occurs over a period of days. Importantly, this temporal pattern is conserved despite significant differences between male and female meiosis I prophase; although homolog pairing occurs in both, synapsis and recombination only occur in females.
We next investigated CID assembly dynamics during male meiosis II and subsequent stages of sperm differentiation (Figure 4). In fixed samples, we did not detect any increase in CID intensity between metaphase (stages M7–M9) and telophase (stages M10–M11) of meiosis II; instead, total centromeric CID intensity per nucleus drops by half, as expected due to the segregation of sister chromatids. We observed that total CID intensity increases gradually beginning in T1–T2 spermatid nuclei, after exit from meiosis II, reaching almost a 2-fold increase in spermatids that have initiated differentiation into spermatozoa (T5+ stages) (Figure 4A and 4B; fixed cells from the same experiment presented in Figure 2). Live imaging confirmed that CID levels increase between telophase II and T4 spermatids (Figure 4C and 4D). Although the exact length of stages T1–T5 is not known, it likely occurs over hours to days, because the entire process of spermatid differentiation to mature spermatozoa takes ∼6 days [42]. Thus, similar to observations in prophase I, CID assembly in spermatids is gradual and occurs over an extended time period.
We next investigated if CID is retained on spermatids after gross histone removal in preparation for protamine exchange (Figure 4E and 4F). We observed in adult testes that CID is present at the late canoe stage (after histone removal), consistent with a previous report [35], and that levels are comparable to levels in spermatids at an earlier stage when histones are still present. Furthermore, CID levels after gross histone removal are comparable to levels in S1 stage primary spermatocytes (Figure 4F). To investigate whether CID is retained at even later stages, in mature sperm, which contain highly condensed chromatin that is inaccessible to antibody staining, we fixed and imaged adult testes from transgenic flies expressing GFP-CID. Mature spermatozoa contain four GFP-CID spots that were clearly visible and spaced along the length of the nucleus (Figure 4G). We conclude that CID is retained at centromeres in mature spermatozoa in adults.
From our fixed and live analyses, we conclude that after premeiotic S phase there are two phases of CID assembly during male meiosis: first, during prophase of meiosis I, and second, beginning in T1 spermatids after exit from meiosis II (summarized in Figure 5). Our results also demonstrate that CID levels increase by more than 2-fold in prophase I and are surprisingly reduced by greater than half after the first meiotic division and before the onset of meiosis II. Taken together, the amount of CID in haploid spermatids (T5+) is similar to the amount of CID per nucleus at the beginning of meiosis (stage S1) (compare Figures 2C and 4B, showing quantifications from the same experiment, both normalized to the S1 intensity value). Finally, analysis of adult testes reveals that CID levels on haploid mature sperm are also comparable to levels at the S1 stage, before the meiotic divisions.
Both CAL1 and CENP-C are required for CID maintenance and assembly in mitotic cells in flies and cultured cells [24]–[26], but their presence, localization, and function in meiosis are unknown. We stained larval testes from a transgenic fly line expressing GFP-CAL1 [25] with anti-GFP and anti-CID antibodies and observed that GFP-CAL1 localized at centromeres, and also the nucleolus, in cells at the S3 stage of prophase I (Figure 6A) and earlier stages in the germinal proliferation center (Figure S3A). Surprisingly, GFP-CAL1 foci at centromeres are dramatically reduced by the S5 stage and are almost undetectable in nuclei at late prophase I (M1a). Using live imaging, we observe GFP-CAL1 foci in S1–S3-stage nuclei, but not in cells that have completed meiosis I (stage M5) or II (onion stage spermatids). Note that GFP-CAL1 accumulates in the cytoplasm and the nebenkern mitochondrial derivative, respectively, during these stages (Figure S3A). Furthermore, live imaging of female oocytes revealed that GFP-CAL1 foci are present in cystoblast nuclei but are undetectable in stage 4 oocyte nuclei (Figure S3B).
We next determined if CENP-C is localized at centromeres during meiosis by staining larval testes with anti-CENP-C antibody. Similar to GFP-CAL1, CENP-C is visible as discrete foci that colocalize with CID at the S1 stage (Figure 6B). However, distinct from CAL1, CENP-C is present at centromeres through all stages of meiosis I (stages M1a and M4) and II (stages M7–M11) but is gradually lost from centromeres beginning after telophase of meiosis II (M10–M11). CENP-C loss is coincident with the start of post-meiosis II CID assembly (T1–T2 spermatids) and prior to the continued assembly in stages T4 and later (Figure 6B). We also observed that CENP-C is absent from centromeres on individualizing spermatids in adult testes and is localized to structures peripheral to the nucleus in T4–T5 spermatids, then cleared away from nuclei along the elongating axoneme during later stages of maturation (Figure S4).
We conclude that the centromere proteins CAL1, CENP-C, and CID show differential localization patterns during meiosis. CID is present at centromeres throughout meiosis and is retained on mature sperm (Figure 4). In contrast, CAL1 levels at centromeres are dramatically reduced during prophase of meiosis I, coincident with the time of CID loading, and centromeric CAL1 is not visible after late prophase I through the end of spermatogenesis. Finally, CENP-C is not visible at centromeres after meiosis II, during the second phase of CID loading, and in mature sperm.
Although CAL1, CENP-C, and CID are mutually dependent for centromere localization in both fly cultured cells and embryos [24],[26], the unusual localization patterns observed for CAL1 and CENP-C during meiosis suggested that these proteins may not be essential for CID localization in male meiosis. We depleted CID specifically in larval testes using a UAS-Cid-RNAi line [43] driven by GAL4 under the control of the bam (bag of marbles) promoter (bam-Gal4), which is repressed in germline stem cells and expressed in spermatogonia at the four-cell stage, after completion of two mitotic divisions [44],[45]. In prepupal testes depleted for CID, CID staining was normal in the S1 primary spermatocytes but was dramatically reduced in nuclei at stage S6 of prophase I compared to bam-Gal4 controls (Figure 7A and 7B). Additionally, in cells depleted for CID, CENP-C was delocalized from centromeres and accumulated in the nucleolus (Figure 7A, arrow).
To investigate if CAL1 or CENP-C are required for CID localization in meiosis, UAS-Cal1-RNAi or UAS-Cenp-C-RNAi lines [43] were crossed to lines expressing the bam-Gal4 driver. In prepupal testes depleted for CAL1, centromeric CID levels were normal in S1 primary spermatocytes but were dramatically reduced in nuclei at stage S6 of prophase I, compared to bam-Gal4 control testes (Figure 7A). Thus, CAL 1 is required for CID assembly in prophase of meiosis I. In prepupal testes with RNAi-depleted CID or CAL1, we also observed an elevated frequency of nuclear mis-segregation after the first (stage M6) and second (stages T1–T3) meiotic divisions (Figure 7C and 7D), indicating that CID and CAL1 are required for normal progression through male meiosis. Additionally, CENP-C was present at centromeres in S1 stage cells depleted for CAL1, but in stage S6 of prophase I was significantly reduced at centromeres and accumulated in the nucleolus, as observed in CID-depleted cells (Figure 7A, arrows). These observations in meiotic cells are consistent with previous reports in cultured mitotic cells, which showed that CAL1 is required for both CID and CENP-C localization and that CENP-C accumulates in the nucleolus in the absence of CAL1 [24]–[26]. We conclude that CAL1 is required for centromeric CID assembly and localization of CENP-C in prophase of meiosis I and proper chromosome segregation in both meiotic phases. It is surprising that CAL1 is required for both meiosis I progression and CID/CENP-C prophase loading and maintenance at centromeres, despite being undetectable at these stages (Figure 6).
RNAi depletion of CENP-C in prepupal testes also resulted in reduced CID localization at centromeres in S6 stage cells (although to a lesser extent than the depletion of either CAL1 or CID), indicating a requirement for CENP-C in CID assembly in prophase (Figure 7A and 7B). IF analysis shows that the reduction in CENP-C levels was comparable after CID-, CAL1-, and CENP-C RNAi depletions; this suggests that CAL1 plays a more major role than CENP-C in CID localization in meiosis. Notably, in T1–T3 spermatids depleted for CAL1 or CENP-C, CID levels at centromeres are low and almost undetectable in the case of CAL1 RNAi (Figure 7C), indicating possible roles for CAL1 and CENP-C in the second phase of meiotic CID assembly. In cells depleted for CENP-C, severe defects in chromosome segregation were still observed after meiosis I and meiosis II (Figure 7C and 7D), even though CID still remained at centromeres at levels higher than observed after CID or CAL1 RNAi depletions (Figure 7C), likely due to the additional role of CENP-C in kinetochore assembly and function. Furthermore, depletion of CENP-C in tissues using the MTD-Gal4 driver, which is expressed throughout all stages of spermatogenesis and oogenesis [46], shows that it is required for testes and ovary development, presumably due to its essential role in centromere propagation and kinetochore assembly in mitosis (Figure S5).
We conclude that CAL1 and CENP-C are both required for CID assembly in prophase of meiosis I in Drosophila males and for normal progression through spermatogenesis. Thus, despite differences in CID assembly timing between mitosis and meiosis, and the lack of detectable CAL1 during prophase of meiosis I, the assembly protein requirements for meiosis are similar to mitosis. Further investigations are required to determine if CID assembly in meiosis is more dependent on CAL1 than CENP-C, compared to the equal requirements in mitosis [24].
This study reveals a surprising diversity of CID assembly timing in mitotic and meiotic tissues in the fruit fly Drosophila melanogaster. During mitosis, CID assembly initiates at late telophase and continues during G1 phase in somatic cells of the larval brain. These results are consistent with the timing and dynamics of CENP-A assembly reported for human cell lines [10],[36],[37] and in general, with centromeric histone deposition outside of S phase, during mitosis and G1 phase. Notably, we observed loading in mitosis occurring at a later mitotic stage (telophase/G1 phase) than previously reported for cultured cells (metaphase) or fly embryos (anaphase) [13],[14]. Interestingly, neuroblast stem cells display a subtle difference between cells derived from the same division; the mother cell, which will continue to act as a stem cell, starts CID loading at centromeres 3–6 min earlier than in the daughter cell that is committed to differentiation. It is currently unclear whether this difference in centromere assembly timing is due to differences in the regulation of mitotic exit between stem and daughter cells or is required for or a response to stem cell propagation mechanisms.
We propose that such differences in timing reflect altered cell cycle regulation in cultured cells compared to animal tissues, and our results emphasize the importance of validating cell culture findings in animal models. It is important to note that despite similarities to the timing observed in human cultured cells (late telophase/G1 phase) [10], our results in Drosophila raise questions about whether the analysis of cultured cells in humans and other species reflects the timing of CENP-A assembly in the organism.
Our results also show that the cell cycle timing for CID assembly in meiosis differs from mitosis (Figure 8). In male meiosis, there are two phases of CID assembly, at prophase of meiosis I and after exit from meiosis II, and two phases of chromosome segregation, resulting in haploid spermatids with nuclear CID levels equivalent to those observed at the beginning of meiosis (see Figure 5). In meiosis in Drosophila females, CID assembly also occurs during prophase of meiosis I. Assembly in prophase provides another example of the restriction of CID assembly to a specific part of the cell cycle outside of S phase, but has not been observed previously in mitotic tissues or cultured cells from other organisms. It is also important to note that meiotic prophase in both male and female Drosophila occurs over days, indicating that CID assembly is gradual over this extended time period. Such slow assembly dynamics are unexpected, given that until now studies in mitotic cells indicate that CENP-A assembly is completed in the order of minutes to hours [10],[13],[14],[36],[37]. How CID assembly is first initiated and then continues over such extended time periods awaits further investigation.
It is likely that cell cycle regulators control CID assembly in meiosis as they do in mitosis. For example, a recent study showed that CDK activity inhibits CENP-A assembly in human cells and that blocking CDK activity results in precocious loading in S and G2 phases [27]. Cyclin A is degraded during late prophase of meiosis I [47]. This is consistent with the observed burst in CID assembly during a 10-min time window of late prophase/early prometaphase I, and our previous demonstration that Cyclin A degradation is required for mitotic CID assembly [13]. However, CID assembly also occurs before Cyclin A degradation in meiosis I, implying that other unknown mechanisms initiate and continue assembly prior to late prophase I. Additionally, CID is not loaded between meiosis I and II, even though Cyclin A levels remain low. Instead, the partial degradation of Cyclin B to an intermediate level after meiosis I, which allows for spindle destruction but prevents a second round of DNA synthesis [48], could inhibit CID assembly between meiosis I and II. Moreover, the slow degradation of Cyclin B at the end of meiosis II [49] could contribute to the gradual CID loading in spermatids, as the second phase of CID assembly after meiotic exit is more similar in terms of cell cycle regulation to the telophase/G1 loading observed in mitotic tissues in the animal (this study) and in human cells in culture [10]. However, we also observed that CID assembly occurs in prophase of meiosis I, when Cyclin B levels are high, but does not occur between meiosis I and II, despite low Cyclin A levels. This suggests that CID assembly in meiosis is regulated by other mechanisms in addition to the inhibition of Cyclin/CDK activities, as proposed for mammalian cells [27].
Another striking observation from this study is that during meiosis I, CID assembly occurs prior to chromosome segregation, whereas most mitotic cells previously studied proceed through most of mitosis with half the maximal amount of CID at centromeres [10],[13],[14],[27]. In addition, we observed a greater than 2-fold increase in CID intensity at centromeres during prophase, even though a 2-fold increase would be sufficient to compensate for CID dilution in premeiotic S phase. What is the role, if any, of an increased level of CID at centromeres during the first meiotic division? In meiosis I, bivalent sister chromatid kinetochores are mono-oriented, instead of bi-oriented as they are in mitosis and meiosis II; combined with the maintenance of sister cohesion at centromeres, this ensures that homologs, and not sisters, segregate during meiosis I [48],[50]. We speculate that extra CID may be required during the first meiotic division to assemble or maintain mono-oriented kinetochores and microtubule attachments. This hypothesis could also be extended to incorporate the surprising decrease in CID levels observed between the end of meiosis I and the beginning of meiosis II. Loss of CENP-A during normal cell divisions has only previously been observed as accompanying DNA replication and nucleosome segregation in S phase, events that do not occur between meiosis I and II. Thus, it is tempting to speculate that the additional loss of CID after meiosis I could contribute to the currently unknown mechanism responsible for reorganization of kinetochores from mono- to bi-orientation in preparation for meiosis II.
Using targeted RNAi depletion of centromeric proteins during Drosophila male meiosis, we find that both CAL1 and CENP-C are required for CID assembly in prophase of meiosis I. This is consistent with previous observations in mitotic cells, where CAL1, CENP-C, and CID are mutually dependent on each other for centromere localization [24],[26]. We also find that depletion of CAL1 or CID in larval testes results in CENP-C delocalization from centromeres and sequestration in the nucleolus, again similar to observations in mitosis [24], possibly because it is no longer in a stable complex with CID or CAL1. Our results also show that reduced CAL1 or CENP-C expression results in defective chromosome segregation and that both are required for normal progression through male meiosis. Our finding that T1–T3 spermatids depleted for CAL1 or CENP-C have reduced CID at centromeres (although to a lesser extent in the case of CENP-C depletion) also suggests that CAL1 and CENP-C are required for CID assembly during the second phase of loading in spermatids. However, given that cells with reduced CAL1 or CID already show major chromosome segregation defects after meiosis I, meiosis-specific GAL4 drivers active in later stages of meiosis and spermatogenesis, which are currently lacking [51], are required to directly assay the requirements for CAL1 and CENP-C in the second phase of CID assembly or during fertilization. Requirements for CAL1 and CENP-C in both phases of meiotic CID assembly are surprising, given that centromeric CAL1 levels are greatly reduced during prophase I and at later stages of spermatogenesis and that CENP-C is not localized to centromeres after meiosis II. One intriguing possibility is that CID assembly requires CAL1 and CENP-C removal from centromeres.
Another key observation from our study is the retention of CID at centromeres on mature spermatozoa in spite of an extensive period of chromatin remodeling and histone–protamine exchange during spermatocyte maturation [52],[53]. How CID is protected from histone removal prior to protamine exchange at centromeres remains to be investigated. It is possible that the local chromatin environment at centromeres is refractory to protamine exchange or that additional proteins present at centromeres could provide protection. Because fusion of male and female pronuclei does not occur until telophase of the first zygotic division [54], it is likely that paternal CID at centromeres is required for kinetochore formation and spindle attachment to paternal chromosomes. The amount of paternal CID at centromeres could be critical for the successful epigenetic inheritance of centromere identity and for the viability of the embryo, if paternal CID is diluted during subsequent zygotic divisions. Alternatively, maternal CID could compensate for a reduced level of CID on sperm or establish de novo centromeres on paternal chromosomes. Whatever the mechanism of CID maintenance in the zygote, the regulation of CID assembly on sperm is likely to prove very important in the transmission of epigenetic information and centromere specification into the next generation.
Flies were grown at 25°C on standard medium. Transgenic fly lines expressing GFP-CID and H2Av-RFP were a gift from S. Heidmann [14], and GFP-CAL1 lines were provided by C. Lehner [25]. RNAi lines used were: UAS-CID-RNAi (VDRC #102090), UAS-CAL1-RNAi (VDRC # 45248), and UAS-CENP-C-RNAi (TRiP #34692). The bam-Gal4 (w;; bam-Gal4-VP16, UAS-dcr2) stock was kindly provided by M. Fuller. MTD-Gal4 stock (# 31777) was purchased from the Bloomington Stock Center. The efficiency of RNAi depletion was enhanced by expression of dicer2 (M. Fuller) and propagation at 29°C [43],[55]. y+ry+ flies were used as wild-type for fixed analyses.
Dissection, fixation, and immunostaining of larval and adult testes [38] and oocytes [56] were performed as described previously. Primary antibodies diluted in PBST/FBS were incubated overnight at 4°C. Larval and adult samples were stained with a rabbit anti-CID antibody (Lake Placid, 1∶500), guinea pig anti-CENP-C polyclonal antibody (1∶500) [24], mouse anti-tubulin (Sigma T6199, 1∶100), mouse anti-pan-histone (including histone H1) (Chemicon,1∶150), and mouse anti-GFP (Abcam ab1218, 1∶100). The slides were washed twice for 5 min in PBST and once for 5 min in 1× PBS. All samples were incubated with secondary antibodies (Alexa conjugates from Molecular Probes: goat anti-mouse 546, goat anti-rabbit 488, and goat anti-guinea pig 647) for 1 h at room temperature at a 1∶500 dilution, washed twice for 5 min in PBST, rinsed in 1× PBS, incubated 5 min with 1 µM DAPI in 1× PBS, and washed 5 min in 1× PBS. Prolong Gold antifade reagent (Molecular Probes) was added, and slides were sealed with a coverslip. GFP-CID adult testes were fixed, incubated with DAPI, washed, mounted as described above, and immediately imaged. Ovaries were stained with mouse anti-C(3)G antibody (1∶500) [57] and rabbit anti-CID (1∶200). After overnight incubation at 4°C with primary antibodies, tissues were washed three times for 15 min in PBST. Samples were incubated with secondary antibodies (described above) for 4 h at room temperature, then washed three times for 30 min in PBST, incubated 5 min with 1 µM DAPI in 1× PBS, washed for 5 min in 1× PBS, and mounted on slides as described above. Larval brains were incubated with 5 µM EdU (Invitrogen) for 15 min at room temperature. All images were taken using a DeltaVision Elite microscope system (Applied Precision). A total of 20–30 z sections at 0.2 µM were taken for each image at a constant exposure time. Raw images were deconvolved using SoftWorx (Applied Precision) using conserved ratio, five cycles, and medium noise filtering. Quick projections of images were created in SoftWorx using maximum intensity. Images were uniformly scaled in Photoshop.
Live imaging of larval testes and ovaries was performed based on the methods described in [58]. Larval testes were dissected in Schneider's medium (Invitrogen) supplemented with 200 µg/ml bovine insulin and were placed in a small drop of the same medium on a glass-bottomed dish (P35G-1.0-14-C, MatTeck) containing a wet Kim-wipe for humidification. Testes were disrupted using a fine tungsten needle and the dish was covered before imaging. Live imaging of larval brains was carried out according to [59]. Imaging was carried out using a DeltaVision Elite microscope system. A total of 20–30 z sections at 0.2 µM were collected per time point at a constant exposure time. Images were deconvolved using SoftWorks.
Deconvolved image files from a single slide that were not scaled or projected (.dv format) were analyzed using a script measuring the total fluorescence intensity of CID foci within a single nucleus. Image analysis software was designed with Matlab (MathWorks Inc, Natick, MA) and DIPimage (image processing toolbox for Matlab, Delft University of Technology, the Netherlands). Nuclei were segmented in 3-D using local thresholding of DAPI followed by a watershed algorithm to separate touching nuclei, resulting in a very accurate 3-D volume for each nucleus. Background CID signal was obtained by computing the average pixel intensity of that signal inside nuclei. A wavelet morphological filter was used to enhance intensity peaks of individual centromere foci in the nuclei while reducing noise from nonspecific signals [60]. The volumes of centromeres were then identified by applying a constant threshold on the wavelet filtered image (k-value = 5). The average total intensity of background subtracted CID signal per nucleus was then computed for each class. Thus, we define the total CID fluorescent intensity per nucleus as the total background-corrected 3-D pixel intensity of all foci in a single nucleus. For fixed samples, the mitotic or meiotic stage of each nucleus was classified manually. Average values for each class were scaled by dividing by the average interphase value for larval brain nonstem mitotic cells, the stage S1 value for male meiotic stages, and the cystoblast value for female meiotic stages. Therefore, a value above 1 reflects an increase in CID fluorescent intensity, and a value below 1 reflects a decrease in CID fluorescent intensity with respect to normalized values for each cell stage.
Live movies were analyzed using image-processing modules from the open-source application Fiji [61] controlled with a custom Java code. Total signal intensity for foci pixels inside a selected cell was computed for every frame and normalized to total foci signal intensity at the first frame. The mean background intensity was computed from a background region of interest (ROI) selected by the user. We set the threshold intensity at three times the mean background intensity. Any values above this threshold value inside the selected cell ROI (also manually set by the user) were classified as foci pixels. To correct for fluorophore bleaching at later time points, we computed the average signal intensity for all the foci in each image. We assumed that the observed decrease in mean signal intensity reflects the decrease in fluorophore signal due to bleaching. The values obtained were thus normalized to the average foci intensity at the first time point. The bleaching-corrected total intensity values for the foci pixels were computed as: , where I (tk) is the total intensity value at time tk, p(tk) is the intensity value of a foci pixel inside the cell ROI, and RB (tk) is the normalized bleaching ratio.
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10.1371/journal.ppat.1004694 | Interaction between the Type III Effector VopO and GEF-H1 Activates the RhoA-ROCK Pathway | Vibrio parahaemolyticus is an important pathogen that causes food-borne gastroenteritis in humans. The type III secretion system encoded on chromosome 2 (T3SS2) plays a critical role in the enterotoxic activity of V. parahaemolyticus. Previous studies have demonstrated that T3SS2 induces actin stress fibers in various epithelial cell lines during infection. This stress fiber formation is strongly related to pathogenicity, but the mechanisms that underlie T3SS2-dependent actin stress fiber formation and the main effector have not been elucidated. In this study, we identified VopO as a critical T3SS2 effector protein that activates the RhoA-ROCK pathway, which is an essential pathway for the induction of the T3SS2-dependent stress fiber formation. We also determined that GEF-H1, a RhoA guanine nucleotide exchange factor (GEF), directly binds VopO and is necessary for T3SS2-dependent stress fiber formation. The GEF-H1-binding activity of VopO via an alpha helix region correlated well with its stress fiber-inducing capacity. Furthermore, we showed that VopO is involved in the T3SS2-dependent disruption of the epithelial barrier. Thus, VopO hijacks the RhoA-ROCK pathway in a different manner compared with previously reported bacterial toxins and effectors that modulate the Rho GTPase signaling pathway.
| Many bacterial pathogens manipulate the actin cytoskeleton of mammalian cells to establish pathogenesis via invasion, to evade killing by phagocytes, to disrupt a barrier function, and to induce inflammation caused by translocation type III secretion (T3S) effector proteins. We demonstrated that the T3S effector protein (VopO) of the enteric pathogen Vibrio parahaemolyticus induced robust actin stress fiber formation in infected host cells. Furthermore, this actin rearrangement induced barrier disruption in a colon epithelial cell line. Although many types of effector proteins have been reported, VopO does not share homology with previously reported effector proteins, and no putative functional motifs could be identified. Finally, we determined that the direct binding of VopO to a RhoA guanine nucleotide exchange factor (GEF) is a key step in the induction of stress fiber formation. These findings indicate that VopO plays a unique role in the pathogenicity of V. parahaemolyticus.
| Vibrio parahaemolyticus is a Gram-negative halophilic bacterium that causes acute gastroenteritis in humans after the consumption of contaminated raw or undercooked seafood. The emergence of pandemic strains poses a worldwide health threat [1]. V. parahaemolyticus possesses two type III secretion systems (T3SSs): T3SS1 and T3SS2 [2]. A T3SS is a multisubunit molecular system that delivers bacterial proteins known as effectors directly to the plasma membrane or into the cytoplasm of infected host cells. The translocated effectors then modify certain functions of the host cell by disrupting normal cell signaling processes [3].
T3SS2, which is encoded on chromosome 2, is a major contributor to the enterotoxic effects observed in several animal models [4–7]. The T3SS2-related gene cluster is encoded in an 80-kb pathogenicity island (Vp-PAI), which is conserved exclusively in pathogenic strains [8,9]. Recently, we demonstrated that the F-actin binding T3SS2 effector VopV is necessary for enterotoxicity [10]. During the identification of VopV, we identified several candidate effector genes that are encoded in the Vp-PAI region, but their roles in the pathogenicity of V. parahaemolyticus remain unknown. Consequently, the precise pathogenic mechanisms underlying V. parahaemolyticus infections are not fully understood.
Many bacterial pathogens manipulate the actin cytoskeleton of the host cell using diverse mechanisms during infection [11]. Tissue culture analysis has shown that V. parahaemolyticus T3SS2 causes two dramatic changes in the actin cytoskeleton: the accumulation of F-actin beneath bacterial microcolonies and the induction of actin stress fibers [10,12]. At least three T3SS2 effectors, i.e., VopV, VopL, and VopC, have been identified as actin cytoskeleton modification effectors. VopV exhibits an F-actin binding activity and is responsible for the F-actin accumulation phenotype [10]. VopL contains three Wiskott-Aldrich syndrome protein homology 2 (WH2) motifs, and it elicits an Arp2/3-independent actin nucleation activity and the induction of actin stress fiber formation when expressed in host cells [12]. However, Liverman et al. reported that vopL deficiency only resulted in modest reductions in the amount of stress fibers formed during infection, thereby suggesting that effector(s) other than VopL may contribute to this activity during V. parahaemolyticus infection.
Recently, we identified VopC, which deamidates Rac1 and Cdc42, and it is homologous to a cytotoxic necrotizing factor of uropathogenic Escherichia coli. We found that this effector is involved in the T3SS2-dependent formation of actin stress fibers via the activation of Rac1 [7]. In the absence of VopC, V. parahaemolyticus induces the formation of long, branched, and curved F-actin filaments instead of actin stress fibers in Caco-2 cells. This cytoskeletal modification is completely dependent on T3SS2. In addition, the activation of Rac1 alone is not sufficient to induce stress fiber formation in the absence of bacterial infection. These observations suggest that the formation of complete stress fibers by V. parahaemolyticus requires the coordinated action of VopC with other T3SS2 effector(s).
In this study, we identified a novel actin cytoskeleton-manipulating T3SS2 effector called VopO. VopO induces a high level of stress fiber formation in the host cell by activating the RhoA-ROCK pathway. We also determined that VopO binds directly to GEF-H1, a RhoA guanine nucleotide exchange factor (GEF), and that the GEF-H1-binding activity of VopO is correlated with its stress fiber formation activity. In addition, VopO-dependent stress fiber formation disrupts the epithelial barrier in vitro, as observed previously in vivo in infected intestinal tissue [5,13]. A number of bacterial toxins and effectors that activate or inactivate small GTPases via the direct modification or mimicry of GEFs or GTPase-activating proteins (GAPs) have been identified [14,15], but this is the first report of an effector or a toxin that activates GEFs via direct binding. Overall, these results suggest that VopO is a novel effector with a different mode of action compared with previously reported effectors and toxins that modulate the Rho GTPase signaling pathway.
Previous studies have revealed that two effectors, VopC and VopL, are involved in T3SS2-dependent actin stress fiber formation. Recently, we demonstrated that VopC deamidates and activates Rac1 in infected cells and promotes stress fiber assembly. However, in contrast to a T3SS2-deficient mutant, the vopC deletion mutant still induces the formation of long, branched, and curved F-actin filaments in Caco-2 cells [7]. VopL has been reported to contribute to F-actin stress fiber formation [12]. Therefore, we first investigated whether the induction of T3SS2-dependent stress fibers in HeLa and Caco-2 cells is completely dependent on VopL (S1A, B Fig.).
In agreement with the results of a previous study [12], in both cell types, we observed that the formation of actin stress fibers was somewhat attenuated after infection with a vopL-deficient strain (POR-2∆vopL) compared with the formation resulting from infection with the parent strain (POR-2). However, stress fibers were still observed in POR-2∆vopL-infected cells, whereas no fibers were observed after infection with a T3SS2-deficient strain (POR-2∆vcrD2) or in an uninfected control, suggesting that an unidentified effector is essential for stress fiber formation. The small GTPase RhoA and its downstream effector Rho-associated kinase are major mediators of stress fiber formation [16] (Fig. 1A). GTP binding and hydrolysis induce the conversion of RhoA between GTP-bound (active) and GDP-bound (inactive) forms [17]. The conversion of GTPases from an inactive to an active state is mediated by GEFs. Activated RhoA then propagates downstream signaling by binding to effector proteins such as ROCK. Activated ROCK leads to the formation of contractile bundles of F-actin via the phosphorylation of myosin light chain (MLC) [18,19]. Therefore, to determine whether the RhoA-ROCK pathway is required for T3SS2-dependent stress fiber formation, we employed a ROCK inhibitor (Y27632) and a Rho inhibitor (Rho inhibitor I, a permeable C3 exoenzyme from Clostridium botulinum that inhibits RhoA, RhoB, and RhoC in living cells). Treatment with either the ROCK inhibitor or the Rho inhibitor completely abolished the POR-2-induced formation of stress fibers (Figs. 1B and 1C).
We also examined the requirement for RhoA in stress fiber formation by silencing RhoA using siRNA (Fig. 1D). RhoA knockdown reduced the stress fiber formation induced by POR-2 infection (Fig. 1D), indicating that the RhoA-ROCK pathway is essential for T3SS2-dependent stress fiber formation. The T3SS2 effector VopC selectively deamidates and activates Rac1 and CDC42, but not RhoA, in infected cells both in vitro and in vivo [7]. VopL binds directly to actin and enhances actin filament assembly in vitro [12]. This activity of VopL does not require any Rho GTPases. Furthermore, there are no previous reports of T3SS2 effectors activating RhoA. Overall, these results strongly indicate the existence of an unidentified T3SS2 effector that activates the RhoA-ROCK pathway, thereby inducing stress fiber formation.
Next, we aimed to identify the effector responsible for T3SS2-dependent stress fiber formation. After screening candidate ORFs encoded within the Vp-PAI region, a known pathogenicity island in pathogenic strains [20], we observed that deletion of the vopO gene (vpa1329: Gene ID 1192024) caused a dramatic change in actin stress fiber formation. Stress fibers were not detected when a vopO-deficient strain (POR-2∆vopO) was used to infect either HeLa cells (Fig. 2A) or Caco-2 cells (S1A Fig.). The lack of stress fiber induction when cells were infected with the POR-2∆vopO strain was rescued by in trans complementation with the vopO gene (POR-2∆vopO/pvopO). Immunoblotting analysis using anti-VopO antibodies revealed that VopO is specifically secreted into the culture medium via T3SS2 (S2A Fig.). Deletion of the vopO gene had no effect on the secretion of T3SS2 translocon proteins (VopB2 and VopD2) [21], the secretion of effector proteins involved in stress fiber formation (VopL and VopC) [7,12] (S2A Fig.), or T3SS2-dependent cytotoxicity against Caco-2 cells [22], which is a characteristic effect of T3SS2 in vitro (S2B Fig.).
The enterotoxic activity of the vopO-deficient strain (POR-2∆vopO) appeared to be reduced slightly compared with that of the POR-2 strain, and the enterotoxic activity of the complemented strain appeared to be slightly higher than that of the vopO-deficient strain; however, the differences between the enterotoxic effects of these strains were not significant (S2C Fig.). Interestingly, VopO was involved in the T3SS2-mediated cell invasion phenotype, which was recently identified as a phenotype with VopC activity (S2D Fig.) [7,23]. These observations indicate that VopO is not required for the full secretory function of T3SS2 (S1 Text), thereby suggesting that VopO is a T3SS2 effector involved in the induction of stress fiber formation and the invasive activity of V. parahaemolyticus.
We then examined whether VopO is involved in the activation of the RhoA-ROCK pathway. As a positive control in the subsequent assays, we used nocodazole, a microtubule destabilizer that induces stress fiber formation via the activation of RhoA [24]. As shown in Fig. 2B, RhoA activation by the parent strain (POR-2) was significantly higher than that induced by either its T3SS2-deficient derivative (POR-2∆vcrD2) or an uninfected control. However, the level of RhoA activation was significantly lower in cells infected with the vopO-deficient strain (∆vopO) than that in cells infected with the POR-2∆vcrD2 strain or in uninfected control cells (Fig. 2B). This result suggests that balance of the Rho GTPase activation shifted from RhoA to Rac1 and Cdc42 because of activation via the other T3SS2 effector, VopC, which directly activates both Rac1 and Cdc42 [7,23]. Similar results were obtained by determining the amount of phosphorylated MLC (pMLC) (Fig. 2C). The amount of pMLC increased in a functional T3SS2-dependent manner. However, the amount of pMLC was lower in POR-2∆vopO-infected cells than that in cells infected with the POR-2∆vcrD2 strain or uninfected control cells. These results suggest that VopO has an important role in the T3SS2-dependent activation of the RhoA-ROCK pathway in infected cells.
Next, we used a transfection assay to determine whether VopO itself activates the RhoA-ROCK pathway and subsequently induces stress fiber formation. Both GTP-bound RhoA (GTP-RhoA, the active form of RhoA, Fig. 2D) and pMLC (Fig. 2E) increased significantly when GFP-fused VopO was transiently expressed in HeLa cells. Moreover, the activation of RhoA and the augmentation of pMLC in GFP-VopO-expressing cells coincided with the formation of thick and massive actin fibers at the site of GFP-VopO protein localization (Fig. 2F, arrowheads). These findings indicate that VopO alone can activate the RhoA-ROCK pathway and induce stress fiber formation.
We demonstrated the importance of the RhoA-ROCK pathway in VopO-dependent stress fiber induction. The transition of a small GTPase from an inactive to an active state is mediated by GEFs (Fig. 1A). Sixty-nine types of Rho GEFs have been reported previously [25], some of which contribute to the activation of RhoA [26,27]. We observed that T3SS2-dependent stress fiber formation was also blocked in dominant-negative RhoA-expressing cells (S3 Fig.). Therefore, we hypothesized that VopO may interact with at least one molecule located upstream of RhoA. A T3S effector EspG of enteropathogenic Escherichia coli (EPEC) induces stress fiber formation and is mediated by GEF-H1 activation [28]. GEF-H1 (Lfc in humans), which is a microtubule-regulated Rho GEF, plays a dominant role in RhoA activation [29]. This information let us to hypothesize that VopO might associate with GEF-H1 to induce the stress fiber. To test this hypothesis, we performed a pull-down assay using purified glutathione S-transferase (GST)-fused VopO (GST-VopO) and a HeLa cell lysate. We observed that GEF-H1 coprecipitated with GST-VopO. By contrast, two other RhoA GEFs, LARG and Ect2 [26], did not interact strongly with VopO (Fig. 3A). Interactions were not detected between VopO and RhoA or β-actin. Thus, we confirmed the direct binding of VopO with the recombinant full-length GEF-H1 protein, which was prepared using an in vitro translation system.
As shown in Fig. 3B, the full-length GEF-H1 protein coprecipitated with GST-VopO. GEF-H1 contains four domains: a zinc-finger motif-containing region (Zn), dbl-homology (DH), pleckstrin homology (PH), and α-helical coiled-coil (CC) domains (Fig. 3C) [29]. The DH and PH domains are conserved in the Rho GEF family and are required for its GEF activity [25]. By contrast, the Zn and CC domains are unique to GEF-H1 and they are involved in the regulation of GEF-H1 activity [29]. Thus, we used a pull-down assay to identify GEF-H1 domains that contribute to the binding activity to VopO (Fig. 3D). An N-terminally truncated GEF-H1 (∆N) protein retained relatively weak binding activity with VopO, whereas all of the truncated proteins lost their ability to bind to VopO. Because we could not identify a specific VopO-binding domain in GEF-H1, we hypothesize that VopO might specifically recognize the higher-order structure of GEF-H1. We then assessed the requirement for GEF-H1 in V. parahaemolyticus-induced stress fiber formation using siRNA. As shown in Fig. 3E, no changes in stress fibers were observed when the RhoA GEFs LARG and Ect2 were silenced. By contrast, cells in which only GEF-H1 was silenced exhibited diminished stress fiber formation. Overall, these findings suggest that VopO may target GEF-H1 to induce stress fiber formation. We explored this possibility in the following experiments.
VopO does not share any motifs or any sequence homology with known proteins; however, the Chou-Fasman secondary structure prediction program (http://cib.cf.ocha.ac.jp/bitool/MIX/) revealed that VopO possesses at least four α-helix regions: H1, H2, H3, and H4 (S4 Fig.). We used several truncated forms of VopO (Fig. 4A) to identify the α-helix region(s) in VopO responsible for binding to GEF-H1. In VopO where the first C-terminal α-helix region (∆H1) was truncated, the GEF-H1-binding activity was attenuated slightly compared with that of the full-length VopO (Fig. 4B). By contrast, in the VopO proteins that lacked the second C-terminal α-helix region (H2), ∆H12 or ∆H2, the GEF-H1-binding activity was reduced dramatically.
Next, we used cell transfection to evaluate the stress fiber formation activity of these truncated VopO proteins (Fig. 4C). The stress fibers of ∆H1-expressing cells were somewhat weaker than those of the full-length VopO-expressing cells (Figs. 2F and 4C). By contrast, ∆H12 and ∆H2 did not induce any stress fiber formation. In V. parahaemolyticus-infected cells, the stress fiber-inducing activity of the POR-2∆vopO strain was restored completely or partially by complementation with full-length or ∆H1 VopO, respectively (Figs. 2A, 4D and 4E). By contrast, no stress fiber induction activity was observed in cells infected with the ∆H12- or ∆H2-complemented strains (Figs. 4D and 4E). As summarized in Fig. 4A, the stress fiber-inducing activity of VopO was strongly correlated with its GEF-H1-binding activity.
The interaction between GEF-H1 and microtubules is important for GEF-H1 inactivation [30]. Several T3SS effectors, such as EspG, EspG2, and Orf3 from EPEC, release and activate GEF-H1 by disrupting the host microtubule network following stress fiber induction in infected host cells [28,31]. Therefore, we explored the possibility that VopO might disrupt the microtubule structure. The transient expression of DsRed-fused VopO in cells induced stress fibers (S5A Fig.) but did not induce the destruction of the microtubule network that is observed during transient EspG expression in host cells (S5B Fig.) [32]. In addition, several DsRed-VopO puncta appeared to colocalize with GFP-fused GEF-H1 but they did not significantly affect the association between GEF-H1 and microtubules (S5C Fig.). Furthermore, although microtubule disruption is reported to be EspG-dependent in cells infected with EPEC [28], VopO-dependent microtubule disruption was not observed in V. parahaemolyticus-infected cells (S5D Fig.). Taken together, these results indicate that GEF-H1 is a primary target of VopO during the induction of stress fiber formation. However, the mechanism that allows VopO to activate GEF-H1 is different from that reported for microtubule-destabilizing T3S effectors.
The intestinal epithelial barrier, which includes tight junctions, plays an important role in defense against the invasion of pathogens and commensal microbiota into the lamina propria [33–35]. Junctional adhesion molecule-A-deficient mice, which have a highly permeable intestinal epithelial barrier, are susceptible to enterocolitis [34]. Several studies have also reported intimate relationships between stress fiber formation and the homeostasis of the intestinal epithelial barrier [36–38]. Therefore, we investigated whether stress fiber formation induced by VopO affects the integrity of the epithelial barrier.
The integrity of the epithelial barrier was evaluated by measuring the trans-epithelial resistance (TER) of polarized Caco-2 cells (Fig. 5A). The TER value of Caco-2 cells infected with the parent strain (POR-2) decreased over time. By contrast, the TER value of cells infected with a T3SS2-deficient strain (POR-2∆vcrD2) was nearly identical to that of uninfected control cells. The TER value of cells infected with a vopO-deficient strain (POR-2∆vopO) declined significantly more slowly than that of POR-2-infected cells. We also confirmed the VopO-dependent disruption of the epithelial barrier in a FITC-dextran leakage assay [39]. A mixture of POR-2 and FITC-dextran was used to challenge the apical side of polarized Caco-2 cells and its leakage into the basolateral side was monitored. The amount of basolateral dextran increased dramatically at 12 h after the infection of cells with POR-2 compared with POR-2∆vcrD2-infected cells or uninfected control cells (Fig. 5B). The basolateral dextran levels were significantly lower in POR-2∆vopO-infected cells than those in cells infected with POR-2. Moreover, the reductions in both the TER value and the amount of basolateral dextran observed for the POR-2∆vopO strain were rescued by in trans complementation with the vopO gene (Figs. 5A and 5B).
The comparable cytotoxicities of POR-2∆vopO and POR-2 against Caco-2 cells (S2B Fig.), suggest that disruption of the VopO-dependent epithelial barrier activity was not attributable to cytotoxicity. These results suggest that the stress fiber-inducing activity of VopO disrupts the epithelial barrier function.
T3SS2, which is encoded in Vp-PAI on chromosome 2 of V. parahaemolyticus, is essential for enterotoxicity in several animal models, thereby indicating that it is involved in the pathogenicity of this bacterium [4,5]. Recently, we identified VopV as an enterotoxic T3SS2 effector in a rabbit loop assay [10]. However, the Vp-PAI region encodes many hypothetical ORFs whose biological activities and roles in virulence are not fully understood. In the present study, we determined that a functionally undetermined ORF encoded by Vp-PAI, VPA1329 (VopO), is a novel T3SS2 effector of V. parahaemolyticus, which participates in disrupting the host epithelial barrier by inducing stress fiber formation.
During infection, bacterial pathogens use diverse mechanisms to manipulate the actin cytoskeleton of host cells [11]. In V. parahaemolyticus, T3SS2 has been reported to induce the accumulation of F-actin beneath bacterial microcolonies and the formation of actin stress fibers within infected host cells [10,12]. In this and previous studies, we have demonstrated that at least three T3SS2 effectors, VopO, VopL, and VopC, are involved in T3SS2-dependent stress fiber formation [7,10,12,23]. Although VopL induces stress fiber formation in transfected cells, it is not essential for this process (S1A, B Fig.) [12]. Furthermore, the actin filament assembly-enhancing activity of VopL, dose not require any Rho GTPases. A vopC-deficient strain caused the formation of long, branched, and curved F-actin filaments with a reticular appearance; these fibers were not observed in cells infected with a T3SS2-deficient strain (S1A Fig.) [7]. The expression of a constitutively active form of Rac1 restored the strain’s capacity to induce normal stress fiber formation. VopO activation of the RhoA-ROCK pathway via GEF-H1 binding is essential for stress fiber formation.
These observations indicate that activation of the RhoA-ROCK pathway by VopO is a requisite first step in the induction of stress fiber formation. The activity of VopL, which nucleates actin filaments, appears to enhance the efficiency of stress fiber formation. Stress fibers are contractile acto-myosin structures, which are attached to focal adhesions at both ends of the fiber. The focal complexes formed in lamellipodia are triggered by Rac1 activation [40]. Rac1-null primary mouse embryonic fibroblasts cannot form focal adhesion complexes or induce RhoA-regulated actin stress fiber formation [41]. A vopC deletion mutant lacks the ability to induce vinculin foci [7]. The focal complexes formed by VopC-activated Rac1 may be converted into focal adhesions, and this signaling cascade is necessary for the formation of robust actin stress fibers.
Stress fiber formation plays a role in the maintenance of the epithelial barrier against both pathogen invasion and commensal microbiota [24,33,34,36,38]. Inappropriate induction of stress fibers disrupts tight junctions and leads to several diseases [42]. A recent in vivo study demonstrated that V. parahaemolyticus disrupts the tight junction complex of small intestinal epithelial cells prior to inducing diarrhea in an infant rabbit oral infection model [5]. The involvement of VopO in this phenomenon was difficult to determine using a rabbit ileal loop test in vivo; however, based on TER measurements and FITC-dextran leakage assays, we detected VopO-dependent disruption of the epithelial barrier (Figs. 5A and 5B). These results suggest that VopO is closely involved in the disruption of tight junction complexes, which is consistent with observations in the infant rabbit oral infection model.
V. parahaemolyticus is usually considered to be a noninvasive bacterial pathogen, but several recent studies have demonstrated that this bacterium can invade epithelial cells, which depends on VopC before replicating in the cytosol of the host cells [7,23,43]. In the present study, we found that a vopO deficient strain also abolished T3SS2-dependent invasive phenotype (S2D Fig.). Consequently, we hypothesize that a fairly complex mechanism is required for T3SS2-dependent invasion because the invasive capacity of this bacterium is needed to activate Cdc42, but not Rac1 and RhoA [7], and treatment with nocodazole, which disrupts microtubules and activates GEF-H1, inhibited the invasion of V. parahaemolyticus into HeLa and Caco-2 cells [23,44]. These observations indicate that, unlike VopO-dependent stress fiber formation, activation of the GEF-H1-mediated RhoA-ROCK pathway is not related to V. parahaemolyticus cell invasion. Further studies are needed to understand how VopO promotes invasion by V. parahaemolyticus.
T3S effectors activate Rho family small GTPases in diverse ways. Some effectors share the common motif Trp-xxx-Glu (WxxxE) and functionally mimic GEFs [15,45], whereas other effectors belong to the deamidating toxin family, the members of which directly modify (via deamidation/transglutamination) Rho family small GTPases [11]. In addition to these effectors, EspG, EspG2, and Orf3 in EPEC disrupt the microtubule network, thereby resulting in the release and activation of GEF-H1 [28,31]. However, VopO does not possess a WxxxE motif (or any type of functional motif), shares no sequence homology with these effectors, and does not disrupt microtubule structures in transfected or infected cells (S5B, C, and D Fig.) [32]. In addition, the deamidation of RhoA was not observed in cells infected with V. parahaemolyticus [7]. These results suggest that VopO hijacks the RhoA-ROCK pathway in a different manner compared with previously reported effectors and toxins.
In the present study, we clearly demonstrated that VopO binds directly to GEF-H1 and that the stress fiber-inducing activity of VopO was correlated with its GEF-H1 binding activity (Fig. 4A). GEF-H1 is a RhoA GEF, which is regulated by microtubule binding, phosphorylation, and protein-protein interactions [30,46–51]. The interaction between GEF-H1 and microtubules is particularly important for the suppression of GEF-H1 activation, where the unique N- and C-termini of GEF-H1 are responsible for its association with microtubules [30]. In a previous study, transient expression of either N- or C-terminal-truncated GEF-H1 resulted in higher GEF activity compared with the expression of full-length GEF-H1, thereby suggesting that these domains negatively regulate GEF activity via the DH and PH domains, which are required for the GEF activity of GEF-H1 [30]. Importantly, VopO binds to GEF-H1, which contains the C-terminal domain (Figs. 3C and D), but the expression of DsRed-fused VopO did not induce obvious alterations in GFP-GEF-H1 localization according to microscopic analysis (S5C Fig.). In addition, we did not observe any VopO-mediated increase in the activity of GEF-H1 with RhoA according to an in vitro Rho GEF exchange assay using mant-GTP. Therefore, the biochemical mechanism that allows VopO to target GEF-H1 and how it coordinates with other V. parahaemolyticus T3SS2 effectors during bacterial infection remain unclear. Further research is required to determine how VopO activates the RhoA-ROCK pathway via GEF-H1. A more detailed understanding of the functional mechanism of VopO may provide new insights into how pathogenic bacteria exploit the signaling pathways of Rho family small GTPases.
V. parahaemolyticus strain RIMD2210633 (KP-positive, serotype O3:K6)[2] was obtained from the Pathogenic Microbes Repository Unit, International Research Center for Infectious Diseases, Research Institute for Microbial Diseases (Osaka University, Osaka, Japan). A four-primer polymerase chain reaction (PCR) technique was used to engineer an in-frame deletion mutation, as described previously [4]. All of the bacterial strains and plasmids used in this study are listed in the Supporting Information, S1 Table.
The activation of RhoA was estimated using a G-LISA RhoA Activation Assay Biochem Kit (Cytoskeleton Inc., Denver, CO, USA) or an EZ-Detect Rho Activation Kit (Thermo Fisher Scientific Inc., Waltham, MA, USA), according to the manufacturer’s instructions. One day prior to infection, the culture medium was exchanged with DMEM containing 0.25% fetal bovine serum. HeLa cells were infected with isogenic V. parahaemolyticus strains at a multiplicity of infection (MOI) of 10 for 150 min or treated with 10 μM nocodazole (Sigma-Aldrich, St. Louis, MO, USA) for 30 min as a positive control. After infection or nocodazole treatment, RhoA activation was evaluated by an ELISA (the G-LISA RhoA Activation Assay Biochem Kit, Cytoskeleton Inc., Denver, CO, USA). The amount of GTP-RhoA in the transfected HeLa cells was estimated via a rhotekin pull-down assay using an EZ-Detect Rho Activation Kit. The intensity of GTP-RhoA bands was measured using ImageJ software (NIH, Bethesda, MD, USA).
HeLa cells were infected with V. parahaemolyticus strains at a MOI of 10 for 3 h or treated with 10 μM nocodazole for 30 min. The cell lysates were probed with p(Thr18/Ser19)-MLC and MLC antibodies (Cell Signaling Technology, Inc., Danvers, MA, USA).
HeLa or Caco-2 cells were infected with V. parahaemolyticus strains at a MOI of 10 for 3 h or treated with 10 μM nocodazole for 1 h. To inhibit stress fiber formation, 10 μM Y27632 (Sigma-Aldrich, St. Louis, MO, USA) or 2 μg/mL Rho inhibitor I (Cytoskeleton Inc., Denver, CO, USA) was added for 1 or 2 h prior to infection, respectively. After infection, the cells were washed with ice-cold phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde in PBS. The fixed cells were then stained for F-actin and DNA using Alexa-488 or rhodamine-conjugated phalloidin (Invitrogen, Carlsbad, CA, USA) and Hoechst 33258 (Sigma-Aldrich, St. Louis, MO, USA), respectively. Images were captured using a fluorescence microscope (Biozero BZ-8100, Keyence, Osaka, Japan) or a confocal laser microscope (FLUOVEW FV10i, Olympus, Tokyo, Japan).
The transfection of GFP fused with full-length or truncated VopO expression vectors was performed using Lipofectamine LTX reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. At 15 h after transfection, the HeLa cells were washed with ice-cold PBS and fixed with 4% paraformaldehyde in PBS. The fixed cells were then stained for F-actin and nuclei using rhodamine phalloidin (Invitrogen, Carlsbad, CA, USA) and Hoechst 33258 (Sigma-Aldrich, St. Louis, MO, USA), respectively. Images were captured using a fluorescence microscope (Biozero BZ-8100, Keyence, Osaka, Japan).
Confluent HeLa cells were washed with ice-cold PBS and lysed in 1 mL of ice-cold RIPA buffer [50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 0.1% sodium dodecyl sulfate (SDS), 0.5% deoxycholic acid (DOC), and 1% NP-40] containing an EDTA-free protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA). After agitation for 15 min at 4°C, the lysates were harvested with a cell scraper and centrifuged for 15 min at 10,000 × g. Recombinant 3xFLAG-tagged wild-type or truncated GEF-H1 proteins were prepared using TnT Quick Coupled Transcription/Translation Systems (Promega, Fitchburg, WI, USA), according to the manufacturer’s instructions. The lysates from the HeLa cells or recombinant GEF-H1 proteins were incubated with GST-VopO proteins and glutathione beads (GE Healthcare, Little Chalfont, UK) at 4°C for 4 h. The beads were washed with RIPA buffer and eluted with SDS sample buffer, and the eluates were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE).
The samples used for western blotting were separated by SDS-PAGE. After electrotransfer, the polyvinylidene fluoride (PVDF) membranes (Merck Millipore, Darmstadt, Germany) were probed with anti-GEF-H1, p(Thr18/Ser19)-MLC, MLC, anti-GST (Cell Signaling Technology, Inc., Danvers, MA, USA), anti-LARG, anti-Ect2, anti-RhoA, anti-β-actin, anti-α-tubulin, or anti-FLAG (Sigma-Aldrich, St. Louis, MO, USA) antibodies, followed by horseradish-peroxidase-conjugated goat anti-rabbit or rabbit anti-mouse antibodies (Zymed Laboratories, Inc., South San Francisco, CA, USA). The blots were developed using an ECL Western Blotting Kit (GE Healthcare, Little Chalfont, UK).
A total of 2 x 105 Caco-2 cells were plated into Transwell chambers (Corning Inc., Corning, NY, USA) with a pore size of 0.4 μm (6.5 mm diameter) and cultured for 12–14 days. The medium was changed every 2 days until a steady-state TER (450–550 Ωcm2) was achieved. The cells were infected with V. parahaemolyticus strains, and the TER was measured using an epithelial voltmeter (EVOM, WPI Inc., Sarasota, FL, USA) at the indicated time. The FITC-dextran leakage assay was conducted as described previously [39]. Mixtures of bacteria and 4-kDa FITC-dextran (Sigma-Aldrich, St. Louis, MO, USA) were used to challenge the apical side of the Transwell chambers. At 6 and 12 h after infection, the amount of FITC-dextran on the basolateral side was measured using a PowerScan HT fluorescence plate reader (DS Pharma Biomedical Co., Ltd, Osaka, Japan).
Specific siRNAs for RhoA, GEF-H1, LARG, or Ect2 were purchased from Applied Biosystems. Silencer Select Negative Control #1 siRNA (Applied Biosystems, Foster City, CA, USA) was used as a negative control. The siRNAs were transfected using siPORT NeoFX Transfection Agent (Applied Biosystems, Foster City, CA, USA). At 24 h after transfection, the cells were subjected to western blot analysis to confirm the knockdown efficiency and used subsequently in the infection studies.
All of the data are expressed as the mean and standard error based on at least three determinations per experimental condition. Student’s t tests that assumed unequal variances were used for the statistical analyses. P < 0.05 was considered significant.
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10.1371/journal.ppat.1003242 | HIV Restriction by APOBEC3 in Humanized Mice | Innate immune restriction factors represent important specialized barriers to zoonotic transmission of viruses. Significant consideration has been given to their possible use for therapeutic benefit. The apolipoprotein B mRNA editing enzyme catalytic polypeptide 3 (APOBEC3) family of cytidine deaminases are potent immune defense molecules capable of efficiently restricting endogenous retroelements as well as a broad range of viruses including Human Immunodeficiency virus (HIV), Hepatitis B virus (HBV), Human Papilloma virus (HPV), and Human T Cell Leukemia virus (HTLV). The best characterized members of this family are APOBEC3G (A3G) and APOBEC3F (A3F) and their restriction of HIV. HIV has evolved to counteract these powerful restriction factors by encoding an accessory gene designated viral infectivity factor (vif). Here we demonstrate that APOBEC3 efficiently restricts CCR5-tropic HIV in the absence of Vif. However, our results also show that CXCR4-tropic HIV can escape from APOBEC3 restriction and replicate in vivo independent of Vif. Molecular analysis identified thymocytes as cells with reduced A3G and A3F expression. Direct injection of vif-defective HIV into the thymus resulted in viral replication and dissemination detected by plasma viral load analysis; however, vif-defective viruses remained sensitive to APOBEC3 restriction as extensive G to A mutation was observed in proviral DNA recovered from other organs. Remarkably, HIV replication persisted despite the inability of HIV to develop resistance to APOBEC3 in the absence of Vif. Our results provide novel insight into a highly specific subset of cells that potentially circumvent the action of APOBEC3; however our results also demonstrate the massive inactivation of CCR5-tropic HIV in the absence of Vif.
| The APOBEC3 family of proteins is a potent cellular defense mechanism capable of restricting a broad range of viruses including HIV. HIV requires a critical accessory protein, Vif, which targets APOBEC3 for degradation thereby shielding its genome from lethal mutagenesis. Previous in vitro studies have shown that in the absence of Vif, HIV can be hypermutated by APOBEC3. This potent restrictive function of APOBEC3 has generated strong interest in developing therapeutics based on the APOBEC3/Vif axis. Here we demonstrate in vivo that CCR5-tropic HIV can be efficiently restricted by APOBEC3. However, our results also show that CXCR4-tropic HIV can replicate independent of Vif and escape lethal restriction by APOBEC3. Specifically, we show that thymocytes have reduced expression of A3G and A3F and that direct injection of vif-defective HIV into the thymus results in viral replication and dissemination. Despite continued Vif-independent HIV replication, the virus remained sensitive to APOBEC3 mutagenesis and was rapidly restricted in tissues with higher A3G and A3F expression. Our results provide novel insight into the restriction of HIV in vivo and identify a potentially significant defect in the innate immune defenses that protect the host cell from pathogens.
| Innate immune restriction factors embody specialized barriers to zoonotic transmission of viruses. Substantial consideration has been given to their potential use for therapeutic benefit [1], [2]. The apolipoprotein B mRNA editing enzyme catalytic polypeptide 3 (APOBEC3) family of cytidine deaminases are potent innate immune defense factors capable of efficiently restricting endogenous retroelements as well as a diverse range of viruses including Hepatitis B virus, Human Immunodeficiency virus, Human T Cell Leukemia virus, TT virus, and Human Papilloma virus [3]–[8].
The best-characterized APOBEC3 family members are the immune defense molecules APOBEC3G (A3G) and APOBEC3F (A3F) and their lethal restriction of HIV [5], [9]. HIV has evolved to counteract these powerful restriction factors by encoding an accessory gene designated viral infectivity factor (vif). In vitro studies have elegantly shown that in the absence of Vif, A3G and A3F are encapsidated into nascent virions and deaminate cytosines in the minus strand of HIV DNA during reverse transcription [10]–[12]. APOBEC3 deamination of cytosines in the minus strand of the viral genome occurs at both CC and TC dinucleotide sites, resulting in GG to AG as well as GA to AA mutations in the coding strand of the viral genome [10], [11], [13], [14]. APOBEC3 induced G to A mutations at GG dinucleotide sites are exclusively the result of A3G deamination, while mutations occurring at GA sites can be caused by multiple APOBEC3 proteins including both A3F and A3G [10], [15]. While studies have demonstrated the deleterious effects of G to A hypermutation of the HIV genome [10], [16]–[18], a recent in vitro study showed variable levels of A3G induced G to A mutations suggesting that A3G may contribute to viral diversity [19].
In this study, we use humanized mice for the in vivo study of HIV in the context of a human immune system. Both NSG-hu and NSG BLT mice are systemically reconstituted with multiple lineages of hematopoietic cells including T cells, B cells, and myeloid cells following transplantation with CD34+ hematopoietic stem cells [20], [21]. Additionally, BLT humanized mice are implanted with human liver and thymic tissue under the kidney capsule prior to the transplant of autologous CD34+ cells which results in the development of a bona fide human thymus for T cell development [21]. Like any other model for HIV/AIDS research humanized mice have several strengths and limitations that have to be taken into consideration in the development of experimental plan. Two recent review articles cover this area in significant detail [22], [23]. Despite their limitations humanized mouse models have previously been used for the study of HIV transmission, pathogenesis, prevention, therapy and latency/eradication [20], [24]–[28]. Here we first demonstrate the highly effective inactivation of CCR5-tropic HIV-1 by APOBEC3 when unobstructed by a functioning vif in vivo after intravenous infection. Secondly, we demonstrate that if injected directly into the thymus, vif-defective viruses can replicate escaping absolute APOBEC3 restriction.
To confirm that the mutations disrupting vif do not have a detrimental effect on the replicative capacity of HIV-1JR-CSF, we generated a CCR5 expressing permissive cell line (CEM-SS CCR5) and infected them with wild-type HIV-1JR-CSF or isogenic viruses containing either an irreparable deletion in vif (HIVJR-CSFΔvif) or a one base insertion in vif (HIVJR-CSFvifFS). Replication of both vif-defective viruses was equal to that of wild-type in permissive cells, confirming that the disruption of vif did not have a deleterious effect on HIV-1 in the absence of APOBEC3 (Figure 1A).
To assess the in vivo effectiveness of APOBEC3 restriction of HIV, we intravenously infected NSG-hu mice with wild-type HIV-1JR-CSF (a T-cell CCR5-tropic primary isolate) or HIVJR-CSFΔvif. As early as one week after intravenous infection, widespread replication of wild-type virus was detected as HIV DNA was amplified from every tissue analyzed (Figure 1B). In contrast the vif-defective virus did not sustain replication in humanized mice; as viral DNA was sparsely present (Figure 1B). Notably, HIVJR-CSFΔvif DNA could only be amplified from one organ from each infected mouse suggesting that an extremely low number of infected cells were present. Analysis of the viral DNA sequence from the animals revealed that HIV DNA from mice infected with wild-type virus had no mutations, whereas viral DNA from the HIVJR-CSFΔvif infected mice had numerous G to A mutations consistent with APOBEC3 induced restriction (Figure 1C). The limited number of tissues with cells harboring G to A mutated HIVJR-CSFΔvif DNA one week after exposure suggests that APOBEC3 restriction of vif-defective HIV occurs rapidly in vivo.
While evidence of APOBEC3 restriction of vif-deficient HIV is observed early after infection, we next determined whether HIVJR-CSFΔvif could develop resistance to APOBEC3 and replicate systemically. To address this, we infected humanized mice (n = 8) intravenously with HIVJR-CSFΔvif and monitored them for plasma viral load. Longitudinal analysis demonstrated that HIVJR-CSFΔvif restriction by APOBEC3 is absolute, as no viral RNA was present in the plasma of the mice at any time point in contrast to infection with wild-type HIVJR-CSF (Figure 2A). No revertants or complementary changes arose that restored the ability of HIVJR-CSFΔvif to replicate. In contrast to the widespread presence of viral DNA in all tissues analyzed after wild-type HIV infection, the extremely limited replication of HIVJR-CSFΔvif was confirmed by the absence of HIV DNA in tissues obtained from 4/8 infected mice, and the presence of lethally mutated viral DNA in only a few tissues of the other four animals (Figure 2B). To determine the extent of APOBEC3 hypermutation in the HIVJR-CSFΔvif provirus, we analyzed the sequences for G to A mutations at GG, GA and GY dinucleotide sites which are the targets of the APOBEC3 proteins. We found that 25–65% of the GG sites had been mutated with additional (albeit fewer) mutations present at GA sites, demonstrating extensive APOBEC3 hypermutation to lethally restrict HIVJR-CSFΔvif (Figure 2C). Analysis of the mutational profile in the HIVJR-CSFΔvif DNA from the mice showed that 84% of all the G to A mutations occurred at GG dinucleotide sites whereas only 15% of mutations were present at GA sites and only 1% occurred at GY sites (Figure 2D). Taken together, these results demonstrate that HIVJR-CSFΔvif is unable to overcome the loss of Vif and is lethally restricted by APOBEC3 in vivo.
To evaluate the selective pressure exerted by APOBEC3 on HIV in vivo, we used a mutant isogenic virus containing a one base insertion in vif (HIVJR-CSFvifFS) to intravenously infect 16 humanized mice representing 7 different human donors. Consistent with our previous results, in 10/16 mice intravenously infected with HIVJR-CSFvifFS there was no evidence of virus replication as determined by the absence of viral RNA in the plasma (Figure 3A). However, in the remaining six mice the virus was able to replicate to levels similar to those observed with the wild-type virus (Figure 3A). One salient aspect noted was the almost complete absence of APOBEC3 mutations in viral RNA samples obtained from the plasma from these six mice. Molecular analysis of viral sequences from the peripheral blood from these mice demonstrated that in all six cases a one-nucleotide deletion had occurred that fully restored the vif open reading frame (ORF) highlighting the extreme selective pressure APOBEC3 exerts on HIV in vivo to restore Vif activity or be lethally mutated. Two important issues that should be noted are 1) the virus used for these experiments was generated via transient transfection of 293T cells creating a uniform inoculum and 2) that this repair mutation occurring in vivo is not at a putative APOBEC3 site and therefore it is most likely is a result of a mutation occurring during reverse transcription.
We then analyzed viral DNA from the tissues of all 16 mice exposed to HIVJR-CSFvifFS. In multiple tissue samples from four of the aviremic mice we found no evidence of HIV DNA. In similar samples from six other aviremic mice only low levels of heavily mutated HIV DNA was present in a few tissues (30–60% of the GG dinucleotide sites mutated) (Figure 3B and 3C). The mutational profile of the viral DNA from these animals again showed a preference for GG sites accounting for 87% of all mutations (Figure 3D). In sharp contrast, in mice where the vif ORF was restored virtually intact viral DNA was present in every tissue analyzed, highlighting the strong selective pressure exerted by APOBEC3 on HIV (Figure 3B and 3C). The lack of G to A mutations in the vif-restored viral genome suggested that the virus had evaded APOBEC3 restriction until restoring vif.
The stochastic nature of vif ORF restoration may reflect its occurrence in a specific anatomical location(s). Therefore we directly injected 9×104 TCIU of HIVJR-CSFvifFS into the spleen, liver, lung or human thymic implant of separate humanized mice. Evidence of viral replication in peripheral blood was exclusively found when HIVJR-CSFvifFS was injected directly into the thymus (Figure 4A). In this case, sequence analysis of vif showed a one base deletion restoring Vif expression. Injection of the virus into the spleen, liver, or lung resulted in absolute restriction with no viremia and no residual viral DNA present in any tissue (Figure 4A and 4B)
To determine if restoration of the vif ORF following thymic injection was non-random, we increased the virus inoculum four-fold and repeated the infection, using 3.6×105 TCIU of HIVJR-CSFvifFS injected into the same set of tissues. Again evidence of HIV replication was only observed after intrathymic exposure with 3/3 mice that received the virus directly into the thymus becoming viremic (Figure 4A). Strikingly, despite the high virus inoculum injected directly into the spleen, liver, or lung no evidence of virus replication was observed (Figure 4A). When tissues from these animals were analyzed, we found that only one mouse, FS24, had viral DNA present (Figure 4B) and that it had all been lethally mutated by APOBEC3 (Figure 4C and 4D). These results demonstrate that transient HIVJR-CSFvifFS replication and subsequent vif restoration specifically occurs following a direct thymic exposure. Furthermore, the potent antiretroviral activity of APOBEC3 is highlighted by the absolute restriction of HIVJR-CSFvifFS when the virus is injected into other tissues.
Since the reversion of the vif ORF specifically occurred following injection of the virus into the thymus, we next determined whether A3G and A3F expression was lower in the thymus compared to other tissues. We tested this by comparing A3G and A3F mRNA levels in purified thymocytes (of which >90% are CD4+) with those in CD4+ cells isolated from other tissues in humanized mice. Our results show that thymocytes express 4–8 fold less A3G mRNA and 2.5–3.5 fold less A3F mRNA than human CD4+ cells isolated from the spleen, liver or lung (Figure 5A and Figure S1). Furthermore, no difference in A3G or A3F mRNA expression was found in thymocytes from humanized mice or human thymus. Additionally, A3G in thymocytes was found to be 3–4 fold lower compared to human peripheral blood mononuclear cells (PBMC) by both mRNA and protein expression (Figure 5A and 5B). These results are consistent with the observation that vif reversion specifically occurs following thymic injection of HIVJR-CSFvifFS and suggests that the thymus may support Vif-independent HIV replication.
Based on our observations of reduced A3G and A3F expression in thymocytes and reversion of the vif ORF exclusively occurring with thymic exposure, we considered the possibility that a direct injection of HIVJR-CSFΔvif (the virus containing a non-revertible deletion in vif) into the thymus would result in Vif-independent replication. HIV RNA was transiently observed in the plasma of 2/6 mice following thymic infection with this virus (Figure S2). This low level of replication in some mice is consistent with the results presented above with frame shift containing HIVJR-CSFvifFS (Figure 4A), in which the virus had restored the vif ORF and was able to replicate unimpeded by APOBEC3 after reversion. These results show that recovery of Vif activity is necessary for ongoing replication and viral dissemination by vif-defective HIVJR-CSF.
We hypothesized that the lack of robust and sustained replication of HIVJR-CSFΔvif following direct thymic infection could be due to limited CCR5 expression in the thymus, as <5% of thymocytes express CCR5 whereas 30–40% of thymocytes express CXCR4 [29]–[31]. We therefore introduced the deletion described above into the vif ORF of HIV-1LAI, a CXCR4-tropic virus (HIVLAIΔvif) and confirmed that the disruption of vif did not affect the ability of the virus to replicate in the absence of APOBEC3 (Figure S3). We tested our hypothesis by directly injecting HIVLAIΔvif into the thymus of four humanized mice. Viremia was present in 4/4 animals inoculated in this manner (Figure 6A). In contrast, when HIVLAIΔvif was directly injected into the spleen, liver, or lung of an additional 3 animals viral replication was absolutely restricted (Figure 6A). These results further demonstrate that Vif-independent HIV replication can be sustained following exposure into the thymus but is vigorously restricted in other tissues. Additionally, when HIVLAIΔvif was injected intravenously, sustained levels of viral replication were observed in the plasma of humanized mice; however this replication was lower relative to the parental virus (Figure 6B). Remarkably, unlike wild-type HIVLAI which rapidly depletes peripheral blood CD4+ T cells, infection with HIVLAIΔvif did not deplete CD4+ T cells in the periphery despite sustained viral replication (Figure 6C). Sequencing of viral RNA obtained from the plasma of HIVLAIΔvif infected mice showed significantly fewer G to A mutations compared to the same region of viral DNA isolated from PBMC, suggesting that the infection was likely being sustained in cells with lower APOBEC3 expression (Figure 6D).
Consistent with these results, HIVLAIΔvif DNA was abundant in the tissues of intrathymically or intravenously exposed mice while direct exposure into the spleen, liver, or lung resulted in viral DNA sparsely present in the organs (Figure 6E). Since this virus could not restore Vif expression, its viral DNA had G to A mutations; however, consistent with the low expression of A3G and A3F in the thymus (Figure 5A, 5B and S1), significantly fewer G to A mutations were present in viral DNA amplified from the thymus when compared to the same region of the viral DNA amplified from other organs (Figure 6F). Analysis of the mutational profile in the HIVLAIΔvif DNA from the mice showed that 86% of all the G to A mutations occurred at GG dinucleotide sites (Figure 6G). The presence of hypermutated provirus in several tissues suggests that the HIV was not able to develop resistance to APOBEC3 by second site mutations in the absence of vif, but was instead persisting in a pool of cells that permitted replication (Figure 7). Taken together, these results demonstrate that HIV can sustain replication independent of vif escaping APOBEC3 restriction in vivo.
Our results demonstrate evidence of vif-independent replication of HIV in an in vivo setting. The observation that infection with CCR5 tropic HIV is rapidly extinguished in the absence of vif while CXCR4 tropic HIV lacking vif can sustain replication suggests that vif-independent HIV replication is occurring in a location with a paucity of cells expressing CCR5, such as the thymus where <5% of cells express CCR5 while a far greater number of cells (30–40%) express CXCR4 [29]–[31]. Consistent with this observation, direct injection of vif-deficient HIV into organs resulted in detectable viral replication only following thymic infection. Remarkably, reversion of the vif ORF with HIVJR-CSFvifFS occurred in 100% of thymic exposures while being absolutely restricted when injected into all other organs, highlighting the potent antiretroviral activity of APOBEC3. Interestingly, despite the strong selective pressure applied by APOBEC3, we did not observe any evidence of vif-defective HIV-1JR-CSF altering its coreceptor usage to take advantage of the lower A3G and A3F expression in the thymus. Coreceptor switching is a complicated process involving multiple mutations in envelope that occurs over a period of years in patients [32]. During their short lifespan, coreceptor switching is not common in humanized mice and has only been reported in a single mouse [33].
Our results demonstrate massive inactivation of CCR5-tropic HIV-1 when the protective effects of Vif are absent. These results are consistent with previously published work by Sato et. al. [34]. Under their experimental conditions, these investigators found that vif-defective CCR5-tropic HIV did not replicate at all in humanized mice. Replication of HIV-1 with a functional vif gave a different result. In this case, there was a low level of G to A mutation in both A3G and A3F contexts in viral DNA sequences [34]. Thus, these authors confirmed in the humanized mouse model the early observations of the occasional occurrence of hypermutation of HIV-1 isolated from patients [35]–[38].
Analysis of HIV DNA in aborted infections for G to A hypermutation, the hallmark of APOBEC3 restriction, demonstrated that when HIV DNA was present, there was an overwhelming prevalence of mutations at GG dinucleotide sites indicating that in the absence of vif A3G is the dominant HIV restricting factor in vivo [10]–[12]. This conclusion is further supported by two recent papers using stably expressed A3F or gene targeting to create null mutants to systematically disrupt the individual APOBEC3 proteins that have elegantly demonstrated that A3G is the APOBEC3 family member that induces the preponderance of GG to AG mutations in vif-deficient HIV DNA [15], [39]. One substantial benefit of A3G restriction is that the mutation of GG to AG can be highly effective in inactivating viral genes because of the conversion of tryptophan codons (TGG) to stop codons (TAG, TGA, or TAA).
The lower level of GA to AA mutations that we observed suggests a contributory role for A3F in the overall level of G to A mutations we observed. The impact of A3F remains unclear however since the specificity of A3G for the GG context is not absolute and some of the GA to AA mutations we observed may have been created by A3G. A role for A3F in HIV restriction has been questioned recently but this issue remains unresolved in vivo [39], [40]. Future experiments with humanized mice will address this question.
The results presented here demonstrate that in vivo HIV fails to develop second-site mutations to compensate for the absolute loss of vif to overcome A3G induced mutation, which is in contrast to observations made with in vitro systems with ectopically expressed A3G [41], [42]. This potent restriction of HIV in vivo is not observed by inactivation of other HIV-1 accessory genes [43], [44]. To survive in vivo in the absence of vif, HIV relies on target cells with reduced A3G expression in which it can replicate as shown by the lack of G to A hypermutation in the cell free virus despite the abundance of G to A mutations present in viral DNA in several tissues with high levels of A3G expression. Our analysis CD4+ cells identified thymocytes as a cell population that has reduced A3G expression. Previous analysis of A3G expression from whole tissues did not identify thymocytes as having reduced A3G; however these results are difficult to interpret because of the lower percentage of CD4+ cells in organs other than the thymus [45], [46]. Furthermore, the significance of A3G expression levels in the modulation of both wild-type and vif-deficient HIV replication has been previously demonstrated in Th1 and Th2 cells [47].
The implications of our findings might not be limited to HIV. Rather they might also extend to other viruses and retroelements that are restricted by APOBEC3 proteins [3], [4], [6]–[8], [48], as they may also persist as a result of reduced APOBEC3 expression that affords them the opportunity to replicate. The expansive restricting activity of the APOBEC3 family on endogenous and exogenous retroviruses serves to illustrate the broad therapeutic implications of our observations. This study also raises an important issue that must be addressed if the Vif-APOBEC3 axis is to be used to develop small molecular inhibitors of HIV replication: the well-documented ability of HIV to develop resistance to all current antiretroviral drugs. By incorporating point mutations in the relevant viral genes HIV can develop drug resistance [49]. Our observation of Vif-independent replication after direct injection into the thymus are consistent with previous work in humanized mice [50] and highlight the potential for HIV to escape the effect of a therapeutic Vif inhibitor [51]–[53]. The drug resistant virus would then be capable of systemic dissemination. However, as with other antiretrovirals, the use of combination therapy may prevent the emergence of such resistance. The thymus plays a critical role in HIV infection as it is actively involved in immune reconstitution following suppression of viremia with antiretroviral therapy. While this immune reconstitution occurs better in children than in adults, extensive thymic damage and incomplete virus suppression hinder this process [54]–[56].
Finally, it remains to be established if sublethal restriction by other innate immune defense proteins such as Tetherin, Trim-5-alpha, SamHD1, etc. could allow the replication of other pathogenic viruses [1]. Therefore, our discovery has long lasting implications that provide an alternative view of the dynamic interplay between endogenous immune restriction factors and the broad spectrum of pathogens they control.
All animal experiments were conducted following NIH guidelines for housing and care of laboratory animals and in accordance with The University of North Carolina at Chapel Hill (UNC-Chapel Hill) in accordance with protocols approved by the institution's Institutional Animal Care and Use Committee. UNC-Chapel Hill protocol number 12-170.
Experiments were performed using the CCR5-tropic primary isolate HIV-1JR-CSF (accession # M38429) or the CXCR4-tropic molecular clone HIV-1LAI (accession # K02013) [57], [58]. Mutations disrupting vif were made in regions that did not affect the overlapping 3′ terminus of pol or the splice acceptor site of vpr. A non-revertible 172 nucleotide deletion in the 5′ half of HIV-1JR-CSF vif (HIVJR-CSFΔvif) was constructed by deleting nucleotides 5138 to 5309 between the NdeI and NcoI sites. A second HIV-1JR-CSF with a potentially revertible vif (HIVJR-CSFvifFS) was constructed by inserting a single adenosine after nucleotide 86 in vif by site directed mutagenesis. A non-revertible 178 nucleotide deletion in the 5′ half of HIV-1LAIvif (HIVLAIΔvif) was constructed by deleting nucleotides 4708–4885 between the NdeI and PflMI sites [59]. All constructs were analyzed by direct DNA sequencing prior to virus production. Virus stocks were generated by transfecting proviral DNA into 293T cells using Lipofectamine 2000 (Invitrogen) and tissue culture infectious units (TCIU) were determined using TZM-bl cells essentially as we have previously reported [24], [27], [60]. TZM-bl Hela cells and human embryonic kidney 293T cells were cultured at 37°C, 10% CO2 in Dulbecco's Modified Eagle Medium (Sigma) supplemented with 10% fetal bovine serum, 50 IU penicillin, 50 µg/ml streptomycin and 2 mM L-glutamine (Cellgro). CEM-SS cells were cultured at 37°C, 5%CO2 in RPMI 1640 (Sigma) supplemented with 10% fetal bovine serum, 50 IU penicillin, 50 µg/ml streptomycin, 2 mM L-glutamine, and 1 mM sodium pyruvate (Cellgro).
To generate a permissive cell line that can be infected with CCR5-tropic HIV, CEM-SS cells were transduced with the retroviral vector pBabe-CCR5 obtained from the NIH AIDS Research and Reference Reagent Program [61], [62]. pBabe-CCR5 and the packaging vector pEQPAM were co-transfected into 293T cells using Lipofectamine 2000 (Invitrogen). The culture supernatants were collected after 48 hours and filtered through a 0.45 µm filter. Twenty-four well plates were coated with 40 µg of Retronectin (Takara) and then washed with PBS+2% BSA and incubated twice with 0.5 mL of the vector supernatants for one hour each. CEM-SS cells (3×105) were then incubated in the vector coated wells overnight at 37°C, 5% CO2. The following day, the vector supernatant (0.5 mL) was added to the cells overnight. Transduced cells were selected in complete RPMI containing 0.5 µg/ml puromycin. Fluorescence activated cell sorting was used to isolate the CD4HiCCR5Hi population with a BD FACSAria (Becton-Dickinson), collecting the top 25%.
CEM-SS cells were used to propagate both wild-type and vif-deficient HIVLAI while CCR5 expressing CEM-SS cells were used for spreading infections with both wild-type and vif-deficient HIVJR-CSF. Cells (1×106) were infected with virus stocks normalized to p24Gag or tissue culture infectious units in complete RPMI containing 4 µg/ml polybrene at 37°C, 5% CO2 for 4 hours. The cells were washed extensively with PBS and cultured at 37°C, 5% CO2 in complete RPMI. Cell cultures were passaged every three days and a sample of the culture supernatant was collected for quantification of viral capsid protein by p24Gag ELISA.
Human CD4+ cells from humanized mouse spleen, liver, or lung were isolated using magnetic bead sorting (Stem Cell Technologies). A3G and A3F mRNA expression in cells was analyzed by quantitative RT-PCR (qRT-PCR) essentially as previously described [46], [47]. Briefly, cellular RNA was extracted using the RNeasy kit (Qiagen) per the manufacturer's protocol including the optional treatment with RNase-free DNase (Qiagen) during extraction. Total RNA (10 ng) was used as the template in a one-step RT-PCR reaction with the TaqMan RNA-to-Ct 1 step kit (Applied Biosystems). Primers for human A3G and A3F mRNA [47] and for human TATA Box binding protein mRNA (Applied Biosystems) were used for amplification and human A3G and A3F mRNA levels were normalized as previously described [46]. A3G protein determination was performed by disrupting cells in lysis buffer (50 mM Tris, pH = 8.0, 100 mM NaCl, 25 mM NaF, 25 mM benzamidine, 20 mM β-glycerophosphate, 2 mM Na3VO2, 3 mM EDTA, 10% glycerol, and 0.5% IGEPAL-630). Lysates were centrifuged at 13,000×g for 10 minutes and the supernatant fraction was prepared for SDS-PAGE gel electrophoresis. Separated proteins were transferred to nitrocellulose and immunoblotted for human A3G (NIH AIDS reagent program #9968) [63] and human GAPDH (Cell Signaling Technology #2118). Protein bands were quantitated by determining density using ImageJ software (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij/, 1997–2009).
Mice were maintained with the Division of Laboratory Animal Medicine at the University of North Carolina at Chapel Hill under specific-pathogen free conditions. Humanized mice (BLT and NSG-hu) were generated and analyzed for reconstitution with human hematopoietic cells including human T cells by flow cytometry essentially as previously described [20], [21], [24], [25], [27], [28]. Humanized mice were inoculated with 3×104 or 9×104 TCIU of wild-type HIV-1LAI, 9×104 TCIU of wild-type HIV-1JR-CSF, 3.6×105 TCIU of HIVJR-CSFΔvif or HIVLAIΔvif, or 9×104 or 3.6×105 HIVJR-CSFvifFS intravenously by tail vein injection or into specific organs as indicated in the text. HIV-1 infection of humanized mice was monitored in peripheral blood by viral load analysis as previously described [27].
Tissues were harvested for evaluation of HIV-1 infection essentially as previously described [21]. Genomic DNA from mononuclear cells (5×105–5×106) from animal tissues was prepared using QIAamp DNA blood mini columns (Qiagen) according to the manufacture's protocol. Viral RNA was isolated from plasma using QIAamp viral RNA columns (Qiagen) according to the manufacture's protocol including an optional treatment with RNase-free DNase (Qiagen) during extraction and cDNA was generated using Superscript III Reverse Transcriptase (Invitrogen). Viral DNA or cDNA was amplified by nested PCR using the Expand High Fidelity PCR System (Roche).
All PCR primers amplify both HIV-1JR-CSF and HIV-1LAI and were designed to anneal in regions with the fewest possible putative APOBEC3 deamination sites to avoid potential primer mismatch due to APOBEC3 induced mutagenesis. HIV regions amplified include a 1.5 kb region in pol (RT: HIV-1JR-CSF 2493–4023; HIV-1LAI 2063–3595), a 1.4 kb region including vif and vpr (vif: HIV-1JR-CSF 4941–6399; HIV-1LAI 4511–5969), and a 900 base region in the 3′ viral genome (nef: HIV-1JR-CSF 8722–9634; HIV-1LAI 8328–9211). Amplification of both RT and nef was used to assess APOBEC3 hypermutation while vif was amplified to asses APOBEC3 hypermutation and to confirm the integrity (or restoration) of the ORF. Primer sequences were as follows: RT outer forward primer, GCTCTATTAGATACAGGAGC; reverse primer, CCTAATGCATATTGTGAGTCTG; RT inner forward primer, GTAGGACCTACACCTGTCAAC; reverse primer, CCTGCAAAGCTAGGTGAATTGC. Vif outer forward primer, CAGGGACAACAGAGATCC; reverse primer, GTGGGTACACAGGCATGTGTGG; vif inner forward primer, CTTTGGAA AGGACCAGCAAAGC; reverse primer, GATGCACAAAATAGAGTGGTGG. Nef outer forward primer, GAATAGTGCTGTTAGCTTGC; reverse primer, CTCAAGGCAAGCTTTATTGAGG; nef inner forward primer, TAGAGCTATTCGCCACATACC; nef inner reverse, CTTTATTGAGGCTTAAGCAGTGG. Amplified viral DNA was sequenced and compared to the corresponding proviral DNA sequence used to generate the viruses using the Highlighter sequence visualization tool (www.hiv.lanl.gov).
One-way ANOVA with Bonferroni's multiple comparison test (alpha level, 0.01), Paired two-tailed t tests, and Unpaired two-tailed t tests were all performed using Prism version 4 (Graph Pad, La Jolla, CA). All data were plotted as mean +/− SEM.
The GenBank (http://www.ncbi.nlm.nih.gov/nuccore) accession numbers for HIV-1JR-CSF and HIV-1LAI are M38429 and K02013. The GenPept (http://www.ncbi.nlm.nih.gov/protein) accession numbers for APOBEC3G and APOBEC3F are NP_068594 and Q8IUX4.
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10.1371/journal.pgen.1003854 | Xbp1 Directs Global Repression of Budding Yeast Transcription during the Transition to Quiescence and Is Important for the Longevity and Reversibility of the Quiescent State | Pure populations of quiescent yeast can be obtained from stationary phase cultures that have ceased proliferation after exhausting glucose and other carbon sources from their environment. They are uniformly arrested in the G1 phase of the cell cycle, and display very high thermo-tolerance and longevity. We find that G1 arrest is initiated before all the glucose has been scavenged from the media. Maintaining G1 arrest requires transcriptional repression of the G1 cyclin, CLN3, by Xbp1. Xbp1 is induced as glucose is depleted and it is among the most abundant transcripts in quiescent cells. Xbp1 binds and represses CLN3 transcription and in the absence of Xbp1, or with extra copies of CLN3, cells undergo ectopic divisions and produce very small cells. The Rad53-mediated replication stress checkpoint reinforces the arrest and becomes essential when Cln3 is overproduced. The XBP1 transcript also undergoes metabolic oscillations under glucose limitation and we identified many additional transcripts that oscillate out of phase with XBP1 and have Xbp1 binding sites in their promoters. Further global analysis revealed that Xbp1 represses 15% of all yeast genes as they enter the quiescent state and over 500 of these transcripts contain Xbp1 binding sites in their promoters. Xbp1-repressed transcripts are highly enriched for genes involved in the regulation of cell growth, cell division and metabolism. Failure to repress some or all of these targets leads xbp1 cells to enter a permanent arrest or senescence with a shortened lifespan.
| Complex organisms depend on populations of non-dividing quiescent cells for their controlled growth, development and tissue renewal. These quiescent cells are maintained in a resting state, and divide only when stimulated to do so. Unscheduled exit or failure to enter this quiescent state results in uncontrolled proliferation and cancer. Yeast cells also enter a stable, protected and reversible quiescent state. As with higher cells, they exit the cell cycle from G1, reduce growth, conserve and recycle cellular contents. These similarities, and the fact that the mechanisms that start and stop the cell cycle are fundamentally conserved lead us to think that understanding how yeast enter, maintain and reverse quiescence could give important leads into the same processes in complex organisms. We show that yeast cells maintain G1 arrest by expressing a transcription factor that represses conserved activators (cyclins) and hundreds of other genes that are important for cell division and cell growth. Failure to repress some or all of these targets leads to extra cell divisions, prevents reversible arrest and shortens life span. Many Xbp1 targets are conserved cell cycle regulators and may also be actively repressed in the quiescent cells of more complex organisms.
| Budding yeast that are grown in rich glucose-containing media and are allowed to naturally exhaust their carbon source undergo a series of changes that enable a significant fraction of the cells, primarily daughter cells, to enter a protective quiescent (Q) state [1]. As yeast cells transition to quiescence, they shift to respiration [2] and stockpile their glucose in the form of glycogen and trehalose [3], [4]. These Q cells are significantly denser than their nonquiescent (nonQ) siblings, which enables us to purify them by density sedimentation [1]. The ability to purify Q cells offers a unique opportunity to study this transition.
An important characteristic of all quiescent cells is that they arrest their cell cycle in G1. This requires the G1 to S transition to be stably halted by a mechanism that can be readily reversed when conditions permit. In cycling cells, progression through G1 into the next S phase involves two consecutive waves of G1 cyclin (Cln) expression. CLN3 is transcribed at the M/G1 border [5] and Cln3 associated with the cyclin-dependent kinase (Cdk) activates the transcription of the CLN1 and CLN2 cyclins and other genes that trigger budding and DNA replication [6]–[8]. If the fidelity or timing of S phase is disrupted, there are checkpoint proteins, including Rad53 and Rad9, which monitor incomplete or damaged DNA and delay cell division to allow for reparations [9].
Cln3/Cdk activity is rate limiting for the G1 to S transition during exponential growth. Excess Cln3 results in shorter G1 phases and smaller cells, while loss of Cln3 function prolongs G1 and results in larger cells [10], [11]. Previous studies have shown that the G1 cyclin Cln3, ectopically expressed during stationary phase from the UBI4 promoter, prevents G1 arrest and causes loss of viability [12]. Tetraploid cells also die in stationary phase and this inviability can be completely rescued by deletion of all four CLN3 genes [13]. These deleterious effects indicate that Cln3/Cdk must be tightly controlled during stationary phase and that its deregulation antagonizes entry into the Q state.
In this work, we demonstrate that G1 arrest is initiated before the diauxic shift (DS), which is when all the glucose has been scavenged from the media. CLN3 is a critical target of repression for G1 arrest and for the transition to quiescence. Rad53 checkpoint activity reinforces this arrest in wild type cells and becomes essential when Cln3 is overproduced. Xbp1 is also important for maintaining G1 arrest. Xbp1 is a repressor of CLN3 transcription [14], [15]. It is related to the Swi4/Mbp1 family of transcription factors, which are the DNA binding components of the yeast complexes paralogous to E2F/Dp1 in higher cells [7], [8]. As glucose is exhausted from the media, the XBP1 transcript is induced and it is among the most abundant transcripts in Q cells. Xbp1 binds and represses hundreds of genes, including CLN3 during the post-DS phase of growth. In the absence of Xbp1, cells undergo extra post-DS cell divisions and produce very small cells. These phenotypes are Cln3-dependent. xbp1 mutant Q cells are also defective in the maintenance of and recovery from the Q state. xbp1 Q cells maintain viability, but lose the ability to re-enter the cell cycle. Using Next Generation Sequencing [16], we have identified over 800 transcripts that are repressed three-fold or more by an Xbp1-dependent mechanism and 520 of these contain Xbp1 binding sites in their promoters. Xbp1 binds directly to all seven of the promoters we tested, in vivo, but only in post-DS cells. These findings indicate that Xbp1 is a global regulator specifically during the transition to quiescence. Xbp1's other targets include many genes involved in cell division, with a particular enrichment of genes required for cytokinesis. Many genes whose products localize to sites of polarized cell growth and are involved in cell wall remodeling are targeted by Xbp1. In addition, many metabolic and transport pathways are repressed by Xbp1.
Yeast cells spend most of their time in a non-dividing state triggered by nutrient depletion from their environment. Under the conditions we employ (see Methods), yeast undergo a highly reproducible transition from the logarithmic (log) phase of growth to stationary phase in response to carbon limitation. Figure 1 shows the average of four growth curves in which we monitored cell density, cell number and DNA content as prototrophic W303 cells grew from log phase to stationary phase in rich medium. The turbidity of the culture increases over this time course to an optical density (OD600) of about 24, but the cell number only doubles once after the DS, which occurs between the 12 and 14 hour time points. We have monitored the DNA content of these cells to determine what fraction of cells are in G1, S and G2/M over this time course. Interestingly, the 12 to 14 hour interval shows the sharpest increase in the percentage of cells in G1. This indicates that the signal to slow proliferation is occurring at or before the DS and cells respond by extending or arresting in G1. Figure 1D shows the DNA of wild type cells in log phase (8 hours), immediately after the DS, and one hour later. During log phase, the G1 (1N) and G2/M (2N) cells form two spots or peaks of high density by flow cytometry. The cells in S phase, with intermediate DNA content, are scattered between them and make up about 20% of the cells in the population. At the DS, the percentage of cells in G1 is already double that of log phase cells. This indicates that cells begin to slow the G1 to S transition before the diauxic shift. Also at the DS, we see a drop in the number of cells that are in early S phase (Figure 1D,) which is an indication that the initiation of new DNA synthesis ceases at this time. One hour after the DS, less than 3% of the cells are in S phase, and this pattern persists for at least 34 hours. We conclude that the signal to stop proliferation is received before the cells have scavenged all the glucose from the media and they respond by extending G1. The halt to DNA replication is correlated with and could be triggered by the DS.
To determine how G1 arrest is accomplished, we have assessed the role of several key regulators of the G1 to S transition. Cln3/Cdk activity is rate limiting for the G1 to S transition during exponential growth. To investigate the effects of over-producing Cln3 on Q cell formation, we generated a prototrophic strain carrying five copies of the wild type CLN3 gene (5XCLN3). This strategy maintains all the regulatory features of the wild type CLN3 gene, while it increases the Cln3 expression level. We first verified that the 5XCLN3 construct produces about five-fold higher levels of CLN3 mRNA than wild type as cells grow from log to stationary phase (Figure 2A). To assess the impact of excess Cln3 on the transition to quiescence, we compared Q cell yield in 5XCLN3 cells to cln3 mutant and wild type cells. 5XCLN3 consistently reduces Q cell yield by half, and cln3 mutants increase Q cell production by at least 30%. This confirms that Cln3 activity is above wild type levels in the 5XCLN3 strain and that this excess Cln3 inhibits Q cell formation. It also suggests that cells enter the Q state from G1 and the longer they stay in G1 the more likely they are to achieve a successful transition into this state.
The CLN3 transcript level is high in rapidly cycling cells then it drops abruptly as cells enter stationary phase (Figure 2A). This is not unexpected, because the CLN3 promoter is cell cycle regulated [5], and it is activated by Azf1 in the presence of glucose [17], [18]. In addition, CLN3 is a target of the Xbp1 repressor, which is highly induced by glucose limitation [14].
Xbp1 is a transcriptional repressor that is not expressed during the log phase of growth, but it is induced by many forms of stress, including DNA damage and glucose limitation [14], [19]. When Xbp1 is ectopically produced in log phase cells, it binds to and represses the CLN3, CLN1 and CLB2 cyclin promoters [15]. Xbp1 overproducers also grow slowly and prolong the G1 phase of the cell cycle [14], [20]. This led us to ask if Xbp1 could be important for repressing CLN3 and halting cell division during the transition from log phase to quiescence. xbp1 and wild type cells are identical in size during logarithmic growth, however xbp1 cells are much smaller than wild type cells when grown to stationary phase (Figure 2B). This could be explained if xbp1 mutants continue proliferating under growth limiting conditions and the physical growth of the resulting cells is impaired. Figure 2C shows that this is the case. xbp1 cultures attain a higher cell number at stationary phase than do wild type cells, indicating that they undergo extra cell divisions. This can also be seen as a slower accumulation in G1 (Figure 2D). The xbp1 mutant reaches 80% G1 eight hours after wild type cells. If CLN3 is a critical target of Xbp1, we expected that the ectopic cell divisions, the small cell size, and the G1 arrest delay of xbp1 mutants would depend on the presence of Cln3. We have assayed these phenotypes in the xbp1cln3 double mutant. As predicted, xbp1cln3 cells are the same large size as cln3 cells (Figure 2B), and they undergo fewer cell divisions, as do cln3 cells (Figure 2C.) xbp1cln3 cells also display the same rate of accumulation in G1 that is seen in wild type cells (Figure 2D). This shows that these hyper-proliferative phenotypes of xbp1 are Cln3-dependent. We also expected that 5XCLN3 would share these xbp1 phenotypes. Figure 2B shows that 5XCLN3 cells are the same small size as xbp1 cells during post-diauxic growth, they undergo extra cell divisions like xbp1 (Figure 2C), and 5XCLN3 delays G1 arrest (Figure 2E.)
During logarithmic growth, accelerating the transition from G1 to S causes a sub-optimal S phase and such cells cannot survive without eliciting the replication stress checkpoint [21]–[23]. The fact that excess Cln3 only delays G1 arrest led us to wonder if the replication stress checkpoint also plays a role in restraining cell cycle progression under these conditions. To test this, we combined rad53-21, which lacks checkpoint activity [24] with 5XCLN3. These cells were grown from log phase into stationary phase and assayed for their ability to G1 arrest. Like 5XCLN3, rad53-21 alone has a modest G1 arrest defect. However, Rad53 is critically important for G1 arrest and Q cell formation when Cln3 is in excess. rad53-21 5XCLN3 cells divide more slowly and undergo the DS six hours later than wild type cells. They very gradually accumulate in G1, reaching 50% G1 about 30 hours later than wild type (Figure 2E). These cells also lose viability rapidly as they enter stationary phase (Figure 2F.) After seven days of growth 80% of the rad53-21 5XCLN3 cells were dead based on vital dye staining. rad53-21 5XCLN3 cells are also completely defective in Q cell formation. These results indicate that the excess Cln3 produced by the 5XCLN3 loci is toxic to nutrient-limited cells that do not have Rad53 checkpoint function. It is worth noting that this experiment was carried out at a constant pH in rich medium. Therefore, this loss of viability cannot be due to acidification, as it is in unbuffered, minimal media [25].
Reactive oxygen species (ROS) and DNA fragmentation has been associated with DNA damage and replication stress in yeast and metazoan cells [26], [27]. During log phase, 8% of the rad53-21 5XCLN3 cells were ROS positive (data not shown.) By day five, 43% of these cells contained ROS and 31% showed DNA fragmentation, as detected by TUNEL staining (Figure 2G). ROS was also detectable in 5XCLN3 and rad53-21 single mutants, but they showed no detectable TUNEL positive cells and high viability over this time course, which indicates that they were able to tolerate this level of ROS accumulation. However, the rad53-21 cells contained four times more ROS than wild type cells by day five (Figure 2G). This indicates that wild type cells also rely on Rad53 checkpoint activity during the transition to quiescence.
Rad53 is activated in response to both replicative stress and DNA damage. To see if DNA damage is involved, we combined 5XCLN3 with rad9, which is a DNA damage-specific checkpoint protein [28]–[30]. 5XCLN3 showed no toxicity in the absence of Rad9 (Figure 2F). We conclude that cells utilize the replicative stress checkpoint to reinforce cell cycle arrest during the transition to quiescence. Cells that fail to down-regulate CLN3 during this transition depend on this checkpoint for their survival. Checkpoint failure leads to apoptotic cell death.
Repression of CLN3 is important for the G1 arrest that is initiated by glucose limitation, and our data are consistent with Xbp1 playing a role in that process. However, when we combine xbp1 with rad53-21, there is no additive effect. The xbp1 rad53-21 is no more defective in G1 arrest then rad53-21 alone (Figure 2D). This suggests that Xbp1 may not repress CLN3 under these conditions. To directly assess the role of Xbp1 in CLN3 repression, we used chromatin immunoprecipitation and RNA Next Generation sequencing. Figure 3A (lanes 1 and 2) show that Xbp1 binding to the CLN3 promoter is undetectable in log phase cells, but it is clearly bound in cells harvested after 24 hours of growth. This can be explained by the fact that Xbp1 is induced by glucose limitation. Figure 3B shows the dramatic induction of XBP1 mRNA that begins before the diauxic shift (14 hours) and continues for 48 hours. It is also present at very high levels in Q cells purified from a seven day old culture. In fact, XBP1 ranks within the top 100 most abundant transcripts in Q cells. Figure 3C shows CLN3 mRNA levels over this same time course in wild type and xbp1 cells. The initial pre-DS drop in CLN3 mRNA still occurs, but we see a two to three-fold de-repression of CLN3 from 14 to 48 hours in the absence of Xbp1. It then drops to a very low level in Q cells, and that drop is also Xbp1-independent. This pattern suggests that there may be three distinct mechanisms for establishing and maintaining CLN3 repression and that Xbp1 plays a role in maintaining CLN3 repression during post-diauxic growth.
The fact that the pre-DS drop in CLN3 levels still occurs in xbp1 cells indicates that the initial signaling to slow proliferation is intact. Figure 2C and D also show that cell number and the fraction of xbp1 cells in G1 is very similar to wild type for the first 18 hours. Direct comparison of the FACS profiles of wild type (Figure 1D) and xbp1 cells (Figure 3D) shows that xbp1 cells halt S phase as well as wild type at the 14 hour time point, but by 20 hours a new S phase population has emerged (Figure 3D.) This S phase re-entry is also Cln3-dependent (data not shown.) In contrast, S phase cells are present at the DS and throughout this time course in the 5XCLN3 population (Figure 3E.) It is possible that either the timing or the extent of replication driven by 5XCLN3 makes these cells more dependent upon the Rad53 replication stress checkpoint for viability.
The high level of induction of XBP1 suggests that it may be a major regulator during post-diauxic growth and in Q cells. Two other key properties of Q cells are their ability to rapidly reverse their arrest upon re-feeding, and their longevity during prolonged intervals of arrest. Xbp1 Q cells are defective in both of these processes. Figure 4A shows the recovery cycle of wild type and xbp1 Q cells upon re-feeding. Wild type Q cells have a 90 minute delay, followed by a highly synchronous cell cycle as monitored by budding. xbp1 Q cells initiate budding 30 minutes later and only about half the cells participate. The very small xbp1 Q cells show no indication of budding at the 150 minute time point (Figure 4D.) These small cells initiate budding two hours after wild type Q cells begin to bud.
Q cell longevity is also compromised by xbp1. Figure 4B shows that xbp1 Q cells, suspended in water, retain wild type viability for at least 8 weeks, as assayed by vital dye exclusion. Q cells do not acidify the water over this time course, indicating that they are in a fundamentally different state than stationary phase cultures [25]. However, xbp1 Q cells lose the ability to form colonies more rapidly than wild type Q cells, indicating that they cannot maintain a reversible quiescent state (Figure 4C). After six weeks, 75% of xbp1 Q cells are viable, but only one-third of those can re-enter the cell cycle and form a colony. Interestingly, all of the viable cln3 Q cells can return to the cell cycle at this time point. This irreversible non-dividing state or senescence exhibited by xbp1 Q cells can be delayed, but it is not suppressed by deleting CLN3. This indicates that the premature senescence of xbp1 is not a Cln3-dependent phenotype. We conclude that Xbp1 also targets genes that influence the recovery and longevity of Q cells.
To see if Xbp1 performs a broader repressive function during the transition to quiescence, we looked for transcripts that are repressed when XBP1 is induced. XBP1 mRNA undergoes dramatic oscillations in cells that are synchronized to undergo metabolic and cell cycle oscillations by glucose limitation [31]. Xbp1's known targets (CLN3, CYS3, CLN1 and CLB2) also display metabolic oscillations, and peak out of phase with Xbp1. Using microarray and motif search tools [32]–[34], we identified ten transcripts that undergo metabolic oscillations out of phase with Xbp1 and that contain Xbp1 binding sites in their promoters. We verified that PIS1, DOG2, and CDC10 are bound in vivo by Xbp1 after the DS, just like CLN3 (Figure 3A). We then identified 100 transcripts whose profiles in the metabolic oscillation data set were most closely correlated with the average profile of CLN3, DOG2 and PIS1 (Supplementary Figure S1) [35]. Among those 100 genes, 54 contained Xbp1 binding sites (CTCGAG/A [14]) within 800 base pairs of their translational start sites. Three of these genes encode transcription factors (RCS1/AFT1, RFX1 and NRG2), which we also verified to be in vivo binding sites for Xbp1 by chromatin immunoprecipitation (Figure 3A).
To show that the repression of these transcripts is Xbp1-mediated and to identify other targets, we used our Next-Generation RNA sequencing [16] data to compare transcript levels of genes from wild type and xbp1 cells as they transit from log phase to stationary phase. CLN3 (Figure 3C), and all 54 of the transcripts we identified as having Xbp1 binding sites, were derepressed in one or more of the post-DS time points in the xbp1 mutant. We then identified over 800 transcripts (Supplementary Table S1) that are repressed by Xbp1, three-fold or more, in at least one of the post-DS time points. More than half (520) of these genes contained Xbp1 binding sites within the 800 base pairs upstream of their coding sequences. Figure 5A shows the consensus Xbp1 binding site derived from these 520 derepressed transcripts. We will refer to these 520 genes as direct targets of Xbp1. Figure 5B shows a dot plot comparison of all transcript levels in xbp1 and wild type cells. Direct Xbp1 targets (red dots) are not significantly affected by the absence of Xbp1 during log phase (8 hours). A few transcripts begin to rise in the xbp1 cells at the DS (14 hours), and this trend continues throughout the time course and in purified Q cells. Very few direct targets are down-regulated. This is consistent with our previous findings that Xbp1 functions as a repressor [14], [15], and expands its role as a global repressor specifically during post diauxic growth and quiescence.
Xbp1 expression is induced at 14 hours and remains high across this time course (Figure 3B), but both the levels and the timing of transcription of its targets vary widely. Figure 6 shows the transcript levels of the direct and indirect targets of Xbp1 that are elevated three-fold or greater during post-diauxic growth. Forty of these transcripts are elevated at least sixteen-fold. However, most reach their peak during a specific interval, which varies for each target gene. We speculate that this variation is due to differences in activation. If Xbp1 serves solely as a repressor, the expression of each one of its target genes would still depend on the expression and stability of its activator(s).
To look more closely at all Xbp1-mediated repression, we identified transcripts that are derepressed three-fold or greater at each time point (Table 1). Significant derepression is observed after 18 hours. The majority of these Xbp1-repressed transcripts are involved in biological regulation (p value 10–9). One third are localized to the cell periphery, but only nine are classified as cell wall proteins. 23 are localized to sites of polarized cell growth [36]. Several of these genes are involved in bud site selection or are components of the Cdc42-mediated cell polarization pathway. Components of the septin ring, which separates mother from daughter [37], the cohesion complex, which holds sister chromatids together, and components that facilitate chromosome segregation [38] are repressed by Xbp1 at 18 hours. Two cyclins (CLN3 and CLN1) that drive the G1 to S transition [6] and are known Xbp1 targets [14], [15] are elevated at this time point. Regulators of transcription are also affected. Among these are transcription factors that promote the G1 to S transition (SWI6 [39]), the S to G2/M transition (NDD1 [40], and others that induce alternative cell fates: filamentation (MSS11 and MGA1 [41]), and meiosis (IME1 [42]). Hence, Xbp1 promotes quiescence by repressing multiple targets involved in mitotic growth and by preventing cells from adopting other developmental fates.
After 24 hours of growth, 65 known genes are derepressed in the absence of Xbp1. At this time point, cell wall proteins are highly enriched. These include most of the daughter-specific genes [43]. Six gluconases and the chitinase Cts1, which are responsible for degrading the cell wall and chitin ring between mother and daughter to achieve cell separation [44] are targeted. In addition, cell division and specifically cytokinesis targets are highly enriched. Three late cycle cyclins (CLB4, CLB2 [45] and PCL9 [46]) are also targeted.
By 48 hours, nearly 10% of all genes (515) are derepressed in the absence of Xbp1. At this time point almost half of the known targeted genes are involved in metabolism and the other large class is involved in cell wall biogenesis. 45 cell cycle genes and 25 transcription regulators are also derepressed at this time point. Only one-third of these derepressed genes are also derepressed in Q cells. Xbp1 affects a more diverse group of genes in purified Q cells. Metabolic genes are the largest class. In addition, 42 genes involved in transmembrane transport, including five glucose transporters are repressed by Xbp1 in Q cells.
We also analyzed direct and indirect targets separately. What is striking is that direct and indirect targets are largely in the same pathways. At 18 and 24 hours, mitosis, cell cycle, cell division and cytokinesis are significantly enriched classes in both direct and indirect targets (Supplementary Table S2). At 48 hours, both direct and indirect targets are highly enriched for genes involved in metabolism and cell wall organization.
The dot plots of Figure 5 show that Xbp1 primarily serves as a repressor of transcription. Transcripts whose levels are under-represented by three-fold or more in the xbp1 mutant are rare until the 48 hour time point and in Q cells. However, at these two time points almost 500 transcripts fit this criterion. Unlike the derepressed transcripts, of which 60% are associated with Xbp1 binding sites, only one fifth of the down-regulated genes are near an Xbp1 binding site, which is about what is expected by chance. This is consistent with Xbp1 playing an indirect role at these promoters. To our surprise, there are only 14 transcripts that are under-represented both at 48 hours and in Q cells. At 48 hours, the under-represented transcripts are nearly all involved in ribosome biogenesis (90/257, p value 10–45) and nitrogen metabolism (156/257 p value 10–18). In Q cells, they are highly enriched for ribosomal proteins (47/129, p value 10–42) and genes involved in monosaccharide catabolism (16/129 p value 10–12). The ribosome biogenesis and ribosomal protein transcripts are tightly and coordinately regulated in response to nutrient conditions [47]. It is unclear how Xbp1 influences the expression of these genes. The striking lack of overlap between the transcripts that are under-represented in xbp1 cells at 48 hours versus purified Q cells suggests that these are fundamentally different states.
In rich glucose-containing medium, yeast cells cease growth and division after about 48 hours due to carbon limitation. The resulting culture is a heterogeneous population of live and dead cells. Most of the daughter cells enter a quiescent state and can be purified due to their increased density [1]. Q cells develop unique characteristics including high thermo-tolerance [1] and high levels of glucose stored in the form of trehalose and glycogen [4]. The transition to quiescence does not occur when cells are abruptly deprived of glucose (Li et al, submitted), so there must be some cellular response to its waning supply that signals cells to stop proliferating, stockpile the remaining glucose and enter a quiescent state.
We are investigating the events that differentiate Q cells from nonQ cells and promote their longevity [48]. We find that G1 arrest is an early event in the transition to quiescence. There is a three-fold increase in the fraction of cells in G1 that occurs before glucose is depleted from the medium. At this point, referred to as the diauxic shift (DS), initiation of DNA synthesis is dramatically reduced and most of the cell division that occurs thereafter can be accounted for by the completion of cell cycles that were previously initiated. We have identified several key regulators that are important for achieving this arrest. We find that excess Cln3 activity, expressed from five integrated copies of the wild type CLN3 gene, interferes with Q cell formation, and cells lacking Cln3 produce more Q cells. Cells transitioning to quiescence with excess Cln3 accumulate in G1 more slowly than wild type cells, but they eventually arrest and remain viable due to the activation of the checkpoint kinase Rad53.
Rad53 is an effector of the DNA damage and replication stress checkpoints [49]. Rad9, which is specific to the DNA damage checkpoint [29], [30], is not required for the survival of 5XCLN3 cells, so we conclude that replicative stress, not DNA damage, triggers the checkpoint during the transition to quiescence. We detect delayed G1 arrest and increased ROS accumulation as nutrients become limiting, even in wild type cells carrying rad53-21. This suggests that replication stress occurs and this checkpoint pathway plays a role in restricting cell cycle progression during the wild type transition to quiescence. With excess Cln3, checkpoint function becomes essential and cells lacking it fail to arrest in G1 and undergo apoptosis. Related effects have been observed with excess cyclin E, and other activated oncogenes in higher cells [50], [51] and in yeast [26], [27], [52].
Our data indicate that CLN3 repression is mechanistically different before and after the DS, and that only its post-DS repression is Xbp1-dependent. The initial drop in CLN3 levels and the halt to S phase that we observe at the DS are Xbp1-independent. Only after the DS, the CLN3 promoter is bound and repressed by Xbp1. Cells lacking Xbp1 resume DNA replication and continue to divide after the DS, and this results in a significant population of very small cells. These phenotypes are Cln3-dependent. These data are consistent with Xbp1 playing a role in maintaining repression of CLN3 and G1 arrest as cells transition from growth to quiescence. However, unlike 5XCLN3, xbp1 mutants are not dependent on the Rad53 replication stress checkpoint for viability. We suspect that either the timing or the extent of derepression of CLN3 by xbp1 could explain the Rad53-independence of these cells. A third possibility is that Rad53 acts in the same pathway and upstream of Xbp1 to restrict cell cycle progression. Rad53 has been shown to increase the level of Xbp1 in response to DNA damage [19]. These possibilities are under investigation.
Xbp1 mutant Q cells remain viable, but they are profoundly delayed in cell cycle re-entry upon re-feeding. They are also short-lived as Q cells, entering an irreversible, senescent state more rapidly than wild type. We have not identified the genes responsible for these phenotypes because our data show that Xbp1 plays a global and continuous repressive role in cells as they transition from a dividing to a non-dividing quiescent state. We have identified 520 targets of Xbp1-mediated repression that contain Xbp1 binding sites in their promoters. All seven that we tested are direct in vivo binding sites for Xbp1. Binding is only detected after the DS, which explains why these targets were not identified in previous studies. None of the Xbp1 targets identified by genome-wide location analysis [53] are among the 520 targets we have identified, and only 5 of the 41 transcripts reported to be affected by an xbp1 deletion [54] are among the 822 transcripts that we find are derepressed after the DS. These differences emphasize the need to determine when a transcription factor is active and use those conditions to search for its targets. The consensus binding site we have derived from the 520 targets agrees with that which we initially identified by site selection [14] and that reported by [55].
XBP1 mRNA oscillates dramatically in cells that are undergoing yeast metabolic and cell cycle (YMC) oscillations [31], and we identified many Xbp1 targets by looking for its binding site in transcripts that oscillate out of phase with XBP1. YMC oscillations are achieved by growing the cells to maximum density, starving them for glucose, then restoring a limited amount of glucose, which is immediately imported and cannot be detected in the media [31], [56]. These conditions resemble the DS and they evoke the expression of genes that are induced by glucose starvation and stress, including XBP1. Cell division stops and storage carbohydrates accumulate. This is the quiescence-like phase of the YMC [4]. In the subsequent phase, XBP1 is turned off, and it's targets peak. Then, DNA replication takes place and cells divide. The striking parallels between the events associated with the transitions in and out of quiescence, and those associated with the YMC suggest that YMC oscillations may be the result of switching on and off the signal to arrest in G1 and enter quiescence. It also seems likely that the oscillation of XBP1 expression is responsible for the subsequent YMC oscillations of its many targets. One such verified target, CLN3, and many other cell cycle regulated transcripts have been shown to have different peak time or multiple peaks in the synchronized cell cycles induced by the YMC protocol [57], [58] compared to that of other cell cycle synchronization studies [59]–[61]. Our finding that a global repressor of CLN3 and 800 other transcripts is also oscillating during the YMC time course may explain some of those altered peak times.
Xbp1 negatively regulates the mRNA levels of 15% of yeast genes during post-diauxic growth. When Xbp1 was ectopically expressed during logarithmic growth, only a small number of Xbp1 targets were identified [14]. This suggests that Xbp1 may be more active in the post-diauxic state, either due to modification of Xbp1 or to the presence of co-factors that increase its activity or the accessibility of its targets. Among its many targets, Xbp1 represses the transcription of key activators of mitosis, meiosis, and filamentation. Repressing these genes may promote the quiescent state by reinforcing G1 arrest and by preventing cells from adopting alternative fates that are also triggered by nutrient limitation. Xbp1 also plays a major but complex role in the metabolic shifts that take place as cells shift from glycolysis to respiration to quiescence. Its many structural and regulatory targets involved in cell wall remodeling and cell division indicate that preventing growth is an active and continuous process in quiescent cells. Even basal expression of these genes may be deleterious to the stability of the quiescent state and/or to the orderly recovery from it.
It is striking that transcripts derepressed by xbp1 early in the transition to quiescence are largely cell cycle and growth regulators. At 18 hours, even the gene products associated with the cell periphery are largely sensors and regulators, rather then structural proteins. This is true of both direct and indirect targets. One possible explanation is that xbp1 mutant cells continue to divide during this interval. About 20% of Xbp1 targets are cell cycle regulated at the transcript level (data not shown). These transcripts are not made when cells stop dividing, so anything that promotes ectopic cell division would increase the transcription of these genes. However, only one-third of the 600 most cell cycle regulated transcripts [62] are elevated in the absence of Xbp1 and no particular class is enriched. If their elevated levels were due to continued cell divisions, all 600 would be elevated. We conclude that repression of these transcripts is an active Xbp1-mediated process, and the fact that half of them contain Xbp1 binding sites in their promoters is consistent with that conclusion. By 48 hours, hundreds of transcripts involved in metabolism and cell wall organization are affected. Again this is true of direct and indirect targets. At this point cell division has ceased, and transcriptional activators that promote cell division are likely to be inactive. Without these activators, loss of Xbp1-mediated repression may be of little consequence. However, house-keeping and metabolic genes may be constitutively active and require sustained repression in order to conserve resources. Our data indicate that Xbp1 provides the repression of these genes, perhaps through its recruitment of the histone deacetylase, Rpd3 [19].
We also looked for transcripts that were under-represented in the xbp1 mutant. These were prominent only in the last two time points and they do not show any enrichment for Xbp1 binding sites. This supports the view that Xbp1 functions primarily, if not solely, as a repressor. We expect that the reduced levels of these transcripts are an indirect effect of the many perturbations that arise in xbp1 cells where 15% of genes are expressed at a time when they should be off.
XBP1 mRNA is among the top 1% highest level transcripts in Q cells. Xbp1 has also been shown to be translationally up-regulated in response to both glucose and amino acid starvation [63]. These observations are consistent with Xbp1 serving as a global repressor of transcription as cells respond to nutrient depletion and transition to a non-dividing quiescent state. The longevity and recovery defects we observe for xbp1 mutant Q cells demonstrate the importance of this repression. Xbp1 shares homology within its DNA binding domain with four other S. cerevisiae transcription factors that specify cell fate. Swi4 and Mbp1 associate with Swi6 and serve as activators of mitotic growth [64]. Sok2 and Phd1 play opposing roles in pseudohyphal development [65]–[67]. Xbp1 plays a minor role in sporulation [15] and pseudohyphal development [68], and this work shows that it is an important global repressor during the transition to quiescence. This family of transcription factors is found only in fungi, and may be important targets for anti-fungal drugs. One of the Candida family members, Efg1, is critical for biofilm formation, which renders these pathogens drug-resistant [69]. We note that many Xbp1 targets are also known to affect virulence in bacterial and fungal pathogens. These include PMT1, 2, and 4 [70], ECM33 [71], SMI1 and FKS1 [72]. Understanding Xbp1's regulation and its role in defining the quiescent state may provide important insights with both medical and basic research implications.
The yeast strains were all derived from W303. The auxotrophic markers were corrected in all strains. The strains carrying five copies of CLN3 were generated by integrating additional copies of CLN3 at four different marker loci using the integrating vectors, pRS303-306 [73]. The wild type controls for these studies contain the same empty vectors integrated at the same locations. To generate the W303 prototroph, BY6500, the auxotrophic mutations were replaced with the wild-type sequence by homologous gene replacement and verified by PCR and sequencing. The checkpoint deficient rad53-21 mutant (Allen et al., 1994) was crossed into the W303 background above to generate BY6741 and subsequently crossed with the 5XCLN3 strain to generate BY6698. CLN3, XBP1 and RAD9 were deleted with KanMX as described [74].
Reproducible growth curves were obtained by patching cells from fresh plates onto YEP plus 2% glycerol and growing them overnight to eliminate petites. This patch was used to inoculate 5 ml YEPD, then a further 1/50 dilution was made and grown overnight. This culture was used to inoculate 25 ml YEPD in a 250 ml flask to an optical density (OD600) of 0.02 and allowed to grow at 30°C, shaking at 200 RPM. The diauxic shift was defined as the point at which no glucose was detected in the media, which was determined with glucose detection strips (GLU 300, Precision labs, Inc. West Chester, OH). Quiescent (Q) cells were purified from YEPD cultures that were seven days old using a 25 ml percoll density gradient [1] with minor modifications [48]. Q cell yield is calculated as the percentage of OD600 units loaded that sediment to the bottom nine ml of the gradient. Cell size and cell count was measured on a Z2 Beckman Coulter Counter. All time course data was collected in duplicate or triplicate, averaged and error bars are shown.
Cell viability was monitored using the FungaLight Yeast Viability Kit (Molecular Probes) according to the manufacturer's protocol and the percentage of live cells was plotted over time. Reproductive capacity was assayed as the ability to resume cell division and produce colonies. Serial dilutions were plated on YEPD plates in duplicate and the percentage of colony forming units (CFU) was plotted, using the CFU from the freshly harvested Q cell sample as 100%. The FungaLight and CFU viability data are averages from at least two independent experiments.
Calcofluor staining of bud scars: Approximately 107 cells were collected and mixed with Calcofluor white M2R (Fluorescent brightener 28; Sigma) at a final concentration of 100 µg/ml. Cells were incubated at room temperature for 15 min in the dark then were washed twice with H2O. The stained cells were examined with a Nikon Eclipse E600 microscope with a Nikon Plan Apochromat 60XA/1.40 oil immersion objective and a UV-2E/C DAPI filter (excitation at 330–380 nm). Photomicrographs of cells were taken on a Photometrics Cascade 512B camera and analyzed with MetaMorph version 6.3r2 software (Molecular Devices, Sunnyvale, CA). TUNEL Assay: Cells were fixed with 4% paraformaldehyde at room temperature for 15 min, spun down at 5000 rpm for 5 min and washed once with 0.1 M potassium phosphate 1.2 M sorbitol buffer pH 7.5. Stationary phase cells were first resuspended in 100–200 µl fresh pretreatment buffer (1 M Sorbitol, 25 mM EDTA, 50 mM DTT, pH 8), and then pelleted in a microfuge at 2000 rpm for 3 min at room temperature. These cells were resuspended in 1 M sorbitol and pelleted as before. Cell walls were digested with 50 µg/ml Zymolyase 100T in 1 M Sorbitol buffer (pH 5.8) for 10–45 min at 30°C. Cells were pelleted at 2000 rpm for 3 min, gently washed and resuspended in 15 µl potassium phosphate/sorbitol buffer, transferred to 0.1% polylysine-coated wells of an eight well microscope slide and allowed to settle for 20 min at room temperature. The slide was washed twice with phosphate-buffered saline (PBS). Each well was incubated with 40 µl fresh permeabilization solution (0.1% Triton X-100 in a 0.1% sodium citrate solution) for 2 min on ice, then rinsed with PBS buffer. 15 µl TUNEL reaction mixture (In Situ Cell Death Detection Kit, AP, Roche) was added to each well, slides were covered and incubated for 60 min at 37°C, then rinsed twice with PBS. Cells were observed under the microscope with an FITC filter (excitation at 460–500 nm). 100–200 cells per sample were evaluated.
For flow cytometry, cells were fixed in 70% ethanol for two hours or overnight, washed once with water, then resuspended in .5 ml 50 mM Tris-HCl (pH 8.0) containing 0.2 mg/ml RNAse A and incubated at 37°C for four hours. These cells were spun down, resuspended in .5 ml 50 mM Tris-HCl (pH 7.5) containing 2 mg/ml Proteinase K, and incubated at 50°C for one hour. They were then spun down again and resuspended in .5 ml 50 mM Tris-HCl (pH 7.5) and stored at 4°C. Before analysis, they were sonicated, pelleted, and resuspended in .5 ml I.0 µM Sytox Green (Invitrogen). Percent of cells in G1, S or G2/M phase of the cell cycle were quantified with FlowJo V9.
For ROS assays, approximately 1×106 cells were pelleted, gently washed with PBS, then resuspended in 1 mL PBS. 2.5 µL of a 10 mM carboxy-H2DCFDA (Invitrogen) stock solution was added and the cells were incubated for 30 minutes at 37°C. Cells were washed twice with PBS, resuspended in 1 mL 50 mM Tris-HCl pH 7.5. Cultures were then sonicated and 30,000 cells per sample were collected on a Fluorescence Activated Cell Sorter FACScan cytometer (BD Biosciences, San Jose, CA) and analyzed using Cell Quest software. FACS parameters were set at excitation and emission settings of 495 nm and 529 nm (filter FL-1), respectively. Average from two experiments is reported.
To generate enough cells for RNA measurements during growth from log phase to stationary phase, 5 OD600 of cells were collected every 10 minutes, washed with RNA buffer (50 mM Tris•HCl pH 7.4, 100 mM NaCl, 10 mM EDTA) and frozen for later RNA purification. The levels of CLN3 and ACT1 mRNA were monitored by an S1 nuclease protection assay as previously described [61]. CLN3 and ACT1 transcript levels were measured in each sample of wild type and 5XCLN3 cells. The ACT1, though not invariant, was not affected by excess CLN3 so it could be used to normalize the RNA levels between the two strains.
Next-Generation RNA sequencing was carried out with RNA prepared as above from log phase cells, purified Q cells, and cells grown in YEPD to log phase (8 hours) the DS (14 hours), then 18, 24 and 48 hours. mRNA expression levels following polyA selection were assayed using the HiSeq 2000 next generation sequencing system from Illumina [16], with RNA libraries prepared according to the manufacturer's instructions. FASTQ sequence output files were generated, demultiplexed by the Illumina CASAVA software package and filtered to remove sequences with low read quality. Nucleotide fragments were paired-end sequenced.
The W303 reference genome in FASTA format and gene annotations in GFF were obtained from the Wellcome Trust Sanger Institute's SGRP group. Sequences from each read were mapped to the Saccharomyces cerevisiae W303 reference genome using the Tophat application, a fast splice junction mapper for RNA-Seq reads [75]. Representation of RNA from annotated genes were assessed using HTSeq, a Python package developed by Simon Anders at EMBL Heidelberg, with quantitative expression calculated proportional to the number of reads per length of the modeled exon (MRPKBME). Finally, differential gene representation between treatments were assessed using the R/Bioconductor package DESeq [76]. These data for differentially expressed genes are provided as Supplementary Table S3. The demultiplexed FASTQ files have been submitted to the National Center for Biotechnology Information Sequence Read Archive and are available there as accession SRA098245.
Cells carrying Xbp1 tagged with a Tandem Affinity Purification (TAP) tag [77] or a non-tagged Xbp1 were collected from log phase cultures and cultures that had been growing for 24 hours into stationary phase. Proteins were cross linked to DNA as described [78] and IgG agarose beads (Sigma A2909) were used to pull down in vivo binding sites. PCR primers used to amplify potential targets as well as an unregulated DNA (IRV) are provided as Supplementary Table S4.
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10.1371/journal.pgen.1006407 | Ancient Out-of-Africa Mitochondrial DNA Variants Associate with Distinct Mitochondrial Gene Expression Patterns | Mitochondrial DNA (mtDNA) variants have been traditionally used as markers to trace ancient population migrations. Although experiments relying on model organisms and cytoplasmic hybrids, as well as disease association studies, have served to underline the functionality of certain mtDNA SNPs, only little is known of the regulatory impact of ancient mtDNA variants, especially in terms of gene expression. By analyzing RNA-seq data of 454 lymphoblast cell lines from the 1000 Genomes Project, we found that mtDNA variants defining the most common African genetic background, the L haplogroup, exhibit a distinct overall mtDNA gene expression pattern, which was independent of mtDNA copy numbers. Secondly, intra-population analysis revealed subtle, yet significant, expression differences in four tRNA genes. Strikingly, the more prominent African mtDNA gene expression pattern best correlated with the expression of nuclear DNA-encoded RNA-binding proteins, and with SNPs within the mitochondrial RNA-binding proteins PTCD1 and MRPS7. Our results thus support the concept of an ancient regulatory transition of mtDNA-encoded genes as humans left Africa to populate the rest of the world.
| The mitochondrion is an organelle found in all cells of our body and plays a significant role in the energy and heat production. This is the only organelle in animal cells harboring its own genome outside of the nucleus. Mitochondrial DNA (mtDNA) variants have been traditionally used as neutral markers to trace ancient population migrations. As a result, the functional impact of human mtDNA population variants on gene regulation is poorly understood. To address this question, we analyzed available data of mtDNA gene expression pattern in a large group of individuals (454) from diverse human populations. Here, we show for the first time that the ancient migration of humans out of Africa correlated with differences in mitochondrial gene expression patterns, and could be explained by the activity of certain RNA-binding proteins. These findings suggest a major mitochondrial regulatory transition, as humans left Africa to populate the rest of the world.
| Genetic variants in the nuclear and mitochondrial genomes have been traditionally used to trace the ancient global migration paths of different human populations [1, 2]. Such studies assumed most variants to be neutral and hence merely reflective of the age of the populations studied. However, accumulating evidence suggests that many common variants in the mitochondrial and nuclear genomes have functional impacts [3]. Specifically, ancient mitochondrial DNA (mtDNA) variants and genetic backgrounds (haplotypes, haplogroups) have been associated with an altered tendency to develop a variety of complex traits [3–5] that affected mitochondrial activity in cell culture experiments [6–9] and which conferred adaptive advantages over the course of human evolution [10–12]. Whereas it has been shown that mtDNA variants affected mitochondrial protein activities, such as oxidative phosphorylation (OXPHOS) or the production of reactive oxygen species (ROS), the impact of mtDNA variants on the regulation of gene expression has drawn little attention.
In the nucleus, variants that affect gene expression (eQTLs) can be mapped within intergenic regions, introns and exons [13, 14]. Unlike the nuclear genome (nDNA), most (~93%) of the human mtDNA contains intron-less genes, with the majority of known gene regulatory elements being mapped within the major mtDNA non-coding control region, the D-loop. These transcriptional regulatory elements include the heavy strand and light strand promoters, as well as the three conserved sequence blocks (CSBs I-III). The only known mtDNA gene regulatory element that maps within the coding region is recognized by members of the transcription termination factor family (mTERF) [15]. These proteins correspond to the main regulatory elements that modulate the expression of the entire set of mtDNA genes (N = 37), including those encoding 13 protein subunits of the OXPHOS pathway and 24 RNA components of the mitochondrial translation machinery (22 tRNAs and two rRNAs). All known regulators of mtDNA transcription are imported as proteins from the nucleus [16], namely mitochondrial RNA polymerase (POLRMT), mitochondrial transcription factors A (TFAM) and B2 (TFB2), and mTERF [15]. Recently, however, we and others have shown that additional nDNA-encoded transcription factors, such as MEF2D, the estrogen receptor, c-Jun and Jun-D are imported into mitochondria, where they bind the mtDNA within the coding region outside the D-loop to regulate transcription [17–20]. These findings not only suggest that mtDNA transcriptional regulation is more complex than once thought but also imply that the quest for genetic variants that affect the regulation of mitochondrial gene expression should not be limited to non-coding mtDNA sequences.
The study of eQTLs in the mtDNA lags far behind that of nDNA eQTLs. We were the first to show that an ancient mtDNA control region variants affected in vitro transcription and mtDNA copy numbers in cells sharing the same nucleus but differing in their mtDNAs (i.e., cytoplasmic hybrids or cybrids) [21]. Subsequently, two studies that measured mtDNA transcript levels (among other mitochondrial activities) in cybrids revealed differences among certain mtDNA haplogroups [22, 23]. Despite these advances, a worldwide overview of the landscape of mtDNA transcriptional differences in human populations, similar to what is known of nDNA gene expression [24–26], remains lacking, as do mechanistic explanations for the specific observations made. Such an overview is an essential step towards highlighting candidate mtDNA eQTLs.
With this aim in mind, we analyzed RNA-seq data that was recently made available as part of the 1000 Genomes Project for 454 unrelated individuals of Caucasian and sub-Saharan African origin. Our analyses indicated that samples carrying a combination of certain mtDNA variants presented a distinct mtDNA gene expression pattern. Strikingly, the most prominent finding was that all of samples carrying the African mtDNA haplogroup L diverged from the rest in their pattern of expression, suggesting that mtDNA gene expression diverged between people who left Africa and those who remained in the continent, supporting an ancient regulatory difference. Furthermore, the association of such mtDNA gene expression patterns with SNPs within known regulators of mtDNA gene expression shed light on the possible mechanism underlying this phenomenon.
Levels of gene expression can vary among individuals, tissues and species [27]. As such, we utilized RNA-seq experiments to assess differential mitochondrial gene expression patterns among individuals and ethnicities (Fig 1). To this end, we sought RNA-seq studies addressing a variety of human populations. As a first step, we attempted to compile available RNA-seq datasets from various populations [26, 28–31] to generate the largest and most diverse studied cohort. However, expression pattern clustering analysis grouped RNA-seq samples according to the study of origin, even when considering the same samples that were separately sequenced and analyzed independently by different groups (S1 Fig), thus arguing against co-analysis of RNA-seq data generated by different protocols. Hence, although Sudmant et al. [27] recently showed that differences in gene expression patterns between tissues are greater than are differences between studies, our results reveal that while focusing on a single tissue, differences in gene expression patterns between studies exceeds differences among individuals. Therefore, to avoid such artifacts, we focused our analysis on the largest of the relevant studies, encompassing 462 publicly available RNA-seq samples from Caucasians and sub-Saharan Africans [26], all part of the 1000 Genomes Project [32]. This dataset included results from mRNAs and rRNAs sequencing libraries, here referred as the ‘long RNA’ dataset, as well as short-reads sequencing libraries that includes mtDNA-encoded tRNAs (i.e. the tRNA dataset). In that study, all samples were randomly distributed to seven laboratories and RNA-seq data was generated following an identical shared protocol. In considering the 462 RNA-seq samples, eight of the long RNA dataset did not successfully map to human nDNA and mtDNA reference genomes. Our analysis indicated that this problem stems from uneven numbers of paired reads (STAR mapping criterion), which may reflect lower data quality. To avoid possible technical biases we excluded the mentioned 8 samples from further analysis. The number of reads per base that mapped to mtDNA in the remaining 454 long RNA samples ranged from several hundred in the case of tRNA genes, to nearly half a million for some protein-coding genes (S2A Fig). Sequencing reads corresponding to tRNAs were under-represented in the long RNA dataset likely due to the library preparation protocols used, which involved a size selection step. We partially overcame this limitation by analyzing the tRNA dataset. Here, 16 of the 22 tRNA genes were represented in the tRNA dataset with sufficient numbers of mapped reads for analysis in at least 90% of the samples. For the sake of consistency, we included only those individuals who were represented in the long RNA dataset when considering the tRNA dataset, thereby retaining 440 samples with coverage of up to tens of thousands mapped reads per mtDNA base (S2B Fig).
Lappalainen et al. [26] reported that there were no significant differences between the same samples that were generated in different laboratories. This enabled us to divide the entire dataset into two groups matched according to ethnicity and gender ratio, which were separately treated as biological replicates.
Recently, we, and others, identified RNA-DNA differences (RDD) in three mtDNA sites common to all human individuals and human tissues tested to date [33, 34], as well as in 90% of the vertebrates [35]. Our sequence analysis of the long RNA dataset also identified these three RDDs in all analyzed samples, while excluding sequencing and mapping errors by analysis filters, as previously outlined [33], thus further supporting the quality of our analyzed data.
The polycistronic nature of mtDNA transcription permitted RNA-seq reads that covered the complete mtDNA of all tested individuals. This enabled reconstruction of the entire mtDNA sequence in all analyzed samples, which were aligned to reveal polymorphic positions and reconstruct a phylogenetic tree (S3A Fig). Such analysis enabled the assignment of all individuals to specific mtDNA haplogroups. Since the analyzed samples are part of the 1000 Genomes Project, we extracted the mtDNA sequence from the DNA sequence database of the same samples and used these to construct a phylogenetic tree (S3B Fig). Notably, the topologies and distribution of haplogroups throughout the RNA and DNA-based trees were nearly identical and were in agreement with previously published trees [12, 36]. Therefore, putative human RNA-DNA sequence differences did not affect overall mtDNA tree topology.
We and others previously showed that nDNA harbors a repertoire of mtDNA sequence fragments (NUMTs) that were transferred from the mitochondria during the course of evolution [37, 38]. NUMTs potentially pose an obstacle to mtDNA gene expression assessment, as a subset of RNA reads might originate from NUMTs rather than from the active mtDNA. As a first step to control for such a scenario, we remapped the RNA-seq reads of each sample against their own reconstructed mtDNA sequence (personalized mapping). This approach also controlled for a second possible bias. It is conceivable that gene expression level differences could be affected by exclusion of sequencing reads due to mapping of the RNA-seq reads to a single European reference sequence (the revised Cambridge Reference Sequence (rCRS), i.e. rCRS mapping), resulting in the exclusion of numerous variants [39]. Since the mtDNA is highly variable and since ~130,000 years separates the appearance of the L haplogroup from the remaining mtDNA genetic backgrounds analyzed [10, 40], our sample-specific analysis enforced increasingly accurate and unique read mapping while excluding erroneous mapping to more than a single locus. Secondly, paired-end technology enabled us to exclude reads whose paired read partner mapped to a non-mtDNA locus. Finally, we repeated our read mapping while avoiding the unique-mapping step and compared the results obtained to those realized by unique-mapping analysis. The latter analysis did not reveal any skew in the expression pattern observed for those samples analyzed. We, therefore, concluded that NUMTs had little or no impact on our data.
We asked whether certain mtDNA SNPs associate with differential expression levels of mtDNA-encoded genes. Since we analyzed multiple mtDNA SNPs (including both singletons and lineage-defining SNPs), Bonferroni correction for multiple testing was applied. As mentioned above, initial analysis was performed while randomly dividing the samples into two groups while retaining the proportions of gender and ethnicity. Such analysis, using the personalized mapped samples, revealed correlation between certain SNPs and a distinct expression pattern. Close inspection revealed that all these SNPs corresponded to mtDNA haplogroup L (Fig 2, S1 Table, S2 Table). It is worth noting that analysis based on either the personalized- or rCRS-mapped samples led to comparable expression patterns (S4 Fig). This was despite the fact that the personalized mapping exhibited with excess of mapped reads in L halogroup samples, i.e. a mean of additional 26,197 reads per sample–a 0.09% increase, in the personalized mapping samples. Similarly, there was a slight increase in the number of reads in personalized mapped Caucasian samples, i.e. a mean of additional 5,279 reads per sample–a 0.02% increase. Taken together, regardless of the mapping approach, we conclude that L haplogroup individuals displayed reduced levels of mtDNA gene expression. For the sake of simplicity further analyses were performed using the personalized mapped samples.
To control for possible bias underlying the trend towards lower levels of L-haplogroup mtDNA transcript expression, we considered the expression patterns of nDNA-encoded genes in Africans versus non-Africans. We found 2,380 nDNA-encoded genes that are differentially expressed in Africans (S3 Table), yet unlike the mtDNA genes ~54% showed higher expression, while the rest showed lower expression in the African group (S5 Fig). These findings suggest a lack of bias in the expression pattern of mtDNA-encoded transcripts. To control for possible group assignment bias, we randomly re-divided the samples 500 times, while retaining constant proportions of gender and ethnicities. Following group assignment, we repeated the gene expression normalization process and SNP association analysis. Our results revealed that in more than 60% of the replicated divisions, ten mtDNA-encoded genes (MT-TH, MT-TI, M-TL2, MT-CO2, MT-ND2, MT-ND6, MT-CO1, MT-ATP6, MT-ND3 and MT-ND1) consistently showed significantly reduced expression levels in L-haplogroup samples (Fig 3). These results confirm that African L-haplogroup individuals possess a distinct mtDNA gene expression pattern.
Since mtDNA transcription and replication are coupled in human mitochondria [41], we included mtDNA copy number as one of the covariates in all our eQTL analyses. Nevertheless, we tested whether the differences in expression levels associated with variations in mtDNA copy numbers. We found that variations in mtDNA copy numbers did not differ between L- and non L-haplogroup mtDNAs (Fig 4). This suggests that the variation we observed in mtDNA gene expression patterns was independent of mtDNA copy numbers, a finding in agreement with previous results [42].
We reasoned that the highly significant gene expression differences between Africans and Caucasians may mask intra-population expression variation. To address this possibility we repeated the gene expression analysis separately for Caucasians and Africans. Although this analysis did not reveal any significant intra-population differences while considering the long RNA dataset (S4 Table, S5 Table), our results indicate that in Africans, individuals belonging to haplogroup L3b had significantly higher expression of cysteine tRNA (Fig 5A and S6 Table). While analyzing the Caucasian samples (Fig 5B–5F and S7 Table), we found that tRNA Leucine (2) had higher expression in individuals belonging to haplogroup U5. Secondly, higher expression of tRNA arginine was found in individuals belonging to haplogroup T. Finally, tRNA glycine had higher expression level in individuals sharing SNPs that define haplogroup cluster WI, in individuals harboring a guanine as compared to those having an adenine allele in mtDNA position 10,398 (shared by haplogroups J, K and I), and in individuals with either an adenine or a cytosine in mtDNA position 16,129 as compared to those with a guanine in this position. Hence, our intra-population analysis revealed much significant variation in mtDNA gene expression that was previously masked by the more prominent differential expression between Africans and Caucasians. Such differences may stem, at least in part, from variation in the impact of certain alleles on gene expression, depending on their linked haplotypes (Fig 6). This is best exemplified by the relatively high expression of tRNA glycine in Caucasian haplogroup cluster WI individuals (with the 12,705T allele) as compared to individuals with the 12,705C allele (see also Fig 5D); all Africans harbor the 12,705T allele, which exhibits even lower tRNA glycine expression than the Caucasian 12,705C allele. The latter caused lack of significance while calculating the impact of 12,705 SNPs on gene expression considering Africans and Caucasians together (Fig 6B). Taken together, the impact of mtDNA SNPs on gene expression differences is modified, at least in part, by their linked genetic background.
Since the regulation of all mitochondrial activities is governed by nDNA-encoded factors, we asked which nDNA-encoded genes are the best candidates to modulate the most prominent distinct mtDNA gene expression pattern–that of the L-haplogroup. As a first step in addressing this question, we screened for nDNA-encoded genes that were co-expressed with the mtDNA genes (Pearson correlation). These genes were then subjected to Matrix eQTL [43] analysis to identify differential expression between mtDNA genetic backgrounds (i.e., L or non-L haplogroups), while including gender, mtDNA copy number, and sample resource lab as covariates. After further correction for multiple testing (Bonferroni correction), a list of 2,380 genes remained (S3 Table). GO analysis revealed enrichment in RNA-binding proteins and poly-adenylated RNA-binding proteins (Table 1, S8 Table), suggesting that RNA stability likely plays a significant role in the differential expression of mitochondrial genes in L-haplogroup individuals.
To further explore candidate genes that best explain the distinct L-haplogroup mtDNA expression pattern, we screened for nDNA SNP association. To avoid statistical power issues, we focused our SNP association study on the most comprehensive list of human RNA-binding and transcription-associated mitochondrial genes available [16], supplemented by additional transcription factors that were recently localized to the human mitochondria and bind the mtDNA [18, 19]. This analysis showed 7,665 correlations between a total of 511 non-redundant nDNA SNPs and 15 mtDNA-encoded genes, of which only five SNPs in four nDNA-encoded genes remained significant after correction for multiple testing in both test groups. Notably, these SNPs correlated with the expression of four mtDNA-encoded genes, namely MT-ND2, MT-CO2, MT-CO1 and MT-ND6 (Fig 7A, Table 2, S9 Table).
The indicated SNPs map within the nDNA-encoded genes PTCD1, MRPS7, PNPT1, HEMK1 and GRSF1. Three of these nDNA genes have already been analyzed for their involvement in mitochondrial gene expression. PNPT1 is involved in RNA import into the mitochondria and is a part of the mitochondrial RNA degradosome [44, 45]. PTCD1 takes part in processing primary mtDNA polycistronic transcripts [46] and GRSF1 is involved in processing both the full and tRNA-less polycistrons [47]. The fourth gene, MRPS7, is a mitochondrial ribosomal protein, and therefore takes part in the mitochondrial translation machinery [48] and HEMK1 methylates the mitochondrial translation release factor HMRF1L [49]. In conducting the same analysis on the tRNA dataset, two of the identified SNPs correlated with the differential expression of the L-haplogroup in both groups of RNA-seq samples, namely PTCD1 and MRPS7 (Fig 7B, Table 3, S10 Table). In summary, our eQTL association analysis of reduced mtDNA gene expression in L-haplogroup individuals revealed consistent association of SNPs within PTCD1 and MRPS7, both in the large and the tRNA datasets, suggesting a common mechanism involved.
In this study, while examining RNA-seq data from 454 individuals of African and Caucasian origin from the 1000 Genomes Project, we found distinct patterns of transcript abundance in individuals with certain mtDNA SNPs, corresponding to the mtDNA L-haplogroup (the most abundant African haplogroup). L-haplogroup individuals presented an overall lower expression of certain mtDNA genes (both mRNAs and tRNAs), as compared to individuals corresponding to non-L-haplogroups. Such distinct mtDNA gene expression patterns of the L-haplogroup suggest an ancient regulatory trait that likely existed prior to out-of-Africa migrations. In the future, when large Asian RNA-seq data become available, it will be possible to corroborate this interpretation.
Our mtDNA eQTL analysis, which revealed a significant expression pattern difference between Africans and Caucasians, was based on SNP-expression pattern association, and was not based on prior division into populations. Furthermore, while performing intra-population eQTL analysis we found distinct mtDNA gene expression pattern for specific haplogroups, only while considering the tRNA genes. Finally, we noticed that the expression of tRNA glycine was elevated in individuals belonging to haplogroups W and I, as well as in individuals with a guanine allele in mtDNA position 10,398 and in individuals with either an adenine or a cytosine in position 16,129 (which is found in people belonging to multiple haplogroups). Interestingly, all haplogroup I individuals harbor a 10,398G allele, suggesting that haplogroup I SNPs play a major role in determining differential expression of tRNA glycine. Alternatively, since the SNPs in positions 10,398 and 16,129 occurred multiple independent times during the evolution of man, it is possible that these SNPs have direct functional impact on the expression of tRNA glycine. We interpret all these results to mean that the intra-population expression differences might be context-dependent, and therefore, they were masked by including both Africans and Caucasians in the same analysis. Specifically, masking of such expression differences may occur, at least in part, due to differential impact of certain SNPs on expression, depending on their linked genetic backgrounds (Fig 6). Such differential impact could further be explained by: (A) the eQTL has a small functional impact which is enhanced by additional eQTLs, or (B) the causal eQTL is in linkage with the identified eQTL. However, currently we cannot differentiate which of the suggested explanations is the most plausible. Taken together, our eQTL analysis was not confounded by populations, and therefore revealed candidate mtDNA-encoded eQTLs.
A recent study of mitochondrial activity in six cell lines sharing the same nDNA but diverging in their mtDNAs (i.e., cybrids), revealed differences in activity and transcript abundance among three L-haplogroup and three H-haplogroup cybrids [23]. Similarly, Gomez-Duran and colleagues identified expression pattern differences between haplogroup H cybrids when compared with those of the haplogroup Uk, 5 cell lines each [22]. Since we studied a much larger sample size from highly diverse individuals, we argue that our study better represents the natural population rather than focusing on specific haplogroups. This further underlines the future need to expand our study to include Asians so as to shed further light on mitochondrial regulatory differences from a world-wide perspective. Once cybrid technology has been adapted for high throughput analysis, it would be of interest to apply our genomic analysis to a large collection of cybrids with diverse mitochondrial genomes.
Since the distinct L-haplogroup mtDNA expression pattern was shared between tRNAs and long RNAs that are encoded by both mtDNA strands, it is plausible that the observed differences stem either from early stage transcription or from polycistron stability. Alternatively, since expression pattern differences were limited to certain mtDNA-encoded genes, the underlying mechanism could involve differences in the RNA stability of the mature transcripts or during transcript maturation, as previously suggested [50]. With this in mind, both analysis of co-expressed nDNA-encoded genes and our eQTL association study revealed that RNA-binding proteins with mitochondrial function (i.e., PTCD1 and MRPS7) best explain the distinct mtDNA gene expression patterns of L-haplogroup individuals. Although a lack of association with SNPs in the vicinity of known mtDNA transcription regulators was observed, one cannot exclude future detection of such association when more mtDNA transcription regulators are identified.
In summary, the distinct mtDNA transcript expression pattern observed in African individuals supports an ancient mitochondrial phenotype, as humans left Africa to populate the rest of the world. We identified several candidate nDNA-encoded modulators of this expression pattern, although their direct functional impact remains to be studied. Nevertheless, expression differences were only seen in certain mtDNA genes, despite the fact that all mtDNA genes are co-transcribed in two polycistrons corresponding to the light and heavy strands. This finding, along with the observed association of SNPs in mitochondrial RNA-binding genes, suggests that RNA decay is the best candidate mechanism for modulating the observed expression pattern.
RNA-seq data from lymphoblastoid cell lines (LCL) were obtained from several inter-population studies [26, 28–31]. Datasets were downloaded from:
citation 28 - www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19480;
citation 29 - www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-197/;
citation 26 - www.ebi.ac.uk/ena/data/view/ERR188021-ERR188482 and www.ebi.ac.uk/ena/data/view/ERR187488-ERR187939;
citation 30 - www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54308;
citation 31 - www.ncbi.nlm.nih.gov/sra/?term=SRP026597.
However, since transcript abundance assessment (see below and in the Results section) revealed mtDNA gene expression clusters according to these studies, our analysis focused on data in the largest recently published study so as to avoid analysis artifacts [26]. In that study, RNA-seq libraries were constructed and data were generated in seven different labs that followed the same experimental protocol, relying on randomly distributed samples. This dataset included results from sequenced mRNA and rRNA (‘long RNA’) libraries, as well as small RNA libraries. The largest RNA dataset contained samples from 462 individuals from five world-wide populations (91 CEU, 95 FIN, 94 GBR, 93 TSI and 89 YRI). Eight of the samples were not successfully mapped to the human genome due to too much unpaired reads and were, therefore, excluded, thus leaving 454 samples for further analysis. Small RNA sequences generated from a subset of these samples (N = 452) encompassed most of the individuals described above (87 CEU, 93 FIN, 94 GBR, 89 TSI and 89 YRI). Three of the samples were not included in the long RNA dataset and were, therefore, excluded from further analysis, in addition to eight samples whose RNA sequences could not be mapped to the long RNA dataset. The same was true for an additional sample that was not successfully mapped to the dataset of small RNA libraries (tRNAs in the mtDNA). This left 440 samples for further analysis. Data were downloaded from Arrayexpress (E-GEUV-1 and E-GEUV-2 for long RNA and tRNA, respectively).
The long RNA dataset was downloaded from: http://www.ebi.ac.uk/ena/data/view/ERR188021-ERR188482
The small RNA sequence libraries were downloaded from: http://www.ebi.ac.uk/ena/data/view/ERR187488-ERR187939
Taking advantage of the polycistronic transcription of the mitochondrial genome [51], mtDNA genomes were reconstructed from each of the RNA-seq samples using MitoBamAnnotator [52]. Each reconstructed mtDNA sequence underwent haplogroup assignment using HaploGrep [53]. Finally, multiple sequence alignment and phylogenetic analysis (neighbor joining, 1000 X bootstrap and default parameters) of all reconstructed mtDNA genomes were performed using MEGA 5 [54].
RNA-seq reads of the long RNA dataset were mapped onto the entire human genome reference sequence (GRCh 37.75) using STAR v2.3 [55] and the available genome files at the STAR website (code.google.com/p/rna-star/). Mapping was performed using default parameters, in addition to the [—outFilterMultimapNmax 1] parameter to achieve unique mapping. Since mtDNA sequence variability can impact the number of mapped RNA-seq reads, the reads were remapped against the same human genome files after replacing the mitochondrial reference sequence by the reconstructed mtDNA of each of the relevant analyzed individuals. To this end, a revised index was generated for the new reference genome by replacing the human mtDNA sequence with the reconstructed version. This was conducted separately for each tested sample, with all other files being retained. Most of the parameters used were retained, with one exception. Apart from replacing the mtDNA reference, we further increased our mapping accuracy by allowing fewer mismatches [—outFilterMismatchNmax 8], while analyzing couples of paired reads (a total length of 150bp). The tRNA dataset was mapped using the same parameters and references as in the remapping process described above, with the single exception of no mismatches allowed [—outFilterMismatchNmax 0] so as to reduce mapping errors [56].
Alignment files (SAM format) were compressed to their binary form (BAM format) using Samtools [57] with the default parameter [view -hSb] selected, and sorted using the [sort] parameter. Mapped reads were counted using HTSeq-count v0.6.1.p1 [58], employing the [-f bam -r pos -s no] parameters. Reads were normalized to library size using DESeq v1.14.0 [59] and the default parameters. This protocol was employed for both the long RNA and tRNA datasets.
mtDNA sequences of all individuals were aligned to identify polymorphic positions. In the tRNA dataset, some tRNA genes had no reads in a subset of our analyzed samples. Therefore, only genes presenting with reads in more than 90% of the samples were used, thus leaving 16 tRNA genes for further analysis. For each polymorphic position, the samples were divided into groups according to their allele assignment. As described in Lappalainen [26] et al., using the linear model implemented in the Matrix eQTL R package [43], eQTL mapping was calculated according to the allele assignment, while considering gender, mtDNA copy number and sample resource (i.e. lab of origin) as covariates. A Bonferroni correction was employed to correct for multiple testing. To reduce false positive discovery rate we focused on SNPs shared by at least 10 individuals. To identify possible associations of nDNA-encoded genes with differential expression patterns of mtDNA genes, the analysis focused on known SNPs (as listed by the 1000 Genomes Project) in the dataset of genes with known mitochondrial RNA-binding activity [16]. This dataset was supplemented by transcription factors and RNA-binding proteins that were recently identified in human mitochondria but were not included in MitoCarta (i.e., c-Jun, JunD, CEBPb, Mef2D) [18, 19]. Such prioritization was employed to enable detection of possible correlations with sufficient statistical power. Since our analysis of the mtDNA revealed sites with more than 2 alleles (i.e. 3 alleles at the most) we performed our analysis such that the major allele frequency will not exceed 95%, thus enabling the discovery of the two other minor alleles.
To assess co-expression of nDNA-encoded genes with the mtDNA-encoded ones, Pearson correlation was employed (p < 5.23e-8, after Bonferroni-correction for multiple testing). Briefly, co-expression was sought between the 15 mtDNA-encoded mRNA and rRNA genes and 63,662 nDNA-encoded genes. Then, Matrix eQTL [43] was employed to identify differential expression between L and non-L genetic backgrounds among the significantly co-expressed genes, while including gender, mtDNA copy number, and sample resource lab as covariates. Finally, only nDNA-encoded genes that were significantly co-expressed, and were differently expressed between African and Caucasians (p < 5.5e-6, Bonferroni corrected for multiple testing), were subjected to gene ontology (GO) analysis. To this end, PANTHER [60] was used, categorized according to molecular function.
As the cell lines that were used in the analyzed RNA-seq study were derived from individuals included in the 1000 Genomes Project [61], mapped DNA reads files were downloaded from the 1000 Genomes Project ftp website in BAM format (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data), using Samtools with the [view -hb] parameter. Bedtools was employed to estimate the read coverage of nDNA regions in each individual, using the BAM and global BED files of that individual. Using these files, read coverage over most of the mtDNA sequence (mtDNA positions 1–16,499) was compared to that of randomly selected sets of 100,000 bases from each autosomal chromosome (nucleotide coordinates 20,100,000–20,200,000 in each of the 22 autosomes). Specifically, the above-mentioned read coverage values were used to calculate the ratio between mtDNA and nDNA read coverage. This ratio equals the estimated mtDNA copy numbers.
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10.1371/journal.pgen.1003571 | Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy | ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called “STAP,” to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed (“primary”) TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA.
| Chromatin Immunoprecipitation (ChIP)-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high throughput method to understand transcriptional regulation, especially on a global scale. Here, we utilize 45 ChIP-chip and ChIP-SEQ data sets from Drosophila to explore the underlying mechanisms of TF-DNA binding. For this, we employ a biophysically motivated computational model, in conjunction with over 300 TF motifs (binding specificities) as well as gene expression and DNA accessibility data from different developmental stages in Drosophila embryos. Our findings provide robust statistical evidence of the role played by TF-TF interactions in shaping genome-wide TF-DNA binding profiles, and thus in directing gene regulation. Our method allows us to go beyond simply recognizing the existence of such interactions, to quantifying their effects on TF occupancy. We are able to categorize the probable mechanisms of these effects as involving direct physical interactions versus accessibility-mediated indirect interactions, long-range versus short-range interactions, and cooperative versus antagonistic interactions. Our analysis reveals widespread evidence of combinatorial regulation present in recently generated ChIP data sets, and sets the stage for rich integrative models of the future that will predict cell type-specific TF occupancy values from sequence and expression data.
| A major challenge in the analysis of genomic sequences is the annotation of cis-regulatory elements. Significant progress has been made towards this goal through high throughput methods such as ChIP-chip and ChIP-SEQ that describe the locations where specific transcription factors (TFs) bind to the genome in vivo [1]–[3]. ChIP-based characterization of TF binding profiles can help elucidate specific regulatory interactions between TFs and genes [4]. A number of genome-wide ChIP data sets, corresponding to diverse TFs and cellular conditions, have been generated through the efforts of various laboratories and consortia [1], [3]. Such data sets also offer the opportunity to apply computational and statistical methods to understand the determinants of TF-DNA binding at a quantitative level [5]–[7]. Given the central role of the TF-DNA binding process in the regulatory activity of a TF, such an understanding can provide a holistic view of transcriptional regulation and also set the stage for future computational methods for predicting cell type-specific TF-binding profiles.
The most extensively studied determinant of TF occupancy is the DNA binding specificity of the TF. Various experimental approaches [8]–[11] have been successful in obtaining motifs representing the diversity and relative affinities of DNA sequences bound by an individual TF. An initial expectation is that a TF's motif will allow prediction of its binding levels genome-wide. On the other hand, it is clear that interactions with other TFs can significantly influence binding to regulatory sequences. For example, interaction of Hox proteins with a cofactor results in greater DNA binding specificity [12] and the tramtrack (TTK) protein can regulate transcription independent of its own DNA binding domain via its interaction with the Trithorax-like (TRL, also known as GAGA binding factor) [13]. Furthermore, TF occupancy at a genomic location in a given cell type also depends on the concentration of that TF, as well as the motifs and concentrations of other TFs that might facilitate or inhibit DNA-binding at the location [14].
A number of recent studies have used genome-wide datasets to characterize parameters that correlate with TF occupancy. In several studies, genome-wide measurements of in vivo DNA accessibility were tested for the ability to help describe TF ChIP data. These studies clearly demonstrate that TF occupancy has a close relationship with in vivo DNA accessibility [6], [7], with both factors likely influencing each other [6], [15]–[19]. While these studies reveal that experimental analysis of accessibility can improve modeling of ChIP data, they do not reveal the underlying genomic sequence features that contribute to accessibility. In another study [5], sequence motifs experimentally and computationally identified in Drosophila were shown to contribute to context-specific TF occupancy. Application of discriminative motif analysis to a TF assayed across multiple conditions can successfully identify predictive motifs associated with context-specific binding. However, whether TFs bound to these discriminative motifs contribute to occupancy by direct interaction with the primary TF, accessibility or other mechanisms is not assessed.
In this work, we test the influence of various potential sequence determinants of in vivo TF-DNA binding – the TF's binding motif, as well as the positive or negative influence of other TFs binding in the vicinity – on each of 45 TF-ChIP data sets in Drosophila. For this analysis, we took advantage of over 300 distinct DNA binding specificity motifs determined for individual TFs [20], which encompasses approximately 40% of all predicted Drosophila TFs, and relied upon stage-specific whole-genome RNA-SEQ data [21] to determine which secondary TFs are expressed at the time of the ChIP experiment. We follow the general framework proposed by Kaplan et al. [6], which involves: (1) building computational models that predict TF binding at a location, and (2) assessing how well a baseline model that only uses the “primary motif” (i.e., binding motif of the “ChIP'ed” TF) fits the ChIP data, as compared with more complex models that incorporate additional determinants such as motifs for additional secondary TFs (i.e., TFs other than the ChIP'ed TF). We use the biophysical model STAP [22] to perform these tests. Improvements in the goodness-of-fit measure are evaluated statistically, and a cross-validation framework is adopted to account for differing model complexity in the comparisons. We evaluate each potential determinant separately in order to limit the number of free parameters in the models. For each identified secondary TF, we performed statistical tests to categorize the mechanistic basis of its contribution. In particular, we asked if a secondary motif's influence is likely to be (a) through long-range (≤150 bp) versus short-range (≤30 bp) interactions with the primary motif, (b) through synergistic or antagonistic interactions, and (c) through modulation of local DNA accessibility or direct interactions between TFs.
We find widespread evidence of the effect of secondary TFs on the primary (ChIP'ed) TF's binding levels, including both enhanced occupancy (“cooperativity”) and reduced occupancy (“antagonism”). Cooperative and antagonistic influences of secondary motifs can act through: 1) long-range interactions between primary and secondary motifs, suggesting indirect effects such as chromatin remodeling, 2) short-range interactions with specific inter-site spacing biases, suggesting a direct association, or 3) through overlapping binding sites, suggesting competition for site occupancy.
Two types of experimental evidence support our computational assignments of secondary TFs that influence occupancy via local chromatin architecture or cooperative DNA binding. Extending previous observations [6], [7], we find that DNA accessibility is the primary genomic feature correlated with TF occupancy across the majority of the 45 data sets examined here. We then use accessibility data to re-examine secondary TF motifs that improved prediction of ChIP data in our accessibility-agnostic analysis. We identify several secondary motifs whose contribution is reduced or lost when accessibility information is part of the model, suggesting that the secondary TF influences binding mainly by modulating accessibility patterns. The TFs vielfaltig (VFL, also known as Zelda) and TRL (also known as GAGA factor) appear to synergistically influence the binding of several primary TFs in early and mid-stage embryonic development respectively. Interestingly, the influence of VFL is sometimes imposed through accessibility, while in other cases it is independent of accessibility. In contrast, the influence of TRL is imposed exclusively through accessibility. The TF motifs for extradenticle (EXD), retained (RETN), jing interacting gene regulatory 1 (JIGR1) and homothorax (HTH) commonly antagonize TF occupancy through accessibility-mediated and accessibility-independent mechanisms. We find many cases where the secondary motif's influence remains significant upon accounting for accessibility, thus suggesting alternative mechanisms such as cooperative or antagonistic DNA-binding by the primary and secondary TFs. We identify eight examples where the arrangement of primary and secondary motifs implies cooperative binding via physical interaction, and demonstrate that for all but one of these cases the TFs do, in fact, directly interact in vitro and that several bind cooperatively in vitro to sequences that are occupied in vivo. Overall, our analysis demonstrates that a biophysical model for the combinatorial action of primary and secondary TFs used with an extensive collection of binding motifs for known TFs can describe the mechanistic basis for in vivo patterns of TF occupancy.
We began our analysis with fifty-five TF-ChIP data sets obtained from diverse sources (see Methods). Each TF-ChIP data set was represented by 1000 peaks and 1000 random non-coding sequence windows, all of length 500 bp. This representation was selected with the goal of identifying TF motifs that improve the ability to properly rank the occupancy within the peak group and/or improve the ability to discriminate between peaks and random sequences. The average ChIP score of each window was treated as the TF occupancy level in that window (Methods), and is henceforth called the “ChIP score”. For each data set a Position Weight Matrix (PWM or motif) representing the DNA binding specificity of the ChIP'ed TF was identified (Methods) and designated as the “primary motif”. We used the STAP program [22] (Figure 1A) to predict a binding level, henceforth called “STAP score”, for each sequence window in a data set, using the primary motif from that data set. We then computed the Pearson Correlation Coefficient (CC) between ChIP scores and STAP scores across the 2000 windows in each data set, and call this the “baseline CC” for the data set. This value captures the ability of the primary TF's binding motif to determine that TF's relative occupancy levels both within the most highly bound regions and in peak versus non-peak regions. Since STAP has one free parameter for which it requires training data (sequences and their binding levels), we performed 4-fold cross-validation to obtain STAP scores for all 2000 windows, with 500 test windows in each fold. Out of the 55 data sets, seven did not show a sufficiently high correlation (CC ≥0.15 and p-value<1E-11) here or in any other test that we report in the following sections, and three data sets presented technical problems in the training phase, e.g., inconsistent parameter values learned over different folds of cross-validation. These 10 data sets (Supplementary Table S1) are excluded from the rest of our report. In all of these examples, the TF motif is broadly confirmed by similarity to motifs for the same TF obtained by other methods or to motifs for homologous TFs. Thus, the low correlation may reflect a high degree of recruitment to DNA by other proteins, technical problems with this group of ChIP datasets, or with the model as applied to these datasets.
The results of this first exercise are shown in Table 1 and Figure 2A. We noted the baseline CC in this test to be ≥0.15 (p-value<1E-11) for 39 data sets, with the highest CC reported for the data set “TRL_Cchip_s5_14”, i.e., ChIP-chip data for the TF TRL in stage 5–14 embryo, obtained from the (C)avalli laboratory (see Table 1 legend for data set nomenclature scheme). We repeated this exercise, for all 55 data sets, using a second program, TRAP, also based on a biophysical model of DNA binding [23] with default parameter settings, and noted that CC values from STAP were generally better (Figure 2B), although there were several data sets where the two methods gave almost identical CC values. We also observed from Figure 2B that the ten data sets that we exclude from most of the analyses in this work (red symbols) received poor CC values from both STAP and TRAP. The purpose of this exercise was not to identify a superior method for occupancy prediction; such an attempt would have been biased since we have more experience with STAP than TRAP, and our TRAP analysis was run without training of free parameters. Our goal was to provide evidence that STAP-based predictions provide a reasonable baseline for more advanced models that will be examined below.
Figure 2C provides a scatter-plot visualization of the STAP results on the data set “TRL_Cchip_s5_14”, which has the best baseline CC value (CC = 0.765). Figure 2D provides an alternative visualization of the same results, as an ROC curve showing how the false positive rate of calling a ChIP peak based on STAP scores varies as we vary the STAP score threshold. We see that 89.7% of the 1000 ChIP peaks can be detected using STAP scores while making 10.3% false positive predictions; the Area Under Curve (AUC) is 0.96. Next to TRL, the TF with the highest CC is biniou (BIN), with the data set “BIN_Fchip_s14” exhibiting a CC of 0.654 and an AUC of 0.895 (Supplementary Figure S1). We note that this data set had been previously observed, in [24], to have a high enrichment of the TF motif in ChIP peaks. The ROC for a data set with a more typical value of CC is shown in Supplementary Figure S2 (CC = 0.305, AUC = 0.679). Figure 2E provides a different visualization of the accuracy of STAP predictions, as genome browser tracks of ChIP and STAP scores for the TF BCD on a single gene locus.
The CC values reported above can arise from differences in STAP scores of peaks and non-peaks in a data set, and from correctly modeling the ChIP scores within peaks and/or non-peaks. To examine the contribution of these two types of agreement between data and model, we separately calculated CC values among the peaks and non-peaks (Supplementary Table S10, Supplementary Figures S5A,B). We found several data sets where a significant overall CC was accompanied by a lower but significant CC within peaks, e.g., BCD_Bseq_s5, where the overall CC is 0.560, and the CC within peaks is 0.466 (Supplementary Figure S5A,C). These are examples where the goodness-of-fit arises from discrimination of peaks and non-peaks as well as from quantitative modeling of binding levels. In a few data sets, the signal appears to arise mainly from separation of peaks and non-peaks, e.g., BIN_Fchip_s14, where the overall CC is 0.654 but the CC within peaks and non-peaks is 0.233 and 0.185 respectively. By and large, the CC values within peaks were higher than those within non-peaks, as expected (Supplementary Figure S5B). Interestingly, for a few data sets the correlation within non-peaks was much greater than within peaks. These include UBX_Mchip_s5_14, EN_Mchip_s5_14, DISCO_Mseq_s5_11, EVE_Mseq_s14, and MAD_Bchip_s5, with peaks of the latter two exhibiting significant negative correlation between STAP predictions and ChIP scores (Supplementary Figure S5D). For these TFs, the motif score may be uniformly high in the top peaks, but help discriminate between very low and intermediate occupancy levels in the randomly selected regions, leading to stronger correlation within non-peaks.
We also repeated the evaluation of the above “baseline” model with a modified definition of data sets: now, the 1000 non-peaks of each data set was replaced with 1000 non-peaks randomly chosen from the ChIP peaks of other TFs. CC values analogous to those of Table 1 (Column “CC(M1)”) were computed and compared to those from Table 1 (Supplementary Table S15 and Figure S9). We observed that for a few data sets the new CC value is lower, suggesting that the primary TF motif in these cases may represent common features of TF bound regions. (We visit these cases in a later section.) We also noted several cases where the CC values were significantly higher when using other TFs' peaks as the non-peaks of a data set (e.g., Supplementary Figure S10). We believe such examples better reveal the role of the primary TF motif in determining the TF-DNA binding strength within accessible regions, since all segments considered in the newly defined data sets are ChIP peaks of some TF. Overall, our analysis of primary TF motif scores in different sets of genomic regions supports the idea that the 2000 regions selected for further study can provide insight into diverse types of mechanisms contributing to in vivo TF binding.
STAP uses a simple thermodynamic model to define TF-DNA occupancy for a genomic region based on binding site affinities, the equilibrium constant of the TF for its optimal site, and the TF's concentration level [22]. While the binding site affinity relative to that of the optimal site can be quantified using the PWM [25], the latter two quantities are formally unknown. The formula used by STAP (see Methods) features these two quantities as a mutual product, which is treated as a free parameter in the model. This TF-specific free parameter, henceforth denoted by γ, may lead to less of a difference in the contributions of high and moderate affinity sites. That is, at higher γ values, as would result from a high effective TF concentration, both high and moderate affinity sites may be fully occupied (saturated occupancy) whereas a stronger bias for high affinity sites will be observed at lower values (Figure 1A). As noted above, we use a cross-validation setting where the parameter is trained on three-quarters of the data and used to predict STAP scores in the left-out quarter, and the process is repeated four times. We examined the role of this parameter in the accuracy of the STAP model by varying it in the broad range 10−1 to 105 and recording the CC at each value of γ. As shown in Figures 3A–C, the optimal parameter value varies across data sets, between 100 to 104, with a roughly equal split into low, medium and high regions of the allowed range. All experiments reported in the rest of this paper were constrained to use γ in the range 100–104. We note that a value of γ = 100 indicates that the optimal site has a fractional occupancy of 0.5 at cellular levels of TF concentration, while a value of γ = 104 indicates a fractional occupancy of ∼1.
Figures 3A–C also reveal that for any given data set there is a substantial variation in the accuracy of STAP scores as we vary the TF-specific γ parameter. For instance, the CC value in the data set “TWI_Fchip_s9” (TWI at stage 9, source: Furlong lab ChIP-chip data) is about 0.25–0.30 at the two extreme values of γ (10−1 and 105 respectively), but reaches a much higher value of 0.42 at γ = 102. This dependence on the γ parameter, along with the variability of optimal γ across data sets underscores the importance of this parameter in the model. The parameter is analogous to the motif transition probability parameter in HMM-based models used in motif scanning, and our observation highlights the need for data set-specific training of this parameter in order to achieve the most accurate predictions. More generally, we conclude that simply adding the strengths of motif matches in a window is not necessarily the best way to predict TF occupancy in that window.
For a given TF, the γ parameter is proportional to the TF's concentration level in the experimental conditions. Therefore, if we have ChIP data on the same TF from two different stages, the optimal γ values ought to reflect the relative concentration levels in those stages. The examined collection of data sets included eight such pairs of data sets comprising ChIP data for the same TF from two different developmental stages. We therefore plotted the ratio of the trained γ values in the two stages versus the ratio of the TF's expression levels in those stages. We noted (Figure 3D) that the ratios of γ values were roughly consistent with ratios of expression levels, in that if one ratio is >1, the other ratio is also greater than or close to 1, and not <1. Expression levels were obtained from RNA-SEQ data from whole-embryos and may therefore be only a crude approximation of cell type-specific protein concentrations. This, and the fact that all ChIP experiments were performed on whole-embryo extracts, are expected to affect the sensitivity of this analysis, and may be the reason why we did not see a more quantitative agreement between the two ratios (i.e., points always close to the diagonal).
Our tests so far examined how different aspects of the primary TF, such as its binding specificity and concentration, affect its DNA-binding profile. In the next set of tests, we sought to evaluate the role of TFs other than the primary TF in determining the latter's occupancy. To this end, we used STAP with two motifs – the primary motif and one secondary motif at a time – and allowed cooperative interaction between TF molecules bound at sites within a certain distance, called the “distance threshold”, of each other (Figure 1B). There are now three free parameters: the two γ parameters corresponding to the primary and secondary motif, and a parameter representing the interaction energy between bound molecules of the primary and secondary TF. Evaluations performed under a cross-validation scheme ensured that CC values here are comparable to those in the baseline results from Table 1.
In the first set of tests of cooperative effects, we set the distance threshold to be 150 bp, therefore allowing long-range interaction that is similar to the length of DNA in one nucleosome. (We use “long-range” here to contrast with “short-range” interactions inferred from site pairs with ≤30 bp spacing in the next subsection, but note that “long-range” has different connotations in other contexts, e.g., to refer to interactions beyond enhancer boundaries [26], [27].) For each data set, we tested a secondary motif for every TF among the most highly expressed genes in the appropriate developmental stage, based on RNA-SEQ data [21]. We compared the CC of a (primary motif, secondary motif) pair to that from the primary motif (Table 1), and examined all cases where the improvement in CC was ≥0.04 (see Methods). The improvement, henceforth called ΔCC, was subjected to two different assessments of statistical significance. First, we recomputed the ΔCC with one hundred random variants of the secondary motif (see Methods), and asked what fraction of these random ΔCC values were better than the original ΔCC, thus obtaining a “ΔCC p-value”. Second, we utilized the ΔCC values from every tested secondary motif to compute a Z-score (see Methods). This mimics standard outlier detection procedures and designates a ΔCC value as significant if it appears to be an outlier compared to other observed ΔCC values for this data set. This is analogous to a multiple hypothesis correction, since we test over 50 candidate secondary motifs per data set. Additionally, we required that the cooperative interaction model has a greater CC than a model where the secondary motif alone is used by STAP. Thereby, we identified data sets where the combination of the primary and secondary motifs, through cooperative interactions, can describe the primary TF's occupancy better than either motif in isolation.
This analysis revealed 25 cases of significant improvements (ΔCC ≥0.04, p-value≤0.05 and Z-score ≥3), spread over 18 data sets (Supplementary Table S2). Table 2 tabulates the secondary motif with the most pronounced effect for each of these data sets. We noted that these effects arise mainly from an improved ability to discriminate peaks from non-peaks, and in only 4 (respectively 2) of these 18 cases the cooperativity model improves the CC even within peaks (respectively non-peaks) (Supplementary Table S11, Supplementary Figure S6). Remarkably, for 15 of these 18 data sets, the most influential secondary motif was either VFL (8 cases) or TRL (7 cases). Figure 4A shows an example where the use of VFL as a secondary motif significantly improves the ability to discriminate ChIP peaks from non-peaks. Overall, the VFL motif significantly improves primary TF occupancy predictions for 10 data sets (Supplementary Table S3, Figure S4A), of which 9 were from an early developmental stage (stage 5), and the tenth was from a broader span of developmental stages including stage 5. We noted that VFL is highly expressed in later stages as well and its motif was tested as a secondary motif in the corresponding data sets, but significant influences were not detected in those data sets. VFL has been proposed to play a “pioneer factor” role [28] in early development [29], [30], and its motif has been found to be highly over-represented in so-called “HOT” regions that represent the most accessible regions of the genome [31], [32]. Yanez-Cuna et al. [5] recently showed the VFL motif to be required for DNA-binding by the TF TWI, as well as for regulatory activity of TWI-bound enhancers, and to be enriched in early binding sites of other TFs such as MEF2. Our findings support these strong lines of evidence for an important facilitative role of VFL in determining TF-binding, and explicitly quantify this role for 10 different TF-ChIP data sets.
We found the TRL motif to influence the binding levels of primary TFs in eight data sets overall, of which six are from the later developmental stages 9–14 (Supplementary Table S4). As discussed in [33]–[35], TRL plays an important role in regulating the chromatin structure and packaging large segments of the chromosome into active (euchromatic) or inactive (heterochromatic) domains. The TRL motif was also prominent among sequence signatures of context-specific TF-DNA binding reported in [5], although this previous study did not explicitly quantify its facilitative effect on various primary TFs. It is interesting to note that TRL is the TF with the highest baseline CC (Table 1), reflecting the possibility that TRL-DNA binding is largely dependent on the TF itself and does not require facilitative effects of secondary TFs. This is consistent with speculation that TRL is a “pioneer factor” [28], [36].
In these initial tests, STAP was configured to allow interaction between primary and secondary motif as long as their bound sites were within 150 bps. We next asked if the promiscuous effects of VFL and TRL could be observed when reducing this distance threshold to 30 bps, which would suggest that short-range mechanisms of interaction might be involved. We found that in most cases the effects of these two motifs were not significant at the shorter distance range (Supplementary Tables S17, S18), and in the four cases where significant effects were detected at this range, the magnitude of the effect was lower than that at 150 bp range. A possible interpretation of this finding, especially in light of available knowledge about these two proteins, is that they act as chromatin remodelers over relatively long scales (150 bp or greater) and facilitate TF binding by making binding sites of the primary TF more accessible. (We revisit this point in a later section, by directly examining accessibility data.) Notably, the data sets for VFL and TRL themselves did not reveal any secondary motifs with significant effects, once again supporting a possible pioneer factor role for these two TFs.
While VFL and TRL clearly show the most frequent effects on TF binding, a number of other influential secondary TF motifs were also revealed by our analysis; these are shown in Table 3. For each of these cases we report the ΔCC values at both distance thresholds (30 bp and 150 bp). Of particular interest were the (primary motif, secondary motif) pairs where the ΔCC was significant only at the 30 bp threshold, since this may reflect direct interactions. (These significant short-range interactions were reflected in a better ability to discriminate peaks from non-peaks rather than an improved ranking of the peaks; see Supplementary Table S12 and Supplementary Figure S7.) A case in point is the data set HB_Bchip_s9, for the TF hunchback (HB), where the secondary motif Adh transcription factor 1 (ADF1) improves the baseline CC of 0.204 to 0.303 when modeling heterotypic cooperativity at distance threshold 30 bp. The ΔCC of 0.099 is highly significant (empirical P-value = 0, i.e., no shuffled motif yielded better ΔCC), while that at the 150 bp threshold does not meet our significance criteria. A similar effect was observed for the ADF1 motif on HB ChIP data in stage 5 embryos. We hypothesized that this is evidence for direct physical interaction between HB and ADF1 resulting in modulation of HB binding levels. We searched for sequence signatures of such a hypothesized interaction in the relative spacing of HB and ADF1 binding sites. Examination of the 250 highest ChIP peaks in the data set showed a statistically significant bias (P-value 3E-4, see Methods) for spacing in the range 18–23 bps (Figure 4B). A similar test on 250 non-peaks from the data set showed no bias for this range or any other. This analysis suggests that proximally located binding sites of HB and ADF1 result in increased HB occupancy in ChIP peaks. We examined other data sets where the ΔCC was significant, and found similar evidence of biased inter-site spacing in ChIP peaks (Figure 4B), supporting the hypothesis that direct cooperative interactions may be a key factor in determining TF binding profiles in these cases. In some cases, e.g., the pair (D, TTK) and (GT, TTK), we noticed more than one preferred spacing range, separated by 11 bp, as might be expected due to proper phasing requirements between physically interacting TFs [37]. We also tested for biased inter-site spacing between the TFs Distal-less (DLL) and Zif Zinc-finger protein (ZIF, also called CG10267) (Figure 4B), because the ΔCC was found to be significant for this pair (empirical p-value 0), although the z-score of this ΔCC was 2.435, slightly below our chosen threshold of 3.0.
For each of the predicted heterotypic interactions shown in Figure 4B, we assayed for direct physical interactions between the TFs using a modification of the LUMIER method [38], [39]. In these experiments, one partner is expressed as a fusion to Maltose Binding Protein (MBP) and the other partner as a fusion to luciferase (luc). To avoid possible bridging interaction by other eukaryotic proteins, the proteins were expressed using a purified prokaryotic in vitro expression system and then combined for analysis. MBP-tagged proteins were isolated using amylose beads and the luciferase activity retained on the beads (via primary TF-secondary TF interaction), relative to a negative control with unfused luc, was used to calculate a luminescence intensity ratio (LIR, see Methods). A value of seven or greater was selected as a cutoff for positive interactions. This threshold is based on a set of positive and negative control interactions among bHLH protein dimers examined using this assay (HNP and MHB, unpublished) as well as additional negative controls using luc fused to the TF CLK or MBP without a fusion partner (Supplementary Figure S12). This threshold is more than twice as stringent than those used in previous studies examining protein interactions in cell culture [38], [39] and consequently may exclude some weaker interactions, including some that may only be significant in the context of cooperative binding to DNA. Each predicted interaction pair was examined in both configurations (e.g., the primary TF was fused to MBP in one experiment and to luc in the second). In addition, since Mothers against dpp (MAD) and Medea (MED) are known to bind DNA as a heteromeric complex [40]–[43], it is possible that any interaction computationally identified for one of these proteins is the result of an interaction with the other one. In our in vitro experiments, only direct physical interactions between two proteins are tested. Therefore, each of the predicted interactions for either MAD or MED was also tested with the other.
For five of the eight tested pairs (i.e., those not involving MAD or MED), a clear in vitro interaction was observed in both configurations (Figure 4C, Supplementary Table S9). For the two predicted interactions involving MAD, one of the two configurations gave a signal while the other was just below our selected cutoff. In one additional case, no physical interaction was observed between ribbon (RIB) and MED, but RIB was observed to interact with the MED binding partner, MAD. None of our tested negative controls was near the threshold and the interaction signal for most of the tested pairs was similar to two, well-established positive control interactions for this set of proteins, a MAD-MED heterodimer and a homodimer of giant (GT), which is a member of the bZIP family of TFs that bind DNA as homodimers [44]. Thus, all of the tested predictions are supported by a moderate to strong in vitro interaction, demonstrating that at least some of the short range cooperative interactions identified by our computational model reflect actual physical interactions that were previously unrecognized in large scale protein-protein interaction screens.
The physical interaction of TFs suggests that they may use cooperativity to increase binding of the primary TF to DNA sites with properly spaced binding sites for both TFs [45]. We tested this prediction for three of the above TF pairs using a variation of a previously described microwell assay [46] (Figure 5). The primary TF fused to luciferase and a secondary TF fused to MBP are used in an in vitro pull down assay with biotinylated dsDNA oligos containing a sequence from the ChIP peaks that contains binding sites for both TFs. The TFs are mixed with the biotinylated target site and an excess of unlabeled wild type or mutant competitor DNA. The competitor sequences used to examine cooperative DNA binding of ZIF and DLL are shown in Figure 5A and all sequences are shown in Supplemental Table S16. Streptavidin-mediated recovery of luc-TF/biotin-DNA complexes in the presence of excess wild type competitor (wt) indicates the background signal. In experiments with both TFs present (Figure 5B), the recovery of the luciferase-tagged primary TF in the presence of a competitor with mutations in both TF binding sites (e.g. ΔZIFΔDLL) increased 8–18 fold over the background in the presence of wt competitor (Figure 5B, upper panels). In contrast, little increase was observed when this experiment was repeated without the secondary TF (Figure 5B, lower panels), indicating that the secondary TF facilitates binding of the primary TF to these sites. The specificity of this interaction was confirmed by testing mutant competitor DNAs that disrupt the individual TF binding sites (e.g., ΔZIF or ΔDLL) or that increase the intersite distance by five base pairs (e.g., “+5”). Each of these alterations in the DNA sequence results in reduced competition by the mutant DNA competitor relative to wild type and increased recovery of the primary TF (Figure 5B). Furthermore, reduced competition is observed even when adding two competitors with mutations in one or the other individual TF binding site and each present at the same concentration as the wild type control; thus, high affinity binding requires the two TF binding sites to be present on the same DNA molecule with the proper spacing. These results indicate that the physical interactions detected for each of these pairs mediate cooperative DNA binding to an endogenous sequence from one of the top ChIP peaks.
In light of the possibility that the influence of short range cooperative interactions may be more pronounced when the interacting TFs are at relatively modest concentration levels, we extended the tests reported in Table 3 to include all candidate secondary TFs with expression in the top 50%. The results, shown in Supplementary Table S14, reveal that for several data sets stronger influences are detectable when allowing lower expression levels of the secondary TF. On the one hand, this means that the list of interactions identified in Table 3 is likely incomplete. On the other hand, the list shown in Table S14 must be interpreted with caution since testing more candidate secondary TFs may lead to spurious interactions being reported due to similarity of motifs between two candidates.
Cooperative interactions are not the only manner in which one TF's binding may influence another's. Two TFs competing for overlapping binding sites can modulate each other's binding levels at the location [47]. Our next set of tests searched for evidence of this phenomenon in ChIP data sets. We used a two motif STAP model with no interaction terms, and compared the cross-validation CC from this model to the baseline CC of Table 1. The only way in which a secondary TF site can influence the binding prediction for the primary TF in the two-motif model is if their sites overlap (Figure 1B). The results, shown in Table 4, comprise 17 cases of significant ΔCC over the baseline model (ΔCC ≥0.04, P-value≤0.05, Z-score ≥3). In at least 10 of these cases, the secondary motif's presence is strongly anti-correlated with the primary TF's ChIP score, i.e., the competing motif is more frequent in non-peaks or in lower ranking peaks than in strong peaks. This may imply that the strong peaks exhibit selection against sites of the secondary TF competing with the primary motif. Figure 6A shows three examples of the pattern of overlap between sites of a primary TF and a secondary TF, observed in sequences with high STAP scores and low ChIP scores. We noted that in all of these cases, the overlapping sites tended to be suboptimal matches to either motif.
Two different data sets involving HB, one from stage 5 and the other from stage 9, were influenced by overlapping sites of the RETN motif (Table 4). RETN is a well-known repressor that acts through competitive binding when inhibiting activation by the TF engrailed (EN) [48]. Two other secondary motifs that seem to influence multiple data sets are EXD and HTH. Both of these homeodomain proteins play prominent roles during development as cofactors in repressor complexes with both Hox and other homeodomain proteins. Interestingly, in all three cases where EXD influences binding, there is no correlation between EXD sites and the primary TF occupancy, while in all three cases where HTH exerts an influence, there is a strong negative correlation (∼−0.18) between HTH motif presence and primary TF binding (see Discussion).
The next set of tests was directed at detecting evidence of antagonistic binding at non-overlapping sites. A possible mechanism for such a phenomenon is that of the secondary TF upon binding rendering the local DNA inaccessible, e.g., through recruitment of HDACs [49], as is speculated to be the case with some short-range repressors in Drosophila [50]. We used a two-motif STAP model with a TF-TF interaction term that is fit on training data, and compared the resulting CC to that from the primary motif alone (Table 1). This interaction term was constrained to be <1, corresponding to an unfavorable energy of interaction in the underlying thermodynamics model (Figure 1B). Note that this model incorporates both competitive binding and antagonistic influence from non-overlapping sites. Comparing the CC achieved by this model at either the 30 bp or the 150 bp distance threshold to the baseline (Table 5, Supplementary Figure S4B), we found 35 cases of significant improvements (ΔCC ≥0.04, P-value<0.05, Z-score ≥3). These included 6 data sets influenced by the EXD motif, 4 data sets by the HTH and RETN motifs, and 3 data sets by the JIGR1 motif. We noted that these four motifs were also observed to influence binding through competitive binding to overlapping sites (Table 4) above. However, in such cases where a secondary motif had significant effect on binding levels both in the competitive binding mode as well as the antagonistic binding mode, the magnitude of the effect was always stronger in the latter mode. The strongest case of antagonistic influence at the 30 bp distance threshold was estimated for the data set CAD_Bseq_s5, for the TF caudal (CAD), where the RETN motif improves the CC from 0.178 to 0.401. On the other hand, the strongest influence at the 150 bp threshold was by the EXD motif, also on the CAD_Bseq_s5 data set, where the baseline CC of 0.178 improved to 0.412, and this effect was exclusive to the 150 bp range. In fact, a large majority of the antagonistic binding influences were significant exclusively at either the short (30 bp) or the long (150 bp) range (Table 5). This may suggest that the underlying mechanisms of short and long-range antagonistic influences are different, although we did not observe any motif-specific preferences for one range versus the other.
We searched for inter-site spacing biases that might provide additional insights into the significant antagonistic influences identified above. It was commonly the case that ChIP peaks had a significant bias towards specific spacing values while non-peaks tended to avoid that range (Figure 6B, e.g., D-EXD). Interestingly, though less commonly, such spacing biases were also observed in non-peaks (Figure 6B, Supplementary Tables S5, S19). Even when examining antagonistic influences of the same secondary TF, e.g., HTH, we found some data sets where the spacing bias was exclusive to ChIP peaks and others where the bias was present in non-peaks.
Separate examination of peaks and non-peaks for effects of antagonistic influence revealed that such effects are manifested in a better discrimination of peaks versus non-peaks as well as a better modeling of ChIP scores with peaks alone or, more commonly, within non-peaks (Supplementary Table S13 and Supplementary Figure S8).
Recent work [6], [7] has shown that DNA accessibility data, which reflects nucleosome positioning and other chromatin-related effects, has a very strong correlation with TF occupancy, and when used in conjunction with the primary TF's motif can lead to highly accurate predictions of occupancy. This has been demonstrated in the context of five TFs in Drosophila (data from whole embryo) and six TFs in human (data from two cell lines). These prior results motivated us to examine the same hypothesis for the much larger collection of TF-ChIP data sets studied here. In all of our tests in this section we used DNaseI hypersensitivity data from [19]. In the first tests, we used a high threshold (90th percentile) on developmental stage-specific accessibility to designate “accessible regions”, predicted zero occupancy in inaccessible regions, and used STAP and the primary motif to predict occupancy in accessible regions. Accessibility-filtered STAP scores computed in this manner correlated very highly with ChIP data (Supplementary Table S6), and led to substantial improvements upon the baseline results of Table 1, for 38 of the 45 data sets. This confirms that the observations made by Kaplan et al. and Pique-Regi et al. are manifest over a larger dataset.
The test above showed that motif and accessibility information together provide highly accurate predictions of ChIP scores. A natural question that arises then is: how strong is the influence of the primary TF's motif in determining its occupancy, beyond the influence of accessibility? To answer this question we computed the “semi-partial correlation coefficient” (SPCC) between ChIP and STAP scores, which subtracts or “partials out” the contribution of accessibility information. Technically, this amounts to first predicting ChIP scores using accessibility alone, and then correlating the residual ChIP scores with STAP scores (see Methods). We found that for the majority of data sets the SPCC values (Table 1, column SPCC(m1)) were comparable to the baseline CC values, demonstrating that, as expected, the primary motif plays a major role in shaping TF binding profiles. For ten data sets, SPCC was better than baseline CC, most notably for the data set TIN_Fchip_s9 where the primary motif's correlation improves from 0.428 to 0.507 upon partialing out accessibility. In these cases, factoring out the accessibility effects better reveals the expected relationship between primary motif presence in the sequence and occupancy. In contrast, five data sets showed a dramatically lower SPCC than CC (Table 1); these were related to the TFs VFL, TRL and MED. This is consistent with hypothesis emerging in this work (also see next paragraph) and in recent literature that VFL and TRL have direct influence on accessibility patterns, and partialing out the correlation with accessibility results in much reduced correlation between primary motif and TF occupancy. The third of the trio of TFs identified here, MED, is also believed to direct the co-factor CBP to the genome [51] and thus influence accessibility profiles. The SPCC was lower than CC also for TWI, D and SLP1, though not as dramatically. Sandmann et al. [52] have previously found TWI to bind to a large number of mesodermal enhancers and speculated that its role may be to facilitate chromatin remodeling. D is a SOX domain protein and there has been suggestion that this family of TFs may function as chromatin remodelers [53]. Interestingly, independent evidence in support of the accessibility-mediated effect of VFL, TRL, TWI, SLP1 and MED emerged when we repeated the evaluation of the single motif STAP model (baseline, Table 1) on data sets composed of the top 1000 ChIP peaks and 1000 random non-peaks selected from ChIP peaks of other TFs (Supplementary Table S15 and Supplementary Figure S9). We found the CC on these data sets to be conspicuously below that on the default data sets where the non-peaks were random genomic segments. This implies that the primary motif in these cases is better able to discriminate peaks of the primary TF from random non-peaks than from other accessible regions (peaks of different TFs). This in turn suggests that the motifs of VFL, TRL, TWI, SLP1 and MED may be common features of many ChIP peaks that discriminate them from random non-coding sequences irrespective of the bound TFs.
Our next tests examined the effect of cooperative binding with secondary TFs in the light of accessibility information. Recall that the VFL and TRL motifs had emerged as the most promiscuous influences in our tests above (Table 2), and that their influence was noted as being predominantly long-range (Supplementary Tables S3, S4), leading us to speculate that they may be mediated through modulation of local accessibility. We therefore asked if the improvements in CC due to either of these motifs are observed after removing the effects of accessibility information. We computed SPCC values of the cooperative interaction model after partialing out accessibility (Figure 7A), similar to that described in the previous paragraph. We found that the effects of TRL disappear in all 8 data sets where it had been significant before considering accessibility, adding evidence in favor of our hypothesis that TRL's influence is mediated by accessibility. In contrast, VFL was found to exhibit a more diverse behavior: in 7 of 10 data sets its effects vanished after considering accessibility, while in 2 data sets (CAD_Bseq_s5 and HB_Bseq_s5), a pronounced influence (ΔSPCC ≥0.04) remained even after partialing out accessibility (Supplementary Table S7). These two data sets also showed evidence of an inter-site spacing bias between VFL and the primary motif (Supplementary Figure S3). These findings suggest that VFL's influence on TF binding may involve distinct mechanisms, including not only a general effect on local accessibility, but also more TF-specific mechanisms potentially involving direct interactions with the primary TF.
We repeated the above analysis on data sets where secondary motifs other than VFL and TRL had led to significant improvements in CC through a cooperative binding model (Table 3). The results, shown in Table 6 and Figure 7B, reveal that in most cases the influence of the secondary motif is pronounced even after partialing out accessibility information. This suggests that most of these secondary TFs operate through primary TF-specific interactions rather than by only influencing accessibility. Similar results were obtained when examining the cases of antagonistic influence by secondary motifs (Figure 7C).
We studied mechanistic determinants of TF-DNA binding by computationally modeling genomic occupancy from over 40 ChIP data sets obtained from four different stages of embryonic development, in conjunction with over 300 TF motifs and stage-specific DNA accessibility and RNA-SEQ data. Our ultimate goal is to use the insights revealed here, both general and data set-specific, to develop improved computational tools that can quantify functional TF-DNA interactions genome-wide. Such tools can potentially inform models of TF regulatory networks in the same way that ChIP data is beginning to be used today [1], [4]. We note that characterizing hundreds of TFs by the whole-genome ChIP-SEQ in the vast number of different cellular conditions is not currently feasible. Computational tools therefore offer an attractive alternative, especially if they can be shown to predict cell type-specific occupancy. TF motifs are already being characterized through high throughput technologies such as Bacterial 1-Hybrid [9], SELEX [11], [54], and Protein-Binding Microarrays [55]. Cell type-specific DNA accessibility profiles and TF expression levels only need to be characterized once for a given cell state, and can then be used to predict binding profiles for all TFs. Our work provides initial evidence for the feasibility of this vision. At the same time, we note that the CC values reported here should not be interpreted as correlation coefficients between genome-wide predictions and observed levels of TF binding. The manner in which we chose to evaluate various models, i.e., by examining agreement with ChIP scores on 1000 bound regions and 1000 randomly selected non-peaks, was dictated primarily by the goal of detecting significant influences on primary TF occupancy. We also note that the CC values varied substantially across data sets, from 0.765 for TRL to 0.062 for Dorsal (DL) (Table 1). This variation in model performance may reflect weaknesses of certain data sets or PWMs, or a variable reliance of ChIP scores on the primary TF's binding.
Despite a general appreciation of the potential role of various determinants of TF binding, there have been very few systematic studies of the extent of their influence across a large number of TFs. We review three such studies that set the stage for our own work and explain the main goals and contributions of our work in the backdrop of these important prior studies.
Kaplan et al. [6] studied ChIP-SEQ data on five TFs in early Drosophila development, and concluded that the TF motif and DNA accessibility are the most informative correlates of TF-DNA binding, as determined by the agreement between measured and predicted occupancy profiles. They also used TF sequence signatures to examine the role of competitive and cooperative interactions with other TFs with similar developmental roles and concluded that these interactions do not play a significant role overall. Their negative finding regarding secondary motifs may be limited to the small number of data sets examined, or be a limitation of the specific methodology adopted in the study (including the use of a more limited set of motifs that were available then). Here, we perform much more extensive tests of the role of the above-mentioned binding determinants of TF binding, by analyzing 45 TF-ChIP data sets spanning multiple stages of embryonic development in D. melanogaster. We primarily consider the influence of a large number of secondary TFs that are highly expressed in that developmental stage. In contrast to the earlier findings, we find many cases where the primary TF's binding levels are significantly influenced by the presence or absence of binding sites for other TFs.
In a related study, Pique-Regi et al. [7] considered the problem of classifying primary motif matches within ChIP peaks versus those outside of ChIP peaks, in the context of six ChIP-SEQ data sets from two human cell lines. They found accessibility and specific histone modifications to be the most useful features in this classification task, but did not consider the influence of secondary TFs. However, there are fundamental differences in the goals of our study from that of Pique-Regi et al. Their objective was to build a computational tool for annotating TF-bound sites genome-wide, and therefore their algorithm integrates several variables that correlate with binding, including evolutionary conservation, transcription start site proximity, DNA accessibility and histone marks. On the other hand, our focus is on the influence of variables that are expected to be mechanistic determinants of binding, and whose influence can be reasonably understood within an intuitive biophysical framework. We therefore focus specifically on testing whether and how binding sites of secondary TFs shape the primary TF's binding profile. In this pursuit, we rely upon motif, sequence and TF expression data, treating these as the “predictor variables” with which to model ChIP data. We do not include other variables such as evolutionary conservation (which is not a mechanistic determinant) or start site proximity (whose influence cannot be easily modeled biophysically) as predictors in this statistical exercise. DNA accessibility data is used in our analysis, not to improve occupancy prediction per se, but to answer a specific mechanistic question about how secondary TFs influence binding. Also, there is a fundamental technical difference between the data types modeled in the two studies: the variable we propose to model is not tied to TF-DNA interaction at an individual binding site as in [7], but to the aggregate effect of all binding events within a 500 bp window. For the simplicity, we ask whether a model can predict the actual ChIP score at a genomic position, rather than ask whether a model can predict whether a putative motif match falls within a significant ChIP peak or not.
A recent study by Yanez-Cuna et al. [5] searched for motif signatures of context specific binding of TFs. In particular, they analyzed ChIP data sets for the same TF from two different cellular conditions and asked if peaks exclusive to either condition could be discriminated on the basis of motif presence. They showed that such motif signatures do exist for the seven TFs examined and that general-purpose machine learning methods such as support vector machines can accurately classify context-specific binding sites using tens of motifs. In the same vein, they showed that bound and non-bound regions of a TF can be discriminated using a combination of tens of motifs, for most of the 21 TF-ChIP data sets examined. Additionally, they performed a closer examination of the binding determinants of one particular TF, twist (TWI), and demonstrated that binding sites for the secondary TFs VFL and TTK significantly affect the correct prediction of many context-specific TWI binding sites. While Yanez-Cuna et al. mostly focused on demonstrating that accessory motif signatures can distinguish TF-DNA binding regions in different developmental stages, our primary goal was to precisely identify the most influential secondary motifs for each of 45 different TF-ChIP data sets. To this end, we focused largely on quantifying the influence of secondary motifs and assessing their statistical significance rigorously. By performing our analysis over many data sets, we were able to gain more general insights about the widespread or TF-specific roles of particular secondary TFs. In particular, our statistical tests are geared towards explaining the mechanistic basis of such roles: short- versus long-range effects, synergistic versus antagonistic effects, chromatin mediated versus direct interactions, etc.
The review by Biggin [56] uses findings from recent studies to argue that accessibility is more important than the role of secondary TFs in determining primary TF binding levels. However, we do not attempt here to characterize the effect of accessibility as being stronger or weaker than the effect of interacting TFs. Integrating perspectives from Biggin and others [15]–[17], [57], [58], DNA accessibility in vivo can be considered the result of multiple factors playing out simultaneously, possibly including innate sequence preferences of nucleosome location, a conglomerate of chromatin remodeling activities and displacement of nucleosomes by competition with TF binding. Under this view, there are practical limitations in the approach of directly comparing the improvement in occupancy prediction due to accessibility information to that due to secondary motif information alone. Moreover, while it may be possible to make broad statements regarding the influence of accessibility or other chromatin-related information on TF binding, secondary TFs , due to the combinatorial nature of gene regulation, will be factor-specific in their effects and thus will only be detectable on a few data sets. Accordingly, our goal is to characterize as many of these determinants of TF occupancy, from each ChIP data set, rather than assign any one number to the overall influence of, say, interactions between the primary and secondary TFs, which will be factor dependent by definition.
A related study that examined the effects of secondary TFs on ChIP data is that of Gordan et al. [59] who reported on TF-ChIP data sets in yeast where a secondary motif alone was a better correlate of peak location than the primary motif. In some cases, this may be due to a problem with the primary motif (H.N.P. and M.H.B. unpublished results). In other cases, such a situation may reflect indirect binding of the primary TF to the peak, via physical interaction with the bound secondary TF. It suggests an alternative model of ChIP data, where binding is predicted to be a sum or linear combination of the occupancy values of the primary TF (direct binding) and a secondary TF (indirect binding). We have not explored this model here, and believe that it is an important goal for future studies.
Our approach to including accessibility data in the analysis was to use partial correlations to examine secondary TF influences before and after factoring out the effect of accessibility on ChIP scores. Alternative approaches may directly include accessibility data in the occupancy models, as was done by Kaplan et al [6], who changed prior probabilities of binding in their probabilistic model based on accessibility, and Pique-Regi et al. [7], who included DHS and histone modification data as features in their classifier. Future modifications of our approach will attempt to include accessibility within the biophysical framework of STAP, and may potentially reveal the role of accessibility even more accurately.
An intriguing observation from our analyses was the influence of competitive binding by the secondary TF EXD despite there being no correlation between EXD sites and the ChIP scores of the primary TF. It is puzzling because it suggests that the frequency of EXD sites does not differ between peaks and non-peaks, yet these sites somehow make a significant difference to binding predictions. However, it is possible that the frequency of EXD sites overlapping with primary TF sites is different between peaks and non-peaks, and the advanced model uses the competition for overlapping sites to predict lower occupancy in certain sequences than that predicted by the baseline model, leading to improved agreement with ChIP scores (Supplementary Figure S11).
Our work opens up several important directions of future research into TF-DNA interaction on a genomic scale. While the models we explored used at most one secondary motif in one interaction mode, a more realistic model will require integration of more than one underlying mechanisms influencing primary TF occupancy. Accessibility information will play a crucial role in the predictive ability of such models. In the longer term, an important goal will be to develop integrative models where sequence, TF gene expression and developmental history is sufficient to predict, at least to a good approximation, both accessibility patterns and TF-DNA binding profiles. With the future availability of large collections of TF motifs, such computational surrogates for cell type-specific ChIP data will enable global studies of gene regulatory networks and provide specific regulatory assignments that can be experimentally confirmed.
We used 55 TF-ChIP data sets on 37 TFs active in early stages of Drosophila embryonic development. These include five ChIP-seq data sets and 20 ChIP-chip data sets from BDTNP [60], seven ChIP-chip data sets from the Furlong lab [24], and 21 normalized ChIP-chip and ChIP-seq data sets from the ModEncode project [1], [61]. ChIP data of VFL and TRL were obtained respectively from [29] and [62]. Stage-specific genome-wide DNaseI hypersensitivity (chromatin accessibility) data, which is mapped to genome release 4 coordinates, was downloaded from the first replicate in the BDTNP web site and converted to release 5 coordinates using the liftOver tool and chain files from the UCSC web site (http://hgdownload.cse.ucsc.edu/downloads.html). We used 614 Drosophila transcription factor motifs, corresponding to 322 distinct TFs, from the FlyFactorSurvey database [20]. The motifs were ranked based on expression of the associated TF gene, using RNA-SEQ data [21] for the appropriate developmental stage. In cases where a TF-ChIP data set corresponded to a range of stages, expression values were stage-normalized and averaged before ranking. Motifs corresponding to heterodimeric complexes (such as HLH TFs in complex with DNA) were not considered. Motifs in the top 10% of the expression-based ranked list for the appropriate developmental stage were tested as candidate secondary motifs. The one exception to this are results in Table 3 where the top 25% of the ranked list was considered.
We smoothed each TF-ChIP data set and each DNase I data set by assigning scores to each 500 bps window over the genome, with a 50 bps shift. First, raw “read scores” in a data set were mapped to the nearest genomic position that is a multiple of 50. The score of a 500 bp window was then computed by averaging over all read scores mapped to positions in that window; we refer to this as the “ChIP score” of the window. After this transformation, we selected 1000 non-overlapping, highest scoring windows as “peaks” and randomly extracted 1000 non-exonic, non-overlapping windows without replacement from the remaining genome as “non-peaks”. This set of 2000 windows and their ChIP scores constitutes a TF- and stage-specific data set in our analyses. A “primary” motif was designated for the data set, based on the availability of motifs for the ChIP'ed TF. In cases where there were multiple motifs available for the ChIP'ed TF, the motif with the highest correlation between STAP scores and ChIP scores over all 2000 windows (see below) was selected. “Secondary” motifs tested for potential effects on the primary TF's binding were selected based on expression data, as mentioned above.
We used the STAP program [22] to predict the ChIP score of a window, using the primary motif and optionally a secondary motif for that TF. STAP has one or more free parameters that require training data – a set of sequences and their ChIP scores. Hence, we used cross-validation to train and test various models of TF-DNA occupancy that are encoded by STAP. We randomly divided the 1000 peaks into 4 equal partitions and also the 1000 non-peaks into 4 equal partitions. In each fold of cross-validation, three partitions from the peaks and non-peaks were used as the training set and one partition (i.e., 250 peaks and 250 non-peaks) was the test set. Predicted ChIP-scores on each of the test sets of windows were collected together, and the resulting set of 2000 real and predicted ChIP score pairs were subjected to evaluations. Evaluations on a data set were considered a failure if the STAP parameter values learned in the four folds were widely different; this happened for one data set.
This was described in [22]. STAP considers each molecular configuration σ that specifies which sites in the given sequence are bound by their respective TFs. Following standard statistical physics, the “Boltzmann weight” of σ, denoted by W(σ), represents the relative probability of the system being in configuration σ, and is calculated based on TF concentration and the estimated binding affinity of every bound site in σ. The Boltzmann weight is a product of terms contributed by each TF-bound site in the configuration. This corresponds to the assumption that each bound TF interacts independently with the DNA, with energy contributions that add up [63]. See Figure 1A for an example where the sequence has two sites (‘A’ and ‘B’) for TF ‘A’, or Figure 1B where there is one site for each of two TFs ‘A’ and ‘C’. A site's contribution, q(S), depends on the TF concentration and the strength of site S, and is given by:where [TF] is the concentration of the TF (in arbitrary units), LLR(⋅) is the log likelihood ratio score of a site, computed based on the known position weight matrix (PWM) of the TF [25], Smax is the strongest binding site of the TF, and K(Smax) is the equilibrium constant of the TF binding to this site. The product K(Smax)[TF] is a TF-specific free parameter denoted by γTF. Let Nk(σ) denote the number of bound sites of TF k in configuration σ. The STAP model predicts the occupancy of TF k as:Note that while Nk(σ) counts the number of bound sites for TF k only, the Boltzmann weight W(σ) depends on bound sites for all TFs.
The accuracy of STAP predictions was assessed by computing the Pearson correlation coefficient (CC) between real and predicted ChIP scores of 2000 windows in a data set. To assess the impact of a secondary motif M2 in modeling a data set whose primary motif is M1, we tested STAP in a single motif mode (“STAP(M1)”) and in two-motif mode (“STAP(M1,M2)”) and compared the difference in their accuracies: ΔCC = CC(STAP(M1,M2)) – CC(STAP(M1)).
A secondary motif M2 was deemed as a significant influence on the data set if the following conditions were met:
We also evaluated the best secondary motif effect for each data set by computing an “Area Under ROC” (AUC) value for the interaction model (Supplementary Table S8).
For each significant case of cooperative or antagonistic influence by a secondary motif, we searched for biases in the inter-site spacing between the primary and secondary TFs. Let us assume a pair of motifs (M1, M2) represents the binding specificities of the primary and secondary TFs. To test for a specific spacing bias, say ‘d’ base pairs, between (M1, M2) in a given set of segments, we grouped all pairs of adjacent heterotypic binding sites (located by FIMO program with threshold of e−7 [65]) into those having or not having inter-site distance of d. We counted the number of site pairs in each group and compared these counts to the corresponding counts in a “background” data set using one-tailed Fisher's exact test. The “background” data set was constructed by shuffling the locations of predicted sites in each segment, thus preserving the number of binding sites in each segment, and pooling together 10 such randomized data sets (Kazemian et al., manuscript in review). Tests of spacing bias were conducted on a set of top 250 scoring ChIP peaks and separately on a set of bottom 250 non-peaks.
Semi-partial correlation is a statistical technique generally employed to assess the association of one random variable X with the other random variable Y after eliminating the effect of a third random variable Z on Y [66]. In our tests, X represents predicted TF-DNA binding, Y the experimental TF-DNA occupancy from ChIP, and Z the accessibility. The semi-partial correlation score between X and Y, after “partialing out” Z from Y, is computed as , where is the correlation coefficient between A and B.
Protein interactions were measured in a modification of the previously described LUMIER or LuMPIS methods [38], [39] except that each protein was expressed in vitro rather than in cell culture. Open reading frame (ORF) clones for transcription factors were part of the Berkeley Drosophila Genome Project the collection of universal donor clones [67]. ORFs were transferred into two vectors, pHPT7-FlRluc-BD and pHPT7-MBP-BD (HNP and MHB, unpublished), using Cre Recombinase (New England Biolabs, M0298L). For one TF, Mad, the ORF was PCR amplified ligated into AscI and PmeI restriction sites in each vector. These vectors contain a T7 promoter for in vitro transcription, a loxP site for cloning and either maltose binding protein (MBP) or Renilla luciferase (luc) coding regions. Clone names and primer sequences are provided in the supplementary information (Table S9).
Proteins were made by coupled in vitro transcription/translation using the PURExpress In Vitro Protein Synthesis Kit (NEB, E6800S). All samples were analyzed by Western Blot to confirm that some full-length product was obtained. Luciferase input was measured using the Renilla Luciferase Assay System (Promega, E2820). The proteins were diluted with IP Buffer (150 mM NaCl, 50 mM Tris pH 7.4) such that roughly 106 luciferase counts were added to each sample and an equivalent amount of MBP protein were mixed. Proteins were incubated with gentle rocking for 4°C for 2 hours. Amylose Resin (NEB, E8021S) blocked with 5 percent BSA was added to proteins and incubated with rocking at 4°C for 2 hours. The samples were washed twice with IP buffer and transferred to 96-well plates (Corning, 07-200-589) for luciferase measurements. The luminescence intensity ratio was measured using as follows:
Each experiment was performed in duplicate, the experiments were averaged, and the standard deviation was calculated. Source cDNAs, amplification primers and luciferase data are compiled in Supplementary Table S9.
In vitro synthesis of tagged TFs and luciferase assays were performed as described above. Target sequences were identified from the top ChIP peak regions that contained strong matches to the primary and secondary motifs with a spacing and orientation that was most frequently observed. Other criteria used in selecting target sequences included whether the ChIP peak lies within a known enhancer, and whether its predicted occupancy under STAP's cooperativity model is higher (in rank) than that under the baseline model without cooperativity. Double stranded DNA oligonucleotides were synthesized that contained wild type or altered sequences. One oligonucleotide containing the wild type sequence is biotinylated on the first base. The genomic coordinates for the wild type sequences and all mutant sequences are shown in Supplementary Table S16. Protein-DNA interactions were measured in a modification of a previously described microwell-based assay [46]. Tagged TFs were expressed in vitro rather than in cell culture and diluted with low-stringency binding buffer (140 mM KCl, 5 mM NaCl, 1 mM K2HPO4, 2 mM MgSO4, 20 mM HEPES (pH 7.05), 100 µM EDTA, 1 µM ZnSO4) +1% BSA. Oligonucleotides were annealed and diluted using annealing buffer (50 mM Tris-HCl, 0.2 mM MgSO4, pH 7.0). Annealed oligo mixes were prepared with 5 ul of 1.2 uM biotinylated oligos, 5 ul of 24 µM competitor oligo, 2 ul of 500 ng/ul Poly(dI-dC)*Poly(dI-dC), and 8 ul of annealing buffer (final volume 20 ul) and incubated for 1 hour. 106 luciferase counts of the luc-tagged primary TF and (if appropriate) an equivalent amount of MBP-tagged secondary TF were mixed (30 ul volume). The diluted proteins were added to the DNAs and incubated with gentle rocking at 4°C for 2 hours. Streptavidin coated 96 well plates (ThermoScientific # 15502) were blocked with 5% BSA and low stringency binding buffer. The protein/oligo mixture was added to the plates and incubated for 2 hours at 4°C. The samples were washed twice with low stringency binding buffer. Recovered luciferase activity was measured directly in the plates. All values were normalized by dividing by the luciferase counts recovered in the sample containing an excess of wild type competitor DNA.
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10.1371/journal.pgen.1002517 | The Dynamics and Prognostic Potential of DNA Methylation Changes at Stem Cell Gene Loci in Women's Cancer | Aberrant DNA methylation is an important cancer hallmark, yet the dynamics of DNA methylation changes in human carcinogenesis remain largely unexplored. Moreover, the role of DNA methylation for prediction of clinical outcome is still uncertain and confined to specific cancers. Here we perform the most comprehensive study of DNA methylation changes throughout human carcinogenesis, analysing 27,578 CpGs in each of 1,475 samples, ranging from normal cells in advance of non-invasive neoplastic transformation to non-invasive and invasive cancers and metastatic tissue. We demonstrate that hypermethylation at stem cell PolyComb Group Target genes (PCGTs) occurs in cytologically normal cells three years in advance of the first morphological neoplastic changes, while hypomethylation occurs preferentially at CpGs which are heavily Methylated in Embryonic Stem Cells (MESCs) and increases significantly with cancer invasion in both the epithelial and stromal tumour compartments. In contrast to PCGT hypermethylation, MESC hypomethylation progresses significantly from primary to metastatic cancer and defines a poor prognostic signature in four different gynaecological cancers. Finally, we associate expression of TET enzymes, which are involved in active DNA demethylation, to MESC hypomethylation in cancer. These findings have major implications for cancer and embryonic stem cell biology and establish the importance of systemic DNA hypomethylation for predicting prognosis in a wide range of different cancers.
| DNA methylation is an important chemical modification of DNA that can affect and regulate the activity of genes in human tissue. Abnormal DNA methylation and its subsequent effects on gene activity are a hallmark of cancer, yet when precisely these DNA methylation changes occur and how they contribute to the development of cancer remains largely unexplored. In this work we measure the methylation state of DNA at over 14,000 genes in over 1,475 samples, including normal and benign cells, invasive cancers, and metastatic cancer tissue. Using cervical cancer as a model, we show that gain of abnormal methylation at genes typically un-methylated in stem cells can be detected up to 3 years in advance of the appearance of pre-cancerous cells, while those genes typically methylated in stem cells lose this methylation progressively throughout cancer development. Furthermore, we discover that this process of methylation loss during cancer progression is a marker of poor disease outcome common to all four major women-specific cancers: breast, ovarian, endometrial, and cervical cancers. Finally we demonstrate the relationship between loss of methylation and cancer-specific over-production of a specific protein known to play an active role in removing methylation from DNA. Taken together these findings highlight the complex nature of DNA methylation dynamics in cancer development as well as their potential exploitation for clinical gain.
| Aberrant DNA methylation is one of the most important cancer hallmarks [1], yet its precise role in carcinogenesis and clinical prognosis remains ill-defined [2]. Indeed, the dynamical changes in DNA methylation that happen during carcinogenesis, in particular those prior to morphological changes, have not yet been explored in detail. Moreover, no study has so far reported a DNA methylation signature capable of predicting prognosis across multiple human cancers, unlike gene expression and DNA copy number where such prognostic signatures have been described [3], [4].
Both hyper and hypomethylation are commonly observed in cancer [1]. In contrast to hypomethylation, which seems to target large inter-genic satellite repeat regions, hypermethylation appears to happen locally, preferentially targeting the promoters of genes. Several studies have reported that a statistically high fraction of these promoters map to stem cell PolyComb Group Target genes (PCGTs) [5], [6], many of which encode transcription factors needed for differentiation, and which are normally suppressed in embryonic stem cells through a reversible mechanism mediated by the Polycomb Repressive Complex (PRC2) [7]. This preferential hypermethylation at PCGTs in cancer supports the view that the reversible gene repression of PCGTs in stem cells may be replaced by permanent silencing in cancer, potentially impairing the differentiation capacity of cells [1], [5], [6]. Although there is no causal functional data linking PCGT methylation to carcinogenesis yet, there is accumulating evidence that factors which lead to cancer, for instance age or oxidative damage, are causally involved in DNA methylation at PCGTs [8]–[11].
Another feature of the epigenetic landscape characterising human embryonic stem cells (hESC) was described by Lister et al [12]. Specifically, using single-base-resolution DNA methylation maps, they demonstrated that a substantial fraction of CpGs is heavily (>80%) Methylated in human Embryonic Stem Cells (MESC) (see Materials and Methods for the precise definition of MESC CpGs and Table S1 for the complete list of MESC CpGs on the 27 k array). However, it is unknown at present what role MESCs may play throughout carcinogenesis. Thus, which epigenetic stem cell features are retained or changed in human cancer and even more importantly at which stage during human carcinogenesis these epigenetic changes occur, is still unclear.
Motivated by these outstanding questions, we decided to (i) explore the dynamics of epigenetic changes at stem cell loci (PCGTs and MESCs) throughout all stages of human carcinogenesis and (ii) to investigate their potential role in predicting poor prognosis.
To address our first aim, we used as a model the uterine cervix, since screening programs in place allow easy access to this organ, and cervical carcinogenesis is also one of the few scenarios in humans where DNA methylation changes in the actual cell of origin and occurring throughout disease progression can be analyzed. Specifically, we measured DNA methylation at over 27,000 CpGs in cervical cells and at three different stages: (a) three years before onset of dysplastic changes, (b) at the stage of non-invasive dysplasia, and (c) at the stage of invasive cervical cancer. To address our second aim we analysed DNA methylation data from 5 independent cohorts encompassing a total of 1,026 tumour samples in 4 different gynaecological cancers. In total, we analysed DNA methylation data from 10 independent studies, encompassing normal and cancer tissue from 5 different tissue types, including metastases (Table 1).
Using these data we here report four major novel aspects of cancer epigenetics: (i) Hypermethylation at PCGT stem cell loci occurs up to three years before the first signs of morphological transformation, (ii) hypomethylation at MESC stem cell loci is a hallmark of cancer invasion, affecting both epithelial and stromal compartments, and increases further in metastases, (iii) hypomethylation instability at MESCs defines a stem cell DNA methylation signature that predicts poor prognosis in multiple human cancers independently of standard prognostic factors, and (iv) expression of TET enzymes [13]–[17] is strongly associated with MESC hypomethylation.
All methylation data in this study were generated with the Illumina Infinium Human Methylation27 beadchip array (Materials and Methods), which assesses the DNA methylation status of 27,578 CpG sites located in the promoter regions of 14,495 genes as described previously [18]. Among these CpGs, 3,465 map to PCGTs, whilst 5,943 map to MESC CpGs (Materials and Methods, Table S1 and Table S2). We also made a distinction between CpGs located within Partially Methylated Domains (PMDs) (a total of 4,750 CpGs on the array mapped to PMDs), and those that are not (termed non-PMDs). PMDs demonstrate reduced methylation levels in more differentiated embryonic tissue compared to embryonic stem cells, and consist of focally hypermethylated elements (corresponding overwhelmingly to CpG islands), concentrated within regions of long-range hypomethylation [12]. PMDs were recently described also in cancer [19]. For precise definitions see Text S1.
To investigate the dynamics of DNA methylation in human carcinogenesis we designed a study with samples from three different phases reflecting cervical carcinogenesis: (1) ‘Before Dysplasia (BDy)’: normal cervical epithelial cells collected within the ARTISTIC trial [20], [21] (n = 152) of which 75 developed a cervical intraepithelial neoplasia grade 2 or 3 (CIN2/3) after three years (cases), whereas the other 77 remained normal (controls). These samples were matched for age and HPV status. (2) ‘Dysplasia (Dy)’: age-matched non-invasive dysplastic epithelial cells (CIN2/3) (n = 18, all HPV+) and normal cervical epithelial cells (n = 30, 19 HPV− and 11 HPV+) collected within screening programs [22], and (3) ‘Invasive Cancer (CA)’: invasive cervical cancer tissue (n = 48) and normal cervical tissue (n = 15) collected within a clinical setting. Further details of the samples are described in Text S1 (see also Table 1).
As expected, PCGTs were highly enriched among CpGs hypermethylated in invasive cervical cancer (Figure 1A and 1C). In contrast, CpGs that become hypomethylated in invasive cervical cancer are to a large extent MESCs (Figure 1B and 1D). Most importantly, PCGTs were hypermethylated three years prior to any cytological changes (Figure 1C, OR = 2.44; 95%CI = 2.27–2.63; p<10−100), especially for those PCGT CpGs located within PMDs (OR = 4.81; 95%CI = 4.19–5.52; p<10−100). We verified that PCGT enrichment was also independent of HPV status (P<0.005 for HPV+ and HPV−). Notably, the frequency of hypermethylation remained fairly constant throughout the phases from non-invasive dysplasia to invasive cancer (Figure 2A and Figures S1, S2, S3, S4).
In contrast to PCGT methylation, MESC hypomethylation appears as a progressive process towards invasive cancer: whereas we observed a substantial enrichment of MESCs in the normal samples three years prior to the dysplastic changes (OR = 5.69 and 9.55 for PMD and nonPMD respectively), non-invasive dysplastic samples had an increased MESC enrichment in hypomethylated CpGs (OR = 7.62 and 12.30 for PMD and nonPMD, respectively) and eventually MESC CpGs contributed most significantly to hypomethylated CpGs in invasive cancer (OR = 18.84 and 26.85 for PMD and nonPMD respectively; Figure 1D, Figure 2A, and Figures S1, S2, S3, S4). In order to check that these enrichments are not just a consequence of the baseline methylation levels (i.e. the levels in normal tissue), we estimated the enrichment relative to other CpGs with specific baseline methylation levels (CpGs with mean β-values in normal cervical tissue samples of <0.2 and >0.4). This confirmed that the observed PCGT and MESC enrichment was independent of the initial methylation levels in normal tissue, and that this was particularly true for PCGT/MESC CpGs within PMDs (Figure S5). Thus, MESC CpGs that showed reduced methylation levels (<80%) in normal tissue compared to their levels in hESCs (>80%) were still more likely to exhibit further hypomethylation in dysplasia and cancer than a control set of CpGs with similar methylation levels in normal tissue (Figure S5).
To test if PCGT and MESC methylation changes are also present in cells which are not immediately involved in carcinogenesis we studied white blood cell DNA from women who carry BRCA1 mutations and who are therefore at an 80% lifetime risk of developing breast and/or ovarian cancer. Whereas MESC methylation was not altered, we observed that PCGTs were highly enriched among CpGs hypermethylated in blood cells from BRCA1 mutation carriers (Figures S6 and S7).
Next, we asked if the progressive hypomethylation of MESCs towards invasive cancer is a generic feature of tumour biology. We analysed DNA methylation profiles of breast, endometrial, colorectal and lung cancer (Text S1; Figure 2B and Figures S1, S2, S6, S7), and in all cancer types we observed a significant loss of methylation at MESC CpGs, concurrent with the expected hypermethylation of PCGT CpGs.
As demonstrated in Figure 2A and 2B, PCGT methylation enrichment exists prior to and at the stage of non-invasive dysplasia when analyzing only epithelial cells without stroma and remains constant when studying invasive cancer tissue which contains some stromal components. In contrast, MESC enrichment doubles in the hypomethylated fraction when comparing invasive cancer to non-invasive dysplastic cells. This pronounced enrichment could be contributed by MESC hypomethylation in the cancer-associated stromal component. To test this, we analyzed those PCGTs and MESCs that are enriched in the hyper- and hypomethylated fractions in lung cancer and asked if these CpGs are also enriched in lung cancer associated fibroblasts compared to normal lung fibroblasts [23]. Interestingly, while there was no enrichment of PCGTs (Figure 2C), there was a clear enrichment of lung cancer MESCs among PMD CpGs that are hypomethylated in lung cancer fibroblasts (Figure 2D). This further supports the view that MESC hypomethylation is an important characteristic of cancer invasion, and that it may therefore be a molecular determinant of clinical outcome.
Molecular signatures, and in particular gene expression signatures, involving stem cell genes have been associated with poor prognosis in several cancers [24], [25]. Therefore, given the fundamental role of PCGT and MESC CpGs in the dynamics of DNA methylation in human cancer, as just described, it is natural to ask if DNA methylation changes at these stem cell loci can predict clinical outcome. In particular, we posited that epigenetic instability, as measured by DNA methylation changes from a normal reference, might indicate clinical outcome. To test this idea, we devised an Epigenetic Instability Index (EpI) to evaluate instability for each tumour sample as the fraction of significant DNA methylation changes relative to a corresponding normal reference profile (Materials and Methods). The instability index was divided into 4 types according to the baseline normal reference methylation (0 = unmethylated, 1 = hemimethylated, 2 = methylated) and the nature of DNA methylation changes (0→1/2, 1→2, 1→0, 2→0/1) observed in cancer (Materials and Methods, Figure 3A and 3B). In addition, we considered the EpI restricted to PCGT and MESC stem cell loci, and since very few PCGT CpGs were observed to be methylated (1 or 2) in normal tissue, this resulted in 3 stem cell EpI indices: PCGT (0→1/2), MESC (1→0), MESC (2→0/1). Remarkably, we observed that the demethylation instability index (DeMI) at MESCs (2→0/1) was associated with poor prognosis in endometrial, breast, ovarian, and cervical cancers (Figure 4). In multivariate analysis, the DeMI was a predictor of poor prognosis in all cancers independently of other prognostic factors (Table 2 and Table S3), demonstrating the clinical potential of this DNA methylation stem cell signature. In contrast, the methylation instability index defined at PCGTs only correlated with clinical outcome in ovarian cancer (Table S3). Survival analysis at individual CpG level further demonstrated the consistent enrichment of MESC CpGs among prognostic CpGs hypomethylated in poor outcome samples in all 4 invasive cancers, whereas PCGT CpGs were not consistently enriched in either the hyper or hypomethylated prognostic component (Table S4). There was also substantial overlap between the MESC CpGs which have stable methylation levels in normal tissue and which become hypomethylated in cancer, and prognostic MESC CpGs that are hypomethylated in poor outcome tumour samples (Table S5).
To further demonstrate that MESC hypomethylation is an important determinant of poor outcome in human cancer, we tested if these epigenetic changes progress further in metastatic cancer. Thus, we compared DNA methylation profiles of primary endometrial cancers to extra-uterine metastases of endometrial cancer. Importantly, the DeMI index was higher in metastatic cancer compared to primary tumours, but not so for the hypermethylation instability index at PCGTs (Figure 5A). In fact, the DeMI index demonstrates clinical potential for discriminating primaries that may be destined to metastasize (Figure 5B). From these data we can therefore conclude that while PCGT hypermethylation is an important event in early oncogenesis, which persists at later stages, MESC hypomethylation is a progressive process and a key characteristic of more malignant cancers (Figure 3B).
The ability of the DeMI index to predict clinical outcome in multiple cancers indicates that a core set of MESC CpGs may be involved. To investigate this we ranked the MESC CpGs according to the frequency of hypomethylation in each of the cancers considered. Many CpGs were observed to be hypomethylated in large fractions of tumours (Figure 6 and Table S6). While there were 6 MESC CpGs (FCGR3B, FLJ27255, FCN2, KRT82, CDH13, KRTAP8-1 on chromosome 1, 6, 9, 12, 16 and 21 respectively) commonly hypomethylated at a frequency of at least 10% in all four cancers (P<10−4), there were substantially larger overlaps between related cancers such as ovarian and endometrial cancer (overlap of 98 CpGs, OR = 134, 95%CI = (89–205), P = 3.2×10−124). Gene Set Enrichment Analysis (GSEA) [26] of the hypomethylated MESCs in each cancer also revealed a striking overlap of enriched terms, especially between endometrial and ovarian cancer where we observed widespread hypomethylation at 20q11 and 9q34 (Table S7).
Up until recently it has been assumed that DNA demethylation in cancer is a passive event, occurring as a result of absent re-methylation during DNA replication, with a consequent dilution of this covalent DNA modification. This view has now been substantially challenged by the identification of TET (ten eleven translocation) dioxygenases, which can convert 5-methylcytosine into 5-hydroxymethylcytosine and 5-carboxylcytosine, which thus constitutes a pathway for active DNA demethylation [13]–[17], [27]. In particular, it has been demonstrated that TET3-mediated DNA hydroxylation is involved in epigenetic reprogramming of the zygotic paternal DNA following natural fertilization and that this may also contribute to somatic cell nuclear reprogramming during animal cloning [16]. We therefore analysed mRNA expression of TET1 and two isoforms of TET2, and TET3 (see Text S1 for primer information), to test whether hypomethylation is associated with TET expression. We observed a strong correlation between high TET, in particular TET3 expression, and hypomethylation, specifically at MESC CpGs (Figure 7 and Figure S8). We checked that the anti-correlation of TET expression with MESC CpG methylation was independent of the level of methylation in normal tissue (Figure S9). Although this observation is purely correlative, it is consistent with the view that TET3 overexpression (Figure S10) in cancer contributes to reprogramming of cancer cells via active DNA demethylation.
Epithelial cells of the uterine cervix offer a unique opportunity to study epigenetic alterations throughout carcinogenesis. Our first key result is the demonstration that normal cells of origin acquire methylation changes at least three years in advance of the first morphological changes. Specifically, our data demonstrate that PCGT hypermethylation and MESC hypomethylation are major contributors to early cervical carcinogenesis. This is independent of human papillomavirus (HPV) infection as our study was matched for HPV status, and since PCGT enrichment was observed in both HPV+ and HPV− samples. Importantly, the observed enrichments were also independent of the levels of methylation in normal tissue. That is, MESCs which showed full methylation (i.e. β-value>0.8) or hemi-methylation (i.e. 0.3<β-value<0.7) were preferentially hypomethylated in dysplasia and cancer in comparison to control sets of CpGs with same methylation levels in normal tissue.
The role of PCGT methylation as a very early event is further supported by our finding that PCGTs were highly enriched among CpGs which were hypermethylated in blood cells from BRCA1 mutation carriers, suggesting that BRCA1 is an important regulator of the DNA methylome and that aberrant BRCA1 function could lead to increased predisposition to cancer through increased methylation at PCGT loci. The fact that BRCA1 mutation carriers showed increased PCGT methylation in their blood cells but are at no substantial increased risk to develop blood-borne cancers suggests that PCGT hypermethylation refers a substantial risk but that there are additional factors required (e.g. endocrine, paracrine or viral triggers).
Our second key result is that MESC hypomethylation occurs in both the epithelial and stromal components of cancer and that this is a progressive process, increasing significantly towards invasion and metastatic cancer. This in turn suggests that the level of MESC hypomethylation in primary tumours may be an important determinant of clinical outcome.
Indeed, our third key result is the report of a stem cell (MESC) DNA hypomethylation signature that can predict clinical outcome in multiple human cancers, independently of known prognostic factors. To the best of our knowledge this constitutes the first report of a common prognostic signature in cancer that is based on DNA methylation, and is therefore an epigenetic analogue to the prognostic genomic instability signature presented in [3].
Besides the key distinction of PCGT and MESC CpGs, we also observed that the localisation of CpGs in relation to PMDs was another important facet of the pattern of DNA methylation changes. Specifically, PCGT hypermethylation was observed preferentially within PMDs, while the progressive MESC hypomethylation in cancer was equally strong in PMDs and non-PMDs. We point out that while the PMDs considered here were defined for colon cancer cells, that these broad regions of partial methylation overlap significantly between colon tissue and fibroblasts, suggesting that these regions may be largely similar also between different tissues.
The similarities between normal developmental and cancer epigenetic programming are intriguing. While embryonic stem cells suppress differentiation-inducing genes reversibly via promoter occupancy of PRC2, cancer cells suppress these same genes much more robustly via covalent DNA modification. Even more interestingly, trophoblast cells whose core function is to invade the maternal tissue and form the placenta, are relatively more hypomethylated compared with the inner cell mass, which will differentiate into the embryo [28], supporting the view that hypomethylation may be associated with the capacity to invade neighbouring tissue such as the maternal endometrium. Similarly, the observed correlation between MESC hypomethylation and the malignant potential of cancers suggests that fully methylated MESCs may provide a protective mechanism against invasion. Thus, the fact that the great majority of MESCs exhibit similar high methylation levels in stem cells and normal tissues, means that high MESC methylation may be viewed as an intrinsic property of any normal cell, regardless of whether it is a stem cell or a mature differentiated one. In this model then, hypomethylation at MESCs would lead to a transformed cellular phenotype that is more prone to invasion. In this context however, it is worth pointing out that the observed MESC hypomethylation could also be reflecting changes in the stromal cell content of the tumours. Indeed, the observation that cancer fibroblasts show similar hypomethylation changes at MESC loci suggests that the more frequent MESC hypomethylation in invasive cancers could be partly due to increased numbers of cancer fibroblasts.
It could also be argued that the other DNA methylation changes we have reported here are the result of changes in the stromal and immune cell compartments of the tumours. However, we verified using Principal Components Analysis (PCA) and GSEA analysis [26] on normal liquid based cytology (LBC) samples and separately on age-matched cervical dysplasias (Table 1, “Dy”-study) that the components of variation associated with stromal and immune cell markers were very similar between normal and dysplasia, in stark contrast to PCGTs which showed a dramatic difference with comparatively no variation in normal tissue but representing the dominant component of variation in dysplasia (manuscript in preparation). Thus, the DNA methylation changes at PCGT loci reported here are unlikely to be due to changes in the stromal cell composition of tumours.
Finally, the crucial role of TET3 in DNA demethylation and early development, its overexpression in cancer, and the observed correlation with MESC hypomethylation, supports the view that aberrant developmental programs leading to reprogramming of the epigenome in adult cells may be critical for carcinogenesis. Interfering with these aberrant programs may therefore lead to novel ways to treat cancer.
In summary, our findings suggest that epigenetic deregulation of two distinct sets of genes, both important for stem cell integrity, impact carcinogenesis in different ways: one process involves gain of methylation and is a hallmark of de-differentiation and early oncogenesis, while the other involves loss of methylation and is a key determinant of invasion and clinical outcome.
A recent study used bisulfite sequencing to map, at single-base-resolution, DNA methylation throughout the majority of the human genome in both embryonic stem cells and fibroblasts [12]. For each CpG site, the number of C and T reads covering each methyl cytosine on both forward and reverse strands were provided [12]. The multiple reads covering each methyl cytosine can be used as readout of the fraction of sequences within the sample that are methylated at that particular site (i.e. C reads/C+T reads) [29], and hence, referred as the methylation level of the site. In this study, Methylated in human Embryonic Stem Cells (MESC) CpGs are the CpG sites that were covered by at least 5 reads on both forward and reverse strands (i.e. the total number of C and T reads on both strands > = 5) and the overall mean methylation levels (i.e. the average methylation level of both the forward and reverse strands) is greater than 80%. MESC CpGs were then mapped to those present on the Illumina 27 k array (Table S1). Functional annotation (gene assignment) of the MESC CpGs present on the array was obtained from Illumina and Bioconductor annotation packages.
PolyComb Group Target genes (PCGTs) were defined as CpGs which are occupied by SUZ12 and/or EeD and/or are trimethylated at Lysine 27 on histone H3 in human embryonic stem cells (Table S2, annotation file kindly provided by Benjamin P. Berman and Peter W. Laird) [19].
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10.1371/journal.pgen.1006054 | Ubr3, a Novel Modulator of Hh Signaling Affects the Degradation of Costal-2 and Kif7 through Poly-ubiquitination | Hedgehog (Hh) signaling regulates multiple aspects of metazoan development and tissue homeostasis, and is constitutively active in numerous cancers. We identified Ubr3, an E3 ubiquitin ligase, as a novel, positive regulator of Hh signaling in Drosophila and vertebrates. Hh signaling regulates the Ubr3-mediated poly-ubiquitination and degradation of Cos2, a central component of Hh signaling. In developing Drosophila eye discs, loss of ubr3 leads to a delayed differentiation of photoreceptors and a reduction in Hh signaling. In zebrafish, loss of Ubr3 causes a decrease in Shh signaling in the developing eyes, somites, and sensory neurons. However, not all tissues that require Hh signaling are affected in zebrafish. Mouse UBR3 poly-ubiquitinates Kif7, the mammalian homologue of Cos2. Finally, loss of UBR3 up-regulates Kif7 protein levels and decreases Hh signaling in cultured cells. In summary, our work identifies Ubr3 as a novel, evolutionarily conserved modulator of Hh signaling that boosts Hh in some tissues.
| Hedgehog signaling regulates many important biological processes and has been linked to developmental disorders, wound healing, and cancer. Although the major components in the pathway have been well studied in Drosophila and vertebrates, how the signaling is regulated by different modulators is not well understood. Here, we take advantage of a fly forward genetic screen to isolate Ubr3. We show that it is a novel modulator that regulates Hh signaling. Loss of ubr3 leads to Hh signaling defects in developing eyes of Drosophila, and affects eye, and somite and sensory neuron development in zebrafish. However, Hh signaling is not affected in all cells known to be dependent on Hh signaling as loss of ubr3 in the fly wing and zebrafish inner ear are not affected. This suggests that Ubr3 is a modulator that is only required in some Hh dependent organs/cells. We have shown that Ubr3 down-regulates the levels of Cos2 and its mammalian homolog Kif7, key negative regulators of Hh signaling, through poly-ubiquitination. The poly-ubiquitination of Cos2 by Ubr3 is enhanced by Hh activation, suggesting that it functions in a positive feedback that modulates Hh activation.
| Hedgehog (Hh) signaling regulates numerous developmental processes and is implicated in multiple cancers, wound healing and pain sensation in adults [1–3]. The Hh ligand acts as a morphogen to induce differential cell responses based on distinct activity thresholds of its signaling transduction cascade [4–6]. Mis-regulation of Hh signaling affects cell specification and proliferation during development and causes several types of cancer such as glioblastoma or basal cell carcinoma [7, 8]. In the absence of Hh, the receptor Patched (Ptc) inhibits the G-protein coupled receptor Smoothened (Smo) [9]. Inhibition of Smo promotes the assembly of an antagonistic molecular complex composed of Costal 2 (Cos2), a kinesin-related motor protein, Cubitus interruptus (Ci), the key transcriptional effector of Hh [10, 11], and several protein kinases [12]. This complex phosphorylates the full length, transcriptionally active form of Ci, Ci155. Phosphorylated Ci155 is ubiquitinated by a SCF (Skp1-Cullin1(Cul1)-F-box) E3 ligase complex [13] and partially cleaved to generate a transcriptional repressor form, Ci75, which leads to the transcriptional silencing of Hh target genes [14, 15]. The Hh signaling cascade is activated by the binding of Hh to Ptc and Ihog (Interference hedgehog) [16], resulting in the release of Smo inhibition. Activated Smo can interact physically with Cos2 [17–20]. This interaction prevents the formation of the Hh signaling antagonistic complex and cleavage of Ci155. As a result, levels of Ci155 increase in the cytoplasm, promoting its translocation to the nucleus and the transcription of downstream target genes such as decapentaplegic (dpp) or ptc (Fig 1A).
Previous studies have shown that Cos2 is a key modulator of Hh signaling, and that it facilitates kinase-mediated phosphorylation of Ci and promotes partial degradation of Ci [21]. Loss of Cos2 leads to ectopic activation of Hh signaling and pattern duplications in the Drosophila wing [11], whereas over-expression of Cos2 inhibits Hh signaling [22], suggesting that Cos2 is both necessary and sufficient for Hh signaling. In vertebrates, the core components of Hh signaling are conserved, including Cos2. Cos2 has two vertebrate orthologs, Kif7 and Kif27 [23, 24]. Kif7 has been proposed to function similarly to Cos2, because Kif7 knockout mice and zebrafish mutants show an up-regulation of Sonic Hedgehog (Shh) signaling [25–27]. In addition, Kif7 can interact physically and modulate the activity of the GLI transcription factors, the mammalian homologs of Ci [27, 28]. Moreover, Cos2 can functionally replace Kif7 [27], demonstrating a molecular conservation between vertebrate and invertebrate homologues. In humans, patients carrying KIF7 allelic variants display a spectrum of phenotypic severity ranging from hydrolethalus or Acrocallosal syndromes to Meckel and Joubert syndromes [28, 29]. Hence, proper function of Kif7 activity is essential for correct Hh signal transduction and is likely to be regulated tightly. Previous studies have shown that Cos2 (Kif7) is phosphorylated by a kinase, Fused, which mediates the strength of differential Hedgehog signaling [30, 31]. To date, however, no data support a role for ubiquitination in the regulation of Cos2.
Ubiquitination plays an important role in several steps of Hh signaling [32–34]. Ubiquitination is catalyzed by a cascade of enzymes consisting of ubiquitin-activating (E1), -conjugating (E2), and –ligating (E3) enzymes [35]. E3 enzymes bind, transfer and ligate ubiquitin to particular substrates. The two major types of E3 ligase are the Really Interesting New Gene (RING) domain E3s and the Homologous to E6AP Carboxyl Terminus (HECT) domain E3s [36].
We describe the identification and characterization of Ubr3, a novel regulator of Hh signaling. Ubr3 belongs to the UBR protein superfamily, characterized by a 70-residue zinc finger domain UBR box [37]. Recent studies showed that Ubr3 can polyubiquitinate target proteins [38] involved in multiple biological processes, including olfactory organ function in mice [39], denticle patterning in Drosophila [40], DNA damage repair in yeast [38], apoptosis in flies [41], homoeostasis in the heart [42], and breast cancer [43].
Here we show that Ubr3 promotes Hh signaling by mediating the ubiquitination and degradation of Cos2/Kif7. Loss of Ubr3 elevates the levels of Cos2, resulting in a decrease in Ci155 and transcriptional silencing of Hh target genes. Loss of ubr3 in flies and zebrafish affects eye development, as well as neuronal specification and somite development in zebrafish. Ubr3 regulates the ubiquitination and degradation of Kif7 in mammalian cells, and transcription of the Shh target ptch2 is strongly decreased in the retina of ubr3 mutant zebrafish. Taken together, our data suggest that Ubr3 is an evolutionarily conserved, positive regulator of Hh signaling that regulates Cos2/Kif7 ubiquitination and degradation.
To identify novel components in developmental signaling pathways, we isolated mutations that affect eye and/or wing morphogenesis in a mosaic forward genetic screen of approximately 6000 X-linked lethal mutations in Drosophila [44–48]. We identified an essential complementation group ubr3, consisting of two alleles (ubr3A and ubr3B). Both ubr3A and ubr3B hemizygous mutants die as 1st instar larvae. Homozygous mutant clones of both alleles cause delayed differentiation of photoreceptors in the morphogenetic furrow of eye imaginal discs (Fig 1B and S1A Fig). This is revealed by the delayed expression of Senseless, an R8 photoreceptor marker [49, 50] and Elav (Embryonic lethal abnormal vision), a marker for photoreceptors [51]. Since delayed differentiation of photoreceptors is observed when Hh signaling is lost [52], we hypothesized that ubr3 mutations may impair Hh signaling. To assess the activation of Hh signaling in ubr3 mutant clones, we examined expression of a Hh reporter, dpp-lacZ [53] and the active form of Ci, CiA. Both dpp-lacZ and Ci155 are lost in ubr3 mutant clones in the morphogenetic furrow (Fig 1C–1D’ and S1B Fig). We and others also noticed an increase in apoptosis in ubr3 mutant cells [41]. To exclude the possibility that the Hh signaling defect in ubr3 mutant cells is due to apoptosis, we over-expressed the anti-apoptotic gene p35 in ubr3 clones. As shown in S1C Fig, the delayed differentiation of photoreceptors is not rescued although apoptosis is suppressed (S1D and S1E Fig). Hence, Hh signaling is impaired in ubr3 mutant cells.
Alleles of ubr3 map to a small deficiency that uncovers ~11 genes including ubr3 (CG42593) and l(1)G0193 (S1F Fig). Both alleles (ubr3A and ubr3B) fail to complement the lethality associated with a P-element insertion in ubr3 (S1F and S1H Fig) [54]. ubr3B carries a Leu788>STOP and ubr3A carries a Phe949>Leu in ubr3 (Fig 1E). No mutations were found in l(1)G0193. A genomic rescue construct rescued the lethality of both ubr3 alleles (S1F and S1H Fig), and over-expression of the ubr3 cDNA in ubr3B mutant clones rescued the loss of Ci155 expression in the morphogenetic furrow (S1G Fig). Together, these data show that ubr3 is required for Hh signaling.
ubr3 encodes a 2219 amino acid protein, the Drosophila homolog of the mammalian RING-type E3 ubiquitin ligase n-recognin 3 (UBR3) gene (Fig 1E). Most UBR superfamily member proteins, including UBR1, UBR2, UBR4 and UBR5, function in the N-end rule pathway, a ubiquitin-dependent system where E3 ligases recognize N terminal residues of their targets and degrade them [37]. However, UBR3 does not bind to known N-end rule substrates, suggesting a different molecular function of Ubr3 from N-end rule E3 ligases [55]. Ubr3 contains a UBR moiety, a RING domain and a C-terminal auto-inhibitory (AI) domain (Fig 1E) [38, 39]. All three domains are highly conserved among fly, mouse and human (S1I Fig), suggesting that the molecular function of Ubr3 may be conserved.
To determine the expression pattern and protein localization of Ubr3, we raised a polyclonal antibody against a region between UBR domain and RING domain of Ubr3 (see Materials and Methods). The Ubr3 antibody specifically recognized a single 250 kDa band on Western blots of protein extracts from larval eye-brain complexes (Fig 2A). This band became more intense when a Ubr3 transgene was expressed (Fig 2A). Furthermore, immunofluorescent labeling of eye imaginal discs with our Ubr3 antibody revealed that the signal was severely diminished or lost within ubr3B mutant clones (Fig 2B). Ubr3 is cytosolic and broadly expressed (Fig 2C) and is enriched in the morphogenetic furrow of developing eye discs (Fig 2B), where Ci155 and dpp-lacZ expression is elevated. The Ubr3 proteins in the cytosol are present in puncta that do not show obvious co-localization with a markers for different organelles (S2A–S2G Fig). These data suggest that elevated levels of Ubr3 positively correlate with the activation of Hh signaling.
To assess whether the enriched Ubr3 protein in the morphogenetic furrow (Fig 2B) results from increased transcription of ubr3, we performed in situ hybridization experiments. As shown in Fig 2D, ubr3 was transcribed most abundantly in the morphogenetic furrow, in agreement with the protein enrichment shown in Fig 2B. Over-expression of ubr3 with an eyegone-Gal4 driver (eyg-Gal4; Fig 2E) expanded ubr3 expression domain in eye discs (Fig 2F), whereas ubr3 RNAi knockdown decreased expression of ubr3 in the center of the eye disc (Fig 2G), showing the specificity of the RNA probes. We activated Hh signaling in the eyg positive cells by expressing a dominant-negative Ptc (ptcDN) [56] or by down-regulating the expression of negative Hh regulators Cos2 or Cul1 by RNAi. In all cases, activation of Hh signaling elevated ubr3 mRNA levels in eye discs (Fig 2H–2J). In contrast, down-regulation of Ci by expressing CiRNAi in the equator region of the morphogenetic furrow through eyg-Gal4 (arrow in S2H Fig) resulted in moderate loss of ubr3 transcription (white arrow in S2I Fig). Hence, Hh signaling positively regulates ubr3 expression at both the mRNA and protein levels. To assess whether different levels of Ubr3 proteins contribute in a dosage-dependent manner to Hh signaling, we manipulated the expression levels of Ubr3 in ubr3B/B mutant cells by expressing a ubr3 cDNA at low or high levels. The actin-Gal4 driver used to express the ubr3 cDNA is temperature sensitive and leads to low expression at 18°C and medium to high expression at 25°C [57]. We then assessed Ci155 expression in the mutant clones expressing discrete levels of Ubr3. Interestingly, when Ubr3 was expressed at low level at 18°C, Ci155 expression was only partially restored (arrows in Fig 2K). However, high level of Ubr3 expression in ubr3 mutant cells fully rescued Ci155 expression. In some cells, Ubr3 over-expression induced ectopic expression of Ci155 posterior to the morphogenetic furrow (arrowheads in Fig 2L–2L”). In summary, these data suggest that Hh activation up-regulates transcription of ubr3, which in turn promotes Hh signaling.
To determine how Ubr3 promotes Hh signaling, we assessed the protein expression of key components of the Hh pathway in ubr3 mutant clones. Expression of Ptc and Fused (Fu), a kinase interacting with Cos2, was not obviously affected (S3A–S3B’ Fig), but Cos2 (Fig 3A and 3A’) and Cul1 (S3C and S3C’ Fig) were up-regulated in ubr3 mutant eye clones. Cos2 up-regulation is obvious in the morphogenetic furrow (arrows in Fig 3A’), suggesting that Hh regulates the Ubr3-mediated down-regulation of Cos2.
Cos2 and Cul1 are both negative regulators of Hh signaling and loss of function of either gene causes ectopic activation of Hh signaling in eye discs [11, 13, 58]. Because both genes are up-regulated in cells lacking Ubr3, we tested whether over-expression of either gene is sufficient to phenocopy the ubr3 mutation. Over-expression of Cos2, but not Cul1, results in loss of Ci155 in the morphogenetic furrow, similar to ubr3 mutants (Fig 3B–3C’). Labeling with a Cos2 antibody showed that a subtle increase of Cos2 is sufficient to inhibit Ci155 expression (S3D and S3D’ Fig), implicating that Cos2 up-regulation in ubr3 mutant cells is relevant. Hence, up-regulation of Cos2, but not Cul1, is likely to be responsible for the Hh signaling defects observed in ubr3 mutants. This hypothesis is supported by the observation that reducing Cos2 protein levels in ubr3 mutant clones through cos2RNAi restored Ci155 levels and suppressed the morphogenetic furrow defects (arrows in Fig 3D and 3D’ and S3E and S3E’ Fig). In contrast, over-expression of cul1RNAi in ubr3 mutant clones did not restore Ci155 expression in the morphogenetic furrow (arrowheads in Fig 3E and 3E’), suggesting that Cul1 up-regulation was not the cause of Ci155 loss. One likely reason why Ci155 expression is not restored by Cul1 RNAi in ubr3 mutant clones in the morphogenetic furrow is that Cul1 RNAi does not completely remove Cul1 in ubr3 clones and the residual Cul1-Slimb E3 ligase activity may suffice to mediate processing of Ci155. Moreover, expression of ptcDN in ubr3 mutant clones did not rescue Ci155 loss (arrowhead in Fig 3F and 3F’). These data show that loss of ubr3 causes a decrease in Hh signaling and a reduction in Ci155 that can be restored by Cos2 down-regulation. Hence, ubr3 acts to attenuate the levels of Cos2, which enhances the activity of Hh signaling in the morphogenetic furrow.
The RING domain of Ubr3 is not a canonical RING domain (Fig 4A and 4A’) [59]. To assess whether Ubr3 has E3 ligase activity, we performed an in vitro ubiquitination assay. Immunoprecipitation-purified Ubr3::GFP fusion proteins were incubated with E1 and E2 enzymes (HR6A) [39] and Flag-tagged Ubiquitin (Flag::Ub) peptides. Interestingly, Ubr3 poly-ubiquitinated itself, as shown in Fig 4B. Moreover, the UBR domain fragment may form a dimer when over-expressed, because a band of twice the molecular weight of GFP::UBR (~80 kDa) is detected (Fig 4B). Co-immunoprecipitation assays with the over-expressed UBR domain indicated that it interacts with the Ubr3 full-length protein present in whole cell extracts of S2 cells (Fig 4C). This suggests that Ubr3 interacts with the UBR domain of another Ubr3 molecule and that Ubr3 proteins poly-ubiquitinate each other.
To test whether the up-regulation of Cos2 in ubr3 mutant cells is due to defective degradation by the proteasome, we performed a degradation assay of Cos2 in Drosophila S2 cells. We found degradation of Cos2 proteins begins 6 hours after treatment with a translational inhibitor cycloheximide (CHX) and that the level of Cos2 decreased to 10% after 10 hours of treatment (Fig 5A). Addition of the proteasomal inhibitor MG132 suppressed the degradation of Cos2 (Fig 5A), suggesting that Cos2 proteins are degraded via the proteasome. The degradation of Cos2 is partially suppressed by down-regulation of Ubr3 by Ubr3RNAi and promoted by over-expression of Ubr3 (Fig 5B), suggesting that Ubr3 mediates the degradation of Cos2.
Because ubiquitination is known to regulate protein abundance through proteasome-mediated degradation, Cos2 levels may be regulated via Ubr3-mediated ubiquitination. To determine whether Ubr3 interacts physically with Cos2 and to map which domains are required for this interaction, we performed co-immunoprecipitation assays. As shown in Fig 5C, both the UBR domain fragment and the full length Ubr3 protein interact with Cos2 (lane 2 and lane 3). To exclude the possibility that Cos2 binds to Ubr3 indirectly via microtubules, we treated S2 cells with the microtubule-destabilizing agent Colchicine. The Cos2-Ubr3 interaction is not affected by Colchicine treatment (lane 4 in Fig 5C), suggesting that Cos2 does not bind Ubr3 via microtubules. To identify which domain of Cos2 is critical for the interaction with Ubr3, we tested a series of deletion constructs of Cos2 (Fig 5D) in co-IP assays with the UBR domain. We found that only the fragments bearing the N-terminal motor domain (MD) of Cos2 (Cos2ΔC1, ΔC2, and ΔC3) interacted with the UBR domain (Fig 5E). Hence, Ubr3 binds to the N-terminal MD of Cos2 with its UBR domain.
To detect the ubiquitination of Cos2, we performed immunoprecipitation assays and examined the ubiquitination of Cos2 in S2 cells that express ubr3 (Fig 4C). As shown in Fig 5F, the ubiquitinated Myc-tagged Cos2 (Myc::Cos2) was detected by an anti-hemagglutinin (HA) antibody in S2 cells co-transfected with an HA-tagged ubiquitin construct (HA::Ub; Fig 5F, lane 1, top panel). In addition, the HA signal exhibited a lower mobility shift compared to the major band detected by anti-Myc antibody (Fig 5F), indicating that these bands correspond to the ubiquitinated forms of Cos2. Over-expression of an E3 ligase dead form of Ubr3, in which the residues required for RING domain activity (Fig 4A) were mutated to alanines, did not cause an increase in ubiquitination of Cos2 (Fig 5G, lane 1–3), suggesting that the E3 ligase activity of Ubr3 mediates the ubiquitination of Cos2. In addition, removing the Ubr3 binding domain of Cos2, Cos2ΔN1, abolished most of the ubiquitination of full length Cos2 (Fig 5G, lane 4–6). The residual ubiquitination of Cos2ΔN1 may result from endogenous full length Cos2 that co-precipitates with Cos2ΔN1 through dimerization [10, 11].
To determine whether Ubr3 regulates Cos2 ubiquitination, we examined the levels of Cos2 ubiquitination when Ubr3 was either over-expressed or knocked down by RNAi. As shown in Fig 5F, the co-expression of Ubr3 with Cos2 increased Cos2 ubiquitination, whereas inactivation of Ubr3 by RNAi decreased ubiquitination (lane 2 and lane 3, top panel). A control GFP RNAi (negative control) did not significantly change the level of Cos2 ubiquitination (lane 4, top panel). These results suggest that Ubr3 interacts with and ubiquitinates Cos2.
We next tested whether Hh signaling regulates the ubiquitination of Cos2. Interestingly, we found that the ubiquitination of Cos2 was strongly enhanced by Hh treatment (Fig 6A, lane 2). This increased ubiquitination was abolished by down-regulation of Ubr3 (Fig 6A, lane 4), suggesting that Ubr3 mediates Hh induced ubiquitination of Cos2. This implied that Ubr3-mediated ubiquitination of Cos2 was tightly controlled by Hh signaling. Because Ci is not expressed in S2 cells, Hh-induced ubiquitination of Cos2 cannot be mediated by a positive, transcriptional feedback loop that depends on Ci. We therefore tested whether Hh may promote binding of Ubr3 to Cos2. We performed co-IP assays between the Ubr3 and Cos2 in the presence or absence of Hh. As shown in Fig 6B, the interactions between Ubr3 full-length protein and Cos2 (lanes 1 and 2 in Fig 6B) were strongly increased by Hh. These data show that Hh induces the ubiquitination of Cos2 by promoting the association of Ubr3 with Cos2. Consistent with Hh-induced poly-ubiquitination of Cos2, we also observed a faster degradation of Cos2 upon Hh treatment (Fig 6C and 6C’).
The ladder pattern of the HA signal in Fig 5E suggests that Cos2 is poly-ubiquitinated. We further determined the ubiquitination chain pattern by using a panel of ubiquitin mutant constructs [34]. Compared to wild-type ubiquitin (Fig 6D, lane 1, top panel), a mutated lysine 48 in ubiquitin (HA::UbK48R) abolished the formation of the ubiquitin chain (lane 4, top panel), whereas altered lysine 11 (K11R), lysine 29 (K29R), or lysine 63 (K63R) did not affect chain formation. In addition, mutating all of the lysine residues except lysine 48 (HA::UbK48 only) leads to longer ubiquitination chains (Fig 6D, lane 6, top panel). The single sharp band of Cos2 ubiquitination by K48R indicates a mono-ubiquitinated Cos2 that cannot be further elongated due to the lack of K48. Together, these data indicate that Cos2 undergoes K48-linked poly-ubiquitination.
To determine whether Ubr3 plays a conserved function in vertebrates, we created two independent zebrafish ubr3 mutant alleles using CRISPR/Cas9. The ubr3 gene is predicted to encode a protein of 1808 amino acids, and the ubr3b1250 allele lacks 28 nucleotides (Del 378–405) downstream of the predicted ATG (S4A Fig) leading to a frameshift and early stop codon. The mutant protein should encode only 129 amino acids (S4C Fig), lacking the UBR and RING domains. The second allele, ubr3b1251 carries a 4 nt insertion at position 220 (S4B Fig), also causing a frameshift and early stop codon. ubr3b1251 is predicted to encode a 78 aa protein lacking all functional domains (S4C Fig). Using an anti-Ubr3 antibody, we detected expression of Ubr3 in the developing retina, central nervous system and trunk, which are lost in ubr3b1250/b1251 mutant zebrafish (Fig 7A and 7B). Three independent crosses between single carriers heterozygous for the b1250 and b1251 alleles resulted in progeny with a distinguishable and reproducible retinal phenotype in a Mendelian frequency (f = 0.22, f = 0.27, f = 0.23, n = 270). At the 5-6-somites stage, phenotypically wild-type siblings display optic vesicles characterized by a compacted and stratified epithelium (Fig 7C, 7E and 7G). The optic vesicles of the ubr3 trans-heterozygous mutants failed to form a cohesive and stratified epithelium (Fig 7D, 7F and 7H). Because appropriate levels of Sonic Hedgehog (Shh) signaling are essential for eye morphogenesis [60, 61], we examined the transcriptional levels of ptch2. In zebrafish, ptch2 is a direct target of Shh signaling [62, 63]. In wild-type embryos, a gradient of ptch2 expression was observed within the optic vesicle (dotted area in Fig 7G and 7I). This gradient was characterized by high levels of ptch2-expressing cells localized in the ventral border of the vesicle, and low level expressing cells localized in the dorsal border region and vesicle core (Fig 7I). In ubr3 mutants, ptch2 expression was strongly decreased (Fig 7H and 7J). Consistent with decreased Shh signaling, ubr3b1250/b1251 trans-heterozygous mutants show a 30% increase in the angle of the somite in comparison with phenotypically wild-type siblings (S5A, S5C and S5E Fig). The opening of the somite angle is a common morphological phenotype of mutants with reduced Hedgehog signaling [64–67]. In addition, we observed a gain of Rohon-Beard sensory neurons at the level of the posterior central nervous system (CNS) in ubr3 mutants (S5B and S5D Fig), assessed by expression of a Rohon-Beard sensory neuron marker islet2 [68]. Because Hedgehog restricts CNS dorsal fate acquisition [69], this result supports the interpretation that Hedgehog signaling is decreased in ubr3 mutants. This finding is also consistent with our observation of decreased retinal ptch2 expression in the absence of ubr3 (Fig 7H and 7J). Because Kif7-depleted zebrafish embryos do not show de-repression of Hh target genes in the CNS [27], our findings further suggest that, at least in zebrafish, Ubr3 may regulate not only Kif7 but also other intracellular negative regulators of Hedgehog signaling in the CNS. Different zebrafish Hh signaling mutants show distinct degrees of severity, highlighting the tissue-specific requirements of Hh levels during development [60, 70–74]. Similarly, loss of ubr3 does not result in cyclopia or inner ear defects, showing that these mutants have a less severe phenotype when compared to smoothened mutant animals. Hence, ubr3 zebrafish mutants retain some residual Hh signaling. Thus, our data show that Ubr3 positively regulates Hedgehog signaling in tissues sensitive to high levels of Hh like the mesoderm and neuroectoderm. In addition, the transcription of ubr3 is strongly reduced in smohi1640-/- mutant animals [70], which lose Shh activity (S5F and S5H Fig). In contrast, ectopic activation of the Shh pathway by injection of the mRNA encoding a dominant negative form of PKA (dnPKA) [75] expands the expression domain of Ubr3 (S5G and S5I Fig). These data suggest that Shh signaling promotes the transcription of ubr3 in zebrafish, similar to what we observed in Drosophila. In summary, Ubr3 is required for the transduction of Hh signaling and proper eye morphogenesis in zebrafish.
To test whether UBR3 also plays a role in Shh signaling in mammals, we used C3H10T1/2 mouse mesenchymal cells. These cells respond to Shh and activate Shh target genes [76]. We first confirmed that Ubr3 is expressed in C3H10T1/2 cells by RT-PCR (see Fig 8B’). We then infected these cells with a lentivirus bearing 7 tandem binding sites for Gli (the vertebrate homologue of Ci) that control the expression of a GFP reporter. Addition of either the Shh ligand or purmorphamine, an agonist of Smo [77], to C3H10T1/2 cells induced GFP expression in about 25% of the cells (Fig 8A and 8A’). To determine whether knockdown of UBR3 impairs Shh signaling, we measured the proportion of GFP-expressing C3H10T1/2 cells transfected with one of four different siRNAs against UBR3 or a scrambled siRNA control, followed by purmorphamine treatment. Induction of the Gli::GFP reporter by purmorphamine was suppressed when siRNA reduced the UBR3 levels (Fig 8B), as judged by real time PCR (Fig 8B’). In addition, down-regulation of UBR3 resulted in up-regulation of Kif7 (Fig 8C), the mammalian homolog of Cos2. To assess poly-ubiquitination of Kif7, we purified Kif7 through immunoprecipitation and loaded the Western blot lanes with equal amounts of protein (unlike in Fig 8C where we loaded equal amounts of cells). We observed decreased poly-ubiquitination of Kif7 upon knockdown of UBR3 (Fig 8D). These data indicate that UBR3 regulates Shh signaling through poly-ubiquitination of Kif7 in vertebrate cells, a process that seems to be evolutionarily conserved.
Numerous studies have shown that Cos2 plays a central role in Hh signaling [10, 11, 22, 25, 26, 78, 79]. Cos2 is both necessary and sufficient to regulate Ci [11, 22] and the level of Cos2 protein is critical for activating Hh signaling [80, 81]. Here, we identified Ubr3 as a novel regulator of Cos2 in a forward genetic screen in Drosophila and showed that this gene is conserved in vertebrates and affects Hh signaling. We present evidence that the level of Cos2 protein is tightly controlled through a Ubr3-mediated poly-ubiquitination pathway (Fig 8E).
Although most of the core components of Hh signaling are evolutionarily conserved, there are differences in Hh signaling between vertebrates and invertebrates [82]. For example, Cos2 can be phosphorylated by the kinase Fused [30, 31], but the kinase that phosphorylates Kif7 remains to be identified, because mice lacking Fused have no apparent defects in Hh signaling [83, 84]. Given that a Kif7 phosphatase affects Hh signaling in vertebrates [85] it is likely that phosphorylation of Kif7 is important even if these sites are different than those observed in Cos2. Here, we present the first evidence that the levels of Cos2 and Kif7 proteins are also controlled by poly-ubiquitination via a conserved Ubr3 E3 ligase. The conservation of this mechanism is supported by the finding that fly Cos2 rescues the Kif7 mutant phenotypes in zebrafish [27]. Although we have shown that the degradation of Cos2 protein is regulated by Ubr3 mediated ubiquitination, the increased Cos2 proteins in ubr3 mutant cells may also result from up-regulated transcription of Cos2.
Although Hh promotes poly-ubiquitination and degradation of Cos2 (Fig 6A and 6C), we did not observe a decrease of Cos2 proteins at the morphogenetic furrow, where Hh signaling is activated. Instead, the level of the Cos2 protein is modestly elevated when compared to surrounding tissues/cells (S6A and S6A’ Fig), consistent with a previous finding [11]. We also observe that activation of Hh in S2 cells up-regulates Cos2 (Fig 6C). The observation that activation of Hh signaling promotes the degradation of Cos2 and that Cos2 protein level is increased, but not decreased by Hh activation, suggests that some mechanism other than ubiquitination up-regulates the level of Cos2 protein (Fig 8F). Ubr3-mediated degradation of Cos2 may function as a mechanism to prevent aberrantly high levels of Cos2, thereby toning down Hh signaling. This may also underlie the observation that not all cells that require Hh signaling are affected in flies. This hypothesis is also supported by the finding that loss of ubr3 in zebrafish affects developmental processes that rely on high levels of Shh signaling but does not affect those that respond to low Shh signaling (Fig 7 and S4 Fig).
Cul1 functions downstream of Cos2 to process Ci155, one would anticipate that Cul1 is epistatic to Cos2. This is inconsistent with the observation that down-regulation of Cul1 in ubr3 clones in the morphogenetic furrow of Drosophila eye discs fails to restore Ci155 expression (Fig 3E), whereas down-regulation of Cos2 restores Ci155 levels (Fig 3D). This may be because the RNAi expression does not deplete the protein sufficiently, or because, Cos2 may regulate Ci155 through a mechanism independent of Cul1.
Although our data clearly show that Ubr3 plays a role in Hh signaling at the morphogenetic furrow, we do not observe a loss of Ci155 in ubr3 mutant clones in wing discs (S6B and S6B’ Fig). However, we observed a similar up-regulation of Cos2 in ubr3 mutant clones in wing discs (S6C and S6C’ Fig), implying that Ubr3 mediated poly-ubiquitination of Cos2 may be present in wing discs. The lack of a Hh phenotype in posterior compartment cells of wing discs may be due to another E3 ligase that is functionally redundant and downregulates Cos2. Alternatively residual Ubr3 in ubr3 mutant cells due to perdurance of Ubr3 products may partially downregulate Cos2, allowing activation of Hh signaling. When we sensitized the background by over-expressing ptcDN to ectopically activate Hh signaling, we find that loss of ubr3 strongly suppresses the activation of Hh signaling in clones, gauged by the reduced clone sizes and Ci155 levels (S6D–S6E’ Fig). This may also be the reason why not all tissues display the typical Shh phenotype in zebrafish. In addition, ectopic activation of Hh signaling leads to up-regulated transcription of ubr3 (S6F–S6J Fig), suggesting that the positive feedback of Ubr3 is present in the wing.
Hh signaling shares many similarities with Wnt signaling [86]. Both pathways regulate many developmental processes and induce human cancers when the pathways are aberrantly activated. Moreover, the principal signaling mechanisms are based on similar features. Each pathway is activated through ligand binding of a G-protein coupled receptor, leading to the downstream activation of a transcription factor through phosphorylation-dependent proteolysis. Axin is the scaffold protein that recruits an activation complex in Wnt signaling, which mediates phosphorylation of β-catenin [87]. This function is similar to that of Cos2 in Hh signaling. Interestingly, previous studies have shown that the levels of Axin protein are also regulated by an E3 ligase, RNF146, through poly-ubiquitination [88–90]. Upon activation of Wnt signaling, Axin undergoes tankyrase-dependent poly ADP-ribosylation, which promotes RNF146-Axin interaction [89]. Ubr3 seems to regulate the poly-ubiquitination of Cos2 in a similar manner, given that Hh activation promotes the Ubr3-Cos2 interaction and the ubiquitination of Cos2. Hence, our data suggest further similarities between the Hh and Wnt signaling pathways.
ubr3A and ubr3B mutants were isolated in a forward genetic screen as previously described [45, 48]. y w ubr3A FRT19A/FM7c Kr-Gal4, UAS-GFP and y w ubr3B FRT19A/FM7c Kr-Gal4, UAS-GFP flies were crossed to, y w tub-Gal80, eyFLP, FRT19A; actin-Gal4, UAS-CD8::GFP/CyO and y w UbxFLP, tub-Gal80 FRT19A; UAS-CD8::GFP, actin-Gal4 to generate GFP-labeled ubr3 homozygous mutant clones using the MARCM technique [91]. The ubr3 genomic rescue transgenic fly strain was generated using the P[acman] system, BAC recombineering and transgenic platform developed in our laboratory [92]. ubr3 cDNA transgenic flies were generated through φC31-mediated transgenesis [92]. Additional strains used in the study are as follows: dpp-lacZ [93], Df(1)BSC622 [[94], Bloomington Drosophila Stock Center], P[lacW]CG42593G0307a [[54], Bloomington Drosophila Stock Center] cul1EX, FRT42D/CyO [58], FRT42D/CyO [95], UAS-p35 (a kind gift from Andreas Bergmann), eyg-Gal4 [96], UAS-cos2/CyO [81], UAS-cul1/CyO [58], UAS-ubr3RNAi [P{GD12698}, [97], Vienna Drosophila Resource Center] UAS-cos2RNAi [[97]; Vienna Drosophila Resource center], UAS-cul1RNAi [TRiP. HM05197, [98]; Bloomington Drosophila Stock Center]; UAS-CiRNAi [TRiP.JF01272, [98]; Bloomington Drosophila Stock Center]UAS-ptcDN (a kind gift from Michael Galko). All flies were maintained on standard food at 25°C.
Zebrafish strains were AB wild-type, ubr3b1250, ubr3b1251 and smohi1640. The ubr3 mutations are recessive alleles. Phenotypically wild-type siblings were used as controls and labeled as wild type in Fig 7. Animals were raised in a 10 hour dark and 14 hour light cycle and maintained as previously described [99]. Embryos were staged according to the standard series [100]. All animal use protocols were IACUC-approved.
CRISPR mutagenesis was carried as previously described with minor modifications [101, 102]. The zebrafish ubr3 reference sequence used in this study was XM_009304449.1. Identification of target sequences was done using Zifit software [103, 104]. Candidate sequences were then blasted against the zebrafish genome (Zv9) and those with unique hits were selected. The following target sequences were selected b1250: 5’- GGGGCCTGTGACTGCGGGGA-3’, located in the sense strand, and b1251: 5’-GGCGTTATCGTAGGATCGGA3’, located in the antisense strand (Fig 7, S1 Fig). A guide RNA (gRNA) template was created by PCR. A T7 promoter site was incorporated in the gene specific oligonucleotide, followed by the target sequence and the start of the guide RNA sequence (5’-gttttagagctagaaatagc-3’). The complementary guide RNA scaffold oligonucleotide sequence used was 5’-gatccgcaccgactcggtgccactttttcaagttgataacggactagccttattttaacttgctatttctagctctaaaac-3’. PCR was performed using Phusion polymerase (NEB) following the manufacturer’s recommendations. 10μM of each primer was used for the reaction. The first denaturation step was carried out at 98°C for 30 sec, followed by 40 cycles of denaturation at 98°C for 10 seconds, annealing at 60°C for 10 seconds, and extension at 72°C for 15 seconds. A final extension step was introduced at 72°C for 10 minutes. PCR products were purified using a PCR purification kit (QIAGEN). RNA was transcribed using a MEGAscript T7 kit following the manufacturer recommendations. A volume of 2nl of Cas9 RNA and gRNA were co-injected at a concentration of 100ng/μl each.
Screening of F0 founders and genotyping of F1 carriers were done by PCR and sequencing using the following primers: Primer b1250F (position 101–119): 5’-CTGCAGGAACTGCTGGATAG-3’; Primer b1250R (position 415–433): 5’-ACCCGCTCTCTCTCATCAC-3’. Primer b1251F (position75-94): 5’-TGACAACAGTTCAGGCTTGC-3’; Primer b1251R (position326-345): 5’-GTGGCGTTATCGTAGGATCG-3’.
250 pg of RNA encoding for a dominant negative regulatory subunit of the Protein Kinase A (dnPKA) [75] was injected into 1 cell stage embryos. dnPKA construct was linearized with NotI and transcribed with SP6 using a mMessage mMachine kit following the manufacturer´s recommendations. Embryos were fixed at 27hpf and processed for in situ hybridization against ubr3.
Fly tissues were dissected in phosphate-buffered saline (PBS) at room temperature and fixed with 3.7% formaldehyde in PBS for 20 minutes, followed by permeabilization with 0.2% Triton-X100 in PBS. The primary antibodies and secondary fluorescently-labeled antibodies used were: chicken anti-GFP (1:1000, Abcam), rat-Elav [1:1000, 7E8A10, DSHB, [51], guinea pig anti-Sens [1:1000, [50]], rat anti-Ci [1:50, 2A1, DSHB [105]], rabbit anti- β-galactosidase (lacZ; 1:1000, Abcam), guinea pig anti-Ubr3 (1:1000, this study, see below), mouse anti-Cos2 [1:50, 17E11, DSHB [18]], mouse anti-Ptc [1:100, DSHB [106]], mouse anti-Fu [1:100, DSHB [18]], rabbit anti-Cul1 [1:250, [107]], rabbit anti-GM130 (1:500, Abcam), rabbit anti-Rab5 (1:500, Abcam), rabbit anti-Rab7 (1:500) [108], mouse anti-Rab11 (1:100, BD Biosciences) [109], mouse anti-Complex V (1:500) [110], ER-GFP (1:1000 incubate with cells over night, CellLight ER-GFP, BacMam 2.0. Thermo Fisher Scientific), PNA-biotin (Vector Laboratories). Alexa488-, Cy3- and Cy5- or DyLight649 conjugated affinity purified donkey secondary antibodies (1: 500, Jackson ImmunoResearch Laboratories) and DAPI (0.5 μg/ml, Life Technologies).
Zebrafish immunolabeling was performed as previously described [111] with the following minor modifications. 18-somites stage embryos were fixed in BT-fix overnight at room temperature. Embryos were permeabilized in PBS+1% Tween20 for 5 hours at room temperature. Anti-UBR3 antibody (Sigma Prestige, catalogue #HPA035390) was diluted in 1/500. Biotinylated anti-rabbit was used at 1/500. To detect signal, ABC kit (Vectorlabs) was used. A and B reagents were mixed together at an 1/100 dilution in PBS- Block and pre-incubated for 20 minutes at room temperature, then added to the samples for 25 minutes. Tyramide from the TSA kit (Perkin-Elmer) was diluted 1:50 in pre-warmed buffer reagent, and added to samples for 20 minutes following the manufacturer’s recommendations. The detection reaction was stopped by adding cold PBS+0.1%Tween20, followed by 4 washes in PBS+0.1%Tween20.
Images were acquired using LSM510 and LSM710 confocal microscopes (Zeiss) and examined and processed using LSM viewer (Zeiss), ZEN (Zeiss) and Photoshop (Adobe) software. Immunostained zebrafish embryos were immersed in Vectashield, mounted laterally in a slide chamber and imaged with a Zeiss LSM5 confocal microscope. Live embryos were mounted laterally in 3% methylcellulsose and imaged on a compound microscope using DIC. ISH treated embryos were dissected in 90% glycerol, flat-mounted in 100% glycerol in a slide chamber and imaged on a compound microscope using DIC.
A ubr3 genomic rescue construct was constructed by cloning a 18.3 kb fragment of genomic DNA that contains the ubr3 gene (X: 7,935,666… 7,953,967) [Release 6 Drosophila reference genome, [112]] into P[acman] [92, 113]. ubr3 cDNA was constructed from exon sequences and cloned into pUASTattB using a GENEART Seamless Cloning and Assembly Kit (Life Technologies). GFP was tagged to the carboxyl terminus of the full length ubr3 sequence or a partial sequence encoding only the UBR domain (aa 222–292). The flag sequence was conjugated to the carboxyl terminus of the full length ubr3 sequence in the primer. Ubr3::flag was then amplified through PCR and cloned into pUASTattB through XhoI and XbaI. To generate E3 dead form of flag tagged Ubr3 expression construct, mutations results in all residues shown in red box in Fig 4A changed to alanines were introduced through synthesized DNA which spans 500 bp downstream from RsrII. This synthesized DNA fragment was then cloned together with PCR amplified flag tagged carboxyl fragment of Ubr3 into pUASTattB-Ubr3-flag through RsrII, BbsI and XbaI. The HA::Cos2N1 to 3 constructs were cloned into pUASTattB through EcoRI and XbaI. HA::Cos2ΔN1-2, HA::Cos2ΔC1-3 have been described [114]. Myc::Cos2 was constructed by fusion of 5xMyc tags to the N-terminus of the Cos2 coding sequence. The HA::Ub transgene has been described previously [34]. Kif7::GFP construct is a gift from Dr. Chi-Chung Hui [78].
The 7Gli:GFP reporter of Hedgehog signaling activity contains 7 repeats of the Gli binding site (5’-TCGACAAGCAGGGAACACCCAAGTAGAAGCTC) followed by GFP [115]. The primer pair (forward 5’-TGAAGCTTGCATGCCCTGCAGGACAAGCAGGGAACGCCCAAGTAG and reverse 5’ CTCGAGTACCGGATCCATTATATACCCTCTGCAGACTTGGGTGTTCCCTGCTTGTCG) was used to amplify the Gli binding sequences from the 8Gli-Luc plasmid by PCR. The reverse primer also contains a TATA sequence, which was used to rebuild the TATA box after the Gli binding sites. The destination plasmid pRRL.sin-18.ppt.TCF/LEF:GFP.pre [116] was linearized by PstI and BamHI digestion to cut out the TCF/LEF sequence and the TATA box. The resulting products were recombined into a linearized destination plasmid by infusion cloning according to the manufacturer’s protocol (Clontech).
For Ubr3 antibody production, the sequence encoding aa 751–1500 of Ubr3 was cloned into pET21 expression construct and expressed in E. coli. Purified inclusion bodies were used to immunize guinea pigs.
ubr3 in situ hybridization probes I and II anti-sense sequences contain 2558 to 3576 nt and 3775 to 4770 nt of ubr3 cDNA, respectively. Anti-sense sequences were cloned into pGEM-T vector (Promega). Before transcription, the construct was linearized by SalI. ubr3 RNA in situ probes were transcribed and labeled with a digoxigenin [113] RNA labeling kit (Roche). In situ hybridization to whole-mount discs was performed as previously described [117]. Probe I was used in images shown in Fig 2F, 2G, 2H and 2I and S6J Fig. Probe II was used in images shown in Fig 2E and 2J and S2I, S6F, S6H and S6I Figs.
Zebrafish whole-mount in situ hybridization was carried out as described with minor modifications [118]. Digoxigenin-labeled probes were prepared according to manufacturer´s instructions (Roche). Probe signal was detected using NBT/BCIP mix (Roche). The ptch2 probe was kindly shared by Stone Elworthy (University of Sheffield, UK).
S2 cells were cultured at 25°C in Schneider’s medium (Life Technologies) plus 10% heat-inactivated fetal bovine serum (Sigma), 100 U/mL penicillin (Life Technologies), and 100 μg/mL streptomycin (Life Technologies). Cells were split every 3 days and plated at a density of 106 cells/well in 12-well cell culture plates for experiments. Transfections were carried out using Effectene transfection reagent (Qiagen). Ubr3 dsRNA was synthesized against nt 652–1,191. dsRNAs transfection and dsRNA against GFP have been described [114]. CHX (100 μM, Sigma) and MG132 (50 μM, Sigma) in dimethyl sulfoxide (DMSO) were added to S2 cells 48 hours after transfection and incubated for indicated time. An equal amount of DMSO was added as a negative control (-). 1ug/ml Colchicine (sigma) was incubated with cells for 5 hours before harvest.
C3H10T1/2 cells were cultured at 37°C in 5% CO2 in air in Eagle's Basal medium with Earle's BSS, 2 mM L-glutamine, 1.5 g/L sodium bicarbonate and 10% fetal bovine serum, as described by the ATCC (http://www.atcc.org/). Cells were split when reaching 80–90% confluence. Cells were transduced with lentivirus containing Gli::GFP reporter construct for 16h and then plated on 12-well cell culture plates. On the second day, siRNAs were incubated with Lipofectamine RNAiMAX reagent (Invitrogen) overnight. The sequences of siRNAs against ubr3 are as follows: siRNA 1: GTTATAGCTTTGAATCAGT; siRNA 2: CAGAGTTTGCCTCACGACA; siRNA 3: CAAGATTGGTTTGATGCTA; siRNA 4: CAGAAATTGCTCGCAGAGT. Stealth RNAi™ siRNA Negative Control Med GC Duplex (Invitrogen) was used as control siRNA. 3μg/ml Shh (R&D systems) or 10 μM purmorphamine (Calbiochem) in culture medium, or the same amount of vehicle in culture medium for uninduced controls, was added to cells on the third day. Cells were photographed for GFP fluorescence and harvested 48 h after purmorphamine induction. The number of GFP-positive cells was manually counted and statistical testing was performed with a one way ANOVA followed by Dunnett’s test using uninduced cells as a control.
For RNA extraction, total RNA from C3H10T1/2 cells was isolated by using Absolutely RNA miniprep Kit (Agilent Technologies). cDNA was synthesized using Superscript III First Strand Synthesis System for RT-PCR (Invitrogen). Quantitative real-time PCR (qPCR) was conducted with a Master SYBR Green kit (Applied Biosystems) and gene-specific primer sets on a Step One Plus real-time PCR system (Applied Biosystems). Each experiment was performed with three biological sample repeats and each PCR was performed in triplicate. L19 was used as an endogenous reference. The gene-specific primer sets used were as follows: L19 (RpL19): 5’-GGTCTGGTTGGATCCCAATG-3’ and 5’-CCCGGGAATGGACAGTCA-3’; UBR3: 5’-CTGATTCATAGAGGAGGCAG-3’ and 5’-ATGGAACAGCTGATTCAGAC-3’.
S2 cells were lysed 48 h after transfection with plasmids in lysis buffer (Tris-HCl 25mM, pH 7.5, NaCl 150 mM, EDTA 1mM, NP-40 1%, Glycerol 5%, DTT 1mM) plus Complete proteinase inhibitor (Roche) for 30 minutes on ice, followed by centrifugation. In these experiments to detect the ubiquitination of Cos2 (Figs 5F, 5G and 6A), we treated S2 cells with 50 uM of MG132 24 h before harvesting the cells. The supernatant was then immunoprecipitated with agarose beads conjugated to antibodies recognizing different epitope tags, which had been previously equilibrated with lysis buffer, overnight at 4°C. The beads were then washed 3 times in washing buffer (Tris-HCl 10mM, pH 7.5, NaCl 150mM, EDTA 0.5 mM) before boiling in loading buffer. Western blotting was then performed with each sample. The following beads were used for immunoprecipitation: Chromotek-GFP-Trap Agarose Beads (Allele Biotechnology), Monoclonal Anti-HA−Agarose antibody (Sigma). Protein A resin and anti-Myc (9E10, Santa Cruz) were used for Myc immunoprecipitation. To examine the levels of Cos2 ubiquitination, a denaturing method was used as previously described [34]. Briefly, S2 cells were transfected with Myc::Cos2 and then lysed with denaturing buffer (1% SDS, 50mM Tris, pH 7.5, 0.5 mM EDTA, and 1 mM DTT) and incubated at 100°C for 5 min. The lysates were then diluted 10-fold with regular lysis buffer containing 1.5 mM MgCl2 and subjected to immunoprecipitation with the anti-Myc antibody. The proteins were then resolved on an 8% SDS-PAGE, and an immunoblot was performed using an anti-HA antibody to detect the HA::Ub or HA::Ub mutants. The antibodies used in Western blot analysis are as follows: anti-GFP (1:1000 Zymed or 1:1000, Millipore), anti-Myc (1:5000, 9E10, Santa Cruz), anti-HA (1:5000, Santa Cruz, F7 or 1:1000, 16B12, Covance), anti-Ubr3 (1:5000), anti-actin (1:5000, C4, MP Biomedicals), anti-α-tub (1:1000, Cell Signaling). The intensities of the bands in Fig 5B were quantified using image J software.
In vitro auto-ubiquitination assays were performed as described previously [119] with modifications. In brief, S2 cells were first transiently transfected with GFP, UBR-GFP or Ubr3-GFP. S2 cell cultures were collected 48 hours post-transfection and lysed on ice for 45 minutes under stringent conditions to minimize interactions with other proteins, using 100 μl RIPA buffer (150 mM NaCl, 1.0% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris, pH 8.0) containing 1x Complete protease inhibitors cocktail (Roche) for every 106 cells seeded. The lysates were then added to 30 μl bed volume of Chromotek GFP Trap beads (Allele Biotechnology), previously equilibrated with RIPA buffer, and incubated by rocking at 4°C for 3 hours. After washing with RIPA buffer, 20% of the beads were retained for assessing the expression of GFP protein by Western blot analysis using anti-GFP (1:1000, Zymed). The remainder of the lysates were equilibrated by rinsing twice in 1x Ubiquitination Reaction buffer (50 mM Tris-HCl, pH 7.4, 5 mM MgCl2, 50 mM NaCl, 1 mM dithiothreitol DTT, 1x protease inhibitors cocktail). The ubiquitination reaction was assembled by adding rabbit UBE1 E1 (Boston Biochem Cat.#302) and human recombinant His6-hHR6A E2 (Boston Biochem Cat. E2-612) conjugating enzymes and FLAG-Ubiquitin (Sigma) on ice and incubated at 30°C for 30 minutes. The reactions were stopped by adding 1x Laemmli buffer, after which the samples were boiled for 10 minutes and analyzed by SDS-PAGE and Western blot using anti-FLAG M2 monoclonal antibody (1:1000, Sigma).
For Cos2 ubiquitination assays, S2 cell culture and RNAi were performed as described previously [114]. Transfections were carried out using Effectene transfection reagent (Qiagen). The immunoprecipitation and immunoblot analysis were performed using standard protocols. Myc-Cos2 was constructed by fusion of 5xMyc tag to the N-terminus of Cos2 coding sequence. HA-Ub and Ub mutants have been described [34]. The HA::UbK48 only has mutations at all of the lysine residues with the exception of K48. GFPRNAi has been described. Ubr3 dsRNA was synthesized against nucleotides 652–1191. The following antibodies were used: mouse anti-Myc (1:5000, 9E10, Santa Cruz), anti-GFP (1:1000, Millipore), anti-HA (1:5000, F7, Santa Cruz), and anti-β-tubulin (1:2000, E7, DSHB).
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10.1371/journal.ppat.1002820 | Germline Transgenesis and Insertional Mutagenesis in Schistosoma mansoni Mediated by Murine Leukemia Virus | Functional studies will facilitate characterization of role and essentiality of newly available genome sequences of the human schistosomes, Schistosoma mansoni, S. japonicum and S. haematobium. To develop transgenesis as a functional approach for these pathogens, we previously demonstrated that pseudotyped murine leukemia virus (MLV) can transduce schistosomes leading to chromosomal integration of reporter transgenes and short hairpin RNA cassettes. Here we investigated vertical transmission of transgenes through the developmental cycle of S. mansoni after introducing transgenes into eggs. Although MLV infection of schistosome eggs from mouse livers was efficient in terms of snail infectivity, >10-fold higher transgene copy numbers were detected in cercariae derived from in vitro laid eggs (IVLE). After infecting snails with miracidia from eggs transduced by MLV, sequencing of genomic DNA from cercariae released from the snails also revealed the presence of transgenes, demonstrating that transgenes had been transmitted through the asexual developmental cycle, and thereby confirming germline transgenesis. High-throughput sequencing of genomic DNA from schistosome populations exposed to MLV mapped widespread and random insertion of transgenes throughout the genome, along each of the autosomes and sex chromosomes, validating the utility of this approach for insertional mutagenesis. In addition, the germline-transmitted transgene encoding neomycin phosphotransferase rescued cultured schistosomules from toxicity of the antibiotic G418, and PCR analysis of eggs resulting from sexual reproduction of the transgenic worms in mice confirmed that retroviral transgenes were transmitted to the next (F1) generation. These findings provide the first description of wide-scale, random insertional mutagenesis of chromosomes and of germline transmission of a transgene in schistosomes. Transgenic lines of schistosomes expressing antibiotic resistance could advance functional genomics for these significant human pathogens.
Sequence data from this study have been submitted to the European Nucleotide Archive (http://www.ebi.ac.uk/embl) under accession number ERP000379.
| Schistosomes, or blood flukes, are responsible for the major neglected tropical disease called schistosomiasis, which afflicts over 200 million people in impoverished regions of the developing world. The genome sequence of these parasites has been decoded. Integration sites of retroviral transgenes into the chromosomes of schistosomes were investigated by high-throughput sequencing. Transgene integrations were mapped to the genome sequence of Schistosoma mansoni. Integrations were distributed apparently randomly across each of the eight chromosomes, including the seven autosomes and the sex chromosomes Z and W. Integration events of transgenes were characterized in chromosomes of cercariae that were progeny of schistosome eggs infected with pseudotyped virions. Also, transgenic cercariae were employed to infect mice and transgenes were detected in the F1 eggs. Together these findings confirmed vertical transmission of transgenes through the schistosome germline, through both the asexual and the sexual reproductive phases of the developmental cycle. Moreover, germline-transmitted retroviral transgenes encoding drug resistance to the aminoglycoside antibiotics allowed schistosomes to survive toxic concentrations of the antibiotic G418. These findings represent the first reports of wide-scale insertional mutagenesis of schistosome chromosomes and vertical transmission of a transgene through the schistosome germline.
| The schistosomes are considered the most important helminth pathogens in terms of human morbidity and mortality. More than 200 million people are infected and a further 800 million at risk of schistosomiasis in tropical and sub-tropical latitudes. Treatment and control of schistosomiasis rely on the anthelmintic drug praziquantel; however, there is concern that drug resistance will develop. New therapeutic approaches including vaccines, drugs and diagnostics are needed for this neglected tropical disease [1]–[6]. Through a complex two-host life cycle, schistosomes are transmitted from freshwater snails to humans. Adult schistosomes dwell as pairs in the blood vessels of the intestines and/or urinary bladder, where female worms release eggs that become embedded in the intestinal wall and other organs to elicit chronic immune-mediated disease and other serious complications [7].
Draft genome sequences for Schistosoma japonicum, S. mansoni and S. haematobium were reported recently, landmark events that ushered in the post-genomic era for schistosomiasis [8]–[11]. In brief, the haploid genome size of these blood flukes is 364–397 MB; they have eight pairs of chromosomes, seven autosomes and a pair of sex chromosomes Z and W bearing ∼11,000 protein-encoding genes, the genome is >60% AT, and 40–50% of the genome is constituted of repetitive and mobile elements. In addition to extensive genomic and transcriptomic datasets, functional analysis of target genes to underpin new interventions for schistosomiasis will require both reverse and forward genetics [10]. To date, functional genomics beyond conventional RNA interference have not generally been available for schistosomes (e.g. see [12]–[14]). Nonetheless, reporter plasmids and RNAs have been introduced to several developmental stages [5], [15]–[22]. Moreover, the piggyBac transposon has been shown to competently integrate into schistosome chromosomes [23] and germline transmission of extrachromosomal, plasmid transgenes through several generations has been reported [15]. Development of somatic and germline transgenesis for schistosomes can be expected to facilitate validation of essential genes/gene products to be targeted with drugs or vaccines, as attested by progress with other pathogens e.g. Plasmodium falciparum [24], Toxoplasma gondii [25], Candida albicans [26] and Salmonella enterica serovar Typhi [27].
Recently, it has been demonstrated that pseudotyped murine leukemia virus (MLV), widely used in human gene therapy e.g. [28], can be adapted for genetic transformation of schistosomes. Reporter transgenes can be introduced and expressed; gain-of-function, including expression of firefly luciferase and antibiotic selection [29] and loss-of-function through vector based RNA interference has been achieved [30]–[32]. Here we used MLV for insertional mutagenesis of schistosome chromosomes and investigated target site specificity of integrated MLV retrovirus, employing high throughput sequencing approaches and a revised schistosome genome sequence. In addition, by characterizing integration events in schistosomes that had been exposed to the pseudotyped virions as eggs, we determined that the retroviral genes were transmitted through the germline. In addition, mice were infected by the percutaneous route with transgenic cercariae, after which transgenes were detected in F1 generation eggs. These findings represent the first report of wide-scale insertional mutagenesis of schistosome chromosomes and the first report of vertical, germline transmission of an integrated transgene in schistosomes. Moreover, they indicate how transgenic schistosomes, for example by expressing antibiotic resistance, could advance functional genomics for these neglected tropical disease pathogens.
Vertical or germ line transmission of transgenes mediated by integration competent vectors has not been reported in schistosomes. In order to establish a method of inserting retroviral transgenes in the schistosome germline, we targeted two populations of the schistosome egg. First, eggs were isolated from livers of experimentally infected mice (here termed ‘LE’, ‘liver eggs’), using standard approaches (Figure 1A, left panel). Second, in vitro laid eggs (IVLE), released from cultured schistosomes from 0 to 48 hours after perfusion of the adult worms from experimentally infected mice [33], [34] were collected (Figure 1A, right panel). Eggs derived by both methods were exposed to pseudotyped MLV virions (similar viral titers used in both methods) and the IVLE cultured to maturity (Figure 1A, center panel). Mature eggs were transferred to water, after which some hatched releasing miracidia. Immature Biomphalaria glabrata snails (≤5 mm diameter) were exposed to these miracidia (Table 1). Infected snails were maintained in the laboratory at 24°C for >40 days, then exposed to bright light to induce release of cercariae (Figure 1B). Genomic DNA was isolated from cercariae released from these snails, and transgene copy number and integration sites were investigated.
Twenty seven independent infections (transductions) of schistosome eggs were carried out, 13 on LE, and 14 on IVLE. Virion-free mock infections were performed to determine the viability of the two different egg types. Several prominent trends were seen. In each of 13 transductions of LE, cercariae were shed from infected snails, indicating the high infectivity of miracidia. By contrast, in only five of 14 (36%) experiments on IVLE were snails productively infected with miracidia leading eventually to release of cercariae. In addition, transgene copy numbers determined by qPCR were higher in the cercariae derived from IVLE than LE with a mean of 634 copies/ng of genomic DNA (range, 0–2,057) versus 33 copies/ng of genomic DNA (0–316), respectively (Table 1; Figure 1). In overview, miracidia from LE were more viable and infectious, reflecting higher overall fitness, than those from IVLE, but cercariae originating from IVLE carried a higher chromosomal density of transgenes (by at least one order of magnitude) (Figure 1C).
In addition, in initial investigation of the longevity of propagated transgenes, F1 progeny eggs from one of the lines originating from virion transduced IVLE (Table 1, IVLE experiment 9, the origin of the transgenic line named IVLE_MLV_001) were examined. Transgenic cercariae were employed to infect mice by the percutaneous route and, after 42 days, mouse feces examined for schistosome eggs. Eggs were seen from day 55 onwards (Figure S1, panel A). At day 65, the mice were euthanized and adult worms perfused from the portal system. IVLE (F1) were collected from the adult schistosomes (G0) (Figure S1, panels B, C). The luciferase transgene was detected by end-point PCR in adult worms (G0) (Figure S1, D) and by qPCR in IVLE, i.e. the F1 generation (Figure S1, E); the neoR transgene was also detected in the F1 eggs (not shown).
We also investigated the use of electroporation to transduce eggs with MLV. LE were exposed to MLV by square wave electroporation. In addition, we examined release of cercariae from individual snails rather from populations of infected snails. Higher transgene copy numbers were seen in cercariae from snails infected with miracidia from eggs soaked with MLV, whereas lower copy numbers were seen in cercariae derived from the eggs subjected to electroporation (e.g., Table 1A, experiment number 6 versus 12). The transgene copy number detected in cercariae released from the same group of snails varied from day to day (not shown). Cercariae from individual snails exhibited variable numbers of transgenes indicating that, among batches of infected snails, not all snails were parasitized by transgenic sporocysts (not shown).
Genomic DNA (gDNA) was isolated from populations of schistosomules, adult worms and/or cercariae. Using sonication to fragment the genomic DNA, along with ligation of linkers to the fragments, over fifty Illumina libraries of PCR products amplified to enrich sites of retroviral integrations into the schistosome chromosomes were prepared. Subsequently, high throughput sequencing of the PCR products was undertaken (Figure 2). 5,556,734 paired sequence reads with retroviral start sites were obtained from 50 libraries constructed from both the 5′- and 3′-long terminal repeats (LTRs) of MLV from populations of adults and schistosomules exposed to virions (somatic transgenesis approach), and from cercariae that were the progeny of schistosome eggs exposed to virions (germline transgenesis approach). Sites of integrations of MLV provirus into schistosome chromosomes were predicted using stringent criteria and version 5.1 of the genome of S. mansoni [35]. 1,248 non-redundant events were located, 343 of which were obtained from both 5′- and 3′-LTR libraries from the somatic transgenesis approaches with schistosomules and adults, and 905 from cercarial gDNA libraries from the germline transgenesis (Table 2). Interestingly, three of the integrations included both 5′- and 3′- sequences.
Chromosomal proviral integration sites are illustrated schematically in Figure 3. Integration events occurred broadly and seemingly randomly across each of the schistosome autosomes and both the Z and W sex chromosomes, including into Z-specific regions (Figure 3 A). For each integration event, the site of integration was characterized as intergenic, promoter/5′-UTR (up to 3 kb of the genome upstream of the first exon), exonic or intronic. This analysis revealed that 48.6% of the integrations occurred in intergenic regions, 39.7% in introns of coding regions, 5.1% of the MLV integrations were found in exons, and 6.7% in promoter/5′-UTRs (Table 2 and Figure 3 B). By comparison, 47% of the S. mansoni genome is composed of intergenic sequences, exons, 4.1%, promoter/5′-UTRs, 8.8% and introns 40% of the genome (Figure 3B, left). Table 2 presents details of integrations for 5′ and 3′ libraries from somatic and germline experiments. Comparison of the four functional categories of the genome by Chi-square analysis among the transgenic genome and non-transgenic reference revealed no significant differences. Using recent RNA Seq findings and database [35], levels of expression of genes carrying transgenes were compared to the transcriptomes at large of the MLV transduced developmental stages. No bias was evident for MLV to integrate into genes predicted to be actively transcribed at the developmental stage during viral transduction (Figure 3C).
We had noted deletions in proviral transgenes in schistosome chromosomes in an earlier study [36]. To examine the extent and potential impact of this phenomenon in the current high throughput analysis, we constructed several libraries of the somatic genomic DNAs employing retroviral specific primers that targeted sites at 0, 500, 1,000 and 1,500 bp from the terminus of the retrovirus (Table S1, Figure S2). Integration events were determined in each of the eight library categories, demonstrating truncation of proviral transgenes. Moreover, integrations were located in all library categories, showing that truncations occurred at sites ranging from the 5′- or 3′-termini to as far as 1,500 bp from the terminus (Figure S3). Overall, 75% of the integrations were intact, whereas 25% of integrations exhibited truncations.
Exposing IVLE to MLV lead to transgenic cercariae, as determined by quantitative PCR (qPCR) and high throughput (Illumina) sequencing (Table 1 and above). Given that the transgenic lines carried and expressed the retroviral neoR transgene [36], [37] which confers resistance to the aminoglycoside antibiotic G418 ( = geneticin) [29], we examined whether the germline transgenic progeny (schistosomula), a line named IVLE_MLV_001, exhibited resistance to G418 (Figure 4A). Control, non-transgenic schistosomula were killed by 250 µg/ml of G418: for example, 30%, 56% and 64% were dead by days 4, 6 and 8, respectively. By contrast, >98%, 93% and 75% of schistosomules from the germline transgenic population survived at days 4, 6 and 8, respectively (Figure 4B). The findings indicated that the retroviral transgene encoding neoR transmitted vertically through the germline, rescued the schistosomules from toxicity of the antibiotic. Finally, in preliminary analysis of three additional lines of transgenic cercariae, lines IVLE_MLV_002, IVLE_MLV_003 and LE_MLV_001, neoR transcripts were detected in schistosomules at 48 hours in culture whereas expression was not evident in cercariae of these lines (not shown).
This investigation presents potentially transformative advances for functional genomics of schistosomes and indeed parasitic helminths at large. First, high-throughput sequencing revealed the genomic locations of numerous integrations of MLV and demonstrated that retroviral integration events were widely and randomly distributed along all eight schistosome chromosomes, including the Z/W sex chromosomes of Schistosoma mansoni. Thus, MLV randomly mutagenized the genome of developmental stages of schistosomes. Second, retroviral transgenes were transmitted through the asexual developmental cycle of the schistosome in the intermediate host snail, from miracidia to cercariae, confirming germline transgenesis, and subsequently to eggs of the F1 generation. Third, a germline-transmitted transgene, neoR, encoding neomycin phosphotransferase permitted schistosomules to survive toxic concentrations of the aminoglycoside antibiotic G418.
Detection of transgenes in cercariae showed that MLV successfully integrated into the genome of eggs, and was maintained and vertically transmitted through the germline of the miracidium, mother sporocyst and daughter sporocyst stages. Daughter sporocysts derive only from the germline of the mother sporocyst, and cercariae from the germ cells of the daughter sporocyst. Consequently, transgenes in cercariae can only have arisen through germline transmission [15]. Of >1,200 authentic integration events mapped to the schistosome chromosomes, more than 900 were recovered from transgenic cercariae. All cells of these cercariae would be expected to carry the retroviral transgene(s) whereas, in the somatic approach, transgenic worms would be constituted of mosaics of transgenic and non-transgenic cells. The MLV constructs employed here encode virions that cannot replicate, hence only the transduced cells and their progeny would carry the transgene.
The pseudotyped MLV virion represents a tractable delivery vector to produce transgenic schistosomes, as demonstrated in studies on integration and activity of MLV-delivered reporter transgenes in S. mansoni and S. japonicum [32], [34], [36], [38]–[41]. Advantages of this system include autonomy, since the envelope glycoprotein of the vesicular stomatitis virus binds to an uncharacterized receptor on schistosome cells, initiating transduction by the retrovirus, and the ability of retroviral integrase to insert the proviral transgene into schistosome chromosomes. Among developmental stages that have been targeted by MLV [34], [38], [40], the egg and miracidial stages of S. mansoni provide access to germ cells (see [15], [34], [39]). Here, two forms of eggs were transduced with MLV - eggs from mouse livers (LE) and in vitro laid eggs (IVLE). IVLE provides a compliant developmental stage at which to introduce transgenes into the schistosome germline, since in stages 0 and 1 of the developing egg (staging system of Jurberg et al [42]), cleavage of the zygote has yet to occur [34]. LE – although available in greater numbers than IVLE - range in age and development from newly laid eggs through several weeks, and also include eggs injured by host responses. Transduction by MLV resulted in transgenic cercariae in 10 of 13 experiments with LE, but only in three of 10 with ILVE. However, the copy number of transgenes in genomic DNA isolated from populations of cercariae was ∼20 times higher in larvae originating from MLV transduced IVLE than LE. On the other hand, snails were likely exposed to more LE miracidia, a few to 200 per snail, whereas numbers of IVLE miracidia was likely considerably less – maybe as few as one miracidium per snail. With this regimen, even if the number of transformed miracidia was similar between LE and IVLE, higher numbers of non-transformed larvae may have diluted transgene density in LE-associated cercarial populations, yielding lower transgene copy numbers. To definitively determine whether MLV transduction of IVLE leads to higher transgene density in the schistosome genome, it would be informative to employ identical numbers of IVLE and LE miracidia per snail and compare transgene copy numbers in cercariae. If transduction of IVLE generally leads to higher transgene numbers, this would be advantageous if saturating insertional mutagenesis was the goal. By contrast, transduction of LE may lead generally to lower transgene copy numbers. Approaches targeting LE have apparent advantages since the miracidia more readily infect snails. LE may be preferable where the goal is to express transgenes encoding native or mutant gene products in studies of schistosome gene function. Here, minimizing transgene copy number to avoid non-specific effects e.g. of over expression or mutagenesis may be desirable. In review, whether to target IVLE rather than LE will depend on the goals of the investigation. This is the first report of germline transmission of an integration competent vector in schistosomes and hence opens the door to the development of transgenic lines of schistosomes.
We have begun to establish lines of transgenic schistosomes, and indeed have named the first line IVLE_MLV_001 (this line originated from experiment 9, Table 1B). In early investigation of the durability of the transgenic line and retroviral transgene(s), mice were infected with transgenic cercariae by the percutaneous route. In due course, adult worms were recovered, which released IVLE. Both the neoR and luciferase transgenes were detected by PCR in genomic DNA from the adults (G0) and from the eggs (F1). Hence, retroviral transgenes were transmitted vertically through the asexual (mitotic) and the sexual (meiotic) reproductive phases of the developmental cycle of S. mansoni. It is germane to note that from this transgenic line, monitored so far from G0 eggs to FI eggs, the schistosomes appeared to be biologically fit in that miracidia infected snails, cercariae infected mice, and eggs were passed in mouse feces. Genome instability has been described in schistosomes [43] which may lead to chromosomal rearrangements during mitosis and/or meiosis, and somatic mutations at microsatellite loci may be routine in intra-snail stages [44]. Mitotic recombination leading to loss of transgenes occurs only rarely in model eukaryote species [45], [46]. However, given the transgene copy number in F1 eggs was lower than the parental cercariae, loss of transgenes through genome instability cannot be ruled out. Nonetheless, since the transgene was detected in cercariae derived from MLV-transduced eggs, even if genome rearrangement occurs in the IVLE_MLV_001 line, so far it has not resulted in elimination of the transgene.
In a previous study, a small number of retroviral transgene integration sites (<20 sites) into chromosomes were characterized by employing anchored PCR [36]. This approach constrains detection of integration sites to those in the vicinity of endogenous mobile elements which were targeted with gene specific primers in the anchored PCR [36]. By contrast, high throughput sequencing of randomly sheared genomic DNA (and hence without bias of fragments for PCR anchored regions and/or restriction sites) was utilized here, an approach validated for transposon-based insertional mutagenesis of bacteria [27]. At least 1,248 integration events were mapped within the chromosomes of S. mansoni, both from schistosomules and adults (likely retroviral integrations in surface and/or gut cells), and from cercariae derived from virion transduced eggs (a vertical, germline transmission approach). An improved genome sequence [8], [35], advances in qPCR to quantify retroviral transgene copy numbers [40], and construction of Illumina libraries that employed linker mediated PCR products to enrich for retroviral transgene integrations [27], together enabled characterization of numerous transgene insertions in the genome of S. mansoni.
Mapping these integrations confirmed that MLV integrated randomly throughout the schistosome genome. Integration events occurred in each of the schistosome chromosomes, including the seven autosomes and both the sex chromosomes Z and W, including Z specific regions. MLV integrations were identified in exons, introns, promoter/5′-UTRs, and intergenic regions, in proportion to the extent of these regions. This distribution is reminiscent of that of avian sarcoma-leukosis virus (ASV) in mammalian chromosomes [47]. Insertion site preferences for retroviruses have been characterized in detail in several systems, primarily in human and mouse cells [48]. In these hosts, MLV integrates throughout the entire genome, but with a bias for transcriptionally active regions, especially 5′-untranslated regions of RNA polymerase II driven genes, CpG islands, DNAse I hypersensitive sites and transcription factor binding sites [49]. HIV-1 also distributes throughout the human genome, preferentially in transcriptional units, but without the positive bias of MLV for promoter regions/transcription start sites [50]. Integration site selection by HIV-1 and HIV-1-based vectors is controlled by the LEDGF/p75 protein [51], [52]. Among other retroviruses, xenotropic murine leukemia virus-related virus exhibits a strong preference for transcription start sites, CpG islands, DNase-hypersensitive sites, and gene-dense regions, chromosomal features associated with structurally open transcription regulatory regions [53]. ASV shows a more random genomic integration pattern, with only weak preference for active genes and none for transcription start regions [47], [50]. In CD4+ T lymphocytes, integration by MLV favors outward-facing major grooves on nucleosome-wrapped DNA, similar to the integration pattern of HIV [54]. The distribution of MLV integrations in the S. mansoni genome appeared to be largely random. No bias was observed for MLV to integrate into four genome features examined in detail (intergenic regions, exons, introns and promoter/5′-UTRs) and, moreover, there was no preference apparent for genes predicted to be actively transcribed at the developmental stage during viral transduction. These findings of random integration widely across all eight schistosome chromosomes indicate that MLV has great potential for insertional mutagenesis as a functional genomics approach in this pathogen.
We had observed previously in a small scale study that MLV integrations into schistosome chromosomes were often truncated. The 5′-end of the integrated provirus had lost up to 2 kb, including reporter transgenes [36]. We have now confirmed this phenomenon, but in the ∼300 integrations sampled here, only ∼25% exhibited deletions. (By contrast, the new findings did not confirm presence of a primary sequence motif, gCATcc at the integration site [36] (not shown).) Given that Illumina reads are only ∼75 bp in length and primers targeting internal sites on the LTR of the retrovirus were used to evaluate truncated integrations, many truncated transgenes may not have been detected. This is a technical constraint of the Illumina approach and consequently numbers of truncated transgenes may be underestimated. Truncation events might be evaluated more comprehensively by sequencing approaches that provide longer reads [55]. Truncation of integrated provirus of Human T-Cell Leukemia Virus (HTLV-1), including loss of the 5′-LTR, is known from human tissues where it appears to influence onset of HTLV-1 induced tumors [56]. The unusual behavior of MLV within schistosome chromosomes in terms of absence of integration site preferences along with occasional truncation of provirus may reflect absence or incompatibility of host cofactors in schistosomes that participate in the retroviral developmental cycle. A deeper knowledge of the behavior of MLV in schistosomes could provide leads for novel interventions both for schistosomiasis and/or retroviral infections.
Transgene constructs utilized here included the neoR gene that confers resistance to aminoglycoside antibiotics. Schistosomules of S. mansoni are sensitive to G418, and neoR expression under the control of the 5′-LTR of the MLV retrovirus can confer antibiotic resistance to transgenic schistosomes [29]. We now show that transgenic schistosomules were more resistant to G418 than wild-type schistosomules. Despite occasional truncations in proviral transgenes (Figure S3) [36], this observation indicates that reporter transgenes encoding resistance to G418 are functional. Furthermore, in a discrete analysis of three additional transgenic lines, neoR transcripts were detected in schistosomules whereas expression was not evident in cercariae. This predicts that the 5′-LTR promoter of MLV may be inactive in non-mammalian stages of the schistosome. Studies on schistosome development expression of neoR driven by the 5′-LTR of MLV should be informative.
The findings revealed wide-scale random insertional mutagenesis of schistosome chromosomes. This is the first report of germline transmission of a transgene in schistosomes, in any platyhelminth or indeed any lophotrochozoan. (This is also the first report to present chromosomal integration site preferences for a retrovirus in any invertebrate.) Establishing systems of conditional transgene expression [31], including model antigens [57] and vaccine candidates [58] in schistosomes will facilitate analysis of pathophysiology of schistosomiasis as well as fundamental aspects of host-parasite relationship including genome methylation and epigenetics [59]. Moreover, transgenic schistosomes expressing antibiotic resistance can be expected to expedite functional genomics based advances with these etiological agents of major neglected tropical diseases.
Female Swiss-Webster mice infected with the Puerto Rican NMRI strain S. mansoni were obtained from the Biomedical Research Institute (BRI) Rockville, Maryland. Adult worms were perfused from these mice at seven weeks after infection [60]. Maintenance of the mice infected with S. mansoni at GWU was approved by the GWU Institutional Animal Care and Use Committee of the IACUC of The George Washington University). All procedures employed were consistent with the Guide for the Care and Use of Laboratory Animals.
Mice and B. glabrata snails infected with the NMRI (Puerto Rican) strain of S. mansoni were supplied by Dr. Fred Lewis, BRI. Both adult worms and eggs (LE) were recovered from infected mice [61], using a protocol approved by the Institutional Animal Case and Use Committee of The George Washington University. In addition, eggs laid in vitro by female worms were collected [34]. Briefly, after recovery by perfusion from mice, adult worms were washed and transferred into 74-µm diameter mesh Netwell, 6-well plates where they were maintained in culture at 37°C for 48 h [60]. (Eggs laid after the females have been in culture for >48 hours do not develop correctly [33].) The in vitro laid eggs (IVLE) from these worms fall through the mesh and collect on the bottom of the culture plate. IVLE were collected and concentrated by filtering media through 8 µm mesh Transwell [34]. Thereafter, IVLE were maintained in schistosomula medium where they develop and mature within 7 days. At that point, they were transferred to sterile water and illuminated with bright light to stimulate hatching. Eggs (∼20%) hatched within 120 minutes, releasing miracidia.
Cercariae released from infected B. glabrata snails were mechanically transformed into schistosomula [60]. In brief, cercariae were concentrated by centrifugation (425 g/10 min) and washed once with Dulbecco's modified Eagle's medium (DMEM) supplemented with 200 units/ml of penicillin, 200 µg/ml of streptomycin, 500 ng/ml of amphotericin B and 10 mM HEPES. Cercarial tails were sheared off by 20 passes through 22 gauge emulsifying needles after which schistosomule bodies were isolated from tails by Percoll gradient centrifugation [62]. Schistosomula were washed and cultured in Basch's medium [63] at 37°C under 5% CO2.
Female inbred Balb/c mice were infected with ∼200 cercariae of transgenic line IVLE_MLV_001line by the percutaneous route by tail immersion [64]. From day 42 after infection, feces of infected mice were inspected for S. mansoni eggs; several voided pellets of feces were re-suspended in PBS and aliquots of the fecal slurry on a microscope slide directly observed with a Zeiss Axio Observer A.1 inverted microscope fitted with a AxioCam ICc3 camera (Zeiss). This microscope system also was used to examine IVLE, LE and other developmental stages, as described [34], [60].
In vitro laid eggs (IVLE) were transfected with MLV virions, hatched 6 days later, and the resultant miracidia were employed to infect snails. Cercariae released from those snails were collected ∼50 days after infection and presence of retroviral transgenes in the cercariae verified by qPCR and Illumina sequencing. Schistosomula obtained by mechanical transformation of these cercariae were cultured in the aminoglycoside antibiotic G418 ( = geneticin) at 250 µg/ml [29]. Media and G418 were replaced every second day, from days 0 to 8. A control group of non-transduced (wild type) schistosomula was included. At least 100 schistosomules (range, 106–144) per condition were counted, and viability was assessed as described [29].
Vesicular stomatitis virus glycoprotein-pseudotyped murine leukemia virus (MLV) virions were produced in GP2-293 cells [36], using pLNHX (Clontech), pLNHX_SmAct-Luc, pLNHX_SmAct-GFP, pLNHX_SLGFP, pLNHX basic, pLNHX_ΔD70 or pLNHX-cHS4 [34], [36], [38]. Viral titers were determined using two complementary approaches; first, a functional assay involving titrating virions on NIH-3T3 cells and, second, real time PCR targeting the retroviral genome (Retro-X qRT-PCR Titration Kit, Clontech) [34], [40]. Schistosomula (∼2–5×104), mixed sex adults, eggs isolated from liver and IVLE of S. mansoni were transfected with MLV virions as described [34], [39], [40]. In brief, one to seven days after mechanical transformation of cercariae, schistosomula were cultured in 6 well plates containing one ml of virion preparation with an infectivity of 106–107 colony forming units (CFU)/ml, in the presence of the cationic polymer polybrene. After exposure to virions for 18 hours, culture media were replaced with virion-free media. The following day, retrovirus-exposed schistosomula were harvested, snap frozen and stored at −80°C for downstream analysis. Adult worms were cultured in 24 well plates, in 200 µl of complete medium supplemented with 200 µl of MLV virions (4×105 CFU/ml) and 8 µg/ml polybrene. The worms were washed 18 h later, cultured for a further 24 h, harvested, snap frozen and stored at −80°C.
Eggs from livers of infected mice were cultured in 24 well plates in 500 µl medium containing MLV virions at 105–106 CFU/ml and 8 µg/ml polybrene. Other eggs exposed to the same MLV inoculum were subjected to square wave electroporation [39], and transferred into media containing 8 µg/ml polybrene. Eighteen hours later, eggs were washed to remove unbound virions and polybrene, and cultured for a further 24 h. At this point, eggs were transferred to water, and miracidia released from hatched eggs counted [60]. Miracidia were used to infect B. glabrata snails; en masse infection of groups of snails was undertaken using ≤5 to 200 miracidia per snail (Table 1). Other miracidia were snap frozen and stored at −80°C. IVLE were collected from mixed sex adult worms from 0 to 48 h after perfusion of adult worms from mice [34]. IVLE were exposed to virions (105–106 CFU/ml) in 500 µl culture media and 8 µg/ml polybrene for ∼18 h, after which IVLE were washed to remove unbound virions and polybrene and transferred to media without virions. Media were changed every day for five days, until eggs developed to stages 7 and 8 [42]. At this point, eggs were washed with PBS, transferred to sterile water, and induced to hatch by exposure to light [34].
Total genomic DNA (gDNA) was isolated from transduced and/or transgenic developmental stages of schistosomes, including mixed sex adult worms, schistosomules, and cercariae, using a kit (E.Z.N.A. SQ Tissue DNA Kit, Omega Bio-tek). Concentrations of gDNAs were determined with a spectrophotometer (NanoDrop 1000). To investigate the presence of proviral transgenes and estimate the transgene copy number by qPCR, primers were designed with Beacon Designer (Premier Biosoft International, Palo Alto, CA) to obtain primer and TaqMan probe sequences targeting the firefly luciferase (FLuc from pGL3-Basic, Promega): forward primer: 5′-TGC TCC AAC ACC CCA ACA TC- 3′; reverse primer: 5′- ACT TGA CTG GCG ACG TAA TCC- 3′; probe: 5′-/56-FAM/ACG CAG GTG TCG CAG GTC TTC C/3IABlk_FQ/-3′, and the neomycin phosphotransferase II gene (neoR): forward primer, 5′-GGA GAG GCT ATT CGG CTA TGA C-3′; reverse primer, 5′-CGG ACA GGT CGG TCT TGA C-3′; probe, 5′-/56-FAM/CTG CTC TGA TGC CGC CGT GTT CCG/3IABIk_FQ/-3′.
qPCR reactions were performed in triplicate, using 96-well plates, with a denaturation step at 95°C of 3 minutes followed by 40 cycles of 30 sec at 95°C and 30 sec at 55°C, using a thermal cycler (iCycler, Bio-Rad) and a Bio-Rad iQ5 detector to scan the plates in real time. Reactions were carried out in 20 µl volumes with primer-probe sets and Perfecta qPCR FastMix, UNG (Quanta Bioscience, Gaithersburg, MD). Absolute quantification was undertaken using 250 ng of gDNA samples, including non-MLV transduced samples as negative controls, or copy number standards, i.e. 10-fold serial dilutions of pGL3, from 1.93×1010 copies to 1.93×103 copies. The exact copy number of each diluted plasmid was calculated through the relationship between the molecular mass of pGL3 and the Avogadro constant, NA. Absolute copy number of the luciferase transgene per ng of schistosome gDNA was estimated by interpolation of the sample PCR signals from a standard curve [65].
Total RNA was extracted from pellets of cercariae and two-day old schistosomules from transgenic lines IVLE_MLV_002, IVLE_MLV_003 and LE_MLV_001 (unpublished). Transgene expression was examined by qRT-PCR, as described [29].
Illumina libraries were constructed as described [66]. In brief, genomic DNAs from schistosomula, adults and cercariae (10–20 µg each), isolated as above, were subjected to ultra-sonication using a model S220 Covaris Adaptive Focused Acoustics instrument (Covaris, Woburn, MA), releasing fragments of ∼200 bp in length. The sheared fragments were modified for ligation by end-repair and adenylation with the NEBNext DNA Sample Prep Reagent Set 1 from New England Biolabs (Ipswich, MA), according to the manufacturer's instructions, but using 1.5× the recommended volumes. Thereafter, the fragments were ligated to double-strand adapters (10-fold excess), formed by annealing oligonucleotides Ind_Ad_T (5′-ACACTCTTTCCCTACACGACGCTCTTCCGATC*T-3′, the asterisk indicates phosphorothioate) and Ind_Ad_B (5′ pGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTC-3′). Approximately one µg of the adaptor-ligated fragments was used to specifically amplify the 5′ or 3′ termini of the MLV retrovirus and retroviral insertion sites. PCRs were performed with Jumpstart Taq polymerase (Sigma-Aldrich, St. Louis, MO). For the first round of the semi-nested PCR, a retrovirus-specific forward primer (Table S1, tab “Primer combinations”, designated “PCR1”), and the reverse primer RInv4.0 (5′ TCCCTACACGACGCTCTTCCGATCT-3′) were used with the program 94°C 2 min, [94°C 20 sec, primer-specific annealing temperature 20 sec, 72°C 40 sec]x18, 72°C 10 min. A second semi-nested PCR was set up with 5 µl of the first PCR as template. The retrovirus-specific primer for this reaction (“PCR2”) contained the Illumina P5 end for attachment to the flow cell, the adapter-specific primer included the Illumina P7 end, and an 8 nt tagging sequence (Table S1). For DNA from cercariae, DNA was subjected to 28 cycles for PCR1, and 16 cycles PCR2. PCR product libraries were quantified by qPCR with standards of known concentration, using primers Syb_FP5 (5′-ATGATACGGCGACCACCGAG-3′) and Syb_RP7 (5′-CAAGCAGAAGACGGCATACGAG-3′). Equal amounts of libraries were pooled where appropriate (the two retrovirus 5′ and 3′ ends were sequenced in separate pools), and size separated on an agarose gel. Fragments of 350 to 450 bp were excised and recovered with QiaExII gel extraction columns (Qiagen) following the manufacturer's instructions, but without heating [66].
The DNA fragment libraries were quantified by qPCR, as above, denatured and sequenced for 76 cycles on paired end flow cells on an Illumina GAII platform (Illumina, Inc., San Diego, CA) using custom sequencing primers (Table S1, tab “Primer combinations”) for the first read, the regular Illumina Read1 primer 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′ for Read2, and primer 5′-AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT-3′ for the index read. About 6.5 million (M) reads deriving from the 5′-end of the retroviral transgene were detected (6 M, 227 k, and 296 k, from three flowcell lanes), and 16.6 M from the 3′-end (12.6 M, 203 k, 221 k, and 3.59 M from four flowcell lanes). For cercariae, 6.3 M reads were obtained on one lane for the 5′ end, and 1.5 M for one lane of the 3′ end of the transgene.
Sequence reads from the Illumina FASTQ files were be parsed for ∼100% identity (with one mismatch allowed, below) to the last 7 bp of the 5′-end or 3′-end of the retrovirus sequence. Thereafter, matching sequence reads were stripped of this retrovirus tag, converted to Sanger FASTQ format and mapped to the S. mansoni genome [8], [35]. After an initial round of quality control (removal of PCR adaptors, vector clipping), the 76 bp Illumina sequences were mapped to the v5.1 S. mansoni assembly using SMALT, http://www.sanger.ac.uk/resources/software/smalt/. Sequence matches were considered a genuine integration if (1) the Illumina sequence started with the retrovirus long terminal repeat terminus with at most 1 bp mismatch, (2) the remainder of the sequence uniquely mapped to the reference sequence with at least 40 bp, (3) with a mapping quality ≥30 (corresponding to a 0.1% alignment error rate) and (4) the flanking sequence of the matched region was different to the MLV retrovirus start site by at least 2 bp. Matches that mapped to low complexity regions of the genome were discarded. Multiple matches within 200 bp of each other were classified as one unique match in the genome assembly. We categorized the integration region as exon, intron, intergenic, and promoter/5′-UTR by comparing annotations from GeneDB. Since information on UTRs and/or promoters remains largely unavailable for the majority of genes of S. mansoni, we arbitrarily assigned the canonical 3 kb upstream of first exon of each gene as the putative promoter/5′-UTR region.
To examine the whether truncations occurred in the integrated proviral transgenes, we searched for the presence of integrations in a series of Illumina libraries constructed with eight discrete retroviral specific primers targeting sites at 0, 500, 1,000 and 1,500 bp from both the 5′- and 3′-terminus of the retrovirus (Table S1; Figure S2). These libraries were prepared from gDNAs from schistosomules and adult worms transduced with MLV.
Sequence data from this study have been deposited in the European Nucleotide Archive (http://www.ebi.ac.uk/embl) under accession number ERP000379.
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10.1371/journal.pcbi.1004454 | Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity | The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.
| Human metabolism is controlled through basic kinetic regulatory mechanisms, where the overall system aims to maintain a state of homeostasis. In response to external perturbations, such as environmental influences, nutrition or physical exercise, circulating metabolites show specific kinetic response patterns, which can be computationally modeled. In this work, we searched for dynamic metabolic biomarker candidates and analyzed specific kinetic mechanisms from longitudinal metabolic concentration data, obtained through a cycle ergometry stress test. In total, 110 metabolites measured from blood samples of 47 individuals were analyzed using tandem mass spectrometry (MS/MS). Dynamic biomarker candidates could be selected based on the amplitudes of changes in metabolite concentrations and the significance of statistical hypothesis testing. We were able to characterize specific kinetic patterns for groups of similarly behaving metabolites. Kinetic shape templates were identified, defining basic kinetic response patterns to physical exercise, such as sustained, early, late and other shape forms. The presented approach contributes to a better understanding of (patho)physiological biochemical mechanisms in human health, disease or during drug therapy, by offering tools for classifying dynamic biomarker candidates and for modeling and characterizing kinetic regulatory mechanisms from longitudinal experimental data.
| Basic principles in reaction kinetics of biomolecules were described by the work of Guldberg & Waage [1–3] more than 150 years ago and recently resumed by Voit et al., 2015 [4] in their perspective article "150 years of mass action". The underlying concept is the law of mass action, describing the quantitative aspects of a chemical reaction under ideal conditions. If a substance C is formed by the reaction of substance A and substance B, the production of C can be described by the following equation
ProductC=k*A*B
(1)
where A, B, and C are concentrations changing over time, and k is a rate constant describing the speed of the reaction. Probably the most widely known and used modification of the original model in biochemistry is the Michaelis-Menten rate law (MMRL) introduced by Michaelis & Menten in 1913 [5]
v=VmaxSKm+S
(2)
where v is the reaction rate, Vmax the maximum reaction rate, S the concentration of the substrate, and Km the Michaelis constant (the substrate concentration at half of the maximum reaction rate).
The Michaelis-Menten model describes the reaction kinetics of an enzyme-catalyzed single-substrate reaction, in which the conversion of a substrate S into a product P takes place via the formation of an intermediate complex ES, where k1, k2 and k3 denote reaction rates [4]
E+S←k2→k1ES→k3E+P
(3)
Guldberg and Waage also examined the fact that biochemical systems tend to remain in homeostasis, which is described by the equilibrium constant [6]
Keq=[C]c[D]d[A]a[B]b
(4)
Keq is the equilibrium constant in the general reaction aA + bB↔cC + dD, where a, b, c, d are the number of molecules of A, B, C, D participating, and [A], [B], [C], [D] are the molar reaction concentrations of the reaction components at equilibrium [7].
When analyzing regulatory mechanisms of metabolite kinetics, a key question addresses the effect of external perturbations disturbing homeostasis, e.g., caused by environmental influences, nutrition, drug interventions (pharmacodynamics) or physical activity (studied in this work through clinical exercise testing). These effects can be measured and examined by longitudinal cohort studies, which investigate dynamic changes in metabolite concentrations over time.
In chronic toxicity testing, which occupies a central position in the analysis of dynamic time-course metabolic data, studies are performed to explore the influences of toxic substances on human or animal metabolism. Mechanisms of metabolite kinetics are analyzed, e.g., by investigating the effect of pesticide exposure on children [8,9], by in-vitro examination of drug induced effects in neurotoxicity using brain cell cultures [10], or by analysis of toxic effects of polymers or nanoparticles to the water flea daphnia magna [11,12]. In biotechnological process monitoring, metabolic interactions are analyzed, e.g., in studying the sensitivity of the biocatalyst Clostridium thermocellum to ethanol stress [13], in exploring the forced ageing process of Port wine [14] or by the examination and optimization of cell culture media, as e.g., of Chinese hamster ovary (CHO) cells [15–17]. In pharmacodynamics, time-course data are collected, e.g., to study the effect of continuous exposure of breast cancer cells to an anti-cancer chemotherapy drug on the metabolic level [18] or to explore the metabolism of albumin in patients with systemic inflammatory response syndrome after continuous venovenous hemofiltration [19]. Research questions on kinetic mechanisms in physical exercise cover fundamental work, e.g., on studying the influence of improved metabolic health on patterns in plasma metabolites [20] or analyzing the effects of aerobic exercise on oral glucose tolerance [21].
In this work, in response to an incrementally increased physical load by cycle ergometry and depending on the underlying metabolic regulatory mechanisms, metabolites are expected to show specific kinetic signatures and shape patterns. Expected kinetic response patterns include: a sustained response (mainly constant concentration over time, overlaid with biological or instrumental noise), an early response (main decrease/increase of concentrations shortly after start of activity), a halving interval response (major change in concentration at half time of physical activity, e.g., a sigmoid behavior with a plateau), a late response (strongest decrease/increase of concentration towards the end of physical activity), and a delayed response pattern (first mainly sustained metabolite concentration, then showing a strong reaction after the end of activity during the recovery phase, respectively).
Regarding computational and mathematical aspects of characterizing kinetic regulatory mechanisms, different approaches of fundamental models for the analysis of metabolic processes have been described in the literature: qualitative models for topological network analysis, models of flux balance analysis using stoichiometric network construction and detailed kinetic models representing metabolic processes using ordinary differential equations (ODEs) [22,23]. Furthermore, different intermediate approaches do exist, e.g., the approach of structural kinetic modeling (SKM), approximating local biochemical mechanisms within a metabolic network by a parametric linear representation [23]. An overview on different "approximative kinetic formats used in metabolic network modeling" is given by Heijnen, 2005 [24]. An example for the dynamic simulation of kinetic mechanisms in metabolism—by simulating the mitochondrial fatty acid β-oxidation—is presented by Modre et al., 2009 [25]. Further examples for theoretical network models as well as dynamic kinetic simulations can be found in the context of the e-cell project [26], e.g., models for drosophila [27] or the metabolic simulation of red blood cell storage [28].
With respect to the analysis of dynamic metabolic data, Smilde et al., 2010 [29] distinguish between six groups of methods: methods based on fundamental models, predefined basic functions, dimension reduction, multivariate time series models, analysis-of-variance (ANOVA) type models, and methods based on imposing smoothness. Analyses of periodic or oscillating data can be performed using methods such as Fourier analysis, wavelet transformation or principal component analysis (PCA) with wavelets [29,30]. Hidden Markov models were presented as a way for using basic functions, allowing flexibility and adaptation in modeling [29,31]. In particular, in gene-expression analysis orthogonal polynomials were introduced for qualitative and quantitative modeling [32,33].
Alternative methods for the analysis of longitudinal metabolic data, typically used in nuclear magnetic resonance (NMR) spectroscopy, comprise weighted principal component analysis (WPCA) [34] or analysis of variance (ANOVA) simultaneous component analysis (ASCA) [35]. A statistical framework for metabolic biomarker discovery in NMR data was presented by Berk et al., 2011 [36], introducing a smoothing spline mixed effects (SME) model, combined with an associated functional test statistic. Mishina et al., 1993 [37] suggested analyzing the kinetics of biomolecules by fitting differential equations for the application in pharmacodynamics. A method for investigating between-metabolite relationships by simultaneous component analysis with individual differences constraints (SCA-IND) was presented by Jansen et al., 2012 [38]. A new method for combined analysis of proteomics and metabolomics data using integrative pathway analysis was introduced by Stanberry et al., 2013 [39]. As an example for a web-based, freely accessible online service, Metaboanalyst [40] offers the profiling of longitudinal time-course data on the basis of a multivariate empirical Bayes approach.
Metabolic biomarkers play an essential role in clinical diagnostics because of their ability to provide specific insights by being functional endpoints of human molecular interactions [41]. The general process for the discovery, verification, and validation of metabolic biomarker candidates was described by Baumgartner & Graber, 2008 [42]. This process ranges from experimental study design, over clinical study execution, execution of bioanalytical methods and acquisition of data, consolidation and integration of data, application of bioinformatics algorithms and data mining methods for the identification of biomarker candidates, up to an independent validation of putative biomarkers by clinical trials. In their review article, Baumgartner et al., 2011 [43] give a comprehensive survey of computational data analysis strategies for the discovery of biomarker candidates from metabolic data.
A milestone in clinical application of metabolic biomarkers was set by establishing routine newborn screening programs for inherited metabolic disorders [44]. The search for novel metabolic biomarkers in disease covers a wide range of clinical application areas, e.g., the identification of metabolic markers in prostate cancer by a rule-based feature selection algorithm [45], the search of early markers as well as late markers in planned and spontaneous myocardial infarction [46,47], the investigation of metabolic mechanisms in diabetes [48–50] or the discovery of putative biomarker candidates in chronic kidney disease [51–53].
In this article, we present a computational methodology, aimed at the modeling and characterization of kinetic regulatory mechanisms and the discovery of dynamic metabolic biomarker candidates in physical activity. Dynamic time-course metabolic concentration data are generated from a longitudinal biomarker cohort study by standardized cycle ergometry experiments. In total, 110 metabolites (including metabolite classes of acylcarnitines, amino acids and sugars) are quantitated by a targeted metabolomics approach utilizing mass spectrometry. After a thorough examination of the measured concentration data in terms of data quality assurance and reliability, we selected a set of 30 metabolites relevant in exercise physiology and considered them for data analysis and modeling in this work.
Metabolite concentrations of 47 individuals, showing different lengths in their concentration time curves (depending on the individual’s maximum physical load), are made comparable by means of data preprocessing. Biomarker candidates are selected depending on maximum fold changes (MFCs) (the amplitude of changes in concentrations) and the corresponding P-values resulting from statistical hypothesis testing. Kinetic signatures of metabolites are quantified by a mathematical modeling approach using polynomial fitting, specifying the dynamic response patterns of analyzed metabolites during physical activity. A similarity measure for characterized metabolite kinetic signatures is obtained through specification of groups of metabolites by hierarchical cluster analysis. Kinetic shape templates are identified, specifying common kinetic response patterns and enabling the classification of dynamic metabolic biomarker candidates according to their kinetic patterns. Findings are verified and interpreted through biochemical and metabolic pathway analyses associated with physical activity.
Putative dynamic biomarker candidates are selected from the pool of analyzed metabolites by combined analysis of MFCs in concentrations and corresponding P-values from statistical hypothesis testing (see section Maximum fold changes and statistical hypothesis testing). Results for this data analysis step are visualized as a volcano plot (Fig 1). The plot demonstrates log2 values of MFCs compared to-log10 values of P-values. A significance level of 0.001 was chosen for the selection of statistical hypothesis testing results (horizontal blue line). Moderate biomarker candidates are classified with a MFC greater than 1.20 (vertical blue line). Strong biomarker candidates are classified with a MFC greater than 1.40 (vertical green line). Detailed data of all analyzed metabolites, including MFCs, log2(MFCs), P-values, and-log10(P-values), are summarized in Table 1.
For the analyzed classes of metabolites, putative biomarker candidates could be selected and ranked according to MFCs and P-values. As strong biomarker candidates, acetylcarnitine (C2, MFC = 1.97, P <0.001), showing the highest values in the entire set of analyzed metabolites, propionylcarnitine (C3, MFC = 1.52, P < 0.001) and alanine (MFC = 1.42, P < 0.001) were identified. Valerylcarnitine (C5, MFC = 1.38, P < 0.001), arginine (MFC = 1.36, P < 0.001), glucose (MFC = 1.32, P < 0.001), butyrylcarnitine (C4, MFC = 1.27, P < 0.001), methylmalonylcarnitine (C3-DC-M, MFC = 1.26, P < 0.001), hydroxyvalerylcarnitine (C5_OH, MFC = 1.26, P < 0.001), and octadecadienylcarnitine (C18:2, MFC = 1.21, P < 0.001) were ranked as moderate biomarker candidates.
Kinetic signatures of analyzed metabolites are expected to show specific characteristic regulatory patterns, in response to the incremental increase of physical activity using a cycle ergometry stress test. Kinetic patterns of the selected 30 metabolites, characterized by a polynomial fitting approach (see section Mathematical modeling), are visualized in Fig 2 (acylcarnitines), Fig 3 (amino acids) and Fig 4 (glucose). For standardized visualization of profiles, the vertical axis is normalized to a range of-20% to 40% of relative concentration. Note that acetylcarnitine (C2) exceeds this specified range, showing a maximum increase in relative concentration of 67%.
Different kinetic response patterns were observed. The majority of metabolites show a sustained response, e.g., threonine, with basically constant behavior in concentration over time, however overlaid with biological or instrumental noise. An early response pattern is shown with valerylcarnitine (C5) with an early decrease in relative concentration (of approx. -16%) after starting exercise followed by an increase in relative concentration (to a maximum of 13%). Methionine could be identified as a metabolite showing a halving interval response pattern with characteristics similar to a sigmoid function, showing first a sustained reaction, then an increase in relative concentration at half time of physical activity (by approx. 13%) and followed by a plateau (at approx. 9% of relative concentration) towards the end of physical exercise. Metabolites showing a late response pattern are e.g., acetylcarnitine (C2) with a slight decrease (-10%) and then a strong continuous increase in relative concentration (up to 67%) or alanine with a continuous increase (of approx. 32%) up to the end of exercise. Glucose shows a delayed response pattern (similar to a L-curve / hockey stick function, see section Mathematical modeling) with a minor increase in relative concentration (approx. 2%) at the beginning of exercise, followed by a continuous decrease (down to-12%) and a major steep increase in relative concentration (up to 13%) after the end of exercise during the recovery phase.
Groups of metabolites, showing similar kinetic patterns with response to physical exercise, were identified by hierarchical cluster analysis (see section Hierarchical cluster analysis), resulting in a set of seven distinct clusters. Metabolites and their corresponding cluster affiliations are summarized in Table 2.
Cluster 1 consists of the two amino acids alanine and arginine. Cluster 2 and cluster 3 comprise a multitude of metabolites of similar metabolite kinetics, which show roughly sustained response patterns. In cluster 4, the metabolites octadecadienylcarnitine (C18:2) and glucose are clustered together. Cluster 5 consists of only acetylcarnitine (C2), the metabolite showing the strongest response. In cluster 6, propionylcarnitine (C3) and butyrylcarnitine (C4) are grouped together. Cluster 7 represents valerylcarnitine (C5), a biomarker candidate showing an early response pattern.
Kinetic shape templates, serving for the classification of similar metabolite dynamics, could be specified based on the median concentration curves of each identified cluster (see section Hierarchical cluster analysis). Identified shapes and their characteristics are summarized in Fig 5, based on relative concentration changes in reference to the initial concentration at rest. Identifiers of kinetic shape templates hereby correspond to identifiers of resulting metabolite clusters from hierarchical cluster analysis.
Templates for sustained response patterns, observed in the majority of metabolites, are specified by shapes 2 and 3. Shape 7 specifies a template for dynamic biomarker candidates, showing an early response pattern (valerylcarnitine (C5)). Shape 1 describes a template showing a late response pattern with a continuous increase in concentration (alanine and arginine). Shapes 5 (acetylcarnitine (C2)) and 6 (propionylcarnitine (C3) and butyrylcarnitine (C4)) define further templates for late response patterns, differing in their dynamics in concentration time courses and maximum concentration changes. Shape 4 demonstrates a template for a delayed response pattern, showing characteristics similar to a L-curve / hockey-stick function (glucose and octadecadienylcarnitine (C18:2)).
Dynamic metabolic biomarker candidates are identified and classified through a two-step analysis procedure: first, by analysis of MFCs in concentrations and statistical hypothesis testing, and second, by reviewing and characterizing specified metabolic response patterns and kinetic shape templates.
The majority of metabolites show a sustained response pattern, staying within an interval of relative MFC of less than 20%, being ineligible as putative biomarker candidates. Valerylcarnitine (C5), yielding an early response pattern, was classified as early marker and moderate predictor (MFC = 1.38, P < 0.001). Methionine shows a halving interval response pattern with a sigmoid behavior, but having a moderate amplitude in concentration (MFC = 1.16, P > 0.001) and was therefore not selected as a biomarker candidate. A late response pattern with weak early decrease in concentration was observed with propionylcarnitine (C3) (strong predictor, MFC = 1.52, P < 0.001), and butyrylcarnitine (C4) (moderate predictor, MFC = 1.27, P < 0.001), both classified as late biomarker candidates. Alanine (strong predictor, MFC = 1.42, P < 0.001) and arginine (moderate predictor, MFC = 1.36, P < 0.001) showed a late response pattern with a continuous increase in concentration from the beginning of exercise and were classified as late markers. Highest concentration changes yielded acetylcarnitine (C2), demonstrating a late response pattern with a very strong increase towards the end of exercise. C2 was ranked as strong predictor (MFC = 1.97, P < 0.001) and classified as late marker. Showing basic delayed response patterns, glucose (MFC = 1.32, P < 0.001) and octadecadienylcarnitine (C18:2) (MFC = 1.21, P < 0.001) were identified as moderate predictors and classified as delayed markers.
Thanks to an elaborate body of knowledge in biochemistry, a peculiarity within the process of data analysis in metabolomics lies in the dedicated biochemical interpretation of results [54]. This knowledge is nowadays annotated in public databases, e.g., the Kyoto Encyclopedia of Genes and Genomes (KEGG) [55], and eases the interpretation of findings in the context of annotated biochemical pathways.
In exercise physiology, various biochemical reactions in metabolism play an essential role, predominantly in carbohydrate metabolism (glycolysis and glycogenolysis), in lipid metabolism (β-oxidation of free fatty acids) and amino acid metabolism [6]. During a cycle ergometry stress test, an individual increasingly consumes adenosine triphosphate (ATP); to compensate this energy consumption and maintain homeostatic levels of ATP, its production is up-regulated, first primarily by aerobic processes (respiration), and then anaerobic fermentation. Under the low-impact conditions chosen in this study (low initial output of 50 Watt (W) and slow increase of 25 W every three minutes), the metabolic data demonstrate that the body uses both glycolysis and β-oxidation of fatty acids as readily available energy sources, before protein catabolism contributes in a substantial manner. Of course, the pools of monosaccharides and free fatty acids have to be replenished by glycogenolysis and lipolysis, respectively.
The findings of this work, i.e., identified biomarker candidates of exercise metabolism, and characterized metabolite signatures via specified kinetic shape templates, can be explained through the metabolic regulatory mechanisms in physical activity. Significant changes in concentrations of acetylcarnitine (C2) and closely related short-chain acylcarnitines (C3, C4, and C5) arise from their involvement in the β-oxidation of free fatty acids with acetylcarnitine (the single most significant finding) representing the actual end-point of the β-oxidation of even-numbered fatty acids which constitute the vast majority of dietary fatty acids and of fatty acids in the body's adipose tissue. The strong increase in concentrations of alanine and arginine are representative for an increased production of glucogenic amino acids through high glycolytic activity. This connection is most obvious for alanine, which is the corresponding amino acid of the alpha-keto acid pyruvate and is, thus, a direct mirror of glycolytic or gluconeogenetic flux [48]. The third major finding, the overproduction of glucose after the end of the exercise, is due to the inertia of metabolic regulation. In order to supply the glycolysis with enough fuel, glucose has to be released from its storage by glycogenolysis. At the abrupt end of the exercise, the increased activity of the glycogenolytic machinery cannot be stopped immediately and, therefore, leads to overcompensated glucose levels.
In this work we have presented a computational modeling and statistics approach for the identification of dynamic metabolic signatures through characterization of kinetic patterns of circulating metabolites from a physical exercise study. Dynamic time-course metabolic concentration data were obtained through clinical exercise testing using a cycle ergometry stress test.
The data of 47 individuals from four different groups were analyzed: male and female test persons, with either average physical activity or competitive athletic activity. Lactate concentrations were measured for all individuals as a gold standard for profiling physical activity. Metabolite concentrations were quantitated by a targeted metabolomics approach, combining mass spectrometry analytics with the concept of stable isotope dilution. From the initial set of 110 metabolites (including classes of acylcarnitines, amino acids and sugars), we selected a reliable and quality assured set of 30 metabolites for data analysis playing a possible role in exercise physiology.
Based on the generic process for biomarker discovery in metabolomics, a computational approach for the analysis of longitudinal metabolic concentration data was developed. Computational tools were implemented in R [56] for automating the data analysis workflow. The source file (R script file) and the underlying dataset (Microsoft Excel file) are provided as supporting information (S1 File and S2 File). Individual workload curves, differing in the number of measurements due to variability in the individual's physical capacity and exertion, were made comparable by data preprocessing steps including rescaling and linear interpolation of concentration-time curves.
Putative dynamic biomarker candidates for physical activity were selected by combined analysis of MFCs in concentrations and P-values of statistical hypothesis testing. Kinetic patterns of analyzed metabolites were characterized based on a mathematical modeling approach utilizing polynomial fitting as the method of choice. Metabolite groups, showing similar kinetic response patterns, were obtained by applying hierarchical cluster analysis to the set of characterized metabolite kinetic patterns. Kinetic shape templates could be specified according to the identified clusters, defining basic kinetic response patterns used for classification of dynamic biomarker candidates.
The following kinetic response patterns could be defined: sustained response (basically constant concentration over time, overlaid with biological and instrumental noise), early response (significant change in concentration at the beginning of exercise), late response (continuous decrease/increase towards the end of activity), and delayed response (first basic sustained response, with a strong response and steep decrease/increase in concentration after the end of the exercise during the recovery phase).
The selected two-step data analysis and modeling strategy including MFCs in concentrations and statistical hypothesis testing, and the modeling of kinetic shape templates led to the identification and classification of dynamic metabolic biomarker candidates for profiling physical activity. The highest values for MFCs and P-values in the analyzed set of metabolites were shown for acetylcarnitine (C2) (MFC = 1.97, P < 0.001), yielding a late response pattern, and being classified as strong predictor and late marker. Alanine showed the highest values in the class of amino acids (MFC = 1.42, P < 0.001) and yielded a late response pattern, being classified as strong predictor and late marker. The only considered sugar, glucose, yet playing a key role in physical activity, yielded a delayed response pattern classified as moderate predictor (MFC = 1.32, P < 0.001) and delayed marker.
In terms of biochemical interpretation, findings were verified and interpreted according to their function in metabolic pathways, associated primarily with physical exercise (β-oxidation of fatty acids, glycolysis, and glycogenolysis). Interestingly, biomarker candidates, identified with the highest predictive value, yielded late response patterns. This might be seen in the context that lactate (also a key indicator for profiling physical activity) first shows an almost sustained response pattern before yielding an exponential increase in concentration up to a maximum physical load. The primary occurrence of late response patterns can be interpreted as a consequence of evolutionary developed regulatory mechanisms in metabolism to keep the individual's metabolic system in homeostasis after external perturbations such as spontaneous physical activity.
Using our computational approach we were able to select and classify dynamic metabolic biomarker candidates and to characterize physiologically plausible metabolite kinetic patterns in physical activity, combining the strengths of statistical testing (hypothesis testing), mathematical modeling (curve fitting) and empiric data analysis (hierarchical cluster analysis).
Experimental limitations and confounders in the analyzed data may result from uncertainties about the nutrition of test persons before exercising (recorded in questionnaires but not objectively verifiable), varying individual motivations and consequently different levels of exertion, potential issues during sample taking (e.g., incomplete removal of sweat at the point of puncture), or from general limitations of the analytical approach based on dried blood spots [57]. It should be noted, that at least two test persons obviously consumed nutritional supplements in the form of branch-chained amino acids, influencing the measurement values of xleucine (sum of leucine and isoleucine).
With reference to the selected cohort, it should be noted that the study participants formed a heterogeneous group, i.e., they differed in their level of physical activity and status of training. Therefore, the baseline concentrations and the kinetic patterns may, to a certain extent, depend on the volunteers' differences in physical fitness, or other confounding factors such as anthropometric measures or dietary habits. Although this paper is primarily focused on the methodology for deriving kinetic patterns and not so much on the discovery of exercise-related biochemical mechanisms, the results should be seen with these limitations in mind.
In terms of data preprocessing, the presented data analysis strategy reveals strong indifference towards the handling of outliers because median concentration values are selected from rescaled and interpolated concentration curves. Cut-off values for the selection of metabolic biomarker candidates (utilizing MFCs and P-values) were chosen empirically by reviewing obtained results and assuming that responses, showing changes in concentration within a range of-10% to +10%, are accepted as biochemically and analytically-caused data variability. For kinetic modeling, an empirical approach (instead of applying pre-defined mathematical basic functions) based on polynomial fitting was chosen, allowing for a more physiological characterization of metabolite kinetics. Looking at the complete set of characterized metabolite kinetic signatures, the user can choose an appropriate polynomial degree after visual inspection or by developing a proper statistical quality measure e.g., based on an estimation of the residuals. In a few cases, minor artifacts of approx. 3% in concentration values of the fitted polynomials do occur, obviously resulting from a slight overfitting of curves due to the chosen polynomial degree.
Identification of groups of similarly behaving metabolites by hierarchical cluster analysis is somewhat affected by the number of interpolated points in the concentration curves and by the degree chosen for fitted polynomials. A higher number of interpolation points as well as different degrees of polynomials were tested, showing high stability in cluster analysis, however, at a lower node height of the dendrogram the arrangement of single metabolites changes slightly between the clusters. Note that the selection of clusters basically depends on the chosen height (cut-off) of the hierarchical tree. Depending on the selected cut-off value, two metabolites, i.e., methylmalonylcarnitine (C3-DC-M) and hydroxyvalerylcarnitine (C5-OH) might also be classified as additional biomarker candidates, interesting for further investigation. Specification of kinetic shape templates finally builds upon the number of specified clusters, depending computationally on the selected cut-off in hierarchical cluster analysis and biochemically on the eligibility and meaningfulness of clustered templates in terms of metabolite kinetics.
Metabolic concentration data used in this study have served as a database for the development and validation of novel data mining and biomarker discovery strategies in previously published studies by our group. In Netzer et al., 2011 [58] we presented a two-step network-based approach for the identification of metabolic biomarkers, classifying alanine, acetylcarnitine (C2), propionylcarnitine (C3), and glycine, as strong, and arginine, citrulline, and lysine as moderate biomarker candidates, represented as major hubs in the dynamic network. These findings show high accordance with identified dynamic metabolic biomarker candidates in physical activity using the approach presented in this work, again selecting alanine, acetylcarnitine (C2), propionylcarnitine (C3) as strong predictors, and arginine as moderate marker candidate.
In a second paper [59] we introduced a method for biomarker identification by inferring two different types of networks, i.e., correlation networks and ratio networks. This more theoretical approach calculates scores to prioritize features using topological descriptors. Groups of obese test persons (with a body mass index (BMI) > 30) and healthy controls were compared in this study, which identified highly discriminatory biomarker candidates, i.e., histidine, ornithine, acetylcarnitine (C2), and proline.
In this article, we have presented a computational methodology for dynamic biomarker classification and modeling of kinetic metabolic patterns in physical activity. Insight into kinetic regulatory mechanisms could be provided by characterizing specific kinetic signatures for selected key metabolites within the groups of acylcarnitines and amino acids, and for glucose. A new data analysis strategy for the characterization and classification of dynamic biomarker candidates was introduced. We were able to specify common kinetic shape templates, identified from groups of metabolites showing a similar characteristic in dynamic time-course responses. Findings demonstrated high accordance with previously published data and established biochemical knowledge, e.g., the response of glucose, showing a behavior similar to a hockey stick function with a delayed increase in concentration after the end of physical exercise during the recovery phase.
Due to the selected study design of a cycle ergometry experiment, in which physical exercise was increased incrementally (every 3 minutes by 25 W), known kinetic patterns could be partly confirmed by our observations, in particular in response to the selected workload protocol. Major impact of the presented methodology can be seen in the fact that kinetic mechanisms in metabolism could be qualified and quantified not only through a “strong” mathematical model, but by an empiric deduction and description of de facto kinetic response patterns from quantitated metabolic time-course concentration data, measured under in-vivo experimental conditions.
A further direction of research might be the analysis of additional classes of metabolites and the description and interpretation of kinetic patterns subsequent to active exercise in the recovery phase. Especially for glucose—which increases rapidly in concentration within the analyzed interval of the recovery phase—a prolonged examination time would be highly interesting, since glucose might be expected to be classified as strong predictor. From the computational viewpoint, a very challenging task would be the development of in-silico pathway models, integrating the identified kinetic signatures into a theoretical mathematical model for hypothesis generation and verification (see e.g., Teusink et al., 2000 [60]). The development of a kinetic model based on an ordinary differential equations (ODEs) description including kinetic parameters selected from our research might be an aim for additional research which, however, is beyond the scope of this article.
The approach presented in this work also shows high potential for contributing to other application areas such as pathophysiology and pharmacodynamics. In pharmacodynamics and toxicology (particularly in chronic toxicity testing), for instance, it might be applied to assess treatment effects more accurately by profiling metabolite levels over time instead of looking at end-points only (see [29]). In many complex diseases, the dynamic analysis may well identify more meaningful biomarkers and reveal a deeper insight into the actual pathomechanisms. To name one important example that is actually very close to the present study: in chronic obstructive pulmonary disease (COPD), physical exercise—and bicycle ergometry in particular—is commonly used to assess the severity of the disease and also to model exacerbations of the patients’ condition [61,62]. In this setting, a dynamic depiction of the metabolic changes clearly has the potential to resolve regulatory mechanisms and distinguish cause and effect of the observed alterations (Christian Schudt, personal communication). This is especially plausible for the pathway leading to the synthesis of inflammation mediators such as prostaglandins, leukotrienes, thromboxanes etc., which is closely associated with the pathology of the disease and depends on the release of polyunsaturated fatty acids from phospholipids in a stoichiometric manner [63–66].
In this article, main focus was put on the development of a computational methodology to examine longitudinal metabolic concentration data and to present a basic approach for the mathematical modeling and statistical analysis of dynamic kinetic metabolic mechanisms. As previously stated (see section Methodology), individual metabolic response patterns are partly influenced by different factors such as physical fitness and training status, anthropometric parameters or dietary habits. Because of limitations in the specification and verification of the observed metabolic kinetic patterns, a further research goal might be to systematically investigate the underlying metabolic and physiological regulatory mechanisms by conducting additional hypothesis-driven prospective cohort studies. Furthermore, an extension of this paper is planned that will compare specific groups of interest, e.g., defined with regard to training status (response in lactate increases) or anthropometric characteristics.
Referring to the practical execution of exercise physiology experiments, it should be noted that most commonly only one blood sample is collected, usually after the end of exercise. However, the results of the presented work clearly demonstrate that characterized metabolites show a very differential kinetic characteristic during physical activity. Consequently one-point measurements may lead to misinterpretations and emphasize an obvious need for multiple measurements in exercise physiology (typically before, multiple times during, and after exercise).
This study was conducted in full accordance with the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all study participants, together with a detailed questionnaire on nutrition and health status. In addition, a physician subjected all individuals to a detailed examination to ensure that they could undergo the cycle ergometry test without health risks, and this physician was also present at all times during the exercise to monitor the electrocardiogram (ECG) that was continuously recorded. All laboratory work and data analysis was conducted anonymously.
In this work, longitudinal metabolic concentration data were obtained through clinical exercise testing using a cycle ergometry stress test. General guidance for clinical exercise testing can be found in "Guidelines for Clinical Exercise Testing Laboratories" [67] and "Recommendations for Clinical Exercise Laboratories" [68]. General recommendations for cycle ergometry studies were described by Driss & Vandewalle, 2013 [69], providing technical and clinical protocols including limitations for study design and execution. The overall cycle ergometry experiment was designed as a longitudinal biomarker cohort study, with 47 persons divided into 4 different groups, i.e., male and female individuals, with either average physical activity or competitive athletic activity. Study participants included amateur endurance athletes (16 males / 8 females) and professional alpine skiers (11 males / 12 females). The anthropometric characteristics of the study participants (age, body mass index (BMI), height, and weight) are summarized in Table 3. Detailed information on anthropometric data, the general training status, and the physical load during the cycle ergometer experiment are provided as supporting information (S3 File).
The workload of the cycle ergometry test was increased incrementally by 25 W every 3 minutes up to the individual’s maximum physical load (the basic scheme of the study protocol is depicted in S1 Fig). The initial workload level of 25 W was skipped for all individuals, starting the exercise with a workload of 50 W. The lowest observed maximum workload was 150 W (one individual), and the highest workload level was 425 W, also reached by one individual. From each individual blood samples for metabolite profiling were taken (i) at rest (directly before starting the exercise), (ii) with each new workload level up to individual’s maximum physical load, and (iii) after a short recovery phase of five minutes after the maximum workload (highest Watt level). In addition, for all test subjects, lactate concentrations were measured as a gold standard and reference for assessing physical activity. Concentration-time curves of preprocessed lactate data are visualized in S2 Fig. Median values of lactate concentrations were roughly 1.2 mM at rest, 8.5 mM at maximum workload, and 7.2 mM after recovery. According to the study protocol, lactate samples were taken at 1:30 min, samples for metabolite profiling at 2:30 min after starting a new ergometry workload level. All samples were taken from the earlobe, collected as dried blood spots (DBS) [57] and analyzed under standardized study conditions.
In metabolomics, two basic conceptual approaches are used: untargeted and targeted metabolite profiling methods. Untargeted metabolomics seeks to create a holistic picture of metabolism by trying to identify a comprehensive set of metabolites as a snapshot of a metabolic state, while targeted metabolomics aims at a quantitation of pre-selected metabolites defined by a priori knowledge [70,71]. The two state-of-the-art technologies for analyzing metabolites are nuclear magnetic resonance (NMR) spectroscopy [72] and mass spectrometry (MS) [73]. Dynamic time-course metabolic concentration values, building the basis for data analysis and modeling in this work, were gathered from a targeted metabolomics approach [70,74,75], using triple quadrupole tandem mass spectrometry (MS/MS) [76] coupled with the concept of stable isotope dilution (SID) [77] for metabolite quantitation.
Longitudinal metabolite concentration data were quantified for three classes of metabolites: acylcarnitines, amino acids, and sugars. In total, 110 metabolites were measured: 40 acylcarnitines, 18 amino acids, and 52 sugars. Quantitated concentration data of all measured metabolites were thoroughly examined with respect to data quality assurance and reliability. Metabolites either below the detection limit (LOD) of 50 nM, measurements with lots of missing values or wide variabilities were excluded from this analysis. As result, targeted concentration data of a selected set of 30 metabolites are considered for data analysis in this work: 11 acylcarnitines, 18 amino acids, and 1 sugar. Analyzed acylcarnitines include: carnitine (C0), acetylcarnitine (C2), propionylcarnitine (C3), methylmalonylcarnitine (C3-DC-M), butyrylcarnitine (C4), valerylcarnitine (C5), hydroxyvalerylcarnitine (C5-OH), hexadecanoylcarnitine (C16), octadecanoylcarnitine (C18), octadecenoylcarnitine (C18:1), and octadecadienylcarnitine (C18:2). Amino acids are: alanine, arginine, aspartic acid, citrulline, glutamic acid, glycine, histidine, lysine, methionine, ornithine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, and xleucine (the sum of leucine and isoleucine). Analyzed metabolite within the class of sugars was glucose. Collected data were almost complete, except some missing data at individual’s maximum load (twelve individuals, however lactate could be measured after 1:30 min for all of them), and at 150 W for one test person (no. 9).
Central steps of the selected data analysis workflow include the technical validation of raw data, preprocessing of data, selection of putative dynamic biomarker candidates, mathematical modeling and characterization of metabolite kinetic patterns, identification of metabolite groups with similar kinetic behavior, specification of observed kinetic shape templates, classification of dynamic biomarker candidates and the biochemical interpretation of findings. A flowchart of the used data analysis workflow is shown in Fig 6, representing the whole data-driven process for the discovery of biomarkers in metabolomics. Results from the different steps of the data analysis workflow are exemplarily shown and visualized for glucose, a key analyte, playing a central role in metabolism of exercise physiology and demonstrating a very specific kinetic pattern in response to physical activity.
Raw data of the cycle ergometry experiment were test-wise reviewed and visualized in two different basic ways to obtain a better understanding about the nature of the metabolic time-course data. Concentration data were initially analyzed by building subsets of data, referring to the levels of each individual’s maximum physical load. For each subset a box plot was generated, visualizing the specific measurements (data points of the horizontal axis) versus the metabolite concentrations (see section Clinical study execution). In S3 Fig, resulting box plots for glucose are exemplarily shown. Eight box plots were generated, where the lowest value of individual maximum workload (150 W) resulted in 7 data points (1 test person) and the highest value (425 W) in 18 measurement points (1 test person). Second, analyzed metabolites were visualized as raw concentration curves (exemplarily shown for glucose in S4 Fig), illustrating differences in individual workload and time of exercise of examined test persons. The horizontal axis hereby represents time points of measurements in minutes.
Different lengths of metabolic concentration-time curves, resulting from the variability of each individual’s maximum physical load, were made comparable by rescaling the data in time (S5 Fig). Measurement at rest was defined as 0%, maximum workload of each individual as 100% and recovery value as median value of 117%—resulting in an aligned workload curve to a uniform length. This requires additional data points added to the concentration curves using a linear interpolation approach (Fig 7A).
Metabolic concentration-time curves underwent simple descriptive analysis by generating a box plot representation from rescaled concentration curves (Fig 7B). In a next step, median concentration values were extracted from interpolated concentration curves (S6 Fig), serving as a basis for mathematical modeling by curve fitting (see section Mathematical modeling). This approach perfectly treats the problem of outliers in the data without the need of applying additional methods for outlier detection and removal. However, a small set of extreme outliers was observed that was manually removed after careful visual inspection (in test person no. 14 all data points at recovery, in test person no. 35 all data points at 175 W and in test person no. 42 the data point for glucose at rest). Regarding missing concentration values it should be noted that our dataset was almost complete, except missing values at individuals’ maximum workload in 12 test persons and at 150 W in one individual (no. 9), representing the last data points in the concentration time curves.
Maximum fold changes in metabolite concentrations and P-values of statistical hypothesis testing serve as a score for the ranking of putative biomarker candidates (see section Selection of dynamic biomarker candidates). The combination of fold changes and P-values, visualized using a volcano plot, is described in the literature as method of choice for the analysis and visualization of significant changes (e.g., on microarray data [78,79] or in diverse metabolomics applications [80–82]). As a general approach, especially in genomics studies, this method is usually used for data comparing the starting and end point of dynamic processes such as regulation of gene expression.
In this work, utilizing longitudinal time course concentration data, MFCs are calculated by the difference between observed minimum and maximum concentration values of a metabolite, independently from their timely occurrence. MFCs are calculated based on median concentration values extracted from interpolated concentration curves (see section Data preprocessing). Measurement indices are determined, and consequently, if the minimum concentration occurs earlier in the time course, the ratio of maximum concentration to minimum concentration is calculated and vice versa. This modality is summarized in the following pseudo-code:
if (conc_min_index < conc_max_index)
maximum_fold_change=conc_max/conc_min
(5)
else
maximum_fold_change=conc_min/conc_max
(6)
P-values of statistical hypothesis testing are calculated in a similar way, first by determining measurement index positions of minimum and maximum median concentration values of interpolated concentration curves and in a subsequent step, by extracting interpolated individual concentration values at identified index positions as basis for statistical hypothesis testing. Interpolated minimum and maximum concentration values of all 30 metabolites were assessed with respect to their density distribution by visual inspection using graphical methods such as histogram analysis [83] and quantile-quantile plots [84]. A Shapiro-Wilk Normality test was applied for normality testing of both minimum and maximum concentration data (significance level P = 0.01) [85–87]. Metabolites hereby yielded inhomogeneous distributions (e.g., normal distribution for histidine, octadecanoylcarnitine (C18) or glycine, non-normal distribution e.g., for xLeucine, citrulline or proline, and partly differences in distributions between minimum and maximum concentrations, e.g., for arginine). To ensure comparability between metabolites, a Wilcoxon Signed Rank Test [88] (non-parametric hypothesis test for paired samples) was used for the calculation of P-values (significance level P = 0.001). Since ranks are used for paired hypothesis testing, identical P-values are partly shown for some metabolites. Finally, calculated P-values were adjusted according to the false discovery rate (FDR) correction for multiple comparisons [89].
The initial goal of our work was the mathematical modeling of metabolite kinetic patterns and shape templates, utilizing a set of predefined mathematical basis functions [29]. However, the introduction of predefined basic functions for the analysis of dynamic metabolomics data is and remains a challenge as also discussed by Smilde et al., 2010 [29]. Note that putative basic functions in this work are associated with kinetic patterns in response to linear increasing physical activity and can be basically classified into the following set of shape templates:
Fitting the above-introduced basic functions to measured concentration-time curves was thoroughly examined and tested with the goal to characterize kinetic response patterns according to these theoretical models. In this analysis curve fitting was performed using median metabolite concentration values, extracted from interpolated concentration-time curves (see section Data preprocessing). Our preliminary results demonstrated that the approach of fitting the pre-defined set of mathematical basis functions was not feasible for the measured response curves caused by an incremental increase of physical workload. We therefore revised our initial concept by utilizing polynomial fitting of preprocessed data. This modality enables us to design kinetic response patterns that are physiologically reasonable and relevant. Polynomials of degree n are defined by following equation:
f(x)=∑i=0naixi
(13)
After testing different polynomial degrees, we decided for a degree of nine, showing the best results in terms of curve/shape representation and smoothness (S7 Fig). To ensure comparability of analyzed metabolites after polynomial fitting, relative concentration values were calculated (in percentage of concentration changes with respect to the initial concentration at rest) (see Fig 4).
Note that there are multiple applications in metabolomics using polynomial fitting, e.g. for baseline correction [92,93], prediction of germination curves [94], calculation of mass correction profiles [95] or in spectrum deconvolution [96].
Metabolite groups, showing similar kinetic response patterns, were examined and identified using hierarchical cluster analysis [97]. Cluster analysis was performed based on the concentration values of the fitted polynomials of 9th degree (see section Mathematical modeling).
Results are visualized as a heatmap in Fig 8. Relative workload values of the x-axis are displayed in linear order. Red colors indicate lower values in dynamic time-course concentrations of specific metabolites, lighter colors indicate higher concentration values. The resulting cluster dendrogram is separately shown in S8 Fig. Clusters of metabolites, showing similar dynamic behavior, were empirically identified by cutting the hierarchical tree at a threshold of 35, resulting in seven different metabolite clusters (see section Identification of metabolite groups with similar patterns).
Descriptive statistical analysis for each cluster was performed and corresponding box plots were generated (S9 Fig). Median concentration curves of each cluster, allowing for an accurate representation and specification of kinetic shape templates, were selected (S10 Fig) (see section Specification of kinetic shape templates).
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10.1371/journal.ppat.1002919 | Structural Organization of Pregenomic RNA and the Carboxy-Terminal Domain of the Capsid Protein of Hepatitis B Virus | The Hepatitis B Virus (HBV) double-stranded DNA genome is reverse transcribed from its RNA pregenome (pgRNA) within the virus core (or capsid). Phosphorylation of the arginine-rich carboxy-terminal domain (CTD) of the HBV capsid protein (Cp183) is essential for pgRNA encapsidation and reverse transcription. However, the structure of the CTD remains poorly defined. Here we report sub-nanometer resolution cryo-EM structures of in vitro assembled empty and pgRNA-filled Cp183 capsids in unphosphorylated and phosphorylation-mimic states. In empty capsids, we found unexpected evidence of surface accessible CTD density partially occluding pores in the capsid surface. We also observed that CTD organization changed substantively as a function of phosphorylation. In RNA-filled capsids, unphosphorylated CTDs favored thick ropes of RNA, while the phosphorylation-mimic favored a mesh of thin, high-density strands suggestive of single stranded RNA. These results demonstrate that the CTD can regulate nucleic acid structure, supporting the hypothesis that the HBV capsid has a functional role as a nucleic acid chaperone.
| Many single stranded RNA virus encapsidate their genome through positively-charged domains of their capsid proteins. Hepatitis B virus (HBV) is a double stranded DNA virus which packages a single-stranded RNA pregenome (pgRNA) that is reverse transcribed within the capsid. RNA packaging requires a phosphorylated form of the HBV capsid protein's RNA-binding carboxy-terminal domain (CTD). Although the capsid has been well studied, the internal structures, the CTDs and the packaged RNA, are poorly characterized. By using in vitro reassembly, we have generated empty and pgRNA-filled capsids using phosphorylation-mimic and unphosphorylated forms of the capsid protein. Using cryo-EM image reconstruction, we have been able to show the structure of encapsidated pgRNA and, independently, the CTD in the absence of RNA to visualize early stages of the HBV assembly. We showed that the structural organization of the CTD changes as a function of the phosphorylation. Changes in CTD structure affect the structure of the encapsidated pgRNA, changing it from thin segments of density in the phosphorylated state, suggestive of single-stranded RNA, to thick rope-like structures consistent with duplex nucleic acid in the unphosphorylated state.
| Chronic infection with hepatitis B virus (HBV) can lead to liver failure and cirrhosis; it is also the leading cause of hepatocellular carcinoma. Approximately 350 million individuals suffer from chronic HBV worldwide, and HBV contributes to 600,000 deaths per year [1]. It is a major public health issue and also a great social concern due to the discrimination against those infected with HBV in the endemic regions [2], [3].
HBV is an enveloped double-stranded (DS) DNA virus with an RNA intermediate form. In an infected cell, virion formation is initiated in the cytosol by the binding of a copy of the 3.2 kb “pregenomic” RNA (pgRNA) to the viral reverse transcriptase (RT) and packaging of the pgRNA• RT complex by multiple copies (180 or 240) of the viral capsid protein (Cp183) to form an immature core. Subsequently, the encapsidated single-stranded (SS) pgRNA is reverse transcribed to a full-length minus-stranded DNA that is covalently attached to the priming domain of RT; simultaneously, the RNA template is digested by the RNase H domain of the RT. A complementary, incomplete plus-strand DNA is then transcribed to generate rcDNA [4], [5]. These mature cores can then interact with viral surface proteins for envelopment. By studying the lifecycle of HBV we can identify new targets for development of antivirals as well as gain understanding of the function and behavior of these very specialized molecular machines.
The basic building block of the HBV capsid is the core protein homodimer. Most HBV cores are composed of 120 dimers arranged with T = 4 icosahedral symmetry; about 5% are 90-dimer T = 3 icosahedra [6]–[8]. The core protein has two domains: the N-terminal assembly domain (residues 1–149, which can be expressed as self-assembling Cp149) and the positively charged carboxy-terminal protamine-like domain (residues 150–183, CTD). The assembly domain forms the protein shell of the capsid [8]–[11]. The CTD is dispensable for capsid assembly but required for packaging RNA [10], [12]–[14], which it binds with high affinity [15]. Structures of empty T = 4, CTD-truncated Cp149 capsid have been solved to high resolution by cryo-EM and X-ray crystallography [9], [16], [17]. The Cp149 dimer has an overall shape of an inverted capital ‘T’ [8], [9], [18]. The stem of the ‘T’ is the four-helix bundle dimerization motif, which protrudes outward from the capsid surface. The crossbar of the ‘T’ clusters in a groups of five or six to form the contiguous capsid surface [8], [16]. Numerous pores perforating the capsid surface (located at the twofold (i.e. quasi-sixfold), threefold, and quasi-threefold axes) are proposed to allow nucleotides to diffuse in and out of the capsid during reverse transcription [9]. The CTD, localized to fivefold and quasi-sixfold vertices, extends into the capsid interior [18]–[20].
The capsid affects genome replication through its arginine-rich CTD. Phosphorylation of the CTD is important for RNA packaging and DNA synthesis [13], [21], [22]. The CTD has three SPRRR motifs (S155, S162, and S170) identified as the phosphorylation sites critical for pgRNA packaging [23], [24]. Mutation of these serines to alanine, to mimic the unphosphorylated capsid, suppresses pgRNA encapsidation [22], [25]–[27]. Replacing these serines with aspartate or glutamate to mimic phosphoserine supports pgRNA encapsidation but differentially affects transcription, suggesting that each repeat has an independent contribution to viral replication and may function together as a nucleic acid chaperone [21]. Although the identity of the enzyme involved in capsid phosphorylation is not clear, the serine/arginine-rich protein kinase (SRPK) family has demonstrated HBV kinase activity; SRPK1 physically binds to the CTD of both core homodimer and assembled capsid [15], [28], [29]. Such binding activity implies that the CTD resemble SR proteins, which are nuclear proteins involved in RNA splicing and transport from the nucleus. Indeed, core protein has substantial sequence identity with SR proteins [30]. Strikingly, though CTDs are on the inside of the capsid, the CTD also carries nuclear localization signals [31], suggesting that phosphorylation and accessibility of the CTDs can regulate intracellular trafficking of HBV cores [32], [33].
To test the hypothesis that phosphorylation of core protein CTDs has a structural role, we have determined cryo-EM structures of T = 4 HBV capsids assembled in vitro from unphosphorylated core protein (Cp183-SSS) or from a phosphorylation-mimic core protein (Cp183-EEE, carrying S155E, S162E, and S170E) with or without in vitro transcribed pgRNA. Our results clearly define the spatial location and the structural configuration of the CTDs and the encapsidated pgRNA. The CTD organization changes substantively as a function of phosphorylation state. This effect is transduced to the RNA structure. In unphosphorylated capsids, pgRNA formed an icosahedral cage that was virtually identical to the DS rcDNA in the native HBV virion, suggesting a largely DS state [34]. In the phosphorylation-mimic environment, the pgRNA formed a complicated mesh more consistent with RNA in a single-stranded state. This difference suggests that the HBV genome undergoes transient conformational changes during viral replication, which implies that the CTD is indeed a nucleic acid chaperone.
Purified HBV core protein dimers were reassembled into empty capsids (Cp183e-SSS and Cp183e-EEE) and pgRNA-filled capsids (Cp183RNA-SSS and Cp183RNA-EEE) [15]. RNA-filled capsids were dialyzed into buffered 150 mM NaCl while empty capsids were dialyzed into higher salt, 250 mM NaCl, to ensure stability [15]. Cryo-electron micrographs of Cp183e-SSS, Cp183RNA-SSS, Cp183e-EEE, and Cp183RNA-EEE (Figure 1) showed that all four constructs have similar morphology. pgRNA-filled particles noticeably had an inner layer of density characteristic of RNA-filled capsids [8], [18], [35]. To enhance the signal from the low contrast cryo-images, we translationally aligned particles to generate averaged images (Figure 1, insets). The empty Cp183e-SSS and Cp183e-EEE averages showed a single rim of density, 34 nm in diameter, indicating that they are both hollow spheres (Figure 1A and B). The averages of pgRNA-filled capsids (Figure 1C and D) showed an additional ring, presumably the encapsidated pgRNA; Cp183RNA-EEE appears to have much stronger RNA density than the Cp183RNA-SSS.
To examine the details of pgRNA structure and the interaction between the capsid and pgRNA, we calculated image reconstructions of T = 4 particles to sub-nanometer resolution (Table 1). External views showed that all four types of particle (Cp183e-SSS, Cp183e-EEE, Cp183RNA-SSS, and Cp183RNA-EEE) were very similar (Figure 2A–D); nevertheless, from the central section it appeared that the spikes of the pgRNA-filled capsids adopted a slightly different quaternary structure (Figure 2E–F). As in previously published HBV structures [9], [16], [18], [34], [35], the capsids had pores at each twofold, threefold and quasi-threefold axis (Figure 2). Empty capsids, both Cp183e-SSS and Cp183e-EEE, appeared to have extra density partially occluding their twofold pores (i.e. quasi-sixfolds) (Figure 2A, B, E and F, black arrows). Presumably this density was from free CTDs. pgRNA-filled capsids (Figure 2C, D, G and H) and reconstructions of CTD-truncated particles [8]–[10], [16], [17] did not display this density. The central sections of the density maps showed short segments of density (Figure 2E–H, white arrows), presumably the CTDs, tethered from the capsid inner surface. In the empty Cp183e-SSS and Cp183e-EEE particles, this density was located under each dimer. Under the fivefold vertex, in Cp183e-EEE the CTD density condenses to form a funnel-like structure; in Cp183e-SSS, the equivalent density is weaker and forms distinct extensions.
The differences between Cp183e-EEE and Cp183e-SSS CTD organization were also seen in pgRNA-filled particles. However, in RNA-filled capsids, density extending from the capsid inner surface submerged into the internal ring of RNA density (Figure 2G and H). In addition to the fivefold capsid-RNA connection, in Cp183RNA-SSS there were thin density elements connecting the extremities of the dimer to the RNA ring (Figure 3A). A different conformation for connecting density was observed at the Cp183RNA-EEE inner surface (Figure 3B).
The critical observation was that the RNA density in Cp183RNA-SSS and Cp183RNA-EEE adopts different ordered conformations. The strength and order of RNA density was observed in spite of the fact that the asymmetric RNA was subjected to icosahedral averaging (Figure 2G and H); if the RNA was not in part icosahedral, we would have expected a uniform shell of density. In Cp183RNA-SSS, RNA density was localized under twofold and fivefold vertices (Figure 2G); in Cp183RNA-EEE, the RNA density appeared as radially arrayed segments (Figure 2H). Radial density maps (Figure 3C and D) revealed similar core protein density distributions in Cp183RNA-SSS and Cp183RNA-EEE (at radii of 167, 153, 144 Å). However, the density shell at 113 Å showed that the RNA of Cp183RNA-SSS forms an icosahedral cage where fivefold pentamers are connected across twofolds; whereas in Cp183RNA-EEE, the RNA shell displayed a complicated mesh of density with the strongest density surrounding the fivefold vertices and comparatively weaker segments at the twofold vertices (Figure 3D).
A close comparison between Cp183RNA-SSS and Cp183RNA-EEE revealed the respective differences in the interactions between their CTDs and pgRNA (Figure 3A and B). In Cp183RNA-EEE, strong and continuous density originated from the A subunit and projected towards the fivefold axis and into the pgRNA density (Figure 3B). This stalactite-shaped density, also seen in the empty particle (compare Figure 2G to Figure 2H), is evident in the radial density map at radius of 126 Å (Figure 3D). Additional density, located under the CD dimer, connected down to the pgRNA layer at the quasi-threefold location. In contrast, the density connecting the inner capsid surface and the pgRNA in Cp183RNA-SSS was located directly under both AB and CD dimers. From the AB dimer, density projected straight into the pgRNA layer (Figure 3A and C); whereas in CD dimer, the density projected inward, obliquely toward the threefold axis and eventually immersed into pgRNA density (Figure 3C).
Difference maps (e.g. Cp183e-SSS less a molecular model of Cp149) revealed the overall CTD organization of empty HBV capsids in the unphosphorylated and phosphorylation-mimic states (Figure 4). Both maps were rendered at density levels needed to obtain the expected volume of the core protein (we note the signal was >1 σ). The locations of the CTDs within Cp183e-SSS (Figure 4A) and Cp183e-EEE (Figure 4B) shared a similar distribution, unsurprising as they erupt from the same regions of the contiguous capsid. However the organization and strength of the CTD density varied substantially. The CTD of the Cp183e-SSS formed five pillars of density surrounding the fivefold axis (Figure 4D), a density cluster under the quasi-threefold vertices (Figure 4C), and density that partially occluded the twofold opening.
In Cp183e-EEE the fivefold CTD density, extending from each A subunit, formed a large stalactite-like structure extending to lower radius (Figure 4F). This structural characteristic was also observed in the Cp183RNA-EEE, where the fivefold stalactite density impinged on the pgRNA layer (Figure 3B and D at 126 Å). The last visible residue in the atomic model (Protein Data Bank (PDB) entry 1QGT) was close to the CTD density in both difference maps (Figure 5). However, the mutation of just three residues resulted in the shift of the CTDs from a relatively disordered structure in Cp183-SSS to a quaternary structure in Cp183-EEE that is suggestive of a convergence of five α-helices (See supporting information, Figure S1), one from each fivefold-related subunit. By comparison, the equivalent density in Cp183e-SSS is neither as strong nor cohesive; in fact, the overall CTD density in Cp183e-SSS was weaker and more scattered than in Cp183e-EEE.
CTD density was not quasi-equivalent. Unlike the CTD network observed at the quasi-threefold position, no CTD density was found beneath the threefold vertices (or twofold/quasi-sixfold vertices) in either Cp183e-SSS and Cp183e-EEE (Figures 3C and D, 4C and E).
Difference maps of the empty capsids subtracted from the pgRNA-filled capsids show that pgRNA adopted dramatically different structures in unphosphorylated and phosphorylated capsids (Figure 6). Density assigned to pgRNA in the Cp183RNA-SSS resembled an icosahedral cage (Figure 6A), closely matching the DS rcDNA structure found in the native (presumably unphosphorylated) virion [34]. The RNA density under the fivefold vertices was connected along the icosahedral twofolds. A branch of the RNA density extended from the twofold edge and terminated near the center of the threefold axis. The structural similarity between the pgRNA observed here and the dsDNA in the native virion, along with the thickness of the RNA density suggested that the icosahedrally ordered pgRNA in Cp183RNA-SSS may be largely DS RNA (Figures S2 and S3).
In striking comparison, the pgRNA in Cp183RNA-EEE formed a complicated mesh-like density network (Figure 6B, see Figure S2 for additional radial density maps). The pgRNA in Cp183RNA-SSS appeared to be condensed at the twofold position with a diameter of ∼20 Å consistent with double stranded nucleic acid. pgRNA in Cp183RNA-EEE resembled a net stretched over a sphere, where each segment in the mesh was approximately 7–8 Å thick, about the diameter of single-stranded RNA. Other analyses of the data supported this view. In the circularly averaged image (Fig. 1D) we observed that the pgRNA was stronger than capsid density. In the central cross-section of the three-dimensional map we observed that the pgRNA density was composed of short segments of strong density (Figure 2H). Furthermore, the Fourier shell correlation (FSC) at the radii corresponding to the pgRNA shell (90–118 Å) indicated a resolution of 6 Å at the 0.5 cutoff (Figure S4), which is slightly better than the overall resolution estimated for the whole 3D model (7 Å at FSC 0.5 cutoff). These metrics indicated that the pgRNA within Cp183RNA-EEE had substantial structural order. We suggest that the conformational rearrangement of the pgRNA between expanded and condensed forms depends on Cp183 phosphorylation state.
In this study, we determined the 3-D structures of the CTD and pgRNA of in vitro assembled HBV particles using cryo-EM and 3-D image reconstruction. Based on cell culture studies [22], [25], [26], we used Cp183-EEE to mimic the effects of phosphorylation. Through difference map imaging, subtracting x-ray coordinates of an HBV capsid of C-terminally truncated capsid proteins [9] from selected Cp183 capsids, we found that the inclusion of the EEE mutation in the CTDs had profound effects on the CTD structure. The altered CTD organization resulted in an equally dramatic reorganization of packaged pgRNA.
The phosphorylation state of HBV is believed to change during the course of assembly and reverse transcription. The initial assembly reaction involves a phosphorylated form of the core protein [36], [37]. In a related hepadnavirus, duck hepatitis B virus, the immature phosphorylated capsid becomes hypophosphorylated as it matures [36], [37]. Dephosphorylation likely occurred at the point when the plus-stranded DNA was synthesized [38], [39]. The core protein in DNA-filled cores was unphosphorylated [36], [37].
Our 3-D reconstructions of empty Cp183e-SSS and Cp183e-EEE show that the structures of the HBV assembly domains (residues 1–149) were similar to existing crystal structures and followed T = 4 quasi-equivalence (Figure S5). However, the SSS and EEE CTDs had very different conformations. For both Cp183e-SSS and Cp183e-EEE, CTD density emerged from the capsid near the last visible residue in the atomic coordinates (Figures 4 and 5). The CTD density in Cp183e-EEE appeared to be more compact (Figure 4A and B). The five independent pillars surrounding the fivefold vertex of Cp183e-SSS may be the result of the electrostatic repulsion from the positively charged CTDs (Figure 5A and C). It was anticipated that the positively charged CTD in Cp183e-SSS would be mobile, resulting in weak, incomplete density. We were surprised to find that the three negative charges of the Cp183-EEE mutant resulted in much greater order (Figure 5B and C). The relatively strong density of the fivefold stalactites suggested formation of a tightly folded complex. Typically, compact folds are stabilized by a hydrophobic core, but in this case we suggest that the organization of these complexes is supported by salt bridges. This result leads us to speculate that regulatable salt bridges have a similar structural role in SR proteins, which also require phosphorylation for activity. No similar density formation was observed beneath the quasi-sixfold vertex. It is possible that the larger opening at the quasi-sixfold pore provides extra space to increase CTD mobility.
The distinct structures of the encapsidated pgRNA in Cp183RNA-SSS and Cp183RNA-EEE suggest a novel response by RNA to the phosphorylation state of hepadnavirus core protein (Figure 7). The pgRNA density in Cp183RNA-SSS shares a common organization with the nucleic acid density observed in rcDNA-filled particles (from transgenic mice and human sources) and virus-like particles containing RNA from an E. Coli expression system [8], [34]. These disparate particles both show an icosahedral cage of nucleic acid stretching from fivefold to fivefold. The structural similarity suggests that the nucleic acid was organized by the CTDs. Conversely, the RNA density in the Cp183RNA-EEE capsid was visibly stronger in both the 2-D averaged image (Figure 1D, inset) and the central section of the 3-D reconstruction (Figure 2H).
So, what is the basis of the difference in the strength of the RNA density? We cannot rule out the possibility that some empty capsids may be accidently selected in Cp183-SSS or Cp183-EEE reconstructions though these were not prevalent in sucrose gradient experiments (Figure S6). The pgRNA density in Cp183RNA-SSS is similar to previously published HBV structures with unphosphorylated core protein where the RNA density is always weaker than capsid density [8], [18], [34], even where the amount of encapsidated RNA is about the size of a genome [18]. In surface representations (using a density cutoff based on the capsid volume, Figure 6), the calculated volumes of the pgRNA in Cp183RNA-EEE and Cp183RNA-SSS were about the same, 1.1×106 Å3, a volume consistent with the 3.2 kb pgRNA assuming an average RNA density of 1.7 g•cm−3. This suggests both particles encapsidated the same amount of pgRNA. Indeed, sucrose gradient velocity sedimentation suggested that the majority of the Cp183RNA-SSS capsid contain one pgRNA (Figure S6). Based on charge, Cp183-EEE has less capacity for RNA binding than Cp183-SSS; electrophoretic mobility shift titrations of RNA by Cp183-EEE saturated only when there was sufficient protein for one RNA per capsid [15]. To test for differences in RNA order in our reconstructions, we examined the volume of the RNA density at different contour levels. We found that the relative pgRNA volume in Cp183RNA-SSS decreased faster than in Cp183RNA-EEE; the ratio of RNA volume to capsid volume is shown in Table S1. Thus, we suggest that the difference in the strength of the RNA density is due to the relative disorder of RNA in Cp183RNA-SSS.
The difference in RNA order appears to correlate with differences in structure. In the Cp183RNA-SSS structure, the surface shaded RNA density was thick enough to accommodate DS RNA. In Cp183RNA-EEE the narrow strands of density could only fit single-stranded RNA (Figures 6B, S2 and S3). We suggest that a single-stranded pgRNA structure is more favorable for reverse transcription. The ability of the CTD to affect RNA structure was consistent with the hypothesis that the core protein itself (via the CTDs) can act as an RNA chaperone [21], [40]. The correlation between dephosphorylation of the CTDs [36] and reverse transcription remains to be elucidated. However, the responsiveness of RNA to CTD phosphorylation (i.e. SSS versus EEE) state and the observed progressive changes in phosphorylation [21], [22], [25], [27], [36], [39] suggest that the pgRNA structures of Cp183RNA-SSS and Cp183RNA-EEE shown here are only two of many possible structures along a conformational continuum.
Thus, our data indicate that the CTDs change conformation in response to phosphorylation and transduce a conformational change in the packaged nucleic acid. We propose that these changes are linked to reverse transcription. A similar modulation on the nucleic acid structure by reversible phosphorylation has also been reported for histone H1 protein, which contains SPXX repeats (where X can be K or R) that are similar to the SPRRR motifs found in the HBV CTD [41], [42]. The phosphorylation states of the histone H1 protein seem to affect its secondary structure and are involved in both condensation and decondensation of the chromatin at different stages during the process of DNA replication [43]–[46]. While the Cp183RNA-EEE is a novel structure, the pgRNA in the Cp183-SSS capsid resembles previous unphosphorylated structures containing RNA and DNA. The conformational similarity between the pgRNA in Cp183RNA-SSS and previous rcDNA structures, as well as the dimensions of the pgRNA density, suggests that at least part of the single-stranded pgRNA is condensed into a duplex architecture. It is notable that the ability of a virus capsid to control the structure of packaged RNA has also been observed in Pariacoto virus and flock house virus, even with non-native RNA [47], [48]. Thus, our findings suggest that the conformation of the icosahedrally arrayed CTDs and the packaged nucleic acid represents a mobile compromise of electrostatics and the equilibrium and non-equilibrium thermodynamics that is vital to virus function.
Although the majority of the density corresponding to the CTD was found internally at the quasi-sixfold, threefold and fivefold vertices (Figure 4A and B), it has been long postulated that the CTD is partially exposed to the capsid surface for the purpose of signaling during viral replication [9], [28], [49]–[51]. Our results (here and previously [28]) suggest that the CTD may be accessible through the large quasi-sixfold pore (Figure 7). We observed CTD-attributed density passing through the quasi-sixfold pore in empty capsids (Figure 2A and B inserts, black arrows); similar density has not been observed in CTD-truncated or genome-filled HBV capsids. Indeed, probing empty Cp183e-SSS capsids with the CTD-specific SRPK results in the capsid decorated by SRPK at every quasi-sixfold vertex [28]. The failure of capsids filled with E. Coli RNA to bind to SRPK columns [28] indicates that CTDs are not readily accessible when associated with RNA.
Ning et al. suggested that single-stranded nucleic acid (either pgRNA or single-stranded DNA) in the immature capsids negatively regulates HBV core trafficking by preventing CTD exposure [52]. They observed that secreted enveloped particles contained either empty capsids (over 90%) or DS rcDNA-filled cores. Thus, the blocking hypothesis predicts that mature DS rcDNA-containing capsid shares structural characteristics with the empty capsid [52]. This hypothesis is supported by EM reconstructions. First, in empty capsids, the CTDs were partially exposed through the quasi-sixfold pore (Figure 2E and F). Second, in the presence of the pgRNA, CTDs strongly interacted with the genome; the single-stranded genome obstructed exposure of CTD-associated signals [28]. Third, the partially DS rcDNA in the mature core is expected to be much less flexible and may not be able to interact with all of the CTDs, which suggests that a fraction of the CTDs may regain their mobility in mature DS rcDNA cores [53]. Thus, nucleic acid-regulated exposure of CTDs through quasi-sixfold pores is a likely mechanism for signaling by the HBV core.
In summary, we report sub-nanometer resolution structures of the full-length empty and pgRNA-filled HBV capsids assembled from unphosphorylated and phosphorylation-mimic core proteins. The structures show that the configurations of the RNA-binding CTDs and pgRNA respond to changes in CTD phosphorylation. Our data indicate that phosphorylation affects the structure of CTDs and the CTDs affect RNA organization. Such functional correlation of the CTD implies that the HBV core has nucleic acid chaperone activity. We further provide direct evidence of partially exposed CTDs on the capsid exterior, suggesting how they may play a role in intracellular trafficking and secretion of HBV cores. Even though we cannot draw a complete structural description of HBV maturation yet, the substantial changes of the CTD and pgRNA we observed in this study indicated that HBV is a highly dynamic molecular machine. Nevertheless, in the authentic capsid the viral RT, and possibly host factors, take critical parts in pgRNA packaging; their impact on the structure of encapsidated pgRNA is currently under investigation.
The pgRNA production, the plasmids coded for HBV Cp183-SSS and Cp183-EEE, and the capsid purification were described previously [15]. Capsids stored at −80°C were disassembled by dialysis at 4°C in the disassembly buffers (1.5 M guanidine HCl, 0.5 M LiCl, 50 mM HEPES at pH 7.5, 10 mM DTT for Cp183-SSS, and 1.5 M guanidine HCl, 1.5 M LiCl, 50 mM Tris at pH 9.5, 10 mM DTT for Cp183-EEE). The encapsidated heterogeneous RNA packaged from E. Coli cells was precipitated by a spin of 20,000× g for 15 min at 4°C. Protein dimers were recovered from the supernatant and purified by size exclusion chromatography using an analytical grade Superose 6 column (GE Lifesciences) equilibrated in disassembly buffer. Fractions containing core protein were identified by SDS-PAGE. Cp183-SSS and Cp183-EEE dimers were either used for the reassembly experiments immediately or stored at 4°C for a short period. Formation of the empty capsids (Cp183e-SSS and Cp183e-EEE) was approached by dialyzing the purified dimers in the reassembly buffer (250 mM NaCl, 50 mM HEPES at pH 7.5, 2 mM DTT for Cp183-SSS and 250 mM NaCl, 50 mM Tris pH 7.4, 2 mM DTT for Cp183-EEE). The pgRNA-filled capsids (Cp183RNA-SSS and Cp183RNA-EEE) were prepared by reassembling the purified dimers with in vitro transcribed HBV pgRNA at a molar ratio of protein dimer to RNA polymer = 120∶1 in the reassembly buffer (150 mM NaCl, 50 mM HEPES at pH 7.5, 2 mM DTT for Cp183-SSS and 150 mM NaCl, 50 mM Tris at pH 7.4, 2 mM DTT for Cp183-EEE) overnight. Samples for cryo-EM were further concentrated by Amicon Ultra centrifugal filter units (Millipore, MA). The quality and the concentration of the sample were routinely checked by negative stained EM using 2% uranyl acetate.
The sample preparation and cryo-EM operation were followed well established procedures described previously [28]. Briefly, a drop of 3.5 µl sample solution was applied on a glow-discharged Quantifoil holey-carbon grid (R2/2), blotted with filter paper from both sides for 4 s to produce a thin layer of specimen solution across the holes. The grids were quickly plunged into liquid ethane bath cooled by liquid nitrogen in a cryo-container. All processes described above were performed by a FEI Vitrobot. The vitrified specimen on the grid was then transferred to a Gatan 626DH cryo-holder and kept at the low temperature environment (<−176°C) for the subsequent processing. The cryo-holder was then rapidly inserted into a JEOL-3200FS EM (JEOL Ltd., Japan) operated at 300 kV with an in-column energy filter using a 20-eV slit except for Cp183RNA-SSS. Digitized images were recorded under the low-dose condition (<20 e−/Å2) on an UltraScan 4000 4k×4k CCD camera (Gatan Inc., Oxford, UK) at a nominal magnification of 80,000× (equal to 0.1484 nm at the specimen space) for Cp183e-SSS, Cp183e-EEE, and Cp183RNA-EEE and 40,000× (equal to 0.294 nm at the specimen space) for Cp183RNA-SSS. Images were taken at multiple defocuses to compensate the effect from the contrast transfer function of the EM.
Selected images which fulfilled the criteria of the suitable particle concentration, optimal ice thickness and minimal specimen drift, were used for analysis. Particle images were semi-automatically boxed using program e2boxer.py from EMAN2 software [54]. The defocus level of each micrograph was estimated using RobEM (http://cryoem.ucsd.edu/programs-old.shtm) and only the phase reversal was corrected in the subsequent data processing. The initial starting model for each specimen was reconstructed by an ab initio random model method [55]. Origin and orientation were determined and refined using AUTO3DEM [56]. The refinement processed iteratively with a successively improved 3-D model from each refinement until a stable 3-D reconstruction had been achieved. The resolution of each 3-D reconstruction was estimated by Fourier shell correlation using a threshold value of 0.5. The final 3-D maps of Cp183e-SSS, Cp183e-EEE, Cp183RNA-SSS and Cp183RNA-EEE were reached at the resolutions of 5.5 Å, 5.8 Å, 8.0 Å, and 7.0 Å, respectively (Figure S7). The 3-D reconstructions were visualized using RobEM and Chimera [57].
A CTD-truncated HBV capsid, Cp149model, was calculated from the atomic coordinates (Protein Data Bank entry: 1QGT) using e2pdb2mrc.py and low-pass filtered to 10 Å. The difference map of CTD was calculated by subtracting Cp149model from Cp183e-SSS or Cp183e-EEE; the difference map of pgRNA was calculated by subtracting Cp183e-SSS from Cp183RNA-SSS or Cp183e-EEE from Cp183RNA-EEE. Prior to the subtraction, all maps were normalized based on their average density and the standard deviation because the map calculated from the crystal structure generally has different density values than the cryo-EM reconstruction. The sizes of the maps were scaled, and the difference observed here was less than 1% in all cases. The region corresponding to the capsid shell (at the radii between 125–160 Å) was then used to calibrate the density. The resulting difference map was rendered at the contour level that is equivalent to that component rendered at the estimated full mass of the parental 3-D model.
The cryo-EM density maps have been deposited to EMDataBank.org. The EMDataBank accession number for Cp183e-SSS, Cp183e-EEE, Cp183RNA-SSS, and Cp183RNA-EEE are EMD-2057, EMD-2058, EMD-2059, and EMD-2060, respectively.
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10.1371/journal.ppat.1000933 | Effect of Neuraminidase Inhibitor–Resistant Mutations on Pathogenicity of Clade 2.2 A/Turkey/15/06 (H5N1) Influenza Virus in Ferrets | The acquisition of neuraminidase (NA) inhibitor resistance by H5N1 influenza viruses has serious clinical implications, as this class of drugs can be an essential component of pandemic control measures. The continuous evolution of the highly pathogenic H5N1 influenza viruses results in the emergence of natural NA gene variations whose impact on viral fitness and NA inhibitor susceptibility are poorly defined. We generated seven genetically stable recombinant clade 2.2 A/Turkey/15/06-like (H5N1) influenza viruses carrying NA mutations located either in the framework residues (E119A, H274Y, N294S) or in close proximity to the NA enzyme active site (V116A, I117V, K150N, Y252H). NA enzyme inhibition assays showed that NA mutations at positions 116, 117, 274, and 294 reduced susceptibility to oseltamivir carboxylate (IC50s increased 5- to 940-fold). Importantly, the E119A NA mutation (previously reported to confer resistance in the N2 NA subtype) was stable in the clade 2.2 H5N1 virus background and induced cross-resistance to oseltamivir carboxylate and zanamivir. We demonstrated that Y252H NA mutation contributed for decreased susceptibility of clade 2.2 H5N1 viruses to oseltamivir carboxylate as compared to clade 1 viruses. The enzyme kinetic parameters (Vmax, Km and Ki) of the avian-like N1 NA glycoproteins were highly consistent with their IC50 values. None of the recombinant H5N1 viruses had attenuated virulence in ferrets inoculated with 106 EID50 dose. Most infected ferrets showed mild clinical disease signs that differed in duration. However, H5N1 viruses carrying the E119A or the N294S NA mutation were lethal to 1 of 3 inoculated animals and were associated with significantly higher virus titers (P<0.01) and inflammation in the lungs compared to the wild-type virus. Our results suggest that highly pathogenic H5N1 variants carrying mutations within the NA active site that decrease susceptibility to NA inhibitors may possess increased virulence in mammalian hosts compared to drug-sensitive viruses. There is a need for novel anti-influenza drugs that target different virus/host factors and can limit the emergence of resistance.
| Highly pathogenic avian H5N1 influenza viruses remain a potential pandemic threat. If vaccination is not available in the event of a pandemic, antiviral drugs such as neuraminidase (NA) inhibitors (oseltamivir and zanamivir) will be crucial for disease control. However, the emergence of NA inhibitor–resistant virus mutations can significantly limit the effectiveness of treatment. We used reverse genetics techniques to study the impact of specific point mutations in the NA gene on the pathogenicity of clade 2.2 H5N1 influenza viruses in a ferret animal model. Seven recombinant H5N1 viruses (containing the mutations V116A, I117V, E119A, K150N, Y252H, H274Y, and N294S) were viable and genetically stable in MDCK cells. Three of the NA mutations were of particular significance: (1) E119A conferred resistance to both oseltamivir carboxylate and zanamivir and increased virus virulence in ferrets; (2) H274Y conferred a high level of resistance to oseltamivir carboxylate; and (3) N294S conferred moderate level of resistance to oseltamivir carboxylate and increased virus virulence in ferrets. Strategies to limit the emergence of drug resistance are urgently needed.
| The highly pathogenic avian H5N1 influenza viruses remain of concern as a pandemic threat. Of the 486 cases of human H5N1 infection confirmed between March 2003 and March 2010, ∼60% were lethal [1]. The H5N1 influenza viruses have continuously evolved and increased in genetic diversity: to date, 10 H5 hemagglutinin (HA) clades have been distinguished, some of which are geographically widespread [2]. The most epidemiologically significant clades are 1, 2 (subclades 2.1, 2.2, 2.3), and 7 [3].
Although vaccination remains the primary method of influenza control, effective antiviral drugs are the best option when vaccine availability or efficacy is limited [4]. The anti-influenza drugs, neuraminidase (NA) inhibitors (oseltamivir and zanamivir) [5], are sialic acid analogues that selectively target the NA enzyme of influenza A and B viruses [6], [7]. Both are safe and effective for the prophylaxis and treatment of seasonal H1N1 and H3N2 influenza [5]. There is limited information about the clinical use of NA inhibitors against highly pathogenic H5N1 influenza viruses; however, studies in animal models suggest their efficacy [8]–[10]. The orally administered NA inhibitor oseltamivir was chosen for stockpiling for the national pandemic preparedness plan.
In the event of a pandemic, the effectiveness of therapeutic and prophylactic oseltamivir will depend not only on the correct dosage and duration of treatment but also on the susceptibility of the targeted virus strain. Our group [9], [10] and others [11] have shown that the NA inhibitor susceptibility of H5N1 viruses of clades 1 and 2.2 may differ. We found an 18-fold difference between the in vitro oseltamivir carboxylate (the active methabolite of oseltamivir) susceptibility of A/Turkey/15/06 (H5N1) virus (clade 2.2) and A/Vietnam/1203/04 (H5N1) virus (clade 1) and different treatment efficacy in mice inoculated with these viruses (20% vs. 80% survival on the same regimen) [8], [9]. Recent data showed that previously undescribed drift NA mutations may also decrease the in vitro susceptibility of H5N1 influenza viruses to oseltamivir carboxylate [10]–[13], possibly reducing the efficacy of the drug in vivo. These findings demonstrate the need for systematic evaluation of the impact of natural NA gene variations on anti-NA drug susceptibility and on other biological properties of H5N1 influenza viruses.
Oseltamivir and zanamivir were designed to bind only to highly conserved residues within the active site of influenza A and B virus NA protein [6]. This active site comprises 8 functional residues (R118, D151, R152, R224, E276, R292, R371, and Y406; N2 numbering here and throughout) and 11 framework residues (E119, R156, W178, S179, D198, I222, E227, H274, E277, N294, and E425) [14]. Despite a high degree of conservation of these residues, the NA substitutions identified in NA inhibitor-resistant influenza viruses isolated both in vitro and clinically tend to be NA subtype–specific: E119A/G/D/V, R292K, and N294S in the N2 and N9 subtypes and H274Y and N294S in the N1 subtype [14], [15]. Broad screening of the in vitro susceptibility of seasonal and H5N1 influenza viruses to NA inhibitors together with recent crystal structure data and conformational studies of influenza N1 enzyme identified several additional conserved or semiconserved NA residues (e.g., V116, I117, Q136, K150, D151, and I222) that may also confer resistance [12], [16]–[19]. Importantly, the exact mechanism by which these changes affect susceptibility to a particular NA inhibitor are not yet understood.
Early studies suggested that seasonal influenza viruses resistant to NA inhibitors may be less infective and transmissible in ferrets than their wild-type counterparts [20]–[22]. The two available reports on the fitness of highly pathogenic oseltamivir-resistant H5N1 viruses of clade 1 offered different findings [23], [24]. In ferrets, an oseltamivir-resistant H5N1 virus carrying an H274Y NA mutation replicated approximately 10 times less efficiently in the upper respiratory tract than the wild-type virus [23]. In contrast, neither the H274Y nor the N294S NA mutation compromised the lethality or virulence of clade 1 A/Vietnam/1203/04 (H5N1) virus in mice [24]. This difference in fitness may reflect a difference in virulence, although the question remains to be answered.
In the homogeneous clade 2.2 A/Turkey/15/06-like (H5N1) genetic background, we studied the role of single point NA mutations near or within the enzyme active site on NA inhibitor susceptibility, NA enzyme kinetics, viability, genetic stability, and pathogenesis in ferrets. Seven substitutions were stable in the N1 NA protein and five reduced virus susceptibility to oseltamivir carboxylate or to both NA inhibitors. Infection of ferrets with the recombinant H5N1 viruses caused mild disease of various duration, although NA inhibitor-resistant variants with the E119A and N294S mutations were more virulent than the wild-type virus.
We used the eight-plasmid reverse genetics technique to generate 11 recombinant A/Turkey/15/06-like (H5N1) viruses carrying different NA mutations (Figure 1), that were proposed to affect virus susceptibility to NA inhibitors [12], [16]–[19]. Two NA mutations (H274Y and N294S) were selected based on case reports on the isolation of oseltamivir-resistant variants in H5N1 virus infected patients after treatment with oseltamivir [23], [25] or before administration of the drug [26]. Four NA residues (R111, S247, Y252, and D283) were chosen based on the differences of amino acid alignments of the NA active sites of A/Vietnam/1203/04 (H5N1) virus (clade 1) and A/Turkey/15/06 (H5N1) virus (clade 2.2) (data not shown). Five NA residues (V116, I117, E119, K150, and I222) were selected based on the results of NA enzyme inhibition assays that substitutions at these positions may be linked to reduced drug-susceptibility in avian and human viruses carrying N1 NA [19]. The viability of the recombinant viruses was evaluated by rescue from transfected 293T cells. Viruses with the R111K, I222L, S247N, and D283N NA amino acid substitutions could not be rescued in three independent experiments, clearly indicating that these mutations are not stably maintained in the clade 2.2 A/Turkey/15/06-virus background.
The recombinant wild-type (WT) H5N1 influenza virus and seven mutants (V116A, I117V, E119A, K150N, Y252H, H274Y, and N294S) were successfully rescued. Direct sequencing of their HA and NA genes revealed that all mutations were correctly incorporated and no additional changes were present in either gene (data not shown). All of the viruses grew to comparable titers and formed homogeneous plaques in MDCK cells (diameter, 0.3 to 1.7 mm), although viruses with the Y252H, H274Y, and N294S NA mutations formed significantly smaller plaques than did the WT virus (P<0.01) (Table 1).
To assay the growth and genetic stability of the recombinant viruses in vitro, we serially passaged each virus three times in MDCK cells (Table 1). Virus yields before passaging and after the third passage did not differ significantly. All mutants were able to maintain their plaque phenotype with the exception of those carrying mutations at positions 252 and 274: homogenous large plaques were observed for both Y252H and H274Y viruses after the third passage (Table 1). Sequence analysis after the third passage showed that neither WT virus nor viruses carrying NA mutations at residues 116, 117, 119, 150, 252, 274, and 294 had acquired any additional amino acid changes in their HA or NA genes (data not shown). Thus, our results suggested that all introduced NA mutations remained genetically stable in A/Turkey/15/06 (H5N1) virus background, however, the possibility that some mutations in polymerase or other genes might occur during in vitro passaging cannot be excluded.
An NA enzyme inhibition assay, a reliable phenotypic assay used to characterize the NA inhibitor susceptibility of influenza viruses, showed altered susceptibility in all recombinant H5N1 influenza viruses except that with K150N substitution. The susceptibility of V116A, I117V, and N294S viruses to oseltamivir carboxylate or zanamivir was moderately reduced (mean IC50 increase, 5-63–fold and 3-33–fold, respectively) as compared to that of WT virus. Virus carrying the E119A mutation was moderately more resistant to oseltamivir carboxylate (35-fold increase in mean IC50 value) and markedly more resistant to zanamivir (>1200-fold increase in mean IC50 value) than was WT virus (Table 2). The H274Y mutant was much more resistant to oseltamivir carboxylate (mean IC50 increase, >900-fold) and slightly less susceptible to zanamivir (mean IC50 increase, 3-fold) than WT virus. In contrast, the Y252H NA change conferred increased susceptibility to oseltamivir carboxylate (mean IC50 decrease, 22-fold) (Table 2).
To determine the effect of the NA mutations on viral functional properties, we characterized their kinetic NA enzymatic parameters, including their Michaelis-Menten constants (Km), which reflect NA affinity for the MUNANA substrate, their inhibition constants (Ki) for oseltamivir carboxylate and zanamivir, and their relative NA enzymatic activity (Vmax) (Table 2, Figure 2). NA proteins harboring the V116A, I117V, and E119A mutations exhibited significantly lower affinity for the substrate (mean Km decrease, 4–8–fold) than did other NAs studied. The Ki values of all NA glycoproteins were consistent with their IC50 values (Table 2), suggesting that the reduced susceptibility to both NA inhibitors could be caused by decreased affinity of their mutant NAs to the antiviral drugs.
We also determined NA enzymatic Vmax values for each of the recombinant H5N1 viruses (Figure 2) and calculated their Vmax ratios in relation to that of WT NA glycoprotein (Table 2). NA proteins with mutations at residues 117 and 252 had significantly higher enzymatic activity than WT NA (P<0.01). The other five NA mutations studied significantly reduced the NA activity of A/Turkey/15/06 (H5N1) influenza virus (all Vmax ratios, 0.1) (Table 2, Figure 2). Thus, the eight recombinant H5N1 viruses differed in both their NA enzyme kinetics and their in vitro susceptibility to oseltamivir carboxylate and zanamivir.
Limited information is available on the pathogenicity of H5N1 influenza viruses carrying various drug-resistant NA mutations in ferrets [23], although it can be an indicator of pathogenic potential of these viruses in humans. We inoculated ferrets with 106 EID50 of each virus and observed four different patterns of clinical outcome (Table 3, Figure 3). Recombinant WT virus caused mild clinical disease signs of moderate duration, similar to those caused by wild-type A/Turkey/15/06 (H5N1) strain [10].
Mild but prolonged illness (duration ∼ 10 days, relative inactivity index, RII≈0.01) was seen in ferrets infected with mutant V116A and K150N viruses (Table 3). Despite their weight gain throughout the infection, the ferrets showed a mean peak temperature increase of ∼ 1.0–1.3°C on day 8 post-inoculation (p.i.) (Figure 3A). On days 4, 6, and 8 p.i. the peak nasal inflammatory cell counts in nasal washes was significantly higher in groups of ferrets inoculated with V116A and K150N recombinants than in those inoculated with WT virus (P<0.01). Virus shedding started at 1 day p.i. for mutant viruses and continued until 6 days p.i. Interestingly, on day 1 p.i. virus titers in nasal washes were significantly lower for animals infected with recombinant V116A and K150N viruses than those in animals inoculated with WT virus (P<0.05), but remained significantly higher on day 6 p.i. (P<0.05) (Figure 3A).
Mild, short illness (duration ∼ 5–6 days) was observed in ferrets inoculated with H5N1 viruses carrying I117V and Y252H NA mutations: only slight temperature elevation (mean peak increase, 0.5 to 1.2° C), mild clinical features (RII≈0.05) (Table 3) and minor weight changes were detected (Figure 3B). Cell counts remained at the same level in nasal washes of animals infected with either WT or I117V viruses, and they returned to normal levels in ferrets inoculated with Y252H at day 6 p.i. Comparison of the protein concentrations in the nasal washes showed no significant differences among the I117V, Y252H and WT viruses, suggesting that upper respiratory tract inflammation in these groups was comparable. Recombinant I117V and Y252H viruses replicated less efficiently in the upper respiratory tract than did the WT strain (P<0.05) (Figure 3B).
Virus with the H274Y NA mutation exhibited virulence comparable to that of recombinant WT virus (Table 3, Figure 3C). In contrast, inoculation of ferrets with recombinant E119A and N294S viruses caused markedly different results. Animals showed more pronounced clinical signs of disease, including a slightly greater RII (≈0.3–0.4), and one of three ferrets in each group was euthanized before the end of the experiment due to severe lethargy or excessive weight loss (Table 3). The body temperature elevation and nasal-wash inflammatory cell count were significantly higher in animals inoculated with the E119A mutant than in those inoculated with recombinant WT virus (P<0.01) (Figure 3C). The recovery of ferrets inoculated with the N294S NA mutant was delayed, and they regained no weight during the observation period.
To characterize in more details the disease caused by recombinant E119A and N294S viruses, we increased the number of animals from three to five in each of the pathogenicity groups and repeated inoculation of ferrets with 106 EID50 of the WT, E119A, and N294S viruses (Figure S1). Importantly, all clinical symptoms of disease were similar to those observed in the first experiment (Figure 3C), including a slight increase in RII for the E119A and N294S (0.3 and 0.4, respectively) and substantial weight loss (∼15%) of N294S-infected ferrets. We observed that nasal-wash inflammatory cell counts were significantly higher in animals infected with mutant viruses than in those infected with WT (P<0.01) (Figure S1). Thus, in two independent experiments, the infection of ferrets with viruses carrying E119A or N294S NA mutations consistently caused more severe influenza disease than WT virus. Taken together, our results showed that changes in the framework residues of the NA enzyme active site can markedly affect the pathogenesis of clade 2.2 A/Turkey/15/06 (H5N1) influenza virus in ferrets.
To identify characteristics that may explain the severity of infection caused by E119A and N294S H5N1 mutants compared to WT stain, we evaluated virus replication and tissue tropism in the lungs, nasal turbinate, trachea, spleen, liver, and small intestine of two inoculated ferrets per virus on day 4 p.i. All three viruses were detected in the lungs, nasal turbinate, and trachea; however, only the E119A and N294S mutants were detected in the liver (Figure 4A). Consistent with the more pronounced clinical signs of disease, the E119A and N294S were detected at significantly higher titers in three out of four lobes of the lungs (∼4.9–6.7 log10EID50/gram tissue) and liver (∼1.7–2.9 log10EID50/gram tissue) (P<0.01). Of the three H5N1 viruses, the N294S recombinant yielded significantly higher virus titers in the trachea (∼6.2 log10EID50/gram tissue versus 4.7 log10EID50/gram tissue, P<0.01) (Figure 4A).
We further histologically examined the tissues of inoculated ferrets to investigate the lesions associated with virus replication in infected organs and to obtain more information about the differences in virulence between recombinant WT, E119A, and N294S viruses. Histological changes were detected only in the lungs of infected animals. Bronchopneumonia and bronchiolitis with epithelial necrosis and/or regeneration characterized by bronchiole hypertrophy and hyperplasia were observed in all ferrets on day 4 p.i. (Figure 4B). However, the degree of severity and the number of lung lobes showing pneumonic lesions differed between the three groups. We determined that only two lung lobes of both ferrets inoculated with WT virus had foci of bronchopneumonia. However, the ferrets inoculated with N294S recombinant had bronchopneumonia in ∼ 50% of two lung lobes and in >90% of the other two lobes. The E119A-infected animals revealed multifocal bronchopneumonia in >90% of all four lung lobes (Figure 4B).
Differences in the type and extent of the alveolar pathology were also apparent on histopathologic examination. Of the three H5N1 viruses, the E119A mutant caused very severe and extensive alveolitis with necrosis of the alveolar pneumocytes and interstitial septal walls resulting in the loss of the alveolar architecture and leading to large coalescing spaces of edema fluid, fibrin, inflammatory and hemorrhagic red blood cells (Figure 4B). In the N294S-infected ferrets, alveolitis was characterized by a centrifugal progression of severity from the peribronchiole alveoli to the subpleural alveoli with pneumocyte hyperplasia and extensive infiltrates of inflammatory cells obscuring the lace-like alveolar pattern. In contrast, in the WT group, the peribronchiole and peripheral alveoli had only mild to moderate inflammatory cell infiltrates and a lace-like pattern with a slight thickening of the alveolar interstitial septa (Figure 4B).
Additionally, to assay the genetic stability of the recombinant viruses in vivo, we extracted RNA directly from nasal wash samples obtained on day 6 p.i. and sequenced the NA and HA genes of the dominant virus populations. No amino acid substitutions were identified in the NA gene of viruses isolated from ferrets inoculated with the WT, E119A, or N294S recombinants. A single F291L mutation in the HA gene was identified in 1 of 3 ferrets inoculated with either E119A or N294S mutants.
Taken together, our results indicated that the introduced E119A and N294S NA mutations were stably maintained in A/Turkey/15/06 (H5N1) virus background in ferrets. Further, increased virulence and severity of disease caused by H5N1 viruses carrying E119A and N294S NA changes were associated with higher virus titers and more pronounced local inflammatory response in the lungs compared to WT virus.
Oseltamivir-resistant H1N1 influenza viruses emerged recently and became predominant during the 2007–2008 season in the absence of drug-selection pressure [27], [28]; however, the molecular mechanisms and viral characteristics underlying this phenomenon are unknown. The NA inhibitors are an important component of influenza pandemic preparedness. In the case of H5N1 influenza viruses, genetic diversity and continuous evolution [1], [2] are known to contribute to reduced susceptibility to NA inhibitors and thus to reduced drug efficacy. In the present study, we introduced single point NA mutations into the A/Turkey/15/06 (H5N1)-virus genetic background at framework and functional NA residues to assess their effect on the NA-inhibitor resistance phenotype and on their pathogenicity in a ferret model. Of 11 NA mutations studied, seven located within (E119A, H274Y, and N294S) or near (V116A, I117V, K150N, and Y252H) the enzyme active site were stably maintained and grew to titers comparable to WT virus in MDCK cells. Five of these 7 NA mutations (all but K150N and Y252H) reduced H5N1 virus susceptibility either to oseltamivir carboxylate or to both NA inhibitors.
Our most important finding is that the E119A NA substitution in the clade 2.2 A/Turkey/15/06 (H5N1)-virus background is viable and genetically stable and confers cross-resistance to oseltamivir carboxylate and zanamivir. Structural analysis showed that the glutamate at 119 is one of two conserved amino acid residues that interact with oseltamivir’s carboxylate group to allow a strong, specific bond between the enzyme active site and the inhibitor [29]. The amino acid alanine has shorter side chains than does glutamate, a difference that may impede this interaction. The resistance of the E119A NA mutant to both zanamivir and oseltamivir carboxylate suggests that they interact similarly with the conserved framework residues of viral NA [29]. E119G/A/D/V NA mutations are commonly reported to be associated with NA inhibitor resistance and were identified in influenza viruses of the N2 NA subtype [14], [15]. Further, the E119A NA substitution has been selected in several strains of influenza A (H4N2) and B viruses after in vitro passages in the presence of zanamivir and has resulted in reduced NA activity [30], [31]. In a previous study, the E119A mutant in an A/WSN/33 (H1N1)-virus background could not be rescued [32], suggesting that this mutation may impede the growth of H1N1 viruses more than that of H4N2 viruses. Moreover, the E119G NA mutation significantly compromised viral growth and was genetically unstable in a clade 1 A/Vietnam/1203/04 (H5N1)-virus background [24]. In contrast, we recently found that in mice, E119A mutation is stably maintained in the NA of clade 2 H5N1 virus during oseltamivir therapy and is associated with resistance to both NA inhibitors [13]. Our finding that E119A reduced the NA activity of H5N1 virus 10-fold without compromising viral yield in MDCK cells suggests that these changes in NA activity do not compromise the infectivity of these viruses due to their high replication ability.
In the present study, V116A and I117V mutants showed a low (∼6-fold greater than WT) level of resistance to oseltamivir carboxylate. This finding is consistent with those of Hurt et al [12], who indentified two H5N1 influenza isolates (A/Chicken/Indonesia/Wates/77/05 and A/Chicken/Vietnam/486A/04) that carried these NA mutations and had reduced NA inhibitor susceptibility. Conversely, A/WSN/33 (H1N1) virus with the I117V change was susceptible to both NA inhibitors [33]. This discrepancy in observed phenotype may be explained by usage of different N1 NA proteins of H1N1 and H5N1 viruses and/or to specific changes in different N1 antigens. Our study is the first to our knowledge to fully elucidate the role of the V116A and I117V NA amino acid substitutions in NA inhibitor resistance in a genetically homogeneous H5N1-virus background in the absence of concomitant HA and/or NA mutations. Importantly, residues 116 and 117 are fully conserved in group 1 NA glycoproteins (N1, N4, N5, and N8 [29]), and their location adjacent to R118 may affect one of the three arginine residues that bind the carboxylate of the substrate sialic acid with further effect on virus susceptibility to the anti-NA drugs [29].
We also confirmed previous findings that the presence of histidine at position 252 in recent H5N1 isolates is associated with increased affinity of their NAs for oseltamivir carboxylate [11], [18], [34]. In clade 1 NAs, this residue is normally H252, but in clade 2 NAs it is consistently Y252. Because this mutation increased the binding of A/Turkey/15/06 (H5N1) virus to oseltamivir, we speculate that genetic variation in highly pathogenic clade 2 H5N1 viruses in the absence of drug-selective pressure may reduce oseltamivir susceptibility in vitro (15- to 30-fold differences in IC50 values) [11] and in vivo [9].
We studied the pathogenicity and virulence of the NA-mutant H5N1 influenza viruses in ferrets, an acceptable animal model to evaluate influenza virus disease manifestations [35]. The inefficient transmissibility of H5N1 influenza viruses in these mammals [36] (as in humans) restricted our studies to evaluation of pathogenicity. Importantly, recombinant H5N1 viruses carrying framework NA mutations (E119A and N294S) were more virulent and were associated with significantly higher virus titers in the lungs and liver than WT virus (P<0.01). The molecular basis of this finding is unknown. However, the possibility that after exposure to one NA inhibitor the chance exists that occurring drug-resistant H5N1 mutant could be cross resistant to both antiviral drugs and will be more virulent that the WT virus (like E119A H5N1 recombinant virus) should prompt to re-assess the suitability of single-drug usage. None of the NA mutations studied resulted in decreased viral virulence in the ferret animal model, consistent with our group’s previous finding that NA inhibitor-resistant mutations did not impair the virulence of clade 1 A/Vietnam/1203/04 (H5N1) virus in mice [24]. Here we used A/Turkey/15/06 (H5N1) virus belonging to clade 2.2, whose virulence is lower than that of the A/Vietnam/1203/04 (H5N1) strain [10]. Its NA activity is lower as well but is higher than that of current seasonal human-like N1 NAs even in the presence of drug-resistant NA mutations (data not shown). Therefore, taken together, our results showed that the virulence of H5N1 influenza viruses does not seem to be decreased by the NA-inhibitor resistant mutations studied here. We did not observe a consistent pattern in the effect of enzyme kinetics (Vmax and Km) on virulence in ferrets. None of the recombinant mutants exhibited an increased affinity for the substrate and a higher activity compared to WT NA, which could possibly indicate their overall better fitness. However, recombinant H5N1 viruses carrying I117V and Y252H NA mutations possessed the highest relative NA activity, but ferrets infected with these viruses experienced a mild, brief illness. Additional studies are needed to identify the impact of NA activity and affinity on the duration of influenza disease in vivo.
Our data demonstrate the significance of continued characterization of all H5N1 isolates for susceptibility to NA inhibitors in order to identify novel NA markers of altered susceptibility. We believe that this knowledge is essential for planning appropriate management strategies for a future pandemic. It is noteworthy that alignment of the NA genes of currently circulating highly pathogenic H5N1 influenza A viruses identified the NA mutations studied here in ∼0.1%–1.4% of isolates (namely, I117V was found in 1.4% and V116A and I119A were found in 0.1% isolates), raising concern about the drug sensitivity of a possible pandemic strain. Future studies should focus on the establishment of novel antiviral strategies to minimize the emergence of resistance.
All animal experiments with recombinant H5N1 influenza viruses were performed in biosafety level 3+ facilities at St. Jude Children’s Research Hospital (St. Jude; Memphis, TN, USA). All animal studies were approved by the St. Jude Children’s Research Hospital Animal Care and Use Committee and were conducted according to applicable laws and guidelines.
Madin-Darby canine kidney (MDCK) and human embryonic kidney (293T) cells were obtained from the American Type Culture Collection and maintained as previously described [24].
Eight plasmids were constructed from the DNA sequences of the 8 gene segments of wild-type A/Turkey/15/06 (H5N1) virus for the reverse-genetics generation of recombinant wild-type virus. Recombinant virus was generated by DNA transfection of 293T cells [37], and the point mutations (Table 1, Figure 1) were inserted into the NA gene of wild-type virus by using a Quickchange site-directed mutagenesis kit (Stratagene) [24]. Stock viruses were prepared in MDCK cells at 37°C for 72 h and their entire HA and NA genes were sequenced to verify the presence of the mutations. The recombinant viruses were designated according to their NA mutations (Table 1, Figure 1). All experimental work with the H5N1 recombinant viruses was performed in a biosafety level 3+ laboratory approved for use by the U.S. Department of Agriculture and the U.S. Centers for Disease Control and Prevention.
The NA inhibitors oseltamivir carboxylate (oseltamivir) ([3R,4R,5S]-4-acetamido-5-amino-3-[1-ethylpropoxy]-1-cyclohexene-1-carboxylic acid) and zanamivir (4-guanidino-Neu5Ac2en) were provided by Hoffmann-La Roche, Ltd.
The genetic stability of the viruses was monitored by sequencing of the HA and NA genes after transfection and after three passages in MDCK cells at a MOI of 0.001 PFU/ml. If different subpopulations were identified, those viruses were considered unstable.
The infectivity of recombinant H5N1 viruses was determined in MDCK cells by plaque assay and expressed as log10PFU/ml [9]. Briefly, confluent MDCK cells were incubated at 37°C for 1 h with 10-fold serial dilutions of virus. The cells were then washed and overlaid with minimal essential medium containing 0.3% bovine serum albumin and 0.9% Bacto agar and incubated at 37°C for 72 h. The plaques were stained with 0.1% crystal violet solution containing 10% formaldehyde, and virus yield was determined. Plaque diameter of any 10 plaques was measured by the Finescale® comparator (Los Angeles, CA, USA).
Modified fluorometric assay was used to determine the NA activity of the recombinant H5N1 viruses [38]. We measured the NA enzyme kinetics at pH 6.5 with 33 mM 2-(N-Morpholino)ethanesulfonic acid hydrate (MES; Sigma-Aldrich), 4 mM CaCl2, and fluorogenic substrate 2’-(4-methylumbelliferyl)-α-D-N-acetylneuraminic acid (MUNANA; Sigma-Aldrich; final substrate concentration, 0–2000 µM). All H5N1 viruses were standardized to an equivalent dose of 107.5 PFU/ml. The reaction was conducted at 37°C in a total volume of 50 µl, and the fluorescence of released 4-methylumbelliferone was measured every 92 sec for 45 min in a Fluoroskan II instrument (Labsystems) using excitation and emission wavelengths of 355 and 460 nm, respectively. To measure the inhibitory effect of oseltamivir carboxylate or zanamivir on NA activity, H5N1 viruses were preincubated for 30 min at 37°C in the presence of various concentrations of the drugs (0.00005–5 µM). The kinetic parameters Michaelis-Menten constant (Km), maximum velocity of substrate conversion (Vmax), and inhibitory constant (Ki) of the NAs were calculated by fitting the data to the appropriate Michaelis-Menten equations by using nonlinear regression in the commercially available GraphPad Prism 4 software (GraphPad Software, La Jolla, CA).
Recombinant H5N1 viruses were standardized to equivalent NA activity and incubated for 30 min at 37°C with NA inhibitors at concentrations of 0.00005–100 µM with MUNANA (Sigma-Aldrich) as a substrate. After 1 h, the reaction was terminated by adding 14 mM NaOH and fluorescence was quantified in a Fluoroskan II (Labsystems) fluorometer. The concentration of NA inhibitor that reduced NA activity by 50% relative to a control mixture with no inhibitor (IC50) was determined by plotting the dose-response curve of inhibition of NA activity as a function of the compound concentration. Values are the mean of 2–3 independent determinations.
Pathogenicity was tested in 4- to 5-month-old male ferrets obtained through the ferret breeding program at St. Jude Children’s Research Hospital or from Marshall Farms (North Rose, NY). All ferrets were seronegative for influenza A H1N1, H3N2, and H5N1, and for influenza B viruses. Groups of three ferrets were inoculated intranasally under light isoflurane anesthesia with 106 EID50 of virus in 1 ml sterile phosphate-buffered saline (PBS). Clinical signs of infection, relative inactivity index [35], weight, and temperature were recorded daily. Activity level was assessed by using the following scoring system: 0, alert and playful; 1, alert but playful when stimulated; 2, alert but not playful when stimulated; and 3, neither alert nor playful when stimulated. The relative inactivity index was calculated as the mean score per group of ferrets per observation (day) over the 14-day period. Animals that showed signs of severe disease (respiratory signs [labored breezing, sneezing, wheezing, and nasal discharge], febrility, lethargy, steady weight loss, and neurological signs [hind-limb paresis, ataxia, torticollis, and tremor] and >25% weight loss were euthanized. Body temperature was measured by subcutaneous implantable temperature transponders (Bio Medic Data Systems Inc.).
To monitor virus shedding, nasal washes were collected from ferrets on days 1, 2, 4, 6, and 8 post-challenge. Virus was titrated in 10-day old embryonated chicken eggs by injecting 0.1 ml of serial 10-fold dilutions of the sample (three eggs per dilution) and expressed as log10EID50/ml. Inflammatory cell counts and protein concentrations in nasal washes were determined as described previously [39]. Briefly, the nasal washes were centrifuged at 2,000 rpm for 10 min. The cell pellet was resuspended in PBS, and the cells were counted microscopically in a hemacytometer. The total number of inflammatory cells was calculated on the basis of the initial volume of nasal wash. The protein concentration in cell-free nasal washes was measured by using a protein reagent from Bio-Rad (Hercules).
Two animals inoculated with WT, E119A, or N294S viruses were euthanized by intracardiac injection of Euthanasia V solution on day 4 post-inoculation, and tissue samples (∼0.5 g each) were collected from lungs (4 lobes tested separately), nasal turbinate, trachea, spleen, liver, and small intestine. Samples were homogenized in 1 ml sterile PBS with antibiotics, and the virus titer (log10EID50/gram tissue) was determined in 10-day old embryonated chicken eggs.
Tissues (lung, nasal turbinate, trachea, spleen, liver, and small intestine) were collected at the time of necropsy, fixed in 10% neutral-buffered formalin, and embedded in paraffin. Five-micrometer-thick sections were stained with hematoxylin and eosin and studied by light microscopy.
Serum samples were collected from ferrets 3 weeks after inoculation, treated with receptor-destroying enzyme, heat-inactivated at 56 °C for 30 min, and tested by hemagglutination inhibition (HI) assay with 0.5% packed chicken red blood cells by a standard method as described previously [40].
Viral RNAs were isolated from virus-containing cell culture fluid after transfection, after three passages in MDCK cells or from ferret nasal washes by using the RNeasy Mini kit (Qiagen). Samples were reverse-transcribed and analyzed by PCR using universal primers specific for the HA and NA gene segments, as described previously [41]. Sequencing was performed by the Hartwell Center for Bioinformatics and Biotechnology at St. Jude. The DNA template was sequenced by using rhodamine or dRhodamine dye terminator cycle-sequencing Ready Reaction kits with AmpliTaq DNA polymerase FS (Perkin-Elmer) and synthetic oligonucleotides. Samples were analyzed in a Perkin-Elmer Applied Biosystems DNA sequencer (model 373 or 377). DNA sequences were completed and edited by using the Lasergene sequence analysis software package (DNASTAR).
The virus yield, plaque size, NA inhibitor susceptibility, NA enzyme kinetic parameters (Km, Vmax, Ki), virus titers in ferret organs and nasal wash samples, differences in fevers, weights, total number of cells and protein concentrations in nasal wash samples, mean survival time, and relative inactivity index of ferrets inoculated with wild-type and mutant viruses were compared by analysis of variance (ANOVA). Virus yields and plaque size of recombinant H5N1 viruses after the first and the third passage in MDCK cells were compared by unpaired two-tailed t-test. The probability of survival was estimated by the Kaplan-Meier method and compared between groups of ferrets by using the log-rank (Mantel-Haenszel) test [42]. A probability value of 0.05 was prospectively chosen to indicate that the findings were not the result of chance alone.
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10.1371/journal.pgen.1005154 | Genome-Wide Negative Feedback Drives Transgenerational DNA Methylation Dynamics in Arabidopsis | Epigenetic variations of phenotypes, especially those associated with DNA methylation, are often inherited over multiple generations in plants. The active and inactive chromatin states are heritable and can be maintained or even be amplified by positive feedback in a transgenerational manner. However, mechanisms controlling the transgenerational DNA methylation dynamics are largely unknown. As an approach to understand the transgenerational dynamics, we examined long-term effect of impaired DNA methylation in Arabidopsis mutants of the chromatin remodeler gene DDM1 (Decrease in DNA Methylation 1) through whole genome DNA methylation sequencing. The ddm1 mutation induces a drastic decrease in DNA methylation of transposable elements (TEs) and repeats in the initial generation, while also inducing ectopic DNA methylation at hundreds of loci. Unexpectedly, this ectopic methylation can only be seen after repeated self-pollination. The ectopic cytosine methylation is found primarily in the non-CG context and starts from 3’ regions within transcription units and spreads upstream. Remarkably, when chromosomes with reduced DNA methylation were introduced from a ddm1 mutant into a DDM1 wild-type background, the ddm1-derived chromosomes also induced analogous de novo accumulation of DNA methylation in trans. These results lead us to propose a model to explain the transgenerational DNA methylation redistribution by genome-wide negative feedback. The global negative feedback, together with local positive feedback, would ensure robust and balanced differentiation of chromatin states within the genome.
| DNA methylation is important for controlling activity of transposable elements and genes. An intriguing feature of DNA methylation in plants is that its pattern can be inherited over multiple generations at high fidelity in a Mendelian manner. However, mechanisms controlling the trans-generational DNA methylation dynamics are largely unknown. Arabidopsis mutants of a chromatin remodeler gene DDM1 (Decrease in DNA Methylation 1) show drastic reduction of DNA methylation in transposons and repeats, and also show progressive changes in developmental phenotypes during propagation through self-pollination. We now show using whole genome DNA methylation sequencing that upon repeated selfing, the ddm1 mutation induces an ectopic accumulation of DNA methylation at hundreds of loci. Remarkably, even in the wild type background, the analogous de novo increase of DNA methylation can be induced in trans by chromosomes with reduced DNA methylation. Collectively, our findings support a model to explain the transgenerational DNA methylation redistribution by genome-wide negative feedback, which should be important for balanced differentiation of DNA methylation states within the genome.
| Epigenetic variation of gene expression is mediated by chromatin marks, such as modifications of histones and DNA. Importantly, these marks and associated gene expression patterns can be inherited over multiple generations in both animals and plants [1,2]. Transgenerational epigenetic inheritance, especially the one associated with DNA methylation, is widespread in plants, and that could have a significant impact on evolution [3–5]. The long-term dynamics of DNA methylation has recently been examined genome-wide at single base resolution in the flowering plant Arabidopsis [6,7]; by analysing repeatedly self-pollinated wild type Arabidopsis plants, heritable gain and loss of DNA methylation have been detected, although their frequencies are generally low. A complementary approach to uncover the background mechanisms controlling long-term DNA methylation dynamics is to examine the effects of impaired DNA methylation pattern over multiple generations.
Factors controlling genomic DNA methylation have been studied extensively in Arabidopsis; and many of these factors constitute positive feedback loops to stabilize epigenetic states. Cytosine methylation in the context of dinucleotide CG is maintained by maintenance methyltransferase MET1 [8,9], while cytosine methylation at non-CG site is mediated by chromomethylases (CMTs) [10,11]. The CMTs are recruited to chromatin by methylation of histone H3 lysine 9 (H3K9me), and the H3K9 methylase KYP/SUH4 is also recruited to chromatin with non-CG methylation, generating a self-reinforcing positive feedback loop [11–14]. Both H3K9me and non-CG methylation are silent heterochromatin marks normally found in repeats and transposable elements (TEs); and these marks are rarely detectable in transcribed genes. Exclusion of these marks from transcribed genes depends on the H3K9 demethylase IBM1 (Increase in BONSAI Methylation 1) [13,15]. IBM1 removes H3K9me from transcribed genes, generating another positive feedback loop to stabilize active states [13]. In addition, a positive feedback loop is also found in a process called RNA-directed DNA methylation (RdDM). RdDM is a de novo DNA methylation process triggered by double-strand RNA; and factors involved in this process have been extensively studied [16–20]. The final step of RdDM is DNA methylation of both CG and non-CG sites by the de novo DNA methyltransferase DRM2 (Domains Rearranged Methylase 2), with the RNAi machinery and small interfering RNA (siRNA) functioning as upstream factors. Interestingly, production of siRNA also depends on DRM2 [21,22], suggesting another positive feedback that stabilizes the silent state. Genome-wide DNA methylation profiles have been determined in mutants of these and other factors controlling DNA methylation [11,23,24], although information for the transgenerational effects of these mutations is limited.
Among the Arabidopsis mutants affecting genomic DNA methylation, ddm1 (decrease in DNA methylation 1) is one of the mutations with the strongest effects. Mutant plants show drastic reduction of DNA methylation at both CG and non-CG sites in heterochromatic repeats and TEs [25,26]. The DDM1 gene encodes a chromatin remodeling factor, which is necessary for DNA methylation in heterochromatic sequences [10,27]. Mutation in its mammalian ortholog Lsh induces loss of DNA methylation, suggesting conserved functions across the animal and plant kingdoms [28,29].
A striking feature of the Arabidopsis ddm1 mutant is the progressive accumulation of the developmental defects; initial generations of the ddm1 mutant grow relatively normally, but many types of developmental abnormalities arise after multiple rounds of self-pollinations [30,31]. Some of the abnormalities are due to DNA sequence changes, such as insertion mutations of de-repressed endogenous TEs [32–34] or a rearrangement of repeats [35], but others are due to epigenetic changes in gene expression, which correlate with changes in DNA methylation pattern at the affected loci [36,37].
Here we analyze the transgenerational effects of the ddm1 mutation genome-wide, by comparing DNA methylation of the ddm1 mutants before and after the repeated self-pollinations. This analysis revealed ectopic accumulation of non-CG methylation at hundreds of loci; and unexpectedly, this hypermethylation could only be seen after repeated self-pollinations. Furthermore, when ddm1-derived chromosomes with disrupted heterochromatin were introduced into a DDM1 wild type background, de novo accumulation of non-CG methylation was induced in trans. These results lead us to propose a model in which loss of heterochromatin is progressively compensated for through a negative feedback mechanism that leads to heterochromatin redistribution across the genome.
To understand the changes in DNA methylation patterns during self-pollinations of ddm1 mutant genome-wide, we compared DNA methylation before and after the self-pollination of the mutant. We examined DNA methylation in four individuals of ddm1 homozygous mutants segregated in progeny of a heterozygote (hereafter called 1G for the 1st Generation) and also four lines of ddm1 plants independently self-pollinated eight times (hereafter called 9G) (S1 Fig). In 1G, the ddm1 mutation already induced reduction of DNA methylation in heterochromatic regions [10,25,26]. Methylation in repetitive sequences, such as transposable elements (TEs) (Fig 1D–1F), was much more severely affected than that in low copy sequences, such as genes (Fig 1A–1C). The reduction was found for both CG sites (Fig 1A and 1D) and non-CG sites. In non-CG sites, both CHG sites (Fig 1B and 1E) and CHH sites (Fig 1C and 1F) were affected (H can be A, T, or C). When we compared average DNA methylation of 9G to 1G, two features were noted for both genes and TEs: further decrease of CG methylation and an increased methylation at non-CG sites (Fig 1).
Although the ddm1 mutation immediately induces a drastic loss of DNA methylation in repeats, further reduction of methylation in later generations has been reported for a few CG sites [30]. Our genome-wide analysis revealed that many loci behave in a similar manner (Fig 2A). The progressive reduction of DNA methylation can have significant phenotypic effects; for example, the promoter of the imprinted gene FWA remains methylated in the 1G ddm1 but the methylation is lost stochastically in 9G ddm1 (Fig 2B), generating heritable epialleles that cause late-flowering phenotype [31,36,38]. The progressive reduction is seen genome-wide for both genes and TEs (Fig 1A and 1D).
To compare the features of the regions hypomethylated immediately or gradually, we defined differentially methylated regions (DMRs; details in Materials and Methods). The regions ddm1 affects immediately (1G-WT DMRs) were enriched in dimethylation of histone H3 lysine 9 (H3K9me2) (Fig 2D left and 2E). H3K9me2 is a mark of silent heterochromatin, and these results are consistent with previous reports [10,26]. In marked contrast, however, regions affected slowly (9G-specific DMRs) have much lower level of H3K9me2 in wild type (Fig 2D middle). DDM1 gene function is necessary for CG methylation in heterochromatin, but in the long-term DDM1 also has significant effects on CG methylation in less heterochromatic regions including gene bodies (Fig 2C).
More counter-intuitively, our genome-wide analysis revealed a large number of genes and TEs ectopically hypermethylated at non-CG sites in the self-pollinated ddm1 lines (Figs 3A, 3B, 4A and 5A–5E). The regions CHG hypermethylated also showed hypermethylation at CHH sites (Figs 3D, 5A–5D, and S6A Fig). In addition, although genic CG methylation tends to decrease progressively from 1G to 9G on average (Figs 1 and 2), non-CG hypermethylated regions show an increase in CG methylation (Fig 3D). The CG and non-CG hypermethylation was found reproducibly at specific loci (S8 Fig). The affected loci include BONSAI and other sequences we have reported previously [37,39], but the majority of the affected loci could only be detected by whole-genome bisulfite sequencing (WGBS), because that can detect increased non-CG methylation with high sensitivity even at loci already CG methylated. In addition to genes, a large number of TEs showed increase in non-CG methylation (Figs 3A,3B, 4E, and S9–S11 Figs).
A very unexpected feature revealed by WGBS is that non-CG hypermethylation of genes is almost undetectable in the first generation of ddm1 but is specifically and reproducibly seen in the repeatedly self-pollinated ddm1 lines. In Fig 3A and 3B, many black dots can be seen along the vertical axis in the panels for 9G but not for 1G. The non-CG hypermethylation of genes is not a simple extension of the effect seen in the first generation. This feature can only be detected in later generations (Fig 3C). In order to further understand the transgenerational dynamics, we examined four independently self-pollinated 2G ddm1 plants. If the hypermethylation proceeds equally at each self-pollination, the increase from 1G to 2G would be 1/8 or more of the increase from 1G to 9G, provided that the methylation level should saturate at specific level (the methylation level can not exceed 100%). Interestingly, although hypermethylation proceeded in 2G, the difference between 1G and 2G was much less than 1/8 of that between 1G and 9G, suggesting that the increase is slow initially but accelerated in later generations (S12 and S13 Figs).
How is this non-CG hypermethylation induced? Our genome-wide bisulfite analyses revealed that the genes non-CG hypermethylated in the self-pollinated ddm1 tend to have low levels of non-CG methylation already in wild type plants (Fig 3D), suggesting that preexisting small heterochromatin domains may function as seed for further heterochromatin formation. Interestingly, distribution of H3K9me2 around the DMR is asymmetric; it is enriched in the 3’ of the DMRs (S14 Fig). We have previously shown that the BONSAI gene is flanked by insertion of a heterochromatic LINE in the 3’ region [37] (Fig 4A and S13A Fig). The BONSAI hypermethylation in ddm1 is induced in a strain with the LINE insertion but not found in a strain without the LINE insertion [37]. The DNA methylation spreads from the 3’ LINE to the BONSAI region during repeated self-pollination of ddm1 mutants [37]. Spread of non-CG methylation from 3’ to 5’ regions was also noted at other loci (Fig 5A–5D). When the methylation level differs among the four 9G ddm1 plants, plants with stronger signals tended to show relative centroid positions more upstream than plants with weaker signals, suggesting that the signal spreads from 3’ to 5’ (Fig 5F). These observations suggest that common mechanisms may operate in BONSAI and many, even if not all, affected loci.
We have previously shown that the de novo non-CG methylation in the self-pollinated ddm1 does not require components of the RdDM machinery, such as RDR2, DCL3, and DRM2 [39]. On the other hand, the non-CG methylase CMT3 and H3K9 methylase KYP are necessary for the de novo methylation, suggesting that the ectopic methylation occurs by mechanisms mediated by the heterochromatin marks H3K9me and non-CG methylation [39]. Indeed, the non-CG hypermethylation at the BONSAI locus is associated with ectopic H3K9me (Fig 4B).
The self-reinforcing loop of non-CG methylase and H3K9 methylase activities could be the basis for the acceleration of hypermethylation as the generation proceeds (S13B Fig). As the two processes enhance each other, the positive feedback would accelerate the spread of the heterochromatin in later generations [12, 13].
Increased non-CG methylation has been reported in mutants of the CG methyltransferase gene MET1 [40–42], which results at least in part from a reduction of full-length IBM1 transcript [43]. The IBM1 gene encodes a demethylase for histone H3K9; and mutation in this gene induces accumulation of H3K9me2 and non-CG methylation in gene bodies. Interestingly, developmental phenotypes of the ibm1 mutation also become progressively stronger during self-pollinations [15]. We compared the regions of non-CG hypermethylation in the ibm1 and self-pollinated ddm1. Although an overlap can be detected, the majority of the DMRs in ddm1 mutants before and after the self-pollinations were distinct from the DMRs of ibm1 mutants (Fig 6B and S16 Fig). Just as progressive loss of CG methylation in the ddm1 mutant, ibm1 mutant shows progressive accumulation of non-CG methylation in later generations (Fig 6A, S15 and S16 Figs). This is consistent with a recent report [44] and likely accounts for the progressive developmental defects in the ibm1 mutant.
We examined DNA methylation patterns of the genes and TEs hypermethylated in the self-pollinated ddm1 lines (Fig 6C). Compared to the ibm1 mutant, the peak in the ddm1 was shifted toward 3’ end. Interestingly, the shift of the peak in the hypermethylation was also found for CG methylation (S5D Fig). Although CG methylation of gene body in wild type peaks around the center (S5C Fig), increase of genic CG methylation in 9G ddm1 was not proportional to the methylation of wild type; instead, the increase of CG methylation was shifted toward 3’ regions (S5D Fig). Together with the observation that CHG-hypermethylated genes tend to show CG-hypermethylation (Fig 3D), these results suggest a link between the ectopic CG methylation and non-CG methylation, as we discussed previously [39].
The bias of the hypermethylation signal toward the 3’ region in 9G ddm1 is especially evident in the hypermethylated TEs; the peak was often located outside of the transcription unit for both CHG and CHH methylations (Fig 6C, bottom half). When different families of TEs are compared, the peak in the downstream region was especially evident in the GYPSY-like LTR retrotransposons (S10 Fig). Generally, these TEs lost DNA methylation in 1G ddm1, but regained methylation during the self-pollinations (S5A and S9–S11 Figs).
The ddm1 mutation can induce increased DNA methylation at hundreds of genes and TEs. The hypermethylation can be a direct consequence of impaired DDM1 function, or alternatively, an indirect effect of disruption of heterochromatin in the mutants. To test these possibilities, we examined the effect of chromosomes introduced from ddm1 into wild type DDM1 background. Chromosomes losing DNA methylation in the ddm1 mutants remain unmethylated even after introduction into wild type DDM1 background [25,45]. We examined DNA methylation data of epigenetic recombinant inbred lines (epiRILs) [46]. In the epiRILs, a ddm1 mutant plant was crossed to wild type plant twice to segregate DDM1/DDM1 lines with around one quarter of chromosome segments derived from ddm1. Although remethylation can be induced in regions associated with small RNA, hundreds of DMRs remain unmethylated in the wild type DDM1 background [46,47]. Each of these segregating lines have been self-pollinated seven times, which makes most of the genomic regions fixed in ddm1-derived haplotype or wild-type derived haplotype [46].
We examined if the loci exhibiting hypermethylation in the self-pollinated ddm1 lines also showed hypermethylation in some of the epiRILs. We utilized DNA methylation data for the 123 epiRILs, which are based on immunoprecipitation (IP) of genomic DNA by anti-methylcytosine antibody. As the context of methylation cannot be distinguished, we examined seven loci that show increased methylation in 9G ddm1 but a relatively low level of methylation at CG sites in wild-type. In six out of the seven loci examined, we could detect hypermethylation in multiple epiRILs, suggesting that the hypermethylation can be induced or maintained in the DDM1 background (Figs 7A, 7C, 7E and S17 Fig). In all of them, the hypermethylation showed a strong positive correlation with the amount of disrupted heterochromatin in each of these lines (Fig 7, S17 Fig and S1 Table), suggesting that the hypermethylation was induced or maintained in the background of disrupted heterochromatin in other genomic regions.
The hypermethylation could be induced de novo or alternatively maintained from the parental ddm1. The parental ddm1 plant originally used for making epiRILs was already self-pollinated three times (4G) and that plant also show low level of ectopic methylation at some loci (S17 Fig), which may have the potential to be maintained in DDM1 background [37]. Very importantly, however, the hypermethylation was found even in chromosome segments originated from wild type DDM1 (Figs 7B, 7D, 7F and S18–S23 Figs), demonstrating that the hypermethylation could be induced de novo after the initial crosses and subsequent repeated self-pollinations in the background of functional DDM1.
In order to confirm and extend this observation, we used WGBS for an epiRIL with genome-wide reduction of heterochromatic DNA methylation. The epiRIL98, which contains large amount of chromosomes with reduced DNA methylation, showed CHG hypermethylation in many genes (Fig 8A), which include BONSAI gene (S24A Fig) and genes with body methylation (S24B–S24C Fig). In the CHG hypermethylated genes, the CHG methylation level was generally much higher than that of the parental 4G ddm1 plant (Fig 8B), suggesting that the hypermethylation was amplified or induced de novo in the background of functional DDM1. A large number of CHG hypermethylated genes were found in chromosome regions of wild type haplotype (Fig 8C and S25 Fig), again suggesting that they can be induced de novo. In control epiRILs with much lower levels of disrupted chromatin, the hypermethylation was undetectable, confirming that the disrupted heterochromatin was responsible (Fig 8A). Taken together, these results indicate that the hypermethylation can be induced de novo by trans-acting effects of disrupted heterochromatin.
Here we report short- and long-term effects of the ddm1 mutation. The mutation immediately induces a drastic loss of DNA methylation in heterochromatic regions in the first generation when it becomes homozygous. In later generations, the ddm1 mutation reproducibly induces ectopic accumulation of DNA methylation in hundreds of genes and TEs. This work and previous work [39] suggest that the ectopic methylation occurs by spread of heterochromatin marks mediated by the non-CG methylase CMT3 and H3K9 methylase KYP. Interestingly, this effect was slow in the initial generations but accelerated in later generations, suggesting strong positive cooperativity for the heterochromatin accumulation. That could be explained by the self-reinforcing positive feedback of H3K9me and non-CG methylation [12, 13].
In addition to the local positive feedback, global negative feedback seems important for the DNA methylation dynamics. The ectopic DNA methylation seems to reflect negative feedback of disrupted heterochromatin in other genomic regions, because the ectopic methylation could also be induced in DDM1 wild type background when the genome contains large amount of chromosomal segments with disrupted heterochromatin (Figs 7 and 8). How does the negative feedback work? One possible explanation is that disruption of heterochromatin in the ddm1 mutant results in release of heterochromatin-forming factors such as CMTs and H3K9 methylases, which then become available in other regions. As these factors are normally recruited to heterochromatin, disruption of a large proportion of heterochromatin in the genome would result in increased level of these factors in released conditions, which would induce spread of heterochromatin into normally euchromatic regions and its amplification by the self-reinforcing loop of H3K9me and non-CG methylation (Fig 9).
In the model we proposed, global reduction of heterochromatin induces ectopic non-CG methylation (Fig 9). That would account for the correlation between the global reduction of methylation and ectopic methylation in epiRILs (Fig 7A, 7C, 7E and S17 Fig). An alternative mechanism would be that ddm1 induces change in a specific locus, such as transcriptional de-repression or repression of a specific gene, and the change is inherited in the DDM1 wild type background and induces the ectopic methylation. For example, ROS1 gene expression is reduced in mutants with reduced DNA methylaiton [48], which would lead to hypermethylation at specific loci. However, although ROS1 gene expression is reduced in ddm1, it is expressed almost normally in epiRIL98, which show strong non-CG hypermethylation (S26A Fig). In addition, DMRs hypermethylated in 9G ddm1 and ros1-dml2-dml3 triple mutant do not overlap much, further suggesting that the hypermethylation in 9G ddm1 is not due to reduced ROS1 expression (S26B Fig). More generally, we could not find a locus consistently derived from ddm1 parent in all of the plants showing the high level of ectopic hypermethylation in the six loci (S18–S23 Figs). Although we cannot exclude the possibility that two or more specific loci redundantly mediate the ectopic methylation, a more parsimonious explanation derived from available data would be that the trans-interaction is mediated by global homeostasis.
The de novo methylation in the epiRILs might also be related to mechanisms such as paramutation [49,50], or transchromosomal methylation (TCM) [51]. In these phenomena, methylated sequences induce methylation in related sequences. However, the ectopic hypermethylation in the epiRILs is generally much higher than that of the parental ddm1 (Fig 8B), suggesting that even if paramutation-like or TCM-like mechanisms are involved, the effect should be much amplified during self-pollinations of epiRILs; and the degree of the amplification correlates with global disruption of heterochromatin (Fig 7 and S17 Fig), which is due to the ddm1-derived chromosomes.
This trans-acting negative feedback could also be understood as a hypersensitive reaction to the challenge by active and proliferating TEs. Our genome-wide analyses revealed that many of the TEs can be targets of the negative feedback (Fig 3A and 3B and S9–S11 Figs). Active TEs often keep parts of heterochromatin, which can function as seeds of the self-reinforcing heterochromatin formation.
An increase in non-CG methylation is also seen in mutants of the histone demethylase gene IBM1. However, targets of IBM1 are generally euchromatic and they do not overlap much with regions hypermethylated in the self-pollinated ddm1 lines (Fig 6B and S16 Fig). An increase in non-CG methylation is also found in the maintenance CG methylase gene MET1 [40–42]. As a mechanism for the met1-induced increase in non-CG methylation, loss of IBM1 function is suggested, as IBM1 transcripts become truncated in the met1 mutant [43]. On the other hand, it has been reported that the main targets of the met1-induced accumulation of H3K9me2 are genes with H3K27me3, another modification for silent chromatin [52]. The negative feedback of heterochromatin marks comparable to that seen in the self-pollinated ddm1 lines may also operate in met1 mutants. In our analyses, although regions affected by met1, ibm1, and self-pollinated ddm1 all differ, significant overlaps are noted (S27 Fig). For these mutants, the local triggers for heterochromatin accumulation appear to be distinct, despite the possible overlap in the downstream mechanisms, including the self-reinforcing loop of non-CG methylation and H3K9me.
Heterochromatin homeostasis mechanisms analogous to those we have uncovered in Arabidopsis may also be operating in other eukaryotes. Mice with a disruption of its DDM1 homolog Lsh show global reduction of genomic DNA methylation, but interestingly it is also associated with increased DNA methylation at specific regions [29]. In human cancer, hypomethylation of repeats and TEs is often associated with local hypermethylation of genes, such as tumor suppressor genes [53,54]. In Drosophila, an increase in the amount of heterochromatic Y chromosome can results in a release of silencing at multiple loci in trans [55], suggesting a negative feedback similar to that discussed here. Furthermore, Drosophila modifiers of position effect variegation often function in dosage-dependent manners [56,57], consistent with the pathway proposed in Fig 9. Positive feedback loops would stabilize and enhance silent and active states [12,13,21,58], but they carry the risk of going out of control to excess. A global negative feedback mechanism, together with the local positive feedback, would ensure a robust and balanced chromatin differentiation within the genome, as has been discussed for pattern formation during development [59,60].
In the context of evolution in plants, a large variation in the amount of repetitive sequences is often noted between related species or even within a species [61–63]. On such occasions, fine-tuning of the amount of trans-acting heterochromatin factors would be especially important, as an imbalance would not only immediately affect gene expression level but also influence the epigenotype in a transgenerational manner.
Isolation of the ddm1-1 and ibm1-4 mutants has been described previously [15,25]. Self-pollination of ddm1 lines was described previously [30]. In order to remove heritable effects of the ddm1 mutation, the original ddm1 mutant was backcrossed six times in the heterozygous state. The heterozygous plants were propagated by self-pollination. 1G ddm1 mutant plants were selected from self-pollinated progeny of the heterozygote. 9G ddm1 plants were generated by independently self-pollinating different ddm1 segregants eight times (S1 Fig). Generation of epiRILs has been described previously [64].
The annotations of genes and TEs are based on The Arabidopsis Information Resource (http://www.arabidopsis.org/). TAIR8 was used for analyzing ChIP chip data (Fig 2E), TEG (TE gene) data, and epiRILs data. TAIR10 was used for other analyses. The details of the annotation of TEGs were described in a document in TAIR web (ftp://ftp.arabidopsis.org/home/tair/Genes/TAIR8_genome_release/Readme-transposons).
For the 1G and 9G ddm1 plants and their controls, genomic DNA was isolated from rosette leaves using the Illustra Nucleon Phytopure genomic DNA extraction kit, and genome-wide bisulfite sequencing was performed as described previously [65]. Raw sequence data were deposited in the DDBJ (DNA Data Bank of Japan) Sequence Read Archive (DRA; accession nos. DRA002545, DRA002546, DRA002548, DRA002549, DRA002551, DRA002554, DRA002555, DRA003018, DRA003019 and DRA003020). The adaptor sequences were clipped out using the FASTX-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). Reads were trimmed to 90 nucleotide length (45 nucleotide for the data obtained from GEO—GSE39901) and mapped to reference genomes (Release 10 of the Arabidopsis Information Resources) using the Bowtie alignment algorithm [66] with the following parameters, "-X 500-e 90-l 20-n 1". Only uniquely mapped reads were used. Clonal reads were removed except one with the best quality. Any read with three consecutive methylated CHH sites were eliminated. The level of methylation of cytosine in a genomic region was calculated using the ratio of the number of methylated cytosine to that of total cytosine. For the three epiRILs and two parental lines, whole-genome bisulfite sequencing was described previously [46] and the data are in GEO (GSE62206).
DMRs (differentially methylated regions) were defined by comparing the methylation level of 100-bp windows throughout the genome between two genotypes. The windows with at least 20 sequenced cytosines were used for the comparison. The level of methylation was calculated using the weighed methylation level of each genotype [67]. The windows were selected as DMRs when difference of methylation level was 0.5 or more at CG site or 0.3 or more at CHG sites. For defining contiguous DMR (conDMR), multiple DMRs were merged if they were adjacent to each other or there was only one gap of the 100-bp window. The centroid of cytosine methylation in conDMR was calculated using the relative position within that region weighed by methylation level of each cytosine. In Fig 5F, we used conDMR of 500 bp or longer and overlapping with genes. Each contiguous DMR was aligned according to the orientation of the corresponding gene. The correlation coefficient between the level and the relative centroid position of DNA methylation was calculated among the four 9G ddm1 plants in each conDMR. To plot DNA methylation patterns over genes or TEGs in ddm1 mutants, #1 samples of each genotype (Figs 2A, 3A and 3B) in 1G ddm1 and 9G ddm1 were used. To draw the heatmap of methylation of cytosine, cluster 3.0 [68] and Java Treeview [69] were used.
15-day-old seedlings were fixed with formaldehyde and ChIP was performed as described previously [70], using antibody against H3K9me1 (CMA316) and H3K9me2 (CMA307) [71]. To assure the equal amount of chromatin in each line, input DNA were quantified by quantitative PCR using TaKaRa Dice_Real Time System TP800 and ACT7 primers. Then, input DNA and each sample were diluted according to the estimated input DNA concentrations. Input DNA, mock (without antibody), and ChIP samples were analyzed by PCR. The PCR conditions were as follows: pre-incubation for 2 min at 94°C, 27 cycles at 94°C for 30 sec, 58°C for 20 sec, 72°C for 45 sec and a final extension at 72°C for 4 min. Primers used for the ChIP are listed in S2 Table. In addition to the BONSAI locus, we examined six loci with CHG methylation increased more than 0.3 from 1G ddm1 to 9G ddm1. Three of them were selected for relatively high level of ectopic CHH methylation (H1, H2, H3) and three with relatively low CHH methylation (L1, L2, L3). The increase of CHH methylation from 1G ddm1 to 9G ddm1 is more than 0.2 for the three H loci, and it is less than 0.02 for the three L loci. The lengths of amplicons for the six loci are between 250 bp and 300 bp.
ChIP-seq data of various histone modifications [72] in GEO (GSE28398) were used for our analysis. The coordinates were remapped onto TAIR10 annotation using a script in TAIR [73]. Enrichment of histone modification in a DMR was calculated by the density of ChIP-seq reads, and normalized by the mean and the standard deviation of the density of reads in 100,000 windows randomly chosen across the genome.
The MeDIP-chip data of 123 epigenetic recombinant inbred lines (epiRILs), ddm1 and WT are in GEO (GSE37284). The regions that were methylated (M) in WT and unmethylated (U) in ddm1 were selected as targets of ddm1 mutation using the values for HMM (hidden Markov model) status (M (methylated) or I (Intermediate) or U (Unmethylated)) [46]. Global hypo-methylation index of an epiRIL was calculated as the genome-wide average of the values for HMM status of probes on the chip (M = 0, I = 0.5, U = 1) in the target regions of ddm1 mutation. The data of inference of inherited haplotypes were shown in the previous study [46]. Following are names of lines numbered 1–6 in Fig 7 and S18–S23 Figs. (Fig 7A and 7B and S18 Fig) epiRIL208 epiRIL122 epiRIL98 epiRIL232 epiRIL70 epiRIL114; (Fig 7C and 7D and S19 Fig) epiRIL122 epiRIL208 epiRIL114 epiRIL258 epiRIL438 epiRIL508; (Fig 7E and 7F and S20 Fig) epiRIL208 epiRIL98 epiRIL438 epiRIL508 epiRIL122 epiRIL114; (S21 Fig) epiRIL208 epiRIL73 epiRIL71 epiRIL394 epiRIL98 epiRIL438; (S22 Fig) epiRIL508 epiRIL114 epiRIL122 epiRIL438 epiRIL208 epiRIL93; (S23 Fig) epiRIL208 epiRIL114 epiRIL556 epiRIL71 epiRIL244 epiRIL98.
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10.1371/journal.pntd.0005566 | A pilot study to delimit tsetse target populations in Zimbabwe | Tsetse (Glossina sensu stricto) are cyclical vectors of human and animal trypanosomoses, that are presently targeted by the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC) coordinated by the African Union. In order to achieve effective control of tsetse, there is need to produce elaborate plans to guide intervention programmes. A model intended to aid in the planning of intervention programmes and assist a fuller understanding of tsetse distribution was applied, in a pilot study in the Masoka area, Mid-Zambezi valley in Zimbabwe, and targeting two savannah species, Glossina morsitans morsitans and Glossina pallidipes.
The field study was conducted between March and December 2015 in 105 sites following a standardized grid sampling frame. Presence data were used to study habitat suitability of both species based on climatic and environmental data derived from MODIS and SPOT 5 satellite images. Factors influencing distribution were studied using an Ecological Niche Factor Analysis (ENFA) whilst habitat suitability was predicted using a Maximum Entropy (MaxEnt) model at a spatial resolution of 250 m. Area Under the Curve (AUC), an indicator of model performance, was 0.89 for G. m. morsitans and 0.96 for G. pallidipes. We then used the predicted suitable areas to calculate the probability that flies were really absent from the grid cells where they were not captured during the study based on a probability model using a risk threshold of 0.05. Apart from grid cells where G. m. morsitans and G. pallidipes were captured, there was a high probability of presence in an additional 128 km2 and 144 km2 respectively.
The modelling process promised to be useful in optimizing the outputs of presence/absence surveys, allowing the definition of tsetse infested areas with improved accuracy. The methodology proposed here can be extended to all the tsetse infested parts of Zimbabwe and may also be useful for other PATTEC national initiatives in other African countries.
| Tse-tse flies are vectors of human and animal trypanosomoses, that are presently targeted by the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC) coordinated by the African Union. In Zimbabwe, the government has devoted a full section of the veterinary services to tsetse and trypanosomosis control but the delimitation of tsetse infested areas, which is a pre-requisite to achieve effective control still requires improvement. Here we present a methodology that could help delimit target areas throughout the country, in a pilot study area located in the Masoka area, Mid-Zambezi valley in Zimbabwe, and targeting two savannah species, Glossina morsitans morsitans and Glossina pallidipes. The study, which was carried out in preparation for a vector control campaign, allowed to discriminate areas where tsetse presence was certain, likely or unlikely Habitat degradation due to agricultural activities seemed to play a pivotal role in determining the infestation by tsetse since settled areas had low probabilities for both species which was expected in this group. Application of this model will help reduce the cost of delineating tsetse infested areas in other parts of Zimbabwe and may also be useful for other PATTEC national initiatives in other African countries at a time when funding for tsetse control programmes is reduced.
| Trypanosomosis is one of the major constraints to rural development in sub-Saharan Africa [1]. Tsetse (Glossina spp.), the primary vectors of animal and human trypanosomosis, are found in the semi-arid, sub-humid and humid lowlands of 37 countries across the continent with a potential distribution range of some 8.7 million km2[2]. This disease places approximately 50 million cattle at risk with losses amounting to US$4.75 billion annually [3].
In Zimbabwe, an area of approximately 180,000 km2 of the total 390,757 km2 was deemed to be ecologically suitable for tsetse before the rinderpest epizootic of 1896 [4]. Sustained interventions resulted in the clearance of tsetse flies from most of this area, with 50,000 km2 being cleared since 1980. Tsetse are now confined to approximately 28,000 km2 in North-Western and Northern Zimbabwe. However, tsetse transmitted trypanosomosis remains a challenge in areas close to tsetse infested areas with a total of 240 African Animal Trypanosomosis (AAT) cases being reported to the OIE between 2009 and 2015[5]. A Human African trypanosomosis (HAT) focus also exist in the Hurungwe District and Mana Pools areas in the Northern parts of the country [6]where 25 cases of the acute form of HAT caused by Trypanosoma rhodesiense were detected through passive surveillance between 2009 and 2015 [7]. The country has committed to eradicate tsetse and trypanosomiasis in the framework of the African Union coordinated Pan-African Tsetse and Trypanosomiasis Eradication Campaign (AU-PATTEC), a decision (AHG/156 (XXXVI)) by African Heads of State and Government during the 36th Ordinary Summit of the OAU, Lome, Togo held in July 2000. The distribution of tsetse and their abundance play an important role in the epidemiology of trypanosomosis and often forms the basis for intervention programmes. Insect intervention and pre-intervention programmes require accurate and up–to–date information on the spatial and temporal distribution of target insects[8]. Strategies to control or eventually eliminate the problem posed by trypanosomosis must rely on tsetse ecology and suitable fly distribution data [9]. However, it has been decades since the latest tsetse distribution maps at the continental level were produced [10].
A number of studies have been carried out in order to understand tsetse population dynamics and these have resulted in an increased understanding of the link between the environment and tsetse presence and abundance [11,12]. It has also been established that tsetse are highly dependent on particular habitats for their survival, therefore ecological and land use change has a major impact on fly populations and the associated disease risks [13]. The distribution, prevalence and impact of vector-borne diseases are often affected by anthropogenic environmental changes that alter interactions between the host, the parasite and the vector [14].
Recent advances in geospatial technology have enabled the development of models in the study of diseases and parasites. Georeferenced datasets and spatial analysis techniques have great potential to support the planning and implementation of interventions against human and animal diseases including African trypanosomosis [15]. Geographic Information Systems (GIS) based distribution mapping can help identify areas of occurrence at the micro-level, where species-specific, environmentally friendly control measures can be strengthened[16].
In recent years, tsetse and trypanosomosis distribution models have been developed at different scales. Distribution models have been produced at a continental scale from low spatial resolution data, using the Advanced Very High Resolution Radiometer (AVHRR) data from the NOAA (www.noaa.gov) satellite that present a spatial resolution of 28 km [17]. This level of resolution does not allow an accurate identification of suitable habitats for tsetse flies that are found diluted in the surrounding pixels[11]. On the contrary, studies conducted at a higher resolution in Senegal recently and based on Maximum Entropy (MaxEnt) models assisted in the identification of pockets of infestation that had been missed by surveys [18]. A study conducted in the North-Western parts of Zimbabwe has also shown great potential in modelling the distribution of suitable tsetse habitats, information that can be used in the planning of intervention programmes [19].
Here we propose to combine this approach (Maxent models) to probability models that has been used previously to delimit tsetse control areas and that stipulate that tsetse can still be present despite a series of zero catches [20,21]. The goal is to prepare for control operations in the study area, but also to produce a standardized method allowing optimizing the definition of tsetse infested areas within the framework of PATTEC.
The field work was authorised by the Tsetse Control and Division, Department of Livestock and Veterinary Services.
The study was conducted in Masoka area, Mbire District (16.00° to 16.28°S and 30.1° to 30.28°E) between March 2015 and December 2015 (Fig 1). This area belongs to the Natural Farming Region IV of Zimbabwe which receives between 650 and 800 mm of rainfall annually and is suitable for livestock and drought resistant crop production. During the dry season, most of the vegetation sheds its leaves and annual grasses and shrubs dry out. A concentration of leafy vegetation is left along water courses, although most of these are temporary. The area is part of the Community Areas Management Programme for Indigenous Resources (CAMPFIRE) scheme, which advocates for the conservation of natural resources, including wildlife. The area thus has a variety of wild animals, the most common ones being buffalo (Syncerus caffer), elephants (Loxodonta africana), warthog (Phacochoerus africanus), among other important tsetse hosts. The distribution of these wild hosts in the dry season is mainly influenced by water availability, as more animals were sighted towards Chewore Safari Area, a protected Parks and Wildlife Authority of Zimbabwe Estate. According to a census conducted by Zimstat the community has an estimated population of 1,632 inhabitants distributed among 300 households [22]. Agriculture is the major activity, with production centred on cattle and goat rearing, cotton and small grains production. Cattle form an important source of blood meal for tsetse, especially in areas with low wild host densities [23]. The Masoka community has a herd of 180 cattle (Division of Veterinary Services Nov 2015 Census). The tsetse population occupying this area has not been affected by intervention programmes instituted by the Division of Tsetse Control over the past 19 years [24] with control activities concentrated along Manyame River some 70 km away.
Tsetse data were obtained using a grid based sampling method outlined in the Food and Agriculture Organisation (FAO)/International Atomic Energy Agency (IAEA) entomological baseline data collection manual of 2008 [25]. The study area was divided into a grid of 110 identical cells measuring 2 km × 2 km and a minimum of one and a maximum of three epsilon traps baited with sachets containing a mixture of 3-n-propyl-phenol, o-cten-3-ol and 4-methyl-phenol in the ratio of 1;4;8 [26] were placed in cells perceived to have suitable habitat. In each sampled cell, sites perceived to be suitable tsetse habitat were chosen based on a supervised classification of a SPOT 5 image. A survey team led by an experienced Tsetse Field Assistant, chose the actual site on the ground based on recommendations by Vale [12] in order to maximise on catches. Each sampling site was geo-referenced using a hand-held GPS receiver and monitoring was done after seven days. Samples were collected from 105 sites between March 2015 and December 2015. Captured flies were identified morphologically using identification keys developed by Buxton [27] and Mulligan [28] and specimens were preserved in 90% alcohol.
The temperature is a parameter that plays an important role in the tsetse life cycle and Land Surface Temperature is among the commonly used temperature indicators. Land Surface Temperature (LST) is calculated from the measurement of radiation emitted by the earth surface and it is highly correlated withthe air temperature [30]. 8 days daytime (DLST) and night-time LST (NLST) were extracted at 1km spatial resolution from MODIS MOD11A2/MYD11A2 temperature and emissivity products. The data werefiltered and temporally aggregated into statistics that can be used to describe the thermal profile of the study area. LST is used in many studies of species distribution and spatial epidemiology. In this study, they were used as proxies for both air and soil temperature which play an important role in tsetse habitat selection.
Among the indices commonly used in epidemiological studies are vegetation indices, a measurement of chlorophyll activity. These indices allow the differentiation of bare ground from the vegetation and also of various vegetation types. The most commonly used is the NDVI (Normalized Difference Vegetation Index), in addition to the NDVI, other vegetation indices such as EVI (Enhanced Vegetation Index) can be used but according to Hay [31], EVI is particularly useful since it performs better than NDVI over high biomass areas. The vegetation continuous field (VCF) is also an important vegetation index that can be used to capture the density of tree cover. Regarding our study area, we used both EVI and the VCF (Treecover) to capture the effect of woody vegetation on tsetse habitat. It is also important to note that EVI and other vegetation indices have already been used several times to predict tsetse flies’ density in West Africa [11,18,32,33].
The reflectance, in the mid-infrared is used to measure the radiation of bare soils. This index is correlated with the Land surface temperature. Luxuriant vegetation is characterized by a low MIR. With the EVI, this index allows to characterize the vegetation well as the soil temperature.
Various topographic indices such as slope, topographic wetness index (TWI) and aspect (slope direction) can be extracted from Digital Elevation Model (DEM). DEM, slope and aspect can be used to describe the elevation and exposure to the sun whereas topographic wetness index measure soil humidity. These indices were also used to model habitat suitability for the two species.
A high-resolution remotely-sensed satellite image acquired on the 9th of November 2014 by the Satellite Pour l’Observation de la Terre 5 (SPOT 5 with a spatial resolution of 2.5m) was used to identify suitable areas for tsetse. A supervised classification of land cover was realized with Envi 5.1software (www.exelisvis.co.uk), based on a maximum likelihood classifier (Fig 2). Eighty three polygons were digitized manually and 106 GPS filed observations were used to validate the classification. The supervised classification was validated from the calculation of a confusion matrix and the Kappa Index of Agreement coefficient (0.95). The pair comparisons of the landcover classes gave a separability coefficient between 1.97 and 2, corresponding to an absence of confusion of the pixels allocated within each class [34]. Seven classes of land cover were identified from which 4 (mopane, riverine forest, crop field and bush land), were used as predictors in the habitat suitability models. For each of these classes, the patch density (number of patches) and the surface of patches inside the prediction pixels at a resolution of 250m were calculated. The list of the remotely-sensed data and their spatial and temporal resolution used in the present study are presented in Table 1.
In the second step, we used a statistical model to predict suitable habitats for both species. We used the Maximum Entropy (MaxEnt) (www.cs.princeton.edu/~schapire/maxent), a species distribution model. MaxEnt is a machine learning algorithm that applies the principle of maximum entropy to predict the potential distribution of species from presence-only data and environmental variables. We resampled climatic and environmental data to a spatial resolution of 250 m and used them as the known features in determining the suitability index of each tsetse species within the study area. Each tsetse species, G. morsitans and G. pallidipes was modelled separately and for each we used presence data and a set of randomly generated pseudo-absence. We used leave one out cross validation (LOOCV) to compute all the model quality metrics. The model was trained n times (n = sample size) and each time we removed one observation for validation and at the end we aggregated the n metrics calculated on the validation point. The absence data were used only to assess the accuracy of each model and set a threshold for the model. We used the Receiver Operator Characteristic (ROC) curve and the associated, Area Under the Curve (AUC) as a metric for assessing the quality and performance of our prediction [37]. An AUC with values closer to 1 indicating excellent prediction. The MaxEnt software was used through its R interface in the dismo package [38].
A total of 73 cells (292 km2) of the 110 cells were sampled with 105 traps. Survey results demonstrated a mean density of 0.27 (sd = 0.54) flies/trap/day for G. m. morsitans, with a presence in 40 sites distributed in 31 cells (124 km2) The mean density of Glossina pallidipes was 0.05 (sd = 0.16) flies/trap/day, with a presence in 15 trapping sites within 13 cells (52 km2) (Fig 3).
The first plan of the ENFA showed that G. m. morsitans occurrence was positively correlated with vegetation indices (EVI, Riverine Forest, Average tree cover and MIR). However, most of the temperature indices exhibited a negative correlation to the species (Fig 4). Mopane woodland patch density and aspect exhibited an important influence on the habitat for the species as they were strongly correlated with the specificity axis. Average EVI accounted for most of the variance and fell outside the cloud of average conditions available in the study area.
The occurrence of G. pallidipes also showed a positive correlation to vegetation related indices with most of the temperature indices exhibiting a negative correlation. The topographic wetness index (TWI) was positively related to G. pallidipes but negatively with G. m. morsitans whilst night land surface temperature was the only temperature related covariate which showed a positive correlation with the occurrence of both species.
The habitat suitability models for G. m. morsitans and G. pallidipes had an Area Under the Curve (AUC) of 0.89 and 0.94 respectively (Fig 5). Both figures were close to one although the G. pallidipes model had a better prediction ability. However there were differences in covariates contributing to the models. The most contributive variable was “aspect” in the G. m. morsitans model and “riverine forest patch density” in the G. pallidipes model.
The resultant maps depicting habitat suitability for the species (Fig 6) show a wider area suitable for G. m. morsitans than G. pallidipes. There was a concentration of suitable habitat to the west of the study area which is a protected wildlife area.
We applied the probability model to 42 grid cells where no G. m. morsitans were caught. The analysis indicated a probability of G. m. morsitans presence below 0.05 (the level of risk accepted) in 10 grid cells where no tsetse were captured whilst 32 grid cells had a probability greater than 0.05 that G. m. morsitans was still present despite a sequence of zero catches. We observed the area infested with G. m. morsitans to be 124 km2 (28%) and a further 124 km2 (28%) had a high probability of being infested. An area of 40 km2 (9%) had a low probability of tsetse presence whilst the remaining 148 km2 (34%) were not sampled (Fig 7).
We also applied the probability model to 60 grid cells where no G. pallidipes were captured. The analysis indicated a probability of tsetse presence below 0.05 (the level of risk accepted) in 24 grid cells where no tsetse were captured whilst 36 grid cells had a probability greater than 0.05 that G. pallidipes was still present despite a sequence of zero catches. Area infested with G. pallidipes was therefore observed to be 52 km2 (10%), area with a low probability of tsetse presence was 96 km2 (22%) whilst the remaining 148 km2 (34%) were not sampled (Fig 7).
Habitats suitable for G. m. morsitans and G. pallidipes can be modelled using presence data and environmental variables [19]. This study produced habitat suitability models at a high resolution (250 m), a level which can be translated into operational plans. The habitat suitability models produced in this study had relatively large AUCs, an indication of a good predictive power. This showed that the habitat of both species, to a large extent, can be explained by the covariates used.
The two species under consideration, G. m. morsitans and G. pallidipes were positively correlated with vegetation indices on the first plan of the ENFA, indicating that the requirement for these covariates for these species was different than the mean conditions in the study area. The link between vegetation and tsetse has been well established through various studies [11,12]. Whilst both species were found in habitats along watercourses during the dry season, G. m. morsitans was also captured in deciduous woodlands of predominantly mopane trees. Studies by Vale at Rekomichi showed that there was variability in G. m. morsitans catches across vegetation types with seasonal effects evident whilst catches of G. pallidipes were distinctly higher in thickets than in mopane woodlands [12]. According to Cecchi et.al., deciduous woodlands and deciduous shrub-lands with sparse trees account for over 50% of the total distribution of the morsitans group [9].
In their model in North Western Zimbabwe, Matawa et al, observed that higher altitude was not associated with suitable tsetse habitat for both G. m. morsitans and G. pallidipes [19]. They attributed this to the effect of altitude on other climatic factors such as temperature. In our study however, the effect of altitude could not be fully explored as the elevation in the study area was more homogenous than the in North-Western Zimbabwe hence there was little variability to examine. Aspect, however, seemed to play a role in the determination of suitable habitat probably due to its association with the amount of sunlight received and subsequently temperature.
This study also demonstrated a negative correlation between suitable tsetse habitat and day land surface temperature which is a measure of air temperature. This negative correlation means G. m. morsitans and G. pallidipes require lower temperatures than the average values in the study area. Whilst studies on artificial refuges by Vale could not pin-point the exact temperature at which all tsetse occupy refuges, they clearly demonstrated that temperatures beyond 30°C affect tsetse [39]. Further work by Hargrove and Muzari revealed an increase in catches of male and pre-full term pregnant female tsetse in refugia at around 32°C [40]. Although no temperature measurements were made in this study, online weather sources reported episodes of maximum temperatures in excess of 40°C in the study area between October and December, values which compare well with 42.5°C observed by Hargrove and Muzari during 1998 [40]. Maximum temperature has also shown to have significant effect on tsetse survival [41] with laboratory studies showing an increase in daily mortality due to temperature [42]
The aim of modelling is to improve the quality of intervention plans leading to a reduction in costs hence accuracy of the models is of paramount importance if they are to be the basis of intervention. The distribution of tsetse hosts is a critical determinant of tsetse distribution, particularly in the morsitans group [13,23]. We however obtained very good predictions in the present study, probably because the density of these wild hosts is correlated with the vegetation habitats that were integrated in the prediction models.
This study confirmed that the absence of tsetse catches in traps does not imply absence in a locality [12,21]. Unlike other probability models build using vegetation only [21] this study used the habitat suitability model, a factor which captures major characteristics of the habitat thus increasing the robustness of the model. Suitable tsetse habitats are influenced by more factors other than vegetation alone thus the probability model produced in this study has got a greater chance of detecting grids with higher chances of infestation.
The probability model showed greater chances of both G. m. morsitans and G. pallidipes presence in wider parts of the study area than observed through surveys. A number of factors can be attributed to this result. Firstly, the absence of tsetse in traps, especially G. m. morsitans, could have resulted from a lower efficiency of traps in capturing the species (0.001) [20], a parameter which is linked to the behaviour of the species. Resting G. m. morsitans respond more to moving objects than G. pallidipes [43]. Whilst great care was taken to place traps in optimal sites, siting in itself is a factor which can influence the efficiency of traps [44]. The model however, still showed a high probability of G. pallidipes presence, contrary to catches recorded in traps which were low despite a better trapping efficiency (0.01) [20].
This new methodology is presented here for the first time and will allow a great enhancement of future tsetse sampling efforts. It has the potential to generate surface information (raster data) from point data (trap catches) thus providing operational information to guide planning and decision making. The model can be applied in planning the placement of insecticide treated targets as it is grid based and can also be applied to direct the focus of further surveys. Most remote sensing products are now freely available making the processing of data much cheaper thus helping national entities working on tsetse control programmes to make informed decisions in the judicious allocation of scarce resources (Prioritization of target areas based on assessed risk). Previous applications of probability modelling on riverine species in West Africa allowed the detection of isolated pockets of tsetse in areas which had been missed by surveys [18,45]. This study also demonstrated that information on areas not surveyed within target areas can be generated to guide the planning process. This is of importance as some areas can be difficult to access whilst at times resources may be limiting to obtain data from every location of the target area. However, the presence of predicted suitable habitats in these not sampled areas will be the basis to consider them as infested or not but this will need to be confirmed by additional sampling efforts.
Whilst local conditions may differ from place to place, we believe adoption of the methodology presented here would assist the country in the drafting of elaborate tsetse control and survey plans for implementation under the AU-PATTEC initiative. The methodology can also serve as a template for other PATTEC national initiatives and can be extended to assess the success of vector control programmes.
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10.1371/journal.pbio.1001915 | Intense Sperm-Mediated Sexual Conflict Promotes Reproductive Isolation in Caenorhabditis Nematodes | Conflict between the sexes over reproductive interests can drive rapid evolution of reproductive traits and promote speciation. Here we show that inter-species mating between Caenorhabditis nematodes sterilizes maternal individuals. The principal effectors of male-induced harm are sperm cells, which induce sterility and shorten lifespan by displacing conspecific sperm, invading the ovary, and sometimes breaching the gonad to infiltrate other tissues. This sperm-mediated harm is pervasive across species, but idiosyncrasies in its magnitude implicate both independent histories of sexually antagonistic coevolution within species and differences in reproductive mode (self-fertilizing hermaphrodites versus females) in determining its severity. Consistent with this conclusion, in androdioecious species the hermaphrodites are more vulnerable, the males more benign, or both. Patterns of assortative mating and a low incidence of invasive sperm occurring with conspecific mating are indicative of ongoing intra-specific sexual conflict that results in inter-species reproductive incompatibility.
| The sexes have divergent reproductive interests, and conflict arising from this disparity can drive the rapid evolution of reproductive traits and promote speciation. Here we describe a unique reproductive barrier in Caenorhabditis nematodes that is induced by sperm. We found that mating between species can sterilize maternal worms and even cause premature death, and we were able to attribute this phenomenon directly to the sperm themselves. Sperm from other species can displace sperm from the same species and, in some cases, can invade inappropriate parts of the maternal reproductive system and even their non-reproductive tissues. We find that mating to males of another species harms females far more than does within-species mating. Overall, our observations are consistent with ongoing sexual conflict between the sexes within species, arising as a byproduct of sperm competition among the gametes of different males. Finally, patterns of assortative mating indicate that mating behaviours that reduce the likelihood of costly inter-species mating have evolved in this group of animals. These findings support an important role of sexual selection and gametic interactions contributing to reproductive boundaries between species, as predicted by evolutionary theory.
| Rarely do reproductive interests of males and females perfectly align. Sexual selection can accelerate the evolution of the traits and molecules mediating reproductive encounters, and this can lead to sexual conflict [1],[2]. Components of the reproductive system that mediate male-female interactions, such as reproductive tract morphology, sperm and egg traits, and molecular components of seminal fluid all diverge rapidly between many species [3],[4]. The particularly forceful process of sexual antagonism drives co-evolutionary arms races between sex-limited traits that exact or counteract harmful, but self-serving, effects on the other sex [2],[4],[5]. Ongoing sexually antagonistic coevolution that operates within a species might generate mismatched interactions between gametes or other reproductive tract components when mating occurs between species. When such mismatches interfere with normal conspecific reproduction [6],[7], they have the potential to instigate or magnify reproductive isolation between species [8],[9]. Selection for traits that prevent the deleterious consequences of inter-species mating for the parents or hybrid offspring may result in further trait evolution [10]. Though pre-mating reinforcement behaviours have received much attention and debate [10]–[12], post-mating mechanisms of gametic isolation, such as conspecific sperm precedence, also can play key roles in pre-zygotic reproductive isolation [13],[14].
The prevailing view of gametic isolation between species is that fertilization precedence of conspecific sperm can provide a potent reproductive barrier, mediated by cryptic female choice, sperm competition, or incompatibilities between female reproductive tracts and heterospecific ejaculates [15]–[18]. Conspecific sperm precedence occurs both in species with internal and external fertilization, governed by a broad variety of proximate mechanisms [14],[19],[20].
Alternatively, Drosophila provide examples of inter-species mating harm, for example, owing to an overly engorged “insemination reaction mass” that exacts a fitness cost on females [6],[7], and female Carabus beetles suffer ruptured reproductive tracts from physical damage upon inter-species matings [21]. Within species, male seminal proteins can manipulate female physiology in a manner sub-optimal for females but beneficial to males [22]. While coevolution between the sexes may obscure the traces of such sexual antagonism (as for other forms of genetic conflict [23]), interactions between divergent populations and species can unmask the underlying conflicts by revealing mismatched male and female traits [8].
Caenorhabditis nematodes provide a powerful system to examine both sexual antagonism and its modulation by reproductive mode. Males, females, and hermaphrodites will mate readily and promiscuously in lab culture, and mechanical harm incurred from multiple mating reduces longevity and survival in C. elegans hermaphrodites [24] and C. remanei females [25]. Male-derived chemical cues also are thought to accelerate female and hermaphrodite aging [26]. Following copulation, C. elegans hermaphrodites can expel male ejaculates [27], and males deposit copulatory plugs that inhibit re-mating [28],[29] and induce larger brood sizes in their partners [30]. In response to experimentally elevated sperm competition, C. elegans evolve larger sperm [31]. Though anatomical evolution in Caenorhabditis is conservative, these intra-specific dynamics suggest there may be substantial inter-species divergence in cryptic reproductive traits.
Evolutionary transitions in reproductive mode from highly outbreeding to highly self-fertilizing are expected to reduce intra- and inter-sexual conflict [32],[33]. Three species of Caenorhabditis have independently evolved androdioecy (hermaphrodites and males) from dioecy (females and males), such that hermaphrodites are capable of self-fertilization in addition to being fertilized by males [34]. These androdioecious species manifest a “selfing syndrome” analogous to plants that includes reduced sperm size and low mating vigor [35],[36]. Hermaphrodites from such species with relaxed sexual selection might be particularly susceptible to adverse effects of mating to vigorous males from closely related species that have a recent history of strong sexual selection (the “weak inbreeder, strong outbreeder” or WISO hypothesis [33],[37]). Despite the generally limited understanding of Caenorhabditis ecology and inter-species interactions in their rotting fruit and vegetal habitats, some species are sympatric [34],[38], putting them at risk for inter-species encounters. Species readily mate with one another in the laboratory, and the animals' transparent bodies provide literal windows into postmating-prezygotic, and postzygotic, reproductive interactions and barriers [39]–[41].
Here we describe an unprecedented postmating-prezygotic reproductive barrier in Caenorhabditis nematodes, induced directly by sperm cells, that imposes potent fitness costs to females and hermaphrodites. Theory predicts species with selfing hermaphrodites to be more susceptible to inter-species harm and less capable of inducing harm [33],[37]. Theory also predicts that rapid divergence in sexually selected traits will produce heterogeneity in harmful effects between species pairs and consequently may fail to yield phylogenetic signal in the magnitudes of effect [2],[4]. In this counterpoint to mechanisms of conspecific sperm precedence, we affirm a potent role for sexual conflict as a pre-zygotic isolating barrier between species.
In no-choice mating arenas, Caenorhabditis copulate repeatedly (Figure S1; Text S1), and will readily mate with different species [41]–[43]. We first examined the impact of mating the self-fertile hermaphrodites of C. briggsae, C. elegans, and C. tropicalis [44] with males of other species (Figure 1A–1C; Text S1). Despite the presence of abundant self-sperm, offspring production was invariably compromised. By contrast, mating to conspecific males leads to increased reproductive output, because hermaphrodite sperm stores are supplemented by the male sperm (Figure S2) [45]. The extremity of reduced reproductive output upon inter-species mating, however, depends in part on the identity of maternal and paternal partners. For example, C. briggsae and C. elegans hermaphrodites produced <5% and <30% of their normal brood, respectively, when mated with any of seven dioecious species of Caenorhabditis tested (Figure 1A and 1B). C. tropicalis hermaphrodites, however, showed similarly striking sensitivity to males of C. brenneri, but were resistant or less sensitive to males of other species (Figure 1C).
In crosses between C. briggsae hermaphrodites and C. nigoni males, a single heterospecific mating event is sufficient to strongly depress reproductive output (Figure 1D), and prolonged exposure accelerates hermaphrodite mortality beyond that seen in conspecific or reciprocal crosses (Figure 1G). Increasing the abundance of conspecific sperm in hermaphrodites by mating them with conspecific males immediately before or after the heterospecific matings was insufficient to prevent or rescue sterilization of C. briggsae hermaphrodites by C. nigoni males (Figure S2). Males from androdioecious species of both C. elegans and C. briggsae were markedly less able to reduce the reproductive output of heterospecific hermaphrodites compared to the effect of dioecious males (Figure 1E and 1F). However, there is no clear association between degree of sterilization and phylogenetic distance (Figure S3A), although notably C. tropicalis is most sensitive to males of its closest tested relative, C. brenneri.
The above observations demonstrate that (i) hermaphrodites of all three self-fertile species are susceptible to adverse effects of mating with males of at least some other species and (ii) males of all species are capable of adversely affecting the reproductive output of hermaphrodites of at least some species. Such disparities among species in gametic isolation are predicted to be a common outcome of sexual selection on sperm competition [37]. Consistent with intense intra-species sperm competition, multiple mating is readily observed in laboratory populations of outbreeding species (Figure S1; Text S1), while the rarity of males in self-fertile populations [46],[47] suggests that multiple mating in these lineages is rare. Additionally, males from highly selfing species are less harmful to heterospecific mates, consistent with weak inbreeder, strong outbreeder (WISO) dynamics [33],[37].
To assess the generality of heterospecific sterilization, we also examined male effects on females of dioecious species (Figure 2). Despite their similar sperm sizes [48],[49], heterospecific mating of dioecious males from C. remanei, C. nigoni, and C. brenneri reduced the reproductive output of females of C. remanei and C. nigoni (Figure 2A and 2B). However, males from highly selfing species lacked the capacity to compromise the fecundity of heterospecific females (Figure 2A and 2B).
In Drosophila, different levels of sexual antagonism can lead to reduced female survivorship in inter-strain matings [50]. To explore whether heterospecific Caenorhabditis matings exert negative effects beyond reproductive output, we quantified maternal survival in the four cross combinations involving C. briggsae and its outbreeding sister species, C. nigoni. Mating with C. nigoni males significantly reduced the lifespan of C. briggsae hermaphrodites relative to conspecific mating (Figure 1G), but did not adversely affect conspecific C. nigoni female survival in our assay (Figure 2C), consistent with additional harmful effects of heterospecific mating beyond sterilization. Matings between another closely related selfer-outbreeder pair produced a different pattern, in which males from the selfing C. tropicalis exerted a much weaker effect on C. wallacei female longevity than did the other three cross combinations (Figure S4A). Matings between dioecious species yielded heterogeneous impacts on maternal survival. C. nigoni and C. sp. 5 males produced no significant reduction in female survival beyond that seen for conspecific matings (Figure S4B), whereas C. nigoni asymmetrically harmed C. remanei survival (Figure S4C). Similar to sterilization, then, inter-species mating affects maternal survival with species-pair dependencies in both outbreeding and selfing species. This observation is consistent with distinct evolutionary resolutions of sexual conflicts in different lineages. Additionally, we see a compelling effect of reproductive mode: in selfing species, hermaphrodites are more vulnerable, the males more benign, or both (as for C. briggsae).
One possible mechanism of reduced female fitness is competitive displacement of conspecific sperm by heterospecific sperm. Hermaphrodite Caenorhabditis make relatively small sperm that compete poorly with even conspecific male sperm, and their conspecific males make sperm that are smaller than those of dioecious species' males [31],[48],[49],[51]. Thus, the sterilization of hermaphrodites could result from the displacement of self-sperm by larger, yet ineffectual, heterospecific sperm [41]. To address this issue, we labeled C. elegans and C. nigoni males with vital dyes, mated them to phenotypically female C. elegans fog-2 animals, and observed the transferred sperm from each male in live animals. Indeed, within a few hours of mating, we observed strong displacement of the smaller C. elegans male sperm from the spermathecae by the larger C. nigoni sperm (Figures 3A and S6C). However, sperm displacement does not explain the adverse effects of heterospecific mating on survivorship nor does it account for the seeming irreversibility of sterility induced by males upon their mating partners.
C. briggsae hermaphrodites mated to C. nigoni males display striking germ line abnormalities, including disorganized proximal germ cells and ectopic, distally localized diakinesis-stage oocytes (Figure 3C). In addition, embryos often formed distal to the oviduct (Figure 3D), and, more rarely, we observed egg-laying defects (Figures 3G and S5). The proximal mass of disorganized germ cells is reminiscent of ovulation-defective mutant phenotypes in C. elegans [45],[52],[53]. Consistent with this, DNA staining of C. briggsae hermaphrodites revealed extensive endomitotic oocyte accumulation after one day of mating with C. nigoni males (54%, n = 78; Figure 3F), which increased further after a second day (91%, n = 70). No endomitotic oocytes were observed among C. briggsae hermaphrodites mated to conspecific males (n = 51; Figure 3B), indicating that C. nigoni males promote oocyte maturation defects in C. briggsae hermaphrodites.
Males transfer both sperm and seminal fluid components to mates, and in principle, either might negatively affect their partner's reproduction and lifespan after heterospecific matings. To distinguish these possible mechanisms, we again applied a fluorescent vital dye to males, mated them to conspecific or heterospecific individuals, and observed transferred sperm in live animals. After six hours of mating to C. briggsae hermaphrodites, conspecific male sperm had localized to the spermathecae and uterus in all animals (n = 52), as expected (Figure 4A; Movie S1). In a striking contrast, we found that 90% of C. briggsae hermaphrodites mated heterospecifically to C. nigoni males had sperm present in ectopic locations in the distal and proximal gonad, whereas only 10% of animals had sperm properly localized exclusively to the spermathecae or uterus (n = 188; Figures 4F and S6A; Movies S1 and S2). This appears to begin when many sperm penetrate the distal spermathecal valve, which normally separates maturing oocytes from sperm. Further, 7% of the hermaphrodites showed invasion of sperm into the body cavity (n = 159; Figure 4F; see Movie S1).
Sperm of C. nigoni males also commonly invaded the gonad past the spermatheca when mated to other species (C. elegans and C. remanei; Figures S5, S6A–S6C), but the effect was most extreme in C. briggsae hermaphrodites, consistent with its pronounced deficits in reproductive output. Just three hours after mating, more than 50% (n = 72) of C. briggsae hermaphrodites had C. nigoni sperm in the distal gonad (cf. 90% six hours post mating; Figure S6A and S6D), whereas ∼20% (n = 39) of C. elegans and 2.9% (n = 35) of C. tropicalis hermaphrodites were similarly afflicted (Figure S6B). Ectopic sperm also were very rarely observed in C. tropicalis hermaphrodites mated heterospecifically to C. brenneri and C. remanei (0.9%, n = 111 and 0%, n = 18, respectively; Figure S6B), consistent with the infrequency of sterilization observed for this species. The ability of C. nigoni sperm to overrun the spermatheca is greater than that of other species tested. C. remanei sperm were rarely found in ectopic locations in C. nigoni females (5%, n = 100; Figure S6A), and ectopic sperm originating from C. briggsae males were never observed (n = 70; Figure 4A and 4E). Notably, the distinctly stronger sterilization of C. tropicalis by males of C. brenneri than C. nigoni occurs in the face of similar sperm localization patterns three hours post-mating (Figures 1B and S6C), further supporting the idea that factors other than sperm displacement are important in heterospecific sterilization. Intriguingly, in conspecific matings with C. nigoni and C. remanei, 5%–8% of females had small numbers of sperm in ectopic gonad locations (Figures 4C, 4D, and S6A). This low but detectable incidence of mislocalized sperm in conspecific matings is indicative of ongoing sperm-driven sexual conflict within dioecious species.
Negative effects of seminal components contributed by somatic glands can potentially trigger harmful reactions in females and hermaphrodites [54]–[56]. We therefore tested for a direct role of sperm in causing harm to C. briggsae hermaphrodites by feminizing the germ line of C. nigoni males via fog-3(RNAi) (Figure 5A and 5B) [57]. Treated males have a normal somatic testis, mate normally, and can deposit seminal components including the copulatory plug, but transfer no sperm. The reproductive output (Figure 5C) and survival (Figure 5D) of C. briggsae hermaphrodites mated to spermless C. nigoni fog-3(RNAi) males is strikingly higher than that of animals mated to wild-type C. nigoni males. These observations implicate sperm infiltration into the gonad arms (and potentially into the body cavity) as the principal cause of male-induced permanent harm in heterospecific matings.
The extreme fitness costs to inter-species mating might select for behaviours that promote assortative mating [10],[12],[14],[19]. We first examined female avoidance of heterospecific males because Caenorhabditis males mate indiscriminately when given the opportunity, and mating pheromones produced by virgin females are strongly attractive to males across species [43],[58]; tests for assortative mating in choice experiments have not been reported previously between Caenorhabditis species. We created assay populations with an equal mixture of females from each of two species and males from one of them (reciprocally), and then quantified the incidence of avoidance behaviour and of mating success of each species, as evidenced by copulatory plugs placed onto the vulva. This assay design presumes that dioecious males mate indiscriminately [43] and thus that female and hermaphrodite behaviour will dominate mating outcomes (Text S1).
Owing to their aggressive sperm, we first focused on copulatory responses to C. nigoni males. Overall, we observed that C. nigoni females exhibit a conspecific mating bias (Figure 6A–6C). We introduced C. nigoni females and males to either C. briggsae hermaphrodites (Figure 6A), C. elegans hermaphrodites (Figure 6B), or C. remanei females (Figure 6C). C. nigoni males were favoured by conspecific females over all heterospecific mating partners. When C. remanei and C. nigoni females (Figure 6C) were presented with C. remanei males, the females of both species showed no mating biases and were equally likely to mate with the males. This contrasts with the conspecific mating bias we observed when these females were presented with C. nigoni males (Figure 6C). This discrepancy suggests stronger assortative mating bias in favour of conspecifics by C. nigoni females compared to C. remanei females.
We also tested C. nigoni females in the presence of either C. brigggsae hermaphrodites and males (Figure 6A) or C. elegans hermaphrodites and males (Figure 6B). Interestingly, C. nigoni females mated more readily with the heterospecific males than did the hermaphrodites to males of their own species (Figure 6A and 6B). The insensitivity of C. nigoni female reproductive output to having mated with androdioecious males (Figure 2A) could partially contribute to a lack of mate avoidance towards this class of heterospecific males (Figure 6A and 6B), although female sex pheromones [58] and hermaphrodite mating avoidance behaviours [43] also could contribute to this outcome. In addition to the mating outcomes, C. briggsae hermaphrodites were distinct in that they crawled away from the mating area when in the presence of C. nigoni males (t = −2.449, degree of freedom [df] = 44.672, p = 0.018). We interpret leaving the mating area as avoiding copulatory attempts by males.
Here we show that sperm transferred from matings between species of Caenorhabditis nematodes induce severe damage to females, compromising their reproduction and longevity. This phenomenon is the antithesis of the conspecific sperm precedence observed in diverse organisms, but still yields a form of gametic isolation owing to sperm displacement and ectopic sperm migration by heterospecific sperm. Caenorhabditis females commonly mate with multiple males (Figure S1; Text S1), and sperm competition selects for more aggressive sperm within species [31]. Attracted to oocyte-secreted chemical cues, sperm must repeatedly crawl to the spermatheca (the site of fertilization) as embryos push them into the uterus (Figure 7) [45],[59]. We propose that intra-specific sperm competition between males within the reproductive tracts of multiply mated females acts to select for aggressive sperm in an evolutionary intra-species arms race. Sperm migration into distal portions of the gonad would represent a byproduct of sperm competition with harmful consequences for females, that in turn leads to counter-selection for female resistance [4],[60],[61]. While co-evolution within a species largely keeps these interactions matched, male and female changes can fail to complement one another in an inter-species context. Although such divergence need not have precipitated the initial isolating events, they nevertheless contribute to extant reproductive isolation among species. It remains to be tested whether heterospecific sterilization by sperm could also be co-opted as a weapon in inter-species resource competition when multiple Caenorhabditis species inhabit the same resource patch.
This model of sperm competition (Figure 7D) and sexual conflict predicts sexual antagonism as an ongoing selective pressure within species of Caenorhabditis. We find evidence supporting this idea in the low incidence of ectopic sperm migration observed in conspecific matings for C. remanei and C. nigoni (Figure S6A). This model also could explain fertility patterns reported for crosses between parthenogenic and amphimictic Aphelenchus nematodes [62], suggesting applicability of this mode of gametic isolation across diverse taxa. Interactions between species with overlapping geographic ranges, as occurs for some Caenorhabditis including C. briggsae with C. nigoni [34] and C. tropicalis with several species [38], could select for behavioural or gametic strengthening of species boundaries or yield reproductive character displacement [10],[63], consistent with some patterns of assortative mating and sperm migration observed in our experiments (Figures 6, S6A, and S6B). However, further tests comparing sympatric and allopatric genotypes are needed [10],[14]. This model further predicts the evolution of distinct cellular and molecular mechanisms mediating male sperm vigor and female resistance in different species, and relaxation of selection in some highly self-fertilizing hermaphrodite species [33],[37]. Consistent with what we observe empirically, differences among species in the mechanisms of evolutionary response to intra-species sexual conflict will create phylogenetic heterogeneity in how strongly the inter-species mismatches manifest as harm to females and hermaphrodites.
We addressed several other potential mechanisms that could contribute to the compromised maternal fecundity and longevity upon inter-species mating. (i) Trauma through copulation may occur in Caenorhabditis [24], and repeated matings in Drosophila melanogaster can induce female sterility and mortality [54]. We can rule out this possibility because germline-feminized males with normal copulatory behaviours do not affect maternal reproductive output or longevity (Figure 5C and 5D). We also exclude repeated matings as an explanation for reduced reproduction because a single inter-species mating event also severely diminishes hermaphrodite reproductive output (Figure 1D). (ii) We can rule out insufficient sperm or oocytes as the cause of sterilization, as hermaphrodites cease laying embryos despite the abundance of oocytes and sperm in the reproductive tract (Figure 3) [40]. (iii) The reduction in progeny production after heterospecific mating could result from superior fertilization capability of heterospecific sperm, leading to mortality of hybrid embryos, as post-zygotic reproductive isolation is nearly complete between most species studied here. However, we and others have observed insufficient numbers of dead eggs to account for the overall reduction in reproductive output [40]–[42]. (iv) Larger sperm from males of another species also could potentially outcompete and displace the conspecific sperm from the spermatheca, yielding fewer progeny [64]. Indeed, such displacement occurs initially following heterospecific matings to males of a species with larger sperm (Figure 3A). However, hermaphrodite sperm for C. briggsae, C. elegans, and C. tropicalis all are similarly small [48],[49], and yet these species differ starkly in susceptibility to the negative effects of mating to heterospecific males (Figure 1A–1C). Likewise, the disparity in sperm size of males from different dioecious species does not correlate with the extremity of reduced progeny production (Figure S3), and would not be expected to shorten lifespan. These observations reinforce the direct effects of sperm mislocalization in the maternal reproductive tract as the primary mechanism of male-induced permanent harm in inter-species matings.
Although our experiments demonstrate the key role of sperm cells in heterospecific harm upon mating, non-sperm components also could contribute. For example, upon maturation, C. elegans sperm cells release by exocytosis the contents of their lysosome-like membranous organelles (MOs) [65], and it is conceivable that molecules from this sperm cell-derived component of the seminal fluid could interact with female tissues. Indeed, the nematode major sperm proteins (MSPs) act as hormone-like signaling molecules to oocytes and gonad sheath cells, delivered in sperm-derived vesicles, in addition to MSP performing cytoskeletal functions [66],[67]. Additionally, components of the seminal fluid that derive from cells of the male somatic gonad play important roles in reproduction [28],[68]. Such seminal factors might compromise female tissue in some way that makes it more susceptible to adverse effects of heterospecific sperm, perhaps by inducing dilation of the gonadal sheath cells bordering the distal end of the spermatheca. In C. elegans, gst-4 and daf-2 mutants show defects in dilation of the spermathecal valve [69] and many proteins affect sheath contraction [59], suggesting potential pathway targets for compounds transferred by males during mating.
In C. elegans, oocyte-derived chemical cues guide sperm migration to the spermathecae [59]. The attractant comprises a complex mixture of F-series prostaglandins derived from poly-unsaturated fatty acid (PUFA) precursors, whose synthesis and modulation depends on a suite of fatty acid desaturases, glutathione S-transferases, cytochrome P450s, insulin-like signaling, and communication between the somatic gonad and germ cells [69]–[72]. In C. elegans mutants of these genes, conspecific sperm fail to localize efficiently to the spermathecae, instead occurring more readily in the uterus [69],[70]. Heterospecific sperm migrate to the spermathecae efficiently in C. elegans and C. briggsae hermaphrodite reproductive tracts (Figure S6A–S6D) [41], indicating conservation of the sperm attractants and their detection between species. The high density of aggressive heterospecific sperm in their spermathecae then sets the stage for invasion of sperm into ectopic locations. However, sperm localization patterns for C. tropicalis suggests that its oocytes might secrete a sperm cue that is only weakly attractive, or a chemical mixture that attracts sperm efficiently only for some species (Figure S6B). Consequently, heterospecific sperm occur with lower density in the spermathecae, with correspondingly reduced likelihood of invasion into the distal gonad (Figure S6B). This hypothesis could underlie the limited adverse effects of heterospecific mating on C. tropicalis hermaphrodites. It might even implicate the weak sperm attractants as an evolutionary response through gametic reinforcement [20] if negative effects of inter-species mating in sympatry occurred in C. tropicalis' past [38]. Finally, it is conceivable that differences among species in sperm attraction to distinct prostaglandins, or other compounds, coupled with secretion of them by more distally developing oocytes could encourage ectopic sperm migration.
The mechanism by which sperm migrate ectopically past the spermathecal boundary remains a key unsolved problem. Moreover, the breaching of the ovary basement membrane and migration of sperm into the body cavity through amoeboid movement, bearing a striking resemblance to features of metastasis in cancer [73]–[75], motivates further investigation of the molecular basis of tissue integrity and resistance to cellular invasion [76].
Animals were maintained according to standard C. elegans procedures [77], with the exception of increased agar concentration in NGM plates to 2.2% in order to discourage animals from burrowing underneath the surface of the plate. Cultures were maintained at 20°C and 25°C. See Text S1 for strains of each species used for experiments: C. afra (sp. 7), C. brenneri, C. briggsae, C. elegans, C. latens (sp. 23), C. nigoni (sp. 9), C. portoensis (sp. 6), C. remanei, C. tropicalis (sp. 11), C. wallacei (sp. 16), C. sp. 5 [44].
Crosses consisted of placing one hermaphrodite at the fourth larval stage (L4, penultimate stage of development) with six heterospecific males overnight (18–24 hours) on a 35 mm diameter Petri dish with a 10 mm diameter bacteria spot (E. coli OP50). Hermaphrodites (Figure 1A–1F) that successfully mated (presence of a copulatory plug) were transferred daily and we measured reproductive output as the yield of viable adult progeny from two days of egg laying following the final mating event (representing >90% of lifetime brood size). Control hermaphrodites were individuals allowed to produce self-progeny.
For matings involving females (Figure 2A and 2B), we first mated them to conspecific males overnight and the subsequent day we mated treatment females to heterospecific males. In all cases except one (C. briggsae×C. nigoni), matings are incapable of yielding viable hybrid progeny (few hybrids are produced by C. briggsae×C. nigoni [40],[78]). Therefore, reproductive output measures the number of conspecific progeny of females (or, equivalently, self-progeny of hermaphrodites).
The single mating treatment (Figure 1D) consisted of placing ten young adult hermaphrodites (C. briggsae) with 40 young adult males (C. nigoni) on a 35 mm Petri dish with a 10 mm diameter bacteria spot. After an hour, mated hermaphrodites were isolated to individual 35 mm diameter Petri dishes, transferred daily, and allowed to lay eggs in order to measure progeny production.
In situations when both female and hermaphrodites are used, they will be referred to as XX animals as they both have two X chromosomes. Seven L4 XX animals, depending on the species, were placed with ten heterospecific or conspecific males per plate and left overnight. The next day, XX animals were assayed for mortality by being touched on the head with an eyebrow hair glued to a toothpick. If the animal performed a backwards locomotive response to the touch, it was scored as alive. If it did not, it was scored as dead. This was performed every day for at least seven days. Every two days, XX animals and males were transferred to new plates in order to prevent the confusion of progeny with parents. Additionally, in these assays, XX animals were kept under continuous mating conditions: when males died or crawled off the plate, they were replaced with new males. XX animals that crawled off the plate were excluded from the lifespan measurements.
The nuclei of animals were visualized using Hoechst 33258 staining. Seven XX animals were mated with ten heterospecific or conspecific males per plate for 1–3 days, and then XX animals were fixed in 100% methanol overnight at 4°C. The animals were then washed three times in M9 buffer and incubated in 1 µg/ml Hoechst in M9 buffer for 5 minutes, followed by mounting for fluorescent microscopy and imaging.
Male sperm were fluorescently labeled in vivo with MitoTracker Red CMXRos (Invitrogen) [71]. Males were incubated in 1 mM dye for 2–3 hours, and then left on a plate to recover overnight. Subsequently, these males were mated with virgin young adult XX animals for 1–4 hours (matings with C. elegans males were allowed to run overnight). Virginity was assured by isolating XX L4 animals from males before reaching adulthood. Mated XX animals were then mounted on 10% agarose pads [79] or 2% agarose pads and immobilized with 50 mM sodium azide for differential interference contrast (DIC) and fluorescence imaging. Automated time-lapse photography (1–10 frames per second) was performed with the Open Lab software package and a Zeiss Axioskop 2 equipped with DIC and fluorescence microscopy.
A 929 base pair fragment including coding sequence homologous to fog-3 was PCR amplified from C. nigoni genomic DNA using primers flanked with 5′ T7 promoters. The reaction was gel purified using the QIAquick kit (Qiagen), and the resultant template was then used for in vitro transcription using the MAXIscript kit (Ambion) to make dsRNA. The dsRNA was recovered using phenol-chloroform extraction and isopropanol precipitation, and the dsRNA was then introduced into the animals via maternal microinjection. The male progeny of injected animals were scored for the feminization of germline (Fog) phenotype using DIC microscopy via standard methods [77]. The worms were mounted on 2% agarose pads and immobilized with 50 mM sodium azide. Only males with clearly defined oocytes and no observable sperm were used for sterilization and lethality experiments. Fog males were allowed to recover for 30 minutes on a plate in a drop of M9 buffer. These males were capable of performing the mating behaviour and of depositing copulatory plugs (and presumably other seminal fluids). These males were then assayed for their ability to sterilize and prematurely kill C. briggsae hermaphrodites. These males were then used for experiments as described above. Control wild-type males were mounted, immobilized, and allowed to recover for the same amount of time in order to remove these as confounding factors.
We focused our assortative mating assays on C. nigoni males, as their aggressive sperm results in sterility and increased mortality (Figure 1). We expect males to mate indiscriminately [41],[43]; therefore, XX animal behaviours (preference or avoidance) should account for the majority of mating biases observed.
Assortative mating assays consisted of placing ten virgin C. nigoni males with ten virgin conspecific and/or heterospecific mating partners on a 35 mm diameter Petri dish. The three treatments involved presenting males to (i) ten conspecific, (ii) ten heterospecific (C. remanei, C. elegans, or C. briggsae), or (iii) a mixture of five conspecific and five heterospecific mating partners. See Text S1 for results of (i) conspecific and (ii) heterospecific treatment. Control assays consisted of males (C. remanei, C. elegans, or C. briggsae) following the same treatments as above with C. nigoni females as the heterospecific species.
We recorded successful mating by the presence of a copulatory plug deposited by a male onto an XX animal's vulva. We also recorded whether any XX animals left the 3 mm diameter (5 µl) bacterial spot mating area, which, we reasoned, was effective in avoiding copulation. We limited the mating period to 10 minutes to ensure males only mated once (male∶female ratio >1 was used to more easily observe successful copulations with inefficient males of androdioecious species). This 10 minute mating period was determined by preliminary experiments with a male placed with multiple conspecific females. In order to visually distinguish the two female/hermaphrodite species from one another, strains with pharyngially expressed GFP (C. briggsae PS9391) or RFP (C. nigoni VX0092) markers were used, which we presume exerts no direct effect on mate choice. See Text S1 for observed mating frequencies.
All statistical analyses were performed using IBM SPSS Statistics v.20, unless otherwise noted. We conducted non-parametric tests for measures of reproductive output, owing to non-normal distributions and heterogeneous variances. To assess the effect of heterospecific matings on reproductive output (i.e., extent of sterilization), we compared the control (selfing for hermaphrodites and conspecific matings for females) to each treatment (heterospecific mating) using Mann-Whitney U tests with Bonferroni correction for multiple testing. We used Kaplan-Meier survival analysis to test for an effect of mating on survival of females or hermaphrodites. The survival analyses were performed with the OASIS online application [80] and SPSS.
In experiments that explored assortative mating with a mixed species treatment (five conspecific and five heterospecific mating partners; Figure 6), an index of mating bias was calculated as the difference between the number of mated C. nigoni females and the number of mated individuals of the other maternal species present in the arena, divided by the number of C. nigoni females present in the arena (five). Positive values indicate a mating bias towards C. nigoni females over the female (or hermaphrodite) species that they were paired with, negative values indicate the reciprocal, and a value of zero indicates no mating bias (a lack of preference or avoidance). Negative values were not observed in our experiments. We then tested for a significant difference from zero with two-tailed one sample t-tests.
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10.1371/journal.pntd.0002404 | Molecular Assays for Determining Mycobacterium leprae Viability in Tissues of Experimentally Infected Mice | The inability of Mycobacterium leprae to grow on axenic media has necessitated specialized techniques in order to determine viability of this organism. The purpose of this study was to develop a simple and sensitive molecular assay for determining M. leprae viability directly from infected tissues.
Two M. leprae-specific quantitative reverse transcription PCR (qRT-PCR) assays based on the expression levels of esxA, encoding the ESAT-6 protein, and hsp18, encoding the heat shock 18 kDa protein, were developed and tested using infected footpad (FP) tissues of both immunocompetent and immunocompromised (athymic nu/nu) mice. In addition, the ability of these assays to detect the effects of anti-leprosy drug treatment on M. leprae viability was determined using rifampin and rifapentine, each at 10 mg/kg for 1, 5, or 20 daily doses, in the athymic nu/nu FP model. Molecular enumeration (RLEP PCR) and viability determinations (qRT-PCR) were performed via Taqman methodology on DNA and RNA, respectively, purified from ethanol-fixed FP tissue and compared with conventional enumeration (microscopic counting of acid fast bacilli) and viability assays (radiorespirometry, viability staining) which utilized bacilli freshly harvested from the contralateral FP. Both molecular and conventional assays demonstrated growth and high viability of M. leprae in nu/nu FPs over a 4 month infection period. In contrast, viability was markedly decreased by 8 weeks in immunocompetent mice. Rifapentine significantly reduced bacterial viability after 5 treatments, whereas rifampin required up to 20 treatments for the same efficacy. Neither drug was effective after a single treatment. In addition, host gene expression was monitored with the same RNA preparations.
hsp18 and esxA qRT-PCR are sensitive molecular indicators, reliably detecting viability of M. leprae in tissues without the need for bacterial isolation or immediate processing, making these assays applicable for in vivo drug screening and promising for clinical and field applications.
| M. leprae, the causative agent of leprosy, cannot be grown on laboratory culture media. This characteristic, along with its extremely long generation time of 12–14 days, makes the study of the pathogenicity of this organism and the experimental testing of new drugs for the treatment of leprosy extremely difficult. We developed two M. leprae-specific quantitative reverse transcription PCR assays and tested their utility as biological markers of M. leprae viability in tissue specimens. These assays could detect high viability of bacilli growing in immunosuppressed mice as well as the inhibitory effects of anti-leprosy drug treatment, or of the host immune system in immunocompetent mice. The RNA preparations were also successfully used for detection of host gene expression. The application of these assays to various experimental models would benefit characterization of the infection or novel drug screening. Furthermore, because these assays utilize fixed tissues, their potential application to clinical and field settings could enable monitoring of M. leprae viability in conjunction with the host immune response during treatment.
| Mycobacterium leprae, an obligate intracellular pathogen and the etiologic agent of leprosy, cannot be grown in axenic medium. This characteristic, in conjunction with its extremely slow generation time of 12–14 days, hinders experimentation addressing even the most fundamental questions regarding its genetics, metabolism, sensitivity to anti-microbials, and pathogenicity. Live animal models are required for bacterial cultivation. Limited growth occurs in the footpads (FPs) of conventional mice [1], [2], whereas more prolific growth is attained in immunosuppressed rodents [3]–[6] and armadillos [7]. In these models bacterial multiplication is measured in terms of months to years.
Microscopic counting of acid fast bacilli (AFB) is used to enumerate M. leprae [2], [8], [9]. Although this method is considered the gold standard, it is time consuming, labor intensive and restricted with regard to specificity. It conveys the total number of bacteria present and does not distinguish between live and dead bacilli. More recently, a molecular technique for the enumeration of M. leprae based on real time PCR amplification of the repetitive element, RLEP, was described [10]. RLEP PCR had correlative results with microscopic counting and allowed for rapid and specific quantification of M. leprae from both mouse and armadillo tissues. Like microscopic counting, it does not provide absolute data on the viability of M. leprae. AFB counts and RLEP PCR yield viability information only indirectly, as bacterial numbers increase over time in a growing population.
For many years, the only way to truly assess the viability of a particular population of M. leprae was to inoculate serial dilutions of freshly harvested bacilli into the FPs of passage mice [11], [12]. Requiring hundreds of mice and at least a year of subculture to complete, this method, while effective, is highly impractical due to the length of time to obtain results, as well as the cost and numbers of experimental animals required. In an effort to simplify and expedite viability determination for M. leprae, a number of assays have been developed which investigate surrogate markers of viability, such as cell wall integrity or metabolism. These include measurement of morphologic index [13], [14], PGL-1 synthesis [15], [16], generation of intracellular ATP [16]–[20], palmitic acid oxidation in the BACTEC system [9], [21] and by radiorespirometry (RR) [9], [18], [20], [22], [23], and various viability stains [17], [24]–[27]. Currently, the most commonly used techniques are RR and the BacLight viability stain [27].
Improved methods for viability determination that are more sensitive and user friendly would be helpful for clinical and research purposes. A number of molecular assays have been proposed for determining M. leprae viability in environmental or clinical samples based on 16S ribosomal RNA [28]–[30] or messenger RNA (mRNA) ([31], [32]. Recently, a quantitative reverse transcription (qRT)-PCR based molecular assay was developed for M. leprae which used a gene transcript for sodA mRNA [30]. This assay was 100% specific for M. leprae and applicable as a viability indicator for bacilli recovered from short term macrophage cultures. Furthermore, the molecular data from the in vitro experiments showed a strong correlation with RR and BacLight viability staining.
The current studies built upon these principles with three objectives in mind. First, we sought to develop a sensitive and simple molecular assay which could accurately determine viability of M. leprae in infected tissue. Second, we examined the feasibility of eliminating the bacterial isolation steps and determining viability using nucleic acids isolated from ethanol-fixed FP tissues. Lastly, we evaluated the capacity of the molecular assays to monitor drug efficacy by comparing two leprosy drugs in a high bacterial burden, athymic mouse FP model. Results showed that when compared to the conventional methods of RR and BacLight viability staining, the hsp18 and esxA qRT-PCR assays were sensitive and reliable biological indicators of M. leprae viability in tissues, and that concomitant host gene expression could be monitored from the same RNA preparations.
These studies were performed under a scientific protocol reviewed and approved by the National Hansen's Disease Programs Institutional Animal Care and Use Committee (Assurance #A3032-01), and were conducted in accordance with all state and federal laws in adherence with PHS policy and as outlined in The Guide to Care and Use of Laboratory Animals, Eighth Edition.
M. leprae, strain Thai-53, is maintained in athymic nu/nu mice through serial passage. Freshly harvested bacilli are stored at 4°C and used within 24 hours of harvest [9]. In this study, BALB/c and athymic nu/nu mice (Harlin Sprague-Dawley, Inc., Indianapolis, IN) were infected by inoculating each hind FP with 3×107 M. leprae in 0.03 ml PBS (Irvine Scientific, Santa Ana, CA) [33], [34]. FPs were harvested on Day 1 post infection and at 4, 8, 12 and 17 weeks.
At 18 weeks post infection, groups of M. leprae-infected nu/nu mice were treated, by gavage, with rifampin (RMP, 10 mg/kg) or rifapentine (RPT, 10 mg/kg) emulsified in hydroxypropyl-β-cyclodextrin/L-α-phosphatidylcholine. All drugs were purchased from Sigma-Aldrich (St. Louis, MO). Each drug was administered as a single dose, five daily doses, or twenty daily doses (5 days per week for 4 weeks). Control mice were given vehicle only. FPs were harvested 1 month after completion of treatment for each regimen.
The feet were disinfected with 70% ethanol and Betadine, the skin removed, and the FP tissue excised. Viable M. leprae were collected immediately from the right FPs. The left FP tissues were stored in 70% ethanol at −20°C until processed for DNA and RNA purification.
To harvest the bacilli, the right FP tissues were minced and gently homogenized in hand-held Tenbroeck tissue glass grinders (Fisher Scientific, Pittsburgh, PA) in 2.5 ml RPMI (Life Technologies, Grand Island, NY) containing 50 µg/ml ampicillin (Sigma-Aldrich) and trypsin. After incubation at 37°C for 15 minutes and slow speed centrifugation (100× g) for 1 minute to remove most of the tissue debris, the supernatants were pelleted (10,000× g for 30 minutes), resuspended, and sonicated in RPMI+10% FBS (Hyclone Laboratories, Logan, UT)+ampicillin. These bacterial suspensions were subjected to microscopic counting, BacLight viability staining, and RR.
Three smears were prepared from each FP sample and counts of bacilli in twenty microscopic fields per smear were calculated to determine the number of AFB present in that particular FP [8]. Data are reported as mean +/− S.D. of 4–10 mice per group.
RR was performed as described previously [9]. Briefly, M. leprae from individual FPs were suspended in 1.0 ml of BACTEC 7H12B medium (Becton Dickinson, Franklin Lakes, NJ) in a 6 ml glass shorty vial (Wheaton Industries Inc., Millville, NJ) with a loosened cap. The vial was placed into a liquid scintillation vial with a 2″×4″ strip of Whatman #42 filter paper (Fisher Scientific) that had been soaked in Kodak concentrate I (Eastman Kodak Co., Rochester, NY) and dried. 14CO2 evolution was measured daily for seven days. Results were calculated as cpm 14CO2 per 106 bacilli and reported as mean +/− S.D. of 4–10 mice per group.
M. leprae from individual FPs were washed twice in sterile saline and stained using a BacLight Viability Staining Kit (Life Technologies) as previously described [27]. Briefly, the bacterial suspension was incubated for 15 minutes at room temperature in 6 µM Syto9 and 30 µM propidium iodine. The bacteria were washed with sterile saline and the pellet resuspended in 5% glycerol in saline. Five µl of the suspension was spread onto a slide, and viability was determined by counting the red and green bacilli, indicating dead and live bacteria, respectively, under a Nikon fluorescence microscope. The excitation/emission maxima are 480 nm/500 nm for Syto9 and 490 nm/635 nm for propidium iodide. Results are calculated as percent viability and reported as mean +/− S.D. of 4–10 mice per group.
RNA and DNA were purified from the left FPs using a previously described protocol [30]. Individual fixed FPs were removed from the ethanol, rehydrated, minced, suspended in 1.0 ml TRIzol reagent, and homogenized twice in FastRNA blue tubes using the FastPrep FP 24 instrument (MP Biomedicals, Solon, OH). Tubes were chilled on ice for 5 minutes, after which 200 µl of chloroform-isoamyl alcohol was added. After vortexing for 10 seconds and centrifugation at 700× g at 4°C for 5 minutes, the supernatants were transferred to new tubes, spun again at 14,000× g for 10 minutes, and the RNA collected from 300 µl of the aqueous phase. After incubation at −70°C overnight, the precipitated RNA was resuspended in 30 µl DEPC treated water, and contaminating DNA was removed using a Turbo DNA-free kit (Life Technologies). The purified RNA (150 µl) was stored at −70°C. DNA was purified by adding 100 µl of 10 mM Tris-EDTA and 150 µl of chloroform-isoamyl alcohol to the remaining aqueous phase and interphase material, homogenizing in the FastPrep 24 FP instrument twice, and centrifuging at 14,000× g for 10 minutes. The aqueous phase (200 µl) was precipitated with 5 M ammonium acetate and two volumes of cold ethanol, incubated at −70°C overnight, washed in 70% ethanol, dissolved in 30 µl 1× TE, and stored at −70°C.
RNA from 3×103 M. leprae, as determined from the number of RLEP genome equivalents from each specimen, was reverse transcribed. Titration experiments had shown that RNA from this number of nu/nu mouse-derived viable bacilli would consistently give a strong signal in the RT-PCR reactions. The RNA was converted to cDNA using an Advantage RT-for-PCR kit (Clontech, Mountain View, CA) consisting of reverse transcriptase, Advantage cDNA polymerase mix, and random hexamer primers at 42°C for 1 hour, 94°C for 5 minutes, and 4°C for 5 minutes. For mouse gene expression, 1 µg RNA was reverse transcribed to cDNA using the same conditions. Control for DNA contamination consisted of equivalent amounts of RNA, polymerase mix, and primers without the reverse transcriptase.
Molecular enumeration of M. leprae was determined using the purified DNA fraction from each specimen via Taqman technology using primers and a probe for a common region of the RLEP family of dispersed repeats in M. leprae as previously described [10]. Molecular viability of M. leprae was determined using the cDNA generated from the RNA fraction for each specimen and qRT-PCR. Primers and probes for each target sequence were designed using Primer Express 2.0 software (Life Technologies): hsp18 primers: forward – cgatcgggaaatgcttgc, reverse – cgagaaccagctgacgattg, probe - 6Fam-acaccgcgtggccgctcg; esxA primers: forward – ccgagggaataaaccatgca, reverse – cgtttcagccgagtgattga, probe - 6Fam-tgcttgcaccaggtcgccca. Five µl cDNA were added to the reaction mixture and real time PCR was performed using cycling conditions of 40 cycles of 60°C annealing, extension for 60 seconds, and 95°C denaturation for 15 seconds. PCR and data analyses for all assays were performed on a 7300 RealTime PCR System (Life Technologies). Results of the Taqman assays were applied to a standard curve generated by preparing 4-fold serial dilutions of a known number of M. leprae. Results were reported as mRNA equivalents for each gene transcript analyzed.
Mouse gene expression was evaluated utilizing cDNA and commercially available specific primer sets and probes for TNF, IFNγ, and CCL-2, and Universal Master Mix (Life Technologies). Data was analyzed by the ΔΔCT method and expressed as a log-fold increase in expression over uninfected FPs. GAPDH was used to normalize for template variation. Results are reported as mean +/− S.D. of 4–10 mice per group.
Data were analyzed using unpaired t tests or the non-parametric Mann-Whitney test and compared by group and within a group over time using SigmaPlot 12.0 software (Systat Software, Inc, Chicago, IL). Data was considered significant at P<0.05.
Microscopic counting of AFB is shown in Figure 1A. On Day 1 post infection, 4.78×106±1.30×106 and 5.03×106±2.23×106 AFB were recovered from the BALB/c and nu/nu FPs, respectively. This recovery of approximately 16% of the inoculum is typical considering the architecture of the mouse FP and is consistent with previous reports [10], [35]. In the nu/nu FPs, the number of M. leprae steadily increased over the infection period reaching 4.25×108±3.46×108 by 17 weeks post infection (p<0.001). In contrast, the number of AFB in the BALB/c FPs remained steady at 4 weeks and then declined at 8 (p<0.001), 12 (p = 0.006), and 17 (p = 0.035) weeks.
The conventional viability assays, BacLight staining and RR, were performed on M. leprae isolated from the BALB/c and nu/nu mouse FPs throughout the infection period to determine its viability. Using the BacLight staining method (Figure 1B), M. leprae from BALB/c and nu/nu FPs were 78.71±10.53 and 82.34±7.87 percent viable, respectively, on Day 1. Percent viability increased to 90.51±4.90 in the nu/nu FPs by 12 weeks (p = 0.029) post infection. In BALB/c mice, M. leprae viability decreased to 60.72±7.16 percent by 4 weeks (p<0.001) and to 29.79±8.90 percent by 8 weeks (p<0.001) post infection. Percent viability held at this level for the remainder of the infection period.
Using the RR assay (Figure 1C), M. leprae viability reported as 14CO2 generated per 106 bacilli showed a slight increase by 4weeks (p = 0.049) in the nu/nu mice. In contrast, M. leprae from BALB/c FPs exhibited a 1.5 log decline in metabolic activity by 4weeks post infection (p<0.001).
RLEP enumeration, performed on DNA purified from the left FP tissues, demonstrated that 6.27×106±5.88×106 M. leprae were recovered from BALB/c FPs and 4.07×106±3.78×106 were recovered from nu/nu FPs on Day 1 (Figure 2A). An initial lag phase was evident in the nu/nu FPs yet growth reached 9.70×107±1.35×108 (p = 0.015) by 17 weeks post infection. In the BALB/c FPs, there was a significant decrease in the number of M. leprae at 4 (p = 0.022), 8 (p = 0.003), 12 (p<0.001) and 17 (p = 0.002) weeks.
hsp18 and esxA qRT-PCR assays yielded strong signals when evaluated for use as indicators of M. leprae viability in our models. In both assays, expression of the transcripts was maintained in the nu/nu FPs on the order of ∼105 hsp18 (Figure 2B) or esxA (Figure 2C) equivalents per 3×103 M. leprae over the course of infection. In contrast, these assays demonstrated a sharp decline in viability in the BALB/c FPs by 8 weeks to 1.95×103±8.76×102 for hsp18 (p<0.001) and 4.30×103±1.69×103 for esxA (p<0.001), and remained on the order of 103 equivalents for the remainder of the infection period.
The conventional and molecular assays were compared in a multibacillary FP model for their capacity to monitor drug efficacy. Athymic nu/nu mice were infected with 3×107 M. leprae. At 18 weeks post infection, rifampin or rifapentine (each at 10 mg/kg) were administered to groups of mice for 1 treatment (1×), 5 daily treatments (5×) or 20 doses at 5 days per week for 4 weeks (20×). FPs were harvested 1 month post treatment for each regimen.
Results of the conventional viability assays are shown in Figure 3. One treatment with rifampin or rifapentine did not decrease M. leprae viability when measured by BacLight staining (Figure 3A) or RR (Figure 3B). Rifapentine reduced M. leprae viability to 56.83±4.11 percent (p<0.001) after 5 daily treatments and to 27.78±6.05 percent (p<0.001) with 20 doses as measured by BacLight staining (Figure 3A). Twenty doses of rifampin decreased viability to 37.71±6.79 percent (p = 0.057). Rifampin was more effective when assessed using the RR assay (Figure 3B) and decreased M. leprae metabolic activity by approximately 1 log (p<0.001) after 5 treatments. Rifapentine treatment reduced metabolism >2 log (p = 0.016). Both rifampin and rifapentine were active at 20× (p = 0.016).
In agreement with the conventional viability assays, neither rifampin nor rifapentine at a single dose decreased M. leprae viability when assessed by hsp18 (Figure 4A) or esxA (Figure 4B) qRT-PCR. Control FPs expressed ∼105 hsp18 or esxA equivalents per 3×103 M. leprae. Five doses of rifapentine reduced this to 1.88×103±1.37×103 hsp18 (p = 0.001) and 3.04×103±1.33×103 esxA (p = 0.001) equivalents, while 5× rifampin showed no significant decrease in expression in either assay. However, these molecular assays demonstrated that both rifampin and rifapentine were highly effective at 20 doses when measured by hsp18 expression (p = 0.016 and p = 0.019, respectively) or esxA expression (p<0.001 and p = 0.009, respectively).
The RNA preparations were also examined for cytokine and chemokine expression. As shown in Table 1, high levels of TNF were expressed by Day 1in the FPs of both strains of mice. TNF expression increased in BALB/c FPs by 8 weeks (p = 0.031) but decreased in nu/nu FPs (p = 0.028). Little or no IFNγ was expressed by either strain on Day 1, but expression increased by >2 log in BALB/c (p<0.001). IFNγ expression also increased in nu/nu FPs (p = 0.005) but not to the extent of BALB/c. Similar levels of CCL-2 were expressed by both strains at both time points.
The lack of an in vitro cultivation system for M. leprae has made determination of its viability extremely difficult in experimental models of the disease and in human lesions because current techniques require large numbers of purified, viable bacteria. This restricts investigation into the pathogenicity of M. leprae as well as the experimental testing of novel drugs for the treatment of leprosy. Therefore, the purpose of this study was to develop a simple and sensitive molecular assay for determining M. leprae viability directly from infected tissues. Two M. leprae-specific qRT-PCR assays based on the expression levels of esxA and hsp18 were developed and tested in the mouse FP model using both immunocompetent and immunocompromised mice. These qRT-PCR assays could detect high viability in the athymic nu/nu FP as well as killing of M. leprae by the host immune system in the BALB/c mouse, or by antimicrobial treatment of nu/nu mice having highly multibacillary FPs. The RNA preparations were also successfully used for detection of host cytokine expression.
The hypothesis tested in this study was that viability is related to the expression of specific genes; therefore, monitoring a specific M. leprae gene transcript(s) by qRT-PCR should provide a simple and sensitive assay for determining its viability. Molecular methods have been developed to ascertain the viability of several infectious organisms [36]. Early studies used levels of ribosomal RNA as a marker of viability [37]–[40]. However, its long half-life and inconsistent retention made it somewhat less accurate, especially for short term experimentation. Because of its relatively short half-life, mRNA has been used successfully as a viability indicator for a number of pathogens [41]–[43] including M. tuberculosis [44]–[46]. The choice of transcript was an important consideration in all of these studies, not only for sensitivity but also for its expression under a variety of circumstances. In addition, different viability assays have varying abilities to differentiate cell death, which is often highly dependent on how the organism is killed and how cell death is defined [41], [47].
Initially we tested the expression profiles of several M. leprae genes as potential indicators of viability in our system. These included: sodA, encoding superoxide dismutase A and which was used successfully in the M. leprae-infected macrophage cultures [30]; gap, encoding glyceraldehyde-3-phosphate dehydrogenase; ML2138C, encoding a probable transmembrane protein; hsp18, encoding the 18 kD heat shock protein; and esxA, encoding the ESAT-6 protein. These transcripts were chosen based on their high expression levels in DNA microarray experiments at 6 months post infection in the athymic nu/nu FP model [48]. However, sodA, gap, and ML2138C genes were not transcribed in sufficient quantities at the early time points in the FP models and therefore did not possess the requisite sensitivity (data not shown). In contrast, the hsp18 and esxA-based assays were highly expressed in both immunocompetent and immunosuppressed mice. Moreover, they were both able to accurately determine loss of viability when using two different methods of bacterial killing, i.e. immunologically mediated and anti-microbial drugs, which strengthens the validity and usefulness of these assays.
In the present study, athymic nu/nu and BALB/c mouse FPs were inoculated with a relatively high dose of M. leprae. We chose this dose for infection because, as an immunizing dose in BALB/c immunocompetent mice, it would be recognized and killed in the first 1–2 months of infection; yet, in the immunocompromised athymic nu/nu mice the inoculum would continue to grow. When inoculated with fewer M. leprae, (e.g., 103 to 104), the maximum growth attained in an immunocompetent mouse FP is approximately 106 bacilli [1]. This growth plateau, which is seen at approximately 6 months post infection, is due to death of the bacilli by the immune response. Once bacterial numbers peak, viability decreases with a half-time of loss of 25 days [12]. Using the hsp18 and esxA qRT-PCR assays, a strong signal could be obtained with RNA from 3×103 M. leprae.
Over the years a variety of techniques have been developed in an attempt to circumvent the inability to culture this organism. Each assay measures different aspects of M. leprae viability and has provided insights into its unique properties. For our conventional viability determinations, we used RR [9], [18], [20], [22], [23] and BacLight viability staining [27]. RR measures the oxidation of 14C-palmitic acid to 14CO2; thus, viability is defined in terms of the metabolic activity of the bacterial population. Viability staining uses fluorescent dyes that bind nucleic acids, with one dye that can penetrate cell membranes and one that cannot. A differential staining pattern is exhibited by live bacteria with intact cell membranes versus dead bacteria with damaged membranes. Thus, viability in this assay is assessed in terms of membrane integrity of individual bacteria. While these assays work very well for in vitro viability determinations, and for ex vivo use, they also have their limitations. Both require immediate, labor intensive purification of M. leprae and processing of samples as the bacilli rapidly lose viability once removed from the host. The bacterial population must be relatively clean and free of most host tissue debris to obtain clear results, especially for the BacLight assay. For RR, a minimum of 106 organisms are required for accurate readings. Furthermore, matters regarding the use of radioisotopes, including worker safety and waste disposal, must be considered. Although valuable in the laboratory, these conditions make these assays impractical for evaluating M. leprae viability in biopsy specimens in clinical or field settings where the reagents and equipment required for bacterial isolation and viability assessment are not readily available.
Therefore, in conjunction with the development of the molecular assays, we evaluated the feasibility of eliminating the bacterial isolation steps and assessing viability on nucleic acids purified directly from ethanol-fixed FP tissue. Our success with this protocol certainly indicates potential for use with biopsy specimens in the clinic or field. Although we have not evaluated formalin-fixed paraffin-embedded tissue (FFPE) for RT-PCR, Su, et al. [49] have shown that ethanol fixation for samples slated for RT-PCR is far superior to FFPE and that these tissues can be stored for weeks. Our unpublished results show that RNA can be preserved for months in 70% ethanol at room temperature. Thus, tissues can be easily fixed and stored in 70% ethanol for transport back to the laboratory for processing [50], [51].
A potential application for these molecular viability assays is in the monitoring of treatment. Lesions of multibacillary patients often contain numerous bacilli even years after completion of multidrug therapy, inciting concern over the possibility of inadequate treatment, lack of compliance, or drug resistance. Our studies using well defined models, i.e. immunocompetent and athymic nu/nu mice infected with a well characterized inoculum [9] of known viability and duration of infection, have allowed characterization of the optimum parameters for use of the hsp18 and esxA transcripts as viability indicators for M. leprae in tissues. Further studies, of course, must be done to determine whether these parameters will suffice for assessing viability of M. leprae in patient samples.
RMP is a very effective anti-leprosy drug and an integral part of the WHO multidrug regimen [52]. Early investigations with RMP found it to be more rapidly bactericidal than dapsone [53], [54], with reports that even a single treatment of patients rendered M. leprae non-infectious for mice [55]. Similarly, treatment of M. leprae-infected immunocompetent mice with a single dose of RMP had a significant bactericidal effect [54], [56]–[58]. Subsequently, several investigators examined various therapeutic regimens, with and without RMP, using M. leprae infection of athymic nu/nu mice. The nu/nu model removes the likely contribution of the immune system to aid the drug therapy in its bactericidal effects and treatment of a high bacterial burden, “lepromatous” infection can be tested. The efficacy of rifampin in this model has been variable [59]–[63]. Early studies showed that single [60] or intermittent [59] dosing with RMP was not effective and that 99.99% killing was not achieved with each dose.
In a large, well-controlled trial designed to determine the effectiveness of single dose rifampicin to prevent leprosy in close contacts in a high endemic area, Moet, et al. [64] found a 57% reduction in the overall incidence of leprosy in the treatment group at 2 years. This efficacy was maintained but not improved at 4 and 6 years of follow-up [64], [65]. However, when they evaluated subgroups of contacts, chemoprophylaxis with single dose rifampicin was less effective in contacts of patients with multibacillary disease and in contacts that were seropositive for PGL-1. They postulated that the bacillary load in these contacts at the time of treatment may have already been too high to be eliminated.
In our present study, the purpose of the RMP and RPT treatments was to validate the molecular viability assays, and we used a high bacterial burden, lepromatous model. Neither a single dose nor 5 daily doses of RMP were effective when measured by either BacLight staining or the molecular assays, although a decrease in metabolic activity was detected by RR after 5 doses. Treatment with 20 daily doses, however, showed strong inhibition in all assays. RPT, in contrast was effective at 5 daily doses, likely due to its longer retention and more potent anti-mycobacterial activity [58], [66]–[69]. Altogether, these collective findings underscore the adjunctive role played by immunity in successful chemotherapy and emphasize the issues which must be considered when treating the susceptible host.
An added bonus to monitoring gene expression in situ is that host gene expression can be determined using the same RNA preparations. If using the conventional assays in our experimental studies, separate groups of mice must be employed for host gene expression and M. leprae enumeration and viability determinations. With the molecular assays, both host and bacterial expression can be determined using the same RNA samples, thereby greatly reducing the number of animals required for an experiment. Likewise, successful application of these techniques in the clinical setting could enable the monitoring of M. leprae viability and correlation with the immune response. A particular need would be during the treatment of reactional episodes, an aspect of leprosy which is still poorly understood.
In conclusion, these results show that the hsp18 and esxA qRT-PCR assays are reliable indicators for determining M. leprae viability in tissue specimens. Their sensitivity and simplicity make them useful for initial in vivo drug screening. Moreover, their ease of use makes them attractive for potential application in clinical and field settings, and both M. leprae viability as well as host responses during treatment can be monitored.
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10.1371/journal.pntd.0003174 | Serological Diagnosis of Paracoccidioidomycosis: High Rate of Inter-laboratorial Variability among Medical Mycology Reference Centers | Serological tests have long been established as rapid, simple and inexpensive tools for the diagnosis and follow-up of PCM. However, different protocols and antigen preparations are used and the few attempts to standardize the routine serological methods have not succeeded.
We compared the performance of six Brazilian reference centers for serological diagnosis of PCM. Each center provided 30 sera of PCM patients, with positive high, intermediate and low titers, which were defined as the “reference” titers. Each center then applied its own antigen preparation and serological routine test, either semiquantitative double immunodifusion or counterimmmunoelectrophoresis, in the 150 sera from the other five centers blindly as regard to the “reference” titers. Titers were transformed into scores: 0 (negative), 1 (healing titers), 2 (active disease, low titers) and 3 (active disease, high titers) according to each center's criteria. Major discordances were considered between scores indicating active disease and scores indicating negative or healing titers; such discordance when associated with proper clinical and other laboratorial data, may correspond to different approaches to the patient's treatment. Surprisingly, all centers exhibited a high rate of “major” discordances with a mean of 31 (20%) discordant scores. Alternatively, when the scores given by one center to their own sera were compared with the scores given to their sera by the remaining five other centers, a high rate of major discordances was also found, with a mean number of 14.8 sera in 30 presenting a discordance with at least one other center. The data also suggest that centers that used CIE and pool of isolates for antigen preparation performed better.
There are inconsistencies among the laboratories that are strong enough to result in conflicting information regarding the patients' treatment. Renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM.
| Paracoccidioidomycosis (PCM) is a neglected systemic fungal infection prevalent mostly in South America. Serological tests have long been established as rapid, simple and inexpensive tools for the diagnosis and follow-up of PCM. However, different protocols and reagents are used. We compared here the performance of six Brazilian reference centers for serological diagnosis of PCM. Each center provided 30 sera of PCM patients, with positive high, intermediate and low titers, which were defined as the “reference” titers. Each center then applied its serological routine test to the 150 sera from the other five centers blindly as regards to the “reference” titers. Surprisingly, all centers exhibited a high rate of discordances (mean of 31 discordant scores in 150 sera tested). When the scores given by one center to their own sera were compared with the scores given to their sera by the other centers, a high rate of major discordances was found (a mean of 14.8 sera in 30 presented a discordance with at least one other center). We concluded that there are inconsistencies among the laboratories that can potentially result in conflicting information regarding the patient's treatment. Renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM.
| Paracoccidioidomycosis (PCM) is a neglected systemic fungal infection prevalent mostly in South America. Despite the significant progress in several areas of knowledge since it was described by Adolpho Lutz, in 1908, it still shows high rates of morbidity and mortality and low visibility [1]. In rural areas of Brazil there are approximately four new cases per million inhabitants, making it the third cause of death from chronic infections, with 1.65 cases per million [2].
The gold standard of PCM diagnosis is the visualization of yeast cells with typical multiple budding aspect in a clinical sample or isolation of the fungus in culture medium [3]. The latter has either low sensitivity when samples obtained from non-sterile sites (e.g., sputum) are used, or is more sensitive in sterile, deep-seated site samples, which, however, are not frequently available. In addition, the growth of P. brasiliensis can take several weeks [3], [4]. Serological tests have been established since the 70's contributing to the rapid, simple and inexpensive diagnosis of the mycosis [5]–[8].
Several antigenic preparations, including sonicated extracts and filtered phase concentrated of cultures of the yeast form of the fungus, have so far been used for the serological diagnosis of PCM [9]. Early on some authors reported on the issue of variability in the antigen preparations [10], [11]. The growth of yeast cells is performed in culture media and conditions such as incubation time, temperature, size of inoculum, with or without agitation, can lead to differences in the antigens produced in different diagnostic centers.
In fact, different protocols and antigen preparations are currently used by these centers for the serological diagnosis and follow up of patients with PCM. Most centers use semiquantitative immunoprecipitation techniques, either the double immunodiffusion (DID) or counterimmunoelectrophoresis (CIE), or both [7], [12]–[14]. However, their performance is not routinely checked, in part due to the lack of external standards. Only an internal positive control is used, which in most centers is a patient's serum with a known positive titer. Moreover, the few attempts put forward to standardize the routine serological methods used in PCM patients have not succeeded [15]. One major reason is that the reference centers have been carrying out in house methodologies for many years with apparently satisfactory performances [11], [15], [16]. However, unfortunately in most instances these centers do not have feedback regarding the clinical correlation from the physicians assisting the patients.
To address this issue, we compared the performance of laboratories from six medical mycology reference centers in Brazil that carry out routine serological diagnosis of PCM. The results show that there are inconsistencies among the laboratories, strong enough to result in conflicting information regarding the patient's treatment, and that renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM.
Six reference centers that traditionally and routinely perform serological diagnosis of PCM participated in this study. They all have made significant scientific contributions to the improvement of the serological diagnosis of this mycosis and for that reason were invited to participate in the study: Mycoses Immunodiagnosis Laboratory, Adolfo Lutz Institute, São Paulo (IALSP); Myco-serology Laboratory, Department of Microbiology, Immunology and Parasitology, Federal University of São Paulo (UNIFESP); Clinical Mycology Laboratory, Pharmaceutical Sciences School, São Paulo State University (UNESP), Araraquara, SP; Serology Laboratory, Clinics Hospital, Ribeirão Preto School of Medicine of the University of São Paulo (FMRPUSP); Medical Mycology Laboratory, Laboratory of Teaching and Research in Clinical Analysis from Maringá State (LEPAC); and Medical Mycology Laboratory Clinics Hospital of the Medical School (LIM53) and Tropical Medicine Institute, University of São Paulo (IMTSP).
Each center was requested to provide 30 sera of PCM patients from their repository, with positive high, intermediate and low titers according to their own criteria. The anonymized sera were numbered 1–30 and aliquots of 120 µl were sent to the remaining five centers to perform their own serological assays. Thus each center performed their usual serological assays in 150 sera from 5 different centers blindly with regard to the “reference” titer of the sera. The results were then sent directly to the coordinating center (IMTSP), which analyzed the data. In addition, the coordinating center also provided all centers with aliquots of 6 healthy donor sera, as negative controls. These donors did not have previous history of tuberculosis or any other significant infectious disease, and the sera were non-reactive for PCM and histoplasmosis.
The study was approved by the Human Research Analysis Ethics Committee of the Hospital das Clínicas da Faculdade de Medicina da USP, accession number #7915.
All centers employed an immunoprecipitating technique, either semiquantitative DID [17] or CIE [18]. The isolates used for antigen preparation are shown in Table 1. Two antigens were used: (a) the somatic antigen, obtained through sonication (100–150 W for 30′) of the cells grown for 15 days in Fava Netto's medium at 35°C [19] and (b) the culture filtrate (metabolic antigen), obtained from yeast cells grown in Negroni's medium for 7–10 days (log phase growth) at 37°C [11]. The sonicated antigen is kept frozen (−20°C) while the culture filtrate is stocked at 4°C [11], [19]. Under these conditions, they are stable for several years. Reactivity of each new batch is tested comparatively with the previous one using patients' sera with high, intermediate and low titers, as well as with a control negative sera and sera from patients with other fungal infections. Briefly, for the DID, glass slides (25×75 mm) were covered with melted purified agar gel punched according to a pattern (a central well surrounded by six wells). The antigen solution was placed in the central well while the peripheral wells were filled with the patient's sera and, as a positive control, either a patient's serum with a known positive titer or rabbit hyperimmune serum. Slides were incubated in a moist chamber at room temperature (20–25°C) and washed with 5% sodium citrate followed by 0.9% saline. They were dried and stained with Coomassie Brilliant Blue R (Sigma, USA). The CIE is also based on the diffusion of proteins but an electric current is applied through a buffered diffusion medium to accelerate the migration of antibody and antigen, with formation of the precipitation lines after around one hour. For the CIE, the glass slides were covered with 1% buffered agarose gel (pH 8.2) and two parallel rows of wells were punched in the gel. The patient's serum samples and positive control were applied to the anodic side and the antigens to the cathodic side of the slides. All sera were diluted two-fold in 0.9% saline and were tested from the undiluted sample. After electrophoresis, the slides were washed in 0.9% saline, dried and stained with Coomassie Brilliant Blue R. The differences in the protocols used by each center are detailed in Table 1.
All centers provided, as requested, 30 sera of PCM patients from their repository, collected within the last five years. These sera were then assayed blindly with regard to their titers by the other 5 centers. For this, the centers were randomly assigned A to F and the sera were numbered 1 to 30 by three of the authors (GMBDN, CPT, GB) who did not participate in the serological assays. To allow comparison among the centers' results, titers were transformed in scores ranging from 0 (negative) to 3 (high titers) according to each center's criteria as described in Table 2. Scores of the sera provided for this study ranged from 1 to 3, with score 1 corresponding to healing titers, and scores 2 and 3 corresponding to active disease with, respectively, low and high titers.
Each center's set of sera was assayed by the other five centers. The results from the donor center, arbitrarily defined as the reference score for their own sera, were then compared with the results of the other five centers. Discordance was defined as a different score, which could be minor, i.e., without a putative clinical consequence for the patient, or major, when the discordance could potentially lead to conflicting decisions regarding the patient's treatment. Minor discordances were between (a) scores 0 and 1: in both cases, either a negative serological result, or a low (healing) titer, would suggest inactive disease and both, in association with clinical and other data, eventually indicate treatment cessation; or (b) scores 2 and 3, both of which are associated with active disease. Major discordances were between scores 2 and 0 or 1, and between 3 and 0 or 1, which, when associated with proper clinical and other laboratorial data, may have led to a different treatment of the patient. Comparisons among laboratories were done using the Chi-square and Fischer exact test. Differences were considered significant when p<0.05.
All centers exhibited a surprisingly high rate of “major” discordances when the scores given by each center to the sera provided by the other 5 centers were compared with the “reference” scores (Table 3). There was some variability in the rate of discordances among the centers, ranging from 22 (15%) to 45 (30%) “major” discordant scores out of 150 scores given, and a mean number of discordant scores of 31 (20%). In fact, the rates of discordances differed significantly among the centers (p = .0007, Chi-square). Minor discordances were also highly frequent, ranging from 16 to 52 out of 150 scores given and a mean of 36 (24%) (Table S1).
Analysis of the performance using the scores given by one center to their own sera (reference score) and comparing them to the scores given to their sera by the remaining five other centers, showed a high rate of major discordances as well (Table 4). For example, 15 out of the 30 (50%) center A's reference scores were discordant with at least one of the remaining centers' scores, and, eight out of the 30 reference scores (27%) were discordant with two or more of the remaining centers. Again variability in the rates of discordance was detected among the centers: in the first comparison, it ranged from 9 (30%) to 23 (77%) scores (p = .0157, chi-square) and for the second comparison it ranged from 2 (7%) to 10 (33%) scores (p>0.05, Chi-square). The mean numbers of sera presenting a “major” discordance were respectively 14.8 and 7.1. In all, considering the 180 references scores provided by the 6 centers to their own sera, 79 (44%) of them presented a major discordance with at least one of the other center's score, and 43 (24%) presented a major discordance with at least two other centers' scores (Table 4). Minor discordances were also frequent when this other analysis was used: for the reference center A, “minor” discordances with at least one other center were found for 19 of their sera (Table S2). In all, 95 of the 180 sera (53%) presented a “minor” discordance with at least one other laboratory result (Table S2).
The 6 control negative sera provided by one of the centers were also negative (score 0) when assayed by the other 5 centers, with the exception of one serum that received a score 1 (titer 1∶2) by laboratory C. This titer is consistent with a healing titer or a non-specific reaction according to this laboratory criterion.
Since each lab has its own, in house, assay for detection of anti-P. brasiliensis antibodies, we anticipated that “minor” discordances (i.e., slight and clinically not relevant differences in the titers of antibodies) would occur with some frequency. Unexpectedly, we found a high rate of “major” discordances (i.e. differences in scores that may have led to different clinical managements: maintenance or interruption of the treatment). In an attempt to understand the reasons for these discrepancies, the influence of two main variables that discriminated the centers with regard to their protocols were evaluated, namely the technique employed (DID [n = 3 centers] vs. CIE [n = 3]) and type of the antigen (single P. brasiliensis isolate [n = 4 centers] vs. pool of isolates [n = 2]). Gathering the 150 scores given by each one of the 3 centers performing the DID technique to the 5 other centers' sera, of a total of 450 scores, in 343 instances there was agreement and in 107 major discordance; the same analysis for the 3 centers using the CIE technique showed more concordant scores (n = 369) and less discordant scores (n = 81, p = 0.04, Fischer exact test). Among the 300 scores given by the 2 centers using a pool of isolates, the proportion was 47 discordant and 253 concordant scores. This proportion was significantly higher than that obtained with the 4 centers using only one isolate: 141 discordant and 459 concordant scores (p = 0.007, Fischer exact test). Thus, the type of the reaction and antigen preparation may be factors that influence the accuracy of the serological result. Regarding the antigen preparation, not only gp43, but several other components in both the somatic and culture filtrate antigens react with the patients' sera [20], [21], [22]. The amount of these components in the antigen preparations not only varies among the strains, but also in a single strain depending on the number of repeated subculturing, medium used, log phase of growth when the fungus is harvested, among other factors. This is probably a major factor in the inconsistencies among centers. Other particularities that likely influenced the accuracy of the serological results (such as duration of reaction, incubation time, expertise and background of the technician responsible for performing the assay, etc.), could not be assessed in the present study because it was not designed to evaluate these factors.
The present study demonstrated a high rate of discordance among centers that are considered to be reference centers for the diagnosis and serological follow up of PCM patients. Due to the fact that, per request, only sera from PCM patients were provided by these centers, we could not analyze the performance of the serological tests for the diagnosis of PCM. For this, sera of patients with other mycoses and infectious diseases would also be required. However, the high rate of discordances found certainly raises some suspicion with regard to this issue. We illustrate this possibility with one of the sera from center B, whose donor was a 56 year-old patient with chronic non-specific respiratory symptoms, initially and presumptively diagnosed as pulmonary tuberculosis (TB) at a community health care unit. TB treatment was ineffective, the pulmonary symptoms worsened and he developed a pneumothorax. Microbiological evaluation was negative on both sputum and bronchoalveolar lavage. Diagnosis of PCM was made based on the history of having lived in an endemic area, a suggestive chest X-ray, and a 1∶32 titer on the CIE test for PCM (score 2, active disease). A similar active disease score was given by centers E and F, but centers A, C and D gave titers corresponding to score 1 (1∶1, 1∶8 and 1∶2, respectively), suggestive of healing disease, which could potentially delay the diagnosis and the beginning of antifungal treatment.
Relapses and recrudescence are commonly reported during the prolonged (usually >1 year) antifungal therapy of this mycosis. In Argentina, Negroni et al [23] reported that 14.3% of the patients relapsed after a follow up of 15 months. In Brazil, Marques reported 13.8% of relapses after 10 years of follow up, although almost half of the relapses occurred in the first 3 years, when the patients were still on or just off antifungal therapy [24]. Serological follow up has been shown to be an important tool in the early diagnosis of relapses [13], [14], [25]. The factor most commonly reported as contributing to the failure of the anti-fungal treatment is poor compliance due to socio-economical factors such as alcoholism, unemployment and/or long distance from the local drug provider [26]. Although decisions regarding the interruption or prolongation of drug therapy are not made solely based on the serological result, we speculate that in certain cases the relapses would be related to inadvertent therapy discontinuation due to misleading serological monitoring. On the other hand, some patients may undergo unnecessary prolongation of the antifungal therapy. In any case, it is clear from the present study that an effort from the medical mycology community must be undertaken (or re-undertaken) to improve better standardization of the serological diagnosis of this mycosis. Our results suggest that particularly the type of antigen (pool vs. single isolate) and technique (DID vs. CIE) should be addressed.
Efforts should also be made at the same time to develop and standardize P. lutzii serological diagnosis tests. This is a new species in the Paracoccidioides genera recently described that is endemic in some areas of South America where the patients' sera were reported to not recognize the P. brasiliensis' antigens in conventional serological tests [27]–[29]. This issue could not be addressed here since the 6 reference centers participating in the study were located in P. brasiliensis endemic areas and provided sera only with positive serological results. However, occasionally reference centers outside P. lutzii endemic areas may handle sera from PCM due to P. lutzii and release false negative serological results. This has already been documented [30] and will certainly be more common owing to the increasing migration rates in South American countries, particularly Brazil.
Finally, high discordance rates may well occur in the diagnosis of other endemic mycoses such as histoplasmosis, coccidioidomycosis and blastomycosis, all of which are endemic in some areas of South America and that are covered by the reference centers involved in this study or by other reference centers. The efforts to improve the serological diagnosis should also be addressed for these mycoses that, like PCM, remain among the most neglected diseases in South America.
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10.1371/journal.ppat.1005235 | Depletion of M. tuberculosis GlmU from Infected Murine Lungs Effects the Clearance of the Pathogen | M. tuberculosis N-acetyl-glucosamine-1-phosphate uridyltransferase (GlmUMtb) is a bi-functional enzyme engaged in the synthesis of two metabolic intermediates N-acetylglucosamine-1-phosphate (GlcNAc-1-P) and UDP-GlcNAc, catalyzed by the C- and N-terminal domains respectively. UDP-GlcNAc is a key metabolite essential for the synthesis of peptidoglycan, disaccharide linker, arabinogalactan and mycothiols. While glmUMtb was predicted to be an essential gene, till date the role of GlmUMtb in modulating the in vitro growth of Mtb or its role in survival of pathogen ex vivo / in vivo have not been deciphered. Here we present the results of a comprehensive study dissecting the role of GlmUMtb in arbitrating the survival of the pathogen both in vitro and in vivo. We find that absence of GlmUMtb leads to extensive perturbation of bacterial morphology and substantial reduction in cell wall thickness under normoxic as well as hypoxic conditions. Complementation studies show that the acetyl- and uridyl- transferase activities of GlmUMtb are independently essential for bacterial survival in vitro, and GlmUMtb is also found to be essential for mycobacterial survival in THP-1 cells as well as in guinea pigs. Depletion of GlmUMtb from infected murine lungs, four weeks post infection, led to significant reduction in the bacillary load. The administration of Oxa33, a novel oxazolidine derivative that specifically inhibits GlmUMtb, to infected mice resulted in significant decrease in the bacillary load. Thus our study establishes GlmUMtb as a strong candidate for intervention measures against established tuberculosis infections.
| The synthesis of the Mtb cell wall involves a cascade of reactions catalyzed by cytosolic and cell membrane-bound enzymes. The reaction catalyzed by GlmUMtb (an enzyme with acetyltransferase and uridyltransferase activities) generates UDP-GlcNAc, a central nucleotide-sugar building block of the cell wall. Apart from cell wall synthesis UDP-GlcNAc is an essential metabolite participating in other cellular processes including disaccharide linker and mycothiol biosynthesis. GlmUMtb shares very little sequence similarity with eukaryotic acetyltransferase and uridyltransferase enzymes. Many pathogens have alternative pathway(s) for foraging GlcNAc from the host. The present study was undertaken to see the effects of depleting GlmUMtb on pathogen survival in the host animal. We have generated a conditional gene replacement mutant of glmUMtb and find that depletion of GlmUMtb at any stage of bacterial growth or in mice infected with Mtb including a well-established infection, results in irreversible bacterial death due to perturbation of cell wall synthesis. We have developed a novel anti-GlmUMtb inhibitor (Oxa33), identified its binding site on GlmUMtb, and shown its specificity for GlmUMtb. The study demonstrates that GlmUMtb is a promising target for therapeutic intervention and Oxa33 can be pursued as a lead molecule.
| The cell wall, which contains a number of virulence determinants, is the first line of defence for survival of the pathogen in the hostile host environment [1]. The mycobacterial cell envelope includes three layers of cell membrane and a cell wall made up of peptidoglycan, mycolic acid, arabinogalactan and lipoarabinomannan (LAM) [2–4]. Most existing first line and second line drugs used to treat TB such as isoniazid, ethambutol, ethionamide and cycloserine, act on enzymes engaged in the synthesis of different cell wall components [5]. The current high mortality rates of infected individuals as well as increasing incidence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) among patients underscore the importance of finding new targets for therapeutic intervention.
GlmUMtb is a bi-functional enzyme, with acetyltransferase and uridyltransferase activities catalyzed by the C- and N- terminal domains respectively (Fig 1A) [6,7]. The carboxy-terminal domain of GlmUMtb transfers the acetyl moiety from acetyl CoA onto glucosamine-1-phosphate to generate N-acetylglucosamine-1-phosphate (GlcNAc-1-P). The N-terminal uridyltransferase domain of GlmUMtb then catalyzes the transfer of UMP (from UTP) to GlcNAc-1-P to form UDP-GlcNAc (Fig 1A) [6]. The UDP-GlcNAc thus produced is among the central metabolites that is required for the synthesis of peptidoglycan, lipid A of LAM, arabinogalactan, Rha-GlcNAc linkers, mycothiol (required for maintaining redox homeostasis) [8–14]. The crystal structure of M. tuberculosis GlmU (GlmUMtb) displays two-domain architecture with an N-terminal α/β- like fold and a C-terminal left-handed parallel-β-helix structure [15,16]. Unlike its orthologs, GlmUMtb has a long carboxy-terminal tail which displays little secondary structure [17]. Results from transposon mutagenesis experiments have indicated glmUMtb to be an essential gene, supported by the fact that M. smegmatis is unable to grow in the absence of glmUsmeg [18–20]. However, no studies have addressed the question of whether both the activities of GlmUMtb are independently essential for the growth or survival of the bacterium.
While the enzymes required for the synthesis of UDP-GlcNAc are well conserved among prokaryotes, they are very different from those found in eukaryotes, making GlmUMtb an attractive putative drug target [21,22]. Researchers have developed compounds that inhibit the activities of the orthologs of GlmUMtb (GlmU from T. brucei, P. aeruginosa, E. coli, H. influenza and X. oryzae) in vitro [23–30]. Bioinformatic analyses and kinetic modelling data advocate GlmUMtb to be a potential target for the development of suitable inhibitors [31]. In concurrence with these predictions, effective inhibitors have been developed against, the acetyltransferase and uridyltransferase domains of GlmUMtb [32,33]. However, the precise sites of inhibitor-protein interactions and the efficacy of the inhibitors ex vivo or in vivo have not been investigated. Subjecting Mtb cultures in vitro to gradual decrease of oxygen (hypoxic stress) results in reprogramming of metabolic pathways and up-regulation of stress response genes, and is considered to be an in vitro model for the dormancy [34,35]. The importance of GlmUMtb for growth under hypoxic conditions and in an in vivo infection model is yet to be investigated. In the present study we have generated a conditional gene replacement mutant of glmUMtb and used this mutant to investigate any role GlmUMtb may play in modulating the growth of the bacterium in vitro, ex vivo and in vivo. The data presented here demonstrate that GlmUMtb is a viable and promising target for therapeutic intervention against tuberculosis.
As the tetracycline-inducible system is an effective means to regulate gene expression [36], we introduced the integration-proficient pST-KirT-glmU construct (wherein glmUMtb gene was cloned under a promoter that shuts down upon ATc addition; S1A Fig) into Mtb H37Rv (Fig 1B). Whereas the expression of GlmUMtb from its native locus remained unaltered, the expression of FLAG-GlmUMtb in Rv::glmU strain was drastically compromised in the presence of ATc (Western blot inset, Fig 1B). This merodiploid strain was transduced with temperature sensitive phage, and the fidelity of homologous recombination at the native locus was confirmed by amplification across the replacement junctions using appropriate primers (Fig 1C). A comparison of GlmUMtb expression in the presence and absence of ATc revealed that the protein was not detectable by western blot analysis after 6 days of growth in the presence of ATc (Fig 1D). While the growth of Rv∆glmU in the absence of ATc was similar to Rv, in the presence of ATc the growth was drastically compromised (Fig 2A). A comparative analysis of growth by spotting of serially diluted cultures of Rv and Rv∆glmU grown in the presence versus absence of ATc showed that GlmUMtb depletion by addition of ATc led to complete inhibition of growth, with no growth detected after 6 days (Fig 2B). Interestingly, analysis of GlmUMtb expression every 24 hours post-ATc addition uncovered significant reduction in GlmUMtb expression by the third day itself (Fig 2C). These results indicate that GlmUMtb is required for the Mtb survival. To determine the impact of GlmUMtb depletion on cellular morphology we carried out SEM and TEM imaging analysis of Rv and Rv∆glmU cells grown for three days in the absence or presence of ATc. SEM analysis revealed severe morphological perturbations in the absence of GlmUMtb, with the bacilli showing wrinkled surface and fused cells (Fig 2D). TEM analysis showed that whereas in Rv and Rv∆glmU cell wall structure and thickness are comparable, there was a marked decrease in cell wall thickness in Rv∆glmU cells where GlmUMtb was not expressed (cells grown in the presence of ATc; Fig 2E and 2F).
Next we used the Wayne model to investigate the consequence of GlmUMtb depletion on the dormant bacteria under hypoxic conditions [35]. Accordingly, hypoxia was established and maintained for 42 days with depletion of GlmUMtb or addition of INH for either 22 days, or for 2 days (Fig 3A, line diagram). In agreement with previous reports, we observed that bacteria were tolerant to INH under hypoxic conditions [37] (Fig 3C), with a thicker cell wall being observed under hypoxic conditions compared with the normoxic cultures (Fig 3D and 3E). Depletion of GlmUMtb for 22 days resulted in complete clearance of growth (Fig 3B), which was also reflected in severe morphological perturbations and drastic reduction in cell wall thickness (Fig 3D and 3E). Significantly, GlmUMtb depletion for as less as 2 days decreased cell viability by three orders of magnitude (Fig 3B) and decrease in cell wall thickness (~18%; Fig 3D and 3E). Taken together, the data suggests that the absence of GlmUMtb in hypoxic condition leads to aberrant cell wall thickness and morphology, eventually leading to the death of the cell.
Biochemical investigations have shown that the N-terminal fragment (1–352 amino acids) and C-terminal fragment (150–495 amino acids) of GlmUMtb can independently undertake uridyltransferase and acetyltransferase activities respectively (Fig 4A and 4B) [15,17]. The active site residues that are necessary for these activities have also been identified (Fig 4A and 4B) [17]. To investigate if both activities are essential for cell survival, we have generated previously reported truncation mutants GlmU-N and GlmU-C [38]. We also generated GlmUK26A and GlmUH374A, the uridyltransferase and acetyltransferase active site mutants, and GlmUDM wherein both the active site residues were concomitantly mutated. GlmUMtb wild type and mutant proteins were purified (Fig 4C) and their uridyltransferase and acetyltransferase activities were assayed. While GlmU-C and GlmUK26A mutants showed acetyltransferase activity, as expected they did not show any uridyltransferase activity (Fig 4D). On the other hand GlmU-N and GlmUH374A had uridyltransferase activity but not the acetyltransferase activity (Fig 4D). As expected the double mutant did not have either uridyl or acetyltransferase activity (Fig 4D). Next complementation assays using one or other truncations / active site mutants were carried out. The FLAG-GlmUMtb and the complemented untagged wt-GlmUMtb proteins were found to be expressed at similar levels (Fig 4E). The episomally expressed wt-GlmUMtb could rescue the Rv∆glmU phenotype in the presence of ATc (Fig 4F). Contrastingly, while the various GlmUMtb mutant proteins were expressed at levels comparable to that of FLAG-GlmUMtb (Fig 4E); none of them rescued the growth defects of the Rv∆glmU strain in the presence of ATc (Fig 4F). These results indicate that both uridyltransferase and acetyltransferase activities of GlmUMtb are essential for pathogen survival and imply that the only source of the metabolites GlcNAc-1-P and UDP-GlcNAc is through the GlmUMtb mediated synthesis pathway.
Mtb cells devoid of an intact cell wall have been found to be capable of surviving inside the host [39,40]. Some pathogens have been reported to resort to cell wall “recycling” for the synthesis of UDP-GlcNAc, and others have been known to utilize GlcNAc from the host for this purpose [41–44]. However, such mechanisms have not yet been reported in Mtb. To investigate these possibilities we examined the impact of GlmUMtb depletion on survival of the pathogen in the host. Using an ex vivo THP-1 infection model we observed ~80% phagolysosome fusion in the absence of GlmUMtb (Fig 5A and 5B; compare Rv∆glmU with Rv∆glmU +ATc). This was also reflected in the survival pattern of the pathogen upon depletion of GlmUMtb (Fig 5C), with survival being strongly compromised in absence of GlmUMtb. The impact of GlmUMtb depletion was evident as early as 24 h post-infection, with a dramatic drop in survival by 48 hours post-infection (Fig 5C). The consequences of GlmUMtb depletion on survival of the pathogen in vivo were evaluated using guinea pig infection model. CFUs obtained 24 h after infection suggested efficient and equivalent implantation of both wild type and mutant bacilli in the lungs of guinea pigs (Fig 5D). Discrete bacilli were observed in the lungs of guinea pigs infected with Rv and Rv∆glmU 28 days post-infection (Fig 5E and 5F). In contrast, the lungs of the guinea pigs infected with Rv∆glmU in the presence of doxycycline were clear (Fig 5E and 5F). In addition splenomegaly was significantly reduced upon depletion of GlmUMtb (Rv∆glmU + Dox; S2A and S2B Fig). Whereas the bacillary load in the lungs and spleen of guinea pigs infected with Rv and Rv∆glmU were comparable, we did not detect any bacilli when the Rv∆glmU infected guinea pigs were administered Dox (Fig 5D). In accordance with these observations, while the gross pathology of lungs infected with Rv and Rv∆glmU displayed considerable granulomatous architecture, normal lung parenchyma was observed upon GlmUMtb depletion (Fig 5E). These results suggest that the presence of GlmUMtb is obligatory for mycobacteria to survive in the host.
It was apparent from the data presented above that the addition of ATc or Dox at the time of inoculation or at the time of infection does not allow mycobacterial cell growth or survival in the host. In the ideal candidate for therapeutic intervention, inhibiting the activity of/ depleting the enzyme at any stage of the infection should result in pathogen clearance. We assessed this parameter of GlmUMtb by providing ATc at different stages of bacterial growth (early, log and stationary phases) and investigating its influence on cell survival in liquid cultures. Addition of ATc to Rv∆glmU cultures on the 2nd, 4th or 6th day after inoculation significantly thwarted growth (Fig 6A). A similar analysis of bacterial growth by serial dilution of cultures followed by spotting on solid medium also revealed that viability was compromised by ~2 log fold 48 h after the addition of ATc, indicating that GlmUMtb depletion negatively impacted cell survival regardless of which stage of cell growth it was depleted at (S3A Fig).
The influence of GlmUMtb depletion on an established ex vivo infection was estimated by providing ATc 24 h post-infection in a THP-1 infection model. As expected the bacillary load in THP-1 cells infected with Rv and Rv∆glmU were similar at 0 and 24 h after infection (Fig 6B). In contrast, while at 96 h post-infection the bacillary load for Rv and Rv∆glmU- infected THP-1 cells remained the same, the addition of ATc to Rv∆glmU- infected THP-1 cells 24 h after infection decreased the pathogen load by ~2.5 log fold, indicating that the reduction of GlmUMtb levels impacts pathogen survival even in an established ex vivo infection (Fig 6B). We extended this investigation to analyze the effect of GlmUMtb depletion from a fully-infected lung using murine infection model. As anticipated, the bacillary load in the lungs of mice infected with Rv and Rv∆glmU were comparable both on Day 1 and on Day 28. Administration of Dox to Rv∆glmU infected mice for the next 56 days (Day 28 to Day 84) drastically decreased the CFUs in the lungs (Fig 6C) and the pathogen was completely cleared from the spleen (S3B Fig). Unlike the lungs of mice infected with Rv and Rv∆glmU, mice infected with Rv∆glmU to whom Dox was administered displayed a total absence of lesions and granulomas in the lungs (Fig 6D and 6E). Collectively, these data suggest a fundamental role for UDP-GlcNAc, the end product of the GlmUMtb -mediated enzymatic reaction, in modulating the persistence of Mtb infection.
In addition to the acetyltransferase and uridyltransferase active site pockets, GlmUMtb also contains an allosteric site. Binding of any suitable molecule/inhibitor to the allosteric site would prevent the conformational change essential for GlmUMtb uridyltransferase catalytic activity. To target the allosteric site on GlmUMtb we drew on crystal structure data of H. influenza GlmU (GlmUHI) bound to its allosteric small molecule inhibitor (S4A and S4B Fig) [27]. Alignment of the GlmUMtb and GlmUHI allosteric pocket residues suggested that the interacting residues were conserved between the two proteins (S4C and S4D Fig). The Asinex database was screened against shape as described (S5A Fig) and the resulting 43 hits were biochemically characterized for their ability to inhibit GlmUMtb uridyltransferase activity. One of the promising molecules was used for further structural optimization (S5A Fig). Of the 53 structurally optimized compounds one molecule, namely (4Z)-4-(4-benzyloxybenzylidene)-2-(naphthalen-2-yl)-1,3-oxazol-5(4H)-one (Oxa33; Synthesis scheme provided in Figs 7A and S5B), was found to be an efficient inhibitor of GlmUMtb activity with an IC50 of 9.96±1.1 μM (Fig 7B). Isothermal titration analysis suggested an adequately high affinity binding for the compound (Ka = 2.35×106 M-1), with a binding stoichiometry of 0.7 (S6A Fig). We sought to identify the residues in GlmUMtb that are critical for interacting with Oxa33. Docking and MD simulation studies revealed polar, non-polar and hydrophobic interactions between Oxa33 and the allosteric site residues (Figs 7C, S6B and S6C). Based on the obtained data a panel of GlmUMtb proteins each carrying a single mutation was created, the mutant proteins were purified (S6D Fig), and their uridyltransferase activity assayed. While all the mutants had similar levels of uridyltransferase activity there was a substantial increase in their IC50 values, suggesting a loss of interaction with Oxa33 (Fig 7D and 7E).
To decipher the mechanism of Oxa33 mediated inhibition of uridyltransferase activity, we superimposed the GlmUMtb-Oxa33 complex with the unbound GlmUMtb structure. Upon Oxa33 binding, the loop regions (in the range of 3–6 Å) at the uridyltransferase active site undergo significant conformational changes, decreasing the active site volume, which results in occlusion of the substrates (Fig 7F). Differential scanning fluorimetry (DSF) analysis of GlmUMtb in the presence of Oxa33 showed a 3°C shift in protein melting temperature (Tm) validating the conformational changes (S5E Fig). Interestingly we also observed much higher relative fluorescence units (~10000 vs 2500) in the presence of Oxa33, which is likely due to the compound induced structural changes facilitating increased binding of the dye (S6E Fig). Together, these data demonstrate that Oxa33 binds to the allosteric site at N-terminal domain of GlmUMtb and inhibits its uridyltransferase activity by causing structural changes.
Subsequently we investigated the ability of Oxa33 to inhibit the in vitro growth of Mtb H37Rv. Oxa33 inhibited the in vitro growth of Mtb H37Rv with a minimum inhibitory concentration (MIC) of ~75 μM (~30 μg / ml) and a maxium bacteriocidal concentration (MBC) of ~150 μM (~60 μg / ml). To ascertain if this inhibitory effect was due to inhibition of GlmUMtb activity we overexpressed GlmUMtb in the cells prior to drug treatment and determined the effect of this on the MIC value (Figs 8A and S6A). Whereas the inhibition of growth in the presence of INH was similar with or without GlmUMtb overexpression in the cells (Fig 8A, lower panel), Oxa33 failed to inhibit cell growth even at concentrations as high as 150 μM (60 μg/ ml) (Fig 8A, upper panel). Interestingly when sub lethal concentration of Oxa33 was provided, the MIC of INH decreased from 32 to 16 ng/ml (S7B Fig). The impact of Oxa33 on THP1 cells 24 h after infection with either Rv or Rv::glmUtet-on was also investigated. In concurrence with the in vitro growth data, overexpression of GlmUMtb alleviated Oxa33-mediated clearance of Mtb from THP-1 cells (Figs 8B, S6D and S6E). These results suggest that the inhibition of mycobacterial growth by Oxa33 is specifically due to inhibition of endogenous GlmUMtb.
Finally, we analysed the efficacy of Oxa33 in clearing bacilli from infected lungs using a murine infection model. Oxa33 compound is highly hydrophobic in nature. After trying many solvents, we could successfully resuspend it in 2.5% Tween-80. Prior to performing the experiments we examined the maximum dose tolerance and survival analysis to determine the toxicity (S8A and S8B Fig). Based on the data obtained we chose 50 mg / kg as the appropriate dose. Since it was difficult to predict the fate of Oxa33 during the process of digestion, we avoided using the oral administration route. We chose intra peritoneal route for administering the compound as the intravenous (I/V) injection of Tween 80 (solvent) in the animals was known to cause hypersensitivity and anaphylactic shock [45,46].
Groups of mice were infected with Rv and were treated with vehicle, INH, or Oxa33 at 28 days post-infection, for a duration of 56 days (Fig 8C, line diagram). Compared with the vehicle-treated group where we observed a marginal increase in bacillary load, a significant reduction in the bacillary load was observed in the lungs and spleen of both, INH- and Oxa33-treated groups (~4 and 2.5 log fold, respectively for lungs) (Figs 8C and S9A). This was also reflected in the gross pathology and histopathology of lungs (Fig 8D and 8E). Although in vitro MBCvalues of Oxa33 was ~150 μM (60 μg/ml), it seems to be a relatively more efficacious in vivo, which could be due to its accumulation in the lungs of the infected mice. To investigate this possibility uninfected mice were treated with 50 mg/kg Oxa33 for a period of 3 weeks or 8 weeks. In order to estimate the concentration of Oxa33 in the lung, we first determined the absorbance spectra for Oxa33, which gave a clear peak at 401 nm (S10A Fig). We determined the A401 at different concentrations of Oxa33 and the standard curve was plotted (S10B and S10C Fig). Oxa33 was extracted from the lungs and its concentration was determined. The concentrations of Oxa33 in the lungs were in the range of ~200–300 μg /lung at 3 weeks and ~800–1300 μg/lung at 8 weeks (S10D Fig). The accumulation of Oxa33 in the lungs is ~13 to 18 fold higher than the MBC values, which may be the reason for higher potency of Oxa33 in vivo compared with the in vitro experiments. Taken together, the results presented in this study establish GlmUMtb to be an effective target against which new sets of inhibitors may be developed.
Cell wall provides the structural rigidity and protects bacteria from various environmental and physiological insults. Biosynthesis of the cell wall of bacteria is a complex process requiring enzymes localized to different cellular compartments [47]. Due to the essentiality of the enzymes involved they are considered attractive targets for anti-microbial therapies. The majority of the first line and second line anti-tuberculosis drugs from the existing regimen target enzymes involved in cell wall synthesis [5]. These include Isoniazid and Ethionamide targeting enoyl-[acyl-carrier-protein] reductase and inhibiting mycolic acid synthesis, Ethambutol targeting arabinosyl transferase and inhibiting arabinogalactan biosynthesis, and Cycloserine targeting D-alanine racemase and ligase, which inhibits peptidoglycan synthesis [5]. However most of these drugs are not very effective against dormant/ latent Mtb [48].
UDP-GlcNAc is a critical metabolite both in prokaryotes and eukaryotes. In eukaryotes it is mainly utilized for O- or N- GlcNAcylation, sialic acid biosynthesis and hylauronic acid biosynthesis [49–51]. In addition to the peptidoglycan synthesis [10], in gram negative bacteria UDP-GlcNAc is required for the synthesis Lipid A subunit of lipopolysaccharide [52] and in gram positive bacteria it is required for Rha-GlcNAc linker [53], arabinogalactan [54], teichioc acid synthesis [55]. In few prokaryotes, UDP-GlcNAc has also been shown to be required for sialic acid [56], N-GlcNAcylation [57] and poly (-GlcNAc-)n [58]. GlmUMtb, an enzyme with dual activity, synthesizes a core metabolite necessary for the synthesis of cell wall peptidoglycan, UDP-GlcNAc [6]. Interestingly, we found that depleting GlmUMtb during both normoxic and hypoxic growth resulted in substantial decrease in cell viability (Fig 3). This may be due to the requirement of UDP-GlcNAc, which in addition to participating in cell wall synthesis is also required for other cellular processes such as mycothiol biosynthesis (to maintain redox homeostasis) [14,59]. However, the TEM data clearly shows decreased cell envelop thickness even in hypoxic conditions (Fig 3). Although the CFUs do not change significantly the cells may be undergoing significant replication, which might be balanced by death [60]. Alternatively, new cell envelop may be required even if the bacteria are not replicating. Thus one can rule out the possibility that decreased viability may well be due to requirement of UDP-GlcNAc for the cell envelop synthesis.
While UDP-GlcNAc is a critical metabolite for both prokaryotes and eukaryotes, the enzymes involved in its de novo synthesis are significantly different [10]. In addition, both prokaryotes and eukaryotes can utilize GlcNAc from different sources to synthesize UDP-GlcNAc through salvage pathways [61–63] (S11 Fig). Capnocytophaga canimorsus, a member bacteria from Bacteroidetes phylum lacks endogenous GlmM and GlmU required for the synthesis of GlcNAc and it instead relies on GlcNAc obtained from forages glycans from the host mucin and N-linked glycoproteins [42]. Depending on the enzymes of the salvage pathway present in the bacterial system, it would require either both the activities or only the uridyltransferase activity of GlmUMtb for UDP-GlcNAc synthesis. Till date the presence of alternate salvage pathway in Mtb has not been demonstrated. However, even with an operating salvage pathway GlmUMtb is essential for the utilization of host GlcNAc to form UDP-GlcNAc (S11 Fig). In line with this, we find that depletion of GlmUMtb during ex vivo or in vivo infection either at the start or after infection has been definitively established leads to clearance of pathogen.
GlmUMtb and the acetyltransferase and uridyltransferase enzymes found in eukaryotes share very little sequence similarity. Although efforts have been made by different groups to target bacterial GlmU proteins, the specificity of these inhibitors for GlmU in vivo have not been established [23–30]. Most GlmU inhibitors characterized till date target either the acetyl- or uridyltransferase active sites. In contrast, inhibitors of GlmUHI target the allosteric site near the uridyltransferase active site [27]. The interaction of the inhibitor with the enzyme via this allosteric site perturbs the active site conformation of the protein, thus inhibiting uridyltransferase activity [27]. In the present study we have used shape based designing and developed a novel oxazolidine molecule, Oxa33, and characterized its ability to bind to the GlmUMtb allosteric site. MD simulation and mutation of critical interacting residues to defined the possible allosteric site residues required for Oxa33 binding (Fig 7). DSF (S6E Fig) and structural superimposition (Fig 7) supports that inhibition of uridyltransferase activity is due to structural changes in the N-terminal domain of GlmUMtb. Further in order to determine the specificity of Oxa33, GlmUMtb over expressing strains of Rv was used to determine the MIC. Both in vitro and ex vivo results (increased MIC or MBC) validate that Oxa33 specifically binds to GlmUMtb inside the bacteria. Administrating the Oxa33 to fully infected (28 days) mice resulted in partial ablation of pathogen load in the lungs. Taken together results presented here demonstrates that GlmUMtb is a viable and promising target for therapeutic intervention and Oxa33 can be pursued as a lead molecule, which needs to be developed further to improve its efficacy.
Restriction enzymes and Phu DNA polymerase were purchased from New England Biolabs. pENTR/directional TOPO cloning kit (Invitrogen), pQE2 (Qiagen), were procured from the respective sources. Analytical grade chemicals and oligonucleotide primers were procured from Sigma. Malachite green phosphate assay kit (POMG-25H) was purchased from BioAssay System (Gentaur). Electron microscopy reagents were purchased from Electron Microscopy Sciences. Media components were purchased from BD Biosciences. Doxycycline hydrochloride was purchased from Biochem pharmaceutical.
The hexa-His tag in the pST-KiT construct[15] was replaced with an N-terminal FLAG tag, and the tetracycline repressor gene (tetR) was replaced with a reverse tetR (r-tetR) from pTC28S15-OX [64] to create plasmid pST-KirT. To generate the integrating shuttle plasmid pST-KirT-glmUMtb, the glmUMtb gene was excised from pQE2-glmUMtb using NdeI-HindIII digestion and was subcloned into the corresponding sites on pST-KirT. The resulting pST-KirT-glmU construct expresses GlmUMtb in the absence of inducer ATc. Upon addition of ATc, ATc binds to the r-TetR repressor resulting in the conformational change that would allow it to bind to the operator seqeunces in PmyctetO (S1 Fig) [64]. The integration-proficient plasmid containing the inducible glmUMtb gene was electroporated into mycobacterial cells to create the merodiploid strain Rv::glmUMtb. 5’ and 3’ genomic flank sequences of glmUMtb (approximately 1 kb on either side) were amplified, the amplicons digested with PflMI, and ligated with the antibiotic resistance cassette along with the oriE and cosλ fragments generated from pYUB1474 construct, to generate the allelic exchange substrate (AES) [65]. The AES was linearized using the unique PacI site and then cloned into temperature sensitive shuttle phagemid phAE159 at the PacI site. A conditional gene replacement mutant of RvΔglmU was created from the merodiploids with the help of specialized transduction methodology (S1A Fig) [66]. RvΔglmU recombinants obtained were analyzed by PCR amplification to verify the fidelity of the recombination event.
H37Rv (Rv) and RvΔglmU cultures were grown in Middlebrook 7H9 medium supplemented with 10% ADC (albumin, dextrose and catalase), or in 7H11 medium supplemented with 10% OADC (oleic acid, ADC). To analyze bacterial growth in vitro, Rv and Rv∆glmU mutant bacterial cultures were inoculated at A600 of 0.1, in the presence or absence of anhydrotetracycline (ATc), and A600 was measured every 24 h for 6 or 8 days. For spotting analysis, cells were harvested by centrifugation, washed twice with PBST (0.05% Tween 80) to remove ATc, resuspended in 7H9 medium, and serially diluted in the same medium, followed by spotting 10 μl aliquots of the various cell dilutions on 7H11 agar plates to assess cell viability. To determine the impact of GlmUMtb depletion during hypoxia in Rv and RvΔglmU strains, we established hypoxia in 1.5 ml HPLC tubes or 500 ml flasks with penetrable caps, following modified Wayne model [35]. ATc (2 μg/ml) or isoniazid (INH) (50 ng/ml) were injected into the cultures at different time points and the number of CFUs were determined after 42 days. Scanning and transmission electron microscopy (SEM & TEM) analysis of Rv and Rv∆glmU mutant grown in the presence or absence of ATc were performed as described earlier [67]. Transmission electron microscopy was performed using standard protocols. Briefly, bacteria was fixed in 2.5% gluteraldehyde and 4% paraformaldehyde, dehydrated in graded series of alcohol and embedded in Epon 812 resin. Ultrathin sections were cut and stained with uranyl acetate and lead citrate [68]. SEM images were procured using Carl Zeiss Evo LS scanning electron microscope, and TEM images were captured using Tecnai G2 20 twin (FEI) transmission electron microscope.
Site directed mutations of glmUMtb were generated with the help of overlapping PCR and the amplicons were cloned into NdeI-HindIII sites of pQE-2, pNit and pST-KT vectors [15,69]. The tetracylin repressor (TetR) expressed from the plasmids binds to the operator sequence in the promoter PmyctetO in the absence of ATc (S1B Fig) [70]. Addition of ATc to TetR alleviates the repression thus inducing the expression of GlmU. pST-KT-glmU was electroporated into Rv to generate Rv::glmUtet-on strain. pNit-glmU (wild type and mutated) constructs were electroporated into Rv∆glmU to generate Rv∆glmU::glmUwt/mutant strains. Rv and Rv∆glmU::glmUwt/mutant strains were grown in the presence or absence of ATc as described above. GlmUMtb was expressed and purified using plasmid pQE2-GlmUMtb, as described earlier [15]. Whole cell lysates (WCL) isolated from Rv, Rv∆glmU and Rv∆glmU::glmUwt/mutant strains that had been grown for 5 days in presence or absence of Atc, were resolved on 10% SDS-PAGE, transferred to nitrocellulose membrane, and probed with anti-GlmUMtb and anti-GroEL1 antibodies as described earlier [15,67].
THP1 infection experiments were carried out with either unlabelled or FITC-labelled Rv and Rv∆glmU strains at 1:10 MOI, as described earlier [71]. For examination of cells under a fluorescence microscope, infected cells (48 h post-infection) were labelled with Lyso Tracker red DND 99 dye (50 nM) and mounted with Antifade (Invitrogen) mounting agent. To determine CFUs per infected cell, the infected cells were lysed in PBS containing 0.1% TritonX-100 for 15 min and different dilutions were plated on OADC-containing 7H11 agar plates. For animal infection experiments, Rv and Rv∆glmU strains grown till A600 of 0.6 were used to infect 3 to 4 week old guinea pigs or ~ 2 month old mice as described previously [72,73]. We initially used guinea pig model system as it has robust immune response. However, for the remaining experiments we chose to use Balb/C mice model of infection as the cost associated with performing the experiments and the amount of Oxa33 required for guinea pig experiments was prohibitive. To determine the implantation dosage, the bacillary load in the lungs of guinea pigs or mice was determined 24 h post-infection. To investigate the impact of glmUMtb depletion on survival of the pathogen, doxycycline hydrochloride (Dox, 1 mg/ kg with 5% dextrose in drinking water) was provided to Rv and Rv∆glmU-infected animals as indicated, either from the time of the infection (guinea pig experiment), or 4 weeks post-infection (mice infection experiments). To assess the impact of INH or Oxa33 treatment on pathogen survival, Rv-infected mice (4 weeks post-infection) were supplied with INH (25 mg/ kg body weight, with 5% dextrose in drinking water) or Oxa33 (50 mg/ kg body weight, with 2.5% Tween 80, injected intra peritoneally) every third day for 8 weeks. Bacillary loads in the lungs and spleens of infected guinea pigs and mice were determined 4 weeks and 12 weeks post-infection. Histopathological evaluation of the harvested organs was performed as described earlier [67,72,73].
ROCS (Rapid Overlay of Chemical Structures), a shape based technique for rapid similarity analysis was used to assess the compounds. Gaussians and shape tanimoto were used to assess the volume and shape overlaps of the compounds, respectively. As the chemical functionality is critical, the chemical feature based similarity was also considered using ROCS colour score whose force field was composed of SMARTS patterns of the chemical functions [74,75]. The shape tanimoto score and scaled color score were considered during the selection of the compounds for further virtual screening. The compounds selected were subjected to molecular docking studies using Glide v5.8 of Schrödinger molecular modelling suite 2012 (Glide v5.8, Schrödinger). The compounds were subjected to a series of docking protocols–high throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP) docking. As the docking progresses from HTVS to XP, the algorithm differs, which starts from a simple docking of compounds and ends with docking protocol with high precision and parameterization while cutting off the number of compounds.
To the glycine solution (3.0 g, 39.89 mmol) in water under constant stirring at 0°C, NaOH (3.19 g, 79.78 mmol) was added. This was followed few minutes later by the addition of 1-naphthoyl chloride (7.20 mL, 47.86 mmol) in 1, 4-Dioxane (20 ml) and the contents were stirred at room temperature for 6 h. The reaction mixture was concentrated to half the volume and 60 ml EtOAc was added. The EtOAc layer was washed with sat NaHCO3 (2 × 30 mL) followed by H2O (2 × 20 mL). The separated organic layer was dried and concentrated over anhydro Na2SO4 to obtain solid compound, which was washed with hexanes to get 2-(1-naphthamido) acetic acid (8.30 g, 90%) as a white solid (M.P. 152°C1). 2-(1-naphthamido)acetic acid (2.0 g, 8.73 mmol), NaOAc (0.21 g, 2.62 mmol) and 4-benzyloxybenzaldehyde (1.85 g, 8.73 mmol) were taken in acetic anhydride and heated at 106°C for 3 h. The solid formed were filtered and washed with water to remove traces of acetic anhydride, and ethanol to remove unreacted aldehyde and other organic impurities. Final compound 4-(4-(benzyloxy)benzylidene)-2-(naphthalen-1-yl)oxazol-5(4H)-one (Oxa33; 3.14 g, 88%), purified as a yellow solid, was confirmed with nuclear magnetic resonance (NMR) [76].
To investigate the binding of Oxa33 to GlmUMtb, we performed Isothermal Titration Calorimetry (MicroCal 2000 VP-ITC, GE Healthcare) [28]. Oxa33 was re-suspended in dialysis buffer (25 mM Tris pH 7.4, NaCl 140 mM and 15% glycerol), 100 μM of MgCl2 containing 2% DMSO. 625 μM of Oxa33 was injected for titrations from syringe (rotating at 307 rpm) into ITC cell containing 25 μM of GlmU or blank buffer at 25°C. Each injection lasted for 20 sec with 300 sec interval between every step. The quantity of heat associated by every injection was calculated by combining the area beneath every heat burst curve (microcalories/second vs. seconds). Data was corrected for the buffer signal and fitting was done by one-site binding model. Origin software (version 7.0) was used to obtain different thermodynamic binding parameters.
Oxa33 was evaluated for its cytotoxic activity in THP1 cells with the help of alamar blue assay. Serially diluted inhibitors (in 2.5% DMSO) incubated with 5 x 103 differentiated THP-1 cells in 96 well plates for 3 days. After 3 days cells were incubated for 5 h with 10 μl of alamar blue and color development was measured using micro-plate reader at 570 nm.
Molecular dynamics (MD) simulation for the protein-ligand complex was carried out for a time scale of 20 ns so as to analyze the stability of molecular interactions between ligand and protein employing Newton’s Laws of Motions. Desmond molecular dynamics system v3.1 was used for carrying out the simulations employing OPLS-AA force field [77]. The protein-ligand complex was solvated using TIP3P water model which was setup as an orthorhombic solvent box, keeping a cut-off of 10 Å from any solute atom in all directions [78]. Na+ counter ions were added in order to neutralize the system. A cut-off of 14 Å was maintained for calculating the solvent-solvent and solute-solvent non-bonded interactions. Initially, the system was minimized keeping the convergence threshold criteria of 1.0 kcal.mol-1.Å-1 so as to allow the adjustment of atoms to the system environment. A simulation for each system was performed using isothermal-isobaric ensemble (NPT) including a relaxation process. Under NPT, the system was simulated for 12 ps using a Berendsen thermostat and a Berendsen barostat with temperature of 10K and a pressure of 1 atm. The later step of relaxation protocol included the simulation of the system for 24 ps with a temperature of 300 K and 1 atm pressure with and without restraints on solute heavy atoms. M-SHAKE algorithm was used with an integration time step of 2 fs for rearranging the hydrogen bonds in the simulation [79]. The temperature and pressure of the system were maintained at 300 K and 1.013 atm respectively. The molecular dynamics simulation was run for 20 ns recording the trajectory frames at an interval of every 4.8 ps and the trajectory analysis was carried out using the Simulation Event Analysis of Desmond.
Uridyltranferase assays were performed using malachite green phosphate detection kit as described previously [17]. Acetyltransferase activity of GlmUMtb was carried out in the presence of 500 μM each of GlcN-1-P and acetyl-CoA in a 30 μl reaction volume for 30 min at 30°C as described earlier [80]. To determine the percent inhibition by different compounds the enzyme was preincubated with either 5% DMSO or 100 μM compounds for 30 min prior to performing uridyltransferase activity assays. In order to determine the IC50 values, GlmUwt/mutant proteins were preincubated with different concentrations of Oxa33 compound for 30 min followed by the uridyltransferase assay. To determine minimum inhibitory concentration (MIC), 5x105 bacteria of Rv or Rv::glmUtet-on (overexpressing GlmUMtb) cultures (grown in the presence or absence of 2 μg/ml ATc) were mixed with 100 μl of 2.5% DMSO or different concentrations of Oxa33/ INH in 96-well plates, and incubated at 37°C for 6 days. After 6 days, 40 μl of resazurin dye (0.02% in 5% Tween-80) was added to each well and the colour change was observed after 12 h.
Experimental protocol for the animal experiments was approved by the Institutional Animal Ethics Committee of National Institute of Immunology, New Delhi, India (the approval number is IAEC# 315/13). The approval is as per the guidelines issued by Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Govt. of India.
Student’s t-test (two tailed non parametric) was used to analyze the significance of cell wall thickness, THP1 and animal infection experimental results. SigmaPlot version 10.0 and GraphPad Prism version 5.0 was used for the statistical analysis and for plotting the results.
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10.1371/journal.pbio.2005046 | The human lymph node microenvironment unilaterally regulates T-cell activation and differentiation | The microenvironment of lymphoid organs can aid healthy immune function through provision of both structural and molecular support. In mice, fibroblastic reticular cells (FRCs) create an essential T-cell support structure within lymph nodes, while human FRCs are largely unstudied. Here, we show that FRCs create a regulatory checkpoint in human peripheral T-cell activation through 4 mechanisms simultaneously utilised. Human tonsil and lymph node–derived FRCs constrained the proliferation of both naïve and pre-activated T cells, skewing their differentiation away from a central memory T-cell phenotype. FRCs acted unilaterally without requiring T-cell feedback, imposing suppression via indoleamine-2,3-dioxygenase, adenosine 2A Receptor, prostaglandin E2, and transforming growth factor beta receptor (TGFβR). Each mechanistic pathway was druggable, and a cocktail of inhibitors, targeting all 4 mechanisms, entirely reversed the suppressive effect of FRCs. T cells were not permanently anergised by FRCs, and studies using chimeric antigen receptor (CAR) T cells showed that immunotherapeutic T cells retained effector functions in the presence of FRCs. Since mice were not suitable as a proof-of-concept model, we instead developed a novel human tissue–based in situ assay. Human T cells stimulated using standard methods within fresh tonsil slices did not proliferate except in the presence of inhibitors described above. Collectively, we define a 4-part molecular mechanism by which FRCs regulate the T-cell response to strongly activating events in secondary lymphoid organs while permitting activated and CAR T cells to utilise effector functions. Our results define 4 feasible strategies, used alone or in combinations, to boost primary T-cell responses to infection or cancer by pharmacologically targeting FRCs.
| The lymph node microenvironment contains an abundance of immune cells that interact with and within an intricate structural framework created by fibroblastic reticular cells. In mice, fibroblastic reticular cells are known to regulate T-cell activation, proliferation, and function, but in humans, they are poorly understood. We investigated interactions between human T cells and human fibroblastic reticular cells from tonsils and lymph nodes. When T cells were activated in the presence of human fibroblastic reticular cells, their proliferation and differentiation were reduced, without altering effector T-cell function, shown through cytokine production. We identified 4 molecular mechanisms that were responsible, concurrently used by all human fibroblast donors tested, and reversible upon addition of specific inhibitors to the cocultures. To establish the relevance of this finding outside of in vitro coculture, we showed that T-cell proliferation was increased in live human tonsil tissue slices when the fibroblastic reticular cell inhibitors were added. This work demonstrates that human fibroblastic reticular cells regulate T-cell activation and provides new information on the mechanisms used, which may be useful to design clinical strategies that improve T-cell responses.
| Stromal cells create specialised lymphoid support compartments within secondary lymphoid organs. The signals they feed leukocytes have profound effects in many aspects of activation, proliferation, and differentiation [1].
Fibroblastic reticular cells (FRCs) construct the internal segregated structure of secondary lymphoid organs by acting as a scaffold for lymphocyte migration and secreting chemokine C-C motif ligand 19 (CCL19) and chemokine C-C motif ligand 21 (CCL21) to bring T cells and dendritic cells to the central T-cell zone and chemokine C-X-C motif ligand 13 (CXCL13) to bring B cells to outer B cell zones. Lymphocyte survival is further supported through secretion of survival factors interleukin 7 (IL-7) and B cell activating factor (BAFF) [2,3].
Several papers demonstrated that mouse lymph node–derived FRCs reduce T-cell proliferation. In mice, when T cells have been activated less than 15 h, cyclooxygenase-2 (COX2)-driven prostaglandin E2 (PGE2) is suppressive [4], while comparative neutralisation experiments showed that nitric oxide plays a larger role past 15 h, when most T-cell division occurs [5–7]. T-cell function is impaired, shown through reduced interferon gamma (IFNγ) production [6–7]. Effects on memory T-cell differentiation have not been assessed in mice.
Human FRCs are still almost entirely unstudied, though it has been shown that podoplanin (PDPN+) cells analogous to mouse FRCs are found in human secondary lymphoid organs and that they secrete extracellular matrix components as well as CCL21 [8,9]. A recent study, citing as-yet unpublished data, said that human FRCs do not produce nitric oxide in response to IFNγ activation [9]. We therefore questioned whether prior mouse FRC research accurately modelled human FRC biology. The effects of FRCs on human T cells are unknown, and their mechanism/s of action have not been tested, though COX2 is expressed [4,9].
The role of human FRCs in T-cell regulation is likely to be highly relevant to human health. Mouse studies show far-reaching effects of FRCs for immunity against influenza and other pathogens [3,10,11], and it is hypothesised that suppression of effector T-cell activation within lymph nodes reduces immune-mediated pathology against the lymph node structure [1,12]. Accordingly, virally infected FRCs are associated with T-cell persistence and chronic viral infection [12].
Here, we show that human FRCs block proliferation and modulate differentiation of newly activated naïve human T cells, without requiring T-cell feedback. Suppression was constitutive, and we identified 4 molecular mechanisms operating simultaneously: indoleamine-2,3-dioxygenase (IDO), COX1 and 2 enzymes responsible for PGE2 production, adenosine 2A receptor (A2AR), and transforming growth factor beta (TGFβ). Coinhibition of these factors reversed FRC-mediated suppression in vitro and permitted us to observe T-cell activation on a living tonsil slice.
It is important to first understand the key cells and molecules involved in regulating T-cell activation during inflammation, infection, cancer, and autoimmunity in order to treat immune-mediated pathologies and immune deficiencies. Here, we show that human FRCs, via 4 pathways—which are druggable individually or in small combinations—are a likely pharmacological target to boost the primary immune response.
To test the effect of FRCs on naïve T-cell activation, we isolated and culture-expanded FRCs from either cadaver-origin lymph nodes or live-donor tonsils using a published digestion protocol [13] or through explant culture. Cultured FRCs used in this study were identified as CD45−, CD31−, PDPN+ cells across multiple passages, and they expressed genes and proteins characteristic of published FRC phenotypes from freshly isolated mouse and human FRCs [14,15] (S1 Fig), including expression of PDPN, CXCL12, α smooth muscle actin (αSMA; ACTA2), lymphotoxin beta receptor (LTBR), platelet-derived growth factor receptor (PDGFR) α and β, vimentin (VIM), detectable but low levels of BAFF, mucosal vascular addressin cell adhesion molecule 1 (MADCAM1), Desmin, CD34, and receptor activator of NF-κB ligand (RANKL). As expected, chemokines CCL21 and CCL19 were switched off by cultured cells; transcription in mice has been shown to be regulated by lymphatic flow [16]. We identified a distinct subset of stromal cells present in freshly isolated tonsil but missing from our cultures; these were CD45−CD31−EpCAM- CD90+ CD73− PDPN− cells (S1 Fig) with unknown function.
We activated carboxyfluorescein succinimidyl ester (CFSE)-labelled T-cells from peripheral blood mononuclear cells (PBMCs) in the presence of human FRCs and found that they underwent fewer divisions after 96 h (Fig 1A and 1B). We then activated CFSE-labelled PBMCs and allowed T-cells to divide freely for 48 h. Cells were harvested and plated with or without FRCs and reactivated for a further 48 h. While reactivated T cells proliferated freely, the addition of FRCs after the initial activation and prior to the second activating stimulus imposed a significant brake on proliferation (Fig 1C). Thus, FRCs could hamper the proliferation of both naïve and pre-activated, actively divided T cells.
Next, we examined surface marker expression changes in responding T cells. After 96 h of activation with or without FRCs, T cells showed normal up-regulation of early activation marker CD69, but significantly fewer T cells up-regulated the interleukin 2 receptor alpha (IL-2Ra) chain, CD25 (Fig 1D). Together with data showing that FRCs could halt the proliferation of pre-activated T cells, these results suggested that the suppressive mechanism occurred well downstream of T-cell receptor ligation and did not involve steric hindrance. Suppression was responsive to dose (Fig 1E), and human FRCs did not produce nitric oxide when stimulated (Fig 1F).
We questioned whether FRCs may be inducing apoptosis or permanently anergising responding T cells. Cell cycle analysis showed no increase in apoptosis in either CD4+ or CD8+ T cells (Fig 1G). Instead, in the presence of FRCs, fewer T cells entered the DNA-synthesis phase (S phase), compared to T cells that were not cocultured with FRCs (Fig 1H), with no change to G0/G1 or G2/M phase (S2 Fig). Separation of activated, suppressed T cells from FRCs, followed by reactivation alone in culture, showed that T cells were not permanently anergised by activation in the presence of FRCs (Fig 1I).
We probed the nature of the suppression mechanism by screening small molecular inhibitors, agonists, and blocking/neutralising antibodies in a similar coculture assay, in which CFSE-labelled T cells were activated in the presence or absence of FRCs and/or inhibitors and analysed after 4 days.
Inhibition or blockade of TGFβ receptor, IDO, COX1/2, and A2AR signalling all restored T-cell proliferation (Fig 2A–2D) to varying degrees, while interleukin 6 (IL-6) and programmed cell death ligand 1 (PD-L1) inhibition had no effect (Fig 2A–2D), and inhibitors did not alone significantly alter T-cell differentiation phenotypes (S3 Fig).
All 4 mechanisms were utilised in all donors but to varying degrees, with no conserved predominance (S4 Fig). This imposed challenges in how to most appropriately assess the overall effect of FRC suppression on T-cell biology in a meaningful manner, given human variance and the potential for redundancy. Donor-to-donor variance was minimal when all 4 mechanisms were targeted at once, and since all mechanisms were operational in all donors, we reasoned that this was the best means of assessing the net biological impact of FRCs on T cells. We therefore chose to suppress all mechanisms simultaneously to investigate downstream effects on T cells.
To assess whether these 4 molecular targets were together sufficient to entirely block FRC suppression, we created an inhibitory cocktail of all 4 inhibitors and treated PBMCs that were exposed to stimulatory signals for 96 h. In the presence of FRCs, proliferation of both CD4 and CD8 T cells occurred at control levels (Fig 2E and 2F). The inhibitor cocktail did not alone significantly increase T-cell proliferation in the absence of FRCs (Fig 2E and 2F).
Strikingly, the presence of FRCs influenced the fate of differentiating naive CD4+ and CD8+ T-cell populations (CD62L+CD45RO−). Their presence during T-cell activation decreased differentiation of CD4+ and CD8+ T cells to a central memory phenotype (CD62L+CD45RO+) while increasing the proportion of CD4+ naïve (CD62L+CD45RO−) T cells. Effector (CD62L−CD45RO−) and effector memory phenotype T cells (CD62L− CD45RO+) and CD8+ naïve T cells were not affected (Fig 3A and 3B). Memory phenotype cells were further profiled by expression of CD27 and by relative proliferation; both subsets yielded expected phenotypes (S5 Fig). Blockade of TGFβR, IDO, COX1/2, and A2AR signalling reversed the effects (Fig 3A and 3B). Results were comparable regardless of whether anti-CD2/3/28-coated beads or PHA-L/IL-2 was used as the activating stimulus.
Since CD25 is the alpha chain of the IL-2R, and since we observed selective inhibition of CD25 expression by FRCs (Fig 1D), which was present as early as 24 h after stimulation and did not occur in the presence of inhibitors (S6 Fig), this led us to question whether FRC-mediated effects on IL-2 signalling were evident. Coculture with FRCs did not alter signal transducer and activator of transcription 5 (STAT5) phosphorylation (Fig 4A and 4B), leading us to conclude that this signalling pathway was not mechanistically important; we tested other signalling molecules phosphorylated extracellular signal–regulated kinase 1/2 (pERK1/2), phosphorylated STAT1 (pSTAT1), phosphorylated STAT3 (pSTAT3), phosphorylated STAT6 (pSTAT6), and p38 mitogen-activated protein kinase (MAPK) and found no mechanistic insight within T cells (S7 Fig). IL-2 production, as a downstream transcriptional target of IL-2 signalling, was also not altered (Fig 4C). Together, these results demonstrated that an inhibition of the IL-2 signalling pathway was not driving T-cell suppression. Accordingly, the proportion of T regulatory cells (Tregs) was also neither increased nor decreased in stimulated cultures (S8A and S8B Fig), and depletion of CD25+ cells did not prevent FRC-mediated suppression (S8C Fig), suggesting that the effect of FRCs did not occur via cross-talk with Tregs.
Production of IFNγ and tumour necrosis factor alpha (TNFα) were also unaffected by coculture with FRCs (Fig 4C), suggesting that while FRCs inhibit activation and differentiation of T cells, once T cells are active, their effector functions are not impaired. This finding was confirmed using antigen-activated chimeric antigen receptor (CAR) T cells (Fig 4D) and is a finding not reproduced in mouse models, in which IFNγ production is reduced following FRC coculture [6].
Given the broad differences here observed between mice and humans, we decided to test whether T-cell cross-talk with human FRCs was required for or important to suppression. In mice, having T cells and FRCs in close proximity is important, since separation by transwell blocks the majority of the suppressive effect of FRCs [5,6]. Residual suppression in the presence of the transwell is likely to be due to secretion of PGE2 [4]. Unlike mouse FRCs, suppression was retained when FRCs were separated from T cells by a cell-impermeable transwell barrier (Fig 4E). Moreover, FRC-conditioned media diluted 1:2 in complete media suppressed T-cell activation indistinguishably from FRCs (Fig 4F), showing that FRCs secrete suppressive factors constitutively and that, unlike mouse FRCs [5,6] or human mesenchymal stromal cells [7], they do not require cross-talk from activated T cells. Accordingly, steady-state cultured FRCs expressed high levels of PGE2 synthesis enzymes COX1 (PTSG1) and COX2 (PTSG2) (Fig 4G). COX1 is usually a constitutive source of PGE2, while COX2 is more commonly inflammation inducible yet was expressed in otherwise unstimulated FRC cultures (Fig 4G) [4]. COX2, A2AR, and transforming growth factor beta receptor type 2 (TGFβR2) protein staining was detectable on ERTR7+ T-zone FRCs (TRCs) present in frozen tonsil tissue sections (S9 Fig) from patients who were not suffering from infection at the time of surgery. We were unable to detect IDO staining, suggesting that this protein alone may be inducible under acute inflammatory conditions.
As a method of cross-confirmation to show that FRCs preproduce suppressive factors prior to seeing T cells, we preincubated FRCs with inhibitors, prior to a wash step, and then added T cells and an activation stimulus. T cells stimulated with pre-inhibited FRCs had high CD25 expression after 24 h, similar to FRCs with inhibitors and higher than uninhibited FRCs (S6 Fig), as also shown in Fig 1D. CD25 expression was used as a biomarker for FRC suppression of T cells, as T-cell division was not measurable until 48 h, and the pre-incubation step was, because of receptor turnover, not effective beyond 24 h.
Next, we looked for a means to validate these in vitro results. Activation of T cells in situ on secondary lymphoid organ tissue slices has not previously been shown, to our knowledge. We decided to test whether this could be due to a suppressive effect of stromal cells, by stimulating T cells in situ within slices of freshly donated tonsil, in the presence or absence of the inhibitory cocktail. Tonsils were obtained within 2–4 h of surgery from healthy donors, immediately sectioned, and then incubated in media containing phytohaemagglutinin-L (PHA-L) and recombinant human interleukin 2 (rhIL-2), with or without the inhibitor cocktail added. After 96 h, T cells were imaged or isolated by mechanical disruption and analysed by flow cytometry.
A significantly higher proportion of CD4+ and CD8+ T cells were observed in active cell cycle (Ki67+) in the presence of the inhibitory cocktail, shown through flow cytometry (Fig 5A and 5B) and immunofluorescence (Fig 5C). Controls showed baseline expression (Fig 5A–5C). Immunofluorescent imaging showed increased Ki67 staining in the T-cell zone when inhibitors and the activating stimulus were both present (Fig 5C). Ki67 staining robustly reproduced in vitro results and demonstrated that it is possible to inhibit stromal-induced T-cell suppression in situ.
Taken together, these results show that human FRCs strongly influence the activation and differentiation of naïve T cells by constraining initial proliferation and skewing their differentiation away from a central memory phenotype without altering effector cytokine production or signalling. Mechanisms of action involved PGE2, COX1/2, TGFβ, and A2AR. Coinhibition of these factors permitted us to observe T-cell activation on a living tonsil slice for the first time, to our knowledge.
Identifying the cells and molecules that regulate T-cell activation during inflammation, infection, cancer, and autoimmunity is a fundamental first step towards creating effective therapies for immune-mediated pathologies and immune deficiencies. Human immunological studies are commonly carried out in vitro, with validation in mice. Here, we show that the presence of a human microenvironment influences T-cell activation both in vitro and in situ, with important functional and mechanistic differences from previous observations in mice.
Previous studies using mouse lymph nodes have established that FRCs are important for regulation of T-cell proliferation, largely through provision of nitric oxide [5–7] with an early role for PGE2 [4,6]. Changes to differentiation were not observed in mice, but FRCs did compromise effector cytokine production and therefore T-cell function [6].
By contrast, our results showed key differences to mice. Human FRCs utilised 4 pathways independent of nitric oxide to control T-cell proliferation and differentiation. Inhibiting TGFβR, A2AR, IDO, and COX1/2 completely reversed the suppressive effect of FRCs and restored differentiation of T cells with a central memory phenotype to normal levels. Similarly, the inhibition of these targets in situ using tonsil slices allowed T cells to overcome the prohibitive effect that stromal cells imposed.
Unlike mice, bidirectional T-cell signalling to FRCs was not required for T-cell suppression or for expression of A2AR, TGFBR, and COX2 protein by TRCs. Expression of IDO was not detected, and it is expected that IDO is induced by acute inflammation, as reported in dermal and bone marrow fibroblasts [17]. It will be of interest in the future to explore the factors and kinetics governing its induction.
Once activated, polyclonal and CAR T cells both showed normal effector cytokine secretion, which again differs from mice. It is currently unclear whether these are bona fide biological differences between mice and humans or due to the preferential study of immunologically naïve mice raised in a specific pathogen-free environment. It would be interesting to see whether FRCs from mice that have undergone several rounds of a self-limiting infection would constitute a more representative model for human FRCs.
The finding that T-cell function in the presence of FRCs is maintained is highly relevant to cancer immunotherapy. Secondary lymphoid organs are a primary tumour site for lymphoma and leukaemia and a prominent metastatic site for many other cancers. They are an important site for transfused CAR T-cell activity, with one study showing CAR T cells heavily infiltrating lymph nodes of patients with lymphoma at >30% of T cells [18] and another showing CAR T transcripts detectable in lymph nodes up to 3 mo post-infusion and at higher levels in lymph nodes than in blood [19]. Mouse data suggested that FRCs would limit IFNγ production by effector T cells; these results show that human FRCs only impose effects on naïve T-cell proliferation and differentiation and not effector function. Secretion of key effector cytokines IFNγ, TNFα, and IL-2 is unchanged in the presence of FRCs after T cells are activated, which is the case for CAR T therapy.
Much like nitric oxide as a mechanism in mouse FRCs, human FRC molecular mechanisms are extremely complex, and the precise effects on T cells are not easily elucidated, despite their clear biological importance and decades of intense study. TGFβR signalling, the COX1 enzyme, A2AR, and IDO all have well-described suppressive effects on T-cell activation. But apart from IDO, none are uniformly anti-inflammatory; rather, each factor is capable of shaping the T-cell response in a complex manner dependent on the stimulus, costimulating factors, and the microenvironmental cytokine milieu [20,21]. As such, inhibitors targeting the molecules profiled in this study have been investigated for highly diverse applications. A2AR inhibitors, for example, are used to treat rheumatoid arthritis (methotrexate [22]) while being investigated as an immunomodulatory treatment for cancer, for which the goal is inhibition of immunosuppression [23]. The level of TGFβ signalling to naïve T cells is an important factor in enforcing their quiescence, and naïve T cells in patients with autoimmunity have reduced expression of TGFβRI and increased capacity for T-cell proliferation [24], but TGFβ blockade enhances vaccine and immunotherapy responses [25]. With similar complexity, murine FRCs have a role in deletional and suppressive tolerance [5,6,26] while promoting healthy immune responses [3,10].
IDO has a well-defined role in T-cell suppression. It oxidises tryptophan to kynurenine metabolites [27], which both deprives effector T cells of tryptophan, inducing proliferative arrest [28], and exposes them to immunosuppressive kynurenine, which can impair T-cell growth and survival [29]. IDO is a well-described mechanism of tumour immune evasion in mice [28] and shows direct effects in human T cells [30–32], though a phase 3 trial of combination IDO inhibitor and programmed cell death protein 1 (PD-1) inhibition recently failed to improve progression-free survival compared to PD-1 inhibitor immunotherapy alone (clinicaltrials.gov identifier: NCT02752074). IDO affects the earliest stages of TCR signalling through down-regulation of Vav1 and inhibition of F-actin reorganisation [30,31], as well as inhibition of the mammalian target of rapamycin (mTOR) pathway [29], and, as we observed, prevents cells progressing to S phase of the cell cycle [33].
COX1 and COX2 are enzymes involved in prostaglandin synthesis. Our inhibitor is capable of blocking both, but our data show that COX1 is a major mediator of FRC suppression, since FRC-conditioned media strongly suppressed T cells, and cultured FRCs only expressed COX1 constitutively. COX1 can also be involved in the earliest inflammatory events, after which COX2 becomes the predominant inflammatory isoform [34]. In humans, PGE2 is the most abundant member of the prostanoid family, and most PGE2 is secreted by professional antigen-presenting cells (APCs) and stromal cells [21]. PGE2 is capable of mediating diverse effects depending on stimuli that are not well understood, but its role in suppression of T-cell activation and proliferation has been reported since 1971 [35], with newer studies also describing skewed differentiation [36] and induction of a suppressive phenotype in non-Treg CD4+ T cells, which were capable of suppressing the proliferation of other T cells undergoing activation [37]. Reported mechanisms include down-regulation of CD25, up-regulation of CD46, and altered responses to costimulation [36].
Extracellular purinergic mediators, such as adenosine triphosphate (ATP) and adenosine, are powerful immunomodulators. They signal through A2AR as an important step in the resolution of inflammation, providing well-described suppressive influences on the function of T cells, dendritic cells, macrophages, mast cells, platelets, natural killer (NK) cells, B cells, fibroblasts, and neutrophils to prevent excessive tissue pathology [20,38,39]. Accordingly, methotrexate is a clinically important A2AR antagonist used to treat rheumatoid arthritis and other autoimmune diseases [22]. Adenosine production is increased in inflammation and in low-oxygen-tension microenvironments, and activation of A2AR increases intracellular cAMP, which inhibits cytokine responses. Accordingly, inhibition of A2AR awakens tumour-reactive CD8+ T cells in mouse models [40]. The oxygen tension in human lymphoid organs is likely to be low: murine lymphoid organs exhibit low oxygen tension in vivo at 0.5%–4.5% oxygen, which impacts upon the speed at which effector T cells differentiate [41], and while data on healthy lymph nodes are lacking, low partial pressures are reported for other human organs and tissues [42].
Certain human T-cell subsets are known to express CD39, which converts ATP to AMP [43], while human FRCs express high levels of CD73, which converts AMP to adenosine. A2AR signals reduce secretion of proinflammatory IL-1β and IL-6 [22], both of which are produced at high levels by FRCs in response to inflammation [11], and increases production of collagen I [44].
TGFβ is a highly evolutionarily conserved immunomodulatory molecule [45]. Our inhibitor blocked signalling receptor component activin receptor-like kinase 5 (ALK5; TGFβRI), whose signals are transduced by phosphorylation of SMAD2 and SMAD3 proteins [46]. TGFβRI−/− mice develop a lethal inflammatory disease [47], and neutralising TGFβ increases antitumour responses of CD8+ T cells [48]. However, TGFβ can impose both pro- and anti-inflammatory functions in responding human T cells, depending on their differentiation state and inflammatory cytokines encountered [45]. As an anti-inflammatory agent, TGFβ inhibits CD4+ and CD8+ T-cell clonal expansion and differentiation and inhibits the activation of high-affinity T cells [49–51], relevant to our findings, and it also promotes the survival of lower-affinity T cells. These effects can occur in a paracrine fashion by acting on APCs and other cells and by reducing CD25 expression [51]. TGFβ signalling blocks the clonal expansion of T cells in vivo and blocks differentiation of T helper 1 (Th1), T helper 2 (Th2), and cytotoxic lymphocyte (CTL) T cells in favour of T helper 17 (Th17; in the presence of IL-6) or peripheral Treg (pTreg; in the presence of retinoic acid and IL-2) [50].
The described pathways could potentially also link together. PGE2 can increase the expression of IDO in dendritic cells [52]. Similarly, signalling pathways downstream of PGE2 ligands EP2–4 involve cAMP, which is derived from ATP [21], potentially linking the suppressive actions of COX1 inhibition and A2AR inhibition. However, these potential interactions are very poorly studied and understood and require extensive further study.
Suppression of T-cell proliferation within secondary lymphoid organs seems paradoxical, but these and other results [5–7] show it is conserved in mice and humans, despite utilising different mechanisms. Mouse studies clearly show that it operates in vivo, in isolated FRCs in response to inflammation [5]. Here, we show that it operates in vitro using human cells and in situ in human tissues and that FRCs also secrete key suppressive factors in the absence of current inflammation. One hypothesis is that this mechanism helps prevent bystander damage to stromal cells in a lymph node teeming with inflammatory cytokines and antigen [1,12], and the mechanisms described all have well-charted effects in the resolution of inflammation [39]. Accordingly, viral infection of FRCs is associated with viral persistence in mice [12], and a loose association has been observed in humans and rhesus macaques in studies of HIV and Ebola virus, though studies are correlative [1].
While human FRCs did not affect the secretion of effector cytokines from activated cells, they did impose effects on the proliferation of activated cells. FRCs added to pre-activated, rapidly dividing cultures halted their division, and when activated, suppressed T cells are removed from the suppressive influence of FRCs, they begin rapidly dividing within 24 h, despite being washed thoroughly to remove the inflammatory cytokine milieu, and without being given a new activating stimulus. Thus, the effects of FRCs are not limited to naïve T cells.
This raises the possibility of pharmacologically targeting FRCs as a means to promote a stronger immune response. It would be valuable to study whether FRC inhibition could benefit patient groups who do not mount a robust response to vaccination, or to boost responses to cancer vaccines.
The in situ activation assay revealed a clear disconnect between the study of human immunology in vitro, in which T cells are frequently activated in isolation, and in situ, in the presence of the microenvironment. Our work highlights the importance of considering the microenvironment for in vitro human immunology studies. Conditions that robustly activated T cells in culture were entirely insufficient to activate T cells in situ. Addition of the inhibitor cocktail significantly increased T cell activation in situ but not to in vitro levels, suggesting the presence of additional physical or chemical inhibitory factors that warrant further study. Similarly, in mice, rare FRCs were observed up-regulating the suppression-mediating nitric oxide synthase 2 (NOS2) enzyme during an in vivo T-cell immune response [5], which was sufficient to robustly impair T-cell activation. These processes are clearly observable yet subject to kinetics and fate decisions we have yet to fully understand—suppressive yet finely tuned to permit T-cell activation and foster immunity.
The in situ activation assay has potential to test drugs in development for their effects on T-cell activation and proliferation. A technical challenge, requiring further study, is the ability to isolate slices from equivalent areas of tissue. The proportion of naïve T cells within a single donor varied hugely from slice to slice. As such, it was not yet possible to use this method to assess differences in subtler immunophenotypic changes, such as differentiation status. Another important caveat is our lack of transcriptomic data from freshly isolated human FRCs. Cultured FRCs in this study lacked a PDPN-low/negative subset that was present in freshly isolated tonsil FRCs, and the function of this subset in humans is not yet known.
Together, this work suggests that FRCs utilise druggable targets (individually or in combinations) with the potential to boost the generation of new immune responses within secondary lymphoid organs—for example, following vaccination of relevant patient groups or for the treatment or prevention of malignancy.
All tissues were obtained from consenting donors from the National Disease Research Interchange (NDRI) resource centre or Human Biomaterials Resource Centre (HBRC), Birmingham (HTA licence 12358, 15/NW/0079), under project approval number REC_RG_HBRC_12–071. All tissues were obtained and utilised in accordance with institutional guidelines and according to the principles expressed in the Declaration of Helsinki.
Human tonsils were obtained from presently healthy children and adults undergoing routine tonsillectomy for a medical history of recurrent infection or obstructive sleep apnoea. Human blood was obtained from healthy adult donors. Human lymph nodes were procured from cadaveric donors, transported intact in DMEM on ice, and processed for flow cytometry or cell culture within 24 h. All tissues were obtained and utilised in accordance with institutional guidelines.
Tonsils and lymph nodes were enzymatically digested using a published protocol [13] or grown through explant culture and used at passage 1–3. Briefly, tissues were cut into small pieces and grown in a low volume of complete media with antibiotics (alpha-MEM with 10% FBS, with penicillin, streptomycin, and a mycoplasma elimination reagent) for 24 h to allow adhesion to the tissue culture plate. Following this, tissues were covered with media containing antibiotics and grown for 5 days to permit fibroblasts to emerge. Tissue was then discarded and cells culture-expanded in complete media without antibiotics. Using this method, a monolayer of >99% pure FRCs was achieved within 2 wk. Ten-fold expansion was taken to equal 1 passage. FRCs were defined as CD45−, CD31−, PDPN+. Very rarely, cultures down-regulated expression of PDPN after passage 3; this did not affect their transcriptome or suppressive T-cell interactions (not shown); nonetheless, such cultures were not used experimentally to ensure uniformity.
FRCs (2 × 104) were plated in a 96-well flat-bottom plate in complete media (alpha-MEM, 10% FBS) and allowed to adhere for 4 h. FRCs were always used in experiments prior to passage 3. Mononuclear leukocytes were isolated from whole blood using a density gradient and then counted using a haemocytometer and Trypan Blue viability dye. Where stated, T cells were purified using the Pan T-cell isolation kit (Miltenyi Biotec) according to the manufacturer’s instructions and at a purity >90%, or CD25 depleted (Miltenyi Biotec) according to the manufacturer’s instructions and at a purity >90%. Mononuclear leukocytes or T cells (5 × 105) were added, together with a stimulant: either CD2/3/28 T-cell activation beads (2 beads/T cell) (Miltenyi Biotec) or PHA-L (1 μg/ml) + rhIL-2 (100 U), as stated in figure legends. Inhibitors were added at the following concentrations: SB431542 10 μM (TGFβ signalling pathway inhibitor through blockade of ALK5, 7, 4, Sigma), Indomethacin 5 μM (Cox1/2 and PGE2 synthesis inhibitor, Sigma), SCH 58261 10 μM (A2AR inhibitor, Sigma), 1-methyl-D-Tryptophan (1-MT) 1 mM (Indoleamine-2,3-dioxygenase inhibitor, Sigma). SCH 58261 and SB431542 10 μM were stored in DMSO. PGE2 inhibitor was reconstituted in ethanol; 1-MT was reconstituted in methanol and pH adjusted to 7.0. The final volume per well was 200 μl, and all cells and inhibitors were resuspended in complete media without antibiotics. All 4 inhibitors used together at stated concentrations are referred to as the ‘inhibitor cocktail’.
FRCs (1–2 × 104) and Chinese hamster ovary (CHO; 5 × 104–1.25 × 105) cells were plated in a 96-well flat-bottom plate in complete media (DMEM, 10% FBS with IL-2 [25 IU/ml]) and allowed to adhere for 4 h. CRT-3 (1 × 105) or mock-transduced T cells were added and incubated for 18 h at 37°C/5% CO2. Culture supernatants were collected at 18 h, and the levels of IFNγ were titrated in culture supernatants using the ELISA method. Briefly, plates (Nunc) were coated with anti-human IFNγ Ab diluted in coating buffer (0.75 μg/ml) and incubated at 4°C overnight. After blocking the wells using buffer containing PBS plus 0.05% (v/v) Tween 20 and 0.1% (w/v) bovine serum albumin (BSA), supernatants were added to each well. Biotin-labelled mAb in incubation buffer was added to each well, and streptavidin-HRP was used as enzyme. The reaction was developed using 3,3′,5,5′-tetramethylbenzidine (TMB) substrate and stopped by adding 1 M hydrochloric acid. The plates were washed after each step using PBS with 0.05% (v/v) Tween 20. Reading was performed using a microplate automatic reader (Biorad) at a wavelength of 450 nm.
Tonsils <2 h from surgery were sliced into multiple 0.4–0.6 mm sections using a sterilised carbon-fibre microtome blade or embedded in low-melting-point agarose and sectioned using a vibratome, collected into ice-cold PBS. Sections were randomised between groups and cultured in complete media containing PHA-L (1 μg/ml) + rhIL-2 (100 U), with or without the inhibitor cocktail described above. Multiple slices were used per treatment group. After 96 h, each slice was pushed through a cell strainer to create a single-cell suspension and stained for flow cytometry or embedded in OCT buffer and snap-frozen for sectioning and imaging. To minimise differences arising from sectioning different areas of tissue, data from multiple slices were averaged to obtain a single data point per donor.
Cells were harvested at stated time points and stained for 20 min in FACS buffer (PBS with 2% FCS and 2 mM EDTA) using antibodies as described in S1 Table. Cells were fixed and permeabilised using a commercial kit (BD) and then stained for intracellular proteins using the following antibodies: cells were resuspended for flow cytometry, filtered through 100 μm mesh, and acquired using flow cytometry. Analysis utilised commercial analysis software (TreeStar or DeNovo Software). tSNE analysis was performed using 1,000 iterations, with perplexity 30 and theta 0.5, displaying a proportional number of events.
Human tonsils were embedded in O.C.T. Compound (Sakura) and then flash frozen using dry ice. Then, 10–12 mm transverse sections were generated on a cryostat (Bright Instrument Company) and collected on adhesive slides (Leica). Sections were air-dried for 2 h at room temperature (RT) and then fixed in cold acetone for 25 min. Sections were air-dried overnight for immediate immunolabelling or stored at −80°C until further use. Frozen tonsil sections were air-dried for 15 min at RT and rehydrated for 5 min with 1X PBS. Sections were then permeabilised for 15 min with 0.3% Triton X-100 (ThermoFisher Scientific) and washed 3 times with 1X PBS. Sections were blocked for 1 h in 1% BSA (Sigma-Aldrich) and 5% goat serum in PBS in a humidified chamber. Sections were incubated overnight at 4°C with primary antibodies diluted in 1% BSA. After incubation, sections were washed 3 times with 1X PBS and then incubated with secondary antibody for 1 h. Secondary antibodies included goat anti-rat Alexa 546 (ThermoFisher), goat anti-rat Alexa 546 (ThermoFisher), goat anti-rat goat Alexa 647 (ThermoFisher), donkey anti-rabbit Alexa 488 (ThermoFisher), goat anti-rabbit Alexa 647 (ThermoFisher), donkey anti-mouse DyLight 594 (ThermoFisher), and donkey anti-goat Alexa 555 (ThermoFisher). Sections were then incubated with DAPI for 1 min, followed by 3 additional washes with 1X PBS. Negative controls utilised incubation with PBS with relevant serum or relevant isotype, followed by secondary antibody. Finally, sections were mounted in antifade mountant (ThermoFisher Scientific) for imaging. Immunofluorescence images were taken with a confocal microscope Zeiss LSM 880, using ZEN Pro imaging system.
Tonsil-derived FRCs from 3 donors (in-house) and bone marrow–derived MSCs from 3 donors (Lonza and expanded in-house) were grown in culture to P3 in the presence of hFGF (4 μg/ml) and harvested in logarithmic growth phase. Total RNA was extracted using an RNA extraction kit (Qiagen) and purified using a cleanup kit (Qiagen). Samples were quality tested using an Agilent Bioanalyzer 2100 and the Agilent RNA 6000 nano kit. RIN numbers for all samples ranged from 9.5 to 10. Samples were then sent to BGI (Hong Kong) for library preparation and sequencing. Briefly, library preparation utilised poly-A enrichment followed by Ribozero depletion. Samples were run in a high-performance sequencing machine (Illumina) over 2 lanes, resulting in approximately 60 million reads per sample. Data were then trimmed for adapter sequences before analysis. Bioinformatics alignment and further analysis was done in-house using commercial software (Partek). For a visual representation of gene expression, TPM was used for normalisation. The heatmap was made using Morpheus (https://software.broadinstitute.org/morpheus). Data are accessible at monash.figshare.com doi: 10.4225/03/5a2dae0c9b455.
Data were tested for normality using D’Agostino and Pearson normality test. Normally distributed data of 2 groups were compared using an unpaired t test, or of 3 or more groups using an ANOVA with a multiple comparison test, as described in figure legends. When data were not normally distributed, 2 comparisons were made using a Mann-Whitney test. When fold-change data were compared to a normalised value of 1, a 2-tailed Wilcoxon signed rank test was used. P < 0.05 was taken as significant.
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10.1371/journal.ppat.1004897 | Natural Killer Cell Sensing of Infected Cells Compensates for MyD88 Deficiency but Not IFN-I Activity in Resistance to Mouse Cytomegalovirus | In mice, plasmacytoid dendritic cells (pDC) and natural killer (NK) cells both contribute to resistance to systemic infections with herpes viruses including mouse Cytomegalovirus (MCMV). pDCs are the major source of type I IFN (IFN-I) during MCMV infection. This response requires pDC-intrinsic MyD88-dependent signaling by Toll-Like Receptors 7 and 9. Provided that they express appropriate recognition receptors such as Ly49H, NK cells can directly sense and kill MCMV-infected cells. The loss of any one of these responses increases susceptibility to infection. However, the relative importance of these antiviral immune responses and how they are related remain unclear. In humans, while IFN-I responses are essential, MyD88 is dispensable for antiviral immunity. Hence, a higher redundancy has been proposed in the mechanisms promoting protective immune responses against systemic infections by herpes viruses during natural infections in humans. It has been assumed, but not proven, that mice fail to mount protective MyD88-independent IFN-I responses. In humans, the mechanism that compensates MyD88 deficiency has not been elucidated. To address these issues, we compared resistance to MCMV infection and immune responses between mouse strains deficient for MyD88, the IFN-I receptor and/or Ly49H. We show that selective depletion of pDC or genetic deficiencies for MyD88 or TLR9 drastically decreased production of IFN-I, but not the protective antiviral responses. Moreover, MyD88, but not IFN-I receptor, deficiency could largely be compensated by Ly49H-mediated antiviral NK cell responses. Thus, contrary to the current dogma but consistent with the situation in humans, we conclude that, in mice, in our experimental settings, MyD88 is redundant for IFN-I responses and overall defense against a systemic herpes virus infection. Moreover, we identified direct NK cell sensing of infected cells as one mechanism able to compensate for MyD88 deficiency in mice. Similar mechanisms likely contribute to protect MyD88- or IRAK4-deficient patients from viral infections.
| Type I interferons (IFN-I) are innate cytokines crucial for vertebrate antiviral defenses. IFN-I exert antiviral effector functions and orchestrate antiviral immunity. IFN-I are induced early after infection, upon sensing of viral particles or infected cells by immune receptors. Intracellular Toll-like receptors (TLR) are selectively expressed in specialized immune cell types such as plasmacytoid dendritic cells (pDC), enabling them to copiously produce IFN-I upon detection of engulfed viral nucleic acids. pDC or intracellular TLR have been reported to be crucial for resistance to experimental infections with many viruses in mice but dispensable for resistance to natural infections in humans. Our aim was to investigate this puzzling difference. Mice deficient for TLR activity mounted strong IFN-I responses despite producing very low IFN-I levels and controlled the infection by a moderate dose of murine cytomegalovirus much better than mice deficient for IFN-I responses. Deficient TLR responses could be compensated by direct recognition of infected cells by natural killer cells. Hence, we identified experimental conditions in mice mimicking the lack of requirement of TLR functions for antiviral defense observed in humans. We used these experimental models to advance our basic understanding of antiviral immunity in a way that might help improve treatments for patients.
| Type I interferons (IFN-I) orchestrate vertebrate antiviral defenses through two complementary mechanisms [1]. These cytokines induce multiple Interferon Stimulated Genes (ISG) coding for effector molecules of cell-intrinsic antiviral immunity. IFN-I instruct antiviral innate and adaptive immunity, in part by promoting the maturation of dendritic cells (DC) for potent activation of natural killer (NK) cells and CD8 T lymphocytes. Genetic deficiencies compromising IFN-I responses dramatically increase susceptibility to viral infections in mice and men [2]. In addition to IFN-I, type III interferons (IFN-III) also appear critical for antiviral defense, based on the analysis of mutant mice, and on the strong association between resistance to viral infections and polymorphisms affecting these genes in humans [1, 3, 4]. IFN-I and IFN-III share the same signaling pathways and downstream target genes. However, while the IFN-I receptor (IFNAR) is ubiquitously expressed, the receptor for IFN-III is selectively expressed in epithelial cells [1].
The contribution of different cell types and molecular sensors to IFN-I induction during viral infections is the subject of debate. IFN-I can be induced by two major mechanisms in infected hosts [1]. Potentially all host cell types are equipped with innate immune sensors of endogenous viral replication that can trigger IFN-I production. Certain immune cell types are also able to sense viral infection in their surroundings and consequently produce high levels of IFN-I without being infected. This ability is especially strong in the plasmacytoid subset of DC (pDC). pDC recognize and engulf viral particles or material derived from infected cells. Subsequent detection of nucleic acids by toll-like receptors (TLR) 7 and 9 in specialized endosomes leads to MyD88- and IRAK4-dependent IFN-I induction [5]. On the other hand, while patients genetically deficient for MYD88 or IRAK4 show enhanced susceptibility to mycobacteria, they are resistant to most viral infections [6–8]. MyD88 is critical for signaling not only by all TLRs except TLR3 but also by the receptors for all members of the interleukin-1 (IL-1) cytokine family [9]. Hence, in humans, pDC, TLR7/8/9 and all the IL-1 cytokine family are largely redundant for antiviral defense, in particular with regards to induction of protective IFN-I responses [8]. In contrast, mice genetically deficient for Tlr7, Tlr9 or Myd88 show enhanced susceptibility to a broad range of pathogens including many viruses [8]. pDC production of IFN-I has been proposed to be essential in mice for the control of acute systemic viral infections in particular with herpes viruses [10–12] or coronaviruses [13]. pDC may also contribute to prevent the establishment of chronic infections with certain viruses [14–16]. Here, we designed experimental studies to investigate what could explain this reported discrepancy between humans and mice for the importance of MyD88 responses in antiviral defense. During viral infections in mice, the impact of MyD88 inactivation or pDC depletion had only been assessed on the basis of IFN-I production. Here, we also examined how it affected the induction of protective IFN-I responses. We also investigated whether NK cell responses could compensate for MyD88 deficiency for host resistance.
We used experimental infection by murine cytomegalovirus (MCMV), a natural pathogen of mice [17], for which IFN-I responses are critical for protection both in vitro in macrophages [18–21] and in vivo [22, 23]. pDC sense MCMV infection through TLR7/9 [24–27] and constitute the major source of IFN-I in vivo [10, 24, 28, 29]. However, infected stromal cells have also been described as potent IFN-I producers in the spleen, 8 hrs after infection [30]. NK cells sense MCMV infection through their activating receptor Ly49H, allowing them to specifically recognize and kill infected cells through binding to m157, a viral protein expressed at their surface. pDC depletion prior to infection leads to a dramatic decrease of serum IFN-I levels but only to a modest and transient increase in viral loads in most organs [5, 10, 24, 29]. In contrast, either NK cell depletion, or deficiency in endosomal TLR activity, decreases the ability of C57BL/6 mice to control viral replication and increases morbidity and mortality [24–27, 31]. It is assumed that the enhanced susceptibility to certain systemic viral infections of mice deficient for endosomal TLR activity is largely due to their decreased IFN-I production [8, 10–13, 24, 27]. However, whether tissue responses to IFN-I are reduced in these animals has not been examined. Moreover, other immune functions are dampened or lost in these animals, including IL-12 production [5] and, in MyD88-deficient mice, responses to all member of the IL-1 cytokine family [9]. Moreover, the respective contributions of IFN-I-, MyD88- and Ly49H-dependent responses to overall resistance to MCMV infection has not been rigorously investigated in parallel in mice of the same genetic background, deficient for one or more of these responses. We thus designed a study to rigorously explore how MyD88 genetic deficiency in mice affects their responses to MCMV infection, in particular their ability to mount strong type I IFN responses and to resist disease development, for comparison with the analysis previously published in patients genetically deficient for MYD88 [7]. In both species, MyD88 deficiency does not only affect direct viral sensing by TLRs but also signaling by all IL-1 family cytokines including IL-18. We also wanted to explore whether the consequences of MyD88 deficiency could be modulated by mutations or polymorphisms in other immune genes, for example those encoding NK cell activating receptors. In steady state conditions, in mice, IL-18 has been reported to be crucial for NK cell functional maturation, enabling them to respond to activation with synthetic stimuli [32]. Hence, using MyD88-deficient animals to examine the interaction between DC and NK cell direct sensing of infected cells might not seem optimal since MyD88-deficiency could affect NK cell responses in a cell-intrinsic manner. During MCMV infection in vivo, contradictory results have been reported regarding the role of IL-18 in the promotion of the proliferation of Ly49H+ NK cells, with no role observed in one study [33], an absolute requirement reported in a another study [34], and an important contribution but not an absolute requirement in a third study [35]. In any case, IL-18 has been described as a cytokine crucial for IFN-γ production by NK cells in the spleen but not in the liver and appears to be dispensable for resistance to MCMV infection under conditions where IL-12 and NK cells are critical [36]. In other words, IL-18 does not seem to be required for NK cell-dependent protection against primary MCMV infection. CD8 T cell responses are also important for immune defense against CMV infection in human and mice [5]. TLR or IL-1 stimulation of DC are considered to be critical for the induction of protective antiviral cellular adaptive immunity [37, 38]. CD8 T cell responses against MCMV are altered in mice affected in their MyD88 or NK cell activities [24, 39–42]. However, how NK and MyD88 responses are integrated in the shaping of antiviral CD8 T cells responses has not been examined.
We used a series of mutant BALB/c mice expressing or not Ly49H, and deficient or not for MyD88 or for the receptor for IFNAR. We characterized their immune response and overall resistance to MCMV infection. We used BALB/c congenic animals, C.B6-Klra8Cmv1-r/UwaJ mice referred to as BALB/c-Ly49H+ mice in the manuscript, which carry most of the C57BL/6 NK gene complex, not only limited to Ly49h [43]. Contrary to the situation observed in BALB/c mice, in BALB/c-Ly49H+ mice NK cells play a major role in the control of an in vitro grown WT MCMV Smith virus. This protective NK cell activity is almost completely abrogated when using an in vitro grown MCMV Smith virus strain lacking the m157 gene [44]. Here, we compared viral control and mortality between BALB/c and BALB/c-Ly49H+ mice upon infection with a K181 virus strain lacking the m157 gene, Δm157 MCMV [45]. Upon infection with a moderate dose of salivary gland-extracted Δm157 MCMV, BALB/c-Ly49H+ mice did not control viral replication more efficiently or survive better than BALB/c animals (S1A–S1C Fig). Altogether, these observations strongly suggest that most of the differences observed in the increased resistance to MCMV infection of BALB/c-Ly49H+ mice as compared to BALB/c animal likely result from the much more efficient recognition of infected cells by their NK cells, due to the interaction of the Ly49H NK cell activation receptor with the viral m157 protein expressed at the surface of infected cells. Nevertheless, some contributions for other genes encoded in the NK gene complex cannot be totally excluded. We chose to inject MCMV intraperitoneally because it is the most frequent route of inoculation used with MCMV and because it rapidly causes a systemic infection, which resolution is thought to more stringently depend of systemic production of IFN-I by pDC than for local infections. Indeed, with several other viral infections, including infections with the herpesviruses HSV-1 and HSV-2, pDC were shown to contribute to IFN-I production only under condition of systemic viral spread and not under conditions where the infections is performed and controlled in epithelia [12, 46]. Moreover, in mice, pDC depletion compromises the control of HSV-1 or HSV-2 only for systemic but not local infections [12]. Our results demonstrate that, in our experimental settings, MyD88 but not IFN-I responses are largely redundant for control of a systemic herpes virus infection in mice. This contradicts current thinking but reconciles the requisite of mice and men for innate antiviral defense. We identify direct recognition of infected cells by NK cells as one innate immune sensing mechanism able to compensate in part for the loss of MyD88 activity. Our results also highlight an unexpected ability of low levels of IFN-I to induce strong cell-intrinsic antiviral immunity in vivo, emphasizing the importance of measuring responses to, rather than production of, cytokines to assess their physiological role.
To assess the contribution of pDC activation and endosomal TLR7/9 triggering to IFN-I responses during MCMV infection, we analyzed the induction of IFN-I and ISG in the spleen or blood of BALB/c mice knocked-out for MyD88 or TLR9, or depleted of pDC through administration of the 120G8 mAb directed against Bst2. 120G8 mAb injection dramatically reduced circulating IFN-I titers and splenic Ifnb1 expression at d1.5 after infection (Fig 1A and 1B). However, unexpectedly, only a slight impairment was observed in splenic induction of 3 canonical ISG at d1.5 and 3 after infection (Fig 1B). Consistently, 120G8 mAb injection in BALB/c mice did not compromise splenic viral control at d6 (S2A and S2B Fig). Because the administration of the 120G8 mAb resulted in an efficient and selective, even though not entirely specific, depletion of pDC during MCMV infection (see materials and methods), we can rigorously conclude that pDC and their high systemic production of type I IFN are not required for induction of strong type I IFN responses and relatively efficient control of MCMV infection in our experimental settings. Similar results were obtained for splenic induction of Ifnb1 and ISG in BALB/c MyD88-/- and BALB/c TLR9-/- mice (Fig 1B and S2C Fig). In contrast, ISG induction was completely abrogated in IFNAR-/- animals (Fig 1B). Thus induction of strong IFN-I responses in the spleen of MCMV infected mice seems not to require pDC, MyD88 or TLR9 functions and can be induced by very low levels of IFN-I.
Systemic levels of IFN-I during MCMV infection are controlled by a balance between pDC and NK cell activation [5]. High systemic IFN-I production not only requires the ability of pDC to sense viral nucleic acids through functional endosomal TLR, but is also promoted by viral replication, which is normally limited by NK cell activity. Hence, to generalize our observation that very low levels of IFN-I production are sufficient to induce strong IFN-I responses, we next measured pangenomic ISG induction in mice with low serum IFN-I titers (Fig 1C) and splenic pDC IFN-I expression (S2A and S2D Fig) resulting either from a primary immune deficiency (MyD88-/- animals) or on the contrary from early control of the virus by NK cells (Ly49H+ animals). A strong induction of most IFN-I/III genes was observed in BALB/c mice at d1.5 after MCMV infection. This progressively decreased over time to become undetectable by d6 (Fig 1D). In contrast, hardly any induction of IFN-I/III genes was seen in BALB/c-Ly49H+ and BALB/c-Ly49H+ MyD88-/- animals, and only a weaker and delayed induction was observed in BALB/c MyD88-/- mice (Fig 1D). However, strikingly, a very strong ISG induction was observed in all mouse strains, already at d1.5 after MCMV infection. This was maintained until d3 in all mouse strains and was still strong at d6 in BALB/c and BALB/c MyD88-/- mice (Fig 1E and S2E–S2G Fig). Thus, induction of strong IFN-I responses in the spleen of MCMV infected mice does not require pDC, MyD88 and TLR9 function and can be induced optimally under conditions where IFN-I/III are undetectable not only in the circulation but also in the spleen both at protein and mRNA levels. Hence, contrary to the commonly accepted dogma, pDC, MyD88 and TLR9 are dispensable in mice for induction of strong IFN-I responses against systemic infection with a herpes virus, in our experimental settings.
We next examined to what extent IFN-I responses are required for defense against MCMV under our experimental conditions. We tested susceptibility to MCMV infection of 6 different mouse strains, with deficiencies in either IFNAR or MyD88, and expressing or not Ly49H (Fig 2), or selectively depleted for pDC (S3 Fig). We infected these animals with serial doses of MCMV, to define the LD50 inoculum at which 50% of the animals were killed (Fig 2A). As expected, BALB/c-Ly49H+ mice were the most resistant to infection. BALB/c IFNAR-/- and BALB/c-Ly49H+ IFNAR-/- mice were the most susceptible, with an LD50 more than 50 times lower than that of BALB/c-Ly49H+ animals. Thus, IFN-I responses are critical for defense against MCMV infection even in mice bearing the Ly49H activating receptor allowing direct sensing of infected cells by NK cells. Strikingly, BALB/c-Ly49H+ MyD88-/- and BALB/c MyD88-/- mice harbored clearly higher LD50 than IFNAR-/- animals. Hence, MyD88-/- mice were significantly more resistant than IFNAR-/- animals, despite their low to undetectable production of IFN-I. In addition, pDC depletion did not increase the susceptibility of BALB/c mice after infection with the LD50 (S3 Fig). Thus, the induction of IFN-I responses to levels allowing a significant control of MCMV infection does not require MyD88 and pDC functions.
The sensitivity to MCMV infection conferred by IFNAR deficiency was independent of the expression of Ly49H. This was not the case for MyD88 deficiency. BALB/c-Ly49H+ MyD88-/- mice were clearly more resistant than BALB/c MyD88-/- mice (Fig 2A). Conversely, the sensitivity to MCMV infection associated with Ly49H deficiency was aggravated by MyD88 deficiency, since BALB/c mice were more resistant than BALB/c MyD88-/- mice (Fig 2A). BALB/c and BALB/c-Ly49H+ MyD88-/- mice exhibited similar LD50. Hence, the deficiency in TLR-mediated MCMV sensing by DC and in responses to IL-1 cytokine family can be compensated at least in part by direct sensing of infected cells by NK cells and vice versa. Indeed, under conditions of infection with a moderate MCMV dose, below the LD50 of BALB/c MyD88-/- mice, control of viral replication was significantly less efficient in these animals than in the three other mouse strains examined (Fig 2B). NK cell activation during MCMV infection depends on IL-12, IL-18 and IL-15 [5, 35]. The induction and/or activity of IL-12 and IL-18 are both strongly decreased in MyD88-/- C57BL/6 mice. This correlates with a significant impairment of NK cell responses under conditions of high viral inoculum [5]. In contrast, here, the survival and viral titers observed suggested that functional antiviral NK cell responses were induced in BALB/c-Ly49H+ MyD88-/- mice. Thus, we examined NK cell functions in BALB/c mice deficient or competent for MyD88, and expressing or not Ly49H (Fig 3). At d3 after MCMV infection, the Ly49H+ NK cells from BALB/c-Ly49H+ MyD88-/- mice were clearly activated although to a significantly lesser extent than in BALB/c-Ly49H+ animals (Fig 3A–3D and S4A and S4C Fig). Diminished IL-12 and type I IFN production by DC as well as cell-intrinsic loss of IL-18 signaling could all have contributed to the decrease in NK cell proliferation, production of IFN-gamma and expression of Granzyme B observed in MyD88-/- mice, according to previous reports which analyzed the contribution of these different responses during MCMV infection [23–26, 35, 36, 47, 48]. In any case, at d6 after MCMV infection, the activation of Ly49H+ NK cells was higher in BALB/c-Ly49H+ MyD88-/- mice as compared to BALB/c-Ly49H+ animals (Fig 3A–3D). Moreover, NK cell depletion led to a significant increase in viral replication not only in BALB/c-Ly49H+ mice but also in BALB/c-Ly49H+ MyD88-/- animals (Fig 3E and S4D Fig). Thus, NK cells can be sufficiently activated so as to contribute significantly to the control of MCMV infection despite a primary immune deficiency that abrogates viral sensing by DC through TLR7/9, as well as the responses to IL-18 and all other IL-1 family cytokines, provided that the NK cells can directly recognize virally infected cells through triggering of a dedicated NK cell activating receptor. Ly49H+ NK cells from BALB/c-Ly49H+ IFNAR-/- mice were significantly activated at d3 and d6 post infection (Fig 3A–3D), to levels similar to those observed in BALB/c-Ly49H+ MyD88-/- mice. However, in contrast to the situation in the latter animals, in BALB/c-Ly49H+ IFNAR-/- mice, viral replication did not appear to be curtailed by NK cell activity, since it was already very high in untreated mice and was not increased further upon NK cell depletion (Fig 3E). These results suggest that Ly49H+ NK cells are similarly activated in BALB/c-Ly49H+ IFNAR-/- and BALB/c-Ly49H+ MyD88-/- mice but fail to control viral replication in the former because the absence of IFN-I response allows fast and widespread virus replication overwhelming the antiviral functions of Ly49H+ NK cell. Thus, MyD88 but not IFNAR deficiency can be compensated at least in part by direct sensing of infected cells by NK cells in our experimental model of systemic MCMV infection.
A strong activation of Ly49H- NK cells was observed in all infected mice (Fig 3F–3I and S4A and S4B Fig). Hence, NK cell activation in MyD88-/- mice did not require cell-intrinsic Ly49H signals. IL-12 and IL-18 production were decreased in MCMV-infected BALB/c MyD88-/- and BALB/c-Ly49H+ MyD88-/- mice (S4E Fig). However, similarly to what happened with the IFN-I response, IL-12/18 stimulated genes were strongly induced in infected MyD88-/- mice (S4F and S4G Fig). Hence, persistence of NK cell activation in MyD88-/- mice likely resulted from residual IL-12 production and subsequent IL-12-dependent responses.
We next investigated the functions and numbers of antiviral CD8 T cells in the different mouse strains under study. Contrary to our expectations, the in vivo cytotoxic activity of anti-MCMV CD8 T cells was similar in the four mouse strains examined at d6 after infection (Fig 4A and S5A Fig). The CD8 T cells from Ly49H-/-MyD88-/- mice even exhibited a significantly stronger expression of IFN-γ and Granzyme B directly ex vivo without any restimulation (Fig 4A and S5B Fig). Moreover, depletion of CD8 T cells led to a significant increase of viral replication both in BALB/c and BALB/c MyD88-/- animals (Fig 4B and S5B Fig). Thus, during MCMV infection, CD8 T cells are hyperactivated in BALB/c MyD88-/- animals and contribute to the control of viral replication. Hyperactivated CD8 T cell responses can contribute to liver immunopathology and death during MCMV infection [49]. However, CD8 T cell depletion led to a higher mortality of BALB/c MyD88-/- mice (Fig 4C), ruling out a deleterious role of CD8 T cells in our experimental settings. Upon infection, there was a strong and significant increase in the numbers of total and anti-MCMV CD8 T cells in all of the three mouse strains expressing MyD88 and/or Ly49H, but not in BALB/c MyD88-/-animals (Fig 4D). Hence, since the efficiency of viral control in vivo by CD8 T cells depends on the local effector-to-target ratio in infected tissues [50], the enhanced susceptibility of BALB/c MyD88-/- mice to MCMV infection likely resulted in part from their failure to expand their antiviral CD8 T cells. As compared to the other mouse strains, the BALB/c MyD88-/- mice exhibited a significant increase in their splenic expression of genes associated with CD8 T cell exhaustion or with fibrosis and a decrease in the expression of the gene signature of red pulp macrophages (S5C and S5D Fig). This is consistent with the early and strong depletion of those cells previously reported in mice lacking efficient antiviral NK cell responses and infected with a high dose of MCMV [51]. At d4 post infection, BALB/c MyD88-/- and BALB/c IFNAR-/- mice also harbored a marked disruption of their spleen architecture (Fig 4E). Altogether, these observations suggest that early, strong and irreversible damage is caused to secondary lymphoid organs upon infection in Ly49H-/-MyD88-/-animals. This possibly is a consequence of persistently high levels of viral replication that cause a loss of the support function of these organs for all T cells and favor exhaustion of antiviral CD8 T cells. The early and profound loss of splenic red pulp macrophages and stromal cells occurring in BALB/c MyD88-/-animals, as witnessed by microarray analysis, likely contribute significantly to the loss of the specific micro-anatomical niches required for sustaining high numbers of antiviral CD8 T cells. Thus, either efficient antiviral NK cell activity or MyD88 functions are necessary to promote antiviral CD8 T cell responses while simultaneous failure of both responses strongly compromises antiviral adaptive immunity.
IFN-I responses are essential for defense against most viral infections in mice and men [2, 52–54]. However, it has been claimed that the requirements for pDC and endosomal TLR in IFN-I production differ fundamentally during natural infections of men and experimental challenges of laboratory mice [8], since IRAK4 or MyD88 genetic deficiencies only increase susceptibility to viruses in mice. However, whether IFN-I responses are impaired in infected MyD88-/- mice and whether this contributes to their enhanced susceptibility to viral infections had not previously been rigorously examined. Moreover, it is unknown whether other immune responses could compensate for MyD88 deficiency during resistance to viral infections in mice as must occur in humans.
Our results show that IFN-I responses are critical for defense against MCMV infection in mice. However, in our experimental settings, pDC and MyD88 are not required for the induction of protective IFN-I responses while they are crucial for high systemic production of these cytokines. Contrary to expectations [8, 11, 12], in mice, high systemic production of IFN-I resulting from endosomal sensing of viruses by pDC is dispensable for relatively efficient intrinsic, innate and adaptive immune responses to our model of systemic infection by a herpes virus. This observation is consistent with the redundancy of pDC and TLR7/9 functions but not IFN-I responses for antiviral defense in humans [2, 6–8, 53, 54]. Moreover, even if mice deficient for MyD88 are more susceptible than wild-type animals to MCMV infection, this can be compensated by other modalities of innate sensing of the infection, namely in our experimental set-up by direct NK cell recognition of infected cells. Hence, while IFN-I, IFN-γ and cytotoxic responses are all necessary for immune defenses against MCMV infection and most other experimental viral infections in mice, access to these functions can be promoted by a number of complementary and/or partly redundant pathways. These include the triggering of different innate immune recognition receptors and the mobilization of distinct cell types with overlapping antiviral activities (Fig 5). Indeed, Ly49H+ NK cells can promote early control of MCMV replication in the absence of CD8 T cells [55, 56] and vice versa [57, 58], likely because they exert partly overlapping functions including IFN-γ production and cytotoxicity [59–62] (Fig 5). However, in RAG-deficient C57BL/6 mice lacking B and T cell responses, immune escape variants mutated for m157 are selected over time and ultimately cause the death of the mice after several weeks [55, 56]. Hence, context-dependent redundancies in innate and adaptive antiviral immune responses likely contribute to the robustness of host defenses under physiological conditions despite the immune evasion strategies developed by many viruses, as illustrated also by the demonstration of redundancies and complementarities between NK cells, CD8 T cells and CD4 T cells in the prevention of MCMV reactivation in latently infected mice lacking humoral immunity [63]. It is likely that other innate immune effector cells contribute to context-dependent redundancies in antiviral defense mechanisms in mice, including NK T cells [64–68] or γδ T cells [69, 70]. Similarly, the absence of overtly increased susceptibility to viruses in patients genetically deficient for endosomal TLR activity due to loss-of-function mutations in MYD88 or IRKA4 likely results from their preserved ability to mount protective intrinsic and innate immune responses that efficiently control primary infections. These involve not only IFN-I responses but also innate cytotoxic responses by NK, NK T or γδ T cells [71–73], before adaptive immunity induction. However, it is possible that pDC and MyD88 are necessary for efficient host defense under specific conditions of viral infections, including during herpesvirus infections consecutive to bone marrow transplantation where adaptive immunity and some innate immune responses are suppressed and the conditions of viral replication may be analogous to a high dose challenge.
Our study demonstrates that even undetectably low levels of IFN-I/III are sufficient to induce a strong and widespread expression of genes involved in cell-intrinsic antiviral immunity during a systemic viral infection, including in MyD88-/- mice. This observation is consistent with a recent report showing that strongly increasing IFN-I production by pDC during MCMV infection, while preserving other antiviral immune responses, does not improve control of viral replication [74]. On the contrary, high systemic IFN-I production by pDC might even delay the induction of antiviral adaptive immunity due to negative effects on the DC and CD8 T cell compartments [39, 75]. Hence, delivery of small amounts of IFN-I in a tightly controlled spatio-temporal manner could be a better way to treat certain viral infections or cancers than administration of high systemic doses of cytokines. The latter treatment may not induce better cell-intrinsic immunity but rather induce systemic responses associated with severe side effects [1]. In addition, given current technological limits to cytokine detection, our results suggest that to investigate whether a cytokine plays a physiological role in a disease, one should quantitate the effect of abrogating cytokine-mediated signaling on disease evolution, and functionally assess how perturbing cytokine activity affects disease outcome, rather than only titrating cytokine concentration.
One may wonder why infected hosts produce high systemic levels of IFN-I if very low to undetectable levels of these cytokines are sufficient to induce a strong expression of ISG and their downstream protective antiviral functions. One possibility is that high systemic production of IFN-I by spleen pDC in the absence of protective NK cell responses is necessary to promote defense or homeostasis of distant organs. It was recently published that elevated levels of IFN-I has systemic effects which promote epithelial turnover and wound repair [76]. However, since MyD88-deficient mice harbor only a mild increase in susceptibility to MCMV infection as compared to IFNAR-deficient mice, these types of systemic IFN-I responses might be rather required to prevent concomitant heterologous infections or for defense against viruses able to cause chronic infections, by allowing a systemic state of pathogen alert with induction of protective responses in all tissues. Indeed, a similar role was recently reported for IFN-γ production by tissue-resident memory CD8 T cells, whereby it induced tissue-wide responses able to protect the skin against viral variants able to escape adaptive immunity or against heterologous infections [77, 78].
In d6 infected BALB/c-Ly49H+ MyD88-/- mice, not only Ly49H+ but also Ly49H- NK cells are significantly more activated than in BALB/c-Ly49H+ mice. NK cells are also significantly activated in infected animals deficient in Ly49H and MyD88 signaling. Hence, NK cells do not require direct sensing of infected cells for their activation, even at late time points after infection. Rather Ly49H protects mice against MCMV by allowing cytokine-activated NK cells to specifically recognize and kill infected cells. The mechanisms promoting NK cell activation in MyD88-/- mice merit further study. IL-12 and IL-18 play important roles in this process in wild type animals [35, 36, 47]. The analysis of the expression pattern of target genes of these cytokines in the spleen of infected mice showed that a significant and sustained induction of cytokine responses are still observed in MyD88-/- animals despite a dramatic decrease in the expression of the cytokines themselves. Hence, similarly to IFN-I, IL-12 requires MyD88 for high level production but not for functional induction of NK cell activation.
Our result showed that NK cell sensing and killing of infected cells can compensate MyD88 but not IFNAR deficiency for the control of systemic MCMV infection in BALB/c mice. In contrast, others have reported that IFN-I responses are not required for NK cell activation and antiviral activity in C57BL/6 mice [42, 79]. This might be due in part to differences between C57BL/6 and BALB/c mice. Alternatively, this might be due to differences between virus strains. In the other studies, relatively low inoculum doses of in vitro produced viruses were used. Viruses deriving from the pSM3fr parental strain are highly attenuated in vivo due to a mutation in the viral MCK-2 chemokine [80]. Under these experimental settings, Ly49H+ NK cell responses were shown to completely or moderately compensate deficient IFN-I responses to promote control of viral replication. Further, NK cell activity was reported to be dispensable for late (d7) control of viral replication in C57BL/6 mice competent for IFN-I responses in contrast to our observations using BALB/c-Ly49H+ mice infected with moderate doses of salivary gland virus. The morbidity and mortality associated with the infections was not documented [42, 79]. Hence, it is possible that the previously reported compensation between IFNAR deficiency and NK cell activity resulted in part from the use of highly attenuated conditions of infection allowing clearance of MCMV and survival even in mice deficient for both IFNAR and Ly49H.
In our study, the higher susceptibility to MCMV of BALB/c MyD88-/- mice as compared to BALB/c-Ly49H+ MyD88-/- or BALB/c mice was associated with a dramatic decrease in the number of antiviral and total CD8 T cells in the spleen. BALB/c MyD88-/- mice harbor a striking disruption of their spleen architecture as early as d4 after infection. Microarray analyses showed a strong induction of genes associated with fibrosis and a downregulation of the red pulp macrophage transcriptomic signature at d6 post-infection in the spleen of BALB/c MyD88-/- animals. Hence, in the absence of efficient antiviral NK cell responses, MyD88 responses are required to protect the secondary lymphoid organs from extensive tissue damage. This includes preventing the loss of cell types and micro-anatomical structures necessary to support protective antiviral immune adaptive responses.
In summary, our study shows that MyD88 responses are dispensable for the induction of protective IFN-I responses against a systemic herpesvirus infection in mice. We show that the enhanced susceptibility of MyD88-deficient mice to MCMV infection is not attributable to loss of IFN-I responses, to decreased NK cell activation or to compromised CD8 T cell priming. Rather, it results from severe tissue damage to the spleen leading to a failure to sufficiently amplify the compartment of effector antiviral CD8 T cells. Moreover, we show that direct recognition and sensing of virus-infected cells by NK cells can compensate for MyD88- but not IFNAR-deficiency and promote resistance to a systemic MCMV infection in our experimental settings. These results challenge our current understanding of the requirements for the induction of protective responses to systemic viral infections in mice, in particular the role of pDC and endosomal TLR7/9 [8, 10–12, 24–27]. These results are consistent with the conclusions drawn from the studies of human patients suffering from primary immunodeficiencies [2, 6–8, 53, 54]. Hence, immune responses might not differ between mice and men as much as is sometimes suggested. Rather, it is important to define experimental settings in mice that best reflect clinical observations in humans, in order to take advantage of this versatile animal model to advance basic immunological knowledge in a way that might benefit human health [81]. Early disruption of lymphoid organ architecture during human immunodeficiency virus infection has been proposed as an important cause for the failure of the host to control viral replication and for disease development [82]. Understanding the mechanisms leading to the disruption of the architecture of lymphoid organs early during viral infections in mice and how MyD88 or NK cell responses can prevent this process could open novel avenues to treat viral infections in humans.
The animal care and use protocols (ID no. 11-09/09/2011) were designed in accordance with national and international laws for laboratory animal welfare and experimentation (EEC Council Directive 2010/63/EU, September 2010), and approved by the Marseille Ethical Committee for Animal Experimentation (registered by the Comité National de Réflexion Ethique sur l’Expérimentation Animale under no. 14).
8 to 12 week old mice of different strains were used for experiments (see supplemental materials and methods in S1 File). Animals were bred under pathogen-free conditions at CIML. Infections were initiated at d0 by i.p. injection of 2.5 x 103 pfu salivary gland-extracted MCMV K181 v70 [23, 49] or Δm157 [45] strains (third and second in vivo passages, respectively), unless specified otherwise. pDC, NK cells or CD8 T cells were depleted in vivo by intraperitoneal delivery of 500μg anti-Bst2 (120G8) mAb, 100μg anti-NK1.1 mAb or 150μg anti-CD8β mAb. Antibodies were injected on d-1 before MCMV infection, followed by injections on d1, 3 and 5 for NK cell and pDC depletion, and on d2 and 7 for CD8 T cell depletion. Control mice were treated with Rat IgG. Cell depletion efficiency was assessed by flow cytometry as detailed in S1 File. Bst2 expression was increased on B cells, NK cells and cDC in infected mice. However, even under these conditions, the levels of Bst2 expressed by these cells were much lower than that observed on pDC. No depletion of B cells, T cells and NK cells was observed in our experimental conditions, although we cannot completely exclude that a very small fraction of some of these cell types was affected due to a higher expression of Bst2, for example plasmocytes. However, the numbers of cDC in infected mice treated with anti-Bst2 antibody was half that of infected animals treated with isotype control (S6 Fig).
Spleen leukocyte suspensions were prepared using DNAse I and collagenase D [23], stained as detailed in S1 File, and acquired on a FACSCanto II Flow Cytometer (BD Bioscience).
Pieces of spleen were harvested and stabilized overnight in RNAlater solution (Qiagen). High-quality total RNA was prepared and used for qRT-PCR or microarrays as previously described [23]. Relative gene expression was calculated with the ΔΔCt method using Hprt as housekeeping gene for normalization. Viral titers were measured as absolute levels of expression of the Ie1 gene [23]. Microarray analyses were performed as previously described [23]. Gene set compositions are given in S1 Table. Data have been deposited in the GEO database under reference GSE62729.
Serum IFN-α2/α4 levels were determined by ELISA (eBioscience) according to the manufacturer’s instructions.
Antigen-specific CD8 T cell–mediated in vivo cytotoxicity was assayed as described [83].
Splenic lymphocytes from mice infected with MCMV were isolated at d6 post infection and CD8 T cells were analyzed for H-2L(d)/IE-1(168YPHFMPTNL176) binding and intracellular IFN-γ or Granzyme B expression.
Infected mice were monitored daily for signs of morbidity (weight loss, piloerection, hunched posture and lethargy). Imminent death was defined as loss of 20% initial body weight or development of severe lethargy (unresponsiveness to touch) established in a preliminary experiment using death as the endpoint.
Spleen section were prepared, stained and imaged as previously described [28] and detailed in S1 File.
Statistical analyses were performed using a nonparametric Mann-Whitney test performed with Prism 6 (GraphPad Software) for all experiments except survival analyses where a Mantel-Cox test was used. ns, non significant (p > 0.05); *p < 0.05; **p < 0.01; ***p < 10–3.
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10.1371/journal.ppat.1006819 | Infection and depletion of CD4+ group-1 innate lymphoid cells by HIV-1 via type-I interferon pathway | Innate lymphoid cells (ILCs) are severely depleted during chronic HIV-1 infection by unclear mechanisms. We report here that human ILC1s comprising of CD4+ and CD4- subpopulations were present in various human lymphoid organs but with different transcription programs and functions. Importantly, CD4+ ILC1s expressed HIV-1 co-receptors and were productively infected by HIV-1 in vitro and in vivo. Furthermore, chronic HIV-1 infection activated and depleted both CD4+ and CD4- ILC1s, and impaired their cytokine production activity. Highly active antiretroviral (HAART) therapy in HIV-1 patients efficiently rescued the ILC1 numbers and reduced their activation, but failed to restore their functionality. We also found that blocking type-I interferon (IFN-I) signaling during HIV-1 infection in vivo in humanized mice prevented HIV-1 induced depletion or apoptosis of ILC1 cells. Therefore, we have identified the CD4+ ILC1 cells as a new target population for HIV-1 infection, and revealed that IFN-I contributes to the depletion of ILC1s during HIV-1 infection.
| Innate lymphoid cells (ILCs), including ILC1, ILC2 and ILC3 populations, represent a novel cellular family of the immune system and have potentials to produce large amounts of T cell-associated cytokines in response to innate stimulation in the absence of specific antigen stimulation. ILCs have emerged as central players in homeostatic and inflammatory conditions, and correlated with the pathogenesis and progression of multiple human diseases. It is reported that ILCs are depleted in HIV-1 infected patients. However, it is not clear whether HIV-1 can infect ILCs and how ILCs are depleted during HIV-1 infection. Here, we find that ILC1s consist CD4+ and CD4- subsets and both are present in various human lymphoid organs. We show that HIV-1 can directly infect CD4+ ILC1s. HIV-1 infection leads to activation, depletion and functional impairment of ILC1s in humans and in humanized mice in vivo. Blocking IFN-I signaling prevents HIV-1-induced apoptosis of ILC1s both in vitro and in humanized mice in vivo. Our study reveals the CD4+ ILC1 population as a new target for HIV-1 infection and identifies an IFN-I mediated mechanism of ILC1 depletion during chronic HIV-1 infection.
| Innate lymphoid cells (ILCs) represent a novel family of cellular subsets that produce large amounts of T cell-associated cytokines in response to innate stimulation in the absence of antigens [1, 2]. Based on the expression of specific transcription factors, cell surface markers and signature cytokines [1, 3, 4], ILCs can be divided into three groups. Group 1 ILCs (ILC1s) have been defined as lineage-CD127+CD117- cells and can produce interferon (IFN)-γ and depend on T-bet for their functions [5]. Group 2 ILCs (ILC2s) are a population of lineage-CD127+CRTH2+ cells that preferentially produce IL-5 and IL-13 and require GATA3 for differentiation [6]. Group 3 ILCs (ILC3s) are lineage-CD127+CD117+cells that have the potential to produce IL-17 and/or IL-22, and are dependent on RORγt [3, 7]. An increasing number of studies have indicated that ILCs represent a heterogeneous family of cells [8–10]. ILC1s were recently divided into CD4+CD8-, CD4-CD8+ and CD4-CD8- cell populations, and ILC3s comprise CD62L+ naïve cells and HLA-DR+ ILC3 subsets [8]. These novel ILC subsets still need to be explored with regard to their functionality and clinical significance in humans.
ILCs have emerged as central players in homeostatic and inflammatory conditions. In particular, changes in the number of ILCs have been found to be associated with the pathogenesis and progression of a number of human diseases including chronic infections and inflammatory diseases [1, 3, 11–13]. For example, IFN-γ production by intraepithelial ILC1s promotes inflammation in mouse models of colitis, and blocking of IFN-γ reduces disease severity [12]. In addition, ILC1s may also contribute to human inflammatory bowel diseases, as their numbers have been found to be higher than normal in patients with Crohn’s disease [5, 12]. Changes in the number and function of ILCs have also been documented during HIV-1 or SIV infection. Further, it has been reported that SIV infection results in persistent loss of IL-17-producing ILCs, especially in the jejunum [14]. NKp44+ ILC3s are also rapidly depleted in the intestinal mucosa during acute SIV infection [15]. In HIV-1-infected patients, too, ILCs are found to be severely depleted [16–18]. We have previously demonstrated that in HIV-1/SIV infection, ILC3s are depleted through plasmacytoid dendritic cell (pDC) activation and CD95-mediated apoptosis [17] or TLR signaling [19]. However, it is not clear whether HIV-1 influences ILCs through infection and how ILCs are depleted, especially ILC1s, during HIV-1 infection.
In this study, we first showed that tissue ILC1s, as reported previously in the case of human peripheral blood mononuclear cells (PBMCs) [20], consist of CD4+, CD8+ and CD4-CD8- cells, three populations that widely exist in various lymph organs in human. In addition, we found that CD4+ ILC1s exhibit significant differences from CD4- ILC1s with regard to their phenotype, cytokine production and expression profile of transcriptional factors. Thus, we have identified a previously unknown CD4+ ILC1 population that serves as a target for HIV-1 productive infection. We showed that HIV-1 can infect, activate and preferentially deplete these CD4+ ILC1s. Our data was also indicative of the pathogenic effect of sustained type I interferon (IFN-I) signaling during HIV-1 infection, including depletion of ILC1s.
It was recently reported that ILC1s in human peripheral blood contain CD4+, CD8+ and CD4-CD8- subpopulations [20]; however, it is unclear whether these cell populations are present in human lymphoid organs. Here, we investigated the distribution of each ILC1 subpopulation in various human lymph organs. By gating on live human CD45+ cells that were negative for lineage-specific surface markers of B cells (CD19 and CD20), T cells (CD3), conventional natural killer (NK) cells (CD16), monocytes and dendritic cells (CD14, CD11c and CD123), and surface markers of hematopoietic precursors (CD34), ILC2 cells (CRTH2) as well as ILC3 cells (CD117), we identified ILC1s as hCD45+Lin-CD117-CRTH2-CD127+CD56- cells (S1A Fig). Similar to the results of a previous study [20], we found that ILC1s comprise of CD4+CD8-, CD4-CD8+ and CD4-CD8- subpopulations (S1A Fig). All the ILC1 subsets don’t express the T cell marker TCRαβ, TCRγδ and NK cell marker CD94 which excludes T cell and NK cell contamination; while they express CD5 (S1B Fig). More importantly, we found that the all the three ILC1 subsets, including CD4+ ILC1s, were all present in various human lymphoid organs including the spleen, bone marrow, large intestine, small intestine and liver perfusion (Fig 1A). Further analysis indicated that CD45+ cells constituted 0.019%–0.818% of the total ILC1 cells (Fig 1B) and CD4+ ILC1s constituted 2.35%–39.2% of the total ILC1s in different organs (Fig 1C).
We further investigated the expression of transcriptional factors such as T-bet and eomesodermin (Eomes) in the three ILC1 subsets in human peripheral blood (Fig 1D). We found that CD4+ and CD4-CD8- ILC1s expressed lower levels of T-bet than CD8+ ILC1s (Fig 1E, left). In addition, CD4+ ILC1s also expressed lower levels of Eomes than CD4- ILC1 subsets in the blood (Fig 1E, right). We also examined the expression of T-bet and Eomes in ILC1 subsets from various human lymphoid organs by flow cytometry (S2A Fig). We found that in most tissues that we examined, CD4+ ILC1s expressed lower levels of T-bet than CD8+ or CD4-CD8- ILC1s. Notably, the expression levels of T-bet were significantly lower in all ILC1 subsets from the small intestine than in the corresponding subsets from the other organs. This indicates that ILC1s present in the small intestine may have a unique function or activity (S2B Fig). CD4+ ILC1s also expressed lower levels of Eomes than CD4- ILC1 subsets in the blood, spleen, bone marrow and liver perfusion. However, the opposite phenomenon was observed in the large and small intestine, where CD4+ ILC1s expressed higher levels of Eomes than CD4- ILC1s (S2C Fig). These data suggest that the transcriptional factor profiles of ILC1s differ according to subsets and tissue types. In particular, CD4+ ILC1s are characterized by lower expression of T-bet and Eomes transcriptional factors in human peripheral blood.
With regards to phenotypic characteristics, CD4+ ILC1s in peripheral blood expressed CD45RA, the NK cell-related molecule CD161, the chemokine receptors CCR6 and CXCR3, death receptor CD95 and adhesive molecule CD11a, which indicate an immature phenotype. However, peripheral blood CD4+ ILC1s did not express the integrin CD103; activation markers CD69, CD38 and HLA-DR; proliferation marker Ki67; and death molecules DR5, caspase 1 and caspase 3. Moreover, they expressed low levels of the ILC progenitor marker IL-1R1 (S3 Fig). However, there was no significant difference in the expression of most of the molecules between CD4+ and CD4- ILC1 subsets from peripheral blood.
As for functionality, we evaluated cytokine production by peripheral blood ILC1 subsets after PMA/ionomycin or IL-12/IL-18 stimulation (Fig 1F and 1G). We found that CD4+ ILC1s produce more TNF-α and lower levels of IFN-γ than CD8+ and CD4-CD8- ILC1s under PMA/ionomycin stimulation (Fig 1F). Under IL-12/IL-18 stimulation, CD4+ ILC1s also produced lower levels of IFN-γ but similar levels of TNF-α than CD8+ and CD4-CD8- ILC1s (Fig 1G). These ILC1 subsets produced no detectable IFN-γ and TNF-α without stimulation. These data indicate that peripheral blood ILC1 subsets are characterized by functional heterogeneity, and that CD4+ ILC1s preferentially produce TNF-α in response to stimulation, as opposed to CD4- ILC1 subsets, which produce lower levels of TNF-α.
Taken together, these comprehensive analyses indicate that CD4+ and CD4- ILC1s exist in various human lymphoid tissues, and their relative numbers, transcription and functionality depend on the subsets and the tissues. In particular, CD4+ ILC1s display relatively unique expression of transcriptional factors, immune phenotypes, and cytokine production in relation to CD4- ILC1s.
Since a significant proportion of ILC1s express CD4, the receptor for HIV-1 infection, we investigated whether HIV-1 can infect CD4+ ILC1s. First, we examined the expression of the HIV-1 co-receptors CCR5 and CXCR4 on ILC1s by flow cytometry. Both CCR5 and CXCR4 were expressed on CD4+ ILC1s from human PBMCs and the spleen of humanized mice (Fig 2A). CD4- ILC1s also expressed comparable levels of CCR5 and CXCR4 (Fig 2A). Further analyses indicated that 12% of human CD4+ ILC1s express CCR5, while 60% express CXCR4 (Fig 2B). The expression of CCR5 and CXCR4 was also detected on CD4+ ILC1s in lymphoid organs, including the spleen, peripheral lymph node and bone marrow, and peripheral blood from humanized mice, but the expression level was slightly lower than that in human PBMCs (Fig 2B).
We then examined whether HIV-1 can infect human CD4+ ILC1s. We infected resting human PBMCs with the CXCR4 tropic virus NL4-3 and the CXCR4 and CCR5 dual-tropic virus R3A in vitro. Productive infection by HIV-1 was detected by staining of the HIV-1 protein p24 in ILC1s (Fig 2C) and in CD3+ T cells (control cells) (S4A Fig). We found that HIV-1 p24 protein was detected in 2.2% of ILC1s after R3A infection and in 3% of ILC1s after NL4-3 infection (Fig 2D), which was comparable to the p24 levels in CD3+ T cells (S4A Fig). A neutralizing monoclonal antibody (Clone CH31) specific to the CD4 binding site [21] blocked both R3A and NL4-3 infection by 90% (Fig 2C and 2D and S4 Fig). We also found that HIV-1 infection down-regulated CD4 expression in ILC1s (Fig 2C), as observed in T cells (S4 Fig). Interestingly, when PBMCs were activated by PHA (Fig 2E and 2F), both ILC1s and T cells were infected at higher levels by HIV-1 in vitro (Fig 2E and 2F and S5A and S5B Fig). These results indicate that HIV-1 can productively infect ILC1s via the CD4 receptor.
We also examined whether HIV-1 also infected ILC1s in vivo in human patients and in humanized mice. We purified CD4+ ILC1s from HIV-1-infected patients and determined the cell-associated HIV-1 DNA level by real-time PCR. On average, we detected 800 copies of cell-associated HIV-1 DNA in one million CD4+ ILC1s (Fig 2G). As controls, 3200 copies of HIV-1 DNA were detected in one million CD4+ T cells, while no HIV-1 DNA was detected in CD8+ T cells (Fig 2G). HIV-1 can effectively infect and replicate in vivo in humanized NOD-Rag2-/-γc-/- (NRG-hu) mice transplanted with human CD34+ hematopoietic stem cells [22], a highly relevant model for studying HIV-1 induced pathology in vivo [23]. We therefore investigated whether CD4+ ILC1s could be directly infected by HIV-1 in vivo in humanized mice. At 3 weeks after R3A infection, 4.9% of ILC1s expressed p24, while 2.7% of CD3 T cells were positive for p24 (Fig 2H, left). To exclude the possibility that the p24 protein detected here was from virions taken into cells by endocytosis, we infected humanized mice with an engineered R3A reporter virus which expresses the mouse CD24 gene in the Vpr ORF [24]. We found that 8% of ILC1s expressed the mouse CD24 protein (Fig 2H, right). Taken together, our results show that HIV-1 can productively infect CD4+ ILC1s both in vitro and in vivo.
We next investigated whether HIV-1 infection also activates ILC1s in patients. We analyzed the expression of CD38 and Ki-67 in ILC1s (Fig 3A and S6 Fig). Both CD4+ and CD4- ILC1s expressed higher levels of CD38 and Ki67 in HIV-1-infected patients than in the healthy control (HC) subjects, while highly active antiretroviral therapy (HAART) reduced the activation and proliferation of both CD4+ and CD4- ILC1s (Fig 3B). As expected, HIV-1 also activated CD8 T cells in HIV-infected patients, and that the activation level was significantly decreased after HAART (Fig 3C and 3D). Further, the percentage of Ki67-expressing CD4+ ILC1s, but not CD4- ILC1s, was found to positively correlate with the plasma HIV-1 viral load (Fig 3E and 3F). In contrast, Ki-67 expression in CD8 T cells was not correlated with plasma HIV-1 load in these patients (Fig 3G). These data indicate that HIV-1 infection activated both CD4+ and CD4- ILC1s. In particular, the activation of CD4+ ILC1s, the HIV-1 target population, was positively correlated with the HIV-1 viral load.
We next investigated whether HIV-1 infection depletes ILC1s in vivo. Compared to the HCs, ILC1s in CD45+ cells were significantly reduced in the peripheral blood of patients with chronic HIV-1 infection (Fig 4A and 4B), and HAART partially reversed the reduction of total ILC1s (Fig 4A and 4B). Further analysis indicated that the percentage of both CD4+ and CD4- ILC1s in total CD45+ cells was lower in patients with HIV-1 infection than in the HC subjects, while only the CD4+ ILC1s but not CD4- ILC1s were significantly rescued by HAART (Fig 4C). The absolute cell counts of total ILC1s and CD4+ and CD4- ILC1s were found to be largely reduced in patients with chronic HIV-1 infection as compared to those of HC subjects; and HAART successfully recovered the absolute cell counts of total ILC1s and CD4+ ILC1s but not CD4- ILC1s (Fig 4D). Correlation analysis indicated that the percentage of peripheral CD4+ ILC1s was negatively correlated with the plasma HIV-1 viral load (Fig 4E) and positively correlated with the CD4/CD8 ratio in the HIV-1-infected subjects (S7 Fig).
We further examined whether ILC1s in the gut were also depleted by HIV-1 infection in humans, which is the key lymphoid organ in HIV-1-associated pathogenesis. As shown in Fig 4F, CD4+ ILC1s were significantly depleted in the large intestine in HIV-1-infected patients as compared to the HC donors. The summarized data also showed that the percentage of total ILC1s within CD45+ cells was significantly decreased in the large intestine in patients with HIV-1 infection (Fig 4G). Further analysis indicated that the percentage of both CD4+ and CD4- ILC1s was reduced during chronic HIV-1 infection (Fig 4H). Importantly, when gated on ILC1 populations, the percentage of CD4+ ILC1s was largely decreased and the percentage of CD4- ILC1s was increased accordingly, which indicates that the CD4+ ILC1s were preferentially depleted (Fig 4I). These data indicate that CD4+ ILC1s from both peripheral blood and large intestine are preferentially depleted during chronic HIV-1 infection.
ILC1s can produce large amounts of Th1-associated cytokines in response to innate stimulation. We next analyzed whether persistent HIV-1 infection affected the cytokine production ability of ILC1s. As shown in Fig 5A, IFN-γ and TNF-α production by both CD4+ and CD4- ILC1 subsets induced by PMA/ionomycin stimulation were significantly lower in HIV-1-infected patients than in HCs. Similar phenomena were also observed when the ILC1s were stimulated by IL-12 and IL-18 (Fig 5B). HAART failed to rescue the function of ILC1 subsets, with the exception that IFN-γ production was rescued by HAART after IL-12 and IL-18 stimulation (Fig 5A and 5B). We thus conclude that chronic HIV-1 infection impaired the ability of the remaining ILC1s, including CD4+ ILC1s, to produce cytokines.
We next examined how HIV-1 infection leads to ILC1 depletion. We discovered that chronic HIV-1 infection significantly up-regulated active caspase-3 expression in both CD4+ and CD4- ILC1s (Fig 6A and 6B). In contrast, caspase1 was not significantly up-regulated in CD4+ ILC1s (and only slightly increased in CD4- ILC1s) of patients with HIV-1 infection as compared to the HC subjects (S8A and S8B Fig). HAART could significantly decrease the expression of active caspase-3 in both CD4+ and CD4- ILC1s (Fig 6A and 6B), correlated with rescued ILC1s. These findings indicate that HIV-1 infection leads to depletion of ILC1 subsets via apoptosis-dependent mechanisms.
We further investigated whether the Fas/FasL pathway was involved in the apoptosis of ILC1s (up-regulation of active caspase-3), as reported in ILC3s in our previous study [17]. We found that expression of CD95 was significantly up-regulated in both CD4+ and CD4- ILC1s from patients with chronic HIV-1 infection compared with HC subjects (Fig 6C and 6D). HAART decreased the expression of CD95 in CD4+ but not CD4- ILC1s (Fig 6C and 6D). In contrast, the expression of death receptor 5 (DR5) was not up-regulated in ILC1 subsets in patients with chronic HIV-1 infection (S8C and S8D Fig). Notably, the expression of caspase-3 and CD95 was also up-regulated in CD8+ T cells in HIV-1-infected patients as compared to HC subjects (S9A and S9B Fig).
We then investigated whether the Fas/FasL pathway is involved in the apoptosis of ILC1 subsets. After in vitro stimulation with the anti-CD95 agonist antibody, both CD4+ and CD4- ILC1s from HIV-1-infected patients displayed higher levels of active caspase-3 expression than those from HCs (Fig 6E). Accordingly, the number of live CD4+ and CD4- ILC1s was significantly reduced after treatment with the anti-CD95 agonist antibody as compared to the IgG control in HIV-1-infected patients but not in the HC subjects (Fig 6F). Thus, the number of live CD4+ and CD4- ILC1s in HIV-1-infected patients was markedly less than that in HC subjects in response to in vitro stimulation with the same anti-CD95 agonist antibody (Fig 6F). This indicates that ILC1 subsets from HIV-1-infected patients are more sensitive to Fas/FasL signaling than those from HC subjects. We conclude that the Fas/FasL pathway is actively involved in the apoptosis of ILC1 subsets in patients with chronic HIV-1 infection.
Sustained IFN-I signaling has been reported to be correlated with and contribute to SIV and HIV-1-induced immune pathogenesis [25–27]. We have proved that depletion of pDCs or blocking IFN-I signaling prevents HIV-1-induced T cell and ILC3 depletion in vivo [17, 26, 28]. We thus investigated whether IFN-I signaling also contributes to HIV-1-induced ILC1 depletion in vivo. We treated HIV-1-infected humanized mice with the anti-IFNAR1 mAb [26] from week 6 through week 10 after infection. At 10–12 weeks after infection, we terminated the mice and measured ILC1 number and phenotype in each group. We found that blockade of IFN-I signaling with the anti-IFNAR1 mAb rescued both CD4+ and CD4- ILC1s cells in percentages (Fig 7A–7C) and in numbers (Fig 7D) as compared to the isotype IgG control group. In addition, we found that blocking the IFN-I pathway significantly decreased CD95 expression on CD4+ ILC1s in humanized mice with persistent HIV-1 infection (Fig 7E and 7F). We further cultured PBMCs from HIV-1-infected patient ex vivo in the absence or presence of pDC-depleting 15B mAbs conjugated with the SAP toxin (immune toxin 15B-sap) or the anti-IFNα/β receptor blocking antibody. We observed significant downregulation of both CD95 and active caspase-3 expression in CD4+ ILC1s from HIV-1-infected patients cultured in vitro in the presence of the immune toxin 15B-sap or anti-IFN-α/β receptor antibodies as compared to the IgG control (Fig 7G). Therefore, depletion of pDCs or blockade of IFNAR1 both prevents HIV-1 induced ILC-1 depletion in vitro (Fig 7H). These data indicate that IFN-I signaling contributes to ILC1 depletion during chronic HIV-1 infection.
Since HAART fails to restore ILC function in HIV-1-infected patients, we therefore investigated whether blocking IFN-I signaling combined with combined antiretroviral therapy (cART) in vivo can rescue the function of ILC1s in HIV-1 infected humanized mice. We treated HIV-1 infected mice with cART at 4 weeks post infection (wpi). As reported [26], 3 week after cART, the infected humanized mice received α-IFNAR1 mAb treatment for 3 weeks from 7 to 10wpi. The function of ILC1 was analyzed at 12wpi. Interestingly, we found that cART alone restored IFN-γ and TNF-α production by splenic ILC1s under PMA/Ionomycin stimulation (S10B Fig). IFN-I blockade in combination with cART did not further increase IFN-γ and TNF-α production by splenic ILC1s (S10B Fig). The result is different from human studies which indicated that HAART cannot rescue ILC1 function (Fig 5). One possible reason for the differences is that we started cART treatment in humanized mice at early infection phase (4 weeks post HIV-1 infection), while in HIV-1 infected patients HAART is usually initialized years after infection at chronic phase of the infection included in our study. Further studies are needed to unveil the effect of IFN-I signaling on ILC1 function.
Our study investigates the heterogeneity of ILC1s in human lymphoid organs, and provides the first piece of evidence to show that HIV-1 can directly infect CD4+ ILC1s and lead to their activation, depletion and functional impairment in vivo in humans and in humanized mice. Successful HAART rescued the number of CD4+ ILC1s but not cytokine production activity via the inhibition of Fas/FasL-mediated apoptosis of ILC1s. This study, therefore, is the first to identify CD4+ ILC1s as important HIV-1 target cells, and may serve as a novel target of HIV-1 therapies aimed at human immune reconstitution.
Through a comprehensive analysis of lymphocytes from human spleen, bone marrow, large intestine, small intestine and liver, we found that human ILC1s consist of CD4+, CD8+ and CD4-CD8- populations and that all these populations are widely present in all lymphoid organs, which has not been described in previous studies [8, 9, 20]. We also observed that CD4+ ILC1s expressed immature phenotypes and lower levels of Th1-associated transcriptional factor T-bet and Eomes than CD4- ILC1s and higher level of TNF-α in response to stimulation. It Is not clear where these CD4+ ILC1s are developed and how the immature ILC1 subsets traffic to various lymphoid tissues. A recent study based on mass cytometry- and t-SNE-based analysis showed ILC1s were undetectable across different human tissues [10]. However, in a re-analysis on CyTOF dataset, ILC1s are clearly clustered in lymphoid tissues [29]. Another report also suggested that ILC1s reported in previous studies may be attributable to CD5+ T cell contamination [30]. However, CD5 is also expressed and functions independently of T cells [31]. Indeed, ILC1s express high levels of CD5 in our study and previous report [32]. Therefore, the use of CD5 with CD4 or CD8 in ILCs without confirming surface CD3 or TCR expression does not definitively identify CD4+ and CD8+ T cells [20]. Furthermore, human patients with RAG1 deficiency, who lack T cells, are characterized by the presence of circulating ILC1s at frequencies comparable to those of ILC2s and ILC3s [33]. ILC1s have also been cloned under T-cell-promoting conditions, and have been detected in inflamed intestinal tissues of patients suffering from Crohn’s diseases [5, 34]. Taken together, our data provide a comprehensive description of the heterogeneity of CD4+ and CD4- ILC1s (not T cells) across various lymphoid tissues in humans.
The identification of CD4 expression on ILC1s led to the question of whether this population can be infected by HIV-1. Our results clearly showed that CD4+ ILC1s also express CCR5 and CXCR4 and can be productively infected by HIV-1 both in vitro and in vivo. The relative infection and replication of HIV-1 in CD4+ ILC1s is comparable to that in CD4 T cells. Interestingly, PHA activation of PBMC enhanced HIV infection in both CD4+ ILC1 and T cells. These results indicate that CD4+ ILC1s are HIV-1 target cells and possibly support HIV-1 persistence in patients with chronic HIV-1 infection. Therefore, we identified CD4+ ILC1s as a new target for HIV-1 infection. Further studies to identify whether CD4+ ILC1s serve as an HIV-1 reservoir in HIV patients during HAART will be important for developing strategies for HIV-1 treatment.
It has been reported that HIV-1 infection leads to depletion of all ILC subsets, including ILC1s, in circulation [16, 17, 19] and lymphoid organs [18]. We discovered here that HIV-1 infection also depleted ILC1s in the large intestine of patients. Unlike the results of a previous study [16], we found that HAART can rescue the number of peripheral ILC1s in HIV-1-infected patients. This discrepancy could be explained by the difference in the cohorts enrolled in the two studies. Differences in the time of HAART onset may lead to differences in immune reconstitution [35, 36], which may then affect the restoration of the number of ILC1s. Our results indicate that HIV-1 infection depletes ILC1s both in circulation and in lymphoid organs. Of particular note, we found that CD4+ ILC1s were preferentially depleted within the total ILC1 population, which indicates that they are more sensitive to HIV-1-induced apoptosis.
The mechanism underlying HIV-1-induced depletion of ILC1s is poorly defined. We have reported previously that HIV-1 infection induces depletion of ILC3s via Fas/FasL signaling in a pDC/IFN-I-dependent manner [17]. In the present study, we found that the depletion of ILC1s was also associated with cell apoptosis mediated by the Fas/FasL pathway during HIV-1 infection. We therefore tested the pDC/IFN-I axis in humanized mice with HIV-1 infection. Our data clearly showed that blocking IFN-I signaling with an antibody against IFNAR1 prevented HIV-1-induced depletion of ILC1s in vivo in humanized mice. Furthermore, blocking IFN-I signaling or depletion of pDCs during in vitro culture of PBMCs from HIV-1 infected patients also significantly reduced ILC1 apoptosis and rescued their number. We thus have demonstrated that pDC and IFN-I signaling plays a critical role in ILC1 depletion during chronic HIV-1 infection.
Our data demonstrate that HIV-1 infection not only depletes ILC1s but also leads to their activation and functional impairment, as indicated by the significant decrease observed in their production of cytokines, including IFN-γ and TNF-α. Interestingly, HAART rescues ILC1s in number but fails to recover their function of cytokine production in HIV-1-infected patients. In HIV-1-infected humanized mice, however, we found that HAART starting during early phase of infection (4wpi) rescued both ILC1 number and functions in IFN-γ and TNF-α production. This differential effect of HAART on ILC1 function may be due to different treatment time in patients and in humanized mice. For patients in the study, HAART was usually initialized years after HIV-1 infection at chronic infection phase; while HAART was given at early phase of HIV-1 infection (4 weeks) for humanized mice in the study. Indeed, our findings are similar to a previous report in which antiretroviral therapy initialized during acute infection could preserve ILCs in patients [16]. These data also indicate that depletion of ILC1s and their functional impairment may be mediated by various mechanisms during short acute and long chronic infection. We have recently found that pDC depletion or blockade of IFN-I signaling could significantly reduce residual immune activation and restore anti-HIV immunity in HIV-1-infected humanized mice without or with cART [26]. Future studies should focus on the differential mechanisms underlying cell depletion and functional impairment of ILC1 subsets, and determine whether HAART combined with IFN-I blockade can restore ILC1 function in chronic HIV-1 infection in human patients.
In summary, we identified subset- and tissue-dependent heterogeneity of ILC1s and provided evidence to show that CD4+ ILC1s are a novel target for HIV-1 infection. Further, we demonstrated that IFN-I signaling contributes to the depletion of ILC1s, at least partly through the Fas/FasL pathway during HIV-1 infection. These new findings, therefore, extend our earlier findings which show that sustained pDC activation and IFN-I production contributes to HIV-1 pathogenesis. Therefore, blockade of the pDC/IFN-I axis will be a novel therapeutic stratagem to reverse HIV-1-induced pathogenesis, including ILC1 depletion and impairment.
Approval for animal work was obtained from the University of North Carolina Institutional Animal Care and Use Committee (IACUC ID: 14–100). The study protocol on human samples was approved by the Institutional Review Board and the Ethics Committee of Beijing 302 Hospital in China. The written informed consent was obtained from each subject. All samples were anonymized in the study. Human tissue samples, including the spleen, small intestine, large intestine, bone marrow and liver perfusion, used in this study were obtained from adult donors who had undergone liver transplantation as healthy controls. Gut mucosa from HIV-1-infected patients were obtained for pathological diagnosis. Written informed consent was obtained from each donor. Complete RPMI media were used for all cell isolation experiments. Human fetal livers and thymuses (gestational age 16 to 20 weeks) were obtained from medically indicated or elective termination of pregnancies through a non-profit intermediary working with outpatient clinics (Advanced Bioscience Resources, Alameda, CA). Written informed consent from the maternal donor was obtained in all cases under regulations governing the clinic. All animal studies were conducted following NIH guidelines for housing and care of laboratory animals. The project was reviewed by the University’s Office of Human Research Ethics, which determined that this submission does not constitute human subjects research as defined under federal regulations [45 CFR 46.102 (d or f) and 21 CFR 56.102(c)(e)(l)].
Thirty HIV-1-infected HAART-naïve individuals and 12 HIV-1-infected patients who underwent successful HAART were enrolled in our study (S1 Table). The majority of these individuals had been infected with HIV-1 via sexual transmission, while a few subjects were paid blood donors. Twenty-six uninfected subjects were employed as healthy controls (HCs). The study protocol was approved by the Ethics Committee of Beijing 302 Hospital, and written informed consent was obtained from each subject.
Immune cells from human samples were isolated according to previously reported protocols. In brief, peripheral blood mononuclear cells (PBMCs) and bone marrow cells were isolated by Ficoll-Hypaque density gradient centrifugation of heparinized blood of enrolled subjects. The spleen was first ground on ice, after which the cells were collected and filtered. The liver perfusion was directly filtered and concentrated by centrifugation (750 g, 15 min, 20°C), and was layered onto the Ficoll gradient. The small intestine and large intestine were first finely minced using scalpels, and were then incubated with 0.8 mg/mL collagenase type IV (Worthington-Biochemical) and DNase I (Roche) for 1 h before they were filtered through a 70-mm strainer. The filtered cells were collected and isolated in a similar manner to PBMCs. Upon isolation, all the cells were cryopreserved in 90% fetal calf serum plus 10% DMSO for subsequent assay.
We constructed NRG-hu mice using a previously reported method [22]. Briefly, human CD34+ cells were isolated from 16- to 20-week-old fetal liver tissues (Advanced Bioscience Resources, Alameda, CA). The tissues were digested with liver digest medium (Invitrogen, Frederick, MD). The suspension was filtered through a 70-μm cell strainer (BD Falcon, Lincoln Park, NJ) and centrifuged for 5 min to isolate mononuclear cells by Ficoll gradient centrifugation. After selection with the CD34+ magnetic-activated cell sorting (MACS) kit, CD34+ hematopoietic stem cells were injected into the liver of each irradiated (300 rad) 2- to 6-day-old NRG mouse (0.5 × 106/mouse). More than 95% of the humanized mice were stably reconstituted with human leukocytes in the blood (60%–90% at 12–14 weeks). The level of engraftment was similar in each cohort. All the mice were housed at the University of North Carolina at Chapel Hill.
Total leukocytes were isolated from the spleen of humanized mice as previously described [22]. Lymphoid tissues, including red blood cells, were lysed with the ACK buffer, and the leukocytes were stained and fixed with 1% formaldehyde before FACS analysis. The total cell number was quantified by Guava Easycytes with the Guava Express software.
An R5-tropic strain of HIV-1, JR-CSF (NIH AIDS reagents program, Cat# 2708), was used for inducing persistent HIV-1 infection. Viruses were generated by transfection of 293T cells (SIGMA-ALORICH, Cat# 12022001-1VL). R3A-HSA was constructed by replacing the vpr gene with mouse heat stable antigen (HSA; CD24) as reported previously. Humanized mice with stable human leukocyte reconstitution were infected with JR-CSF or R3A-HSA at a dose of 10 ng p24/mouse, through an intra-orbital injection. Humanized mice infected with mock-transfected 293T cell culture supernatant were used as control groups. For acute HIV-1 infection, viral genomic RNA present in the plasma was measured by real-time PCR (ABI Applied Biosystem). An X4 and R5 dual-tropic strain of HIV-1, R3B/Av1v2, was used for the in vitro experiment.
Fresh PBMCs were incubated with the infectious HIV-R3A stock, NL4-3 stock or mock stock with or without the neutralizing monoclonal antibody (Clone CH31) for 2 h at 37°C. Then, the cells were incubated in complete RPMI 1640 medium at a density of 2 × 106 cells/ml in the presence of IL-2 (50 IU/ml) and IL-7 (20ng/ml) for an additional 3 days. Alternatively, fresh PBMCs were activated with phytohemagglutinin (PHA, 5 μg/ml) or medium in the presence of IL-2 (50 IU/ml) and IL-7 (20 ng/ml) for 24 hours. Then the cells were incubated with the infectious NL4-3 stock or mock stock for an additional 4 days. Intracellular p24 expression on ILC1 subsets or CD3+ T cells was determined by flow cytometry as described above.
An anti-IFNAR1 blocking antibody was developed as per our recent report [26]. Briefly, the human IFNAR1 expression cell line 293T was first incubated with the supernatant of the hybridoma and then incubated with the PE-labeled goat anti-mouse IgG secondary antibody. Then, an IFN-I reporter cell line 293T stably transfected with a mouse A2 promoter-driven EGFP was used to screen antibody clones that could block human IFNAR1 signaling. Humanized mice with HIV-1 infection were treated intraperitoneally with anti-IFNAR1 blocking antibodies from 7 to 10 weeks post-infection twice a week at a dose of 400 μg/mouse at the first treatment and 200 μg/mouse for the following treatments. The same dose of mouse isotype IgG2a control was used in all the experiments. Alternatively, the HIV-1-infected mice were treated with combination antiretroviral therapy (cART) as reported [26]. HIV-1 infected, cART treated mice were treated i.p. with IFNAR1 blocking antibodies from 7 to 10 wpi twice a week with 400 μg/mouse at the first injection and 200 μg/mouse for the following treatments. A same dose of mouse isotype IgG2a control was use in all experiments.
Flurochrome-conjugated antibodies or regents obtained from Biolegend, BD Bioscience, eBioscience and R&D Systems were used in the study. Live/dead fixable violet dead cell dye (LD7) was purchased from Molecular Probes (Eugene, OR). For humanized mice, live human leukocytes (Y7-mCD45-hCD45+) were analyzed for ILC1 subsets and other cell subsets or phenotypes with CyAn FACS (Dako, Beckman Coulter, Denmark). The data were analyzed with the Summit Software. For human PBMCs and various tissue-derived lymphocytes, dead cells were excluded using the fixable viability dye eFluor 450 (eBioscience). The remaining live CD45+ cells were analyzed for phenotypic expression with FACS CANTO II, and the data obtained were further analyzed with the FlowJo software (TreeStar, San Carlos, CA). Cytokines, including IL-2, IL-12 and IL-18, were purchased from PeproTech (Rocky Hill, NJ).
For surface marker staining, leukocytes were incubated with antibodies on ice for 30 min and then washed and fixed for further analysis. For staining of HIV-1 gag p24, transcriptional factors, Ki67 and the apoptotic marker active caspase-3, the cells were stained with the surface marker first, and then permeabilized using a Cytofix/Cytoperm kit (BD Bioscience) and stained for intracellular protein. Alternatively, fresh cells were mixed with caspase-1 for 2 h for caspase-1 staining and were then subjected to surface staining.
For intracellular cytokine detection, freshly isolated cells were stimulated for 6 h by culturing with PMA (50 ng/ml, Sigma) and ionomycin (1 μM, Merck) in the presence of BFA (1 μM). Alternatively, the cells were incubated with IL-12 (20 ng/ml) plus IL-18 (20 ng/ml) for 12 h, followed by Golgi-stop for an additional 6 h. The cells were then collected for surface marker staining; this was followed by cell permeabilization and intracellular cytokine staining. For CD107a staining, the cells were incubated with anti-CD107a antibodies from the onset of stimulation. Then, the cells were further incubated with BFA for an additional 6 h.
Freshly isolated PBMCs from HC and HIV-1-infected patients were enriched for ILCs by depletion of CD3+ T cells, CD14+ monocytes and CD19+ B cells using microbeads (Miltenyi Biotech, Germany). Then, the enriched cells were sorted on a FACSAria II (BD Biosciences). CD4+ ILC1s were isolated by sorting on live cells, singlets, scatter, and lineage-CD56-CD127+CD4+ cells (lineage including CD3, CD14, CD16, CD19, CD34, CD11c, CD123, CD117 and CRTH2). CD4+ and CD8+ T cells were directly sorted from PBMCs. Then, nucleic acid was extracted by sorting CD4+ ILC1s, CD4+ T cells and CD8+ T cells using the DNAeasy minikit (Qiagen) to measure total cell-associated HIV-1 DNA. HIV-1 DNA was quantified by real-time PCR according to our previous protocol. DNA from serial dilutions of ACH2 cells, which contain 1 copy of the HIV-1 genome per cell, was used to generate a standard curve.
Frozen PBMCs from HCs and HIV-1-infected patients were thawed and cultured in complete RPMI (RPMI 1640 containing 10% heat-inactivated fetal bovine serum, 2 mM l-glutamine, 100 U/ml penicillin and 100 mg/ml streptomycin sulfate) (Cellgro, Manassas, VA) with IL-12 (10 ng/ml), IL-18 (10 ng/ml) and IL-2 (50 IU/ml) for 12 h. Then, the cells were collected to perform in vitro assays. The cells were incubated in the presence of plate-bound anti-CD95 monoclonal antibody or isotype control antibody (5 μg/ml, clone CH11, Millipore) for an additional 24 h. Alternatively, the cells were incubated with 15B mAb conjugated with the toxin sap (15B-sap, 8 ng/ml) to deplete pDCs or with anti-IFN-α/β receptor antibodies (10 μg/ml, Millipore) to block IFN-I signaling for an additional 72 h. Then, the cells were harvested, and the number of live cells was counted and stained for active caspase-3 and/or CD95 expression by ILC1 subsets.
Data were analyzed using GraphPad Prism software version 5.0 (GraphPad software; San Diego, CA, USA). The data represent the mean ± s.e.m values. One-way ANOVA was used for primary comparisons between different groups, and the result was represented by the overall p value. Secondary comparisons between any two different cohorts of mice or patients were performed using a two-tailed unpaired Student’s t-test. Correlations between variables were evaluated using the Spearman rank-correlation test. Results were considered significant at p values <0.05.
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10.1371/journal.pntd.0003596 | Evaluation of the Diagnostic Accuracy of a New Dengue IgA Capture Assay (Platelia Dengue IgA Capture, Bio-Rad) for Dengue Infection Detection | Considering the short lifetime of IgA antibodies in serum and the key advantages of antibody detection ELISAs in terms of sensitivity and specificity, Bio-Rad has just developed a new ELISA test based on the detection of specific anti-dengue IgA. This study has been carried out to assess the performance of this Platelia Dengue IgA Capture assay for dengue infection detection. A total of 184 well-characterized samples provided by the French Guiana NRC sera collection (Laboratory of Virology, Institut Pasteur in French Guiana) were selected among samples collected between 2002 and 2013 from patients exhibiting a dengue-like syndrome. A first group included 134 sera from confirmed dengue-infected patients, and a second included 50 sera from non-dengue infected patients, all collected between day 3 and day 15 after the onset of fever. Dengue infection diagnoses were all confirmed using reference assays by direct virological identification using RT-PCR or virus culture on acute sera samples or on paired acute-phase sera samples of selected convalescent sera. This study revealed: i) a good overall sensitivity and specificity of the IgA index test, i.e., 93% and 88% respectively, indicating its good correlation to acute dengue diagnosis; and ii) a good concordance with the Panbio IgM capture ELISA. Because of the shorter persistence of dengue virus-specific IgA than IgM, these results underlined the relevance of this new test, which could significantly improve dengue diagnosis accuracy, especially in countries where dengue virus is (hyper-) endemic. It would allow for additional refinement of dengue diagnostic strategy.
| Dengue disease has become a major global public health concern, but an ideal diagnostic test that permits early and rapid diagnosis is not yet available. Improving diagnostic performance in this area is a major challenge and necessitates the development and evaluation of new efficient, accurate methods. According to the kinetics of dengue infection in serum, virus isolation and nucleic acid or antigen detection are the most specific diagnostic methods during the early acute stage of disease; serology is often used for diagnosis later in the course of infection. In order to provide an earlier and reliable dating of the dengue infection, few recent studies showed that the detection of specific IgA in the serum is a useful diagnostic parameter. Exploring that new approach, this study was carried out to assess the performance of a Platelia Dengue IgA Capture assay for dengue infection detection, newly developed by Bio-Rad, using 184 well-characterized samples provided by the French Guiana NRC sera collection of the Institut Pasteur in French Guiana. This study revealed good overall performances of this test, constituting promising assistance in dengue diagnosis, especially in hyper-endemic countries.
| Caused by any of four dengue virus serotypes (i.e. DENV-1 to DENV-4), dengue infection is currently the most significant arthropod-borne viral disease [1]. Whereas the World Health Organization (WHO) estimated in 2009 that 50–100 million infections occur each year [1], a recent study estimated that 390 million dengue infections occurred annually, of which more than 90 million were symptomatic [2]. World Health Organization has identified approximately 100 tropical and sub-tropical countries around the world where populations experience a high risk of dengue exposure. Because of its rapid spread and its impacts on human health, dengue epidemic has become a major and global public health concern.
Without specific therapeutics or vaccines, dealing effectively with this disease emergency requires innovative and appropriate diagnostic tools [3–6]. During the acute phase of the disease, dengue diagnosis is based on direct viral detection targeting the genome, especially by RT-PCR approaches, virus isolation on culture cell, or a viral antigen, the non-structural protein 1 (NS1) by ELISA or rapid tests. Indeed, the virus and soluble NS1 circulate in patients’ blood and persist for 5–7 days after fever onset. Indirect methods of dengue diagnosis are based on dengue-specific antibody detection, particularly the specific immunoglobulin M (IgM) by an IgM antibody-capture enzyme-linked immunoabsorbent assay (MAC ELISA), but also virus-specific IgG and IgA. Different commercial kits are available to detect these specific antibodies [1,3,5,7].
In response to the disease, IgM could be detected in 50% of cases early after infection (within 3–4 days after fever onset) and the majority of infected individuals become positive for IgM by day 5–6. IgM have been described as persisting until about 3 to 8 months post onset [7]. As for IgM, IgG are generally detectable at the end of the first week of illness (within 7–10 days after fever onset), and still detectable in serum after several months, and probably even for life. Concerning anti-dengue IgA antibodies kinetics, their positive detection often occurs one day after the beginning of the IgM time frame (on average at 5.5 days after the onset of fever) reaching their highest titres around day 8 following this onset. The IgA titre decreases rapidly until it reaches undetectable levels by day 40, indicating a shorter persistence of dengue virus-specific IgA in serum than IgM and IgG [6–11].
The dengue virus and circulating antibodies displayed well-known dynamic patterns: these patterns are influenced by the infecting serotype and patients’ clinical status, which shows significant differences following a primary or secondary dengue infection [1,7,11]. In secondary infection, IgM antibodies appear earlier or within the same time frame, but are usually at lower titres than during primary infection. They may even be undetectable. During secondary infection, the dominant antibodies are IgG, present from the previous infection and detectable at high levels, even in acute-phase serum samples. Concerning IgA dynamics in secondary infection, these antibodies in serum appeared to be slowly increasing during the first days, until reaching a higher level than in primary dengue infections [8,11].
Notably because both IgM and IgG antibodies persist for several months or years after infection, an IgM or IgG positive result from one serum sample is no more than suggestive: only seroconversion from paired serum samples-or a four-fold IgG titre increase- can confirm a recent dengue diagnosis [1]. But in practice, due to difficulties in obtaining blood samples taken on two occasions with an interval of no less than fifteen days apart, the serological analysis is carried out from a single acute-phase serum specimen and therefore provides only probabilistic diagnosis. The interpretation of these indirect tests is especially difficult in countries where dengue virus is hyperendemic and where other flaviviruses circulate, which could induce serological cross reactivity [1].
In this type of epidemiological context, obvious ways to overcome these diagnostic issues are to combine several diagnostic tests based on different approaches (direct and indirect, or multiple indirect tests) and to develop new tests targeting new infection markers. IgA thus appears to be an early, high-quality serological marker. Because dengue-specific IgA antibodies are detectable in acute-phase serum and persist for a shorter period of time than dengue-specific IgM, some studies have recognized the value of IgA detection in sera for dengue virus diagnosis using ELISA and immunofluorescence assays [7,9,12–14]. Helping to narrow the time frame of marker detection after infection, the IgA based method could be a more informative diagnostic tool and a better marker of recent dengue infection [8,10–13].
In keeping with these aims, a novel ELISA test for the detection of anti-dengue virus IgA from human sera, Platelia Dengue IgA Capture, was recently developed by Bio-Rad. The main objective of our study was to evaluate the performance of this serological dengue diagnostic test, based on specific IgA detection in clinical samples from patients exhibiting a dengue-like syndrome. Dengue diagnosis was confirmed using reference assays (detection of DENV RNA by RT-PCR and virus culture). Accuracy was also evaluated in relation to both DENV infecting serotype and DENV immune status. The second objective was to compare the Platelia Dengue IgA Capture performance to the PanBio Capture IgM test for dengue diagnosis in serum.
A total of 184 human sera were used for this study in order to reach a precision of 5% of the performance of the index test and to conform the STARD requirements [15]. The index test, Platelia Dengue IgA Capture, was evaluated using dengue diagnostic reference assays, as described below. Sera were provided from the French Guiana NRC (National Reference Center) sera collection (Laboratory of Virology, Institut Pasteur in French Guiana), stored at -80°C. The collection encompasses sera collected between 2002 and 2013 from patients exhibiting a dengue-like syndrome (fever, arthralgia, headache and/or myalgia). These sera were collected either for diagnostic purposes or for identifying the DENV serotype from patient sera already found positive for NS1 antigen in the context of epidemiological surveillance.
The 184 sera selected for this evaluation included two groups: a group of 134 sera from confirmed dengue-infected patients and a second group of 50 sera from non-dengue infected patients. Sera were classified according to the onset of fever (day 0 was defined as sera collected within 24h after the onset of fever).
A patient with febrile illness consistent with dengue fever was defined positive for DENV infection if an acute-phase serum sample was found positive for either RT-PCR targeting viral RNA [16] and/or viral isolation in Aedes pseudoscutellaris cell (AP61) [17]. Dengue-positive samples were selected to achieve a balanced collection of sera sampled between the third and the fifteen day following the onset of fever and of sera infected by the four DENV serotypes. All dengue-positive patients constitute the “dengue group”.
A patient with febrile illness consistent with dengue fever was defined negative for DENV infection if at least two of the three following analyses were obtained: (i) Negative RT-PCR or viral isolation from samples collected on day 0 and day 5 of the disease [16,17]; (ii) Negative NS1 detection of sera obtained prior to five days (Platelia Dengue NS1 AG, Bio-Rad); (iii) Negative in-house IgM capture assay on no less than 8 days sera [10]. All dengue-negative patients constitute the “non-dengue group”.
Convalescent serum was included in this evaluation, provided that its own paired acute-phase serum sample was available. Finally, all sera were analyzed for the presence of anti-dengue IgM and IgG using the Dengue IgM and Dengue IgG capture assay from PanBio (Panbio Dengue IgM Capture ELISA, Panbio Dengue IgG Capture ELISA—Australia), conducted according to the manufacturer’s instructions.
No research-specific blood collection was performed for the study purpose. All the analyzed samples were remaining samples resulting from diagnosis procedures following blood collection required by the care for any patient presenting dengue-like symptoms in French Guiana, and kept by the NRC biobank for both health and scientific purposes. According to the French legislation (article L.1211–2 and related of the French Public Health Code—FPHC), biobanking and secondary use for scientific purpose of remaining human clinical samples are possible as long as the corresponding patients (or their parents if less than 18 years of age) were previously informed and had given no oral objection (documented in the medical or laboratory files) to them. Whenever no information are available concerning patient’s objection, a waiver from one of the 39 French Ethical Committees (Comités de Protection des Personnes—CPP) could be sought according to the FPHC. In the present research, those two requirements are fulfilled. Information had been given to patients through the brochure entitled “Information for patients” during the prospective blood collection, and no immediate or delayed patient’s opposition was reported by the clinicians to the Arboviruses NRC. Study ethical approval and information waiver for retrospective remaining samples were obtained from the CPP Sud-Ouest Outre-Mer III (CPP # DC 2013/27). Moreover, in application of French legislation (article L.1243–3 and related of the FPHC), the NRC biobank for research purpose had been declared to both the French Ministry for Research and the CPP Ile de France I (declaration #2010/1223). The NRC database was declared to the French Data Protection Agency (Commission Nationale de l’Informatique et des Libertés, CNIL # 1248768) and provided clinical information about the age and sex of each patient, the date of serum collection and the date on which symptoms began.
Platelia Dengue IgA Capture (Bio-Rad Laboratories—Marnes La Coquette, France) is a microplate immunoassay using immuno-capture format for detection of specific IgA against DENV in human serum or plasma. Intra- and inter-assay repeatabilities were already assessed using 4 samples, tested in the same assay in 32 replicates: coefficients of variation ranged from 2.3% for the medium positive samples to 26.8% for the low negative one (cf. the analytical and clinical performance report from Bio-Rad). The test was used strictly following the instructions provided by the manufacturer. Briefly, 200 μl of 1/100 diluted sera of each 184 patients were distributed in each well then incubated for 1 h at 37°C. The plate was then washed four times and 200 μl of conjugate were added and incubated for 1 h at 37°C. After a 4-time washing step, revelation was carried out with a TMB substrate solution for 30 min at room temperature then stopped with 1N sulfuric acid. Optical densities (OD) were read at 450/620 nm using a plate reader within 30 minutes after stopping the reaction. Results were expressed in ratio = (OD of tested sample) / (appropriate Cut-Off). Results were interpreted as positive, negative or equivocal using the ratio provided with the kit: positive result when ratio was greater than 1, negative lower than 0.8 and equivocal between 0.8 and 1.
Detections of specific dengue IgM and IgG were carried out using the Panbio Dengue IgM Capture ELISA and the Panbio Dengue IgG Capture ELISA kits according to manufacturer’s recommendations. An IgM/IgG ratio was used to distinguish between the primary and the secondary dengue virus infections on serum samples showing positive IgM and positive IgG results, as recommended by the World Health Organization [1]. Using patient’s sera at 1/100 dilution, dengue infections were classified as primary if the IgM/IgG OD ratio was greater than 1.2 and as secondary if that ratio was lower than 1.2. In addition, a host immune status was defined in the acute sample as being due to a primary infection when it was found positive for IgM and negative for IgG, and due to a secondary infection when it was found negative for IgM and positive for IgG. Others equivocal results or combinations were defined as unclassifiable.
All serum analyses were tested blinded to the confirmed dengue samples (virus isolation and/or RT-PCR positive). Continuous variables were expressed as median and interquartile ranges (IQ1-IQ3) or mean ± SD and categorical variables as percentages. Differences among percentages were analyzed using the Fisher’s exact test and differences among continuous variables were analyzed using the Kruskal-Wallis test. In case of global significant differences between the groups, Bonferroni correction was applied.
AUROCs were calculated with 95% confidence intervals for Platelia Dengue IgA Capture and Panbio Dengue IgM Capture ELISA and compared using the non parametric Delong test [19,20]. Kappa (K) coefficient was calculated to evaluate the concordance between Platelia Dengue IgA Capture and Panbio Dengue IgM Capture ELISA results, using the interpretation scale of Landis-Koch [21]. Results were considered statistically significant when p<0.05. The sensitivity and the specificity for the assays were calculated based on confirmed dengue with the binomial exact 95% CIs. STATA 12.2 (StataCorp, College Station, Texas) software was used for all statistical analyses.
One hundred and eighty-four patients presenting a dengue-like syndrome were included to estimate the Platelia Dengue IgA Capture performances. Gender (94 females, 51.1%; 90 males, 48.9%) was equally distributed between both groups (p = 0.120) (Table 1). The mean patient age was 35.8 ± 17.6 years, with age ranging from 1 to 90 years old. Mean age of the dengue positive patient group (dengue group) was 34.8 ± 1.4 years; mean age of dengue negative patient group (non-dengue group) was 36.5 ± 2.9 years, with no statistical difference between the two groups (p = 0.58).
Only one sample out the 184 tested sera was found equivocal, which represents only 0.54% of inconclusive results (Table 2). Out of the 134 dengue group sera, the IgA index assay detected 124 positive, indicating a sensitivity of 93% (95% CI, 87% to 96%). Six sera out of the 50 non-dengue group ones were also found IgA positive, demonstrating a specificity of 88% (95% CI, 75% to 95%). Out of the 10 IgA seronegative sera from dengue positive patients, 8 were collected 3–4 days after the onset of fever, with the other 2 collected 6 days following fever onset. Seropositivities for IgA broadly increased from 50% for sera collected 3–4 days after the onset of fever to 100% for those collected seven days after.
In that study, the IgA index assay displayed a sensitivity of 93% (124/134; 95% CI, 87% to 96%), while the PanBio Dengue IgM kit positive for IgM detected 95% of the sera (127/134; 95% CI, 90% to 98%). These differences were not statistically different (p = 0.25). As observed for IgA, seropositivities for IgM also rose from 56% for sera collected 3–4 days after the onset of fever, to 100% for those collected five or more days after fever onset.
Moreover, ROC curves were drawn for both IgA index test and Panbio IgM capture assay, both of which showed very good performances, with Area Under ROC around 0.95 (Fig. 1). The comparison between the two AUROC showed no difference (p = 0.4135). When using a qualitative approach, the results are similar with an almost perfect kappa coefficient, equal to 0.8632.
The novel Platelia Dengue IgA Capture assay’s performance is very good: the overall sensitivity and specificity of the IgA index test are 93% (124/134) and 88% (43/49), respectively, and only 0.54% (1/184) of the results was inconclusive. These results suggest that the Platelia Dengue IgA Capture assay is well correlated with dengue diagnosis on clinical sera from patients exhibiting a dengue-like syndrome. The development of this new assay could contribute to the improvement of dengue diagnostic performance, currently a major challenge in managing dengue disease [1,6]. Even if the detection of specific IgA in the serum has been evaluated as a useful diagnostic parameter [8,10,13,14], there is still no single available ELISA commercial kit to measure IgA antibodies. The only available Dengue IgA kit was recently developed by MP Diagnostic ASSURE as a rapid test (Dengue IgA RT), and even when evaluated under different conditions and inclusion criteria, the performances were lower, with an overall sensitivity of 61.0% and specificity of 85.1% by comparing the index test results with IgM detection by ELISA [22–24].
In concordance with theoretical IgA antibodies kinetics observed in serum, sensitivities of the Platelia Dengue IgA Capture index test vary according to the sera collection day. Sensitivity was highest when estimated on convalescent samples (day 8 to 15 collected sera), compared to acute samples (day 3 to 7 collected sera), displaying sensitivities of 100% for convalescent versus 84% (95% CI, 73% to 92%) for acute samples.
The effect of the collection day of sera data was also observed in the analysis concerning the infecting dengue virus serotype: performances obtained showed significantly lower accuracy in detecting serotype 1. To the best of our knowledge, no existing studies have reported such differences observed in a study based on anti-dengue immunoglobulin detections. Results of serological IgM test obtained on these samples suggest the same trend of better performance observed for serotype -2, -3 and -4 detection. Our dataset features, and particularly the small size of the groups, does not allow for more thorough statistical analysis of the specific effects of each of these variables however, this point could be fully explained by the differences observed between the collection day of sera data: matched to the median (IQ1-IQ3) numbers of days after the onset of fever, DENV-1 sera displayed the lowest value, i.e. 7(5–8), compared with 8 (6–10), 10 (7–14), 7 (6–10) for DENV-2, DENV-3, DENV-4 sera respectively.
The IgA test displayed good sensitivity in detecting immune status, with better efficiency at detecting secondary infections, when evaluated according to the IgM/IgG ratio method recommended by WHO. This test’s ability to detect IgA for primary and secondary dengue infections is a major criterion for quality and relevance, particularly in dengue (hyper)-endemic area where the detection of dengue secondary cases at early stage of infection is especially important, because secondary cases are more frequently associated with severe outcomes. This point should be correlated with collection day medians, which are lower for primary sera (7 (5–10) days), and higher for secondary ones (8 (6–11) days).
Moreover, the results leading to a specificity of 88% (6 sera found positive by the Platelia Dengue IgA Capture among the non-dengue group) could be correlated either with a previous dengue infection, preceding the recent dengue-like syndrome associated with these clinical samples, or with a cross-reaction potentially induced by another etiological agent, because of a higher risk of exposure to multiple flavivirus infections in French Guiana. A high degree of cross-reactivity is frequently observed among flavivirus infection serology, particularly where the circulation of multiple flaviviruses compromises the local specificity of such measurements.
In addition, it is interesting to note that global diagnostic performance reaches 100% for both specificity and sensitivity when adding the NS1 diagnosis assay. As expected, combining the NS1 assay with an IgA assay will enhance the sensitivity of detection. The estimated accuracy of this test evaluated under our criteria could provide better diagnostic value if combined with another direct diagnostic test, as commonly illustrated and recommended [1,4,6,7,25,26].
Finally, the comparable overall performances estimated for both IgM and IgA serological marker measurements make this IgA index test as reliable as the IgM test. Even if the most frequently used serological test is the IgM capture ELISA format, based on the fact that around 80% of patients are IgM positive by the 5th day following the onset of symptoms [1], its limitations from the anti-dengue IgM antibody persistence over several months make their detection can be due to infection up to several months earlier. They do not necessarily indicate an acute dengue infection. Because of longer IgM persistence in serum, IgA based method could be a more informative diagnosis tool because it is a marker of an earlier dengue infection, which narrows the time frame of infection and increases diagnostic accuracy [8,10–13].
To conclude, these results suggest that the Platelia Dengue IgA Capture assay is an acceptable test, with overall sensitivity and specificity of about 90%. This new test could contribute to dengue diagnosis, especially in countries where dengue virus is endemic and where many serotypes of dengue viruses are circulating. Using the IgA test assay to measure a good quality serological marker detectable in acute-phase serum and persisting for a shorter period of time than dengue specific IgM allows a more accurate dating of infection. It reduces the window of potential recent dengue infection and refines the diagnostic strategy for dengue adopted by physicians. Moreover, it would be interesting to increase the sample numbers per day after fever onset enough to minimize the effect of this variable on others. Lastly, it appears essential to design and to conduct prospective diagnostic evaluations during different phases of dengue epidemic in an endemic area [5,27].
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10.1371/journal.pgen.1002511 | Ultrafast Evolution and Loss of CRISPRs Following a Host Shift in a Novel Wildlife Pathogen, Mycoplasma gallisepticum | Measureable rates of genome evolution are well documented in human pathogens but are less well understood in bacterial pathogens in the wild, particularly during and after host switches. Mycoplasma gallisepticum (MG) is a pathogenic bacterium that has evolved predominantly in poultry and recently jumped to wild house finches (Carpodacus mexicanus), a common North American songbird. For the first time we characterize the genome and measure rates of genome evolution in House Finch isolates of MG, as well as in poultry outgroups. Using whole-genome sequences of 12 House Finch isolates across a 13-year serial sample and an additional four newly sequenced poultry strains, we estimate a nucleotide diversity in House Finch isolates of only ∼2% of ancestral poultry strains and a nucleotide substitution rate of 0.8−1.2×10−5 per site per year both in poultry and in House Finches, an exceptionally fast rate rivaling some of the highest estimates reported thus far for bacteria. We also found high diversity and complete turnover of CRISPR arrays in poultry MG strains prior to the switch to the House Finch host, but after the invasion of House Finches there is progressive loss of CRISPR repeat diversity, and recruitment of novel CRISPR repeats ceases. Recent (2007) House Finch MG strains retain only ∼50% of the CRISPR repertoire founding (1994–95) strains and have lost the CRISPR–associated genes required for CRISPR function. Our results suggest that genome evolution in bacterial pathogens of wild birds can be extremely rapid and in this case is accompanied by apparent functional loss of CRISPRs.
| Documenting the evolutionary changes occurring in pathogens when they switch hosts is important for understanding mechanisms of adaptation and rates of evolution. We took advantage of a novel host–pathogen system involving a bacterial pathogen (Mycoplasma gallisepticum, or MG) and a songbird host, the House Finch, to study genome-wide changes during a host-shift. Around 1994, biologists noticed that House Finches were contracting conjunctivitis and MG from poultry was discovered to be the cause. The resulting epizootic was one of the best documented for a wildlife species, partly as a result of thousands of citizen science observers. We sequenced the genomes of 12 House Finch MG strains sampled throughout the epizootic, from 1994–2007, as well as four additional putatively ancestral poultry MG strains. Using this serial sample, we estimate a remarkably high rate of substitution, consistent with past implications that mycoplasmas are among the fastest evolving bacteria. We also find that an array of likely phage-derived sequences known as CRISPRs has degraded and ceased to recruit new repeats in the House Finch MG strains, as compared to the poultry strains in which it is diverse and rapidly evolving. This suggests that phage dynamics might be important in the dynamics of MG infection.
| Populations of animals are under constant threat from bacterial pathogens, which can be particularly destructive following a switch to a new host or the evolution of novel virulence mechanisms. Understanding the rate and process of evolutionary change in pathogens is thus important to assessing the risks of pandemics and developing means to predict and avoid such catastrophic events. In 1994, a strain of Mycoplasma gallisepticum (MG) was identified as the causative agent of an emerging epizootic in House Finches, a wild songbird inhabiting Eastern North America [1]. This bacterial pathogen frequently causes disease in commercial chicken and turkey flocks, but it had never been reported in House Finches or any songbird, leading to the suggestion that the epidemic began when MG expanded its host range from poultry to this phylogenetically distant songbird. MG prevalence reached 60% in some areas, and killed an estimated 225 million finches in the first three years after detection [2]. The early detection of the epizootic allowed research and citizen-science teams to track its rapid spread throughout eastern North America in exceptional detail, making it one of the best documented wildlife pathogen outbreaks [3]–[7].
Although previous genome-wide studies have clarified rates of measurable evolution in viral pathogens [8], [9] and in bacterial populations evolving under laboratory conditions or as human pathogens [10]–[18], less is known about rates of genetic change in bacterial pathogens of non-mammalian vertebrates, particularly on short evolutionary time scales. Genome-wide and gene-specific estimates of point substitution in bacterial lineages measured over centuries [19] to millions of years [20] suggest maximum substitution rates on the order of 10−7 to 10−9 per site per year. Although recent work suggests the rate may be even faster for several bacterial species [12], [14], [19], the number of studies documenting whole-genome changes in bacteria during host switches is still small, particularly for wildlife pathogens [21], [22]. As part of ongoing surveillance, field isolates of MG obtained from infected finches were sampled at multiple time points from the start of the epidemic in 1994 to 2007, providing a genetic time series beginning immediately after the host switch, as well as an opportunity to directly measure the tempo and mode of evolution in a natural bacterial population whose genome is as yet uncharacterized.
To characterize patterns of genomic change during its host switch between distantly related avian species, we sequenced whole genomes of 12 House Finch MG isolates from this 13-year time series, with four samples each from the beginning (1994–1996), middle (2001) and recent (2007) periods (Table S1). In addition, to identify putative source strains as well to determine if differences between the House Finch MG strains and the ∼1 Mb published reference Rlow strain from chicken [23] were ancestral or derived, we sequenced four additional strains from chicken and turkey based on phylogenetic analysis of a smaller multistrain data set (Figure S1). Our sequence, SNP filtering and between-platform cross-validation protocols yielded a high quality 756,552 bp alignment encompassing 612 genes (Tables S2, S3, S4, Text S1, Figure S2), and allowed us to monitor point substitutions, genomic indels, IS element insertions, and other changes across the entire genome (Figure 1), including the entire array of clustered regularly interspaced short palindromic repeats (CRISPR) of all 17 strains (finch and poultry isolates).
All House Finch MG samples were collected in the southeastern U.S. (Table S1), with an emphasis on the well studied population in Alabama [24], [25]. The population structure of Eastern House Finches before the epizootic was virtually panmictic [26], suggesting that there is likely to be little geographic structuring of MG in the east, a hypothesis that could be tested with additional data. The 12 House Finch strains from the three time periods spanned the known temporal and phylogenetic diversity of this lineage, and included strains that have been used to study host response to pathogen infection in House Finches [27]. To determine genetic diversity and phylogenetic identity of putative source populations of the House Finch MG strains, and to aid in sampling chicken and turkey strains for sequencing, we first analyzed a previously published data set [28]. Phylogenetic analysis of 1,363 bp obtained from four genomic regions for a large sample (n = 82) of MG strains suggests that turkeys rather than chickens were the source of House Finch MG and that the MG lineage colonizing House Finches first passed multiple times among chickens and turkeys (Figure S2). Although this analysis suggests frequent host switches between chickens and turkeys, which diverged 28–40 MYA [29], [30], it also suggests a single switch to the House Finch, a songbird species diverged from chickens by ∼80 MYA [31].
The whole genome alignment contained strong signals of a founder event as a result of colonization of House Finches. The total nucleotide diversity (π) in the House Finch strains for the four-gene region was only 3.1% of the diversity in circulating poultry strains prior to the epizootic, and only 2.3% of the poultry diversity when considering the entire House Finch MG genome [28] (Figure 2 and Table S5). In agreement with the four-gene analysis, our whole genome sequencing showed that the four sequenced poultry isolates were much more genetically diverse than the 12 House Finch isolates, possessing a total of 13,175 SNPs as compared to only 412 SNPs among the House Finch isolates (Table S2). The House Finch MG diversity corresponds to π = 0.00014, or roughly 1 SNP every 1,800 bp. Consistent with purifying selection acting over the longer time period encompassing the divergence of House Finch and poultry MG strains (as opposed to acting after the host-switch among House Finch strains alone), there was a stronger bias against non-synonymous substitutions among the more diverged poultry strains than among the recently diverged House Finch MG strains (Table S6). Across the entire genome, only 147 (35%) of the SNPs among the House Finch isolates were phylogenetically informative; the majority (265 or 64%) appeared as singletons.
To further quantify House Finch MG demography, we used a statistical model, the Bayesian skyline plot implemented with BEAST, that utilizes information on dates of sampling to estimate changes in genetic diversity through time [32], [33] (Text S2). The analysis is broadly consistent with field observations suggesting a mid-1990s origin followed by rapid population expansion, though it estimates that the House Finch MG lineages coalesced roughly in 1988, several years prior to the observation of sick birds in the field (estimated MRCA of the House Finch MG strains is 19.2 years prior to 2007 [95% HPD 16.9 – 21.7]; Figure 2d). Discrepancies between coalescence times and observed outbreaks in host populations have been observed for other pathogens, and could possibly be due to selective or demographic effects, or in our case low sample size [12]. Phylogenetic analysis suggests substantial turnover in the standing SNP variation between sampling intervals, with strong clustering of the 2007 strains, which are distinguished from other House Finch strains by 85 diagnostic SNPs (Figure 3). We found that one of the sequenced turkey strains, TK_2001, was highly similar in sequence to the House Finch strains and shares a number of genomic deletions and transposon insertions as well as duplications and losses of CRISPR spacers (see below) with the House Finch MG strains. This turkey strain may represent a poultry lineage close to the source lineage for House Finch MG (Figure 3).
In addition to SNPs in House Finch MG we found five large genomic deletions that occurred by 2007 and amounted to ∼42, 245 bp and encompassing 34 genes relative to the chicken Rlow strain (Figure 1 and Figure 3, Table S7). Three of these deletions are phylogenetically informative among the 17 MG strains (Table S7), but their conflicting phylogenetic distribution underscores the presence of recombination (see next section). Two deletions totaling 9,275 bp were shared among all strains except the reference. In addition, we detected six novel IS element insertions in the House Finch MG lineage (Text S3, Table S8) and three of the genomic deletions were likely mediated by illegitimate recombination between flanking IS elements (Table S7). In addition to the 34 genes deleted as part of genomic deletions, we found evidence for pseudogenization of 19 genes relative to the chicken MG reference (Text S3, Table S9). Two genes appear to have been disrupted by transposon insertions and 17 genes were pseudogenized by frameshift or nonsense mutations (Table S9). The substantial gene losses we detected, a total of 52 genes (∼8.6%) fixed in the House Finch MG lineage, presumably as a result of the bottleneck during host switch. By contrast, we failed to find a single novel gene in House Finch MG that was not also found in the poultry MG strains (Text S5). Comparative analysis with other Mycoplasma genomes showed that 15% of these lost genes also lacked a homologue in the other genomes surveyed whereas 13% had a homologue in every genome (Table S9).
Despite the small amount of genetic variation segregating among our House Finch Mycoplasma samples (only 412 SNPs), it is not possible to construct a phylogenetic tree for these strains that is free of homoplasies. Although the four 2007 strains and all 2001 strains except AL_2001_17 clearly formed well defined clades based on 85 and 28 SNPs, respectively, establishing the phylogenetic relationships for the other 5 House Finch MG strains exclusively via SNPs was not possible (Text S6, Figure 3). Although a total of 16 SNPs were phylogenetically informative for the placement of these five strains, the largest cluster of SNPs that were phylogenetically consistent was seven, and overall, 13 different trees were supported by at least 3 SNPs each. Similarly, substantial homoplasy was found among the four newly sequenced poultry strains and the Rlow reference. Although 6,152 SNPs were parsimony informative for these five strains, the unrooted tree with the best support was in conflict with 4,619 (75%) of these SNPs. These patterns are expected if sites are being shuffled by recombination or horizontal gene transfer (HGT) among isolates, and analysis of the entire data set found strong support for this (Text S4, Figures S3, S4, S5). Using the pairwise homoplasy index test [34] revealed a statistically significant signal of recombination (p<10−9). This signal comes predominantly from the four newly sequenced poultry strains because there is not enough genetic variation to make this test significant when only the House Finch strains are considered. However if we apply to the House Finch MG strains the homoplasy test by Maynard-Smith and Smith [35], which is found to perform well in situations of low nucleotide diversity [36], we again obtain a significant signal for recombination (p<10−6). We conclude that, despite a significant signal for recombination in both the poultry and House Finch strains, the House Finch MG cluster as a whole is a distinct and easily identifiable phylogenetic lineage with a long branch separating it from the poultry strains (Figure 3).
Coalescent analysis [32] of the 12 House Finch isolates sampled at different dates suggested an extraordinary point substitution rate of 1.02×10−5 substitutions per site per year (95% HPD 7.95×10−6 to 1.23× 10−5 (Text S2), consistent with earlier suggestions that Mycoplasma may be among the fastest evolving bacteria [37]. This rate of point substitution is not restricted to House Finch MG strains but was also found in the poultry strains when analyzed separately (Text S2), suggesting that rapid evolution was characteristic of MG prior to the House Finch epizootic. We estimated a similar substitution rate when considering only the four-gene multistrain alignment use to identify poultry strains for sequencing (Text S2). We verified that our estimate of substitution rate is robust to different protocols for SNP identification, statistical models and data sets (Figure 4; Text S7). Altogether we estimated the substitution rate within a coalescent framework on 34 combinations of SNP calling and model assumptions and found consistent estimates throughout (Text S1, Figure 4, Figure S6). In addition, we achieved a similar estimate using a Poisson regression approach as well as a root-to-tip regression (Text S7 and Figure 4).
In addition to a high estimated substitution rate in MG, we found a mutation in the gene-encoding UvrB that could elevate this rate yet further. UvrB is an essential part of the nucleotide excision repair system, which has been posited to be the most important pathway for maintaining genomic integrity in Mycoplasma [38]. The mutation truncates the UvrB protein by three amino acids (Table S10) and raises the possibility of the origin of a mutator strain in House Finch MG [39] as the C-terminal of this protein is essential for its function [40]. Consistent with this idea, we found 14 instances of adjacent SNPs among the 12 House Finch isolates, a notable excess in an alignment with only 412 variable sites (Table S11). Moreover, 12 of these 14 are CC→TT double substitutions, which are normally repaired by the UVR system (Table S10). For 13 of the 14 doublets, both sites are inferred to have mutated on the same branch of the tree, suggesting single mutational events, and the proportion of doublet mutations involving the same base was drastically higher (92.8%) in lineages with the UvrB mutation as compared to those without (p<0.0001; Table S10). Nonetheless, these doublet mutations are not required to achieve the high rate of substitution that we measured. They account for less than 7% of the segregating variation and removal of these doublet sites does not affect the high estimated substitution rate. The UvrB mutation is found in all of our House Finch MG strains as well as the turkey strain TK_2001, but not in the ancestral chicken strains or the reference chicken strain. Thus, the mutation appears to have arisen on the lineage leading to the House Finch.
In some bacterial systems, CRISPRs have a well-recognized function in bacterial immunity and defense against phage, although they may possess additional functions, such as gene regulation [41]–[44]. We extensively catalogued CRISPR repeats in the House Finch and ancestral poultry strains (Figure 5, Text S8, Table S12). In so doing we observed drastic changes in the CRISPR system between House Finch and poultry strains (Figure 5) [45]–[48]. The House Finch MG strains from 1994–96 contain up to 50 unique spacers, none of which is shared with the four divergent poultry genomes, which each contained a unique set of 36 to 147 spacer regions consistent with a high rate of turnover for a population actively acquiring new spacer sequences. We found that less than 1% of the 302 unique spacer sequences had similarity to any sequences in the House Finch MG genomes and that none of the remaining spacers had any similarity to sequences in Genbank, indicating an external source for these sequences (Text S8). Surprisingly, no novel spacer elements are present in any of the House Finch MG samples or TK_2001, indicating that the CRISPR array ceased recruiting additional spacers around the time of host switch into the House Finch. In fact, over the 13-year period of the epizootic, the number of unique spacers present in the CRISPR array of the samples decreased to 28 (Figure 5). Further evidence for degradation of the CRISPR locus following the host switch is the complete loss of the four CRISPR-associated (i.e. “CAS”) genes in all of the 2007 isolates, a loss that likely renders the CRISPR system in House Finch MG non-functional [45].
We conducted whole-genome sequencing on a unique 13-year serial sample of Mycoplasma strains circulating in wild House Finches to characterize genomic changes accompanying a host shift from poultry in the mid-1990s as well as to obtain a very high substitution rate for this avian pathogen. Previous estimates using serial samples and/or the known timing of events presumably tied to the divergence of bacterial strains have generally found much lower rates. An estimate of 2.0×10−6 was obtained for Staphyloccous aureus [12], 1.1×10−7 for Buchnera [19], 7.42×10−7 in Yersinia pestis and 1.4×10−6 in Heliobacter pylori [14]. Disentangling the effects of recombination and point substitution can be challenging and some previously published substitution rates are likely to be upper bounds rather than point estimates [12]. Our estimate appears to be among the highest reported for a bacterium, and is consistent with other reports of exceptionally high substitution rates in mycoplasmas [37].
Estimates of substitution rates can be influenced by the interval over which sequences are sampled, with estimates taken from short time intervals often exceeding those taken on biogeographic or geological time scales [49]. However the small number of SNPs that we detected segregating in House Finch MG populations suggest negligible effects of multiple hits on our estimate, and our use of a coalescent model suggests that effects of ancestral polymorphism on substitution rate estimates should be adequately accounted for [32], [50]. Additionally, our estimates of substitution rate were robust to many potential complicating factors, including SNP calling protocol and whether poultry or House Finches were used as the host for sampled sequences. Given the history and genetic isolation of the House Finch MG strains, the influence of recombination or lateral gene transfer on our estimate of substitution rate is likely also minimized (Text S7).
The CRISPR dynamics we observed in House Finch MG differ from that seen in other pathogen and bacterial populations. A recent study of Y. pestis CRISPR arrays from 131 strains [51] indicated a slower pace of CRISPR evolution than observed in MG and pattern of evolution in which acquisition of novel sequences does not play a prominent role. This study found that in Y. pestis the first part of the CRISPR arrays were conserved and that over 76% of all spacer sequences derived from within the Y. pestis genome. Similarly, a recent study of E. coli and Salmonella genomes found that strains within 0.02% divergence typically have identical CRISPR loci [52] and that spacer sequences were often matched to elements of the E. coli genome. Additionally, some spacer sequences were shared between strains within a species exhibiting over 1% sequence divergence. These observations and an estimated substitution rate on the order of 10−10 per site per year suggested that E. coli strains that had diverged for 1,000 years sometimes shared identical CRISPR loci, suggesting patterns of evolution different from that expected for a rapidly changing adaptive immune system primed to combat phages, a conclusion that was supported by later work [53].
By contrast to the pattern seen in these γ-proteobacteria, none of the House Finch MG strains in this study have the same CRISPR locus despite differing at only 0.01–0.02% of sites and likely having last shared a common ancestor less than 20 years ago. Our serial sampling suggests that the loss of spacer sequences and the CRISPR system itself can take place on very short time scales in Mycoplasma. Unlike the patterns seen in E. coli, Y. pestis, and Salmonella, the poultry MG strains in our study did not share any spacer sequences, even though they differed by ∼1%. These strains had very large CRISPR arrays and 99% of all spacer sequences did not match any known sequence in their genome or in the databases. Therefore the MG CRISPR loci studied here differ from the those observed in some γ-proteobacteria, a group for which CRISPR dynamics can appear functionally unrelated to ecology or immunity [53]–[55].
Instead, our finding of rapid evolution and degradation of the CRISPR loci more closely resembles patterns found in other bacterial groups, particularly those in which CRISPR is involved in phage defense [56]. CRISPRs are found in only 40% of sequenced bacteria investigated thus far, and often have major roles in bacterial immunity in several lineages investigated in detail [45]. We were surprised to find a gradual degradation and ultimate apparent functional loss of the CRISPR system in House Finch MG after the host switch and a shift in CRISPR dynamics appears to be a major correlate of host switch in this system. One possible explanation for this pattern is that MG experienced release from its ancestral phage parasite community (or other mobile genetic elements such as plasmids) following introduction into the House Finch. Loss of traits upon removal of the agent of selection is a common evolutionary response, as are population expansions of animals and plants when introduced into novel habitats unaccompanied by their parasites [57].
Despite the large amount of ecological research focusing on this host-pathogen system [3]–[7], at present nothing is known about phages that infect MG or their role in its evolutionary dynamics. Therefore the hypothesis of parasite release as a driver of CRISPR loss is purely speculative. We know of no phage known to infect the Pneumoniae phylogenetic group of mycoplasmas and the few phages known to infect Mycoplasma have proven difficult to characterize [58]. We might expect Mycoplasma bacteriophages to be host-specific given that they seem to be unusual in their ability to bind to a bacterium with no cell wall and a diverse assortment of surface proteins [58]. However, we are not aware of even basic data on the degree to which Mycoplasma might be susceptible to the many bacteriophages that they presumably encounter in their environment. Although phage represent one possible source for these novel ∼30 bp sequences, another possible explanation for the source of the spacer sequences is that they derive from plasmids. Although unprecedented (we know of no examples of a naturally occurring plasmid in the Pneumoniae mycoplasmas), such a scenario could raise the possibility of easier genetic manipulations in MG where development of such tools has been challenging [59]. Of the many other possibilities that could explain the observed degradation of the CRISPR loci, we can at least rule out self-interference as an explanation in derived MG strains, given that there is only a single CRISPR cluster in House Finch MG [54]. Measurement of costs, possible advantages and consequences of CRISPR loss, as well as functional and evolutionary assays and surveys of phage diversity will help determine if the rapid and deadly spread of Mycoplasma following their expansion into the House Finch was facilitated by a lack of phage predation, a short-term advantage of CRISPR degradation or some other, possibly neutral, mechanism. Although our sequence data is suggestive, explicit functional studies will also be required to demonstrate CRISPR functionality or lack thereof in poultry and House Finch MG and its role, if any, in phage defense.
Genome evolution of MG during its host-switch from poultry to House Finches adds to a growing list of host-switches that are successful in the complete absence of novel genes [21], [60], [61] and bacterial lineages exhibiting high rates of point substitution [14]. Mycoplasmas are some of the fastest evolving organisms on earth [62] having lost many of the repair mechanisms present in other bacteria [38] and this high mutation rate could help introduce deleterious mutations and contribute to the substantial level of pseudogenization that was observed in this study. The high basal substitution rate in MG may well be elevated yet further by UvrB mutation that we detected, a mutation that could have consequences for the long term genomic integrity of this MG lineage, particularly if it remains genetically distinct from and unable to exchange genes with the poultry MG lineages with a functional UvrB. Alternatively, given the short (3 amino acid) truncation of this gene in the House Finch strains, another explanation for the greatly increased number of doublet mutations in the lineage carrying the UvrB truncation is that selection has not had enough time to remove them as it has for poultry strains without this mutation. Although mutator strains are known to have a selective advantage in rapidly evolving laboratory and natural populations [39], [63], additional functional and experimental work will be required to determine the selective and functional effect of the mutation we have detected in UvrB, and over what time scales such selective effects might persist. For this and other endeavors, serial sampling of additional bacterial populations in nature will further clarify the rate at which genomes are remolded during host switches in the wild.
DNA sequence data for 4 gene fragments collected from 74 strains in Ferguson et. al. [28], was combined with data from 8 strains newly sequenced in this study to yield a Large Sample Multiple Sequence Alignment (LS-MSA) 1,363 bp in length (Figure S2). We estimated nucleotide diversity and the standard deviation of this estimate within and among subgroups of these sequences using DNAsp version 4.10.9 [64] (Table S5). In estimating diversity of MG strains sampled from chickens and turkeys, we restricted analysis to those strains sampled during 1994–1996 for comparison with our earliest House Finch strains sampled in a similar time interval.
Twelve strains of MG isolated from House Finches in the Southeastern US were sequenced with the Roche 454 Gene Sequencer. The average coverage level was 9.4X (Table S1). Additionally, four MG strains isolated from poultry hosts and selected based on their positions in the multistrain phylogenetic tree were sequenced with the Illumina sequencing platform to an average coverage of ∼410 X (Tables S2, S3, S4, Text S1, Figure S2).
Using a coalescent model and a Bayesian framework as implement in BEAST v1.52 [32] we estimated the mutation rate and times to common ancestry from a 13-taxon alignment composed of the reference MG genome and all of the House Finch MG strains whose genomes were sequenced in this study (Text S2). We also ensured that the conclusions from this inference were not sensitive to the SNP calling procedures or the choice of substitution models (Text S2, S7, Figure S6). In order to compare the mutation rate between the poultry and House Finch MG populations, these quantities were similarly estimated from the 82 taxon LS-MSA after removing nine laboratory strains from the alignment that likely experienced different population dynamics than the wild strains and had unknown sampling dates. A Poisson regression model was also used to estimate substitution rates by counting mutations along a single lineage assumed to span the dates of sampling for each strain (Text S7).
We catalogued IS elements using BLAST and the ISFinder database [65, Text S4]. We tested for evidence of genetic recombination between MG strains using the genome sequences from our 4 poultry and 2 House Finch strains using the pairwise homoplasy index test [34] as implement in splitstree4 [66], and the homoplasy test by Maynard-Smith and Smith [35]. Further evidence for the presence of recombination and the number of nonrecombining blocks was provided by other methods (Text S6, Figures S3, S4, S5).
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10.1371/journal.pgen.1004242 | FRA2A Is a CGG Repeat Expansion Associated with Silencing of AFF3 | Folate-sensitive fragile sites (FSFS) are a rare cytogenetically visible subset of dynamic mutations. Of the eight molecularly characterized FSFS, four are associated with intellectual disability (ID). Cytogenetic expression results from CGG tri-nucleotide-repeat expansion mutation associated with local CpG hypermethylation and transcriptional silencing. The best studied is the FRAXA site in the FMR1 gene, where large expansions cause fragile X syndrome, the most common inherited ID syndrome. Here we studied three families with FRA2A expression at 2q11 associated with a wide spectrum of neurodevelopmental phenotypes. We identified a polymorphic CGG repeat in a conserved, brain-active alternative promoter of the AFF3 gene, an autosomal homolog of the X-linked AFF2/FMR2 gene: Expansion of the AFF2 CGG repeat causes FRAXE ID. We found that FRA2A-expressing individuals have mosaic expansions of the AFF3 CGG repeat in the range of several hundred repeat units. Moreover, bisulfite sequencing and pyrosequencing both suggest AFF3 promoter hypermethylation. cSNP-analysis demonstrates monoallelic expression of the AFF3 gene in FRA2A carriers thus predicting that FRA2A expression results in functional haploinsufficiency for AFF3 at least in a subset of tissues. By whole-mount in situ hybridization the mouse AFF3 ortholog shows strong regional expression in the developing brain, somites and limb buds in 9.5–12.5dpc mouse embryos. Our data suggest that there may be an association between FRA2A and a delay in the acquisition of motor and language skills in the families studied here. However, additional cases are required to firmly establish a causal relationship.
| Some human genetic diseases are caused by dynamic mutations, or expansions of a short repeated sequence in the genome that can be unstably passed on from generation to generation. A subset of these dynamic mutations known as fragile sites can be seen as a break or gap on the chromosome when cells are cultured under specific conditions. To date eight folate-sensitive fragile sites (FSFS) have been characterized, and all are due to CGG-repeat expansions within the 5′ UTR or promoter region of the respective gene. When the repeat expands in size, it becomes hypermethylated and the adjacent gene or genes are transcriptionally silenced. For at least four of the eight known fragile sites this silencing of the associated gene(s) lead to intellectual disability syndromes such as fragile X. In this work we describe molecular characterization of an autosomal FSFS called FRA2A on chromosome 2. As the molecular cause of FRA2A, we identify an expansion of a CGG repeat which subsequently results in silencing of the neighbouring gene AFF3. This gene is one of the four autosomal paralogss of the AFF2/FMR2 gene which, when mutated, is the cause of the FRAXE syndrome. We find that FRA2A expression is associated with highly variable developmental anomalies in the three FRA2A families studied.
| Dynamic mutations are heritable unstable expansions of short, genomic repeat sequences. Various pathogenic mechanisms have been associated with dynamic mutations [1], [2] and at least 40 neurological, neurodegenerative and neuromuscular disorders are known to be caused by these types of mutations [3], [4]. Expansions of these unstable sequences may occur in promoters, coding regions, introns and 3′ and 5′ untranslated regions (UTR) of genes [5], [6], [7]. Known and putative disease mechanisms include aberrant splicing [8], loss or gain of function of the encoded protein [9], [10], the expanded repeat itself [11] or its RNA transcript [12], [13] and Repeat Associated Non-ATG translation (RAN translation) [14], [15]. The size threshold at which a repeat becomes unstable and/or pathogenic varies widely, from the expansion of only a few trinucleotide repeats in e.g. ARX-associated infantile epileptic encephalopathy (MIM 308350) to over a thousand repeats in e.g. DMPK-associated myotonic dystrophy (MIM 160900), FXN-associated Friedreich ataxia (MIM 229300) and FMR1-associated fragile X syndrome (MIM 300624) [16], [17], [18].
Fragile sites represent a specific subset of dynamic mutations that are visible as gaps or breaks on metaphase chromosomes from cells cultured under specific conditions. Fragile sites are categorised by the nature of the inducing culture condition and the population frequency of the mutation [19]. FRAXA is a rare, folate sensitive fragile site (FSFS) associated with a trinucleotide repeat (CGG) expansion mutation in the 5′ UTR of the FMR1 gene resulting in fragile X syndrome, the most common inherited intellectual disability syndrome [20]. Twenty-six other FSFS have been reported cytogenetically but only eight of these have been molecularly characterized: FRAXA [20], FRAXE [21], FRAXF [22], FRA16A [23], FRA11B [24], FRA10A [25], FRA12A [26] and FRA11A [27]. To date, all characterized FSFS are due to a CCG/CGG trinucleotide repeat expansion. The expanded repeat and any adjacent CpG island become hypermethylated and transcriptionally silenced at a locus-specific repeat size-threshold [28]. At least four of the eight characterized rare, folate sensitive fragile sites are associated with a neurodevelopmental disorder. The relevance of folate sensitive fragile sites to intellectual disability (ID) is strengthened by five independent population studies that have all shown that autosomal folate sensitive fragile sites are overrepresented in people with ID compared to control groups without ID, with a prevalence of 1.2% and 0.27% respectively [reviewed in 29]. It thus seems likely that as yet uncharacterized CGG/CCG repeat expansions may be associated with neurodevelopmental problems.
An autosomal FSFS on chromosomal band 2q11.2-q12 has been previously described [30], [31], [32]. We studied three families with FRA2A-expression and a wide spectrum of neurodevelopmental and other anomalies. We identified expansion of an intronic CGG repeat leading to hypermethylation of at least one promoter of the AFF3 gene in all FRA2A carriers and we hypothesise that the associated transcriptional silencing of AFF3 in the brain may be responsible for some of the developmental features observed in FRA2A carriers.
Using the simple repeat track on UCSC genome browser (GRCh37/hg19) we identified three candidate CGG repeats in the FRA2A containing region (2q11-12). One of these repeats is located within the LAF4/AFF3 gene (chr2:100721261–100721286; hg19), an autosomal homolog of the FRAXE-associated FMR2/AFF2 gene. In order to determine whether this CGG-repeat is expanded in FRA2A we used metaphase FISH analysis on a FRA2A-expressing individual (Figure 1; AII.3) with the BAC clone RP11-549H5 (chr2:100,588,792–100,759,365; hg19) encompassing the repeat. The FISH signal spanned the FRA2A fragile site (Figure 2A). Consistent with this the FISH signals from probes RP11-436F6 (AC010736) and RP11-506F3 (AC074387) were centromeric and telomeric to FRA2A, respectively. To establish co-location of the CGG repeat with the fragile site, long-range PCR-generated probes L10K (chr2:100721983–100733233; hg19) and L18K (chr2:100700447–100718834; hg19) were targeted to the genomic regions on either side of the (CGG)n repeat. These probes did indeed flank the fragile site, locating FRA2A to a 3.1 kb interval within the AFF3 gene (Figure 2B). The second red (L18K) FISH signal observed at 2q13 (Figure 2B) is the result of two copies of a 24 kb low copy repeat flanking the NPHP1 gene (Figure 2C) (chr2: 110520380–110538822 and chr2:111347822–111366260; hg19). A copy of this sequence is also located adjacent to the CGG-repeat within the AFF3 gene in the region covered by L18K.
PCR-based amplification and sequencing of the AFF3 CGG repeat in 200 control chromosomes revealed it to be highly polymorphic with a length ranging from 3 to 20 copies (Figure 3). The most frequent CGG allele contains eight repeats (as does the genomic reference sequence; chr2:100721261–100721286; hg19). To exclude the possibility that apparently homozygous control individuals are in fact heterozygous for the detected allele in combination with a large expansion that is not detected by this protocol, we amplified these samples with gene specific repeat primed PCR (Asuragen Inc., Austin Texas, USA). This protocol enables us to detect expansions up to 300–500 repeats. However, no expanded repeats were detected in control chromosomes, and the genotype distribution agreed with Hardy-Weinberg equilibrium (P>0.05).
PCR amplification of the repeat in the FRA2A-expressing individuals from AI.1, AII.3, AII.4, AIII.1, BII.1 and CII.1 (Figure 1) showed a single CGG allele in the normal size range. An additional smaller fragment was detected in subject AII.4. Sequence analysis of the smaller PCR product showed a 134-bp deletion encompassing the entire CGG repeat as well as some flanking sequences (Figure S1). This deletion was not detected in 800 control chromosomes. To visualize the repeat expansion in the FRA2A-positive individuals, we performed Southern blotting on HindIII digested genomic DNA of all available members of the three families (AI.1, AII.3, AII.4, AIII.1, BI.2, BII.1, CI.1, CI.2 and CII.1) and control samples. A 4.4 kb fragment was observed in all cases and controls. In five affected FRA2A-positive individuals we detected additional large fragments or smears compatible with the presence of an expanded allele (Figure 4). Two FRA2A-negative parents of FRA2A-positive individuals (BI.2 and CI.2) showed additional larger fragments indicative of repeat expansion. No evidence of an expanded fragment was observed in the control samples or in FRA2A-negative individual CI.1. Interestingly, individual AI.1 who had been reported as showing a low level of FRA2A-positive cells showed no fragments suggestive of an expansion mutation. A Southern blot of the same samples after NcoI digestion gave very similar results (data not shown).
A gene specific repeat-primed PCR assay was used for accurate sizing of the repeat expansion mutations. The mothers in family B and C both showed a slightly expanded allele (±120 and 106 repeat units, respectively) in addition to an allele in the normal size range (15 and 17 repeat units, respectively) while their offspring show one allele of 8 units compatible with paternal inheritance and one allele with a large expansion of more than 300 units (Figure S2). This strongly suggests that the expanded allele in both families was inherited from the mother (Table 1). In family A, the apparently FRA2A-positive individual AI.1 showed no evidence of an expanded allele on either Southern blot analysis or on repeat primed PCR. This apparent discrepancy could be resolved genetically using the microsatellite markers (D2S2209/AFMA246XE9 and D2S2311/AFMB355ZG1, Figure S3). The FRA2A-positive daughters of AI.1, AII.3 and AII.4, were shown to have inherited a different non-expanded allele (8 CGG units) from him, while they share a common allele with the mother (AI.2). Their FRA2A-positive grand-daughter, AIII.1 also inherited this maternal allele. The expansion was therefore probably inherited from AI.2, with the 4% FRA2A expression in AI.1 representing a false-positive cytogenetic result. Unfortunately DNA was not available from AI.2 to determine if she also carried an intermediately expanded allele.
The RefSeq AFF3 gene model consists of 23 coding exons and two 5′ non-coding exons together spanning 558 kb genomic DNA [33]. Here, the 5′ non-coding exons are named exons 1 and 2 with the first coding exon called exon 3. An AFF3-specific cDNA probe encompassing the final 5 exons was used for northern blot analysis. A major transcript of approximately 8 kb, corresponding to the predicted size was detected in several tissues, including brain, placenta and lung (data not shown).
To determine the precise location of the AFF3 transcriptional start sites (TSSs) in relation to the expanded repeat we used Cap Analysis of Gene Expression (CAGE) data from the FANTOM4 consortium. FANTOM4 produces sequence tags from the 5′ end of mRNAs from many different tissue sources and species and maps these to the reference genome [34]. Mapped CAGE tags reveal the sites of transcription initiation at single nucleotide resolution and provides a semi-quantitative measure of steady state mRNA levels for those transcripts using a tags per million (TPM) metric. The TPM scores for three different human tissue groups (brain, immune and other tissues) are plotted in Figure 5B. AFF3 is transcribed in telomeric to centromeric orientation and the x axis of these graphs represent the hg19 genomic coordinates. The location of the 25 annotated exons of the RefSeq model of the AFF3 gene (NM_001025108) is represented above the graphs using the same genomic coordinates. To assess the transcriptional activity of AFF3 during early brain development we mapped transcriptome sequencing reads of mRNA (RNA-seq) from three different human fetal brain samples to the regions surrounding the TSS identified by CAGE (Figure 5C).
CAGE tag sequencing demonstrates two robust TSSs. The most 5′ TSS corresponds to the 5′ end of exon 1 at position chr2:100759169 (GRCh37/hg19). This TSS is highly expressed in immune tissues with a mean of 300 tags per million shown by the blue bar in the middle graph (Figure 5B). There is no obvious transcription of exon 1 on RNA-seq from human fetal brain (Figure 5C, left-hand side). A second robust TSS was identified in CAGE data from brain and other tissues which mapped within intron 2 as shown by the blue bars in the top and bottom graphs in Figure 5B. The highest levels are seen in the brain (mean of 60 tags per million). An expanded representation of this region is shown in the right-hand side of Figure 5C. This shows no evidence of transcription of exon 2 but strong expression in exon 3. This also shows evidence of an alternative exon 1 immediately 3′ to the TSS (Figure 5C, right hand side, black arrow). The TSS lies immediately downstream of the CGG repeat (Figure 5D) suggesting this expansion prone repeat is located within the core of an alternate AFF3 promoter.
FANTOM4 CAGE data also encompasses a range of mouse tissues. From this we can demonstrate that both the exon 1 TSS and intron 2 TSS are evolutionarily conserved and functional TSSs. Although the CGG repeat itself is not conserved, a region of low compositional complexity flanked by highly constrained non-coding sequences is a conserved feature of the intron 2 TSS promoter (Figure 5).
Whole mount in situ hybridization (WISH) using riboprobes targeted to the 3′UTR of Aff3 was used to determine the developmental expression pattern in mouse embryos at 9.5, 10.5, 11.5 and 12.5 days post coitus (dpc). This has shown site and stage specific expression of Aff3. The most striking areas of expression are in the somites, the upper limb buds, the diencephalon/prosomere I and the fusing primary palate (Figure 6).
In all rare, folate-sensitive sites characterised to date, CGG repeat expansions are associated with hypermethylation of the surrounding CpG island. Bisulfite sequencing indicated hypermethylation of the CpG island in all five affected FRA2A carriers AII.3, AII.4, AIII.1, BII.1 and CII.1, while in healthy control individuals this region was not methylated (figure S4). In order to quantify the methylation level, we subsequently subjected all samples to pyrosequencing. This technique allows accurate quantification of methylation across individual CpG sites [35]. A methylation frequency of 50% would be consistent with complete methylation of the expanded allele as all affected individuals in this study are heterozygous. We analyzed a short region of genomic DNA (chr2:100721843–100721885; hg19) adjacent to the CGG repeat in all available family members, containing four analysable CpG dinucleotides (Figure S1 and Table 2). Methylation percentages are congruous across the 4 CpG-sites in each individual (p-values ranging from 0.417 to 1.000) and are consistent with hypermethylation of the CGG repeat region in individuals carrying an expanded allele. There is some suggestion that the methylation frequency may be increasing upon generational transmission.
To exclude a non-specific effect of increasing age on the methylation of this region, we pyrosequenced 72 individuals from 24 unrelated two-generation families. The ages within this control group varied between 0 and 11 years for the children and between 23 and 53 years for the parents, which is comparable to the age distribution in our FRA2A families at the time of DNA collection. No methylation above the threshold was detected in any control individual (data not shown). In the FRA2A-carriers the methylation level for each of the four CpG sites differed significantly from the level determined in this control population (p-values≤0.001 for CpG site 1,2 and 4 and p<0.004 for CpG site 3), suggesting that expansion of the repeat is associated with hypermethylation of the region surrounding the repeat.
In AII.4, mosaic for a CGG-repeat expansion and a deletion, the promoter region on the expanded allele was hypermethylated as determined by bisulfite sequencing. The allele with the 134-bp deletion was not methylated, as determined by Southern blotting after double digestion with BamHI and NotI (data not shown).
The level of AFF3 expression in lymphoblastoid cell lines is too low to be reliably measured by RT-qPCR. To determine if FRA2A results in transcriptional silencing of AFF3 in cis, we utilized single nucleotide polymorphisms (SNP) mapping within the open reading frame. Two such SNPs were found to be heterozygous in the genomic DNA of affected FRA2A carrier BII.1. Rs4851214 maps to exon 14 and heterozygous individuals display both an T and a C peak (c.1499T/C) on Sanger sequencing, while rs13427251 maps to exon 25 and heterozygotes for this SNP show both an A and a T peak (c.5475A/T). Sequencing cDNA from BII.1, revealed only a C peak at rs4851214 (Figure 7) and only the A allele peak was seen for rs13427251. These results are consistent with monoallelic expression of AFF3 in this individual. Genomic DNA from BII.1's mother (BI.2) is homozygous for the rs13427251 T allele (g.5475T/T) indicating that it is the maternal T allele carrying the CGG expansion that is silenced in BII.1. cDNA of BI.2 showed a heterozygous signal for rs4851214 (c.1499T/C) indicating that both AFF3 alleles are transcribed in the mother despite partial methylation of her expanded allele.
Six FRA2A carriers were initially included in this study (Figure 1, Table 3), four in Family A and one each in Families B and C. Individual AIII.1 is currently too young to make any conclusion about cognitive development. Individual A1.1 has no discernible affected phenotype and he also has the lowest expression of the fragile site. The molecular analysis presented above strongly suggests that the 4% apparent expression of this case represents a false positive and so was excluded from the clinical analysis. Two FRA2A carriers AII.3 and AII.4 are adults; both had global delay in their neurocognitive development to a level that merited genetic investigations during childhood and their long-term placement in special educational facilities. However, as adults both of these individuals are functioning at a normal level and are in full time employment. This raises the possibility that they had a true delay in development rather than a fixed disability. Something similar is observed in FRAXE patients as most adult FRAXE males adapt to live a normal life. Individual CII.1 was born prematurely and had significant respiratory distress, which confounds the unambiguous interpretation of the cause of her mild developmental delay. BII.1 has the most significant delay, currently without a plausible non-genetic explanation. Thus all four of the characterized true FRA2A carriers did have significant delay in their motor and language development.
To determine whether the FRA2A carriers with neurodevelopmental anomalies had additional mutations in the protein coding region of the AFF3 gene, mutation analysis of all coding exons was performed. No sequence abnormalities were detected in any of the affected FRA2A carriers, except in subject AII.4, in which a 6-bp deletion was identified in exon 14, removing two amino acids: Threonine and Alanine (position 619 and 621 respectively) (Figure 5A). Both amino acids are found in a region, enriched with proline, serine and glutamic acid residues and located between the transactivation domain and the nuclear localisation signal (NLS). According to different prediction software (mutationtaster, mutation assessor, Indelz), the deletion is benign. Moreover, this 6-bp in-frame deletion was also present in the unaffected father (AI.1).
We provide compelling evidence that the molecular basis of the FSFS FRA2A is a CGG repeat expansion in an alternative promoter which is active in the brain and is located in the intron immediately 5′ to the first coding exon of the major AFF3 transcript. The FRA2A-associated repeat is polymorphic in the general population. Repeat primed PCR showed all individuals with an expansion of over 300 repeat units expressed FRA2A in more than 20% of their cells. The expansion was associated with hypermethylation of a CpG island adjacent to the alternative promoter and, in at least one case, resulted in transcriptional silencing of AFF3. These results are consistent with the epigenetic effects that have been described in other FSFS. Within each of the three families studied here higher levels of methylation correlate well with neurodevelopmental delay, higher repeat size and silencing of AFF3. However, there are striking disparities in the absolute levels of methylation observed between the families. For example individuals AII.3 and AII.4 both have >300 repeats and had evidence of neurodevelopmental delay during childhood but have lower levels of methylation than BI.2 (∼120 repeats, biallelic expression of AFF3) and C1.2 (106 repeats) neither of whom showed evidence of developmental delay. A likely explanation for this is that the assay used here was performed on peripheral blood-derived cells whereas the phenotype in which we are interested is developmental and neural. Many developmental loci appear to show tissue specific differences in DNA methylation [36]. In this regard the ability to model brain development using cerebral organoids from patient-derived pluripotent cells [37] may enable more interpretable transcriptional and epigenomic analyses of the consequences of CGG-repeat expansion on AFF3.
Nonetheless, all individuals for whom a significant methylation frequency was measured, show an expanded allele in the pre- or full mutation range. Repeat sizes of >300 do correlate with neurodevelopmental effects and expression of the fragile site in a significant percentage of cells. Carriers of an expanded allele in the premutation range are phenotypically normal but may show lower levels of expression of the fragile site.
In one individual (AII.4) a mosaic deletion of 134-bp removed the entire CGG repeat and the CpG island on the deleted allele remains unmethylated (data not shown). A similar combination of a full mutation with an expanded repeat and a deletion encompassing the repeat has been reported in several fragile X syndrome patients and recently also in a myotonic dystrophy type 1 case [38], [39]. In the fragile X syndrome, the phenotype of deletion cases is often indistinguishable from that of carriers of an expanded repeat, a reported exception being an unaffected individual where the deletion is the major allele present, and the transcription and translation start sites are preserved [40].
AFF3 belongs to a family of nuclear transcription activating factors including AFF1/AF4, AFF2/FMR2 and AFF4/AF5q31 [33], [41], [42]. These proteins form super elongating complexes (SEC) with active P-TEFb (positive transcription elongation factor) and AF9/ENL. SECs regulate the induction and expression of different subsets of genes. AFF3 is the closest paralog of AFF2, and is 36% identical on the amino acid sequence level. They share functional domains including the N-terminal Homology Domain (NHD), the C-terminal Homology Domain (CTHD) involved in intranuclear localization and binding of G-quadruplex RNA structures, and the ALF domain that promotes protein degradation through the proteasome pathway and the transactivation domain (TAD). Intriguingly, the highly conserved intron 2 TSS sequences and to a lesser extent the CGG repeat itself, are predicted to have a strong propensity to form G-quadruplex structures (Figure 5D, orange bars) with the most downstream of these being present in the 5′ UTR of the produced transcript. Given that AFF3 is known to bind G-quadruplexes, there is the potential for AFF3 autoregulation at this promoter. Both AFF2 and AFF3 localize to nuclear speckles and modulate splicing efficiency [43]. The expression pattern of murine Aff3 overlaps to a considerable extent with that of murine Aff2 [43], [44].
FRAXE is associated with loss of expression of AFF2 through dynamic repeat expansion of a CGG repeat in the 5′ UTR. FRAXE causes an X-linked non-syndromic intellectual disability [45]. AFF2 may play an important role in learning, memory, and language-learning processes [46]. Moreover, rare missense variations in the highly evolutionary conserved sites of the AFF2 gene might be associated with autism spectrum disorder [47]. The Aff2 knockout mouse model shows specific cognitive deficits, including an impaired conditioned fear memory over longer periods and enhanced long-term potentiation in the hippocampus [48], [49]. Aff3 expression is upregulated in cortical neurons during the initial steps of cortical differentiation and is downregulated in postnatal cortex, indicating its involvement in brain development [44]. We have shown that Aff3 shows strong regional expression in the developing mouse brain.
AFF3 is thus a reasonable candidate for the neurodevelopmental features seen in FRA2A carriers in our families. Our data predict that FRA2A carriers are haploinsufficient for AFF3, at least in a subset of tissues. A confident assignment of causality to the association of AFF3-associated repeat expansion mutations with neurodevelopmental anomalies is confounded by the rarity of the fragile site and the strong bias in clinical ascertainment. It is, however, intriguing that delay in motor and language development appears to be a common feature in the individuals presented here and this may represent a true delay in development rather than a fixed disability. AFF3 deficiency may then be involved in the speed of skill acquisition without impairing the developmental capacity.
A de novo microdeletion of 500 kb on chromosome 2q11.2 removing only AFF3 [50] has been reported in a girl with a severe multisystem disorder consisting of a mesomelic skeletal dysplasia (fibular agenesis, abnormal and triangular tibiae, short neck), urogenital tract malformations, delayed psychomotor development and recurrent apnoea leading to respiratory arrest at the age of 4 months. This clinical presentation is clearly very different to those associated with FRA2A but would be consistent with the expression pattern we demonstrate in mouse embryos. The clinical differences may be explained by the fact that the methylation of the repeat and thus the inactivation of the AFF3 gene presumably takes place several weeks after fertilization, so that development during the first weeks is not affected [51]. It is also plausible that the transcriptional silencing associated with FRA2A may by tissue specific given that the alternative promoter that is immediately adjacent to the expansion mutation shows evolutionarily-conserved tissue-specific activity, and appears to be the main driver of AFF3/Aff3 transcription in the brain in humans and mouse.
Both rare and common fragile sites often co-localize with evolutionary breakpoints as was postulated previously by Ruiz-Herrera et al. [52], [53]. We have shown through FISH and BLAST-analysis that the region close to the AFF3 repeat is indeed involved in a chromosomal rearrangement including a duplication and inversion of a 24 kb sequence from 2q13 to 2q11.2 followed by an ancestral head-to–head chromosomal fusion that led at 2q13 that led to the formation of human chromosome 2. The 2q11.2 breakpoint of this rearrangement falls within base pairs of the repeat and is also present in other primates. The 2q13 region also co-localizes with FRA2B, an as yet to be characterized rare fragile site of the same type.
In conclusion, we report a CGG repeat expansion mutation as the molecular cause of the fragile site FRA2A. FRA2A expression is associated with methylation of an AFF3 promoter and apparent transcriptional silencing of AFF3. It is currently difficult to unequivocally link FRA2A to a specific neurodevelopmental phenotype but it is plausible that haploinsufficiency for AFF3 in the developing brain is related to a true developmental delay and possibly mild intellectual disability.
The ethics committees of the participating study centers approved the study protocol and all participants gave their written informed consent. The study was in accordance with the principles of the current version of the Declaration of Helsinki. The fetal brain tissue was collected with informed written consent and ethical approval by Southampton and South West Hants LREC. The fetal tissue was obtained following surgical termination of pregnancy and staged according to the Carnegie Classification [54], [55].
Peripheral blood lymphocyte-derived metaphase chromosome preparations from individual AII.3 were obtained using standard methods. An AFF3-containing BAC-clone from the RPCI library, RP11-549H5 (AC092667), and clones mapping centromeric (RP11-436F6, AC010736) and telomeric (RP11-506F3, AC074387) to AFF3 were obtained from the BACPAC Resource Center (Oakland, California, USA). Long Range-PCR (LR-PCR) was used to generate probes of 10 kb and 18 kb situated respectively immediately 5′ and 3′ to the promoter region of AFF3. The following primer pairs were used: L10K (forward 5′-TGCAGGAATGAATGAAGGGCAAGCAA-3′ and reverse 5′-TGGCCTCTGGGTGTCGACTTCAAACT-3′) and L18K (forward 5′-ACAGTTTGGCTTGACCGGGAGGGTTT-3′ and reverse 5′- TCAAAAATGTTCCCTTGCCCACAGTGC-3′). LR-PCR was performed using the Expand Long Template PCR System (Roche, Basel, Switzerland) according to the manufacturer's instructions. The amount of BAC DNA used per reaction was 5–10 ng. All probes were labelled with digoxigenin-11-dUTP or biotin-16-dUTP (Roche, Indianapolis, IN) by nick translation. DNA hybridisation and antibody detection were carried out as described previously [56]. At least five metaphases were analysed for each hybridisation, using a Zeiss Axioplan 2 fluorescence microscope equipped with a triple band-pass filter (#83000 for DAPI, FITC and Texas Red; Chroma Technology, Brattleboro, VT). Images were collected using a cooled CCD camera (Princeton Instruments Pentamax, Roper Scientific, Trenton, NJ) and analysed using IPLab software (Scanalytics, Vienna, VA).
PCR amplification of the normal FRA2A CGG repeat was performed with the aid of 2.5× PCR Enhancer solution (Invitrogen, Carlsbad, CA, USA) using a forward primer P1 (5′-GGCCGTAAAAGCCACGAGAGAGGG-3′) and a reverse primer P2 (5′-CTTGCGCGCAGGCACACTCAAGAG-3′) derived from the sequences flanking the repeat. PCR products were sequenced and subsequently analysed by use of an ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA).
A Southern blot was created by digesting 10 µg of DNA, extracted from blood or Epstein-Barr-virus transformed cells, with the restriction enzymes HindIII and NcoI (Fermentas GmbH, St. Leon-Rot, Germany) in separate reactions. The digested DNA was then separated by electrophoresis on a 0.7% agarose gel. No ethidium bromide was added during this electrophoresis step to avoid product-related smearing on the gel that would cause overestimation of the mosaicism of repeat sizes [57]. After subsequent denaturation and neutralisation the DNA fragments were transferred to Hybond N+ membranes. Hybridisation was performed at 65°C using a specific 32P-labeled 992 bp PCR probe (forward primer 5′- AGCCTTTGTTCCTGGGAATGCTGTCTCAAT -3′ and reverse primer 5′- GGAAAGGCAGGTGATCAGCTAGAAGGGTG -3′).
Repeat primed PCR was performed to interrogate the number of CGG repeats in the AFF3 gene with Asuragens CGG Repeat Primed PCR system designed for detection of Fragile X expanded alleles. Triplet repeat primed PCR (TP-PCR) uses a locus-specific forward primer that flanks the repeat. The reverse PCR primer of the primer pair is designed to hybridise within the CGG repeat region as it contains a (GCC)5 tail. This generates amplicons of various sizes as the reverse primers bind to multiple locations during TP-PCR. As the number of CGG repeats increases, a characteristic ladder profile appears on the fluorescence electropherogram enabling the rapid and inexpensive identification of expanded repeats that may have been missed using current PCR methods. Samples were PCR-amplified by preparing a master mix containing 11.45 µl of GC-rich AMP buffer, 0.25 µl of FAM-labelled AFF3 forward primer (5′-GGCCGTAAAAGCCACGAGAGAGGG-3′), 0.25 µl of AFF3 reverse primer (5′-CTTGCGCGCAGGCACACTCAAGAG-3′), 0.5 µl of CGG primer (5′- TACGCATCCCAGTTTGAGACGGCCGCCGCCGCCGCC-3′), 0.5 µl of nuclease-free water, and 0.05 µl of GC-rich polymerase mix from Asuragen (Austin, TX). 2 µl of the DNA sample, typically at 20 ng/µl, was added before transferring the plate to a thermal cycler (9700, Applied Biosystems, Foster City, CA). Samples were amplified with an initial heat denaturation step of 95°C for 5 minutes, followed by 10 cycles of 97°C for 35 seconds, 62°C for 35 seconds, and 68°C for 4 minutes, and then 20 cycles of 97°C for 35 seconds, 62°C for 35 seconds, and 68°C for 4 minutes with a 20 second autoextension at each cycle. The final extension step was 72°C for 10 minutes. After PCR, 2 µl of the PCR product was added to a mix with 11 µl HDF and 2 µl Rox 1000 standard. After a brief denaturation step, samples were analysed using the ABI Prism 3130 Genetic Analyzer.
The methylation status of the AFF3 associated CGG repeat and the surrounding region, was analysed by bisulfite sequencing. Genomic DNA collected from lymphoblastoid cell lines and saliva (subject AIII.1) was bisulfite-treated using the EpiTect Bisulfite Kit (Qiagen, Venlo, Netherlands) according to manufacturer's guidelines. Bisulfite treatment converts all non-methylated cytosines into thymines, while methylated cytosines remain unchanged. Primers specific for the methylated bisulfite converted DNA (forward 5′- GGTGAGAAATAAAAAGAAAGGAG -3′ and reverse 5′- CCTCAACAACCCTAAAATACC -3′) were designed. After PCR amplification, the CGG surrounding area (chr2:100721494–100721911; hg19) was sequenced using an ABI Prism 3130 DNA sequencer. Moreover we have performed pyrosequencing using the AFF3_002 PyroMark CpG assay according to manufacturer's instructions (Qiagen, Venlo, Netherlands) and analysed the results on a PyroMark Q24. Methylation cut-off value was set at 10%.
The expression pattern of AFF3 in human tissues was studied using a multiple-tissue Northern blot (FirstChoice Northern Human Blot I, Ambion, Austin, TX, USA). The specific AFF3 probe was a 655-bp PCR product [forward primer 5′-TATCGAGTGTGGAAATGCAA-3′ and reverse primer 5′-TGAGGTCCCTATGACAGGTG-3′] and radiolabelled by the addition of 32P-dATP and 32P-dCTP (MP, Irvine, California, USA). Hybridisation was performed according to the manufacturer's instructions.
Total RNA-sequencing data from the Illumina Human Body Map 2.0 project (GSE30611) was obtained from the NCBI Gene Expression Omnibus. Data from brain (ERR030882, female, 77y), ovary (ERR030874, female, 47yj) and lymph node (ERR030878, female, 86y) was downloaded in the form of 2×50 bp reads and imported into CLC genomics workbench v6.01. Transcriptomics analysis was performed within CLC Genomics using the human reference genome version hg19. Default settings were used, apart from a smaller expected insert size of 50 bp. Additionally total RNA was isolated from the human fetal brain tissues (FB1 54 gestational days (GD), FB2 47 GD, FB3 59 GD) according to the Trizol (Invitrogen) protocol. The preparation of amplicon libraries and RNA-Seq analysis were performed following standard Illumina TruSeq protocols and reads of length 50 bp were produced on the Illumina GAIIx platform. The fetal tissue was obtained following surgical termination of pregnancy and staged according to the Carnegie Classification [54], [55]. CAGE tag data was obtained from the FANTOM4 consortium as both pre-defined CAGE tag clusters (http://fantom.gsc.riken.jp/download/Tables/human/CAGE/promoters/tag_clusters/ and http://fantom.gsc.riken.jp/4/download/Tables/mouse/CAGE/promoters/tag_clusters/) and as genome aligned individual tags (http://fantom.gsc.riken.jp/4/download//Tables/human/CAGE/mapping/). Coordinates were converted from the hg18 reference genome assembly to hg19 using LiftOver (http://genome.ucsc.edu/cgi-bin/hgLiftOver). Statistical analysis was performed R (http://www.R-project.org/; version 3.0.0).
The AFF3 transcript NM_002285 (http://www.ncbi.nlm.nih.gov/) was searched for cSNP's. The cSNP's in FRA2A patients were tested by PCR followed by sequencing at the genomic and at the cDNA level. RNA was isolated from Epstein-Barr-transformed lymphoblastoid cells using Trizol (Invitrogen, Carlsbad, CA, USA) and converted to cDNA using Superscript III reverse transcriptase (Invitrogen, Carlsbad, CA, USA). The following primer sets were used: SNP1 (rs4851214) in exon 14 (forward primer 5′- AGTGATGAAGAGGAGAATGAACA -3′ and reverse primer 5′- ATAGGAGGCTTGTGGGGATTA -3′) and SNP2 (rs13427251) in exon 25 (forward primer 5′-GTGTGTCTGGTATGTTTACAC-3′ and reverse primer 5′-GGATCAGCATCTAGTCTAAG-3′). Sequencing products were analysed on an ABI 3130 Prism automatic sequencer.
The Aff3 riboprobe for whole mount in situ hybridisation to mouse embryos was generated by in vitro transcription from a PCR template amplified from the Aff3 3′UTR using mouse genomic DNA as a template. T3 (for sense probe) and T7 (for antisense probe) binding sites were added to the forward (5′-AATTAACCCTCACTAAAGGCTCTCCAACCGGATCCAGAAT-3′) and reverse (5′-TAATACGACTCACTATAGGAGCCCATGGCACCTCTCT-3′) primers. The WISH protocol and OPT scanning was performed exactly as previously described [58].
All coding exons of the AFF3 gene were PCR amplified at the genomic level using standard protocols for all patients and relatives to exclude the presence of any other disease-causing mutation. PCR products were enzymatically purified and sequenced. Sequences were analysed with an ABI Prism 3130 DNA sequencer.
For the marker analysis, genomic DNA was isolated from peripheral blood from all available family members using standard procedures. Highly polymorphic microsatellite markers, D2S2209 and D2S2311, were selected from the Marshfield genetic map in the proximity of the repeat. These markers are both dinucleotides with an average heterozygosity of 71%. Analysis was performed by a Go Taq DNA polymerase mediated PCR, with fluorescently labeled primers. Fragment analysis of amplified products was performed using an ABI PRISM 3130 XL Genetic Analyzer (Applied Biosystems). Allele identification was done with Gene mapper v3.7 software (Applied Biosystems).
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10.1371/journal.ppat.1007041 | HCMV triggers frequent and persistent UL40-specific unconventional HLA-E-restricted CD8 T-cell responses with potential autologous and allogeneic peptide recognition | Immune response against human cytomegalovirus (HCMV) includes a set of persistent cytotoxic NK and CD8 T cells devoted to eliminate infected cells and to prevent reactivation. CD8 T cells against HCMV antigens (pp65, IE1) presented by HLA class-I molecules are well characterized and they associate with efficient virus control. HLA-E-restricted CD8 T cells targeting HCMV UL40 signal peptides (HLA-EUL40) have recently emerged as a non-conventional T-cell response also observed in some hosts. The occurrence, specificity and features of HLA-EUL40 CD8 T-cell responses remain mostly unknown. Here, we detected and quantified these responses in blood samples from healthy blood donors (n = 25) and kidney transplant recipients (n = 121) and we investigated the biological determinants involved in their occurrence. Longitudinal and phenotype ex vivo analyses were performed in comparison to HLA-A*02/pp65-specific CD8 T cells. Using a set of 11 HLA-E/UL40 peptide tetramers we demonstrated the presence of HLA-EUL40 CD8 αβT cells in up to 32% of seropositive HCMV+ hosts that may represent up to 38% of total circulating CD8 T-cells at a time point suggesting a strong expansion post-infection. Host’s HLA-A*02 allele, HLA-E *01:01/*01:03 genotype and sequence of the UL40 peptide from the infecting strain are major factors affecting the incidence of HLA-EUL40 CD8 T cells. These cells are effector memory CD8 (CD45RAhighROlow, CCR7-, CD27-, CD28-) characterized by a low level of PD-1 expression. HLA-EUL40 responses appear early post-infection and display a broad, unbiased, Vβ repertoire. Although induced in HCMV strain-dependent, UL4015-23-specific manner, HLA-EUL40 CD8 T cells are reactive toward a broader set of nonapeptides varying in 1–3 residues including most HLA-I signal peptides. Thus, HCMV induces strong and life-long lasting HLA-EUL40 CD8 T cells with potential allogeneic or/and autologous reactivity that take place selectively in at least a third of infections according to virus strain and host HLA concordance.
| Understanding the mechanisms of immune control of viral infection is crucial to improve diagnosis and to design efficient immunotherapies. CD8 T lymphocytes are key components of the cellular immunity against human cytomegalovirus (HCMV), a widespread pathogen that cause severe illness and poor outcome in immunocompromised hosts such as transplant recipients and HIV-infected patients. In this study we characterized a population of non-conventional CD8 T lymphocytes directed against the viral protein UL40 and presented by the non-classical HLA-E molecules in blood samples from HCMV seropositive hosts. This immune response was detectable in around 30% of hosts, may represent up to 38% of total blood CD8 T lymphocytes, persists for life and thus seems to belong to the common immune arsenal against HCMV. Genetic factors related to the host and to the different strains of HCMV are critical parameters for the existence of this immune subset. Although specifically induced in response to HCMV infection, a key feature of these cells is their potential ability to be also responsive against multiple HLA molecules. In conclusion, HCMV infection frequently leads to the long-term persistence of a large subset of lymphocytes with potential side effect requiring attention in contexts such as autoimmunity and transplantation.
| Human cytomegalovirus (HCMV; human herpesvirus 5) is the prototype member of β-herpesvirus and a widespread opportunistic pathogen. In healthy individuals, primary infection is asymptomatic and is followed by a life-long, persistent, infection that is controlled by host immune system [1]. Nevertheless, HCMV is a major cause of morbidity and mortality in immunocompromised individuals such as transplant recipients or HIV-infected patients. HCMV is the most common cause of congenital infection in the world that can result in neurodevelopmental delay and sensorineural hearing loss. HCMV disease can manifest in many forms, including infectious mononucleosis, hepatitis, post-transplant arteriosclerosis, pneumonia, colitis, immune senescence, and alteration to the immune repertoire [1]. The impact of HCMV on the outcome of solid organ transplantation (SOT) is substantial. HCMV not only causes a highly morbid and potentially fatal illness but also indirectly influences other relevant outcomes, such as allograft acute and chronic rejection, other opportunistic infections, post-transplant lymphoproliferative disorders, vascular disease, and overall patient and allograft survival [2, 3]. Because of the magnitude of its direct and indirect impacts, there have been extraordinary efforts to define strategies for its prevention, monitoring and treatment [1, 4].
Cellular immune response is the major mechanism by which HCMV replication is controlled [5, 6]. Large human HCMV-specific T-cell responses have been described in numerous published reports, particularly in the transplantation setting and in ageing [7, 8]. HCMV-specific T cells in healthy adults can constitute as much as 10% of the total memory CD4 and CD8 T cells that recognize multiple viral proteins, notably, pp65, IE1, IE2 and gB [9, 10]. Suppression of the number and function of HCMV-specific CD4 and CD8 T cells allows reactivation of the virus from latency, leading to uncontrolled viral replication and clinical disease in immunocompromised hosts, including SOT recipients [5].
The CD8 T-cell response appears as the most important component of the anti-HCMV immune response [7], although CD4 T cells and natural killer (NK) cells also play a significant role [11, 12]. Expanded HCMV-specific responses are often thought to be a requirement for protection and could result from the life-long latency of HCMV in specific cells, interspaced with episodic reactivations that gradually increase response size in a process called inflation [13, 14]. HCMV-specific T cells were first described as those able to recognize the immunodominant antigen immediate early 1 (IE1) [15], but later studies emphasized the importance of T cells that target a tegument phosphoprotein of 65kDa (pp65/UL83) [16]. The original epitope identification studies focused on NLVPMVATV, a HLA-A*02 restricted epitope within pp65 that was defined as a “typical” epitope because of its common detection in HLA-A*02–positive individuals. The identification of other less common epitopes targeted by HCMV-specific T cells has extended the panel of HCMV-reactive T cells in humans [9, 10]. In murine models, subdominant epitopes have been shown to be protective [17]. Despite technical advances in terms of HCMV-specific T-cell response monitoring [18], a correlation between T-cell responses and clinical protection has not been established to date. This underlines a need for a global analysis of anti-HCMV T cell responses at both qualitative and quantitative levels investigating response numbers, sizes, hierarchy levels, peptide specificities, time course and duration.
HCMV-specific CD8 T cells directed against UL40 epitopes presented by HLA-E have more recently emerged as an additive piece in the complexity of anti-HCMV immune response [19]. HLA-E is a poorly polymorphic non-classical (MHC-Ib) HLA molecule. Although more than 20 HLA-E alleles have been registered, only two nonsynonymous HLA-E alleles: HLA-E*01:01 (HLA-E107R) and HLA-E*01:03 (HLA-E107G) that differ by a single amino acid (R107G) have been found in most populations [20]. Cell surface expression of HLA-E depends on binding of a conserved 9-mer peptide naturally provided by the N-terminal signal peptide of classical HLA-I or HLA-G molecules. HCMV UL40 signal peptide contains a 9-mer sequence with an exact sequence identity to endogenous HLA-E–binding peptides. The prototype of UL40 peptide loaded on HLA-E molecules is VMAPRTLIL provided by the AD169 HCMV strain [21]. As a consequence, HCMV UL40 promotes efficient cell surface expression and stabilization of HLA-E independently of TAP function [21, 22]. HLA-E containing peptides engage two types of receptors. HLA-E binds the NK cell inhibitory receptor CD94/NKG2A [23, 24] and, thereby, promotes efficient protection against lysis by CD94/NKG2A+ NK cells [22, 23, 25, 26]. In addition to CD94/NKG2A, HLA-E interacts with CD94/NKG2C, albeit with lower affinity. CD94/NKG2C is an activating receptor predominantly expressed on a relatively small population of NK cells. Interestingly, the frequency of this CD94/NKG2C+ NK subset increases in HCMV-infected individuals [27] [6]. HLA-E/UL40 (HLA-EUL40) complexes also trigger TCR-dependent activation of a subset of CD8 αβ T cells [28–30]. UL40-specific/anti-HCMV HLA-E–restricted CD8 cytotoxic T-cell responses have been reported in healthy donors and in kidney and lung transplant recipients and associated with a possible harmful impact on graft endothelial cells [30] and allograft survival [31]. Characterization of these CD8 T-cell subsets in healthy and transplanted population remains sparse and no longitudinal study is available. In healthy hosts, the beginning and duration of HCMV infection are usually unknown, thus monitoring the development of T-cell responses starting at the time of infection is not possible except in the setting of organ transplantation where post-graft primary HCMV infections are frequent and require a specific follow-up.
Our study investigated the presence of circulating HLA-E-restricted CD8 T cells in a cohort of kidney transplant recipients (KTR, n = 121) during either an active HCMV infection (at primary infection or at reactivation) or at latency and in HCMV seropositive (HCMV+) healthy volunteers (HV, n = 25). Using a set of HLA-E tetramers refolded with 11 different UL40 epitopes to cover the diversity of HCMV clinical strains, we provide here a quantitative analysis of HLA-EUL40-restricted anti-HCMV T-cell response in hosts. The frequency, the magnitude, the time course of HLA-EUL40-restricted anti-HCMV CD8 T-cell responses, as well as the phenotype and the specificity of peptide recognition of these subsets, were documented ex vivo in comparison to the conventional HLA-A*02pp65 CD8 T-cell responses. Altogether our findings reveal that HCMV induces early long-lasting HLA-E–restricted, UL40-specific unconventional CD8 T-cell responses that often parallels HLA-I-restricted CD8 T cells. Although their induction seems initially restricted by both the viral infecting strain and host’s HLA-I, extended peptide recognition may occur allowing these effector responses to potentially target self and allogeneic, donor-specific, HLA-I peptides.
Although UL40-specific HLA-E-restricted CD8 T-cells have been described in a few HCMV seropositive (HCMV+) individuals [28–30, 32], only sparse data are available concerning their characteristics and post-infection occurrence. To address this point, we performed a retrospective detection and quantification of circulating HLA-EUL40-restricted CD8 T-cell responses in a cohort of kidney transplant recipients (KTR, n = 121) and in HCMV+ healthy volunteers (HV, n = 25). Our study cohort included transplanted patients segregated into 4 groups according to recipient’s HCMV serology (HCMV- and HCMV+) and, for HCMV+ patients, the status of infection (primary, latent, active) at 12 months post-transplantation. Demographic and clinical characteristics of the cohort are presented in Table 1. UL40-specific HLA-E-restricted CD8 T cells were analysed ex vivo in blood samples after PBMC isolation using a multi-parameter (CD3+CD8α+TCRγδ-) flow cytometry assay subsequent to the blockade of the CD94 receptor using a specific blocking mAb. Our protocol was adapted from Pietra et al. [29] and allows a sensitive (threshold of detection: 0.1% of total CD8 TCRαβ T cells) and peptide-specific analysis of HLA-EUL40 CD8 T-cell populations (S1 Fig). Detection of HLA-A*02:01/pp65 CD8 T (HLA-A*02pp65) cells was carried out in parallel for a comprehensive analysis of unconventional (HLA-E-restricted) versus conventional (HLA-A*02:01-restricted) anti-HCMV responses. Banked blood samples, harvested at M12 post-transplantation, were investigated using a set of HLA-E tetramers loaded with 8 different UL4015-23 peptides to encompass the usual UL4015-23 variability among common HCMV strains [33, 34]. The 8 HLA-E tetramer/peptide complexes were tested individually. Fig 1 shows the distribution of HLA-EUL40 CD8 T-cell responses (detected for at least 1 tetramer HLA-E/UL40 peptide complex) versus HLA-A*02pp65 CD8 T-cell responses in the various groups. In HCMV- transplanted patients no HLA-EUL40 nor HLA-A*02pp65 T-cell response was detected, consistent with the concept that these responses are induced by and specific to HCMV infection. HLA-EUL40 CD8 T cells were detectable in all HCMV+ subgroups (primary, latency, reactivation) and were present in an overall of 28.7% of HCMV+ transplanted recipients and 32.0% in HCMV+ blood donors. By comparison the overall incidence of HLA-A*02pp65 T-cell responses in HLA-A*02 patients and healthy hosts was 65.0% and 46.1%, respectively. HLA-A*02pp65 T-cell responses were roughly similar in frequency upon primary (58.3%), latent (68.4%) and active (66.7%) infection while HLA-EUL40 CD8 T-cell responses were lower upon primary infection compared to other groups. Interestingly, HLA-EUL40 CD8 T-cell responses was more frequent in HLA-A*02 as compared to non HLA-A*02 hosts (37.5% versus 20.0% for transplant recipients and 46.1% versus 16.7% for HV; p = 0.0318). In HLA-A*02 recipients, HLA-EUL40 CD8 T-cell responses were found either associated with (32.2%) or independent (16.1%) of a HLA-A*02pp65 T-cell response. Coexistence of HLA-EUL40 and HLA-A*02pp65 CD8 T-cell responses also occurs in 33.3% of healthy hosts. Together these results reveal a very high incidence (up to 46%) of HLA-EUL40 CD8 T-cell responses in HCMV+ hosts with no significant difference between transplanted patients and healthy individuals suggesting that antiviral and immunosuppressive regimens have no impact of these cell subsets at M12. These cells are detected more frequently in hosts carrying an HLA-A*02 allele. Unconventional CD8 T cells can be detected independently of detectable conventional HLA-A*02pp65 T-cell response. Presence of HLA-EUL40 CD8 T cells early post-infection (primary or reactivation) as well as at latency suggests long lived cell subsets consistent with memory anti-HCMV response. Of interest, the lack of HLA-EUL40 CD8 T cells in HCMV- transplant recipients may also indicate that, although a full sequence homology between common UL4015-23 peptides and signal peptides from most HLA-A, -B and -C molecules, presentation of allogeneic (i.e. donor) HLA-I signal peptides (HLAsp) by HLA-E-expressing uninfected cells in the graft doesn’t drive the generation of consequent anti-donor HLA-EHLAsp CD8 T-cell response.
Detection of HLA-EUL40 CD8 T cells suggested that these unconventional responses occur more frequently in HLA-A*02 carriers. Genotyping of HLA-A was then performed to decipher this association. Firstly, HLA-A*02 allele frequency was 28% in the HCMV+ hosts in our study, a value similar to those found in the HCMV- counterpart (36%, p = 0.1982) (Fig 2A). Our findings indicate that HLA-A*02 allele frequency was significantly higher in HCMV+ hosts with HLA-EUL40 responses than in non-responders (44% versus 22%, p = 0.0026, Fig 2A). Next, distribution of HLA-A*02 genotypes were compared between HCMV+ and HCMV- individuals. A similar distribution of HLA-A*02 genotypes was observed in both groups (Fig 2B). However, HCMV+ hosts that display HLA-EUL40 responses were more frequently hosts carrying two HLA-A*02 alleles than hosts without response (19% versus 0%, p = 0.0002). Similar analysis was performed for HLA-E alleles and genotypes. HLA-E sequencing allowed us to discriminate the two major HLA-E*01:01 and HLA-E*01:03 alleles. These variants differ in a single amino acid at position 107 when an arginine (R) in HLA-E*01:01 is replaced by a glycine (G) in HLA-E*01:03 resulting in different thermal stabilities and lengths of interaction with cognate receptors [33]. HLA-E allele distribution was found equal for HCMV- and HCMV+ hosts (Fig 2C, left panel) and no difference in HLA-E allele frequency was observed among HCMV+ individuals with or without HLA-EUL40 responses (57.0% versus 53.0%, and 43.0% versus 47.0%, for HLA-E*01:01 and *01:03 respectively, p = 0.7541, Fig 2C, right panel). An equal distribution of HLA-E genotypes was calculated for HCMV+ and HCMV- hosts (p = 0.1661) (Fig 2D, left panel). However, a significant change occurs in HLA-E genotypes for hosts that display or not HLA-EUL40 responses (p = 0.0323) with a higher prevalence of heterozygous HLA-E*01:01/HLA-E*01:03 in hosts with HLA-EUL40 responses (Fig 2D, right panel). No impact of donor HLA was found. These findings support a role for immunogenetic factors in the occurrence of HLA-EUL40 responses upon HCMV infection and associate HLA-A*02/A*02 and HLA-E*01:01/HLA-E*01:03 genotypes as independent (p = 0.85) positive factors promoting HLA-EUL40 responses.
Next, we sought to determine the specificity of HLA-EUL40 CD8 T-cell responses toward UL40 peptide provided by the host’s HCMV infecting strain. To this aim, DNAs isolated from whole blood samples from transplant recipients undergoing either a primary infection (n = 18) of a reactivation (n = 7) of HCMV during the 12 months post-transplantation were used for UL40 protein (AA 1–221) sequencing. Sequencing identified a total 32 UL40 sequences for the 25 infected patients, some patients carrying more than one infecting strain (Table 2). Overall variability of full UL40 protein among strains is reported in S2 Fig and was consistent with a previous report [34]. UL40 variability affects 38 AA along the sequence but mostly concentrates within the region encoding the signal peptide (UL401-37) and in particular inside UL4015-23, the HLA-E binding epitope (Fig 3A). Notably, AA22 and to a lesser extend AA20 that correspond to the peptide position P8 and P6, respectively, two critical residues for the interaction with the CD94/NKG2-A or -C or with the TCR of specific T cells [32] [36], were the most variable, with respectively 48.2% and 19.6% of AA variability and up to 5 and 3 different AA (Fig 3A and 3B). In contrast, residues 16 (P2), 21 (P7) and 23 (P9) that correspond to the 3 major anchor AA for the peptide binding pockets of HLA-E, were highly conserved [37]. Three major UL4015-23 sequences (VMAPRTLIL, VMAPRTLLL, VMAPRSLLL) accounted for 62.5% of the HCMV strains detected in patients (15 out of 25) while 10 other UL40 sequences were found only in a single patient (Table 2). These data confirmed that consensus UL4015-23 sequences such as VMAPRTLIL and VMAPRTLLL are highly predominant in clinical strains. Interestingly, VMAPRTLIL and VMAPRTLLL UL40 sequences are fully homologous to signal peptide sequence for the majority of HLA-A and HLA-C alleles excluding the most common HLA-A*02 and HLA-C*07. Since banked blood samples were available for 23 of these patients, we next assessed the presence of HLA-EUL40 CD8 T-cell responses using dedicated HLA-E/UL40 tetramers. HLA-EUL40 CD8 T cells were detected in 6 out of the 23 patients (26.1%) and illustrated for 4 out of the 6 in the Fig 3C. As shown in Fig 3C, when HLA-EUL40 CD8 T-cell responses were investigated using HLA-E tetramers loaded with the UL40 peptide that we identified in their own infecting strain, HCMV strain-specific HLA-E-restricted T cells were detected in patients. Importantly, percentages of HLA-EUL40 CD8 T cells vary from 2.9% up to 38.6% of total CD8 αβT cells in the blood sample at the time of detection. Fig 3C also illustrates the complexity of the patterns of HLA-EUL40 CD8 T responses. Indeed, while in a large majority of hosts, homogenous CD8α bright populations were observed exemplified in patients #108 and #109, in few hosts, such as #026, multiple populations that display various levels of CD8α expression (low and high) were observed. This may reflect either the detection of concomitant subsets of HLA-EUL40 CD8 T at a particular time point or different stage of activation for a single population or both. Thus, our data sustain previous report showing the UL4015-23 nonapeptide as a highly polymorphic region inside the viral UL40 protein [34]. Our data show that UL40 polymorphism also drives (strain-specific) antigen-specific HLA-E-restricted T cells. However, in our study only a limited set of canonical UL40 peptides were found in the majority of clinical infecting strains (such as VMAPRTLIL, VMAPRTLLL and VMAPRSLLL identified in 20 out of 32 strains) and allowed strain-specific HLA-EUL40 CD8 T cells. Interestingly, about a third of patients were infected by an HCMV strain carrying a non-canonical UL4015-23 sequence that display variant amino acid on the residues P1, P3, P4, P5, P6 and P8. Thus we speculate that such HCMV strains for which no HLA-EUL40 CD8 T-cell response was detected in our assays may hold UL40SP probably not able to bind HLA-E. Nevertheless, we cannot rule out the possibility that detection of HLA-EUL40 CD8 T cells was underestimated in our study due to the lack of tetramers loaded with the full set of UL40 sequences identified in clinical isolates.
To further characterize the HLA-E-restricted anti-HCMV T-cell responses, time course of these responses during the acute phase of infection and beyond, and T-cell activation markers were monitored post-infection in patients (n = 16) with either a primate infection or a reactivation of the virus. Results from 3 patients are illustrated in the Fig 4A that summarizes the most frequent profiles that we observed. Upon primary infection (exemplified by patient #109), HLA-EUL40 CD8 T cells develop early and most often concomitantly to HLA-A*02pp65 T-cell response. HLA-EUL40 CD8 T cells are detected in blood 1 month post-infection (patient #107 and #109) and may even precede detection of HLA-A*02pp65 T-cell response (patient #109). HLA-EUL40 CD8 T-cell response can be either predominant (patient #109) or lower in frequency among total CD8 αβT cells compared to conventional HLA-A*02pp65 response (patients #107 and #108). Patient #108 illustrates a HCMV reactivation with a pre-existing HLA-A*02pp65 population leading to a clear increase in the percentage of HLA-A*02pp65 CD8 T cells at the time of reactivation and a de novo induction of HLA-EUL40 CD8 T cells. For the 3 patients, consistent long term (M9-12 post-infection) responses were observed ranging from 1.2 to 15.6% for HLA-EUL40 CD8 T cells and 0.4 to 47.7% HLA-A*02pp65 CD8 T cells. Activation markers (CD69 and PD-1) were analysed by flow cytometry for both anti-HCMV CD8 T-cell subsets at each time point. Fig 4B reports on the relative expression of CD69 and PD-1 investigated ex vivo at M6 post-transplantation for the 3 recipients. Overall, we found that both subsets display similar rate of CD69+ cells. In contrast, there were striking differences in the programmed death-1 (PD-1) expression between the 2 subsets with a lower percentage of expression for PD-1 on HLA-EUL40 CD8 T cells as compared to HLA-A*02pp65 CD8 T cells (Fig 4C). These discrepancies were found at all time points post-induction (S3 Fig). More than 45% of HLA-A*02pp65 CD8 T-cell subsets express sustained levels of PD-1+ after a primary infection (patients #107 and #109) and up to 100% upon reactivation (patient #108). These investigations that shape the temporal occurrence of HLA-EUL40 CD8 T cells post-infection reveal that both responses, conventional and unconventional, may be very close in kinetic, persistence and in percentage of total CD8 T cells in blood. However, although similarly activated early post-infection, low expression of PD-1 could be a feature of HLA-E restricted anti-HCMV T-cell responses.
The functional and phenotype description of HLA-EUL40 CD8 T cells is rather limited. Our phenotypic analyses by flow cytometry, performed ex vivo for 3 patients (#107, #108 and #109) confirmed that HLA-EUL40 T cells belong to the CD3+CD4-CD8αβ+TCRαβ+ T cell subset. HLA-EUL40 T cells exhibited a phenotype (CR45RAhighCD45ROlowCD27-CD28-CD57+/-CCR7-, S4 Fig) consistent with effector memory T-cell response as previously reported [38]. Furthermore, in our study, to better characterize anti-HCMV HLA-E-restricted responses, HLA-EUL40 CD8 T-cell lines were generated by cell sorting using for each patient an HLA-E tetramer loaded with the UL40 peptide identified in their own HCMV circulating strain. (Fig 3C). PBMCs from 5 HCMV+ patients with a primary infection or a reactivation (KTR #104, #105, #107, #108, #109) were sorted and then amplified in vitro to reach a purity>95% (defined by tetramer staining using the HLA-E/UL40 complexes employed for sorting).
Amplified HLA-EUL40 T cells were used for the analysis of T-cell receptor β chain variable region (TCR-Vβ) expression by flow cytometry using 24 antibodies reactive to 70% of the human TCR-Vβ repertoire. Given the fact that HLA-E is a poorly polymorphic gene and that HLA-EUL40 CD8 T cells recognize a restricted number of UL4015-23 peptides, the question of the existence of a public T-cell repertoire between individuals was raised. Consistently, only few analysis of TCR sequences from UL40-specific T-cell clones have been reported yet and display a limited number of TCR including Vβ3, 5.1, 9, 16, 22 [29]. HLA-EUL40 CD8 T-cell population expressing a dominant Vβ chain sub-family was obtained for 3 patients while another one gives rise to oligoclonal populations (from 2 to 6 subsets detected) with variable distribution (Fig 5A). This suggests the sorting of multiple, coexisting, HLA-EUL40 CD8 T-cell populations in this patient. Interestingly, a broad TCR-Vβ repertoire was found with 16 Vβ identified (Vβ1, 2, 3, 5.1, 7.1, 8, 9, 12, 13.1, 13.2, 13.6, 14, 16, 17, 22 and 23) thus enlarging the Vβ repertoire previously described for these cells. For patient #104 that exhibits oligoclonal T-cell populations only 19% of Vβ repertoire was identified suggesting that this patient carry a dominant Vβ not detectable in our assay. No dominant Vβ was identified for patient #109 with oligoclonal HLA-E-restricted subsets covering 82% of its repertoire.
Next, amplified HLA-EUL40 CD8 T-cell populations were investigated for their capacity to produce TNF in response to TCR engagement in a peptide-specific manner. To this aim, the 5 enriched populations were stimulated with 11 HLA-E/UL40 peptide tetramers, used individually, for 5h before intracellular TNF staining. The set-up of experimental conditions are depicted in S5 Fig. In most cases, T cells were highly stimulated (up to 50% of cells expressing TNF) by the HLA-E tetramers loaded with the peptide corresponding to UL4015-23 identified in their own infecting stain (Fig 5B). However, consistent stimulations (10 to 50%) were also obtained for HLA-E tetramers loaded with other peptides. Interestingly, T-cell activation can be induced by peptides that correspond to self and donor-specific allogeneic HLASP supporting the idea that these T cells may be auto- and/or alloreactive (Fig 5B). In most of cases, changing in P8 or P6 residues of UL4015-23 peptides diminished or abolished the reactivity of T cells, showing the relative importance of these two amino acids for the interaction of the HLA-E complexes with the TCR. Such cross-stimulation was observed similarly for T-cell populations containing a single dominant Vβ subset or oligoclonal subsets. Magnitude of the stimulation was peptide-dependent and differs for each T-cell subpopulation. In most cases the dominant peptide issued from the infecting strain and used for sorting, gives the highest score of T-cell activation. Together, these data may suggest that a single dominant Vβ subset of HLA-EUL40 CD8 T cells induced in a UL40 peptide-dependent manner could be activated by HLA-E molecules presenting UL40 peptides with a degree of homology including a panel of HLASP.
The use of 8 different HLA-E/UL40 peptide tetramers allowed us to decipher the spectrum of HLA-EUL40 responses generated post HCMV infection. This assay provided a qualitative and quantitative analysis of HLA-E-restricted responses for the 31 HCMV+ transplanted patients and HV initially found to carry at least one HLA-EUL40 CD8 T-cell response. Responses were analyzed to define, for each individual, both peptide specificity and relative strength of the responses (percentage of subset among total circulating CD8 T cells). As a result, consistent responses were observed for the 8 tetramers tested. VMAPRTLLL, VMAPRTLIL, VMAPRTLVL, VMAPRTVLL, VMAPRSLLL and VMAPRSLIL are the most frequently recognized peptides by HLA-EUL40 responses in terms of both occurrence and magnitude. The number of circulating HLA-E-restricted CD8 T cells varies in the range of 0.1% (detection threshold) up to around 40% of total TCRαβ CD8 T cells. These percentages were similar or even higher than those we obtained for HLA-A*02:01-restricted responses (Fig 6A).
An overview of the panel of HLA-EUL40 T-cell responses detected in patients and HV is provided in the Fig 6B. This analysis shows that HCMV+ subjects usually display HLA-EUL40 T-cell responses against more than a single HLA-E/peptide complex. The total number of responses (from 1 to 8) detected as well as the nature of UL40 peptide recognition (specificity and magnitude) is variable among the hosts. Similar variability is observed for HV and transplant recipient populations. These ex vivo findings sustained our results above obtained with cell lines and showing that a monoclonal HLA-EUL40 CD8 T-cell subset can be activated by a set of UL40 peptides. Nevertheless, we cannot exclude that a multiplicity of responses can also coexist in hosts resulting from coinfection.
Considering the ability of HCMV to generate HLA-EUL40 T-cell responses that recognize multiple peptides we next sought to determine whether the detected HLA-EUL40 CD8 T cells may target autologous or allogeneic (i.e. provided by the transplant donor) HLA-I signal peptide in the KTR. To this aim, sequence of HLA-I (-A, -B, -C) signal peptide carried by the host (KTR or HV) or by the transplant donors were compared to the UL40 sequences targeted by HLA-EUL40 CD8 T-cell responses to identify self and allogeneic, donor-specific or non donor-specific, peptides, respectively. Potential self or allogeneic recognition mediated by anti-HCMV HLA-E-restricted T-cell subset are presented in the Fig 6C. Due to full sequence homology between UL40 viral peptides and HLA-I signal peptides, most of UL40-induced responses were found to recognize at least one autologous HLA peptide for all HCMV+ individuals. Moreover, in most cases (70% of responders) HLA-EUL40 responses may also potentially target transplant HLA-I signal peptide presented by HLA-E molecules on the graft.
Ex vivo HLA-EUL40 tetramer staining allowed us to provide a qualitative and quantitative assessment of unconventional CD8 T cells directed against HCMV. This unconventional T-cell subset is restricted by the MHC-Ib, HLA-E molecule, and targets UL40 signal peptide (UL4015-23). A number of conclusions can be drawn from this study. First, a major finding was the high prevalence of this CD8 T-cell population investigated in HCMV+ transplant recipients and healthy volunteers. HLA-EUL40 CD8 T cells were detected in 31 out of 105 (29.5%) HCMV+ hosts. About half (46.1%) of HCMV+ healthy HLA-A*02 blood donors possesses detectable HLA-EUL40 CD8 T cells. An equal proportion of HLA-A*02 blood donors (46.1%) possess HLA-A*02pp65 CD8 T cells and 1/3 of these individuals display both anti-HCMV CD8 T cells. Although, in our cohort of transplant recipients, HLA-A*02pp65 specific T cells were more frequently detected than HLA-EUL40 CD8 T cells, the latter were found in over 35% of kidney transplant recipients. Nevertheless, we cannot exclude that these values were underestimated since ideally, a broader panel of HLA-EUL40 complexes would be used for an exhaustive detection. Moreover, T-cell populations below 0.1% (our threshold of detection) were not considered. Together these results support the idea that HLA-E-restricted T-cell response belongs to the usual T-cell response against HCMV UL40.
Conventional T-cell responses to HCMV peptides, such as dominant responses to the pp65 and IE epitopes presented by HLA-A*02 and HLA-B*07, can regularly reach 5–10% of total CD8 T cells in the blood of healthy adults and even greater with up to 30% of total CD8 T cells are reported in some studies [9, 10, 39]. However, there is extensive variability in the size of T-cell responses between individuals. The reasons for this variability are not fully understood but may include the dose and timing of infection, as well as the HLA restriction element. Here we show that similar disparity also occurs for HLA-E-restricted anti-UL40 CD8 T cells with frequencies varying from 0.1% to over 38% of total CD8 T cells according to the hosts (median value: 2.2%). These values are the highest reported for this unconventional subset ex vivo. Previous studies established ex vivo percentages of HLA-E-restricted anti-UL40 CD8 T cells from 0.05% [31] to 14% [30, 32]. Thus, HLA-E-restricted responses mirror HLA-Ia-restricted responses in both frequency and magnitude. Our longitudinal analysis demonstrated that these T-cell populations develop early post-infection and expand quickly to reach maximal rate between 2 to 11 months post primary infection and within 1 month post reactivation. Tetramer staining of HLA-EUL40 CD8 T cells showed continued expansion post-infection and stabilization at high frequencies. In our cohort study, anti-HCMV HLA-E-restricted, and to a lesser extent HLA-A*02-restricted T-cell responses appear more frequent during latent and reactivations/secondary infections than during primary infections. Although this difference may be due to variations in the time interval between infection and the time point selected for detection assay (M12) among individuals or an effect of immunosuppressive regimen, this could also reflect a delay in HLA-EUL40 CD8 T-cell induction.
A key point of this study is to provide evidence for a positive correlation between HLA-A*02 allele and the occurrence of HLA-E-restricted anti-HCMV CD8 T cells. Firstly, using HLA-EUL40 tetramer staining, anti-HCMV HLA-E-restricted were detected more often in HLA-A*02 hosts. Next, HLA sequencing further confirmed a significantly higher rate of hosts carrying at least one HLA-A*02 allele among HLA-EUL40 CD8 T-cell responders compared to non-responders. Moreover, all HLA-A*02+/+ HCMV+ individuals (n = 6) developed an HLA-EUL40 CD8 T-cell response. The positive correlation between HLA-A*02 allele and HLA-EUL40 CD8 T-cell response could be related to the sequence of HLA-A*02 signal peptide (VMAPRTLVL). Indeed, HLA-EUL40 CD8 T-cell responses that have been identified in HCMV infection typically involved epitopes that are structurally related to canonical HLA-I leader sequences but foreign to the hosts [19, 40, 41]. Consistent with the paucity of the VMAPRTLVL sequence among viral strains, UL40 sequencing of host’s circulating strains allowed us to identify the VMAPRTLVL sequence only in a single clinical strain out of 32. Thus, it could be suspected that the presence of HLA-A*02 decreases the chances that a host will present a signal peptide derived from a different HLA-I allele, one that could cause negative selection of HLA-EUL40 reactive TCR. In that respect, when HLA-A*02 is present, deletion of HLA-EUL40-responsive T cells is less likely.
HLA-E*01:01 (HLA-E107R) and HLA-E*01:03 (HLA-E107G) alleles only differ in a single amino acid at position 107 and the frequencies of these two variants are equal in most populations [20]. It has been shown that the HLA-E*01:03 variant is usually expressed at higher levels than HLA-E*01:01 [33]. Although located outside the peptide-binding groove, the mutant AA at position 107 may also possibly affect the conformation of HLA-E or its association with β2-microglobulin resulting in the presentation of different peptide repertoires [42]. We found no HLA-E allele preference associated with the establishment of an HLA-EUL40 CD8 T-cell response. Instead, we report a higher prevalence of HLA-E*01:01/*01:03 heterozygous among individuals carrying an HLA-EUL40 CD8 T-cell response. Interestingly, it has been demonstrated for HLA-E and for the non-human primate HLA-E ortholog that a large panel of identified peptides can be presented by all allotypes [43]. Both alleles present a limited set of peptides derived from class I leader sequences physiologically [42]. In stress conditions (viral infections, tumors), HLA-E can present peptides from other sources than the signal sequences of classical HLA-I molecules [38, 44]. Recent studies demonstrated that the HLA-E alternative peptide repertoire observed in pathophysiological conditions seems not to be shared equally by the two HLA-E alleles [42, 45]. Comparing the HLA-E*01:03-restricted peptides to those of HLA-E*01:01, Celik et al. demonstrated that the peptide repertoire of both alleles greatly differs in the absence of HLA class I molecules leading to functional disparity between both alleles [45]. Consistent with these observations, it is likely that bearing both *01:01/*01:03 alleles may improve HLA-E stability and the diversity of peptide presentation and thus increase HLA-EUL40 T-cell responses as suggested by our findings. In transplant recipients, the impact of donor HLA was investigated in parallel to the impact of recipient of donor HLA. We found no significant impact neither for HLA-A,-B,-C or HLA-E alleles nor for a mismatch between donor and recipient for HLA-A,-B,-C or HLA-E.
An elegant study from Wang et al. suggested that HCMV-specific CD8 TCR repertoire diversity is more important than CD8 T-cell response magnitude for the control of persistent HCMV infection [46]. Using a single-cell based approach for the clonotype analysis of human CD8 TCRαβ repertoires they demonstrate a high prevalence of both TCRα and TCRβ public motif usage. Our analysis of TCR Vβ usage by HLA-EUL40-specific T cells investigated after in vitro expansion showed no predominating TCR Vβ usage for HLA-EUL40-specific T cells, indicative of an unbiased T cell response. A donor-specific focus revealed diverse and unique TCR Vβ chain repertoire in each host. Analysis of TCR Vα repertoire remains to be performed to fully define T-cell repertoire diversity.
Ex vivo phenotype analysis at distance from the infection revealed that HLA-EUL40 CD8 T cells belong to effector memory cells, most probably TEMRA, that display CD45RAhigh/CD45ROlow. Chronic viral infections result in decreased function of virus-specific cellular and humoral immunity that occurs via upregulation of specific inhibitory receptors expressed on the immune cells. Our data showed that HLA-EUL40-restricted CD8 T cells express lower level of PD-1 as compared to HLA-A*02pp65-restricted CD8 T cells. It has been reported that CD8 T cells expressing high levels of co-inhibitory molecule PD-1 during the chronic infection are characterized by lower proliferation, cytokine production, and cytotoxic abilities [47]. PD-1 plays a significant role in establishment of virus-specific CD8 T-cell exhaustion and has been identified as a major regulator of T-cell exhaustion during chronic HIV/SIV infection [47]. Markedly upregulated on the surface of exhausted virus-specific CD8 T cells, PD-1 expression correlates with impaired virus-specific CD8 T-cell function and with elevated plasma viral load in chronic viral infections [48]. In our study, low levels of PD-1 expression compared to conventional HLA-A*02-restricted CD8 T cells appear as a feature of HLA-E-restricted CD8 T cells. The functional significance of the low PD-1 expression still requires investigations. It could be speculated that low PD-1 level on HLA-EUL40 CD8 T cells may reflect low TCR affinity as recently reported for antigen-specific CD8 T cells targeting melanoma peptides [49]. This feature could be important for homeostatic survival and proliferation to ensure long-term T cell survival [50].
It is interesting to notice that elected tropism of HCMV for endothelial cells also coincides with elevated basal level of HLA-E on this cell type as well as on hematopoietic cells as we previously reported [51]. Basal HLA-E expression can increase upon cellular stress caused by viral infection or heat shock and in inflammatory and cancer cells [41]. It can be speculated that HLA-E-expressing infected ECs play a role as both a trigger and a target of HLA-E-restricted CD8 T cells. We previously demonstrated in vitro the capacity of HLA-EUL40 CD8 T cells to efficiently kill primary allogeneic endothelial cell cultures presenting a homologous HLA signal peptide though HLA-E [30]. Consequently, HLA-EUL40 CD8 T cells could be involved in vascular injury and transplant rejection. The presence of UL40-specific CD8 T cells in the blood of lung transplant recipients was significantly associated with allograft dysfunction, which manifested as Bronchiolitis Obliterans Syndrome [31]. Although deciphering the clinical impact of HLA-EUL40 CD8 T cells was not the focus of the present study, clinical data indicated no significant impact on graft function (serum creatinine and proteinuria) at M12 post-transplantation (Table 1 and S1 Table). This could suggest that although we detected (by tetramer staining or TNF production) a multiplicity of HLA-EUL40 CD8 T-cell responses induced by HCMV and potentially cross-reactive toward a broad set of peptides including self and allogeneic HLAsp, their activation may be either controlled by co-inhibitory receptors or functionally impaired. Another important finding in the setting of organ transplantation also emerges from our work. No HLA-E-restricted CD8 T cells were detected in HCMV- transplant recipients suggesting that allograft does not induce per se consistent HLA-E-restricted CD8 T-cell response against allogeneic (i.e. donor) HLA-EHLAsp complexes as speculated in earlier studies [19].
The function(s) of HLA-EUL40 CD8 T cells still remain to be established in regard to the control of HCMV infection. Efficient lysis of infected cells expressing high levels of HLA-E (i.e. endothelial cells, monocytes) could be a primary function expected for this effector CD8 T-cell subset. Regulatory functions for some HLA-E/Qa-1-restricted CD8 T-cell populations have been well established in mice [52] and more recently in humans [53]. Considering the high expression of HLA-E on CD4 T and B cells [54], a regulatory role for HLA-EUL40 CD8 T cells in the homoeostasis of anti-HCMV cellular immune response cannot be excluded beyond an action on the elimination of infected cells. Moreover, our findings provide evidence for self and allogeneic HLA peptides as potential targets and triggers (for their maintenance) of HLA-EUL40 CD8 T cells supporting effector and regulatory functions for these unconventional CD8 T cells beyond HCMV infection.
To conclude, HCMV UL40 induces specific HLA-E-restricted CD8 T-cell response with similar occurrence, magnitude, time course and long term persistence that pp65 viral protein. HLA-A*02 allele and HLA-E genotype are key determinants positively associated with HLA-EUL40 CD8 T cell response. HLA-EUL40 CD8 T cells are effector cells induced by HCMV in a strain-dependent manner that may specifically target and eliminate infected cells. We demonstrated that HLA-EUL40 CD8 T cells also display a potential reactivity toward self and allogeneic HLA peptides that may also contribute to the pathogenicity of HCMV, especially in immunocompromised patients.
Banked biological samples (PBMCs and DNAs) were issued from the DIVAT biocollection (CNIL agreement n°891735, French Health Minister Project n°02G55). This retrospective study was performed according to the guidelines of the local and national ethics committees (CCPRB, CHU de Nantes, France). Blood samples collected from anonymous healthy volunteers (n = 25) were obtained from the Etablissement Français du Sang (EFS Pays de la Loire, Nantes) and collected with donor’s specific and written informed consent for research use.
A total of 121 patients who underwent kidney (105/121) or kidney-pancreas (16/121) transplantation in our center (Institut de Transplantation/Urologie/Nephrologie, ITUN, CHU de Nantes, France) between 2006 and 2013 were retrospectively enrolled in our cohort study. The cohort includes 4 groups of transplant recipients defined by their HCMV status (Table 1). The groups were defined according to the HCMV serology of the recipient (HCMV- or HCMV+) and for HCMV+ the status of infection (primary, latent, reactivation) at M12 post-transplantation: HCMV active infection (AI) was defined by having at least two consecutive PCR with a viral load (VL) > 3 log10, expressed as number of viral DNA copies (log10cop) per 106 cells. No statistical difference (p > 0.05) between the groups was found related to the age of the recipients at the day of transplantation, gender ratio, frequency of HLA-A*02 genotype, and the post-transplant time for the blood samples. There is also no statistical difference between the groups concerning the gender ratio of transplant donors, the rank of the transplantation and the duration of cold ischemia. Mismatches of total HLA-I and/or HLA-II for each donor/recipient pairs were equal in the groups. Finally, expected statistical differences between the groups only appeared related to HCMV primary infection status at 12 months post-transplantation. Healthy HCMV+ seropositive blood donors (n = 25) were also recruited in this study. No statistical differences were founded between HV and KTR patients related to age or gender ratio.
Frozen PBMCs isolated from blood samples issued from kidney transplant recipients were prospectively stored at the Centre de Ressources Biologiques (CRB, Nantes, France). Cells were thawed 24 hours before use in RPMI-1640 medium (Gibco, Saint Aubin, France) supplemented with 8% human serum, 2 mM L-glutamine (Gibco), 100 U/mL penicillin (Gibco), 0.1 mg/mL streptomycin (Gibco) and 50 U/mL human recombinant IL-2 (Proleukin, Novartis Pharma, Rueil-Malmaison, France). Blood samples from HCMV+ HV’s were provided by the Clinical Development and Transfer Facility (DTC Facility, INSERM/SFR Federative Structure Research Francois Bonamy, Nantes, France). PBMC were isolated by Ficoll density gradient (Eurobio, Les Ulis, France) and used immediately.
For HCMV monitoring, EDTA blood samples were collected for blood donation from healthy volunteers, patient’s follow up or during the acute phase of HCMV infection. HCMV serology was performed using the LIAISON CMV IgG; LIAISON CMV IgM and LIAISON CMV IgG Avidity tests (Diasorin, Saluggia, Italy). Additional evidence of active HCMV replication was examined using an in-house real time HCMV PCR in whole blood, adapted from [55]. The combination of positive CMV IgG, positive IgM, and positive PCR was used for confirmation of primary HCMV infection. For UL40 sequencing, DNA were extracted using QIAsymphony system (Qiagen, Courtaboeuf, France) from 200μL of whole blood samples with the QIAamp DSP DNA Mini Kit (Qiagen). The HCMV UL40 region (858bp) was amplified by PCR using a protocol adapted from [56]. Briefly, the following specific forward and reverse primers were used for a long PCR: forward 5’-TCCTCCCTGGTACCCGATAACAG-3’ and reverse 5’-CGGGCCAGGACTTTTTAATGGCC-3’. Standard reaction mixtures were realized using SYBRPremix Ex Taq kit (Takara Bio Europe, Saint-Germain-en-Laye, France), with the following PCR parameters: one cycle of 94°C for 12 min, then 50 cycles of 94°C 30 sec, 63°C 30 sec and 72°C for 1 min 30, and finally one cycle of 72°C 7 min. PCR products were analyzed by electrophoresis through a 9% non-denaturing acrylamide-bisacrylamide 37.5–1 gel stained with ethidium bromide. PCR products were purified using the enzymatic method ExoSAP-IT USB (Affymetrix, Thermo Fisher Scientific, Villebon-sur-Yvette, France). Bidirectional sequence was performed using the fluorescent BigDye terminator method (Big Dye version 1.1 Cycle Sequencing Kit, Applied Biosystems, Courtaboeuf, France) and sequencing reactions were run on Applied Biosystems ABI Prism 3130 XL. Nucleotide and amino acid sequences analyses were performed using Seqscape software (version 2.5, Applied Biosystems). All sequences were imported and aligned in MEGA5 software using the UL40 sequence from Human Herpesvirus 5 (Merlin strain) as reference sequence (NCBI Reference Sequence: NC_006273.2). Sequence LOGO were created using the Los Alamos HIV Database tool Analyse Align (http://www.hiv.lanl.gov/content/sequence/ANALYZEALIGN/analyze_align.html), which was based on WebLOGO3.
Banked genomic DNAs (gDNAs) from the transplant donor/recipient pairs (n = 121) analysed in this study and available in the DIVAT biocollection (62 donors and 106 recipients) were harvested. Genomic DNA was extracted from blood samples issued from the HCMV+ HV (n = 25) using usual proteinase K/phenol-chloroform method and subsequently used for genotyping. For HLA-E*01:01 and HLA-E*01:03 determination, a first PCR product was generated from gDNA encompassing exon1 to 3 coding for the alpha domains and using the following primers: forward 5'-TCCTGGATACTCATGACGCAGACTC-3’ and reverse 5'-CCTCTTACCCAGGTGAAGCAGCG-3’. Next, a second run of amplification was performed into two separated nested PCR targeting exons 1–2 and exon 3, respectively with the primer pairs: 5-'TCCTGGATACTCATGACGCAGACTC-3’ and 5'-ATCTGGGACCCGAAGATTCGA-3’, 5'-TCGAATCTTCGGGTCCCAGAT-3’ and 5'-CCTCTTACCCAGGTGAAGCAGCG-3’. DNA sequencing was performed with BigDye Terminator v3.1 kit (Applied Biosystems) according to the manufacturer's instructions on the DNA Sequencing Core Facility (INSERM/SFR François Bonamy, Nantes, France), using a 48-capillary Applied Biosystems 3730 automatic system (Applied Biosystems). Sequences were analyzed using Chromas 2.33 software (Digital River GmbH, Shannon, Ireland) using a SNP at AA position 107 to discriminate between *01:01 and *01:03 alleles. HLA-A,-B,-C genotypes of transplant donors/recipients pairs and HV were performed by either the EFS (Nantes, Pays de la Loire) or Histogenetics (Ossining, NY, USA), by using PCR-SSO (and completed by PCR-SSP if necessary) and based on the IMGT/HLA database nomenclature (www.ebi.ac.uk/ipd/imgt/hla/).
Nine-mers UL4015-23 peptides from 11 different HCMV strains (VMAPRTLIL, VMAPRTLLL, VMAPRTLVL, VMAPRTVLL, VMAPRSLIL, VMAPRSLLL, VMTPRTLVL, VMAPQSLLL, VTAPRTLLL, VTAPRTVLL, VMAPRALLL) and the UL83 pp65495-503 peptide (NLVPMVATV) were synthesized (purity>95%) and purchased from Proteogenix SAS (Schiltigheim, France). HLA-E*01:01/UL4015-23 (HLA-EUL40) and HLA-A*02:01/pp65495-503 (HLA-A*02pp65) complexes were generated as described previously [57]. Recombinant HLA proteins were produced in E.coli and refolded with 15μg/mL of each UL4015-23 peptide for HLA-E-monomers or pp65495-503 peptide for HLA-A*02-monomers. Next, HLA-monomers were biotinylated for 4h at 30°C with 6μg/mL BirA (Immunotech, Marseille, France), purified and tetramerized with BV421- or APC-labelled streptavidin (BD Biosciences, Le Pont de Claix, France). Tetramerization was confirmed by gel filtration chromatography (Superdex 200 column, Sigma-Aldrich, Saint-Quentin Fallavier, France).
To investigate the frequency of the anti-HCMV CD8 T-cell responses in individuals, PBMC (3x105 per condition) were pre-incubated with a blocking anti-CD94 mAb (clone HP-3D9, 5 μg/mL, BD Biosciences) for 20 min at 4°C to completely abrogate the non-specific staining of CD94/NKG2+ T cells by HLA-E-tetramers (S1 Fig). PBMCs were then incubated with one of the different BV421-labelled HLA-E- or HLA-A*02-tetramers (10 μg/mL, 30min, 4°C), before costaining (30min, 4°C) with the following antibodies: anti-CD3 (clone SK7/Leu4, BV786, 2 μg/mL, BD Biosciences), anti-CD8α (clone RPA-T8, BV650, 0.1 μg/mL, BD Biosciences) and anti-TCR γδ (clone 11F2, APC-Vio770, 3 μg/mL, Miltenyi Biotec, Paris, France). Dead cells were excluded using NucRed Dead 647 ready probes reagent (Life technologies). As a control of tetramer staining, a FMO condition (Fluorescence Minus One; all labelled-markers except one) without tetramers was performed for each sample. Acquisition was performed on a BD LSR II and analyses were performed using BD DIVA Software v6.0 as described below. Compensations were performed by using anti-mouse κ chain Ab-coated beads (anti-mouse Ig, κ chain/negative control compensation particles set, BD Biosciences) incubated with corresponding Ab at the same concentration during 15 min at room temperature. Data acquisition for the 121 KTR and 25 HV was normalized with application settings based on the KTR#001 patient. Gating analysis strategy was identical for all samples (S1 Fig).
To follow-up the development of HCMV-specific T-cell subpopulations in KTR, banked PBMCs from 16 KTR prospectively collected at 1, 2, 3, 4, 5, 6, 7, 9, 10,12 and 13 months post-transplantation were used. For each time point tested, UL40-specific HLA-E-restricted (HLA-EUL40) and pp65-specific HLA-A*02:01-restricted (HLA-A*02pp65) T cells were concomitantly stained and quantified as described above with the complementary mAbs: anti-CD69 (clone FN50, BUV395, 2 μg/mL, BD Biosciences) and anti-PD1 (clone EH12 (.1), PE, 2 μg/mL, BD Biosciences). Acquisition and analysis was performed on a BD LSR Fortessa X-20 with BD DIVA Software v8.0. Longitudinal samples for each patient were all stained and acquired in the same experiment.
HLA-EUL40 T cells were sorted for 5 transplant recipients (#104, #105, #107, #108 and #109) from PBMCs harvested at 12 months post-transplantation as previously described [58]. Briefly, streptavidin-coated beads (Dynabeads M-280 Streptavidin, Invitrogen, Villebon sur Yvette, France) were saturated with HLA-E/UL4015-23 monomers before incubation with PBMCs (5x106) for 4h. The UL4015-23 peptide corresponding to the own HCMV infecting strain was selected for each patient. HLA-EUL40 T cells were isolated by immunomagnetic sorting and then expanded for 21–30 days as follows: cells were seeded in 96-well plates (3x103/well) and stimulated with phytohemagglutinin (1 μg/mL, PHA-L; Sigma-Aldrich) in the presence of irradiated EBV-transformed B-cell lines and allogeneic PBMC from healthy donors (EFS, Nantes) as feeder. Cells were grown in RMPI-1640 medium supplemented with 8% human serum, 2 mM L-glutamine, 100 U/mL penicillin and 0.1 mg/mL streptomycin and human recombinant IL-2 (150 U/mL). Purity (>95%) of each T cell population was defined after 14 days of culture by tetramer staining.
The use of tetramers to activate T cells has been extensively reviewed by Wooldridge and colleagues [59]. T-cell activation by soluble peptide–MHC-I tetramers is very sensitive for inducing a full range of effector functions. In addition to inducing a normal pattern of T-cell signaling [60] tetramer activation results in lytic granule release, a full profile of cytokine and chemokine release and the production of a wide range of cell surface activation markers [61]. In the present study, a series of preliminary experiments were performed to set up the assay measuring T-cell activation in response to HLA-E/UL40 peptide tetramers. Representative results from these preliminary assays are illustrated in the S4 Fig. To determine the peptide specificity of HLA-EUL40-restricted T cells, purified cell lines (1x105 cells /condition) were stimulated for 5h at 37°C in 96-wells plates with one of the 11 HLA-E/UL40-tetramers at 20 μg/mL in RPMI 1640 medium in the presence of Brefeldin A (10 μg/mL, Sigma). Next, cells were incubated with an anti-CD8α mAb (clone RPA-T8, 1 μg/mL, BioLegend) for 30 min at 4°C before fixation with 4% paraformaldehyde. After permeabilization with 0.1% (w/v) saponin (Sigma-Aldrich), cells were stained for 30 min at room temperature with an anti-TNFα mAb (clone cA2, Miltenyi). Cells were finally washed twice in PBS-0.1% (v/v) BSA and 0.1% (w/v) saponin before sample acquisition on BD FACS Canto II.
Phenotypic analyses were performed on PBMCs from 3 patients. Analysis of T cells before activation was performed ex vivo using the following mAbs: anti-CD3 (clone UCHT1), anti-TCR αβ (clone T10B9.1A-31/T10B9), anti-TCR γδ (clone B1), anti-CD45RA (clone HI100), anti-CD45RO (clone UCHL1), anti-CD28 (clone CD28.2), anti-CD27 (clone M-T271), anti-CD57 (clone NK-1) from BD Biosciences; anti-CD8β (clone SIDI8BEE) from eBioscience (Thermo-Fisher); anti-CD4 (clone RPA-T4), anti-CD8α (clone RPA-T8) from Miltenyi and anti-CCR7 (clone 150503) from R&D Systems. For Vβ TCR repertoire analysis, purified HLA-EUL40 T cells (2x105) were incubated 30 min at 4°C in PBS-0.1% (v/v) BSA with the TCR Vβ Repertoire Kit (IO Test Beta Mark–TCR Vβ Repertoire Kit, Beckman Coulter, Villepinte, France). This kit allows detection of the following Vβ TCR: 1, 2, 3, 4, 5.1, 5.2, 5.3, 7.1, 7.2, 8, 9, 11, 12, 13.1, 13.2, 13.6, 14, 16, 17, 18, 20, 21.3, 22 and 23. All Abs were used at saturating concentration conforming to the manufacturer’s recommendation.
Data are expressed as medians + interquartile range between Q1 and Q3, or percentages. Appropriate non-parametric statistical analysis (Kruskall-Wallis test, Mann-Whitney, Fischer’s exact test or Pearson’s chi-squared test with adequate post-tests) was performed using GraphPad Prism(GraphPad, San Diego, CA) and R softwares. The type I error rate α (probability threshold of rejecting the null hypothesis given that it is true) was set to 0.05. A p-value <0.05 was considered to represent a statistically significant difference.
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10.1371/journal.ppat.1004822 | The EBNA3 Family of Epstein-Barr Virus Nuclear Proteins Associates with the USP46/USP12 Deubiquitination Complexes to Regulate Lymphoblastoid Cell Line Growth | The Epstein-Barr virus (EBV) nuclear proteins EBNA3A, EBNA3B, and EBNA3C interact with the cell DNA binding protein RBPJ and regulate cell and viral genes. Repression of the CDKN2A tumor suppressor gene products p16INK4A and p14ARF by EBNA3A and EBNA3C is critical for EBV mediated transformation of resting B lymphocytes into immortalized lymphoblastoid cell lines (LCLs). To define the composition of endogenous EBNA3 protein complexes, we generated lymphoblastoid cell lines (LCLs) expressing flag-HA tagged EBNA3A, EBNA3B, or EBNA3C and used tandem affinity purification to isolate each EBNA3 complex. Our results demonstrated that each EBNA3 protein forms a distinct complex with RBPJ. Mass-spectrometry revealed that the EBNA3A and EBNA3B complexes also contained the deubquitylation complex consisting of WDR48, WDR20, and USP46 (or its paralog USP12) and that EBNA3C complexes contained WDR48. Immunoprecipitation confirmed that EBNA3A, EBNA3B, and EBNA3C association with the USP46 complex. Using chromatin immunoprecipitation, we demonstrate that WDR48 and USP46 are recruited to the p14ARF promoter in an EBNA3C dependent manner. Mapping studies were consistent with WDR48 being the primary mediator of EBNA3 association with the DUB complex. By ChIP assay, WDR48 was recruited to the p14ARF promoter in an EBNA3C dependent manner. Importantly, WDR48 associated with EBNA3A and EBNA3C domains that are critical for LCL growth, suggesting a role for USP46/USP12 in EBV induced growth transformation.
| Epstein-Barr virus (EBV) is a gammaherpesvirus implicated in the pathogenesis of multiple malignancies, including Burkitt lymphoma, Hodgkin lymphoma, post-transplant lymphoproliferative disease (PTLD), nasopharyngeal carcinoma, and gastric carcinoma. EBV infection of resting B-lymphocytes drives them to proliferate as lymphoblastoid cell lines (LCLs), an in vitro model of PTLD. LCLs express a limited EBV gene repertoire, including six nuclear proteins (EBNA1, 2, 3A, 3B, 3C, and LP), three integral membrane proteins (LMP1, 2A, and 2B), and more than 30 micro RNAs. EBNA2 and the EBNA3 proteins are transcription factors that regulate viral and cell gene expression through the cell DNA binding protein RBPJ. In this study, we established LCLs transformed by recombinant EBV genomes in which a Flag-HA epitope tag is fused in-frame to the C-terminus of EBNA3A, EBNA3B or EBNA3C. Using these LCLs, we purified endogenous EBNA3 complexes and identified the USP46 deubiquitinating enzyme (DUB) and its associated chaperones WDR48 and WDR20 as EBNA3 binding proteins. We find that EBNA3s interact primarily with the WDR48 protein and that loss of WDR48 interaction with EBNA3A or EBNA3C impairs LCL growth. This study represents the first characterization of EBNA3 complexes from LCLs and implicates the USP46 DUB complex in EBNA3 mediated gene regulation.
| Epstein-Barr Virus (EBV) is a herpesvirus that establishes lifelong asymptomatic infection in up to 95% of the human population [1]. In vitro, EBV infection of resting B lymphocytes drives them to proliferate as lymphoblastoid cell lines (LCLs) [2,3]. The EBV genome resides in LCLs as a non-integrated episome and expresses a limited gene repertoire called latency III, which includes genes encoding six nuclear proteins (EBNA1, 2, 3A, 3B, 3C, and LP), three integral membrane proteins (LMP1, 2A, and 2B), and more than 30 micro RNAs (miRs) [1]. Latency III driven B lymphocyte proliferation in vivo is normally controlled by a vigorous cytotoxic T cell response [4]. In the absence of an effective immune response or in collaboration with various environmental or genetic co-factors, EBV latent infection can result in malignancies, including Burkitt and Hodgkin lymphomas, post-transplant lymphoproliferative disease (PTLD), as well as nasopharyngeal and gastric carcinomas [1].
Extensive genetic and biochemical data support the model that EBV latency III gene expression usurps growth and survival signaling pathways in B lymphocytes normally triggered by antigen recognition and CD4+ T cell co-stimulation [1,5]. LMP1 expression results in constitutive NF-kB activation that is essential for LCL outgrowth and survival. The ability of LMP1 to self-associate allows it to activate, in a ligand independent manner, molecules that transduce signals from receptors in the TNF superfamily [6,7,8,9,10]. The other two latent membrane proteins, LMP2A and LMP2B, are not required for LCL transformation in vitro [11]. The ability of LMP2A to engage B cell receptor signaling molecules may be important for maintaining viral latency or for the growth and survival of EBV infected cells in vivo [12,13]. EBNA2 is an acidic transactivator that is targeted to promoters through an interaction with the RBPJ DNA binding protein, a component of the Notch signaling pathway [14]. EBNA2 and its co-activator EBNALP are the first genes expressed during EBV latent infection and result in upregulation of promoters including c-myc, EBV LMP1, LMP2A, and EBNA essential for latency III transformation [15,16]. Global analysis of EBNA2 and RBPJ binding in LCLs has implicated EBF1 and other B cell transcription factors as pioneering factors for EBNA2 binding of promoters and enhancers [17]. In contrast, the role of the EBNA3 proteins in LCL transformation is less clearly defined.
The EBNA3 protein family is defined by a ~300 aa region of homology in their N-termini; there are no known homologs outside of EBV and the closely related primate lymphocryptoviruses. EBNA3A, EBNA3B, and EBNA3C share a common exon structure consisting of a short 5’ exon and a longer 3’ exon arranged in a tandem array that likely arose from triplication of a single ancestral EBNA3 gene [1]. Reverse genetic analyses have demonstrated that EBNA3C is essential for LCL transformation, while EBNA3B is dispensable [18,19]. The requirement for EBNA3A is probably intermediate. EBNA3A truncation or conditional inactivation abrogated transformation in multiple studies. However, LCLs have been generated under appropriate conditions, using feeder cells, with an EBV genome deleted for EBNA3A [20]. The most convincing evidence of the unique requirement for EBNA3A and EBNA3C derives from LCLs in which either EBNA3A or EBNA3C has been rendered conditional by fusion to a mutant estrogen hormone binding domain [21,22,23,24,25]. In this system, LCL growth arrest induced by EBNA3A inactivation could be rescued only by exogenous EBNA3A expression, but not by expression of additional EBNA3B or EBNA3C [22,23]. Similarly, EBNA3C inactivation results in termination of LCL growth that can only be restored by EBNA3C [21,24,25].
Cell cycle effects of the EBNA3 proteins, particularly EBNA3C, have been documented in many other systems. EBNA3C can overcome serum deprivation and disrupt the G1 checkpoint in REFs, NIH3T3, and U2OS cells [26]. Additionally EBNA3C overexpression can disrupt mitotic spindle checkpoints and produce aneuploidy [26,27]. EBNA3A and EBNA3C can cooperate with HRAS in classical transformation assays [28]. Multiple mechanisms of EBNA3C mediated effects have been suggested, including inhibition of accumulation of the CDK inhibitors p27KIP1 and p16INK4A, Rb degradation via the SCF ubiquitin ligase, c-myc stabilization, and binding to, and inactivation of cyclinA-CDK complexes [29,30,31]. In LCLs, the growth effects of EBNA3A and EBNA3C appear to be primarily due to suppression of the CDKN2A gene products p16INK4A and p14ARF [32]. Conditional inactivation of either EBNA3A or EBNA3C results in p16INK4A and p14ARF accumulation and cessation of growth. Moreover, siRNA knockdown of both gene products restores growth despite EBNA3A or EBNA3C inactivation. EBNA3A and EBNA3C effects appear to be at the level of CDKN2A transcription as changes in protein levels are accompanied by concomitant increases in mRNA and a substantial reduction of the repressive H3K27me3 modification at the CDKN2A promoter [32]. Furthermore, p16INK4A null B lymphocytes can be transformed into LCLs in the absence of functional EBNA3C protein [33].
Although the mechanism by which EBNA3A and EBNA3C cooperatively suppress CDKN2A is unknown, gene co-regulation by the EBNA3 proteins appears to be frequent. In LCLs, 52 out of 287 genes reported as EBNA3A regulated were found to also be regulated by EBNA3C [34]. In Burkitt lymphoma cells, EBNA3A and EBNA3C are both required for suppression of BIM, a pro-apoptotic Bcl-2 family member [35]. Genome-wide analysis of EBNA3A, EBNA3B, and EBNA3C effects in BL31 cells infected with recombinant EBV genomes [36], suggested that about half of the cell genes differentially expressed as a result of deletion of one EBNA3 ORF are similarly affected by deletion of at least one of the other EBNA3s. In that study, overlap among genes regulated by EBNA3B and EBNA3C was particularly extensive [36].
A large number of interacting proteins have been suggested to be important for EBNA3 activities. Of these, RBPJ is the best established as a mediator of transcriptional and LCL growth promoting effects [37,38,39,40]. RBPJ is bound by the conserved N-terminal EBNA3 domain, which unlike Notch and EBNA2, does not interact with the RBPJ’s beta-trefoil domain. Instead, the EBNA3s bind to the N-terminal rel-homology domain (NTD) of RBPJ [37,41]. Although biochemical assays suggested that the EBNA3-NTD interaction could inhibit RBPJ DNA binding, genome-wide co-localization between EBNA3 proteins and RBPJ has been demonstrated by ChIP-seq [42,43]. Genetic analyses have demonstrated that interaction of both EBNA3A and EBNA3C with RBPJ is essential for CDKN2A promoter repression and maintenance of LCL growth [32,33]. A second cell protein important for CDKN2A regulation is CtBP1, which interacts with the C-terminal regions of EBNA3A and EBNA3C [44,45,46]. Mutation of the CtBP1 binding sites in EBNA3A and EBNA3C impairs their ability to support LCL growth. By contrast, RBPJ binding mutants are completely defective in maintenance of LCL growth [21,24].
The strength of evidence supporting a role for other interacting proteins in mediating EBNA3 growth effects varies considerably. Although significant progress has been made in mapping the EBNA3 domains critical for LCL growth, for most interacting proteins, mutations within the EBNA3 proteins that selectively disrupt their binding have yet to be identified. In parallel with efforts to correlate ongoing genetic analysis of the EBNA3 proteins with interacting protein binding, we set out to devise a means of purifying endogenous EBNA3 complexes from LCLs and to determine their protein constituents. To that end, recombinant EBV genomes in which DNA encoding a flag-HA (F-HA) epitope is inserted in-frame to the C-terminus of the EBNA3A, EBNA3B, or EBNA3C ORF were constructed. These genomes were used to transform primary B-lymphocytes into three cell lines: EBNA3A-F-HA, EBNA3B-F-HA, and EBNA3C-F-HA LCLs. Using tandem affinity purification and LC/MS/MS, we characterized the protein composition of endogenous EBNA3A, EBNA3B, and EBNA3C complexes in these LCLs.
Here we show that each EBNA3 protein is associated with the USP46 and USP12 deubiquitylase (DUB) complexes, and that the domains of the EBNA3A and EBNA3C proteins that bind these DUBs are important for maintenance of LCL growth. In the presence of EBNA3 proteins, RBPJ and the USP46/USP12 enzymes become associated and, when purified, these EBNA3 containing complexes exhibit DUB activity. Using CRIPSR/Cas9 we provide evidence that USP46 is essential in 721 LCLs, but dispensable in 293T cells. Further, using chromatin immunoprecipitation we demonstrate increased binding of WDR48 to the p14ARF promoter in the presence of functional EBNA3C protein. We propose a model in which EBNA3s serve as adaptor proteins between USP46/USP12 and RBPJ, recruiting these DUB complexes to chromatin to regulate transcription.
In order to study endogenous EBNA3 complexes from LCLs, we generated recombinant EBV genomes in which flag and HA epitope tags are fused in-frame with the carboxyl-terminus of EBNA3A, EBNA3B, or EBNA3C, using a previously described EBV BACmid [47]. These recombinant EBV genomes were used to transform B lymphocytes into LCLs, designated EBNA3A-F-HA LCL, EBNA3B-F-HA LCL, and EBNA3C-F-HA LCL, respectively, and collectively referred to as the EBNA3-F-HA LCLs. Additionally, a control a wild-type LCL was created using the unmodified EBV BACmid as the transforming genome. Western blotting of the three EBNA3-F-HA and wild-type LCLs revealed that RBPJ, EBNA1, EBNA2, EBNALP, and LMP1 levels in whole cell extracts were indistinguishable among the different cell lines (S1 Fig). The epitope tagged EBNA3 proteins were expressed at levels comparable to their wild-type counterparts and, as expected, migrated as slightly higher apparent molecular weights than the untagged proteins. Interestingly, the EBNA3B-F-HA LCL was hypomorphic for EBNA3C expression and exhibited a slower rate of growth than the other LCLs. A similar reduction in EBNA3C expression and rate of growth was previously reported in an LCL in which the EBNA3B gene was replaced by a chloramphenicol cassette [48]. Thus, the fusion of flag-HA tags to each of the EBNA3 open reading frames resulted in transformation competent EBVs that express latency proteins at levels comparable to those seen in wild-type LCLs.
RBPJ immunoprecipitation efficiently retrieves four of the six EBV nuclear proteins (EBNA2, EBNA3A, EBNA3B, and EBNA3C) from LCL lysates (Fig 1, left panels). Although previous work had suggested that EBNA2 and EBNA3C exist in distinct complexes [49], efforts to further investigate whether EBNA3 proteins exist in distinct complexes have been hampered by varying degrees of cross-reactivity of among available EBNA3A, EBNA3B, and EBNA3C antibodies [43]. Using flag immunoprecipitation on EBNA3A-F-HA LCL lysates, we found that EBNA3A and RBPJ were efficiently precipitated, but no EBNA1, EBNA2, EBNA3B, EBNA3C or LMP1 was detectable (Fig 1, right panels). Immunoprecipitations using flag resin on EBNA3B-F-HA or EBNA3C-F-HA LCL lysates produced similar results: RBPJ and the tagged EBNA3 protein were readily detectable, but other EBV latency proteins were not. Control immunoprecipitations for HA (Fig 1, left panel) and flag (Fig 1, right panel) from wild-type LCLs, did not precipitate any detectable RBPJ, EBNA2, or EBNA3 proteins. Thus, EBNA2, EBNA3A, EBNA3B, and EBNA3C all associate with the same cell DNA binding protein, but appear to form distinct RBPJ complexes in LCLs.
In order to identify proteins that associate with EBNA3A, EBNA3B, or EBNA3C under physiologic conditions, we purified EBNA3 complexes by tandem affinity purification (TAP) from LCLs expressing flag-HA tagged EBNA3A, EBNA3B or EBNA3C and from the wt LCL as a control. For each LCL, LC/MS/MS fingerprinting identified between 63–174 peptides of the epitope tagged EBNA3 protein and 95–148 peptides corresponding to RBPJ (Table 1). For each EBNA3-F-HA LCL, the purified protein complex contained peptides from the corresponding epitope tagged EBNA3 protein and no peptides that mapped to the other untagged EBNA3 proteins expressed in that LCL. No peptides from other EBV proteins, such as EBNA1, EBNA2, and EBNALP, were detected in any complexes. Additionally, 14 peptides corresponding to CtBP1 were detected in the EBNA3A complex, but not in the EBNA3B, EBNA3C or control TAPs (Table 1). We also detected 105, 99, and 3 total peptides corresponding to WDR48 in purified EBNA3A, EBNA3B, and EBNA3C complexes, respectively. Importantly, in the EBNA3A and EBNA3B complexes we also detected the known WDR48 associated proteins WDR20 (28 and 4 peptides, respectively), USP46 (15 and 8 peptides, respectively), and USP12 (7 and 4 peptides, respectively) (Table 1). Thus, analysis of tandem affinity purified EBNA3 complexes lends further support to model that each EBNA3 protein, while highly associated with RBPJ, exists in a distinct complex that does not contain other EBV nuclear proteins. Further, our results identify the USP46 (and USP12) deubiquitinases and their associated chaperones WDR48 and WDR20 as members of the EBNA3A and EBNA3B protein complexes in LCLs. Because USP46 and USP12 are highly homologous (~90% identity) and we could confirm a robust association with USP46 by Western blotting (discussed below), we chose to focus our subsequent attention on the USP46/WDR48/WDR20 complex.
Given that the EBNA3 proteins share other protein binding partners (e.g., RBPJ and CtBP1) and because we had previously identified WDR48 as a cell protein that interacts with flag-EBNA3C aa365-545 [50], the small amounts of this protein detected in the EBNA3C was unexpected, particularly given the much larger amounts associated with EBNA3A and EBNA3B. We considered the possibility that WDR48 became dissociated from EBNA3C during complex purification. Therefore EBNA3C complexes were rapidly immunoprecipitated from EBNA3C-F-HA LCLs and blotted for associated proteins. Co-precipitated RBPJ was readily detected, as were members of the USP46 complex, including USP46, WDR48, and WDR20 (Fig 2). Additionally CtBP1, a known EBNA3C interacting protein [45] was detectable under these conditions even though it was not detected in the TAP-MS experiments. Thus, EBNA3C also appears to target the USP46 complex; however, this complex appears to be less stably associated with EBNA3C-F-HA than it is with either EBNA3A-F-HA or EBNA3B-F-HA.
Although originally described as an endosomal protein [51], WDR48 has subsequently been isolated from other cellular compartments, including as a chaperone for USP1 in the nucleus as a component of the Fanconi anemia DNA repair pathway [52]. Lehoux et al., found that USP12 and USP46 fused to red fluorescent protein were predominantly cytoplasmic in C33A cervical carcinoma cells, but were recruited to the nucleus via an interaction with the HPV E1 helicase [53]. Because our TAP lysis procedure extracted both nuclear and cytoplasmic proteins (S2 Fig), we wanted to ensure that the USP46/WDR48/WDR20 complex subcellular localization was compatible with formation of a complex with EBNA3 proteins. To this end, we fractionated LCLs into cytoplasm, membrane, nucleoplasm, chromatin, and cytoskeletal components (Fig 3). Fraction purity was monitored by immunoblotting for control proteins of known localization: alpha-tubulin (cytoplasm), BRG1 (chromatin associated), histone H2B (chromatin), and lamin B (nuclear matrix/cytoskeleton). The EBNA3 proteins were predominantly found in the nucleoplasm with a small quantity stably associated with chromatin. Notably, a significant amount of the WDR48, WDR20, and USP46 proteins were found in the nucleoplasm, with the balance being extranuclear. To more directly assess whether EBV latent gene products might affect USP46 localization, we compared fractions derived from EBV negative BL41 cells with fractions derived BL41 cells super-infected with EBV (S3 Fig). Although USP46 was present in the nucleoplasm regardless of EBV status, we consistently observed an increase in nucleoplasmic USP46 levels in EBV positive BL41 cells to varying degrees. We did not observe significant changes in the levels of nucleoplasmic WDR48 or WDR20 in response to EBV infection; however, USP46 was increased in the membrane fraction upon EBV infection (S3 Fig), similar to that seen in LCLs compared to uninfected BL41 cells. As expected, RBPJ was present in the nucleoplasm and, to a much lesser extent, the chromatin fraction in LCLs and BL41 cells. We consistently observed a portion of the cellular RBPJ in the cytoskeletal fraction of LCLs, but not in BL41 cells, regardless of EBV infection. This may reflect RBPJ association with the nuclear cytoskeleton (matrix) as has been previously reported [54]. In summary, these cell fractionation experiments are compatible with EBNA3 proteins associating with a USP46 DUB complex that resides in the nucleoplasm of B lymphocytes.
To further study the association of the EBNA3 proteins with members of the USP46 complex, Flag tagged EBNA3A, EBNA3B or EBNA3C was co-expressed with Xpress tagged WDR48, WDR20, or USP46. Under these conditions, each of the EBNA3 proteins interacted with WDR48, WDR20, and USP46. In each case, consistent with our LC/MS/MS data, the interaction with WDR48 was the most robust (Fig 4A and 4B). We speculated that the EBNA3 proteins may target the USP46 complex primarily via interactions with WDR48 and that, in our overexpression assay, only a small portion of the WDR20 or USP46 was complexed by endogenous WDR48. To test this, we also assessed the ability of each EBNA3 protein to co-precipitate USP46 in the presence of additional WDR48 protein (Fig 4B). This markedly increased the retrieval of USP46, suggesting a central role for WDR48 in mediating interaction with the EBNA3 proteins. In order to test whether the EBNA3 proteins could recruit this DUB complex to RBPJ complexes, we evaluated the ability of RBPJ and WDR48 to co-immunoprecipiate. In LCLs, WDR48 could be weakly detected in RBPJ immunoprecipations; however in EBV negative BL41 cells, no binding above background could be discerned (Fig 5). These results are consistent with a model in which EBNA3 proteins serve as adaptor proteins to recruit WDR48 to RPBJ in LCLs.
In order to map the EBNA3 residues that mediate interaction with WDR48, additional immunoprepicitation assays were conducted using EBNA3 deletion mutants. Each EBNA3 protein was split into 3 approximately equal fragments, an N terminal region encompassing the RBPJ binding motif, a central region, and a C-terminal domain. These 3 fragments were fused to Flag and co-expressed with WDR48 in 293T cells. This revealed that EBNA3A aa524-944, EBNA3B aa394-938, and EBNA3C aa365-545 interacted with WDR48 as well or better than the full-length proteins (S4 Fig). We further mapped the EBNA3A and EBNA3C interactions using internal deletion mutants that have been assessed for their ability to support LCL growth [21,23] (Fig 6A and 6B). These data revealed that EBNA3A aa827-944 was critical for WDR48 interaction. In the case of EBNA3C, small deletions of aa447-500 or 501–544 disrupted interaction with WDR48, as did mutation of the EBNA3C SUMO interaction motif (E3C509mSIM). Importantly, EBNA3C mutants that were defective for WDR48 association correspond to mutants that are intermediate for supporting LCL growth (Fig 6C). Thus, the EBNA3 domains responsible for association with WDR48, while not as critical as the RBPJ association domains are important for EBNA3 mediated growth effects in LCLs.
In the Fanconi anemia DNA repair pathway, SUMO interaction motifs (SIMs) of the FANCI protein associate with sumo-like domains (SLDs) within the WDR48 C-terminus [55]. Because EBNA3s proteins all contain SIMs [56], and our mapping data implicated the EBNA3C SIM in WDR48 binding, we speculated that these SLDs might be important for EBNA3A, EBNA3B, and EBNA3C interactions with WDR48. We first evaluated WDR48 aa1-634, a previously described mutant (also called WDR48ΔSLD2) that is unable to associate with FANCI [55]. Flag-EBNA3A was able to associate with this mutant comparable to wildtype WDR48 (Fig 7A). By contrast, EBNA3B aa394-938, which associates strongly with full length WDR48, did not bind to WDR48 aa1-634. The strong association of EBNA3C aa365-545 with full length WDR48 was almost completely lost with WDR48 aa1-634 (Fig 7A). To further define the EBNA3A binding site, we constructed additional WDR48 truncation mutants: WDR48 aa1-535, which removes the spacer region between SLD1 and SLD2 and WDR48 aa1-430, which is deleted for both SLDs. Both WDR48 truncation mutants interacted with EBNA3A and WDR48 aa1-430 interacted as efficiently as full length WDR48 (Fig 7B). Thus, EBNA3B and EBNA3C require the WDR48 SLD2 domain for binding, but EBNA3A can associate with the WDR48 N-terminus, independent of the SLDs.
Because EBNA3A aa827-944, which is essential for WDR48 binding, also contains two CtBP1 binding motifs [44], we sought to determine whether CtBP1 binding was separable from WDR48 binding. Deletion of EBNA3A aa920-944 (EBNA3A 1–919) had no effect on CtBP1 or RBPJ binding, but dramatically impaired WDR48 association in co-immunoprepicitation assays (Fig 8). Immunoprecipitations also confirmed that the previously described EBNA3A CtBP1 binding mutant [44] retains the ability to bind to WDR48 with efficiency comparable to wild type EBNA3A (Fig 8B). These data demonstrated that EBNA3A aa827-944 contain distinct binding sites for CtBP1 and WDR48.
In order to assess whether WDR48 binding by EBNA3A might be important for LCL growth, we tested EBNA3A aa1-919, which binds CtBP1 but not WDR48 to binding, in EBNA3A-HT LCL growth complementation. For these experiments EBNA3A-HT cells were transfected with EBNA3A, EBNA3A mutant or control GFP expression plasmid, split, maintained in growth media lacking 4HT and compared with control transfected cells grown in the presence of 4HT (Fig 9A and S5 Fig). In the presence of 4HT (closed square) or transcomplemented wt EBNA3A (closed diamond), LCL growth continued, whereas transcomplementation with EBNA3A CtBP1 binding mutant (open diamond) resulted in modestly impaired growth. By contrast, EBNA3A mutants impaired for RBPJ or WDR48 association were unable to maintain LCL growth under these conditions. Western blotting for p16 expression demonstrated that EBNA3A mutants defective for supporting LCL growth, including the WDR48 binding mutant (EBNA3A aa1-919) were impaired for suppression of p16 expression, compared to wild type EBNA3A (Fig 9B).
We assessed the ability of purified EBNA3 complexes to cleave ubiquitin from the 7-amino-4-methlcoumarin (AMC) fluorophore (Fig 10A) in an effort to determine whether USP46/USP12 deubiqutinase activity is activated or inhibited by association with EBNA3 proteins. EBNA3 complexes, isolated by TAP from EBNA3A-F-HA LCL (closed circle), EBNA3B-F-HA LCL (closed triangle), or EBNA3C-F-HA LCL (closed square) or control WT LCL (X), were incubated with Ub-AMC reaction buffer and fluorescence intensities were measured by fluorometer. For each EBNA3 complex, but not wt control, fluorescence intensity increased with time during the assay, consistent with DUB activity within each EBNA3 complex. Interestingly, the amount of USP46 isolated from EBNA3C-F-HA LCLs was comparable to that seen in EBNA3A or EBNA3B complexes (Fig 10B) further confirming the association of the USP46 DUB complex with EBNA3C in LCLs.
In order to assess the requirement for USP46 expression in LCLs, we attempted to knockout USP46 expression using CRISPR/Cas9 gene editing. We cloned two guide RNA (gRNA) sequences into the pX330 vector, which allows simultaneous expression of the Cas9 nuclease and a gRNA, and transferred this dual expression cassette into pCEP4 to allow hygromycin selection. Each construct was transfected into the 721 LCL and, as a control, 293T cells. We identified multiple 293T cell populations in which no expression of USP46 could be detected (Fig 11). In some cases, low level USP46 expression was detectable, which probably reflects the oligoclonal nature of this experiment. By contrast, we observed no more than an ~50% reduction in USP46 levels in the 721 LCLs (Fig 11, top panels). To ensure that the CRISPR/Cas9 mediated gene editing had worked as intended we PCR amplified and sequenced the targeted region for each USP46 gRNA from one cell population (c3 in each case). Sequencing results demonstrated that in each population, at least one allele had undergone an in-frame deletion (S6 Fig), which would be predicted to abrogate further Cas9 cleavage, but not disrupt the USP46 open reading frame. These sequencing results confirm that the USP46 gene was successfully edited in the 721 LCLs. As a further test, we performed an independent replicate of our USP46 CRISPR/Cas9 knockout in both cell lines (S7 Fig). In 293T cells, 22 of 39 clones were successfully knocked out for USP46 expression, whereas we did not observe any USP46 knockout among 39 randomly selected clones in the 721 LCL. This difference was highly statistically significant (p < 10–8) by a two-tailed Fishers exact test. Our results suggest that the USP46 gene is dispensable in 293T cells, but our inability to generate USP46 null LCLs using the same approach despite evidence for Cas9 mediated cleavage, implicates USP46 in LCL growth or survival.
To more directly assess whether EBNA3C interaction with the USP46/WDR48/WDR20, could recruit the DUB complex to chromatin, we performed chromatin immunoprecipitation (ChIP) assays for WDR48 in EBNA3C-HT LCLs and assayed for enrichment using qPCR [42]. We first examined the EBNA3C binding site within the p14ARF promoter that was recently reported to mediate recruitment of repressor complexes to this promoter. We observed an increase in ChIP signal in the presence of 4HT over that seen with 4HT withdrawal (Fig 12). As controls, we examined two additional sites located near the EIF2AK3 and PPIA genes which are bound by cell transcription factors, but not by EBNA3C [42,57]. At each of these locations, we observed no enrichment for in the permissive (4HT+) condition relative to the EBNA3C inactivation state (4HT-). Total levels of WDR48 and USP46 were unchanged upon 4HT withdrawal (Fig 12B) thus increased signal in the WDR48 ChIP at the p14 was not attributable to increased expression of the constituents of the USP46/WDR48/WDR20 complex by EBNA3C. These results are consistent with an EBNA3C dependent recruitment or stabilization of USP46/WDR48/WDR20 complex binding at the p14ARF promoter.
In this manuscript, we report the first detailed characterization of EBNA3A, EBNA3B, and EBNA3C complexes from LCLs. Despite an extensive literature on putative EBNA3 interacting proteins, endogenous EBNA3 complexes have not previously been isolated and subjected to LC/MS/MS analysis. The use of epitope tags permitted tandem affinity purification of these complexes and minimized the chances that observed differences in composition were attributable to differences in the antibodies used. Our approach has additional advantages in that the proteins were expressed at endogenous levels from their native promoters. Further, because these recombinant EBV genomes were able to transform primary B lymphocytes into LCLs, the epitope tags did not disrupt EBNA3 interactions essential for the transformation process. Despite the large array of proteins reported to interact with each EBNA3, we identified only a limited number of proteins specifically associated with the EBNA3s through the TAP procedure. This limited overlap between binary protein-protein interaction screens and protein complex composition is consistent with results from large scale protein interactome mapping efforts [58]. It is a formal possibility that the purified EBNA3 complexes associated with these cell proteins during the purification procedure. We view this as unlikely since it requires the simultaneous assumption that the interaction is sufficiently strong to be maintained during the TAP procedure, but does not occur endogenously despite these proteins being present in the same subcellular fraction. One important caveat for our analysis is that the TAP procedure, while highly specific, can be insensitive for weak interacting partners. Thus, the complexes defined in our study may be most appropriately described as EBNA3 “core” complexes.
Each EBNA3 complex was found to include RBPJ, a transcription factor in the Notch signaling pathway that is critical for EBNA3A and EBNA3C function in maintaining LCL growth. It was previously known that EBNA2 and EBNA3C exist in separate RBPJ complexes [49]. Our results demonstrate that all four RBPJ-interacting EBNAs (2, 3A, 3B, and 3C) form distinct RBPJ complexes. This has several important implications for the mechanisms by which EBNA3 proteins can act to regulate transcription and their ability to modulate EBNA2 activation [38,59,60,61]. First, despite binding to a distinct domain in the RBPJ N-terminus, EBNA3 proteins are able to exclude EBNA2 which interacts with the RBPJ beta trefoil domain. Further, because EBNA3A and EBNA3C must each interact with RBPJ to maintain LCL growth via p16 promoter repression, it is likely that two different RBPJ molecules, and hence, binding sites are required. Although we did not detect stable interactions among the EBNA3 complexes, it is conceivable that interactions described by other investigators at these promoters are required for cooperative gene regulation observed among EBNA3 proteins [62]. Indeed, it is tempting to speculate that EBNA3 proteins exert their cooperative effects by exploiting paired RBPJ sites in the human genome that are important for activation of specific genes by intracellular Notch [63]. Interactions with other transcription factors are also likely to be important for observed differences in EBNA3A, EBNA3B, and EBNA3C binding observed in ChIP-seq experiments [42,57].
Although we initially embarked on these experiments with the expectation of identifying unique EBNA3A, EBNA3B, and EBNA3C interacting partners, we unexpectedly identified another shared EBNA3 target: the USP46 and USP12 deubiqutinases (DUBs) and their chaperones WDR48 and WDR20 [64,65]. Because we did not find peptides corresponding to other EBNA proteins in these complexes, each EBNA3 protein appears to target RBPJ and the USP46/USP12 DUB complexes independently. Each EBNA3 bound most strongly to WDR48, and USP46 binding was enhanced by WDR48 co-transfection, consistent with WDR48 being the primary mediator of EBNA3 binding to the USP46/USP12 DUB complexes. It is notable that EBNA3B and EBNA3C target the WDR48 SLD2 domain, whereas EBNA3A interacts with the WD repeats of WDR48. Thus, the EBNA3 proteins bind to WDR48 via their highly divergent C-termini and do not all target the same WDR48 subdomains. Whether these distinct strategies for targeting the WDR48 protein are an accident of positive selection or account for differences between EBNA3A and EBNA3C’s roles in LCL growth is not clear. These binding site differences would allow for chromatin associated EBNA3A and EBNA3C to simultaneously bind (and potentially stabilize) a single WDR48 molecule, but we found no evidence for stable binding of both EBNA3A and EBNA3C in a single complex in our LC/MS/MS data. Nevertheless, we find that USP46/WDR48/WDR20 is a component of endogenous EBNA3 complexes purified from LCLs and is bound by domains of EBNA3A and EBNA3C that are important for p16 suppression and LCL growth. Taken together these findings suggest that these DUB complexes are specifically targeted by EBNA3 proteins as part of the EBV lymphocyte transformation strategy.
The ubiquitin specific proteases USP12 and USP46 are close paralogs, that are 89% identical over 357 residues and are both regulated by the WDR48 and WDR20 chaperones [64,66]. Although the physiologic role of these enzymes is incompletely understood, they appear to exhibit partially overlapping substrate specificity [64]. The more distantly related USP1 is also regulated by WDR48, but WDR20 is unique to USP12/USP46 complexes. We did not detect any USP1 peptides in our complexes, suggesting that the even though the EBNA3 proteins interact strongly with WDR48, they are selective for USP12 and USP46 complexes, possibly due to stabilizing interactions with WDR20 or the enzymes themselves. Although the PHLLP1 and PHLLP2 phosphatases have been reported to be components of the USP46 and USP12 complexes, they were not detectably associated with EBNA3 complexes, likely because these phosphatases are predominantly membrane-associated [67,68,69,70]. Because regulation of the steady state levels of the PHLLP phosphatases by USP46/USP12 is a critical regulatory step in the Akt signaling pathway, EBNA3 proteins might influence PHLLP protein levels, and hence, alter Akt signaling by binding this DUB complex. However, PHLLP1 was not detectable in our LCLs and we found no evidence that PHLLP2 levels or Akt phosphorylation were effected by EBNA3C inactivation (S8 Fig). Further, USP46 complexes were present in both the cytoplasm and the nucleoplasm of LCLs and this distribution was also observed in EBV negative B cells. Membrane associated USP46 and USP12 complexes have been implicated in regulating membrane trafficking of receptors, including Notch1 and GLR1 [71,72]. Although it is possible that EBNA3 proteins could affect this regulation, we do not favor this hypothesis as the levels of membrane associated USP46 in LCLs are not reduced, but slightly higher than that observed in EBV negative BL41 cells.
Our inability to derive USP46 null LCLs using CRIPSR/Cas9 gene editing is consistent with USP46 playing an essential role in LCL growth or survival that it does not play in 293T cells. Based on our observation that WDR48 plays a dominant role in mediating EBNA3 association with the USP46 DUB complex and binds to EBNA3A and EBNA3C domains that are important for regulation of p16, we suspect that the DUB complex interaction is important for transcriptional effects of the EBNA3 proteins. We considered the possibility that this interaction contributes to the long half-life of EBNA3 proteins; however the steady state levels of EBNA3A and EBNA3C WDR48 binding mutants were not detectably different than wild type (Fig 6) and there was no detectable difference in protein turnover in the presence of cyclohexamide (S9 Fig). Instead, our results suggest that EBNA3 proteins act as adaptor molecules to target USP46 complexes to promoters via interactions between RBPJ and other transcription factors. This is supported by our observation that WDR48 is recruited the p14ARF promoter in an EBNA3C dependent manner. Given the central role of ubiquitylation in transcriptional activation, we favor the hypothesis that the EBNA3 proteins recruit the DUB complex to remove ubiquitin molecules from other nuclear proteins. However, we are unaware of any unbiased methods for determining the substrates of DUB complexes. Using a candidate substrate approach, we investigated the possibility that ubiquitylation of histone H2A (H2A-Ub) and H2B (H2B-Ub) were targeted by these complexes as has been previously described in Xenopus [73]. However, we found global levels of H2A-Ub and H2B-Ub to be unaffected by the presence EBNA3 proteins (S10 Fig). We did observe decreased H2A-Ub at the p16 promoter upon EBNA3C-HT inactivation (S10 Fig) and no change in H2B-Ub levels. This is consistent with decreased polycomb repression of p16 upon EBNA3C-HT inactivation, but not consistent with recruitment of USP46 to chromatin playing a direct role in deubiquitylating histones at the p16 promoter.
In summary, we find that the USP46/USP12 DUB complexes are a highly associated with EBNA3 proteins in LCLs, interact with domains of EBNA3A and EBNA3C essential for LCL growth, and that DUB activity is preserved in these complexes. The substrates upon which these DUBs act upon in LCLs remain to be determined, despite our efforts to identify effects of the EBNA3 proteins on several candidates. Although we have focused on transcriptional effects of the EBNA3 proteins, it is likely that their ability to associate with the USP46/USP12 DUB complexes explains other observed EBNA3 activities as well, most notably their effects on the stability of cell proteins, including Mdm2, cyclin D, Gemin3, IRF4 or aurora kinase B [27,30,31,74,75]. We believe that the identification of the USP46/USP12 DUBs as components of the EBNA3 complexes in LCLs represents a significant advance in our understanding of the multitude of roles played by EBNA3 proteins in B lymphocyte transformation.
Lymphoblastoid cell lines (LCLs) described in this manuscript were derived by EBV transformation of peripheral blood B lymphocytes from de-identified donors, with written informed consent, which is approved by Partners IRB based on Helsinki recommendations.
293T (obtained from Elliott Kieff, Harvard Medical School), a human cell line transformed by adenovirus 5 and SV40 large T antigen [76], was cultured in Dulbecco’s modified Eagle’s (Gibco) medium. The “wild-type” LCL, created with an unmodified EBV BACmid, was a generous gift from Fred Wang [47] and the 721 LCL was obtained from Bill Sugden [77]. LCLs and P3HR1 ZHT cells [78], a type II EBV cell line, were cultured in RPMI 1640 (Gibco). Media was supplemented with L-glutamine (Gibco), penicillin-streptomycin (Gibco) and 10–15% FetalPlex (Gemini Bio-Products).
pBS-XS-EA contains the XbaI-SalI fragment from the EBV B95-8 genome containing the EBNA3C region, in which the C-terminus of the EBNA3C ORF is mutated to create EcoRI and AvrII sites (GATTCGATTAAGGGGATCCTAGG). pBS-EBNA3C-flag-HA-CAT was created from pBS-XS-EA by inserting an oligo encoding the flag and HA epitopes (AATTGGATGAATTCGCGGCCGCTGGAGGAGACTACAAGGACGACGATGACAAGTCGGCCGCTGGAGGATACCCCTACGACGTGCCCGACTACGCCTAGGACGCGT annealed to CTAGACGCGTGGATCCGCATCAGCCCGTGCAGCATCCCCATAGGAGGTCCGCGGCTGAACAGTAGCAGCAGGAACATCAGAGGAGGTCGCCGGCGCTTAAGTAGG) and a PCR product containing an FRT flanked CAT gene amplified from pKD3 using the primers (CACTGAATTCCTAGGTAGGTGTAGGCTGGAGCTGCTTCGAAG and TTGAATGAACGCGTCATATGAATATCCTCCTTAG). pSG5-EBNA3A-flag-HA-CAT and pSG5-EBNA3B-flag-HA-CAT were created by cloning the NotI/MluI fragment from pBS-EBNA3C-flag-HA-CAT into pSG5-EBNA3A and pSG5-EBNA3B which had been modified to create NotI/MluI sites allowing the flag-HA tag to be fused in-frame with the EBNA3A or EBNA3B ORFs, respectively. Plasmids for expression of EBNA3A and EBNA3C mutants have been previously described [21,23]. pSG5-flag-EBNA3A 1–919 was constructed by PCR amplify of pSG5-Flag-EBNA3A using primer pairs EBNA3A-C919 (ACAACAGCTGGCGGCCGCTACCTTCTAGTTTCAGGGCCTGTGACATTTTGGCCAC) and EBNA3A-N543 (CTCAGGGAATGGCATACCCATTAC), digested with NotI/BssHII and recloned into pSG5-Flag-EBNA3A or pCEP-Flag-EBNA3A. pSG5-Flag-E3A mCtBP1 was constructed same as previously described [44]. Flag-tagged EBNA3B 1–938, 1–544, and 394–938 were constructed by PCR amplification from pSG5-EBNA3B [23] using appropriate pairs of the following primers: E3B-N1 (TTGTACAAAACTGCAGGCATGAAGAAAGCGTGGCTCAG), E3B-C938 (AACTTTGTACGCGGCCGCTTACTCATCGTTCGATGTTTCAGAAG), E3B-C544 (TCACTCTCTAGCGGCCGCTAACCGGTGAAGACACAAGGGCCTC), and E3B-N394 (CTGCCGTACACTGCAGCAGTATACGGCAGGCCCGCGGTG), and cloned into the PstI/NotI sites of pSG5-flag [79]. The expression construct for Xpress-tagged-WDR48 was a kind gift of Jae Jung [51]. WDR48 1–634 (ΔSLD2) was constructed using WDR48-N399 (GCAAAGTGGATTTTGAAGATG) and WDR48-C634 (AGTTCAATTGCGGCCGCCTACAACACAGCAATATCTTCTTC). Resulting PCR product was digested with HpaI/NotI and recloned into pcDNA4-WDR48. WDR48 1–535 (ΔSLD2) was constructed using WDR48 C535stop (TGTTTCATTAAGCGGCCGCTACGTTAACTAACCCCCGGAATCTCGGCAGAGCAGC) and WDR48-N295 (GCACCAGTTCTCAAGATGGAGC). Resulting PCR product was digested with EcoRI/NotI and recloned into pcDNA4-WDR48. Then WDR48 1–535 (ΔSLD2) was digested with HpaI and religated to make WDR48 1–430 (ΔSLD1/2). Xpress tagged USP46 and WDR20 were constructed by amplifying the ORFs from ORFome 5.1 Entry clones (generous gifts of Marc Vidal) using the following primers: TGTACAAAAGGTACCTATGACTGTCCGAAACATCGCCTC + CTTTGTACTCGAGCGGCCGCTATTCTCTTGACTGATAGAATAAAATATATC and TGTACAAAAAGGTACCTATGGCGACGGAGGGAGGAGGGAAG + AACTTTGTACTCGAGCGGCCGCTAAGGATTAAAACTTACCACTTTACCAG, respectively. Resulting PCR products were KpnI/NotI digested and cloned into pCDNA4-HisMax-A (Invitrogen). All constructs were verified by sequencing.
In frame C-terminal flag-HA tags were fused to the EBNA3A, EBNA3B, or EBNA3C ORFs as follows: DNA fragments containing these fusions were either excised as an Xba/SalI fragment (from pBS-E3C-flag-HA-CAT) or PCR amplified (from pSG5-E3A-flag-HA-CAT and pSG5-E3B-flag-HA-CAT), using the E3A-F (TGACGTGGTCCAACATCAGC) and E3A-R (GCGTATTATCAGTGGGTGGAATGGAGGGGGACACACTTCTACACCTTTGCCATATGAATATCCTCCTTAG) or the E3B-F (ACTCCCATGCAGCTGGCACTAAGGGC) and E3B-R (CCCCGCAGTCTGTTGCCCCAGGGTTCATCCCAGTTCTTGTTACATGGGCGCATATGAATATCCTCCTTAG) primers. Fragments were recombined into an EBV-BACmid derived from the B95-8 genome using an inducible lambda red recombinase as previously described [47]. Following transient expression of FLP recombinase, single colonies were plated and screened for excision of the CAT gene.
Recombinant EBV-BACmids were transfected into P3HR1 ZHT cells, selected with puromycin and induced for replication by addition of 4HT. Viral supernatant were collected and used to transform peripheral blood B cells in to lymphoblastoid cells as previously described [23]. LCLs were screened by PCR for recombinant genomes containing the flag-HA fused in frame to the appropriate EBNA3 open reading frame and the absence of co-infecting P3HR1 genomes.
The following antibodies were used for Western blotting, immunoprecipitations and chromatin immunoprepicitation: mouse monoclonal antibodies against HA.11 (16B12, Covance), Flag (M2 and M5, Sigma), Xpress (R910-25, Invitrogen), alpha-tubulin (B-5-1-2, Sigma), Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH; 6C5, Millipore), Beta Actin (Sigma, A5441), BRG1 (PA5-17008, Thermo Scientific), LaminB (sc-6216, Santa Cruz), RBPJ (Hyb-T6709, Cosmo Bio Co), CtBP1 (Q13363, Millipore), WDR48 (HPA038421, SIGMA or rabbit polyconal sera, a kind gift of Alan D’Andrea), WDR20 (A301-657A, Bethyl Laboratories), USP46 (HPA007288, Sigma), p16 (clone JC8, sc-56330, Santa Cruz Biotechnology), NF-kB p65 (8242, Cell Signalling), EBNA1 (13-156-100, Advanced Biotechnologies), EBNA2 (PE2, [80]), EBNA3A (F115P, Exalpha Biologicals), EBNA3B (F120P, Exalpha Biologicals), EBNA3C (A10, [81]), LMP1 (S12, [82]), EBNALP (4D3, a kind gift of Yasushi Kawaguchi [83]). histone H2A (#07–146, Millipore), Ub-Histone H2A (#05–678, Millipore), histone H2B (07–371, Millipore), Ub-Histone H2B (#07–371, Millipore), PHLPP1 (A300-660A, Bethyl), PHLPP2 (A300-661A, Bethyl), Akt (#4691, Cell signaling), p-Akt (#4060, Cell signaling), and GFP (sc-5384, Santa Cruz).
Approximately 6x108 cells from an LCL transformed with either a wild-type EBV BAC or a recombinant EBV BAC expressing either EBNA3A-F-HA, EBNA3B-F-HA, or EBNA3C-F-HA were lysed in 10ml of TAP lysis buffer (1% (v/v) Igepal CA-630, 50mM TrisHCl [pH7.5], 140mM KCl, 10mM NaF, 1.5mM EDTA, and 5% glycerol) containing 10mM β-ME and EDTA-free Complete protease inhibitor (Roche, Mannheim, Germany). Lysed cells were incubated at 4°C for 30 minutes with constant rotation before being cleared by two rounds of centrifugation at 8500rpm for 10 minutes and one spin at 10,000rpm for 20 minutes. Supernatants were diluted as required to match total protein concentration as measured by Bradford assay (BioRad). A 50μl aliquot was saved for Western blot analysis and the remaining supernatants were incubated with 60μl of anti-Flag M2 agarose (Sigma) for 4 hours at 4°C with rotation. The beads were washed extensively with TAP lysis buffer before being eluted with 60μl of 0.4mg/ml Flag peptide (Sigma) in TAP buffer twice at 4°C for 30 minutes with shaking and once at 37°C for 30 minutes with shaking. Elutions were passed through Bio-Spin columns (Bio-Rad) to remove entrained agarose beads and pooled. Agarose-conjugated HA beads, 25μl per sample, (F7, Santa Cruz Biotechnology) were added to the pooled elutions and incubated overnight at 4°C with constant rotation. The beads were washed three times with TAP lysis buffer and eluted with 30μl of 0.4mg/ml HA peptide (Covance) twice at 37°C for 30 minutes with shaking; elutions were spun through Bio-Spin columns and pooled for LC/MS/MS analysis.
Eluted samples (50μl) were mixed with 10μl of 4X LDS Loading Buffer (Invitrogen) separated on a 10% Bis Tris NuPAGE MOPS gel (Invitrogen). Gels were fixed in destain solution (50% methanol and 7.5% acetic acid), rehydrated, stained with Simply Blue Safestain (Invitrogen), cut horizontally into one slice per sample, and destained until transparent. Gel slices were reduced with DTT, alklyated with iodoacetamide, and then rinsed with three alternating washes of 50 mM ammonium bicarbonate and acetonitrile. Each slice was then digested with trypsin by resuspending in 50mM ammonium bicarbonate/10% acetonitrile/5.5g/mL trypsin and incubating at 37°C for 24 hours. Peptides were extracted with one rinse of 50mM ammonium bicarbonate/10% acetonitrile followed by one rinse of 50% acetonitrile/0.1% formic acid, lyophilized, then rehydration in 20μL 96% water, 4% methanol, and 0.2% formic acid.
Digested samples were loaded into 96-well plates for mass spectrometry analysis on a LTQ-Velos Orbitrap XL (Thermo Fisher Scientific) instrument. For each run, 10μL of each re-constituted sample was injected onto an Easy nLC system configured with a 10cmx100um trap column and a 25cm x 100um ID resolving column (Thermo Scientific). Buffer A was 96% water, 4% methanol, 0.2% formic acid and Buffer B was 10% water, 90% acetonitrile, and 0.2% formic acid. Samples were loaded at 5μL a minute for 9 minutes, and a gradient from 0–60% B at 375nl/minute was run over 70 minutes, for a total run time of 115minutes (including regeneration, and sample loading). Velos-Orbitrap (Thermo Scientific) was run in a standard data dependent Top 10 configuration at 60K resolution for a full scan, with monoisotopic precursor selection enabled, and +1, and unassigned charge state rejected. MS2 fragmentation and analysis was performed in the ion trap using CID fragmentation.
Peptides were identified using SEQUEST (Thermo Fisher Scientific) through Protein Discoverer, version 1.2. MS/MS data were searched using 10ppm mass accuracy on precursor m/z and a 0.5Da window on fragment ions. Fully enzymatic tryptic searches with up to three missed cleavage sites were allowed. Oxidized methionines were searched as a variable modification and alkylated cysteines were searched as a fixed modification. Sequential database searches were performed using the NCBI RefSeqHuman FASTA database. Peptides for each charge state were filtered to a false discovery rate (FDR) of 1%.
Subcellular fractionation was performed using the subcellular protein fractionation kit (Thermo Scientific Pierce) according to the manufacturer’s instructions. For each fraction, an amount corresponding to that derived from 400,000 cells was resolved by SDS-PAGE and probed for EBNA3 proteins, RBPJ, and components of the USP46 complex (USP46, WDR20, and WDR48). Fraction purity was assessed by probing for tubulin, BRG1, Histone H2B, and LaminB.
In vitro deubiquitination assays using TAP purified EBNA3s complex and Ub-AMC (U-550, Boston Biochem) as a substrate were performed in 100uL of reaction buffer (20 mM HEPES-KOH at pH 7.8, 20 mM NaCl, 0.1 mg/mL BSA, 0.5 mM EDTA, 20mM beta-mercaptoethanol). Fluorescence signal was monitored in VICTOR X5 multilabel plate reader (Perkin Elmer).
Transfected 293T cells which were harvested from 10cm tissue culture dishes or ten million of LCLs were lysed into IP lysis buffer (1% (v/v) Igepal CA-630, 40mM TrisHCl [pH7.5], 150mM NaCl, and 10mM MgCl2) supplemented with fresh 0.015mg/mL aprotinin (Sigma), 0.5mM PMSF, and 1ug/ml Leupeptin. Lysates were incubated at 4°C for 30 minutes with rotation and cleared by centrifugation at 10,000x g for 15 minutes. Supernatants were pre-cleared by rotating with Sepharose (Sigma) for 1 hour at 4°C and then incubated with anti-Flag M2 agarose, anti-HA magnetic beads (cloneTANA2, MBL), or protein A/G for 2 hours at 4°C with rotation. The beads were washed extensively with IP lysis buffer and either eluted with 0.4 mg/ml Flag peptide or 0.4mg/ml HA peptide in IP lysis buffer at 37°C for 30 minutes with shaking or resuspend into SDS sampling buffer. The proteins were analyzed by Western blotting.
Total-cell lysates or immunoprecipitated proteins were separated by sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis, blotted onto nitrocellulose membrane, and probed with appropriate antibodies. After extensive washing, horseradish peroxidase conjugated secondary antibodies (Jackson Immuno Research) were applied. After incubation for 1–2 hours the membrane was washed again, and developed with chemiluminescence reagent (Perkin Elmer). Western blots were exposed on film and visualized on a KODAK Image Station 4000R (Kodak Molecular Imaging Systems).
Five million of EBNA3AHT-infected LCLs [23] were transfected with 2ug of oriP plasmid DNA expressing EBNA3A, EBNA3A mutant or control plasmid. LCLs were harvested during log-phase growth, washed with complete medium, resuspended in 100ul of buffer V with DNA in a cuvette, transfected using program X-001 of Amaxa Nucleofector (Lonza), and cultured for 3 days in LCL-conditioned medium with 4-hydroxy-tamoxifen (4HT). Cells were then washed with PBS twice, and cultured in complete medium with or without 4HT. Every 4 to 7 days, cell numbers were counted, cultures were split, and the total numbers of viable cells relative to those of the initial culture were calculated.
Cas9 mediated editing of the USP46 gene was accomplished by cloning either of two targeting 20mers for the gRNA (CRISPR-1 in exon3: AAACTTGCTGACGTGCCTGG and CRISPR 2 in exon4: TATTGCGGACATCCTTCAGG) [84,85] into the pX330 plasmid [86]. The Cas9 expression cassette and gRNA were excised from pX330 by PciI/ NotI digestion and cloned into pCEP4 with a modified polylinker sequence, which allowed for hygromycin selection via an self-maintaining episomal plasmid.
Five million of EBV transformed LCLs were harvested during log-phase growth, resuspended in 100ul of buffer Ingenio with pCEP-CRISPR-USP46 plasmid, transfected using program U-001 of Amaxa Nucleofector, and cultured for 2 days in RPMI1640 complete medium. 20,000 cells were plated on 96 well culture plate using RPMI1640 complete medium with 300ug/ml hygromycin for one month. For 293T cells, one million cells were transfected with pCEP-CRISPR-USP46 plasmid using Effectene, recovered for 48 hours, and then subjected to hygromycin selection. Hygromycin resistance cells were harvested and screened with DNA PCR using primer pairs (CRISPR-1 F: GGTGAGCTGGACTCCAATACAGGG and R: GCCAGCTCTTCCTTTTGAGGAGAT or CRISPR-2 F: GGAGGCAGAGGTTGCAGTGAACTG and R: GCAATCACATGCAACATAGCGTAC) and Western blotting analysis. These primers were also used for Sanger sequencing of PCR products. USP46 Western blot signals were quantified and normalized to tubulin signal. For statistical analysis any cell line exhibiting >25% normalized USP46 signal was considered positive.
ChIP assays were performed as described previously [87]. Briefly, 2x106 cells per ChIP were fixed in 1% (wt/vol) formaldehyde and sonicated using cup horns sonication system (Qsonica). After extract clearing by centrifugation, supernatants were diluted and incubated with protein G agarose with salmon sperm DNA (Millipore) for 1 h with rotation at 4°C. Protein G agarose was pelleted and supernatants were used in ChIP experiments. One or two micrograms of antibody were added per 2x106 cells, followed by incubation overnight at 4°C with rotation. Purified DNA was quantified using gene specific primers and iTaq universal SYBR green supermix (Bio-Rad) using a CFX96 touch real-time PCR detection system (Bio-Rad). Primers used for these experiments were as follows: p16 TSS [32], p14ARF [42], EIF2AK3 (F: CTTCCGGACGCAATTACCAATGAG and R: GTAGGAAAGGTATTCCGGGAACTG) or PPIA [57].
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10.1371/journal.pgen.1000672 | Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations | Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic β-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08×10−66. The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1–3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10−4 or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases.
| Although several rare monogenic diseases are caused by defects in enzymes involved in sphingolipid biosynthesis and metabolism, little is known about the major variants that control the circulating levels of these important bioactive molecules. As well as being essential components of plasma membranes and endosomes, sphingolipids play critical roles in cell surface protection, protein and lipid transport and sorting, and cellular signalling cascades. Experimental evidence supports a role for sphingolipids in several common complex chronic metabolic, cardiovascular, or neurological disease processes. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, and discovering the genetic variants that influence their circulating concentrations is an important step towards understanding how the genetic control of sphingolipids might contribute to common human disease. We have identified 32 variants in 7 genes that have a strong effect on the circulating plasma levels of 33 distinct sphingolipids, and 43 matched metabolite ratios. In a series of 3 German MI studies, we see association with MI for variants in 3 of the genes tested. Further cardiovascular, metabolic, neurological, and psychiatric disease associations can be tested with the variants described here, which may identify additional disease risk and potentially useful therapeutic targets.
| Sphingolipids are essential components of plasma membranes and endosomes and are believed to play critical roles in cell surface protection, protein and lipid transport and sorting, and cellular signalling cascades. They are known to have roles in both health and disease [1],[2]. Several rare monogenic diseases associated with sphingolipid biosynthesis and turnover have been identified such as metachromatic leukodystrophy and GM1- and GM2-gangliosidosis, Niemann-Pick, Gaucher, Krabbe, Fabry, Farber, Tay-Sachs and Sandhoff diseases [3]. Defective biosynthesis due to mutations in genes involved in sphingolipid metabolism (e.g.serine palmitoyl transferase (SPTLC1) [4]; ceroid-lipofuscinosis, neuronal 8 (CLN8) [5]; and ceramide synthase (LASS1) [6]) can also lead to disease. Moreover, natural fungal inhibitors of ceramide synthase can result in a broad spectrum of effects including equine leucoencephalomalacia, porcine pulmonary oedema syndrome and liver cancer in rats [7], demonstrating the wide range of processes that include cell proliferation, differentiation and apoptosis underpinned by sphingolipid metabolism. Identifying common genetic variants that influence the balance between individual sphingolipid concentrations represents an important step towards understanding the contribution of sphingolipids to common human disease. To achieve this goal, we conducted a genome-wide association study (GWAS) on plasma levels of 33 major sphingolipid species (24 sphingomyelins and 9 ceramides) in five European populations, both within and across populations. The traits were analysed by individual species (sphingomyelins (SM), dihydrosphingomyelins (Dih SM), ceramides (Cer) and glucosylceramides (GluCer)) or aggregated into groups of species with similar characteristics (e.g. unsaturated ceramides), and expressed as absolute concentrations or as molar percentages within sphingolipid classes (mol%). In addition we examined 43 matched metabolite ratios between the traits as a surrogate for enzyme activity [8] in separate clusters designed to examine sphingolipid metabolism (11 ratios), desaturation (16 ratios) and elongation (16 ratios). All traits displayed substantial heritabilities in that much of the observed variation in sphingolipid levels could be attributed to genetic variation among individuals in each population.
The GWAS for single species and matched metabolite ratios revealed a total of 32 SNPs in five distinct loci reaching genome-wide significance (p values ranging down to 9.08×10−66) (Table 1, Figure 1 and Figure 2, and Table S1 and Table S3). The direction and magnitude of the observed effect sizes for the 22 variants identified in the analysis of single species are summarized in Table 1 with full details in Table S1. For three of the regions (chromosomal regions 4p12, 14q23.2 and 19p13.2), p values reached genome-wide significance in the largest cohort (South Tyrol), and the effect was replicated in the other populations. For two additional loci (11q12.3 and 20p12.1), signals bordered on genome-wide significance in South Tyrol alone, were consistent between all 5 populations and reached genome-wide significance in the meta-analysis. In the single species analysis, the strongest associations for three of the loci (11q12.3, 14q23.2 and 19p13.2) were found with sphingomyelins and dihydrosphingomyelins. The 4p12 locus showed the strongest association with serum glucosylceramides and the 20p12.1 locus showed the strongest association with serum ceramide concentrations. Table S2 shows the p-values for the individual SNPs when included in a multiple regression model, and the fraction of single sphingolipid variance explained by sex, age and all SNPs in the model together. Taken together, the SNPs explain up to 10.1% of the population variation in each trait. Ratios of matched (substrate/product) pairs have been shown to reduce variation in the dataset and increase power of association several orders of magnitude [8]. Analysis of 43 matched metabolite ratios (Table S3) indeed increased power of association up to 10 orders of magnitude on some of the 22 variants above, and revealed an additional 10 SNPs over the same 7 genes reaching statistical significance (see Table S3). Surprisingly no signals from new genes reached genome-wide significance, highlighting the fact that across the 5 cohorts analysed here, the 7 genes identified are the major genes associated with circulating sphingolipid concentrations. Among the 32 significant individual SNPs (Table S4) variants in LASS4 explain up to 7.5% of the variance in some ratios (i.e. in SM16:0/SM18:0), SGPP1 variants explain up to 12.7% of the variance (i.e in SM14:0/SM16:0), FADS1–3 variants explain up to 3.5% of the variance (e.g. in SM16:0/SM16:1), SPTLC3 variants explain up to 4.9% of the variance (e.g. in SM14:0/SM16:0 and SM24:0/Cer24:0), and ATP10D variants up to 4.2% of GluCer/Cer variance. Combined effects of several genes (i.e. SPTLC3 and SGPP1) explains up to 14.2% of the variance in medium chain SM ratios (SM14:0/SM16:0) and, in combination with LASS4, up to 11.2% of the variance in long-chain sphingomyelin ratios (SM22:0/SM24:0).
All SNPs within the associated chromosomal regions are located within or are in linkage disequilibrium (LD) with genes that encode enzymes involved in sphingolipid biosynthesis or intracellular transport (Figure 2). The ATPase, class IV, type 10D (ATP10D) gene, located at chromosome 4p12, encodes a putative serine-phospholipid (phosphatidylserine, ceramide) translocase [9]. Three SNPs at this locus showed genome-wide significant associations with glucosylceramides (C16:0, C24:1) (Table 1, Table S1), with an additional five variants revealed in the ratio analysis (Table S3). SNP rs10938494 gave the strongest association in the single species analysis (p-values of 1.68×10−9 in South Tyrol and 8.03×10−19 in the joint analysis), and was among the strongest association in the ratio analysis (p = 3.04×10−16) along with rs2351791 (p = 6.58×10−17).
Three fatty-acid desaturase genes (FADS1, 2 and 3) are located adjacent to one another in a cluster at the 11q12.3 locus. The FADS1–3 genes encode enzymes that regulate the desaturation of fatty acids by the introduction of double bonds between defined carbons of the fatty acyl chain. Seven SNPs at this locus, distributed in and around the three genes, reached statistical significance in the single species analysis for sphingomyelin 16∶1 levels in the joint analysis, with p-values ranging from 2.99×10−11 (rs174449, close to FADS3) to 6.60×10−13 (rs1000778, in FADS3) (Table 1). The ratio analysis revealed an additional SNP at this locus within the FADS3 gene (rs174450, Table S3), and improved association results for other SNPs several orders of magnitude (e.g. rs1000778 p = 1.29×10−15). Fatty acids are built into ceramides by the ceramide synthases (e.g. LASS4). Unsaturated ceramides can be synthesized exclusively by the introduction of unsaturated fatty acids into the sphingosine/sphinganine chain. The pivotal role of FADS1–3 in the synthesis of unsaturated ceramides is confirmed by the strong associations of SNPs in this cluster to the mono-unsaturated sphingomyelins 16∶1, 18∶1 and 20∶1, which are the end-products of the ceramide biosynthesis pathway (Table 1, Table S1), and the ratios between these and their respective unsaturated precursors (Table S3). Previous studies of sphingolipid metabolites and poly-unsaturated fatty acids (PUFA) have demonstrated associations to SNPs, including rs174537, over the FADS1 and FADS2 genes in several populations [8],[10],[11].
The sphingosine-1-phosphate phosphohydrolase 1 gene (SGPP1) at the 14q23.2 locus belongs to the super-family of lipid phosphatases that catalyze the generation of sphingosine and, together with irreversible cleavage by sphingosine-1-phosphate (S1P)-lyase, strongly influences the pathway of S1P to ceramide (Figure 3). Six SNPs in and around this gene demonstrate the most significant associations with circulating sphingomyelin C14–C16/C22–C24 and dihydrosphingomyelin concentrations (Table 1) in the single species analysis, with a further two SNPs revealed in the ratio analysis. SNP rs7157785 showed the strongest association with sphingomyelin 14∶0 relative content (molar percentage: mol%) with genome-wide significant p-values in all five populations, particularly in the South Tyrol population (p = 2.53×10−28) and joint analysis (p = 9.08×10−66), and demonstrated the most significant association in the ratio analysis. Enhanced SGPP1 activity leads to elevated ceramide levels by shifting the stochiometric balance of SGPP1/S1P-lyase towards sphingosine and ceramide production.
Five SNPs at the 19p13.2 locus showed some of the strongest associations with sphingolipids and all lie within LASS4, the gene encoding LAG1 longevity assurance homologue 4. In the single species analysis SNP rs7258249 showed the highest genome-wide significant association with sphingomyelin 18∶0 mol% (South Tyrol p = 1.04×10−15 and joint analysis p = 2.28×10−27). Several LASS4 SNPs showed statistically significant association with the sphingomyelin species C18 to C20 and with ceramide C20∶0 (Table 1 and Table S1). In the ratio analysis, however, associations strengthened by several orders of magnitude (p value) over those with these SNPs, with rs1466448 demonstrating the most statistically significant association (p = 4.05×10−35). LASS family members, six of which have been identified in mammals (LASS1–6), are de novo ceramide synthases (CerS) that synthesize dihydroceramide from sphinganine and fatty acid (Figure 3). Moreover, LASS enzymes catalyze the re-synthesis of ceramide and phytoceramide from sphingosine and phytosphingosine respectively, which are cleavage products of alkaline ceramidase activity in endoplasmic reticulum (ER) membranes.
The 20p12.1 locus contains the serine palmitoyltransferase long chain base subunit 3 gene (SPTLC3) encoding a functional subunit of the SPT enzyme-complex that catalyzes the first and rate-limiting step of de novo sphingolipid synthesis. One SNP (rs680379) demonstrated association for unsaturated ceramide in the South Tyrol population alone (p = 1.77×10−07) and was genome-wide significant in the joint analysis (p = 8.24×10−15). Significant association was observed also with C16 to C24 ceramides and the sphingomyelins 16∶1 and 17∶0 (Table 1 and Table S1). The ratio analysis strengthened association at this variant (p = 3.3×10−20 for the metabolite ratio SM24:0/Cer24:0) and revealed two further significant variants at this locus (rs3848751 and rs6078866, Table S3).
As matched metabolite ratios can serve as a proxy for enzyme activity [8], in a complementary candidate gene approach, we investigated association signals in our combined single species and ratio datasets at 624 SNPs within or near 40 genes that encode enzymes involved in sphingolipid metabolism, in order to identify the most promising variants within these genes for further analysis. Of these, a total of 70 variants in or near 23 of the genes demonstrate association p values of 10−4 or less (Table S5).
Sex and age adjusted single sphingolipids species displayed strong phenotypic correlations with circulating plasma lipoproteins especially with total cholesterol or LDL-cholesterol (Table S6, e.g. between the sum of saturated sphingomyelin species and total cholesterol: 0.788/0.717/0.794/0.733/0.773 in respectively NPHS/ERF/SOUTH TYROL/CROATIA/ORKNEY; or SM16:1 and total cholesterol 0.737/0.631/0.671/0.6/0.638). This is in agreement with recent lipid profiling of lipoprotein fractions, showing higher proportions of sphingomyelin and ceramides in the LDL fraction [12]. However, among the GWAS hits uncovered in this analysis, only the FADS1–3 cluster overlaps with those reported in large meta-analysis of circulating serum lipoproteins levels (strongest with total and LDL-cholesterol levels) [13]. Several of the variants reported here display suggestive associations with classical lipids in the EUROSPAN cohorts (Table S7). All eight SNPs in the FADS1–3 cluster associate with HDL-cholesterol levels (age-sex adjusted p values between 0.06 and 0.0041) similar to previous observations [8]. Interestingly, the sex-specific age-adjusted results show that these associations seem driven by the association found in males (lowest p = 0.0037 at rs174546). Association with HDL-cholesterol in males is also seen with SNPs in ATP10D (rs2351791, p = 0.01) and SPTLC3 (rs3848751, p = 0.0047). SNPs at ATP10D also associate with LDL-cholesterol, albeit weakly in the total population (rs469463, p = 0.034). In the males only, variants at LASS4 (rs28133, p = 0.043) and SPTLC3 (rs3848751, p = 0.022 and rs6078866, p = 0.02) also associate weakly with LDL-cholesterol levels. Five variants in FADS1–3 and two in ATP10D associate with triglyceride levels, with lower p values in males than in the whole group (p values from 0.017 to 0.009 in FADS1–3 and 0.0071 for rs17462424 in ATP10D). Association of FADS variants with triglyceride levels has also been observed in other populations [8]. As previously highlighted [8], the p values for association with the sphingolipids species were orders of magnitude stronger than with these classical lipids.
Given the reported associations to classical lipids and cardiovascular disease with variants at the FADS1–3 locus [10],[13],[14], and the evidence from functional studies of a role for sphingolipids in atherosclerotic plaque formation and lipotoxic cardiomyopathy [15], we looked in silico in a series of three age- and sex-adjusted GWAS datasets of German myocardial infarction (MI) case-control studies (Ger MIFS I [16] Ger MIFS II [17] and Ger MIFS III (KORA), unpublished) for evidence of association with the major variants associating with sphingolipid concentrations. Variants within three of the genes (ATP10D, FADS3 and SPTLC3) associate with MI in one or more of the studies (Table 2). The protective odds ratios observed for variants in ATP10D and SPTLC3 are on alleles correlating positively with higher metabolite/lower ceramide ratios (i.e. GluCer/Cer and SM/Cer), in support of evidence that increased enzyme/transporter activity that lowers ceramide levels might alleviate the pro-apoptotic effects seen with higher ceramide levels in cardiomyocytes [18]. As previously hypothesised, carriers of FADS variants that are associated with higher desaturase activity may be prone to a proinflammatory response favoring atherosclerotic vascular damage [14].
Direct experimental evidence indicates a role for sphingolipids in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta cell failure, insulin resistance and type 2 diabetes mellitus (T2D) [15]. Until now, the genetic variants that influence circulating sphingolipid concentrations in the general population have been characterized in relatively small cohorts [8]. Here we identified genetic variation with a significant effect on the biosynthesis, metabolism or intracellular trafficking of some of the major sphingolipids species in a large diverse group of European population samples. The SNPs showing association with circulating sphingolipids explain up to 10.1% of the population variation in each trait and 14.2% of some matched ratios (Tables S2 and Table S4). Four of the five loci identified contain genes encoding proteins that are either responsible for de novo ceramide synthesis or for ceramide re-synthesis from sphingosine/sphinganine-phosphates or both (SPTLC3, LASS4, FADS1–3 and SGPP1). Increases in all of these enzymatic activities are predicted to elevate the “ceramide-pool”. The associations are observed not only with ceramides, but also with sphingomyelins, indicating that a considerable proportion of ceramide is converted into the large and more stable “sphingomyelin-pool”. None of the genes involved in ceramide degradation or ceramide-related signaling is genome-wide significantly associated with the traits analyzed, indicating the primary role of genes related to ceramide production in the genetic control of ceramide levels. Of these four loci, the FADS1–3 gene cluster has been the most frequently to be reported linked with disease in recent literature. Variants within in this region have been associated with cardiovascular disease and classic lipid risk factors such as cholesterol levels [10],[13],[14]. Reported variants demonstrating association in these reports (rs174547, rs174570, rs174537 and rs174546) are within the FADS1 and FADS2 genes, but expression studies indicate complex regulation in this region, with the FADS1 SNP rs174547 showing correlation with expression of both FADS1 and FADS3 genes [19], while the FADS1 SNP rs174546 correlates with FADS1 but not FADS2 expression [10]. Our strongest associations with both sphingolipid levels and MI are in or nearest the FADS3 gene, with variants showing less marked association with cholesterol levels than that observed with variants over FADS1 and FADS2 genes (Table S7). It is known that sphingomyelin and ceramides can modulate the atherogenic potential of LDL [20]. Further functional studies will be necessary to determine whether the active mechanism is through FADS3 alone, or in concert with FADS1, FADS2 or both.
Neurological phenotypes associated with FADS2 include attention-deficit/hyperactivity disorder [21] and the moderation of breastfeeding effects on IQ [22]. Little is published regarding disease association with variants at the other four major loci described here. However, a reported association between expression levels of SGPP1 with Schizophrenia [23] along with changes in SPTLC2 (with variants identified in our candidate SNP search –Table S4) and ASAH1, highlights the importance of testing variants in these genes with multiple neurological and psychiatric diseases. Additional neurological associations with candidate genes listed in Table S4 include SGPL1 in Alzheimer's disease [24] and GBA with Parkinson's disease and dementia with Lewy bodies [25],[26]. The wider possible involvement of genes within pathways of ceramide metabolism in Lewy body disease has also been recently reviewed [27].
The fifth locus contains ATP10D, a cation transport ATPase (P-type) type IV subfamily member. The type IV subfamily is thought to be an important regulator of intracellular serine-phospholipid trafficking however the exact function or transport specificity of ATP10D has not yet been described [9]. A novel functional finding of this study is the specificity of the association of ATP10D SNPs to glucosylceramides (among the species tested so far), which provides the first evidence for the functional involvement of ATP10D in intracellular transport of specific species of ceramide (Figure 3). Impaired function of ATP10D may therefore lead to enhanced exposure of ceramide to glucosyltransferases, forming higher concentrations of glycosylceramides that are released into the plasma compartment or may elevate serum glucosylceramide concentrations by impaired transport of glycosylceramide to the trans Golgi network. Mutations of ATP10D (C57BL/6J(B6)) in mice result in low HDL concentrations and these mice develop severe obesity, hyperglycaemia and hyperinsulinaemia when fed on a high-fat diet [28]. Based on the mouse model, increased circulating glucosylceramides in connection with ATP10D function would be one plausible mechanism of contributing to weight gain and early insulin resistance. From the novel association of SNPs in ATP10D to MI (Table 2) seen in German studies, further investigation of the specific role of glucosylceramides in MI and other cardiovascular diseases is warranted.
Thus, sphingolipids play a role in pathological processes leading to common complex diseases, and identification of genetic variants that influence the balance between individual sphingolipid species is an important first step into dissecting out the genetic components in such processes. Associations between the SNPs identified in this study, some of which have a strong effect on the circulating plasma levels, and complex metabolic, cardiovascular, inflammatory and neurological diseases in which a role for a sphingolipid-dependent mechanism is implicated can now be investigated. Modulation of sphingolipids in vivo has demonstrated that this may be a successful preventative strategy for diseases in which sphingolipids play a role, lending hope that, once such disease contributions are identified, successful therapeutic regimes may subsequently be identified.
All studies were approved by the appropriate Research Ethics Committees. The Northern Swedish Population Health Study (NSPHS) was approved by the local ethics committee at the University of Uppsala (Regionala Etikprövningsnämnden, Uppsala). The ORCADES study was approved by the NHS Orkney Research Ethics Committee and the North of Scotland REC. The Vis study was approved by the ethics committee of the medical faculty in Zagreb and the Multi-Centre Research Ethics Committee for Scotland. The ERF study was approved by the Erasmus institutional medical-ethics committee in Rotterdam, The Netherlands. The MICROS study was approved by the ethical committee of the Autonomous Province of Bolzano. For the German MI studies (GerMIFS-I,-II and –III(KORA), local ethics committees approved the studies and written informed conset obtained as published previously.
The ERF study is a family-based study which includes over 3000 participants descending from 22 couples living in the Rucphen region in the 19th century. All descendants were invited to visit the clinical research center in the region where they were examined in person and where blood was drawn (fasting). Height and weight were measured for each participant. All participants filled out questionnaire on risk factors, including smoking. The 800 participants included in the lipidomics studies consisted of the first series of participants.
The MICROS study is part of the genomic health care program ‘GenNova’ and was carried out in three villages of the Val Venosta on the populations of Stelvio, Vallelunga and Martello. This study was an extensive survey carried out in South Tyrol (Italy) in the period 2001–2003. An extensive description of the study is available elsewhere [29]. Briefly, study participants were volunteers from three isolated villages located in the Italian Alps, in a German-speaking region bordering with Austria and Switzerland. Due to geographical, historical and political reasons, the entire region experienced a prolonged period of isolation from surrounding populations. Information on the health status of participants was collected through a standardized questionnaire. Laboratory data were obtained from standard blood analyses. Genotyping was performed on just under 1400 participants with 1334 available for analysis after data cleaning. All participants were included in the lipidomics studies.
The Swedish samples are part of the Northern Swedish Population Health Study (NSPHS) representing a family-based population study including a comprehensive health investigation and collection of data on family structure, lifestyle, diet, medical history and samples for laboratory analyses. Samples were collected from the northern part of the Swedish mountain region (County of Norrbotten, Parish of Karesuando). Historic population accounts show that there has been little immigration or other dramatic population changes in this area during the last 200 years.
The Orkney Complex Disease Study (ORCADES) is an ongoing family-based, cross-sectional study in the isolated Scottish archipelago of Orkney. Genetic diversity in this population is decreased compared to Mainland Scotland, consistent with the high levels of endogamy historically. Data for participants aged 18 to 100 years, from a subgroup of ten islands, were used for this analysis. Fasting blood samples were collected and over 200 health-related phenotypes and environmental exposures were measured in each individual. All participants gave informed consent and the study was approved by Research Ethics Committees in Orkney and Aberdeen.
The Vis study includes a 986 unselected Croatians, aged 18–93 years, who were recruited into the study during 2003 and 2004 from the villages of Vis and Komiza on the Dalmatian island of Vis [30],[31]. The settlements on Vis island have unique population histories and have preserved their isolation from other villages and from the outside world for centuries. Participants were phenotyped for 450 disease-related quantitative traits. Biochemical and physiological measurements were performed, detailed genealogies reconstructed, questionnaire of lifestyle and environmental exposures collected, and blood samples and lymphocytes extracted and stored for further analyses. Samples in all studies were taken in the fasting state.
Lipids were quantified by electrospray ionization tandem mass spectrometry (ESI-MS/MS) in positive ion mode as described previously [32],[33]. EDTA plasma (serum for South Tyrol) samples were quantified upon lipid extraction by direct flow injection analysis using the analytical setup described by Liebisch et al. [33]. A precursor ion scan of m/z 184 specific for phosphocholine containing lipids was used for phosphatidylcholine (PC) and sphingomyelin (SM) [33]. Ceramide and hexosylceramide were analyzed using a fragment ion of m/z 264 [32]. For each lipid class two non-naturally occurring internal standards were added and quantification was achieved by calibration lines generated by addition of naturally occurring lipid species to plasma. Deisotoping and data analysis for all lipid classes was performed by self programmed Excel Macros according to the principles described previously [33]. Nomenclature of sphingomyelin species is based on the assumption that d18∶1 (dihydroxy 18∶1 sphingosine) is the main base of plasma sphingomyelin species, where the first number refers to the number of carbon atoms in the chain and the second number to the number of double bonds in the chain.
DNA samples were genotyped according to the manufacturer's instructions on Illumina Infinium HumanHap300v2 (except for samples from Vis for which version 1 was used) or HumanCNV370v1 SNP bead microarrays. Four populations have 318,237 SNP markers in common that are distributed across the human genome, with Vis samples having 311,398 SNPs in common with the other populations. Samples with a call rate below 97% were excluded from the analysis. Sphingolipid measurements were available for analysis following quality control assessment for 4110 study participants.
Genome-wide association analysis was performed using the GenABEL package in R [34]. A score test was used to test for association between the age- and sex-adjusted residuals of sphingolipid traits (both as absolute concentrations and as relative content of the total sphingolipid pool: mol%) and SNP genotypes using an additive model. The Genomic Control procedure [35] was used to account for under-estimation of the standard errors of effects, which occurs because of pedigree structure present in the data [36]. For the most interesting results and the species ratios, we re-analysed the data using “mmscore” function, a score test for family-based association [37], as implemented in GenABEL. The relationship matrix used in analysis was estimated using genomic data with “ibs” (option weight = “freq”) function of GenABEL. This analysis, accounting for pedigree structure in an exact manner, allowed for unbiased estimation of the effects of the genetic variants (adjusted for age and sex). The results from all cohorts were combined into a fixed-effects meta-analysis with reciprocal weighting on standard errors of the effect-size estimates, using MetABEL (http://mga.bionet.nsc.ru/~yurii/ABEL/). Thresholds for genome wide significance were set at a p value of less than 1.57×10−7 (0.05/318,237 SNPs) for the individual populations. For the overall meta-analysis we chose to use the conservative threshold of 7.2×10−8 [38]. Since many of the traits tested and especially the ratios demonstrate high degrees of correlation, introducing a suitable statistical correction the multiple testing of the 76 correlated traits would be complex. Since Bonferroni correction (unsuitable in this instance) would lower thresholds to values between p = 10−9 to 10−10, and since all five genomic regions have variants with p values <10−10, we report the age-sex corrected p values alone. The threshold for replication of significant results from one population in other cohorts was set at a p-value less than 0.05 divided by the number of SNPs tested. All significant variants reported are in Hardy-Weinberg Equilibrium, and effect directions are consistent across all five populations.
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10.1371/journal.pntd.0006345 | Seroepidemiology of helminths and the association with severe malaria among infants and young children in Tanzania | The disease burden of Wuchereria bancrofti and Plasmodium falciparum malaria is high, particularly in Africa, and co-infection is common. However, the effects of filarial infection on the risk of severe malaria are unknown. We used the remaining serum samples from a large cohort study in Muheza, Tanzania to describe vector-borne filarial sero-reactivity among young children and to identify associations between exposure to filarial parasites and subsequent severe malaria infections. We identified positive filarial antibody responses (as well as positive antibody responses to Strongyloides stercoralis) among infants as young as six months. In addition, we found a significant association between filarial seropositivity at six months of age and subsequent severe malaria. Specifically, infants who developed severe malaria by one year of age were 3.9 times more likely (OR = 3.9, 95% CI: 1.2, 13.0) to have been seropositive for filarial antigen at six months of age compared with infants who did not develop severe malaria.
| In this paper, we used a multiplexed, serologic assessment to identify children with previous or current exposure to or infection with filarial parasites or S. stercoralis (a soil transmitted helminth), enhancing our understanding of co-infections in early childhood. We identified an increasing prevalence of filarial antibodies over time in a population of children as young as 6 months old. In addition, we found a significant association between filarial seropositivity at six months of age and subsequent severe malaria.
| Parasitic helminths and malaria are both highly prevalent globally and overlap extensively in tropical areas [1–3]. In 2016, more than 216 million cases of malaria were estimated to occur with 89 percent of cases occurring in Sub-Saharan Africa [4]. Nearly all cases of severe malaria are due to infection from Plasmodium falciparum [5], although P. vivax is increasingly regarded as a potential cause of severe malaria infection [6].
Lymphatic filariasis, caused exclusively by the helminth Wuchereria bancrofti in Africa, affects an estimated 120 million to 130 million persons globally [7]. W. bancrofti is highly endemic throughout Tanzania and especially in the northeast region [8–10], with an estimated 34 million people at risk of filarial infection and 6 million people affected by filariasis [11]. Filariasis has an overlapping geographical distribution with malaria in Tanzania [12–14] where it shares the same Anopheles vector as P. falciparum [15]. Co-infection is frequent [16, 17], with between 0–11% of school age children co-infected, depending on local ecology, in one study from Mvomero District, Tanzania [16]. In 2000 the Tanzanian National Lymphatic Filariasis Elimination Programme (NLFEP) was launched to distribute ivermectin and albendazole, highlighting the disease burden in this region [18], however the program is limited to individuals greater than 5 years of age.
The interaction of helminth and malaria co-infections is not well understood, and studies have had contradictory conclusions related to the inter-infection effects [19–23]. Differences in W. bancrofti prevalence by age have been previously described [16, 24, 25], but few studies have focused on filarial infection among infants, who are most likely to suffer severe malaria in the context of high malaria endemicity. The effects of W. bancrofti infection on the risk of malaria [12], especially severe malaria among infants, are largely unknown despite the findings of filarial-induced immune modulation on malaria-specific responses [26–28].
To address this gap, we used the remaining serum samples from infants and children in the Mother-Offspring Malaria Study (MOMS) Project, a large cohort study conducted from 2002 to 2006 in Muheza, Tanzania, to estimate the effect of exposure to filarial parasites on subsequent severe malaria infections. We tested the sera for reactivity to crude filarial antigens using a well-established immunoassay. We hypothesized that coinfection with filarial and Plasmodium species will modify immune responses and impact the risk of severe malaria.
Details for the cohort have been previously described [29]. Briefly, starting at birth, serum samples were taken at 3 and 6 months (+/- 2 weeks) of age and then at 6-month intervals, and malaria smears were collected at 2-week intervals during infancy and at 4-week intervals thereafter. All data analyzed were de-identified and anonymized. We used the remaining samples from this study to perform the assays listed below, along with the participant data already collected to perform this exploratory study. A comparison of participant characteristics between the previously published study and the current study is provided in Table 1.
Filarial-specific antibody levels were measured using a multiplex array system modified for filarial and Strongyloides antigen from a technique published by Fouda et al [30] for P. falciparum. Briefly, crude soluble lysates from Brugia malayi adults (BmA) or S. stercoralis larvae were coupled to fluorescently labeled beads. B. malayi antigen is used for filarial assays owing to its high cross-reactive antigenicity with W. bancrofti [31], and its amenability to in vitro culture to generate assay antigen. Ten positive control sera were collected from parasitologically proven infections with W. bancrofti (for BmA) (n = 5) and S. stercoralis (for Strongyloides) (n = 5). Negative control sera were from 19 non-exposed adults in the United States. Samples were assayed in duplicate. Discrepancies between duplicates were evaluated by the coefficient of variance: the highest result was dropped from pairs with a coefficient of variance greater than 0.4. The mean value of the duplicates was used where the coefficient of variance was less than 0.4. The positive cutoff was defined using a receiver operating characteristic (ROC) curve to identify the cutoff point that produced the highest sensitivity and specificity based on positive and negative controls (Fig 1).
Available serum samples from children ≤2.5 years of age were assayed for antibodies to filarial antigen and to S. stercoralis larval antigen. Subsequent risk factor analysis was further limited to visits at 6 months and 1 year of age because this was the age window during which most severe malaria infections occurred. Children were excluded from this analysis if they: 1) had HIV or sickle cell anemia; 2) were twins or triplets; 3) had moved away from the study area since enrollment. Severe malaria was defined using WHO criteria [32].
Logistic regression models were used to assess the risk of severe malaria in the 6 months following sample collection for measurement of filarial serologies. We estimated the risk of severe malaria in the 6-month period following serologies to determine if children who were seropositive were at higher risk of severe malaria than those who were seronegative. To assess confounding, we estimated the association of specific variables known to be related to severe malaria (maternal parity, placental malaria, village, infant anemia, presence of an insecticide treated bed net in the household and malaria transmission season during birth) for their association with both filarial sero-status and severe malaria in this cohort. Only variables that were significantly associated with both severe malaria and filarial sero-status in this cohort were considered confounders. All models included only children who had a positive malaria blood smear in the six months following the visit when filarial serology was assessed, because development of severe malaria requires infection from P. falciparum. Statistical significance was assessed at p < 0.05.
We quantified plasma cytokine levels at 6 months and 1 year of age to assess associations with filarial serology and severe malaria risk, after stratifying by malaria blood smear positivity. We assessed pro-inflammatory (IL1, IL6, IFNγ and TNFα) and anti-inflammatory cytokines (IL4, IL5, and IL10), based on the hypothesis that the balance of pro- and anti-inflammatory cytokine levels may influence severe malaria risk [33]. Cytokine assays were performed as previously described [34, 35]. The detection limits for the different analytes were as follows: TNF-α, 0.10 pg/ml; IFN-γ, 0.04 pg/ml; IL-1β, 0.01 pg/ml; IL-4, 0.3 pg/ml; IL-5, 0.02 pg/ml; IL-6, 1.45 pg/ml; IL-10, 0.02 pg/ml. Values were log transformed after adding one to all values to avoid log transformation of zero and the geometric means were compared using ANOVA. IL4 was analyzed as detectable vs. non-detectable using the detectable limit of 0.3. We accounted for multiple comparisons by using a Bonferroni correction. Statistical significance was assessed at p < 0.0125.
Data for the MOMS study were collected under protocols approved by the International Clinical Studies Review Committee of the Division of Microbiology and Infectious Diseases at the US National Institutes of Health, and ethical clearance was obtained from the Institutional Review Boards of Seattle BioMed and the National Medical Research Coordinating Committee in Tanzania.
A total of 746 serum samples were selected for risk factor analysis as outlined by the flowchart in Fig 2. The proportion of children with positive serology to filarial antigens ranged from 16.8%-60% with the highest proportion seropositive at 2.5 years. The percentage of children with positive serology to Strongyloides antigens ranged from 3.1–8.1% (Fig 3). Because of the low numbers of children with positive serology for Strongyloides, we did not include these in subsequent risk factor analysis (Table 2).
Children with remaining serum samples collected between 6 months and 1.5 years of age (n = 612) were assessed for the occurrence of severe malaria and serologic evidence of filarial infection, with the highest proportion of severe malaria events occurring in the first year of life (Fig 4).
In order to assess risk associated with filarial serology, we used the data for children with samples at 6 months and 1 year who also had blood smear data. To better understand the risk of progressing to severe malaria among those already infected, we further limited the sample to children with at least one positive blood smear: 236 children had at least one serum sample at 6 months or 1 year of age and subsequent or concurrent positive malaria blood smear. 180 children had a serum sample at 6 months of age and a subsequent or concurrent positive malaria blood smear, and 125 children had a serum sample at 1 year of age and a subsequent positive malaria blood smear. Overall, of the 236 children, 70% (n = 166) had a report of treated bed net use at some period during the observation period and 16% (n = 38) had missing information for this variable.
None of the tested potential confounding variables were associated with both severe malaria and filarial antibody positivity in this cohort. We assessed the risk of severe malaria in the 6 months after filarial seropositivity at 6 months and 1 year. We found that infants who developed severe malaria between 6 months and 1 year of age were 3.9 times more likely (OR = 3.9, 95% CI: 1.2, 13.0; p-value = 0.02) to have had positive filarial serology at 6 months of age compared with infants who did not develop severe malaria. Children with severe malaria between 1 and 1.5 years of age were not significantly more likely to have positive filarial serology at 1 year of age than children who did not develop severe malaria (OR = 1.4; 95% CI: 0.27, 7.6; p-value = 0.67).
We did not identify a difference between IL1, IL5, IL6, IL10, TNF-alpha, and IFN-gamma levels by filarial serology at 6 months or 1 year of age when using a significance value of p< 0.0125 among infants who were blood smear positive or negative. Among infants who subsequently developed severe malaria, no significant difference in IL1, IL5, IL6, IL10, TNF-alpha, or IFN-gamma levels by filarial serology was found at either 6 months or 1 year of age (Figs 5 and 6). Likewise, the presence of detectable IL4 was not associated with severe malaria risk during the 6 months after assay (Table 3).
We describe an age-specific increase in prevalence of filarial antibodies beginning in infancy in Tanzania. Although previous studies have identified filarial infection in young children, this study identifies an increasing filarial seropositivity with age starting at 1 year. Additionally, we found that filarial seropositivity at 6 months of age was significantly associated with severe malaria by 1 year of age.
Although transplacental maternal antibodies may play a role in the observed prevalence, particularly at the 6-month measurement, the increasing seroprevalence after 1 year is noteworthy. Weerasooriya et al. described a decline in urinary antigens after 1 year of age among infants born to mothers who were Brugia pahangi antibody-positive [36]; antibodies from breast milk are not known to enter the infant’s circulation [37] suggesting that our results in children 1 year and older indicate the presence of antibodies acquired through filarial exposure rather than through maternal-infant transfer. Many public health studies focus on school age children when describing infections among children. However, our results may indicate that pre-school age children are at increased risk of filarial exposure as well.
This study is also valuable in that it uses a multiplexed serologic assessment to identify children with previous or current exposure or infection for filariae and Strongyloides. The assay data appear consistent using the non-exposed controls, so we are confident that we are detecting both exposures. This obviously provides a framework for adding multiple (up to 50) antigens to gain a comprehensive assessment of seroreactivity in a single assay [38]. Other helminths contribute to polyparasitism in Tanzania including: Onchocerca volvulus, the causative agent of onchocerciasis [39]; S. mansoni and S. haematobium [40] [41]; and multiple species of soil-transmitted helminths. Several challenges exist with identifying the soil-transmitted helminth S. stercoralis infections in young children, a focus of this study. Very young children are incorrectly thought to be at lower risk based on the idea that they are not in direct contact with infective sources. Further, diagnosis may be hindered because measures of active infection (e.g. eggs in stool, parasites in the blood) may lag behind serologic measures of exposure [42].
The significant association between filarial serostatus at 6 months and subsequent severe malaria infection highlights the need for further investigation to assess whether the increased risk is due to a shared vector or if immune modulation is occurring. As such, a primary limitation to this analysis is the potential for confounders in the relationship between severe malaria and filarial serology. Both malaria and filarial parasites are transmitted by mosquitoes, and previous studies have suggested that the same mosquito species may transmit both infections [17, 41].
The association between filarial seropositivity and severe malaria has been reported elsewhere in studies of older children and adults. Increased risk of severe malaria with helminth co-infection has been reported in children aged between 1–15 years in Senegal and Northern Senegal, and helminth coinfection has been associated with an increase in clinical malaria in children aged < 16 years in Zaire [23, 43, 44]. A study of adults in Thailand found an increase in clinical malaria associated with co-infection with intestinal helminths [19]. Conversely, studies in Senegal and Mali found decreases in malaria parasite densities associated with S. haematobium co-infection in cohorts aged 3–15 [21, 45], and no influence on malaria incidence was found in mixed age cohorts in Southwest Uganda and Northern Senegal [46, 47]. These apparent differences in findings may be explained by parasite differences, with S. haematobium co-infections having little or no influence on severe malaria, while Ascaris, S. mansoni, filarial and Strongyloides co-infection may confer increased risk. The differences may also be attributable to the age of the children in this study, as the children in this cohort are younger and the effect of coinfection may be different, or the antibodies present may reflect maternal antibodies.
We assessed potential confounding for a variable indicating the presence of an insecticide treated bednet in the household and this variable was not significantly associated with filarial serology at 6 months or 1 year of age. A modest proportion of children were missing information for this variable. Even among children who had an insecticide treated net in their household, we are unaware of actual utilization rates or integrity of the bed nets, so actual assessment of treated bed net use may be imprecise.
This study has several additional limitations. First, we are unable to determine if the positive filarial serology indicates current or past infection. Because we do not have measures of current infection, we are unable to determine if our results correlate with infection or exposure and are limited to describing the associations with filarial sero-reactivity. However, one advantage of using serology is that we are able to observe cumulative exposure, rather than assessing exposure at a single point in time. Secondly, this well-established serologic assay specifically uses Brugia malayi antigens, based on the substantial antigenic cross-reactivity among all filarial species [8–10, 39], and as a result this assesses filarial exposure without assignment to an exact species. However, the age-specific profile aligns much more closely with W. bancrofti than with O. volvulus infection [48–51] as does the geospatial data. Although we now have filarial species-specific recombinants for W. bancrofti [52–54] and O. volvulus [55], insufficient serum was available to perform these specific assays. Nonetheless, the results still suggest filarial antibodies are an important biomarker of increased risk for severe malaria and further suggest that efforts to reduce exposure to the vectors associated with W. bancrofti or O. volvulus infection may also have a substantial impact on reducing severe malaria.
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10.1371/journal.ppat.1003505 | Discovery of Anthelmintic Drug Targets and Drugs Using Chokepoints in Nematode Metabolic Pathways | Parasitic roundworm infections plague more than 2 billion people (1/3 of humanity) and cause drastic losses in crops and livestock. New anthelmintic drugs are urgently needed as new drug resistance and environmental concerns arise. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product. A chokepoint analysis provides a systematic method of identifying novel potential drug targets. Chokepoint enzymes were identified in the genomes of 10 nematode species, and the intersection and union of all chokepoint enzymes were found. By studying and experimentally testing available compounds known to target proteins orthologous to nematode chokepoint proteins in public databases, this study uncovers features of chokepoints that make them successful drug targets. Chemogenomic screening was performed on drug-like compounds from public drug databases to find existing compounds that target homologs of nematode chokepoints. The compounds were prioritized based on chemical properties frequently found in successful drugs and were experimentally tested using Caenorhabditis elegans. Several drugs that are already known anthelmintic drugs and novel candidate targets were identified. Seven of the compounds were tested in Caenorhabditis elegans and three yielded a detrimental phenotype. One of these three drug-like compounds, Perhexiline, also yielded a deleterious effect in Haemonchus contortus and Onchocerca lienalis, two nematodes with divergent forms of parasitism. Perhexiline, known to affect the fatty acid oxidation pathway in mammals, caused a reduction in oxygen consumption rates in C. elegans and genome-wide gene expression profiles provided an additional confirmation of its mode of action. Computational modeling of Perhexiline and its target provided structural insights regarding its binding mode and specificity. Our lists of prioritized drug targets and drug-like compounds have potential to expedite the discovery of new anthelmintic drugs with broad-spectrum efficacy.
| The World Health Organization estimates that 2.9 million people are infected with parasitic roundworms, causing high-morbidity and mortality rates, developmental delays in children, and low productivity of affected individuals. The agricultural industry experiences drastic losses in crop and livestock due to parasitic worm infections. Therefore, there is an urgent need to identify new targets and drugs to fight parasitic nematode infection. This study identified metabolic chokepoint compounds that were either produced or consumed by a single reaction and elucidated the chokepoint enzyme that drives the reaction. If the enzyme that catalyzes that reaction is blocked, a toxic build-up of a compound or lack of compound necessary for subsequent reaction will occur, potentially causing adverse effects to the parasite organism. Compounds that target some of the chokepoint enzymes were tested in C. elegans and several compounds showed efficacy. One drug-like compound, Perhexiline, showed efficacy in two different parasitic worms and yielded expected physiological effects, indicating that this drug-like compound may have efficacy on a pan-phylum level through the predicted mode of action. The methodology to find and prioritize metabolic chokepoint targets and prioritize compounds could be applied to other parasites.
| Parasitic nematode (roundworm) infections impose an enormous burden of morbidity on humanity [1], [2]. Only a few drugs are commonly used to treat nematode infections, creating a dangerous environment for the emergence of drug resistance. Currently, administering anthelmintic drugs on a yearly basis is necessary to break the infection cycle, but also causes drug resistance in parasites that infect human and animal populations [3], [4]. Many of the drugs used to treat filarial infections, including diethylcarbamazine (DEC), ivermectin, and albendazole, predominately kill nematodes in their microfilarial stage and have a much lower activity level in adult worms [5]. Plant parasitic nematodes have devastating effects on crops, costing $78 billion per year globally [6]. In addition to the possibility of the development of pesticide resistance in plant parasitic nematodes, there are also environmental concerns associated with them. For example, the United States is phasing out methyl bromide (a highly effective pre-plant soil fumigant used on high-value crops) due its ability to deplete ozone in the stratosphere [7]. Thus, there is a pressing need to develop new anthelmintic treatments and pesticides [1] that are highly efficient and environmentally safe.
A systematic way of identifying new targets is by studying metabolic pathways, particularly chokepoint reactions within particular pathways. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product (Figure 1A & B; [8]). If the enzyme catalyzing a reaction that produces or consumes a unique compound can be inhibited, the entire pathway will be blocked, leading to accumulation of the unique substrate or the organism being starved of unique product [8]. The idea of chokepoints and essentiality is further supported by Palumbo et al [9], which demonstrated that lethality corresponds to a lack of alternative pathways in a network that has been perturbed by a blocked enzyme.
Chokepoint analyses have been used for drug target identification in several pathogenic organisms. In two different studies, chokepoint analyses were performed to determine novel drug targets for two parasites: the mitochondrial protist, Entamoeba histolytica [10], and the protozoan parasite Plasmodium falciparum, which causes malaria [8]. Two additional studies have applied chokepoint analysis to find unique drug targets for Pseudomonas aeruginosa [11] (a common bacterium that causes infections) and Bacillus anthracis [12] (the bacterium that causes anthrax). Another study which explored P. falciparum drug targets has evaluated the essentiality of a reaction in a pathway by deleting a reaction in silico and determining if the metabolic network could find an alternative pathway to get to the same endpoint [13]. A chokepoint analysis and the essentiality of a reaction have been combined to find antibacterial drug targets [14]. However, most of these studies have yielded a long list of chokepoints without any prioritization for testing.
The number of nematodes sequenced has risen dramatically recently, with a total of 10 complete nematode genomes being published and around 30 in progress [15], [16]. These newly sequenced genomes provide a unique opportunity to find new anthelmintic drug targets that may be broad-spectrum in nature. The set of 10 sequenced nematode genomes provides representatives from four of the five clades spanning the phylum Nematoda [17] including those that are free-living, and plant, animal, or human parasitic nematodes. In this study, we determine chokepoint reactions using the intersection in all 10 nematode-deduced proteomes (the common/intersection to all ten studied nematodes, CommNem), as well as the complete set of chokepoints within the 10 deduced proteomes (the union of all 10 nematode species, UniNem). We also isolate a group of chokepoints that are only found in a union of parasitic nematodes (ParaNem). All other chokepoint analysis studies have only used a single organism in their analysis, making this pan-phylum analysis much more comprehensive than previous studies. The chokepoints from nematodes are compared to chokepoints in Drosophila melanogaster and Homo sapiens, in addition to the chokepoints found in the publicly available databases, KEGG Drug and DrugBank [18], [19]. Further, targets of insecticides were also investigated. We confirm that chokepoints are meaningful drug targets by identifying chokepoint enzymes that are already known anthelmintic and insecticide targets through this method. Given the list of nematode chokepoints, we prioritize the list by evaluating specific criteria and compare the results to known drug targets from two publically available databases. In addition, we provide a list of enzymes involved in chokepoint reactions that have already known drug associations. Seven of these compounds (referred to as “drug-like compounds” because while pharmacological properties were used to screen out compounds, not all of the compounds in those databases are approved drugs) were experimentally tested in C. elegans and two parasitic nematodes. Three drug-like compounds elicited a deleterious phenotype in C. elegans, and one of these also yielded a deleterious phenotype in the two parasitic species, demonstrating that this prioritized list of drug-like compounds should be further studied for good candidates for repositioning and/or development as potential anthelmintic drugs. We present evidence that one of these drug-like compounds, Perhexiline, acts according to its predicted mode of action. Computational modeling suggested structural differences in the binding site that can be used to develop a more specific, efficacious drug.
The following list of nematode genomes was analyzed: Brugia malayi [20], Caenorhabditis species from WormBase release WS240 (Caenorhabditis brenneri, Caenorhabditis briggsae, Caenorhabditis elegans, Caenorhabditis japonicum, Caenorhabditis remanei), Meloidogyne hapla (http://supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY/cgi-bin/gen_list.cgi?genome=wm; [21]), Meloidogyne incognita (http://www.inra.fr/meloidogyne_incognita/g enomic_resources/downloads; [22]), Pristionchus pacificus (http://pristionchus.org; [23]) and Trichinella spiralis [24]. The Homo sapiens genome was downloaded from Ensembl (Homo_sapiens.GRCh37.57.pep.all.fa) and Drosophilia melanogaster were downloaded from Flybase 5.26 (http://flybase.org/static_pages/downloads/archivedata3.html). The sequences of all the genomes had open reading frames discerned and then translated to protein for analysis (henceforth referred to as ‘proteomes’). Proteins with EC (enzyme commission) numbers associated with them were downloaded from KEGG version 58 [18]. WU-BLASTP (wordmask-seg, hitdist = 40, topcomboN = 1, postsw) was used to screen the proteomes for sequence similarity and find homology to proteins with an associated EC number and best match, scoring below 1e−10. The intersection of ECs (i.e. common ECs, “CommNem”) and the union of ECs (i.e. set of all nematode ECs, “UniNem”) in the 10 nematode proteomes were parsed using PERL scripts developed in-house.
Both KEGG Drug [18] and DrugBank [19] were used to identify potential drugs that bind to targets in the nematode proteomes, H. sapiens, and D. melanogaster. These databases contain some FDA approved compounds, as well as compounds that were known to interact with certain targets. The KEGG Drug and DrugBank databases used for analysis were downloaded on 4/14/2010 and 5/19/2010, respectively. ECs were linking to targets using annotations from the KEGG Drug database. DrugBank contains the protein sequences of the targets, as well as their associated drugs. WU-BLASTP was used to screen the targets in DrugBank against the KEGG genes database to get an EC number annotation that matched within a cutoff score of 1e−10 or better. The EC number associated with the DrugBank target was then associated with the drug within DrugBank.
The reaction database from KEGG v58 [18] was used to identify chokepoint reactions and corresponding chokepoint enzymes. Each reaction equation is listed as a separate reaction with a unique identifier under the ENTRY field. The KEGG reaction database also contains a file that lists the reactions within the reaction database as reversible or irreversible (reaction_mapformula.lst – downloaded 6/21/2011). The entire reaction was extracted from the KEGG reaction database by parsing the EQUATION field, and the reaction_mapformula.lst file was used to obtain the directionality of the reaction such that the reactions could be written with reactants on the left side and products on the right side. If the reaction was reversible, this was also noted in the file because products and reactants would be ambiguous. The reactions were placed into a [compound×reaction number] matrix by parsing an intermediate file that contained the directionality and all the products and reactants for the reaction within the matrix, −1 indicated the compound was consumed (i.e. the compound was listed on the left side of the equation), +1 indicated the compound was produced (i.e. the compound was listed on the right side of the equation), 2 indicated the reaction was reversible, and a zero indicated the compound did not take part in the reaction. To find the chokepoints, the matrix was parsed for compounds that were only produced or consumed in a single reaction. If a compound was produced or consumed in a single reaction, only a single 1 or −1 would be present across the entire compound row within the matrix. In some cases, a compound was uniquely produced or uniquely consumed, but was part of a reversible reaction (i.e. two 2's would be present within a row). If this reaction was the only reaction in which the compound participated, this was also called a chokepoint. The chokepoint compounds were related to EC numbers using the ENZYME field in the reaction database.
The EC numbers corresponding to proteins in the various genomes were mapped to KEGG metabolic pathways active in nematodes. Pathway categories that were not applicable such as photosynthesis, carbon fixation, reductive carboxylate cycle were excluded. The distribution of chokepoint targets and known drugs in metabolic pathways was compared to determine any potential enrichment using Fisher's Exact Test.
Pathways in the KEGG reaction database (v58) were enumerated. First, the KEGG reaction database was broken into separate reaction pathways based on the “PATHWAY” classification. There were 8121 entries in the reaction database, and 5638 had a PATHWAY classification. Only 142 unique reaction pathways were used; due to the large size and overlap with other pathways, rn00240, rn00230, rn01100, rn01110, and rn01120 were not used. For each of the different pathways, a separate [compound×reaction number] matrix was generated as described in the “Identifying Chokepoints” section above. The starting and ending nodes for reaction pathways were generated from this matrix by determining compounds that were consumed but not produced (start nodes) and produced but not consumed (end nodes). Beginning with each of the start nodes, the compounds in all possible pathways were enumerated. The position of the chokepoint within the pathway was determined by the number of compounds in the pathway before the chokepoint, as well as the length of the entire pathway.
Chokepoint enzymes were prioritized by assigning a point for meeting each of the following criteria, then ranked based on number of points: EST-based gene expression found in a parasitic stage for plant parasitic nematodes (egg, J2, J3, J4, adult) and infective/parasitic stages for human and animal parasitic nematodes (embryo, L3, L4, adults); expressed in pharynx, intestine, neurons, muscle, or hypodermis [25], [26], [27] in C. elegans (www.wormbase.org); less than 30% sequence identity to H. sapiens over half the length of the sequence; chokepoint enzyme functioning in two or more pathways; chokepoint enzyme involved in nucleic acid metabolism; and chokepoint is a hydrolase based on their enrichment (classification as EC 3, enzyme commission number). This analysis was performed to determine if certain classes of enzymes were more likely to have drugs associated with them. This information was fed into the prioritization scheme. EST sequences sets for the 10 species were downloaded from Genbank on 7/16/2010: C. brenneri, C. briggsae, C. japonicum, M. hapla, M. incognita, T. spiralis, P. pacificus, B. malayi, and C. remanei. C. elegans EST sequences were downloaded from GenBank on 4/21/2010. The tissue expression data from C. elegans was obtained from WormMart (WS195) on 4/23/2010.
Proteins associated with ECs (using KEGG) were blast searched against protein targets in DrugBank as described above. The ECs from DrugBank were compared to CommNem and UniNem. Cheminformatic properties were obtained by running SMILES strings (SMILES are strings of ASCII characters that describe a compound unambiguously) extracted from DrugBank through the Cytoscape [28] plugin, ChemViz. To prioritize the drugs, drugs were given one point for meeting each of the following criteria: molecular weight ≤500, 0<number of rotatable bonds ≤10, hydrogen-bond donors ≤5, hydrogen-bond acceptors ≤10, logP≤5 [29]. This additional screen was done because the compounds in the drug database are not optimized for Lipinski's rules and thus may not have been “successful” drugs for the disease for which they were developed/tested. For a drug to be effective, it should have a long half-life, so a drug with half-life ≥60 minutes was rewarded with a point. Toxicity information is also important for future testing and therefore, a compound with any available toxicity information was given an additional point. The maximum attainable compound score was 7. Drug-like compounds were also eliminated if placed in the dietary supplement, micronutrient, or vitamin categories by DrugBank, as various vitamins and amino acids were not desired. Nematode proteins were searched against sequences from DrugBank, and then parsed for sequences that had 50% or greater identity over 80% of sequence length. Only these targets were considered in the prioritized list.
Compounds were obtained from the following sources: Perhexiline maleate (1 DB1074 is just perhexiline; CAS: 6724-53-4; P287320) from TRC; Carbidopa (2 DB00190; CAS: 28860-95-9; BML-EI265) and dopamine (4 DB00988; CAS: 62-37-1; BML-AC752) were ordered from Enzo Life Sciences dissolved in DMSO; LT00772250 (Probenecid 5 DB01032; CAS: 57-66-9), LT00255846 3 (similar to DB00993; the DrugBank compound was not available, so a similar compound was ordered), LT00138053 (6 DB01033), LTBB001666 (7 DB00548) were ordered from Ryan Scientific. Compounds formulated in 100% DMSO were tested in microtiter plates containing 50 µl nematode growth media, 1% E. coli and 20 L1 C. elegans. Five concentrations in 4-fold increments (0.078, 0.3125, 1.25, 5, and 20 ppm; ∼25 to 60 µM, depending on the molecular weight of the compound) were tested, and the experiment was repeated twice and a final confirmation test, with the best result reported. The efficacy of a compound was determined based on the motility of the larvae as compared to average motility of control wells containing DMSO only at 48 hours post treatment (by that time the larvae develop to L4's; screening is not performed at a later stage due to the way imaging is done, i.e. comparing exact numbers of parasites in every well). The motility was assessed using a camera-based imaging. The camera takes multiple images of a well and the changes in movement between the images are calculated. An absolute movement value is calculated for each well. On each test plate, multiple wells containing only DMSO are included as a control. The absolute movement value from these wells was averaged and then compared to the movement in the treatment wells. The percent reduction in motility is calculated by dividing the movement in the treatment well by the average movement of the DMSO wells. Controls were used on every plate and in every test (data not shown). Movement was manually assessed at 72 hours post-treatment to determine if there were altered movements or morphological changes not detected by the imaging system.
Compounds formulated in 100% DMSO were tested in microtiter plates containing 50 µl nematode media, fecal slurry and 20 L1 Haemonchus contortus. The experiment was repeated twice at five concentrations in 4-fold increments (0.078, 0.3125, 1.25, 5, and 20 µM). The efficacy of a compound was determined based on the motility of the larvae (when the larvae have developed to L3's) as compared to average motility of control wells containing DMSO only. A MIC90 value was calculated by determining the lowest dose at which there was a 90% reduction in motility as compared to the control wells. The motility was assessed using a camera-based imaging system as described in the C. elegans screen. Larval movement was manually assessed at 72 hours post-treatment to determine if there were altered movements or morphological changes not detected by the camera.
Compounds were tested at two static doses of 50 µM and 12.5 µM in Onchocerca lienalis. Five microfilariae were added to each well of a 96-well microtitre plate. Larvae were assessed at 120 hours post-treatment and efficacy was determined by visually assessing the motility of the larvae in the treated wells as compared to control wells.
While other stages for screening could also be used, our approach was implemented as an early indicator of activity. Progressing to advanced tests against relevant clinical stages should be the next step for future research. In particular, when working with filarial worms, having some filter for prioritizing compounds is helpful, since access to adult stages is often difficult.
Real-time measurements of oxygen consumption rates (OCR) were made using an XF-24 Extracellular Flux Analyzer (Seahorse Bioscience) as previously described [30]. The real-time extracellular flux experiment was designed to evaluate whether Perhexiline decreases OCR via inhibiting mitochondrial carnitine palmitoyltransferase in C. elegans. The concentrations used (25–100 uM) do not have any impact on the movement of the worms (based on examination under the microscope), but do have an impact on the OCR. Synchronized young adult C. elegans were washed with M9 media and plated into XF-24 culture plates at approximately 100 worms/well. OCR measurements were recorded under basal conditions or in the presence of Perhexiline, Etomoxir (Sigma) and/or Ivermectin (Sigma) at various concentrations, over a period of 1.5 hours and 40 minutes. The significance of observed OCR differences was assessed using Student's t-test using GraphPad Prism Version 5.
The treated worms (approximately 100 µl settled volume) were washed in sterile PBS and resuspended in 100 µl TRIzol reagent (Invitrogen). Samples were frozen with liquid nitrogen and homogenized. Following the homogenization, the worm/TRIzol powder was collected and allowed to thaw on ice. A further 0.2 volumes of chloroform were added into samples, and gently mixed, incubated at room temperature for 3 minutes, then centrifugated at 12,000× g for 15 minutes at 4°C. The upper aqueous phase was transferred to a fresh tube and RNA was precipitated by an additional 0.5 volumes of isopropanol followed by incubation at room temperature for 10 minutes. The mixture was then centrifuged at 12,000× g for 10 minutes at 4°C. The supernatant was discarded and the RNA pellet was washed with 500 µl of 75% (v/v) ethanol before centrifugation at 7,500× g for 5 minutes at 4°C. The supernatant was removed and the pellet air-dried. The RNA pellet was suspended in nuclease-free distilled water.
The total RNA was treated with Ambion Turbo DNase (Ambion/Applied Biosystems, Austin, TX). 1 ug of the DNAse treated total RNA went through polyA selection via the MicroPoly(A) Purist Kit according to the manufacturer's recommendations (Ambion/Applied Biosystems, Austin, TX). 1 ng of the mRNA isolated was used as the template for cDNA library construction using the Ovation RNA-Seq version 2 kit according to the manufacturer's recommendations (NuGEN Technologies, Inc., San Carlos, CA). Non-normalized cDNA was used to construct Multiplexed Illumina paired end small fragment libraries according to the manufacturer's recommendations (Illumina Inc, San Diego, CA), with the following exceptions: 1) 500 ng of cDNA was sheared using a Covaris S220 DNA Sonicator (Covaris, INC. Woburn, MA) to a size range between 200–400 bp. 2) Eight PCR reactions were amplified to enrich for proper adaptor ligated fragments and properly index the libraries. 3) The final size selection of the library was achieved by an AMPure paramagnetic bead (Agencourt, Beckman Coulter Genomics, Beverly, MA) cleanup targeting 300–500 bp. The concentration of the library was accurately determined through qPCR according to the manufacturer's protocol (Kapa Biosystems, Inc, Woburn, MA) to produce cluster counts appropriate for the Illumina platform. The HiSeq2000 Illumina platform was used to generate 100 bp sequences.
Analytical processing of the Illumina short-reads was performed using in-house scripts. DUST was used to filter out regions of low compositional complexity and to convert them into N's [31]. An in-house script was used to remove N's, which discards reads without at least 60 bases of non-N sequence. Raw RNA-seq datasets are deposited at SRA (accession numbers: Control - SRR868958, IVM - SRR868932, PER - SRR868957, PER+ETO - SRS868939, ETO - SRS868942.). Gene expression for each sample was calculated by mapping the screened RNA-seq reads to the WS230 release of C. elegans using Tophat [32] (version 1.3.1), and calculating depth and breadth of coverage per gene using Refcov (version 0.3, http://gmt.genome.wustl.edu/gmt-refcov). Gene expression values were normalized using the depth of coverage per million reads (DCPM) per sample [33]. Expressed genes were subject to further differential expression analysis using EdgeR [34] (false discovery rate <0.05, dispersion value 0.01), in order to identify genes differentially expressed in each treatment relative to the control sample. Hierarchical agglomerative clustering (with “unweighted pair group method with arithmetic mean”, and Pearson correlation coefficient similarity settings in XLSTAT-Pro; version 2012.6.02, Addinsoft, Inc., Brooklyn, NY, USA) was used to cluster samples based on the gene expression profiles across all genes, and to cluster all 1,908 genes upregulated in any of the four comparisons.
Interproscan [35], [36] was used to determine associations of genes to Gene Ontology (GO) terms [37]. Interproscan also identified predicted Interpro domains found in each gene. GO term enrichment among genes upregulated in each of the 4 samples was determined using a non-parametric binomial distribution test with a 0.05 p value cutoff for significance, after Benjamini-Hochberg false-discovery-rate (FDR) population correction for the total number of terms [38]. Only GO terms with at least 5 gene members in the C. elegans genome were included in the analysis (501 total).
Perhexiline was downloaded from the DrugBank website as a mol file, then converted to a PDB file using OpenBabel [39]. The PDB file was optimized using Sybyl 7.3 [40] to minimize the Perhexiline structure. In AutoDockTools4 [41], hydrogen atoms, followed by Gasteiger charges, were added, then the non-polar hydrogen atoms were merged. A docking box of 88×68×80 points in the x, y, and z dimensions, with a spacing of 0.375 Å, was used centered at 61.752, 72.8001, 52.0321 and all other parameters were default. The carnitine palmitoyltransferase-2 (CPT-2) macromolecule was taken from the crystal structure of 2H4T [42]. Hydrogen atoms were added, followed by Kollman charges. Then, the non-polar hydrogens were merged on the macromolecule. The docking calculations utilized local search Lamarkian genetic algorithm in Autodock4 [41] using rigid side chains. A total of 250 genetic algorithm runs were done. The results were clustered using Autodock4 with the default parameters.
Our approach identifies chokepoint enzymes as targets of existing drugs or as novel drug targets (Figure 1C). The intersection of nematode genomes (CommNem) yielded 487 proteins conserved among all nematode species studied, of which 169 are conserved chokepoint enzymes (Figure 2 & Table S1 in Text S1). The union of the nematode proteomes (UniNem) yielded 477 chokepoint enzymes (Table S2 in Text S1), of which 24 chokepoint enzymes were only found in parasitic worms (ParaNem). The EC numbers and corresponding FASTA sequences for each of the species investigated can be found on Nematode.net [43]. In all cases, 34–35% of the proteome assigned with an EC number consists of chokepoints (Figure S1 in Text S2). The only chokepoint enzyme present in CommNem and not in H. sapiens is EC: 6.2.1.12. However, 120 chokepoint enzymes from UniNem are not found in H. sapiens. A high overlap also exists between CommNem chokepoint enzymes and D. melanogaster, with only 5 of 169 in CommNem are not present in D. melanogaster (EC: 1.8.4.2, 2.4.2.8, 5.3.2.1, 2.7.1.149, 3.6.1.14).
Some enzyme categories were enriched or depleted based on Fisher's Exact statistical test within the species relative to chokepoint enzymes in KEGG (i.e. KEGGChoke), and all enzymes in the KEGG database (i.e. AllKEGG) (Figure S2 in Text S2). This analysis was performed to determine if certain types of enzymes were more likely to have drugs associated with them. This information was fed into the prioritization scheme. Oxidoreductases were significantly enriched in nematodes and KEGG Drug and DrugBank relative to KEGGChoke (p<0.005). The chokepoints within KEGG Drug and DrugBank were significantly enriched in hydrolase enzymes (p<0.005) when compared to KEGGChoke (all chokepoints in KEGG identified using our approach) as well as AllKEGG (all enzymes with assigned ECs within KEGG). Further, isomerases in DrugBank and KEGG Drug were significantly enriched relative to KEGGChoke. The abundances of enzymes in DrugBank and KEGG Drug significantly differ from KEGGChoke in 3 out of the 6 enzyme categories.
There are 75 drugs in KEGG Drug that are classified as anthelmintic. Much research has also been done to design insecticides, therefore it is interesting to see that these insecticides also target chokepoint enzymes. The insecticides are shown in Table S3 in Text S1, and the DrugBank compounds that are classified as antiparasitic are shown in Table S4 in Text S1.
The nearly complete overlap of CommNem and partial overlap of UniNem chokepoint enzymes with H. sapiens enzymes provide an excellent opportunity to reposition drugs used for other purposes in H. sapiens as anthelmintic drugs. If these drugs show some efficacy, subsequent optimization studies could be performed on these leads to make these drugs bind with higher affinity and specificity to the nematode protein. Out of the 169 chokepoints in CommNem, only 13 have a drug associated with them in KEGG Drug (Table S5 in Text S1 and Table S6 in Text S1). When considering UniNem, a total of 29 chokepoints have ECs associated with a drug in KEGG Drug (Table S5 in Text S1 and Table S7 in Text S1). Out of 446 enzymes involved in chokepoint reactions in H. sapiens, only 35 mapped to ECs associated with a drug in KEGG (data not shown). Of the 977 enzymes in the D. melanogaster genome, 330 are chokepoint enzymes and of the 68 of those that mapped to the ECs in the KEGG Drug database 29 are considered chokepoint enzymes. There are 30 drugs in KEGG that have insecticide activity, but none have ECs associated with them. Only 97 enzymes within KEGG Drug have an EC assigned, of which 39 are associated with chokepoint reactions. Therefore, the UniNem, H. sapiens, and D. melanogaster proteins hit roughly 1/3 of targets with ECs assigned within KEGG Drug.
DrugBank contains the sequences of targets to which the drugs bind, enabling more complete mapping of ECs to protein targets and subsequently to drug-like compounds. Within DrugBank, there are 4774 compounds, and 1289 targets were assigned EC numbers. DrugBank contains 504 enzymes that are involved in chokepoint reactions based on chokepoints derived from KEGG reactions. Based on the number of compounds, KEGG Drug has more compounds than DrugBank with 9447 compounds. However, DrugBank has many more compounds associated with ECs (Figure S1 in Text S2). Due to the large list of targets and compounds, the compounds were prioritized (see Methods). Several of the compounds yielded the maximal compound score of 7. A compound score cutoff of ≥6 was used to prioritize the top drugs that have potential to be repositioned or further optimized as nematode drugs (Figure 3A, Table 1). The compounds identified are drugs that are used to treat hypertension, angina, and Parkinson's disease, and have immunosuppressive and antimicrobial properties.
The chokepoint enzymes were prioritized for the CommNem, UniNem, and ParaNem groups using a simple addition scoring function, with 7 being the maximum possible target score (see Methods and Materials). The results for CommNem and UniNem are shown in Table 2 and ParaNem in Table S8 in Text S1. The maximum target score obtained in CommNem and UniNem was 5, and a cutoff of 4 was used. None of the enzymes in ParaNem met the maximum-target score criteria as well, with 5 being the highest target score attained; therefore a cutoff of 2 was used.
The seven drug-like compounds prioritized based on our cut-off (see Methods and Materials) were experimentally screened in C. elegans (Table 1), and three yielded a phenotype. C. elegans exposed to drug-like compound 2 yielded a slow moving and twitchy phenotype, whereas 7 yielded a jerky, twitchy phenotype in 75% of the worms and 25% of the worms did not move after exposure to the compound. C. elegans exposed to drug-like compound 1 (Perhexiline) yielded a 50% reduction in motility phenotype at 47.3 µM (18.6 ppm), also showed slow movement and twitchy behavior at compound concentrations below the EC50 value. Importantly, Perhexiline (1) caused a 90% reduction in motility (MIC90) at 20 µM in the blood-feeding nematode H. contortus, and 100% reduction in motility in the filarial nematode O. lienalis at 50 µM. Chemical structures of the drug-like compounds are shown in Figure 3A, dose-response curves for Perhexiline (1) are shown in Figure 3C & D, and videos of the effect of Perhexiline (1) on C. elegans and H. contortus and Carbidopa (2) and Azelaic acid (7) in C. elegans are shown in Supplementary Videos (Video S1, S2, S3, S4, S5, S6, S7, S8).
Carnitine palmitoyl transferases (CPT) are chokepoint enzymes with existing drugs, such as Perhexiline (1), inhibiting the mammalian homologs. Two versions of the enzyme (CPT-1 and CPT-2) play important roles in fatty acid metabolism in the mitochondria [44]. Inhibition of CPT leads to a decrease in oxygen consumption rate (OCR) in the mitochondria. Perhexiline (drug-like compound 1) treatment in C. elegans led to a significant decrease in basal OCR in a dose-dependent manner (Figure 5A). The effect of Perhexiline (PER) was equivalent to that of Etomoxir (ETO), a known inhibitor of the mitochondrial outer membrane associated enzyme, CPT-1, which acts with CPT-2 to regulate fatty acid oxidation [45], [46]. The combination of Perhexiline and Etomoxir had an additive inhibitory effect of OCR that was greater than the effects measured with either drug alone (Figure 5B).
OCR was also measured in presence of PER, ETO, PER+ETO and compared to OCR in presence of Ivermectin (IVM), a commercially available anthelminic used to treat nematode infections. IVM, which kills C. elegans at therapeutic concentrations through interference with nervous system function, provides a control for drug-induced toxicity that leads to phenotypic alterations such as paralysis that may indirectly affect oxygen consumption as measured by OCR. The dose response curve (Figure 5C) enabled identification of the 10 uM concentration as applicable for our comparison experiment (see Methods). While the effect of PER, ETO and the additive inhibitory effect of OCR was confirmed by this experiment, the IVM had no significant inhibitory effect of OCR (Figure 5D).
Genome-wide gene expression profiling can be used to investigate if a transcriptional response to drugs carries signatures for drug mechanism of action. Drugs with related mechanisms of action are expected to have similar patterns of molecular functions significantly perturbed. RNAseq-based expression evidence was obtained for all C. elegans genes with 6–11% of the genes being differentially expressed among the four treatments (Table S9 in Text S1). On average 2–8% of genes were upregulated (range 1.7% PER+ETO to 3.9% IVM) and 5.3% were downregulated (range 3.3% PER to 7.1% IVM). Comparison of genome-wide transcriptional responses to PER, ETO, PER+ETO and IVM showed that the transcriptional responses of C. elegans to PER and ETO are significantly closer than any of the two to IVM, confirmed by them being clustered together and having more enriched functions in common (Figure 6A; Table S10 in Text S1). The correlation of gene expression (across the 1,908 differentially expressed genes) between PER and ETO was 0.43, compared to 0.09 between PER and IVM (p<10−10 according to r-to-z Fisher test), showing that PER and ETO elicit a highly similar gene expression response to one another compared to the IVM treatment. PER and ETO cluster together since their targets (CPT-1 and CPT-2) act together to regulate fatty acid oxidation. The difference among PER and ETO, among others, was reflected by a small gene expression cluster near the top of the heatmap (Figure 6A), where we observed a group of genes downregulated in PER but upregulated in ETO. GO enrichment analysis on the genes related to this PER-specific downregulation pattern identified several enriched molecular functions (flavin-containing monooxygenase activity-GO:0004499; flavin adenine dinucleotide binding-GO:0050660; carbohydrate binding-GO:0030246 and NADP binding-GO:0050661) and biological processes (response to heat-GO:0009408; multicellular organismal development-GO:0007275). GO enrichment analysis was performed independently on the upregulated gene sets of each of the four treatments. The number of GO categories enriched in each treatment are shown in Figure 6B, and the specific GO terms in each intersection of Figure 6B can be found in Table S10 in Text S1. Two terms, one biological process (response to heat-GO:0009408) and one cellular component (peroxisome-GO:0005777) were enriched among genes upregulated in PER, ETO and PER+ETO, showing that both heat-responsive genes (primarily HSP70 genes) as well as genes related to peroxisome function were upregulated in all combinations of these treatments. Since CPT-1 is an initiating step in the translocation of long chain fatty acids across the mitochondrial membranes for beta-oxidation [44], [47] and the peroxisome proliferator activated receptor α (PPARα) is a nuclear receptor which stimulates genes involved in mitochondrial fatty acid oxidation and increases expression of those modulating pyruvate oxidation, the observed enrichment of genes related to peroxisome related activity is not surprising. Among the 10 GO terms which were only enriched among the PER+ETO treatment (but not in individual treatments) were two biological process terms related to fatty acid processes (fatty acid beta-oxidation-GO:0006635 and fatty acid metabolic process-GO:0006631), biological functions that are directly related to the function of CPT-1 and CPT-2.
The rat structure of CPT-2 (PDB ID: 2H4T) was used for the docking of Perhexiline, since that is the only species with crystal structures available. One major low-energy cluster with a binding energy of −5.8 kcal/mol resulted and contained 226 of the 250 genetic algorithm runs. Using Autodock 4 [41], Perhexiline was docked into the active site of CPT-2 [42] (Figure 7). The binding site of Perhexiline in CPT-2 does not overlap with the carnitine group in the ST-1326 (bound CPT-2 inhibitor in PDB ID: 2FW3) based on the docking calculations, but overlaps more with the fatty acid chain. The major contacts that Perhexiline makes in its docked configuration include: P133, F134, M135, F370, H372, D376, G377, V378, L381, S590, G601, and F602. H372 is the catalytic residue (Figure 7C). The amine group on Perhexiline makes a hydrogen bond with the backbone carbonyl group on D376. Residues that differ between mammals and nematode include L335, S445, Q447, V597, S598, L599, A615, W620, C623, N624 (Figure 7B).
Given the pressing need for new anthelmintic treatments and pesticides, this study outlines new potential drug targets of global importance found to be conserved in nematodes from different trophic ecologies as well as promising compounds that could lead to new anthelmintic treatments and nematicides. The targets offer the possibility for broad-spectrum drugs and pesticides for nematodes. We also provide a list of already known drugs that could be repositioned or further optimized as anthelmintics. Features of chokepoint enzymes that are known drug targets were analyzed. This is the first study to incorporate a large dataset of pan-phylum genomic data into a chokepoint analysis, provide a prioritized list of targets for broad-spectrum drugs, and test some of the prioritized drug-like compounds experimentally.
This work used the entire KEGG database to determine chokepoint reactions, then compared the homologous enzymes that are predicted to catalyze the chokepoint reactions in the intersection (CommNem) and union (UniNem) of the 10 nematode species with sequenced genomes, as well as drug targets in KEGG Drug and DrugBank. One caveat to this study is the possibility that the absence of complete pathway information may have led to false negative and false positive chokepoint drug targets. For instance, the entire deduced proteomes of some nematodes has not been mapped out due to the draft nature of the genome sequences (e.g. B. malayi genome used in this study). Some chokepoint reactions may utilize a chokepoint compound and produce a product that is also produced by several other reactions. To determine the effect of blocking the chokepoint reaction, modeling of the kinetics and equilibrium constants within the pathways would be required. However, these analyses are beyond the scope of this work. Another caveat surrounding the databases used in this study is the manner that compounds are linked to drug targets in KEGG Drug and DrugBank could yield false linkages between drugs and drug targets. For instance, DrugBank links drug targets and drugs using text-mining programs to search through abstracts in PubMed, as well as manual inspection by trained individuals. As the genomes and databases are improved, the analysis framework outlined here will become more powerful.
Despite limitations of the approach, two out of the six intestinal helminth drugs in the World Health Organization (WHO) Model list target enzymes that catalyze chokepoint reactions. The WHO Model List of Essential Medicines [48] contains a core list of minimum medicines that are needed for a basic health care system. The drugs in this list contain the most efficacious, safe, and cost-effective medicines for certain conditions. The presence of our predicted chokepoint drugs on this list indicates that chokepoint reactions may be useful in providing safe and effective treatments. The two drugs that target chokepoint reactions (listed with their respective targets) include: Levamisole (EC: 3.1.3.1 and EC: 6.1.1.6) and Praziquantel (EC: 2.5.1.18). The next two, Albendazole (DB00518) and Mebendazole target tubulin, which is not an enzyme. The remaining drugs, Niclosamide and Pyrantel, were not in DrugBank or in KEGG Drug and therefore, could not be identified in our study. In the category of antifilarials by the WHO, an additional 6 compounds are listed, but only two have EC associations. The two compounds, Suramin sodium (EC: 3.1.1.4 & 3.5.1.-) and Praziquantel (EC: 2.5.1.18), are both associated with targets that are enzymes involved in chokepoint reactions. Some of the enzyme drug targets are not in CommNem, but are in UniNem. Although it is not on the WHO list, Metrifonate is used as an insecticide and anthelmintic drug and targets an enzyme, EC: 3.1.1.7 (CommNem), which is associated with acetylcholinesterase in a chokepoint reaction.
Considering all anthelmintic drugs, there are also some drugs that are in KEGG that either do not have ECs associated or are not known chokepoints. Within KEGG Drug, Diethylcarbamazine (DB00711) targets two enzymes: EC: 1.9.3.1 (not in a chokepoint reaction) and EC: 1.13.11.34 (involved in a chokepoint reaction) [49]. Nitazoxanide targets EC: 1.2.1.51, which is not a known chokepoint enzyme [50]. Ivermectin (DB00602) and piperazine (DB00592) (two popular anthelminths) do not target enzymes, but target the GABA-A [51] and glutamate-gated chloride channels [52]. For Thiabendazole (DB00730), the metabolizing enzyme cytochrome P450 is a chokepoint. Thiabendazole is thought to inhibit fumarate reducatase [53] (EC: 1.3.99.1, which is not a chokepoint in our study), but the precise mode of action is unknown [54]. Biothionol, Oxamniquine, Niclosamide, Niridazole, and Triclabendazole are not found in DrugBank, and KEGG Drug does not have an EC number associated with them. If DrugBank is searched for drugs used to treat parasitic infections, eleven out of fifteen drugs used to treat parasitic infections that also have assigned ECs are chokepoint reactions in our study. An additional twelve drugs used to treat parasitic infections do not have ECs associated with them. Searching DrugBank for insecticides yielded four out of five drugs that have targets with ECs associated that are chokepoint reactions (Table S3 in Text S1).
Ideal drug targets in nematodes are proteins found only in nematodes and not in their host. The enzyme 4-coumarate-CoA ligase (4CL, EC: 6.2.1.12) is one such enzyme found in the CommNem group and not in H. sapiens in this study. This enzyme class has potential to be very interesting for pan-phylum nematicides. 4CL is involved in many reactions in the phenylpropanoid biosynthesis pathway, but the chokepoint compound is Cinnamoyl-CoA (C00540). Cinnamoyl-CoA feeds directly into the flavonoid biosynthetic pathway in plants and is also a precursor for capsaicin synthesis. The role 4CL plays in nematodes is unclear but it may be involved in host-parasite interactions (due to its position in the flavonoid pathway in plants) or in the production of antioxidants (due to its upstream involvement in capsaicin synthesis, to enable the worm to survive in the host).
During the course of this project, the chokepoint enzymes from the flatworm Schistosoma mansoni were published, and therefore our results were compared to theirs [55]. Out of 607 enzymes that were successfully placed in pathways, 120 were classified as chokepoint enzymes, and only 107 of these chokepoint enzymes were unique. From the chokepoint reactions found in KEGG in our study (2249), 56 chokepoint enzymes overlap with the S. mansoni chokepoint enzymes. Interestingly, there are many similarities between the nematode chokepoint reactions found in this study and the flatworms, with 50 and 33 chokepoint enzymes intersecting the S. mansoni/UniNem and CommNem sets (respectively). Only 1 chokepoint enzyme (EC:2.3.1.39) in the ParaNem set overlaps with the S. mansoni set, but it only obtained a target score of 1.
Several trends between chokepoint enzymes in general and chokepoint enzymes that have drugs associated with them were found. The chokepoint enzymes in CommNem and UniNem could potentially be enriched for drug targets by looking at trends in the KEGG Drug and DrugBank datasets. For instance, enzymes may be higher in priority because they were were significantly enriched in the set of chokepoint enzymes present in KEGG Drug and DrugBank, compared to AllKEGG and KEGGChoke. Ligases were significantly depleted relative to AllKEGG for both DrugBank and KEGG Drugs, so these enzymes would not be weighted as highly because they are depleted in databases of known drugs.
Whether the chokepoint compound was a substrate or a product of the chokepoint reaction did not seem to have any bearing on whether the enzyme was a good drug target. However, the pathway population was different between KEGGChoke compared to the DrugBank and KEGG Drug databases. Within the KEGG Drug and DrugBank databases, enzymes are involved in more pathways compared to KEGGChoke and AllKEGG. For KEGG Drug and DrugBank, enzymes involved in just one pathway are depleted and those involved in more than one are enriched for enzymes within the drug databases. A significant observation between the enzymes associated with chokepoint reactions in the drug databases and the entire list of chokepoint compounds (consumed and created) for various species is the position of the chokepoint in the pathway. Chokepoint enzymes that have known anthelmintic drugs associated with them are found more often around the first 20% (consumed compounds) or around 50% (created compounds) of the pathway length, and chokepoint enzymes that have compounds in KEGG drug associated with them were located around 70% of the pathway length. However, the trend did not exist for chokepoint enzymes associated with compounds in DrugBank, suggesting that this finding may have been an artifact of KEGG Drug. Before conclusions are drawn, the test should be expanded to other drug/protein databases.
Based on the areas studied (where significant differences were seen between a set of all chokepoint enzymes and the drug database), we developed a scoring scheme that helped us prioritize these chokepoint enzyme targets for experimental testing. Many of the targets can be considered broad spectrum, as these proteins are found in all 10 nematode genomes. For instance, nucleoside-triphosphate diphosphatase (EC: 3.6.1.19) scored high on the prioritized list. This enzyme is inhibited by plant compounds, lycorine and candimine, in Trichomonas vaginalis, a parasitic protozoan, which could make T. vaginalis more susceptible to the host immune system [56]. In addition, it is also a possible target for antimicrobial therapy [57]. In S. mansoni, EC: 3.6.1.19 is secreted and also believed to help the worm evade the immune system of the host; there is a new class of antischistosoma drugs (N-alkyl-aminoalkanethiosulfuric acids) that inhibit the enzyme and may negatively impact schistosoma survival [57]. Another prioritized target is nucleoside-diphosphate kinase (EC: 2.7.4.6), which is secreted by T. spiralis and may modulate host cell function [58]. This enzyme has been studied in B. malayi and is expressed during all parasitic stages in B. malayi, and molecular modeling for drug targeting has been performed for it in B. malayi [59].
Repositioning or further optimization of existing drugs may provide a means to obtain much needed anthelmintic drugs at a faster pace, as many of the drugs already have FDA approval. Existing drugs-like compounds may yield a faster path to anthelmintic drugs by providing a known scaffold that may require some optimization. Many drugs in KEGG Drug and DrugBank whose targets also hit nematode ECs have immunosuppresant, anti-inflammatory, antiviral, and antineoplastic activity. For example, Levamisole, an anthelmintic drug, is also a treatment for rheumatoid arthritis [60]. These target proteins may provide insight into how the parasite evades the immune system when it infects the host. Further, other targets with drugs that have immunosuppressant activity may yield a drug that has already been approved that can be repositioned as an anthelmintic drug. For instance, Mercaptopurine (DB01033) and Azathioprine (DB00993) (Table 1), which resulted from the prioritized list of drug-like compounds from DrugBank, both have immunosuppressive properties. In addition, several targets in KEGG Drug with homology to helminth proteins also have immunosuppressive activity, including IMP dehydrogenase (EC: 1.1.1.205). Several chokepoint targets from KEGG Drug with homology to helminth proteins also have antimalarial and antiprotozoal properties, such as phospholipase A2 (EC: 3.1.1.4). The corresponding drugs for various targets are listed in Table S6 in Text S1 and Table S7 in Text S1.
To find promising drug-like compounds for repositioning (or ones which hit scaffolds for which further optimization can be done), drug-like compounds that target chokepoint enzymes were also prioritized and the best candidates were tested in C. elegans and 2 parasitic nematodes. One compound, Perhexiline (PER) (DB01074) (1), yielded an EC50 value of 47.3 µM (18.5 ppm) and caused a slow movement and twitchy phenotype in C. elegans, as well as a deleterious phenotype in H. contortus and O. lienalis, two parasitic nematode species. PER is an approved small molecule drug which is used as a coronary vasodilator and used for angina treatment [19]. According to DrugBank, PER binds to H. sapiens carnitine o-palmitoyltransferase I (CPT-1) and carnitine o-palmitoyltransferase 2 (CPT-2). If PER inhibits CPT-1 or CPT-2 in living parasites, a drop in fatty acid oxidation can be measured by oxygen consumption rates experimentally. The dose-dependent decrease in basal oxygen consumption rates in the C. elegans exposed to PER (Figure 5A) provides indirect evidence that PER is acting via its intended mode of action on CPT-1 and CPT-2. In addition, a comparison of OCR in C. elegans exposed to either PER, ETO (or both) to an anthelmintic with a different mode of action would also provide an independent orthogonal confirmation of the similarity of PER and ETO in their possible mode of action on CPT-1 and CPT-2. Indeed, the lack of an observed decrease of OCR in C. elegans in the presence of IVM (which disrupts neurotransmission processes regulated by GluCl activity) further confirmed our hypothesis (Figure 5). Additionally, transcriptional responses to drugs often carry signatures for drug physiological mode of action. The transcriptional response to PER was measured by RNAseq and compared to that of ETO and IVM. Drugs with a related mechanism of action (i.e., PER and ETO) cluster together, since similar patterns of pathways are expected to be significantly perturbed. The clustering we observed (Figure 6A), as well as a Gene Ontology analysis of upregulated genes which (among other GO categories) includes peroxisome and fatty acid beta-oxidation, provides an additional confirmation of the similarity of PER and ETO in their mode of action (Table S10 in Text S1). Further experimentation, including in vitro enzyme assays, binding studies and drug resistance mutants, would need to be done to validate completely the mode of action and to move from hit to lead. The compound may need to be altered in order to increase efficacy.
There are 6 homologs of carnitine o-palmitoyltransferase (EC: 2.3.1.21) in C. elegans. R07H5.2 (cpt-2) is expressed in the adult and larval intestines of C. elegans and has an embryonic lethal RNAi phenotype, whereas Y46G5A.17 (cpt-1) does not have an RNAi phenotype (see below for a detailed explanation) and is expressed in the intestine, body wall muscle, and rectal gland cells in larva and in the pharynx, reproductive system, vulval muscle, and body wall muscle in adults [61]. The chokepoint compound in this reaction, L-palmitoylcarnitine (L-PC) has been shown to inhibit the Na/K pump in guinea-pig ventricular myocytes [62] and the interaction between L-PC and PIP2 in the membrane regulate KATP channels [63]. A module of the KEGG Fatty Acid Metabolism pathway map is shown in Figure 7A. Using Autodock 4 [41], perhexiline was docked into the active site of CPT-2 [42] (Figure 7B). The binding site of perhexiline in CPT-2 does not overlap with the carnitine group in the ST-1326 based on the docking calculations, which is consistant with biophysical experiments on CPT-1 [64], which showed competitive inhibition with respect to palmitoyl-CoA, but non-competitive inhibition with respect to carnitine. PER binds to residues in the active site that do not differ between mammals and nematodes (Figure 7D), which explains its efficacy in different phyla. Differences in residues between nematodes and mammals are present around the binding site (Figure 7D), and these differences could be exploited to generate a specific and more potent inhibitor by extending the PER molecule into this area.
Two other compounds, Carbidopa (DB00190) (2) and Azelaic acid (DB00548) (7), also showed deleterious movement phenotypes in C. elegans. Carbidopa (8) is an approved small molecule that is an inhibitor of L-DOPA decarboxylase (EC: 4.1.1.28 chokepoint enzyme), which prevents the conversion of levodopa to dopamine (Figure 8). Carbidopa is used in the treatment of Parkinson's disease to reduce the side effects of levodopa, but has no anti-Parkinson actions by itself [19]. L-DOPA decarboxylase was also found to be a chokepoint in flatworm [55], and Methyldopa (9) has been found to inhibit enzyme activity in schistosoma extracts [65]. Methyldopa and carbidopa only differ by one amino group (Figure 3B). Azelaic acid is also an approved drug that targets, 3-oxo-5-alpha steroid 4 dehydrogenase (EC: 1.3.99.5 chokepoint enzyme), as well as thioredoxin reductase, tyrosinase, and DNA polymerase I [19]. Typically, azelaic acid is used to treat acne and has antimicrobial properties.
High throughput RNAi studies in C. elegans can provide evidence that an enzyme has an important in vivo function [66], suggesting that targeting that enzyme using a drug would likely have a deleterious effect on the worm. Similar phenotypes observed for a drug treatment and RNAi (or gene mutation) provide support that the drug is specifically inhibiting the gene product targeted by RNAi. However, high-throughput RNAi data needs to be considered with caution [66], [67], [68], and thus it was not possible to incorporate it into our chemogenomic pipeline. There are a number of reasons why high throughput RNAi experiments can fail to generate a phenotype. One biological reason is that there is a family of genes that encode the enzymatic activity, and knockdown of any single gene will have no effect due to genetic redundancy. This appears to the case for L-DOPA decarboxylase (EC: 4.1.1.28), which is encoded by three paralogs (K01C8.3, ZK829.2, and C05D2.3) that do not show single gene RNAi phenotypes. In some cases however, gene family members can have essential functions, due to divergent protein sequences, subcellular compartmentalized functions and/or unique expression behavior, which may explain why cpt-2 RNAi displays a strong phenotype. Other biological reasons why RNAi may fail to display a phenotype include RNAi resistance for the genes or that the relevant functional cell type is largely resist to RNAi (e.g. neurons). Additionally, high throughput RNAi in C. elegans has, methodologically, a relatively high rate of false negatives. In contrast, the false positive rate for RNAi in C. elegans is generally low, but can occur due to the libraries containing some incorrect RNAi clone IDs. Thus while high-throughput RNAi data can be used as a starting point, gene product hits from chemogenomic pipelines must be individually tested experimentally, including verification of RNAi clone identity, assessment of the extent of knockdown, or through analysis of gene deletions, if available. Finally, when comparing phenotypes generated by RNAi (or mutant) relative to drug treatment, the extent of gene product loss of function and drug-mediated inhibition need to be comparable, with consideration of the developmental stage that is being examined. For the compounds with hits, further experimentation that includes other life cycle stages would need to be performed to determine if the compounds should progress to advanced testing and move it from ‘hit’ to ‘lead’. The compound may also need to be modified to increase efficacy.
This study has yielded many interesting lead drug target hits and drug-like compounds that should be explored further that could potentially yield a next-generation anthelmintic/nematicide or novel drug target.
In this study, we report chokepoint reactions and enzymes that are common to all 10 studied species of nematodes, as well as chokepoint reactions and enzymes that encompass the union of the 10 nematode species. This study goes further than previous studies to try to understand features of chokepoint enzymes that are successful drugs targets, then uses available diverse information to prioritize the nematode chokepoint enzymes for those that are good drug candidates. Scoring high on the prioritized list are targets that are under investigation for treatment of parasites, indicating that the list contains reasonable targets that should be investigated further. In addition, KEGG Drug and DrugBank were examined for existing drugs that could be repositioned or optimized as anthelmintic drugs. Three of the seven compounds were experimentally tested and show efficacy in C. elegans, and one of these three (Perhexiline) shows efficacy in two nematode species with distinct modes of parasitism. A suggested mode of action was also outlined for Perhexiline. Computational modeling results suggest opportunities for higher affinity and specificity using this compound as a starting point. The list of prioritized drug targets and drug compounds has enormous potential for the development of new and urgently-needed anthelmintic drugs and pesticides.
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10.1371/journal.pntd.0005507 | Unraveling Chagas disease transmission through the oral route: Gateways to Trypanosoma cruzi infection and target tissues | Oral transmission of Trypanosoma cruzi, the causative agent of Chagas disease, is the most important route of infection in Brazilian Amazon and Venezuela. Other South American countries have also reported outbreaks associated with food consumption. A recent study showed the importance of parasite contact with oral cavity to induce a highly severe acute disease in mice. However, it remains uncertain the primary site of parasite entry and multiplication due to an oral infection. Here, we evaluated the presence of T. cruzi Dm28c luciferase (Dm28c-luc) parasites in orally infected mice, by bioluminescence and quantitative real-time PCR. In vivo bioluminescent images indicated the nasomaxillary region as the site of parasite invasion in the host, becoming consistently infected throughout the acute phase. At later moments, 7 and 21 days post-infection (dpi), luminescent signal is denser in the thorax, abdomen and genital region, because of parasite dissemination in different tissues. Ex vivo analysis demonstrated that the nasomaxillary region, heart, mandibular lymph nodes, liver, spleen, brain, epididymal fat associated to male sex organs, salivary glands, cheek muscle, mesenteric fat and lymph nodes, stomach, esophagus, small and large intestine are target tissues at latter moments of infection. In the same line, amastigote nests of Dm28c GFP T. cruzi were detected in the nasal cavity of 6 dpi mice. Parasite quantification by real-time qPCR at 7 and 21 dpi showed predominant T. cruzi detection and expansion in mouse nasal cavity. Moreover, T. cruzi DNA was also observed in the mandibular lymph nodes, pituitary gland, heart, liver, small intestine and spleen at 7 dpi, and further, disseminated to other tissues, such as the brain, stomach, esophagus and large intestine at 21 dpi. Our results clearly demonstrated that oral cavity and adjacent compartments is the main target region in oral T. cruzi infection leading to parasite multiplication at the nasal cavity.
| Oral transmission of Trypanosoma cruzi associated with food/beverage consumption is presently an important route of infection in Brazil and Venezuela. Colombia, Bolivia, Argentina and Ecuador have also reported to have acute cases of Chagas disease transmission through the oral route. Significant studies about this form of T. cruzi infection are largely lacking. In addition to the classic cardiac involvement, orally-infected patient progress to a highly symptomatic disease and increased mortality rate (8–35%), surpassing the calculated mortality produced by the disease resulting from the biting of infected insect vectors (5–10%). Here, we explored by in vivo bioluminescent images, qPCR and fluorescence microscopy the primary site of parasite entry and multiplication in oral infection (OI). Our results clearly demonstrated that the oral cavity is the main T. cruzi target region in OI, leading to parasite multiplication at the nasal cavity and parasite dissemination to the brain and peripheral tissues. Interestingly, facial edema, paraesthesia of the tongue, gingivitis and dry cough were already described in affected patients. These findings might be associated to our present data, which describe for the first time the nasomaxillary region as the main target tissue following oral T. cruzi infection.
| Human Chagas disease (American trypanosomiasis) is a neglected tropical illness caused by the protozoan Trypanosoma cruzi. Infection affects 6–8 million people worldwide and is considered a global health problem. Chagas disease is endemic in Mexico, Central America and South America and is also spreading in non-endemic countries through migration of infected people [1]. It can be transmitted by excreta deposition after biting of blood sucking Triatominae bugs, blood transfusion; organ transplantation; laboratory accident; congenitally and orally [2, 3].
Outbreaks of oral transmission of Chagas disease were described in Brazil, Venezuela, Colombia, French Guyana, Bolivia, Argentina and Ecuador [4–9]. All of these outbreaks were associated with contaminated food/beverage consumption as wild animal meat, vegetables, sugar cane extract, açaí pulp, guava juice, bacaba, babaçu and vino de palma [10–12]. From 1968 to 2000, 50% of acute cases in Amazon region were attributed to oral transmission [8] and these numbers reached 70% between 2000–2010 [6]. Venezuela has also reported the biggest outbreak described so far, with two distinct occurrences affecting respectively 103 and 88 people. These outbreaks involved adults and children from urban and rural schools [5, 13]. Mortality rate in orally infected patients is reported as higher (8–35%) when compared to the classical vectorial transmission, through triatomine excreta deposition after biting (<5–10%) [14]. It is well known that both trypomastigotes and metacyclic trypomastigotes are associated with oral Chagas transmission [15–17]. Regarding T. cruzi genotypes, isolates from DTUs I, II, III, IV and VI have been associated with patients from oral Chagas outbreaks [18–25]. Although relevant, there are few reports about T. cruzi oral transmission in the literature. Some authors have demonstrated parasite-mucosa interaction, some aspects of immune response as well as disease outcome after intragastric, pharyngeal or buccal parasite challenge. These models of oral T. cruzi infections present both patent parasitemia and heart parasitism, which indicate systemic infection [26–30]. In addition, T. cruzi glycoprotein gp82 seems to bind gastric mucin, promoting invasion and replication in epithelial cells from the gastric mucosa [31]. This initial invasion is related to establishment of a progressive gastritis and allows further systemic dissemination of the parasite. Nonetheless, the short replication period at this mucosal site induces specific immunity, as protection was observed after a secondary mucosal challenge, involving the production of IgA and IgG antibodies [27]. In orally infected mice, inflammatory infiltrates are observed in tissues such as pancreas, spleen, liver, bone marrow, heart, duodenum, adrenal glands, brain and skeletal muscle. Moreover, it was suggested that intraepithelial and lamina propria lymphocytes are involved in IFN-γ but not IL-4 production in orally infected hosts [27]. Following disease outbreaks caused by T. cruzi food contamination, a clear increase in severity of clinical manifestations was observed in patients, as compared with other types of transmission routes [8, 14]. These observations raise important questions concerning the particular features of T. cruzi entry via the mucosa, including the possible modulation of local immune mechanisms and the impact on regional and systemic immunity [32, 33]. We have recently demonstrated that the site of parasite entrance, through oral infection (OI)–directly in the mouth, as observed in natural infection, or gastrointestinal infection (GI)–directly to the stomach via gavage differentially affects host immune response and mortality. Thus, comparing to GI mice, we observed that OI mice presented elevated infection rate and parasitemia, higher TNF serum levels, more severe hepatitis and milder carditis [15]. This difference in immunological response and infection severity between GI and OI mice raised important questions about the primary site of T. cruzi infection by the oral route and its impact on disease progression.
Bioluminescent imaging is a promising technique that brings the opportunity to approach the in vivo host-pathogen interactions through a highly sensitive and non-invasive way [34]. In addition to allow the follow up of infection progression by keeping the animal alive, this technique also gives the possibility to observe new sites of infection and parasite distribution that are hardly observed by histological techniques [35]. In the past years, some reports developed in vivo bioluminescent analysis both in T. cruzi infected mice and in the invertebrate host [35–37]. In the present work, by employing the bioluminescent technique and real-time qPCR, we followed the dynamics of T. cruzi Dm28c luciferase (Dm28c-luc) distribution throughout the host using our well-established model of OI in mice [15]. The bioluminescence results indicated the nasal cavity as the main primary site of parasite invasion and multiplication in the host. At later moments, luminescent signal progressively increased in the abdomen and genital region, as a result of parasite dissemination. Quantification of parasite load, via T. cruzi satellite DNA (SatDNA) detection by real-time qPCR at 7 and 21 dpi, corroborated the bioluminescence results, showing predominant T. cruzi detection in mouse nasal cavity. Parasite amplification was also observed in the mandibular lymph nodes, pituitary gland, heart, liver, small intestine and spleen at 7 dpi, and was disseminated to other tissues, such as the brain, stomach, esophagus and large intestine at 21 dpi. Our results indicate the oral cavity and adjacent tissues as the main target region for oral T. cruzi infection, leading to parasite multiplication at the nasal cavity.
Male BALB/c mice, aged 6–8 weeks, were obtained from the animal facility of Oswaldo Cruz Foundation (Rio de Janeiro, Brazil) and used in all experiments. Animals were handled according to the rules of the Ethics Committee for Animal Research of Oswaldo Cruz Foundation. The total number of mice used in each experimental set is described in S1 Fig flowchart. Mice were infected via the oral cavity (OI) with trypomastigotes of a Dm28c (DTU- TcI) genetically modified to express the firefly luciferase (Dm28c-luc), Dm28c-GFP or Tulahuén (DTU- TcVI) strains [35, 38].
Parasites were obtained from infected cultures of a monkey kidney epithelial cell line (Vero cells) from the particular Cell Line Collection of the Laboratory on Thymus Research, Oswaldo Cruz Institute. T. cruzi were counted using Neubauer's chamber in phosphate buffered saline (PBS). Mice were maintained starving for 4 hours and then infected with 1x106 trypomastigotes in 50 μL of parasite suspension into the mouth. At the infection moment, mice swallowing time was respected to avoid parasite aspiration.
A control experiment was performed with injection of 50 μL of black ink suspension at the oral cavity or intranasally to validate our protocol of oral infection and to exclude the possibility of an intranasal contamination (S2 Fig).
This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation and the Federal Law 11.794 (10/2008). The Institutional Ethics Committee for Animal Research of the Oswaldo Cruz Foundation (CEUA-FIOCRUZ, License: LW-23/12) approved all the procedures used in this study.
Mouse parasitemia was individually evaluated at different days post-infection (4, 7, 11, 14 and 21 dpi–S1 Fig) by counting trypomastigotes in 5 μL of tail vessels blood. Blood-parasite number was calculated according to the Brenner method.
Photoluminescence signals were measured at different time points post-infection (15 and 60 minutes (min), 7 and 21 dpi–S1 Fig), in anesthetized animal, by ventral and lateral position using the IVIS Lumina image system (Xenogen Corp, CA, EUA). D-luciferin potassium salt (Xenogen) stock solution was prepared in PBS at 15 mg/mL and stored at -80°C.
Analyses of 15 min post-infection imaging were performed with a 5 min pre-incubation of Dm28c-luc with 0.15 mg in PBS (10 μL) of D-luciferin stock solution followed by mouse infection. Photoluminescent images of infected mice were acquired 15 min later.
Images at 60 min post-infection were carried out after intraperitoneal injection of D-luciferin (150 mg/Kg of body weight) followed by an addition of 50 μL of D-luciferin (0.75 mg in PBS) at the oral cavity, just before capturing the images.
At 7 and 21dpi analyses, photoluminescent signals were measured with images starting 15 min after an intraperitoneal injection of D-luciferin solution in potassium salt (150 mg/Kg of body weight).
Mice were placed inside the animal chamber anaesthesia delivery system (Xenogen XGI-8 Gas Anaesthesia system). Isoflurane (1.5%) anaesthesia was applied until the mice became recumbent. These animals were then placed into the image chamber of IVIS Lumina system (Xenogen Corp, CA, USA) and controlled flow of isoflurane, with a nose cone device into the chamber, maintained them anesthetized during the bioluminescence imaging acquisition.
For the analysis of T. cruzi presence in specific organs, mice were injected with D-luciferin at different times post-infection (S1 Fig), and 10 min later mice were euthanized in order to perform single tissue harvest. Tissues were removed, transferred to a culture dish and images acquired at the IVIS Lumina image system.
Acquisition of bioluminescent images of both mice and tissues was performed by 5 min of exposure and the photons emitted from luciferase-expression T. cruzi were quantified using the Living Image 3.0 software program.
Uninfected and six days post infection mice were euthanized, the nasal cavity were isolated and tissue were included in tissue tek (OCT, Sakura, USA). Cryosections (5 μm) of frozen tissues were analyzed using a fluorescent Zeiss microscope (Germany). Images were digitalized using AxioCam HRm and Axio Vision Rel 4.8 software.
DNA extraction was performed from nasal cavity, palate, tongue, esophagus, stomach, small intestine, large intestine, liver, heart, spleen, mandibular lymph nodes, pituitary gland and brain, using the QIAamp DNA Mini kit (Qiagen, CA). Tissues were obtained from dissected infected mice at different time points (60 min, 7 and 21 dpi), individually weighted (maximum 10 mg for spleen and 30 mg for other tissues was used), washed in PBS (except tissues from nasal cavity, mandibular lymph nodes and pituitary gland) and stored at -20°C until DNA extraction. Blood was drawn via cardiovascular perfusion with PBS, immediately after euthanasia. Nasal cavity tissue was obtained after scraping the region. Tissues and organs from non infected mice were used for negative control. The protocol was carried out according to the manufacturer’s instructions and the DNA was eluted with 100 μL of elution buffer (AE). As a qualitative internal reference control, the exogenous internal amplification control (IAC), a pZErO-2 plasmid containing an insert from the A. thaliana aquaporin gene, was used as reported by Duffy (2009). Before DNA extraction, 5 μL (40 pg/mL) of linearized IAC were added to the samples. DNAs were stored at −20 ◦C until use and their purity and concentration were determined using a Nanodrop 2000c spectrophotometer (Thermo Scientific) at 260/280 and 260/320 nm.
According to the international consensus for quantification of Trypanosoma cruzi DNA in Chagas disease patients [39], Quantitative Real Time PCR Multiplex assays using TaqMan probes were performed targeting the satellite region of the nuclear DNA (SatDNA) of T. cruzi and the exogenous internal amplification control (IAC), as described in Duffy et al, 2009. The qPCR reactions were performed in a final volume of 10 μL containing 1.5 μL of DNA template, 5 μL of 2X TaqMan Universal PCR Master Mix (Applied Biosystems, USA), 750 nM of both cruzi1 (5′ASTCGGCTGATCGTTTTCGA 3′) and cruzi2 (5′AATTCCTCCAAGCAGCGGATA3′) primers and 50 nM cruzi3 probe (5′FAM- CACACACTGGACACCAA-NFQ-MGB 3′) specific for T. cruzi SatDNA; 100 nM IAC Fw (5′CCGTCATGGAACAGCACGTA3′) and IAC Rv (5′CTCCCGCAACAAACCCTATAAAT 3′) primers and 50 nM IAC Tq probe (5′ VIC-AGCATCTGTTCTTGAAGGT-NFQ-MGB 3′). Cycling conditions were a first step at 95˚C for 10 min followed by 40 cycles at 95°C for 15 seconds and 58°C for 1 minute. The amplifications were carried out in a ViiA7 Real-Time PCR System (Applied Biosystems, USA). Standard curves for the absolute quantification were constructed by serial dilution of DNA, extracted from 1 x 106 trypomastigotes of T. cruzi (Dm28c-luc and Tulahuén strain), ranging from 105 to 0.5 parasite equivalents (par. eq). Normalization of the parasite load was performed by tissue mass, after the absolute quantification of T. cruzi by real time qPCR and results were expressed as parasite equivalents/tissue mass (g).
Kruskal-Wallis (Dunn’s post-test) or Mann-Whitney tests were used for the statistical analyses. P values < 0.05 were considered statistically significant. Tests were performed using GraphPad Prism 5.
Mice orally infected with T. cruziDm28c-luc were examined for blood parasitemia during the acute phase of infection. Peripheral blood parasites started to be detectable at 7 dpi, with a peak of parasitemia at 11 dpi. At later moments, the number of circulating parasites gradually decreased (Fig 1).
In order to determine the anatomical route of parasites entrance after OI, mice were infected and evaluated by bioluminescence imaging. At 15 and 60 min after OI, mice were placed inside the IVIS Lumina chamber and the images were obtained in ventral (upper panels) and lateral (lower panels) position (Fig 2). Detection of bioluminescence images after 15 min of OI showed that all infected mice analyzed had highest intensity of bioluminescence in the head region, concerning the mouth, nose and eyes. Although less intensive, bioluminescence was also observed in the neck, thorax and at the abdominal region. Bioluminescence signals were consistently observed from either ventral or lateral viewpoints (Fig 2A and 2B). One hour after infection, the major bioluminescence image detected remained in the head region (Fig 2C and 2D).
To confirm luciferase activity in living trypomastigotes, 5x104 Dm28c-luc T. cruzi parasites were incubated in vitro with medium or D-luciferin in 24 well plate (black circle). Medium or D-luciferin (150 μg/mL) substrate was added to the well and, after 5 min of incubation, images were acquired. As demonstrated in S3 Fig, luminescent signals were only detected in D-luciferin treated parasites. Moreover, as in vivo controls, non-infected mice were treated with D-luciferin and bioluminescent signal analyzed. S4 and S5 Figs show that, in absence of T. cruzi infection, D-luciferin was incapable to promote bioluminescent signal.
For ex vivo evaluation of parasites in specific organs, mice were euthanized at 15 and 60 min and 48 hours after OI. The selected head tissues (nasomaxillary region, mandible region, cheek muscle, tongue and eyes) and gastrointestinal tract (esophagus, stomach, small and large intestine) were excised. The ex vivo evaluation of dissected organs and tissues by bioluminescence imaging confirmed the in vivo bioluminescent T. cruzi foci, as most of the signal detected was localized in the head, specifically in the nasomaxillary region (including areas of the nose, nasal cavity and upper oral cavity) (Fig 3A and 3B). A slight bioluminescence signal was observed in the cecum and mandible region in one single animal, 15 and 60 min after infection, respectively (Fig 3B). Furthermore, no luminescent signal was observed in tongue, eyes, cheek muscle, stomach and small intestine at this time (Fig 3B). At 60 min and 48 hours after OI, ex vivo bioluminescence imaging of the heart, brain, spleen, liver, male sex organs, lung and salivary gland was negative (Fig 3C). Taken together, our data suggests that the primary site of T. cruzi invasion due to OI is located at the upper region of the oral cavity, specifically at the nasomaxillary region. To exclude the possibility of an intranasal contamination in our oral infection protocol, mice were inoculated with black ink suspensions at the oral cavity or intranasally. As observed in S2 Fig orally inoculated mice after 5 min showed ink labeling in the tongue and the oral cavity, but were negative in the nasal cavity. In contrast, the intranasal inoculation clearly labeled the nasal cavity (S2 Fig).
To have an overview of parasite distribution at different stages of infection, OI mice were analyzed at 7 dpi, an early stage of infection when blood parasites started to be detected, and at 21 dpi, a late point of the acute phase allowing a better analysis of parasite distribution and the target tissues. On 7 dpi, bioluminescent signal was detected in the head, neck and abdomen. It is noteworthy that the head region (mouth, nose and eyes) remained the major focus of bioluminescence (Fig 4). At 21 dpi, infection was dispersed trough the animal body, including head, ears, abdomen, genital region and thorax. Interestingly, at this moment, the genital region showed to be an important focus of bioluminescence signal (Fig 4).
To accurately identify the infected tissue, images of individual organs were captured at 7 and 21 dpi. Dissected tissues comprise the nasomaxillary region, palate, mandible, tongue, eyes, cheeks muscle, esophagus, stomach, small and large intestines, mandibular lymph nodes, salivary gland, heart, lung, spleen, liver, brain, pituitary gland, mesenteric fat and lymph nodes and male sex organ, including preputial glands, testicles, epididymis fat and penis. To better evaluate the nasomaxillary region, we removed the hard and soft palate exposing nasal septum and nasal cavity.
Ex vivo evaluation of dissected organs and tissues at 7 dpi demonstrated that high bioluminescent signal remained at the nasomaxillary region of the mice (Fig 5A and S6 Fig). Furthermore, after removal of the entire palate, nasal cavity and nasal septum region showed the major bioluminescence signal (Fig 5A). Light foci were also detected in the palate in 75% of OI mice, shown in Table 1, which describes the percentage T. cruzi-positive tissues analyzed (Fig 5A and Table 1 and S6 Fig). Interestingly, at this moment of infection, images of T. cruzi were detected in the brain, located in the olfactory bulb region (Fig 5C and S6 Fig). Bioluminescence was also detected in the cheek muscle, mandibular lymph nodes and mandible in 50% of OI mice (Fig 5A and 5C and Table 1 and S6 Fig) and 66.6% of spleens (Fig 5D and Table 1). A slight bioluminescence signal was observed in the esophagus, liver, large and small intestines, mesenteric fat and lymph nodes (Fig 5B and 5D and S6 Fig). Bioluminescent foci were also detected in male sex organs, specifically in the testicle and epididymis fat in 33.33% of OI mice (Fig 5E and Table 1 and S6 Fig). The bioluminescence signal was undetected at this time in the tongue, eyes, stomach, pituitary gland, salivary gland, lung and heart (Fig 5A, 5B, 5C and 5F and S6 Fig). In agreement with initial bioluminescent images, a large number of T. cruzi Dm28c-GFP amastigote nests are detected in the nasal cavity of OI mice at 6 dpi (Fig 6).
At 21 dpi, bioluminescence was clearly observed in the nasomaxillary region, palate, mandible region, cheek muscle, esophagus, mandibular lymph nodes, spleen, liver, mesenteric fat and lymph nodes and male sex organ (Fig 5A, 5B, 5C, 5D and 5E and S7 Fig). The major affected tissues and organs in the genital region were penis and preputial gland (Fig 5E). In addition, tissues such as the salivary glands, heart and lung started to reveal parasite presence at this moment (Fig 5C and 5F and S7 Fig). At this time of infection, we also observed an increased signal of bioluminescence in the gastrointestinal tract, mostly in the stomach, intestines and mesenteric fat (Fig 5B and 5D and S7 Fig). Bioluminescence signal was observed in 75% of the intestines analyzed and in 50% of stomach and esophagus (Fig 5B and Table 1 and S7 Fig). Finally, at 21 dpi, the ex vivo evaluation revealed that parasites were disseminated to different organs of the body.
In conclusion, at 7 and 21 dpi, T. cruzi spreads to other parts of the body, infecting other organs. The persistence of bioluminescence signal emitted from the nasomaxillary region suggested the existence of a general maintenance of parasite proliferation in this region.
In contrast to the classical techniques, bioluminescence imaging is able to identify small foci of infection in the whole animal, but, in some cases, bioluminescent signal can be under detection limits. Quantitative real-time PCR (qPCR) is an accurate technique to evaluate the presence of parasites in tissues. To examine the parasite burden in target tissues, we collected tissues from orally infected mice at 60 min, 7 and 21 dpi and performed qPCR to compute parasite load. Initially, tissues of the oral cavity, the gastrointestinal tract and adjacent regions, such as the nasal cavity, tongue, palate, mandibular lymph nodes, esophagus, stomach, large and small intestines were all analyzed by qPCR.
Consistent with the bioluminescence results observed in the nasomaxillary region at 60 min (Fig 3B) and 7 dpi (Fig 5A), T. cruzi foci were detected in elevated numbers at the nasal cavity by qPCR. The first hour after infection showed T. cruzi Sat DNA detection in the nasal cavity among 80% of OI mice, with parasite quantification up to 560 parasite equivalents/g (par.eq./g) (mean of 180) (Fig 7A and Table 1). Parasite amplification was also detected in the esophagus, stomach, small intestine and large intestine (Fig 7A), although these tissues were negative by bioluminescence imaging (Fig 3B). Interestingly, at 60 min, SatDNA detection was observed in one OI mouse at the esophagus, small intestine and large intestine (Fig 7A). Furthermore, T. cruzi SatDNA was detected in 75% of the analyzed OI mice in the stomach and mandibular lymph nodes at 60 min, with T. cruzi quantification up to 191.1 (mean of 52.0) and up to 1.63 (mean of 1.0) par.eqs./g, respectively (Fig 7A and Table 1).
In addition, SatDNA T. cruzi quantification in the nasal cavity was much higher at 7 dpi, ranging from 6.2x103 to 7.5x106 par.eqs./g (mean of 2.2x106) (Fig 7A). In this time points after infection, nasal cavity showed the highest parasite load among the analyzed tissues. Interestingly, mandibular lymph nodes also showed high parasite loads, ranging from 31.2 to 6300 par.eqs./g (mean of 3.5 x 103) (Fig 7A). It becomes evident that the mean parasite load detected in the nasal cavity was 103 times higher than in the other organs (Fig 7A).
At 21 dpi, due to parasite dissemination, high levels of par.eqs./g were detected in all tissues (Fig 7A), in accordance to the bioluminescence imaging. In addition, it was not possible to detect parasite presence in the palate and tongue due to PCR inhibition (no amplification of the qualitative exogenous internal amplification control (IAC). To evaluate parasite dissemination throughout the body and to determine if there was any correlation with the bioluminescence signal, we analyzed parasite load in the pituitary gland, brain, heart, spleen and liver at 60 min, 7 and 21 dpi. Ex vivo imaging of the brain, spleen and liver did not reveal any bioluminescence signal at 60 min (Fig 3C). As expected, qPCR results confirmed the bioluminescence imaging and T. cruzi DNA amplification was undetectable in these organs (Fig 7B). Low amount of parasite detection was observed in the heart of a single animal (0.8 par.eq./g), at 60 min (Fig 7B). At 7 dpi, T. cruzi SatDNA was detected in the heart, spleen, liver and pituitary gland (Fig 7B). Finally, at 21 dpi, parasite dissemination favored T. cruzi detection in all analyzed tissues (Fig 7B).
T. cruzi is highly genetically diverse and currently six Discrete Typing Units (DTU), TcI to TcVI, are recognized [38]. TcI, TcII, TcIII, TcIV and TcVI genotype has been reported in oral transmission of acute Chagas disease [18–25]. Because of this biological polymorphism, different strains may present tropisms for distinct tissues (cardiac muscle, myoenteric plexuses in the esophagus and rectum and others tissues) and consequently differences in the clinical forms of the disease [40]. Due to this difference tissues tropism in T. cruzi strains, qPCR of gastrointestinal tract, nasal cavity and heart tissues from OI mice using a different strain (Tulahuén strain, DTU—TcVI) was performed to compute parasite load. Tissues were collected at 60 min and 7 dpi from OI mice.
Consistent with the qPCR results observed in OI mice with Dm28c-luc strain (DTU- TcI) (Fig 7), sixty minutes after infection, T. cruzi foci was detected in elevated numbers at the nasal cavity in OI mice with Tulahuén strain (DTU- TcVI). T. cruzi presence was also detected in the stomach at this time point (Fig 8). However, at 7dpi the highest SatDNA T. cruzi quantification in the nasal cavity suggested intense parasite growing in this tissue, in contrast with the stomach (Fig 8). Altogether, these data confirms that the nasal cavity is the preferential site T. cruzi infection and expansion in oral infection, regardless of DTU strain specificity (Fig 8).
Interestingly, the percentage of OI mice with blood parasitemia at 7 and 21 dpi was 25% and 56%, respectively. However, by assessing the percentage of infected mice in these same points of infection using bioluminescent imaging (evaluating the presence of the bioluminescence signal) and qPCR (evaluating T. cruzi SatDNA amplification in tissue), 100% of OI mice showed both bioluminescent signal and T. cruzi SatDNA amplification in tissues at 7 and 21 dpi. We conclude that the parasitemia is less sensitive to determine the percentage of infection in animals inoculated by the oral route in our model, since the bioluminescence techniques and qPCR showed signs of active infection in mice in these times.
Taken together, bioluminescence and qPCR data showed that at the first moments after OI, T. cruzi is able to infect nasal cavity, mandibular lymph nodes and stomach. However, nasal cavity is the major focus for parasite permanence and replication. These results show parasite distribution kinetics, thus suggesting that T. cruzi may disseminate to other organs (pituitary gland, brain, heart and liver) from the nasal cavity (Fig 9).
In the past years, the number of oral Chagas disease outbreaks in Brazil and other Latin America countries are increasing. Presently, the most common pathway of T. cruzi infection in the Brazilian Amazon is the oral route and, from 2000 to 2013, this pathway of infection was responsible for 70% of acute cases in Brazil [4, 6].These outbreaks were associated with ingestion of contaminated food and beverage[11, 41]. Orally infected patients are frequently highly symptomatic, presenting long-lasting fever, headache, facial and bipalpebral edema, lower limb edema, myalgia, abdominal pain, meningoencephalitis and the classical cardiac involvement [6, 9, 42–44]. Analysis of distinct outbreaks demonstrated that the mortality rate of affected patients in the first two weeks of infection is estimated at 8–35%, considerably higher than the mortality rate from the classical vectorial transmission (< 5–10%). The higher mortality rate can be associated with elevated prevalence of cardiac pathology and absence of an earlier diagnosis [14, 43].
Despite being an important route of infection, there are few studies regarding T. cruzi oral transmission in the literature. Previous data, using histopathology studies, showed signs of a possible T. cruzi penetration in the oral, esophageal, gastric, and intestinal mucosa with a local reaction with eosinophilia, infiltrated lymphocytes and monocytes after oral infection in dog [45]. In contrast, some authors have demonstrated that orally T. cruzi infected mice involves gastric mucosal invasion for the systemic infection. It has been shown, by histological analysis, that T. cruzi infection is observed in the gastric mucosal epithelium. However, parasites were not detected in other areas throughout the gastrointestinal tract, like esophagus and oropharynx. These authors observed that T. cruzi initiates systemic parasite dissemination as a consequence of an oral infection by gastric mucosal invasion [27]. By using intragastric or intrapharyngeal challenge, another group observed that T. cruzi glycoproteins, such as gp82 and gp30, are important for gastric invasion. Prior to invasion, the parasite binds to gastric mucin using these glycoproteins that allow T. cruzi to invade and replicate in the stomach [29, 31, 46, 47]. We have previously shown that the site of inoculation, through the oral cavity (OI) or the stomach (by gavage-GI), differentially affects host immune response and mortality. OI developed a highly severe acute disease with higher parasitemia, TNF serum levels, hepatitis and mortality rates when compared to GI [15], suggesting that the inoculum site is a key factor in Chagas disease progression, possibly modulating local immune mechanisms that impacts in the systemic immunity. In addition, intraperitoneal (IP), intravenous and subcutaneous infection shows higher infection rates and mortality than mucosal ones (oral, intragastric, intrarectal, genitalia or conjunctival) [33, 48, 49].
Here, we searched for the site of parasite entry in the host in orally infected mice. It is well accepted that bioluminescence imaging is an innovative technique that helps the identification of parasite distribution in distinct tissues, allowing a panoramic comprehension of T. cruzi dissemination in the entire animal body [34]. By using bioluminescence technique, we demonstrated that, few minutes after OI, parasites are concentrated in the head region, specifically in the nasomaxillary region (upper oral cavity, nose and nasal cavity). In a lesser intensity, parasites were also detected in the thorax and at the abdominal region. In addition, T. cruzi was detected in the nasal cavity and draining lymph nodes at 60 min post-infection by qPCR, confirming that the nasal cavity has the highest parasite load among affected tissues, in contrast with the stomach and intestines. In the same way, two and seven days after inoculums, images revealed that the nasomaxillary region remains as the major focus of infection.
Interestingly, facial edema is a common feature in affected patients, being described in 57–100% of cases in Brazilian outbreaks of oral infection [6]. Nevertheless, a contaminated sugar cane juice outbreak of oral infection in Paraiba State (Brazil) revealed the presence of bilateral palpebral edema in 92% of orally infected patients [44]. An outbreak with contaminated fresh guava juice in Venezuela showed that 40% of hospitalized patients had facial edema [50]. Moreover, another outbreak in Venezuela involving five members of the same family described that all patients showed edema in the face, mouth and cheek, and edema and paraesthesia of the tongue [51]. Furthermore, other clinical finding in the face region, such as gingivitis and dry cough has been attributed to the penetration of the parasite throughout the oral or pharyngeal cavity [6, 43]. Interestingly, T. cruzi infection and gingival inflammatory foci has been shown at the oral cavity from a chronic Chagas disease patient [52]. These findings might be associated to our present data, which describe for the first time the nasomaxillary region as the main target tissue following oral T. cruzi infection.
The mouth can be targeted by various infectious diseases, including viral, bacterial, and fungal. The oral cavity contains distinct mucosal surfaces composed of sophisticated structures and molecules, such as mucins, in which the microorganisms can bind and colonize the environmental cells [53]. It has been shown that the soft palate is an important site of infection and adaptation of influenza viruses. The soft palate infection may contribute to airborne transmission by providing a mucin-rich microenvironment and perhaps the initial region of infection. In fact, the expression of α 2,3 sialic acids and viral hemagglutinin ligand is detected on the soft palate in the regions of the oral surface, mainly at the basal cells, and the nasopharyngeal tissues from humans and ferret [54]. Interestingly, α 2,3 sialic acids are the main molecule involved in T. cruzi transialidase mediated binding. Transialidase has been considered as an important virulence factor of T. cruzi, due to its ability to reduce host cell immune response and mediate T. cruzi and host cells adhesion [55]. It has been shown that transialidase have adhesive capacity with host sialoglycans, generating “eat me” signals in epithelial cells, facilitating the parasite entry into non-phagocytic cells [56]. Based in these previous studies we can hypothesize that oral T. cruzi infection may occur on the palate, through the interaction of transialidase molecules in the parasite membrane with α 2,3 sialic acids residues present in the soft palate [54]. Other molecules may also be involved in T. cruzi adhesion with oral cavity cells, such as mucins and glycoproteins such as gp82, gp30, gp90 [57].
Seven days after infection reveals that nasal cavity, nasal septum region, palate, cheek muscles, mandible and mandibular lymph nodes are target tissues of the parasite. Surprisingly, the mean parasite load detected by qPCR in the nasal cavity of OI mice with Dm28c-luc (DTU- TcI), is 103 times higher than other tissues. This predominant T. cruzi detection in mouse nasal cavity is also observed in OI mice with other T. cruzi strain (Tulahuén strain, DTU- TcVI. Altogether this data suggesting that nasal cavity is the main site of T. cruzi maintenance and replication following oral infection.
In the line with our findings, Giddings and colleagues demonstrated that nasal cavity is the principal site of parasite infection and replication after conjunctival T. cruzi infection with Tulahuén strain (DTU-TcVI). The predominant invasion occurs through epithelia lining nasal cavity and nasolacrimal ducts. T. cruzi initially replicates within these sites and further spread to draining lymphoid organs with systemic dissemination. In the nasal cavity, parasites were detected in areas such as the submucosa of the epithelial lining the nasal septum, nasal mucosa-associated lymphoid tissue and bone marrow of the facial bones surrounding the nasal cavity [58]. Mice infected with the Tulahuén strain of T. cruzi by the intranasal route shows higher brain parasitism than mice infected by the subcutaneous pathway [49]. It was also observed that parasites gain access to the brain via olfactory nerve tissues. The authors proposed that, within the first moments, parasites invade nasal cavity cells, multiply and then migrate to the brain via the olfactory tissues [49]. Supporting this idea, we have observed that after infection and multiplication of parasites in the nasal cavity of orally infected mice, bioluminescence imaging of T. cruzi at 7 dpi were detected in the bulbous olfactory region of the brain in orally infected mice. Interestingly, parasites were also detected by qPCR in the pituitary gland at 7 and 21 dpi, but not in the central region of the brain at 7 dpi, turning positive at 21 dpi. Thus, we propose that brain infection is subsequent to the nasal cavity and the olfactory nerve tissue commitment. Corroborating our results of T. cruzi detection in the pituitary gland and in the brain, a previous study detected the parasite kinetoplast DNA in the pituitary gland during the acute phase [59].
Despite bioluminescence imaging is able to identify small foci of infection in the tissues and in the whole animal, this technique has limitations and some aspects that should be considered [34, 37]. The detection sensitivity is dependent on several factors, such as the level of luciferase expression, type of tissues, depth of labeled cells within the body and sensitivity of the detection system. Thus, in some cases, bioluminescent signal can be under the detection limit [37, 58–60]. As we have observed in our model, the percentages of T. cruzi-positive analyzed samples by bioluminescence and qPCR are different in some tissue (Table 1). Indeed in both pituitary gland and the heart at 7 dpi the presence of T. cruzi was not detected by bioluminescence, however it was detected by qPCR. This can be explained by higher sensibility of the qPCR compared to bioluminescence, as the qPCR allows detection of at least 0.5 equivalents parasites [61] and bioluminescence does not.
T. cruzi infection has been associated to disturbances in immune-endocrine systems, leading to activation in the hypothalamus–pituitary–adrenal (HPA) axis and high glucocorticoid production. The high glucocorticoid secretion seems to limit the excessive production of pro-inflammatory cytokines, protecting the host from tissue injury and metabolic alterations. Furthermore, the elevated glucocorticoid production in the acute phase is involved in thymus atrophy and immature T CD4+CD8+ cell apoptosis [60, 61].
In Fig 4 we observe that animals analyzed showed differences in bioluminescence signal. Some animals present less intensity of bioluminescence signal in the head, demonstrating that these animals have a lower parasitism in this region in that time point. Note that with 21dpi these same animals presented a larger signal in the region in the nasal cavity, which shows that they may have different evolution kinetics. This does not exclude the fact that they were infected and presented high intensity of signal at the same regions as the others, but not exactly at the same time. These differences between mice in T. cruzi infection can be observed also in parasitemia (Fig 1) or in parasitism load at different tissues (Figs 7 and 8). Interestingly, we can also see in Fig 7A a large difference in parasite load in the nasal cavity with 7 dpi between animals analyzed by qPCR, although not analyzed in the same animals bioluminescence.
Interestingly, with the development of the infection and spread of T. cruzi, we observed the presence of bioluminescence signal mainly in the male sexual organs (testicles, epedidimal fat, preputial gland and epididymis). As described in previous studies, male sex organs are frequently infected in T. cruzi experimental infections, including testes, penis, epididymis ducts and accessory sex glands (prostate, preputial gland and seminal vesicle) of mice infected by IP route [62–65]. In humans some cases of orchitis due to gonadal parasitism during the acute phase of Chagas disease have been described. Furthermore, clinical manifestations of sexual dysfunction such as decreased of libido, erection and ejaculation were reported [66–69]. Although the possibility of sexual transmission of T. cruzi has been suggested, few studies have been published on this theme. In the acute phase of experimental infection, sexual transmission has been described, but with low transmission rates in uninfected and immunosuppressed females through males infected by IP route [70]. Ribeiro and colleagues evaluated the potential of sexually transmission of T. cruzi in the chronic phase with infected males to uninfected females and vice versa by using mice infected via IP route. After copulation, 100% of the animals, both males as females seroconverted (ELISA and IF) and presented T. cruzi DNA in the heart and skeletal muscle [71].
In the present work, we have identified the site of T. cruzi initial invasion and replication after infection through the oral route. Our results demonstrated that oral infection involves T. cruzi passage through the mouth into the nasal cavity, where parasite replication occurs. Then, nasal cavity parasites might disseminate through the olfactory nerve tissues and blood to distant tissues (Fig 9). Thus, the proper oral cavity operates as a potential source of infection, and places the regional innate and adaptive immune systems as central players in the disease progression. Therefore, the elucidation of the tissue/organs targets and the molecular components regulating the establishment of oral T. cruzi infection is critical to understanding the pathogenesis of this current form of Chagas’ disease.
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10.1371/journal.pntd.0001739 | Detection of Antibiotic Resistance in Leprosy Using GenoType LepraeDR, a Novel Ready-To-Use Molecular Test | Although leprosy is efficiently treated by multidrug therapy, resistance to first-line (dapsone, rifampin) and to second-line drugs (fluoroquinolones) was described worldwide. Since Mycobacterium leprae is not growing in vitro, phenotypic susceptibility testing requires a one year experiment in the mouse model and this is rarely performed. Genetics on antibiotic resistance provide the basis for molecular tests able to detect for antibiotic resistance in leprosy.
A reverse hybridization DNA strip test was developed as the GenoType LepraeDR test. It includes DNA probes for the wild-type sequence of regions of rpoB, gyrA and folP genes and probes for the prevalent mutations involved in acquired resistance to rifampin, fluoroquinolones and dapsone, respectively. The performances of the GenoType LepraeDR test were evaluated by comparing its results on 120 M. leprae strains, previously studied for resistance by the reference drug in vivo susceptibility method in the mouse footpad and for mutations in the gene regions described above by PCR-sequencing. The results of the test were 100% concordant with those of PCR sequencing and the mouse footpad test for the resistant strains: 16 strains resistant to rifampin, 22 to dapsone and 4 to ofloxacin with mutations (numbering system of the M. leprae genome) in rpoB (10 S456L, 1 S456F, 1 S456M + L458V, 1 H451Y, 1 G432S + H451D, 1 T433I + D441Y and 1 Q438V), in folP1 (8 P55L, 3 P55R, 7 T53I, 3 T53A, 1 T53V) and gyrA (4 A91V), respectively. Concordance was 98.3% for the susceptible strains, two strains showing a mutation at the codon 447 that in fact was not conferring resistance as shown by the in vivo method.
The GenoType LepraeDR test is a commercially available test that accurately detects for antibiotic resistance in leprosy cases. The test is easy to perform and could be implemented in endemic countries.
| Although leprosy is a curable disease using a combination of antibiotics for one year, the transmission is still active with 230,000 new cases in 2010. Drug resistance has been described and may prevent eradication of the disease. The infectious agent causing leprosy, Mycobacterium leprae, is not growing in vitro and antibiotic susceptibility testing is possible only in the mouse footpad model that requires a one year experiment. Consequently this testing is rarely done and antibiotic resistance rates in leprosy are unknown. This is the reason why we endeavored to set a new diagnosis test that detects for antibiotic resistance in M. leprae. The test is based on the method of a DNA strip test with a multiplex PCR followed by reverse hybridization. It was developed as an easy-to-use test and it will be available in endemic countries, where these kinds of strip tests are already used for detection of drug resistance in tuberculosis. The results of the new test, Genotype LepraeDR, performed on 120 M. leprae strains were concordant with those of the standard PCR sequencing and mouse footpad susceptibility testing.
| Leprosy, the second communicable disease due to mycobacteria after tuberculosis, is still a preoccupying disease as 230 000 new cases have been reported in 2010 (www.who.int/lep/). This disease remains difficult to diagnose and treat in low- and mid-developed countries, especially in rural areas. Global child rate has remained consistently at around 10% of cases for the last years, showing that transmission is still active [1]. Leprosy can be cured if multidrug therapy (MDT) is properly implemented following WHO recommendations: a 6 month regimen for paucibacillary cases and a 12 month regimen (formerly 24-months) for multibacillary (MB) cases both combining rifampin and dapsone, plus clofazimine for MB cases [2]. The relapse rate ranges between 2% and 5% in leprosy depending of the country, and we learned from tuberculosis that relapse cases are at risk of drug resistance [3]. However, in contrast to what we know for tuberculosis, the prevalence of primary and secondary resistance is unknown for leprosy. Consequently, the risk of resistance cannot be assessed and re-treatment regimen cannot be appropriately design.
Mycobacterium leprae is one of the few bacteria that are not growing in vitro. It multiplies only in the mouse footpad [4] and in the nine-band armadillo [5]. The in vivo susceptibility testing model, based on footpad inoculation of mice treated with antibiotics, is available in only an handful of highly specialized laboratories and cannot be spread because it requires one year lasting experiment (M. leprae doubling time is about 10 to 14 days) and expensive facilities [4], [6]. Resistance to anti-leprosy drugs, such as dapsone, rifampin and fluoroquinolones, has been described since 1967 using this in vivo model [6]. Multi-drug resistance, i.e. resistance to at least two of these drugs, has been described in Africa [7], Asia [8] and South America (unpublished data).
In the late 1990's, thanks to PCR and determination of the M. leprae genome [9], molecular methods detecting antibiotic resistance have been set. Rifampin resistance was associated to mutations in the rpoB gene encoding the β subunit of RNA polymerase [10], dapsone resistance to mutations in the folP1 gene encoding the dihydropteroate synthase [11], [12] and fluoroquinolone resistance to mutations in the gyrA gene encoding the subunit A of DNA gyrase [7]. Various methods have been described to detect the mutations listed above such as PCR- sequencing, heteroduplexes formation, and DNA array [13], [14], [15], [16], [17], [18]. However, all these methods require specialized laboratories and are not commercially available. No easy-to-use methods are available in the endemic areas.
The DNA strip assay is a methodology widely used for molecular detection of resistance in tuberculosis [19]. The test is based on a classic PCR and reverse hybridization. Because this methodology has been demonstrated to be simple and robust in developing countries, we aimed to develop a new test based on this technology that easily detect for drug resistance in leprosy.
Hundred and twelve skin biopsies containing M. leprae were studied for the evaluation of the test. They have been sent for leprosy diagnosis to the National Reference Center for mycobacteria (NRC-Myc, Paris, France) from 1989 to 2010 and were all smear-positive for acid fast bacilli (AFB) with a minimum amount of 5×104 AFB/ml. The samples were anonymized and the collection was used under the IRB approval for diagnosis specimens received at Assistance publique Hôpitaux de Paris, Biology laboratories of Pitie-Salpetriere Hospital. The selected biopsies (54% of the collection) were consecutive biopsies for which mouse culture was performed and for which enough quantity of specimen was available for performing the molecular studies. Skin biopsies were prepared as described previously for mouse inoculation and molecular experiments [17], [20].
Eight M. leprae strains, which were previously described and propagated in the nude mouse footpad, were taken as reference strains [8], [21].
DNA from several mycobacterial strains other than M. leprae were tested for analytical specificity: 3 M. ulcerans, 5 M. marinum, 5 M. chelonae, 1 M. scrofulaceum, 1 M. kansasii, 1 M. intermedium, 1 M. terrae, 1 M. malmoense, 1 M. fortuitum. In addition, ten biopsies known to be negative for mycobacteria were also tested for specificity.
The design of the mutated (MUT) and wild type (WT) probes were based on the mutations reported in the literature for the resistant strains: in the rifampin resistance determining region (RRDR) in rpoB [10], [17], [22], in the region determining dapsone resistance (DRDR) in folP1 [11], [12], [20] and in the quinolone resistance determining region (QRDR) in gyrA [7], [23]. The probes are listed in Table 1. Wild type probes, one to four according to the gene, were chosen to span the region affected by drug resistance mutations: WT1 to WT4 for rpoB (the 430–458 region, numbering system of the M. leprae genome TN, GenBank n°NC 002677), WT folP1 for the 53–55 region and WT gyrA for the 89–91 region. Some of the most prevalent mutations in rpoB (S456L and H451Y), in folP1 (P55L) and in gyrA (A91V) were included in the strip as specific probes.
Strips were coated at Hain Lifescience factory (Nehren, Germany) with the different specific oligonucleotides (DNA probes) using the DNA strip technology. Amplification, hybridization and interpretation were performed in a similar procedure as for other GenoType tests [19]. Briefly, 35 µl of 5′-biotinylated primers and nucleotide mix, 5 µl of polymerase buffer, 2 µl of 25 mM MgCl2 stock solution, 3 µl of water and 5 µl of total DNA (20 to 100 ng) were mixed with 1 U of Hot Star Taq polymerase (Qiagen) per reaction. The PCR run was comprised of 35 cycles. After denaturation at 95°C for 15 min, ten cycles at 95°C for 30 sec and at 58°C for 2 min were followed by 25 cycles with a first step at 95°C for 25 sec, a second step at 53°C for 40 sec and a third step at 70°C for 40 sec. PCR ended with 8 min at 70°C. Hybridization was performed using the TwinCubator at a temperature of 45°C. The denaturation solution was mixed with 20 µl of the amplified sample and submitted to the usual protocol for hybridization.
In order to assess positive and negative bands, each DNA strip was stuck on an evaluation sheet after the hybridization, and a template was aligned side by side of the respective strip, with at the top the conjugate control band and at the bottom the coloured M marker band. Positive control bands, i.e. that should appear positive to make the test valid, were the conjugate control, the amplification control, the identification control for the M. leprae species and amplification controls of the rpoB, folP1 and gyrA genes.
Interpretation was as follows for each gene/antibiotic: the strain was predicted to be susceptible when all WT bands were positive and all MUT bands were negative; the strain was predicted to be resistant when at least one MUT band was positive or at least one WT band was negative.
PCR sequencing was performed routinely and prospectively in the frame of NRC-Myc activities, as individual susceptibility to rifampin (rpoB) and dapsone (folP1) for all the 112 biopsies whereas ofloxacin susceptibility was tested for 52 biopsies. PCR sequencing was performed specifically in the frame of the present study for the 8 reference strains.
Total DNA was extracted from biopsies containing M. leprae following the heat-shock procedure [24]. DNA was subjected to three PCRs, one amplifying the RRDR in rpoB gene, one the DRDR in folP1 and one the QRDR in gyrA, as previously described [10], [25]. Typical reaction mixtures (50 µl) contained 1× reaction buffer, 1.5 mM of MgCl2, 200 µM of dNTPs, 1 µM of each primer (Proligo France SAS), 1.25 U of Taq polymerase (Q-Biogene, Illkirch, France) and 5 µl of DNA extract. PCR-amplified fragments were purified by using Montage™ PCR Centrifugal Filter Devices (Millipore, Molsheim, France) and sequenced by the dideoxy-chain termination method with the ABI PRISM BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Courtaboeuf, France). The oligonucleotide primers used for DNA sequencing were those used for PCR. The nucleotide and deduced amino acid sequences were analyzed with the Seqscape v2.0 software (Applied Biosystems).
Animal experiments were performed in accordance with prevailing regulations regarding the care and use of laboratory animals by the European Commission. The experimental protocol was approved by the Departmental Direction of Veterinary Services in Paris, France.
The M. leprae strains were subjected to the mouse footpad susceptibility testing that included 10 untreated Swiss mice as a control group, a rifampin-treated group of 8 mice and a dapsone-treated group of 30 mice as described previously [17],[20]. Dapsone susceptibility testing was stopped in 2001 because of new governmental regulation for antibiotic-free animal feeding. An additional group of 8 ofloxacin-treated mice was inoculated, as described in [7], for the biopsies sampled in patients who have been treated by fluoroquinolones.
The results of the GenoType LepraeDR test were compared to those of the PCR sequencing method for all the 120 M. leprae strains (60 in the case of ofloxacin and gyrA).
The results of the GenoType LepraeDR test were also compared to the results of the mouse footpad model for M. leprae strains that yielded interpretable susceptibility results, i.e. 84 strains tested in vivo for rifampin susceptiblity, and among them 56 for dapsone susceptibility and 5 for ofloxacin susceptibility.
The DNA strip tests were validated with regard to the M. leprae identification band, which was positive with an intensity equal or higher than that obtained with the universal positive control, demonstrating the presence of M. leprae DNA. Thus, the overall sensitivity of GenoType LepraeDR for detecting M. leprae was 100%.
Analytical specificity tested with either DNA from another mycobacterial species (n = 19) or negative skin biopsies (n = 10) was 100% since no positive signal was obtained for the M. leprae identification band. However, hybridization was observed for DNA from M. intermedium and M. malmoense with two of the wild type rpoB bands, due to a high identity between the rpoB genes of these mycobacterial species.
The mutations found in the M. leprae strains by PCR-sequencing are listed in the Table 2. Representative results of the DNA strip tests are shown in Figure 1 for resistant strains and in the Figure 2 for susceptible strains.
The results of the DNA strip test were concordant with those of PCR sequencing for all the 16 rpoB mutations conferring rifampin resistance (Table 3). We observed a positive signal at probes rpoBMUT2 for the 10 strains harboring the mutation S456L and at rpoBMUT1, for the strain harboring the H451Y mutation, since these mutations are present onto the strip as a mutated probe. As expected for these strains, no signals were observed for the wild type probes rpoBWT4 and rpoBWT3, respectively. For the others mutations, the test detected the rpoB mutation through the lack of hybridization with the wild type probes that include the mutated codon (Table 1), e.g. with rpoBWT4 for the two strains harboring the mutation S456M or S456F, with rpoBWT2 for the strain with the mutation Q438V, rpoBWT1 and rpoBWT3 for the strain harboring the two mutations G432S + H451D and rpoBWT1 and rpoBWT2 for the strain harboring the two mutations T433I + D441Y. For two strains carrying a mutation at the codon 447, they were not detected by the DNA strip test since no probe spanning this codon is included in the strip because this mutation was not known to confer resistance. The first of these strains showed a silent mutation and the second showed a mutation leading to the substitution S447C. Although the latter strain appeared susceptible to rifampin in the routine mouse footpad testing, we repeated this test using decreasing dosages of rifampin in order to be sure that the S447C mutation does not confer resistance in M. leprae as a similar mutation does in M. tuberculosis [26], even at a low level. For this purpose, three groups of mice (10 mice per group) were treated by 10 mg/kg (normal dosage), 5 mg/kg or 2.5 mg/kg rifampin. Growth was not observed in any of these groups but occurred in the control untreated group, demonstrating that the strain was really susceptible to rifampin and that the S447C mutation was not conferring resistance. Moreover, the patient, who was an immigrant from Senegal, was cured after being treated by the standard MDT, i.e. the combination of rifampin, dapsone and clofazimine. For the other 102 other strains, no mutations were detected by the RRDR sequencing in rpoB and the DNA strip test.
Concordance was observed between the DRDR sequence in folP1 and the DNA strip test: 22 strains with a folP1 mutation involved in dapsone resistance and 98 strains with a wild-type folP1 sequence (Table 3). Hybridization was observed with the folP1 MUT probe for the 8 strains with the folP1 P55L mutation. For the 14 strains harboring other mutations at codon 55 (P55R) or at codon 53 (T53I, T53A, T53V), there was no signal with the wild type probe, showing that there was a mutation.
Finally, we observed a concordance between the QRDR sequence in gyrA and the DNA strip test results: 56 strains with a wild type sequence showed a gyrA WT band and the four strains with the mutation A91V showed the gyrA MUT band (Table 3).
Concordance was observed between the phenotypic susceptibility results assessed by the mouse footpad model and the genotype detected by the GenoType LepraeDR test. Results are detailed in Table 3 with regard to the antibiotic tested.
Concordance between rifampin phenotypic susceptibility in vivo and the results of GenoType LepraeDR was obtained for all the 84 strains tested. Thirteen rifampin-resistant strains showed either the rpoBMUT1 band (S456L) for 9 strains, or the absence of at least one rpoB WT band for the remaining 4 strains, which indicated a mutation in the RRDR. The exact nature of the rpoB mutation was further identified by PCR-sequencing. All the 71 susceptible strains were founded susceptible by the DNA strip test since all the rpoB WT bands were positive and all of the MUT bands were negative.
Concordance between dapsone phenotypic susceptibility and detection of folP1 mutation by the DNA strip test was obtained for the 48 susceptible and the 8 resistant strains. For all the resistant strains, the folP WT band was negative, indicating a mutation in the DRDR. The folP MUT band was positive for two of these strains, indicating a mutation P55L. In the 6 remaining strains, the exact nature of the folP mutation was identified by PCR-sequencing. For the 48 dapsone-susceptible strains, the folP1 WT band was positive and the MUT band was negative
Finally, results of ofloxacin phenotypic susceptibility were concordant with the results of gyrA obtained by the DNA strip test for the five strains tested in the mouse footpad: one was resistant and showed a positive gyrA MUT band (mutation A91V) with a negative WT band, and the four susceptible strains showed a positive gyrA WT band and a negative MUT band.
Leprosy, after centuries of endemicity when the disease lasted the whole patient life due to a lack of efficient treatment, became a curable disease by combining rifampin and dapsone into a multidrug therapy regimen [2]. Consequently, a dramatic decrease in the prevalent active cases occurred during the two last decades. However, the incidence rate did not decrease showing that leprosy is still an actively transmitted disease [1]. Acquired resistance has been observed for each of the antileprosy drugs following their successive introduction as antileprosy agent [27], [28]. Multidrug resistant strains resulting from the accumulation of distinct resistant traits have been described in several endemic regions [7], [22]. Proportions up to 80% of secondary resistance (patients previously treated) and 40% of primary resistance (patients never treated) to dapsone and up to 40% secondary resistance to rifampin, have been reported through local and limited studies [28], [29], [30]. Since M. leprae is not growing in vitro, it is not possible to measure resistance rates at large scale in endemic countries. Even in highly specialized leprosy centers where the animal model has been set up, it is nowadays very difficult to sustain animal facilities because of ethic rules and safety measures. Molecular detection of resistance to antileprosy drugs has been introduced since genetic bases of resistance were deciphered by expert scientific labs in France, US and Japan Cambau 1997 [10], [11], [12], [31]. We previously showed that mutations in the target genes in clinical M. leprae strains were associated with acquired resistance demonstrated by in vivo drug susceptibility testing: in rpoB for rifampin resistance, in folP1 for high and medium level dapsone resistance, and in gyrA for ofloxacin resistance [7], [17], [20]. These studies demonstrated concordance between genotypic and in vivo phenotypic results. Therefore, in-house molecular detection is being used for individual diagnosis of leprosy cases in countries where PCR sequencing is affordable [15], [31], [32], [33], [34], [35], [36].
Following years of using various in house molecular methods to rapidly detect for drug resistance in M. tuberculosis, particularly to detect for multi-drug resistant cases, i.e. cases resistant to isoniazid and rifampin that cannot be cured by the standard regimen, standardized and commercially available kits, such as the line probe assays, InnoLiPA Rif.Tb and GenoType MTBDR, and more recently GeneXpert RifTB, have been introduced and are recommended in low-income but highly epidemic countries (www.who.int/tb/strategy/en/).
WHO launched in 2008 a programme of surveillance of drug resistance in leprosy using molecular methods relying on a handful of national and supranational reference laboratories. First results obtained for cases reported in 2008, 2009 and 2010, showed that rifampin, dapsone and fluoroquinolone resistance were described but the resistance rates varied from 0 to 10% [37]. This needs confirmation at a larger scale and for an extended time. However this showed that the rates of resistance to antileprosy drugs can be measured by using molecular methods.
The DNA strip technology has been developed as GenoType kits and applied to the molecular detection of antibiotic resistance in various infections such as tuberculosis and Helicobacter pylori diseases [19], [38]. This approach has been shown to be easy to use, requiring only a classic thermocycler and a hybridization chamber at a constant temperature of 45°C. This is the reason we choose to develop a standardized test based on the DNA strip technology able to detect for molecular detection of resistance in leprosy.
The new test, GenoType LepraeDR, was evaluated by systematically testing 120 M. leprae strains studied for genotypic and phenotypic characters of resistance [17], [20], [22]. The results yielded by the test were shown to be 100% concordant with those of the in vivo susceptibility testing whereas the results of PCR sequencing was 98.3% for rifampin, 100% for dapsone, and 100% for fluoroquinolones. Moreover, the two rpoB mutations not detected by the test, located at the codon 447, a codon not included in the test, were in fact not conferring rifampin resistance.
We focused deliberately the present evaluation on AFB-positive specimen from multibacillary leprosy cases for two reasons: (i) first the AFB positivity represents a major clue in leprosy diagnosis that allows concentrating subsequent tests on mot probable cases, an important point in low income countries and (ii) second, the risk of developing acquired resistance by selection of resistant mutants are highest in multibacillary cases. We did not evaluate the performances of the test on either AFB-negative specimen nor on specimen other than skin biopsies (e.g. nasal wabs). The specificity of the test with regard to other mycobacterial species involved in skin infections was assessed for Buruli ulcer and infections due to M. marinum, M. chelonae, M. abscessus, M. fortuitum, M. terrae and other less common mycobacteria. Because of the high identity of the rpoB gene between some mycobacterial species, the results of resistance mutation in rpoB, gyrA and folP genes by the test can be interpreted only when the test identifies the species as M. leprae (positive ML band).
Various other methods have been described to detect mutations in rpoB, gyrA and folP such as PCR sequencing, heteroduplexes, and DNA array [13], [14], [15], [16], [18]. There were mostly used in large laboratories affiliated to Universities of high income countries and collecting strains from endemic countries [34], [39]. Since the reverse hybridization technology is already used in several countries endemic for tuberculosis, the same technology could be also used for the diagnosis of resistance in leprosy in countries where leprosy is still a preoccupying disease, with two objectives: (i) diagnosing resistance at the individual level and (ii) assessing rates of secondary and primary resistance in collaboration with health authorities [1], [37]. Although leprosy is now diagnosed in the field using clinical findings only and no laboratory support is available, such a test can be used complementary to the clinical diagnosis of multibacillary leprosy for (i) relapse cases, especially those who have not been treated by MDT, i.e. before 1982, and (ii) survey of resistance in new cases in defined areas or periods for epidemiological surveillance on the behalf of leprosy public health programmes. Therefore the specimen can be send to a regional lab, especially one used to similar molecular test detecting resistance in tuberculosis. In addition, clinical microbiology laboratories in high income countries, which have usually moderate expertise in leprosy diagnosis and resistance detection, would appreciate the robustness of the test, and such a test can help in diagnosing cases from immigrants or national intertropical territories [40], [41]. Using this technology routinely at the French National Reference Center for mycobacteria during the last two years, we diagnosed 35 cases of leprosy in patients living in France and detected 4 cases with dapsone resistant strains (folP1 mutations as P55L in 3 strains and T53A in one strain) and 1 case with an ofloxacin resistant strain (gyrA A91V mutation) (data not shown). These results, obtained independently of the present evaluation, support the practical interest of this technology.
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10.1371/journal.ppat.1000275 | Viral Mimicry of Cdc2/Cyclin-Dependent Kinase 1 Mediates Disruption of Nuclear Lamina during Human Cytomegalovirus Nuclear Egress | The nuclear lamina is a major obstacle encountered by herpesvirus nucleocapsids in their passage from the nucleus to the cytoplasm (nuclear egress). We found that the human cytomegalovirus (HCMV)-encoded protein kinase UL97, which is required for efficient nuclear egress, phosphorylates the nuclear lamina component lamin A/C in vitro on sites targeted by Cdc2/cyclin-dependent kinase 1, the enzyme that is responsible for breaking down the nuclear lamina during mitosis. Quantitative mass spectrometry analyses, comparing lamin A/C isolated from cells infected with viruses either expressing or lacking UL97 activity, revealed UL97-dependent phosphorylation of lamin A/C on the serine at residue 22 (Ser22). Transient treatment of HCMV-infected cells with maribavir, an inhibitor of UL97 kinase activity, reduced lamin A/C phosphorylation by approximately 50%, consistent with UL97 directly phosphorylating lamin A/C during HCMV replication. Phosphorylation of lamin A/C during viral replication was accompanied by changes in the shape of the nucleus, as well as thinning, invaginations, and discrete breaks in the nuclear lamina, all of which required UL97 activity. As Ser22 is a phosphorylation site of particularly strong relevance for lamin A/C disassembly, our data support a model wherein viral mimicry of a mitotic host cell kinase activity promotes nuclear egress while accommodating viral arrest of the cell cycle.
| Human cytomegalovirus (HCMV) causes life-threatening disease in transplant patients and people with AIDS, and is also an important cause of birth defects. Like all viruses, HCMV must have a way to leave the host cell, so that it can infect new cells. Moreover, as a member of the herpesvirus family, HCMV replicates its DNA in the nucleus, so it must have mechanisms to ensure that its genetic material can exit from the nucleus (nuclear egress). HCMV encodes a protein kinase, UL97, which is required for efficient nuclear egress. We found that UL97 aids nuclear egress by mimicking a host cell enzyme that normally helps break down a protein meshwork in the nucleus during cell division. The enzyme activity of UL97 pokes holes in the meshwork that allow nascent HCMV virions to gain access to the nuclear membrane. UL97 is also an important target for drugs for treating HCMV disease. This work not only helps explain how these drugs act, but also highlights the potential of targeting nuclear egress for the discovery of new drugs.
| Human cytomegalovirus (HCMV) is a pathogen that is especially dangerous in immunocompromised individuals [1]. As is true for all viruses, HCMV replication depends on the interplay between viral and host cell functions. An important example of this interplay is nuclear egress, a stage during which herpesviral DNA-containing capsids (nucleocapsids) exit the nucleus [2]. An important obstacle for the exiting nucleocapsids is a meshwork underlying the inner nuclear membrane known as the nuclear lamina, whose principal components are intermediate-filament proteins known as lamins [3],[4]. There are two major classes of lamins in mammalian cells: A-type lamins, which comprise the four lamins encoded by alternative splicing from the LMNA gene, lamin A, AΔ10, C, and C2 (collectively lamin A/C), and B-type lamins (lamin B), which are encoded by the LMNB1 and LMNB2 genes. A major function of lamins is to help maintain the structure of the nuclear envelope. Accordingly, along with the nuclear envelope, the nuclear lamina must be disassembled during mitosis and then reassembled after mitosis. These dynamic processes are regulated by phosphorylation of lamins. In particular, it is well established that Cdc2/cyclin-dependent kinase (CDK) 1 disassembles nuclear lamina by phosphorylation of specific sites on lamins during mitosis [5],[6],[7]. CDK1 phosphorylation of lamin A/C at Ser22, and of lamin B at the equivalent position, have been shown to be especially crucial for lamina disassembly [5],[8]. It is thought that phosphorylation at this site interferes with head-to-tail interactions between lamins (reviewed in [3],[4]). HCMV arrests cells at the G1/S boundary during the cell cycle [9],[10],[11], and therefore is unable to utilize this normal pathway for dissolution of the nuclear lamina for nuclear egress. Interestingly, despite the G1/S arrest, CDK1 and cyclin B are upregulated in HCMV-infected cells [12],[13],[14]. However, these proteins do not appear to accumulate in the nuclei of infected cells to the extent seen in mitotic cells [14].
It has been proposed, initially from work on murine cytomegalovirus (MCMV), that a complex of two viral polypeptides (UL50 and UL53 for HCMV) recruits calcium-dependent protein kinases C (PKCs), to the nuclear envelope to phosphorylate lamins, disrupt the nuclear lamina, and permit nuclear egress [15],[16],[17]. There is evidence that PKC phosphorylation of lamins is important for dissolution of nuclear lamina (e.g. [18]). However, it has not been demonstrated that recruitment of PKC is sufficient or necessary to cause lamin disruptions during HCMV infection or to permit nuclear egress of HCMV. On the other hand, an unusual protein kinase, UL97, which is encoded by HCMV, has been shown to be required at the stage of nuclear egress for efficient replication of HCMV [19]. Some evidence for UL97 effects on nuclear lamina components has been presented [20]. Interestingly, it has recently been shown that UL97 has activities similar to those of CDKs (as hypothesized [21]), but is not subject to cellular inhibitors of CDK function [22]. UL97 is also a target for specific inhibition by a new antiviral drug, maribavir [23], which is currently in phase III clinical trials.
In this study, using purified UL97, we show that UL97 phosphorylates lamin A in vitro on Ser22, a site utilized by CDK1 to mediate lamin disassembly. In HCMV-infected cells, we detected UL97-dependent phosphorylation of lamin A/C at Ser22, and inhibition of phosphate incorporation into lamin A/C by maribavir. Finally, morphological alterations in the host cell nucleus and nuclear lamina during HCMV infection were observed to depend on UL97. Taken together, our data argue that UL97 directly phosphorylates lamin A/C during HCMV replication to promote lamin disassembly during nuclear egress.
To investigate the role(s) of UL97 during HCMV infection, we searched for candidate substrates of UL97 by implementing a proteomics strategy. Human foreskin fibroblast (HFF) cells infected with wild-type (wt) HCMV strain AD169 (multiplicity of infection (MOI) = 1), with a UL97 deletion mutant (RCΔ97) [24], or with wt virus under conditions where UL97 was pharmacologically inhibited using maribavir [23] were radiolabeled with 32P orthophosphate, and the proteins were separated on 2-dimensional gels (Figure S1). Spots containing labeled phosphoproteins from wt-infected cells that differed from those from the other conditions were excised from the gels, digested with trypsin, and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A number of polypeptides were identified; these are tabulated in Table S1. The protein for which the most peptides were identified was lamin A/C, which is a major component of the nuclear lamina [3],[4]. Given that HCMV UL97 is important at the stage of nuclear egress [19] and that the nuclear lamina forms a barrier to nuclear egress, we hypothesized that UL97 phosphorylates lamin A/C to mediate nuclear egress.
A previous report [20] described the phosphorylation of lamins in anti-FLAG immunoprecipitates from lysates of cells in which FLAG-UL97 was expressed by transfection, but only when lamins were simultaneously immunoprecipitated by anti-lamin antibodies. No controls in which lamins were immunoprecipitated, but UL97 was absent or inactive were shown. Thus, it was not demonstrated that UL97 directly phosphorylates lamins. Additionally, sites of phosphorylation were not reported.
We wished to determine whether UL97 can directly phosphorylate the largest of the A-type lamins, lamin A, in vitro. We expressed lamin A fused to a histidine tag at its N-terminus, and purified it from E. coli (Figure S2). When incubated with purified enzymatically active UL97 fusion protein (GST-UL97) and γ-[32P]-ATP, both GST-UL97 and lamin A became radiolabeled (Figure 1A). However, labeling of lamin A was not observed in the absence of added enzyme, and was almost completely abolished when an equivalent quantity of a catalytically deficient mutant form of GST-UL97 (K355Q) was used in place of wild-type GST-UL97 (Figure 1A). Moreover, treatment with maribavir, a specific inhibitor of UL97 activity, reduced phosphorylation of lamin A by GST-UL97 with a dose-dependence very similar to its inhibition of autophosphorylation of GST-UL97 (Figure 1B). Thus, UL97 phosphorylates lamin A in vitro.
To map the sites on lamin A that are phosphorylated by UL97 in vitro, proteins from kinase reactions, such as those conducted in Figure 1A — except using unlabeled ATP — were digested with trypsin. Phosphopeptides were enriched, and the peptides analyzed by LC-MS/MS. Two major phosphorylated peptides on lamin A were reproducibly detected following phosphorylation by GST-UL97. One of these peptides (residues 12–25) was unambiguously phosphorylated on Ser22, a site whose phosphorylation by CDK1 is crucial for lamin disassembly [5]. A representative MS/MS spectrum showing Ser22 phosphorylation in tryptic peptides released from lamin A incubated with GST-UL97 is presented in Figure 2A. A second major peptide including Ser390 and Ser392 was clearly phosphorylated, but in the initial analysis there was ambiguity as to which of these serines was phosphorylated (see further analysis below). Three other phosphorylated peptides– one containing Ser414, one Ser628, and one Ser652 (Table 1) — were detected more than once, but less frequently than the two major peptides. No phosphopeptides were reproducibly detected in reactions using GST-UL97 K355Q or in reactions containing GST-UL97 and 1 µM maribavir (data not shown).
In order to definitively assign the site of in vitro phosphorylation by GST-UL97 on the second major peptide, we used an isotopically labeled synthetic peptide phosphorylated at Ser392 to identify fragment ions diagnostic for this phosphorylation site. As an additional control, we also treated lamin A with a commercial preparation of CDK1/cyclin B1 complex. For both GST-UL97 and CDK1/cyclin B1 treated samples of recombinant lamin A, no phosphopeptides matching the fragmentation pattern for phosphorylation at Ser392 were detected. Instead, a tryptic peptide (387–397) with phosphorylation at Ser390 was detected (Table 1, Figure 2B and 2C). Evidently, phosphorylation at Ser390 by UL97 or by CDK1 inhibited tryptic cleavage at Arg388. Possible reasons why we observed CDK1 phosphorylation on Ser390 rather than the more generally accepted site, Ser392 [7] include folding of the E. coli expressed lamin A altering the in vitro site specificity of CDK1. Reexamination of the original study [7] suggests that the data presented are consistent with a mixture of peptides; the peptide reported, which is phosphorylated at Ser392, and the longer peptide that we detected, which is phosphorylated at Ser390. Interestingly, CDK1 mediated phosphorylation of murine A-type lamins at Ser390 in vitro has been previously suggested [25]. Regardless, UL97 phosphorylates lamin A in vitro on two sites, Ser22 and Ser390, that are phosphorylated in vitro by CDK1 (Figure 2, data not shown, and [5],[6],[7]).
To examine the role of UL97 in lamin A/C phosphorylation during HCMV infection, we infected cells with wild type (wt) HCMV strain AD169 at an MOI of 1, and at 72 hours post-infection (p.i.) prepared cell lysates from which we immunoprecipitated lamin A/C. We then subjected the lamin A/C immunoprecipitates to tryptic digestion and LC-MS/MS to examine the pattern of phosphorylation (Table 1). Ser22 phosphorylation was again detected (Figure 2D provides an example from an iTRAQ labeling experiment — see below). A phosphopeptide containing either Ser390 or Ser392 was also detected, but in this case, with the aid of the isotopically labeled synthetic peptide phosphorylated at Ser392, the site of phosphorylation was unambiguously identified as Ser392 (Figure 2E). The longer tryptic peptide containing Ser390 that was observed following in vitro phosphorylation was not detected in the lamin A/C immunoprecipitates. Four other phosphorylation sites were also detected (Ser403, Ser406/407, Ser423, Thr424), which are either identical or in close proximity (Ser404) to sites previously detected in studies of phosphorylated proteins from human cells not infected by HCMV [26],[27],[28],[29].
As mutational analysis implicates Ser22 and to a lesser extent Ser392 in lamin A/C disassembly [5], we employed an iTRAQ™-based quantitative MS approach [30] to evaluate whether levels of phosphorylation on these Ser residues were influenced by UL97 during HCMV replication. Cells were infected at an MOI of 1 with wt HCMV or a UL97 deletion mutant, Δ97 [31] or a virus in which the deletion mutation was rescued [31] and lamin immunoprecipitates were prepared at 72 hours p.i. Following digestion with trypsin, peptides from each sample were labeled with a sample-specific iTRAQ reagent. Stable isotopes incorporated into these reagents permit pooling of samples after labeling and subsequent relative quantification of phosphopeptide abundances by LC-MS/MS. In two separate experiments, while levels of Ser392 phosphorylation did not vary between lamin A/C isolated from wt-infected vs. Δ97-infected cells, there were 2–3 fold higher levels of phosphorylation of Ser22 in lamin A/C from wt HCMV-infected cells compared to lamin A/C from Δ97-infected cells (Table 2). Furthermore, lamin A/C from cells infected with the virus in which the deletion was rescued also showed approximately 2-fold higher levels of Ser22 phosphorylation compared to that from cells infected with Δ97 (Table 2). Another pair of previously described mutant and rescuant viruses was also compared, one encoding a catalytically-deficient form of UL97 (AD K355Q), and the other a rescuant of AD K355Q, in which sequences encoding a wild-type UL97 were restored (AD Q355K) [22]. In this experiment, lamin A/C from cells infected with the rescuant (AD Q355K) had 4.1-fold higher levels of Ser22 phosphoryation than that from AD K355Q-infected cells (Table 2). From these results, we conclude that there is UL97-dependent phosphorylation of lamin A/C at Ser22 during HCMV infection.
As a second approach to investigating the dependence of lamin A/C phosphorylation on UL97, we performed radiolabeling experiments with wt HCMV infected cells (MOI = 3) in the presence or absence of 1 µM maribavir, which specifically inhibits UL97 [23],[32], or 15 µM roscovitine, which specifically inhibits CDKs including CDK1 [33],[34]. To minimize the effects of kinase inhibition on earlier stages of the HCMV replication cycle, infection was allowed to proceed for 69 hours p.i., at which time nuclear egress has commenced. The infected cells were then treated with the inhibitors or mock-treated with DMSO-containing vehicle for 1 hour, and then, in the presence or absence of the inhibitors, labeled with [32P]-orthophosphate for 2 hours. Following radiolabeling, cells were lysed. Probing western blots of the lysates with anti-actin antibodies showed that similar amounts of protein were present in each lysate (Figure 3C), Lamin A/C was immunoprecipitated from each lysate, the immunoprecipitates were resolved using SDS-PAGE, and transferred to a membrane. 32P-incorporation into lamin A/C in each sample was measured by exposing the membrane to a phosphorimager. Then, the membrane was probed with anti-lamin A/C antibodies to assess the amounts of lamin A/C in each immunoprecipitate. Similar amounts of lamins A and C were detected in each sample (Figure 3A, lower panel); however, there was somewhat more lamin A and C in the samples from cells treated with maribavir or maribavir plus roscovitine than in the mock-treated sample. Despite that, the incorporation of radioactive phosphate into lamin A and C was visibly less in the sample from infected cells treated with maribavir compared to mock-treated infected cells, (Figure 3A, upper panel). Phosphorimager quantification indicated that the reduction in labeling was approximately 2-fold. In contrast, the CDK inhibitor roscovitine had a less pronounced effect on lamin A/C phosphorylation, with radioactive phosphate incorporation reduced by 20–30% compared to the DMSO treated control (Figure 3A, upper panel). When both drugs were present, radioactive phosphate incorporation was reduced by 60–70% (Figure 3A, upper panel). (Again, there was more lamin A/C, as detected by immunoblotting, in the sample treated with both drugs than in the mock-treated control; thus, the actual effect of the drugs was likely even greater.)
As an additional control, in the same labeling experiment HCMV UL44 was immunoprecipitated, the immunoprecipitates were resolved by SDS-PAGE and transfered to a membrane, and both the amounts of UL44 and the incorporation of 32P into UL44 were measured on the same membrane. In this case, the overall incorporation of radioactive phosphate into HCMV UL44 was not reduced by treatment with maribavir, in fact, it was increased (Figure 3B, upper panel), while the amounts of UL44 were very similar in all the samples (Figure 3B, lower panel). (Further studies of UL44 phosphorylation have shown that UL97 phosphorylates only one or a few sites on UL44, while other kinases phosphorylate a number of other sites robustly (unpublished results).) Thus, transient treatment with maribavir exerted a greater inhibitory effect than transient treatment with a CDK inhibitor on lamin A/C phosphorylation, while not inhibiting the overall phosphorylation of a control protein, UL44.
Morphological deformations of cell nuclei are common in cells that are defective for lamin A/C [35]. Infection by HCMV has long been known to cause distortions in nuclear shape [36], and another study has reported that UL97 mutants do not induce such deformations [37]. Given our identification of lamin A/C as a substrate for UL97, we investigated whether these distortions were associated with alterations in the nuclear lamina. Therefore, we examined nuclear morphology during infection (MOI = 1) by staining the nuclear lamina with a polyclonal antibody against lamin A/C, while co-staining for sites of HCMV DNA replication (replication compartments) using a monoclonal antibody against the viral DNA polymerase subunit, UL44 (Figure 4). In immunofluorescence images of mock-infected cells (Figure 4D), the nuclei stained with the lamin antibody and were oval-shaped with no anti-UL44 staining. In wt HCMV-infected cells at 96 hours p.i. (Figure 4A), anti-lamin A/C staining revealed deformed nuclei, many of which exhibited a kidney shape. This shape was mirrored by those of the replication compartments stained with anti-UL44. Wt-infected cells treated with the UL97 inhibitor maribavir (Figure 4B) and cells infected with a UL97 null mutant virus (RCΔ97) (Figure 4C) showed much less dramatic shape changes, and while nuclei were observed to be somewhat enlarged relative to mock-infected cells, they often retained an oval shape. In addition, the anti-lamin staining appeared more uniform around the rim of the nuclei in mutant-infected cells, maribavir-treated wt-infected cells, or mock-infected cells than in wt-infected cells (compare Figure 4A to 4B, 4C and 4D). These kinds of changes in shape and anti-lamin staining could be observed in wt HCMV-infected cells by 24 hours p.i., although less consistently (data not shown).
We then used the same antibodies and confocal microscopy to investigate these staining patterns and their requirement for active UL97 in more detail. In wt HCMV-infected cells (MOI = 1; 96 hours p.i.), we observed thinning, invaginations (as recently reported [17]), and, in some cells, discrete gaps in anti-lamin staining (Figure 5A and 5B) compared to mock-infected cells (Figure 5E). In contrast, when wt HCMV infection was performed in the presence of maribavir or when UL97 was ablated genetically (two independent isolates of a UL97 deletion mutant), the anti-lamin staining of the nuclear rim resembled that of mock-infected cells with oval shaped nuclei and little or no thinning and few or no gaps (Figure 5C and 5D). To quantify the UL97-dependent changes in nuclear morphology, we surveyed approximately one hundred nuclei from each infection condition by confocal microscopy. These results indicated that nuclear deformities (Figure 6A) and gaps in the nuclear lamina large enough to be visible by light microscopy (Figure 6B) were each significantly more frequent under conditions where UL97 kinase activity was present (p<0.001). Thus, UL97 is required for nuclear deformation and disruptions of lamina during HCMV infection.
We found that UL97 can directly phosphorylate lamin A/C in vitro on sites phosphorylated in vitro by Cdk1, including Ser22. We also detected UL97-dependent phosphorylation of Ser22 on lamin A/C in HCMV-infected cells. Additionally, phosphorylation of lamin A/C was reduced by approximately two-fold when UL97 kinase activity was transiently inhibited in HCMV-infected cells. These findings taken together strongly suggest that lamin A/C is a bona fide, direct substrate of UL97 in infected cells.
Moreover, we found that UL97 is required in infected cells for discrete changes in the nuclear lamina, including gaps visible by confocal light microscopy. Given our results on lamin phosphorylation by UL97, we propose a model in which UL97 phosphorylates lamin A/C on Ser22, a site whose phosphorylation is known to mediate disassembly of nuclear lamina [5],[7]. These phosphorylation events contribute to localized disruptions in the nuclear lamina. These disruptions permit access of HCMV nucleocapsids to the inner nuclear membrane for primary envelopment and budding into the space between the inner and outer nuclear membranes, and thus nuclear egress. This model explains the requirement for UL97 for efficient nuclear egress [19].
We now discuss our results in terms of this model and previous results regarding lamin phosphorylation and nuclear egress.
UL97 directly phosphorylates E. coli expressed lamin A in vitro with Ser22 and Ser390 being the major sites phosphorylated. Neither of the major sites conforms to a preference of UL97 for basic residues in the P+5 position [32], although the sites do contain basic residues at P+6 and P+7. (A minor site (Ser414) phosphorylated by UL97 has an Arg at P+5.) More importantly, the major sites phosphorylated by UL97 were also phosphorylated in vitro by CDK1/cyclin B1. This result and previous results with Rb [22] indicate that UL97 can exhibit at least some preference for sites that CDKs prefer.
We observed UL97-dependent phosphorylation in infected cells of a site that UL97 phosphorylates directly in vitro, and we found that transient inhibition of UL97 with maribavir could inhibit lamin A/C labeling by ∼50%. The simplest interpretation of our data is that UL97 directly phosphorylates lamin A/C in HCMV-infected cells. An alternative interpretation is that UL97 acts indirectly, for example, by increasing the activity and/or expression of host cell kinases that phosphorylate lamin A/C, in particular CDK1. However, if UL97 were acting indirectly to increase lamin A/C phosphorylation by increasing CDK activity or expression, then roscovitine should have been at least as effective at inhibiting lamin phosphorylation as maribavir. Our finding that maribavir was more effective than roscovitine is consistent with UL97 directly phosphorylating lamin A/C rather than increasing the activity or expression of a CDK. This interpretation is also consistent with a study that found that expression of a dominant negative CDK1 did not adversely affect replication of HCMV [38]; if CDK1 were required for disruption of nuclear lamina during nuclear egress, such expression might have been expected to decrease HCMV replication.
Although only one site phosphorylated by UL97 in vitro showed dependence on UL97 for its phosphorylation in virus infected cells, it is striking that this position — Ser22 — is the lamin A/C phosphoacceptor site of greatest established relevance to dissolution of lamina [5]. In particular, substitution of Ser22 with Ala results in a dominant negative mutant protein that inhibits lamina disassembly, while substitutions of Ser392 exert little effect on lamina disassembly by themselves [5]. Similarly, substitution of lamin B at Ser16, the position equivalent to Ser22 of lamin A/C, renders lamin polymers resistant to phosphorylation-mediated disassembly, while substitutions of serines equivalent to Ser390 and Ser392 have little or no effect [8]. Therefore, our results together with these previous studies strongly suggest that UL97 phosphorylation of lamin A/C in infected cells at Ser22 drives lamin A/C disassembly.
We also detected UL97-independent phophorylation of lamin A/C in our infected cell preparations. This could be due to the activities of other kinases in infected cells, due to phosphorylated lamin A/C that existed prior to infection, and/or due to phosphorylation occurring in uninfected cells in our samples.
In previous studies, it was reported that wt HCMV infection resulted in drastic reductions in the amounts of A-type lamins detected on western blots [20],[39], and almost complete loss of lamin staining as detected by immunofluorescence [20], but that neither was observed during infection by a UL97 mutant [20]. However, we observed copious expression of A-type lamins during wt HCMV infection, and, similar to the recent report of Camozzi et al. [17], we observed only discrete changes in lamin staining in immunofluorescence experiments. The failure of others to detect lamin A/C in infected cells may be due to using antibodies that fail to recognize hyperphosphorylated A-type lamins, as previously asserted [20], or some other form of lamin A/C. Additionally, it has been reported that expression of UL97 following transient transfection induces a redistribution of lamin A/C from the nuclear rim to granular intranuclear structures [20],[40]. Whether this was due to lamin A/C phosphorylation or due to toxicity (e.g. apoptosis) following UL97 overexpression was unclear. Regardless, we showed here that UL97 is required for disruptions in the nuclear lamina in HCMV-infected cells.
The simplest explanation for this requirement is that limited phosphorylation of lamins by UL97 causes local disassembly of the lamina. Although we detected gaps visible by confocal light microscopy in only a minority of cells, we emphasize that such gaps are much larger than an HCMV nucleocapsid. We think it is highly likely that nearly all infected cells contain smaller gaps in the lamina that could readily permit access of HCMV nucleocapsids to the inner nuclear membrane during nuclear egress. It may be advantageous for HCMV to induce only localized disruptions of the nuclear lamina. Lamins are important for cellular chromatin organization, DNA synthesis and transcription [3],[35], and thus may play important roles in herpesvirus infection in addition to their service as barriers for nuclear egress. Indeed, during HSV infection, lamin A/C is required for proper targeting of the viral genome and for reduction of heterochromatin formation on viral promoters at early times of infection [41]. Nevertheless, there is more extensive disruption of the nuclear lamina during HSV infection than what we observed during HCMV infection [42],[43].
The requirement for UL97 for the characteristic kidney-shape of nuclei in HCMV-infected cells is consistent with the results of Azzeh et al. [37]. The concave invaginations found in these nuclei are typically adjacent to juxtanuclear structures that appear to be cytoplasmic sites of virion assembly [44], and whose morphology depends on UL97 [37]. It is possible that the earliest UL97-mediated lamin disruptions (we detected gaps in some cells at 24 hours p.i. (data not shown) direct the passage of primary enveloped virions into a particular portion of the perinuclear cytoplasm, and this contributes to the organization of the juxtanuclear structure. Alternatively or additionally, UL97 may prevent aggregation of tegument proteins in the nucleus [45] or be more directly required for formation of the juxtanuclear structure [37]. As noted by Azzeh et al. [37], the juxtanuclear structure seems to impact the nucleus. Thus, it may exert the force that causes deformation of the lamin-depleted nuclear envelope.
A considerable body of evidence establishes that HCMV UL50 and UL53 and their homologs in other herpesviruses play important roles in nuclear egress (reviewed in [2]). It has been proposed that these proteins and their homologs alter the nuclear lamina by recruiting PKCs to phosphorylate lamins [15],[16],[17],[46],[47]. However, PKC phosphorylation of nuclear lamins normally occurs during interphase and is not sufficient to cause dissolution of lamina [6],[25]. Moreover, we did not observe phosphorylation of lamin A/C in HCMV-infected cells at sites known or likely to be phosphorylated by PKC, including Ser5, Thr199, Ser395, Thr416, Thr480, and Ser652 [7],[25]. It may be that UL50 and UL53 act by recruiting PKC to disassemble nuclear lamina, but that the relevant substrate of PKC is lamin B [48] or other nuclear envelope components such as emerin [49],[50]. Alternatively, the roles of UL50 and UL53 during nuclear egress may be independent of protein kinase recuitment. Recently, Camozzi et al. [17] reported that transient overexpression of HCMV UL50 and UL53 was sufficient to induce changes in the distribution of lamin A/C akin to what is observed during HCMV infection. Regardless, during HCMV infection, in the absence of UL97, nuclei remain oval and lamin staining remains intact despite the presence of UL50 and UL53. Thus, much as CDK1 is the crucial kinase for dissolution of lamina during mitosis, UL97, which mimics CDK1 for phosphorylation of lamin A/C, is the crucial kinase for nuclear egress.
Why has HCMV evolved to encode a kinase that mimics CDK1 for phosphorylation of lamin A/C? One possible explanation stems from HCMV arresting the cell cycle at the G1/S boundary [9],[10],[11]. CDK1 is most active in phosphorylating nuclear lamina during M-phase [6]. There are elevated levels of CDK1 and cyclin B1 in HCMV-infected cells [12],[13],[14], which may account for the phosphorylation of Ser22 and Ser392 on lamin A/C that we detected even in the absence of UL97. It is also possible that this phosphorylation contributes to disruption of the nuclear lamina. However, expression of a dominant negative CDK1 did not decrease HCMV replication [38], CDK1 and cyclin B do not appear to accumulate in the nuclei of infected cells to the extent seen in mitotic cells [14], and their elevated levels evidently are not sufficient to disrupt the nuclear lamina by themselves. Interestingly, CDK1 does appear to be required in HCMV-infected cells for the formation of so-called pseudomitoses, in which aberrant spindle poles and chromosome condensation occur [38], and CDKs that are sensitive to roscovitine are involved in several phases of HCMV replication [51].
UL97 mimicry of a CDK for phosphorylation of lamin A/C explains at least part of this viral enzyme's role in HCMV infection. It is notable that UL97 is also required for phosphorylation of a second CDK substrate, Rb, in infected cells [22],[52]. As yet, the importance of phosphorylation of Rb for HCMV replication has not been established. It will be interesting to elucidate further how HCMV uses its CDK-mimic to promote its propagation.
All reagents were from Sigma unless otherwise specified.
HFF cells, isolate Hs27, (American Type Culture Collection) were cultured in Dulbecco's modified Eagle's medium (DMEM) (VWR) supplemented with 10% fetal bovine serum (FBS) as described previously [31]. HCMV wt strain AD169 was obtained from the American Type Culture Collection. Two independent isolates of RCΔ97, RCΔ97.08 and RCΔ97.19, derived from AD169 and containing the Escherichia coli lacZ and gpt genes replacing most of UL97 [24], were generously provided by Mark Prichard (University of Alabama, Birmingham). In some experiments, AD169rv [53], a bacterial artificial chromosome (BAC) clone of HCMV strain AD169, as well as more recently constructed viruses derived from AD169rv, were used: Δ97, a BAC derived UL97 deletion mutant, FLAG97 a rescuant of Δ97, AD K355Q, and AD Q355K [22],[31]. Viral stocks were propagated and titrated as previously described [31].
HFF cells were infected at a multiplicity of infection (MOI) of 1 PFU/cell (inoculum titers were confirmed by back titration) in complete medium (DMEM containing 5% FBS) for 2 h. Inocula were removed, and the cells were washed two times with complete medium. Complete medium, either with or without 1 µM maribavir, was then added and incubation was continued at 37°C. At 3 days post infection (p.i.), cells were rinsed twice with inorganic phosphate-free DMEM (Invitrogen) containing 1% FBS and were then incubated in 2 ml of that medium containing 1 mCi of [32P]-orthophosphate for 2 h. The medium was removed, and the cells were rinsed twice in ice-cold Tris-buffered saline (TBS: 20 mM Tris HCl pH 7.5, 150 mM NaCl) and then scraped into ice-cold TBS. 2D gel electrophoresis was performed by isoelectric focusing (IEF) using Immobiline Dry Strips with a pH gradient from 3 to 10 (GE Healthcare Inc.) in the first dimension and 4 to 20% (Invitrogen) SDS-PAGE in the second dimension as described [54]. Proteins were transferred for 30 minutes at 20 V to a BioTrace Polyvinylidene Fluoride (PVDF) membrane (Pall Corporation, Pensacola, Florida), such that approximately half of the protein was transferred and half remained in the gel. 32P signal from proteins transferred to PVDF was visualized by exposure to a phosphorscreen or Bio-Max Film (Kodak), and remaining protein in the gel was stained with Gel Code Blue colloidal coomassie blue staining reagent (Pierce) and was kept for subsequent MS analysis.
A pET19b vector (EMD Chemicals, Inc.) which was modified to incorporate a human rhinovirus 3C protease cleavage site following the pET19b polyhistidine tag, a generous gift from Tapan Biswas (Harvard Medical School), was used to express lamin A in E. coli. The lamin A sequence was amplified by PCR with an upstream primer (5′CCCCATATGATGGAGACCCCGTCCCAG3′) containing a NdeI site, and a downstream primer (5′TTGCTCGAGTCATGATGCTGCAGTTCTG3′) containing a XhoI site (restriction sites are in italics), from plasmid pJB311 [55], kindly provided by Joel Baines (Cornell University). The resulting plasmid was used to transform E. coli BL21 (DE3) CodonPlus bacteria (Stratagene), and expression was induced at an optical density of 595 nm of 0.5, protein expression was induced with 0.5 mM isopropyl-β-D-thiogalactopyranoside (IPTG) for 20 h at 16°C. Inclusion bodies were isolated and dissolved in 8 M urea, 50 mM Tris-HCl pH 8.0, 0.5 M NaCl, 1 mM dithiothreitol (DTT), 20 mM imidazole and Complete™ protease inhibitor (Roche, 1 tablet/50 ml). The protein was then purified using a nickel column (Amersham Pharmacia) using the same urea buffer with a 20 to 1 M imidazole gradient. Proteins were concentrated and stored frozen and, when required were renatured by dialysis against 0.5 M NaCl, 50 mM Tris-HCl pH 8, 1 mM DTT and protease inhibitors.
GST-UL97 (WT) and GST-UL97 K355Q were expressed from baculovirus vectors as previously described [56], except that in some experiments, newly generated baculoviruses were used to express GST-UL97 and GST-UL97 K355Q fusion proteins. These baculoviruses were based on the Bac-to-Bac system (Invitrogen, Inc., Carlsbad, California) and derived from customized pFASTBAC transfer vectors incorporating an N-terminal glutathione S-transferase tag. Protein concentrations were determined by amino acid analysis at the Molecular Biology Core Facility, Dana-Farber Cancer Institute.
For radioactive in vitro kinase assays, 32 ng of GST-UL97 or GST-UL97 K355Q was used with 0.6 µg of His-lamin A per 20 µL reaction in 50 mM Tris (pH 8.5 at 37°C), 125 mM NaCl, 5% glycerol, 10 mM MgCl2, 2 mM DTT, 5 mM betaglycerophosphate, 100 µM unlabeled ATP and 0.5 µL of [γ-32P]-ATP (3,000 to 6,000 Ci/mmol) (Perkin Elmer Inc., Waltham, MA) and incubated at 37°C for 90 min. For non-radiactive kinase reactions submitted for MS analysis, reactions were scaled up to 120 µL and 2 µg of UL97 or 160 ng of CDK1/cyclin B complex (Cell Signaling Technology, Inc., Danvers, MA) was used, and the final ATP concentration was adjusted to 200 µM. For comparison of maribavir inhibition of UL97 autophosphorylation versus lamin A phosphorylation, the same reaction conditions were used except 50 ng GST-UL97, 0.5 µg of His-lamin A, 50 mM Tris (pH 9.0 at 25°C), 300 mM NaCl, 1 mM DTT, 10 mM MgCl2, 0.5 ml of [γ-32P]-ATP (3,000 to 6,000 Ci/mmol) (Dupont NEN) and specified concentrations of maribavir were combined in a final volume of 10 µl and incubated at 37°C for 2 h. Reactions were terminated by the addition of concentrated SDS-PAGE loading buffer. Samples were heated at 95°C for 5 min and proteins were resolved by SDS-PAGE. Gels were dried onto blotting paper under vacuum, and incorporated 32P was quantified with a phosphorimager (Molecular Imager FX System; Bio-Rad Laboratories Inc., Hercules, California).
For immunoprecipitations of lamin A/C for MS analysis, HFF were infected at an MOI of 1.0 (confirmed by back-titration). At 72 h post infection, cells were rinsed. In non-radioactive experiments, cells were rinsed twice in ice-cold Dubecco's phosphate buffered saline prior to lysis. For radioactive experiments, cells were rinsed three times in 0.5 mL of ice-cold Tris-buffered saline (25 mM Tris pH 8.0, 4 mM KCl, 137 mM NaCl) prior to lysis. Cells were lysed for 30 min at 4°C in 0.5 mL of ice-cold modified radio-immunoprecipitation assay (RIPA) buffer per well. The RIPA buffer used was similar to one previously described [57] except that 300 mM NaCl was used, Triton-X 100 (1%) was used in place of NP-40, and the following components were added: 10 mM EDTA, 10% glycerol, 10 mM betaglycerophosphate, 5 mM NaF, 10 µg/mL each of leupeptin and aprotinin, 1 µg/mL pepstatin A, 10 µM E-64, 2 mM imidazole, 1.2 mM sodium molybdate, 0.5 mM sodium orthovanadate, 4 mM sodium tartrate, 1 mM DTT, and Calbiochem Phosphatase Inhibitor Set I (EMD Chemicals Inc., Gibbstown, New Jersey) was added at 1∶100. Lysates were collected and clarified at 10,000 g at 4°C and supernatants were transferred to new tubes, flash frozen on liquid N2, and stored at −80°C until use.
Two hundred microliters of each thawed lysate was pre-cleared, with rotation, for 30 min at 4°C with 10 µg each of purified mouse IgG1 and IgG2a (Bethyl Labs Inc., Montgomery, Texas) and 20 µL of settled Immunopure Protein A/G (Pierce) in a final volume of 0.5 mL (adjusted by adding RIPA buffer). Then, 220 µL of each sample was added to 15 µL of Protein A/G, which had been pre-incubated with 10 µL JOL2 and 5 µL JOL4 mouse anti-lamin A/C monoclonal antibodies (Millipore Inc., Billerica, Massachusetts). IP reactions were adjusted to a final volume of 0.5 mL with RIPA buffer and allowed to rotate for 1 h at 4°C. IP reactions were then subjected to four 5 min washes in 0.5 mL RIPA buffer and then incubated at 85°C for 5 min in 80 µL of 2× SDS-PAGE sample buffer [57] supplemented with 5% betamercaptoethanol. Immunoprecipitation of lamin A/C from non-radiolabeled cells was performed essentially as above except without pre-clearing.
For immunoprecipitation of HCMV UL44, each sample of cells was lysed in 1 ml EBC2 lysis buffer (50 mM Tris [pH 8.0], 30 mM NaCl, 0.5% NP-40, 2 mM EDTA and 2 mM DTT) supplemented with 10 µg/mL each of leupeptin and aprotinin, 1 µg/mL pepstatin A, 25 mM betaglycerophosphate, 0.5 mM sodium orthovanadate, and 1∶100 of Phosphatase Inhibitor Cocktail 1 and 2 (Sigma). Lysates were pre-cleared then reacted with anti-UL44 antibody 28-21 (kindly provided by William Britt, U. of Alabama) pre-conjugated to Protein A beads. The immunoprecipitations were washed four times in 1 mL cold EBC2 lysis buffer, and resuspended in 40 µL 2× SDS-PAGE sample buffer.
SDS-PAGE and western blotting procedures were carried out as previously described [58] using goat anti-lamin A/C polyclonal antibody N-18 (Santa Cruz Biotechnology), anti-CMV ICP36 (UL44) monoclonal antibody-ViruSelect (Virusys), or anti-beta-actin mouse monoclonal antibody (Abcam) to probe immunoblots.
In early experiments, following SDS-PAGE, lamin A from in vitro phosphorylation reactions or lamin A/C from infected cells in gel slices was reduced and alkylated by DTT and iodoacetamide, respectively. The gel slices were then dehydrated in acetonitrile, and the protein digested with trypsin (500 ng/slice) in NH4HCO3 buffer (50 mM, 50 µL/slice) overnight at 37°C. Trypsin-digested peptide samples were then enriched for phophopeptides using a phophopeptide isolation kit (Pierce). The samples were mixed with binding buffer (10% acetic acid), followed by addition of sample mix (50 µL) to SwellGel Disc (Pierce) resin. The sample-resin mixture, whose pH was maintained below 3.5, was incubated for 8 minutes at room temperature with regular gentle swirling. Resin was washed twice in 50 µL of 0.1% acetic acid, and twice in 50 µL of 0.1% acetic acid, 10% acetonitrile. Phosphopeptides were eluted in elution buffer (50 µL, 100 mM ammonium bicarbonate, pH 9.0) after 5 minutes incubation. Eluted phophopeptides were analyzed by LC-MS/MS at the Taplin Biological Mass Spectrometry Facility at Harvard Medical School.
Subsequent experiments were performed using different protocols depending on whether phosphorylation was assessed following in vitro phosphorylation or following phosphorylation in infected cells. For analyses of in vitro phosphorylated samples, the kinase reactions were treated with 1 µg trypsin (Promega) in 100 mM ammonium bicarbonate pH 8.0 (overnight, 37°C), and lyophilized. Free peptide carboxylate groups were converted to their corresponding methyl esters and the derived peptides were subjected to phosphopeptide enrichment as described previously [59]. Captured phosphopeptides were eluted with 50 mM PO4 buffer pH 8.0 directly onto a 100 µm (i.d.)×8 cm fused silica capillary desalting precolumn (PC) packed with 10/25 irregular-shaped C18 beads. The PC was then connected to a 50 µm×8 cm (5 µm spherical C18 beads) analytical column. LC-MS/MS was performed on a QSTAR XL (MDS SCIEX). The HPLC solvent gradient was 0–7% B in 5 min, 7–63% B in 30 min; solvent A was 0.2 M acetic acid and solvent B was 70% acetonitrile/0.2 M acetic acid.
In later experiments aimed at comparing the phosphorylation sites of UL97 and CDK1 on lamin A/C, in vitro phosphorylated lamin A/C was digested as described above and each digest was analyzed separately on an LTQ-Orbitrap mass spectrometer (ThemoFinnigan) using a method consisting of two data-dependent MS/MS scans, followed by targeted MS/MS scans on precursors corresponding to lamin A/C peptides containing Ser22, Ser392, their phosphorylated counterparts, and a synthetic pS392 peptide (LSPpSPTSQR) containing 613C and 4 15N atoms. This synthetic peptide was loaded onto the PC as an internal control.
For analysis of lamin A/C phosphorylated in cells, lamin A/C immunoprecipitates was processed for subsequent iTRAQ (Applied Biosystems) isotope labeling according to the manufacturer's protocol. Briefly, each sample was separately reduced using 5 mM Tris-(2-carboxyethyl)phosphine (TCEP) (1 hr, 60°C), alkylated using 10 mM methyl methane-thiosulfonate (MMTS) (10 min, room temperature) and digested on the beads with trypsin (Promega) in 0.5 M triethylammonium bicarbonate pH 8.5 (overnight, 37°C). The resulting peptides were then labeled with iTRAQ-114 (mock), iTRAQ-115 (AD K355Q), iTRAQ-116 (Δ97) and iTRAQ-117 (AD169rv) reagents in 70% ethanol, respectively, for 1 hr at room temperature. Following iTRAQ labeling, all four samples were combined and dried by centrifugal concentration (Thermo Savant, Holbrook, New York). To enrich for phosphorylated lamin A/C peptides, 100 µl of 100 mg/ml MassPREP Enhancer in 80% acetonitrile/0.1% trifluoroacetic acid (TFA) were added. After sonication (20 min), the mixed sample was loaded on a well of a TiO2 96-well plate (Waters, Milford, Massachusetts). Peptides were eluted with 100 µl of 300 mM ammonium hydroxide. Following elution, 3 µl of TFA were added to the sample to bring the pH to 2.0 and the sample volume was reduced to ∼20 µl by centrifugal concentration. LC-MS/MS was performed on a QSTAR XL using a Top 3 method. A similar protocol was followed for analysis of AD169rv- (iTRAQ 114), Δ97-(iTRAQ 115), FLAG97 (WT rescue of Δ97)- (iTRAQ 116), mock-(iTRAQ 117), AD K355Q-(iTRAQ 114) and AD Q355K-(iTRAQ 115) HCMV-infected cells, except that a targeted MS/MS method (on phosphorylated Ser22 and Ser392) was used for the phosphopeptides and a Top 6 was used for the TiO2 loading flowthrough. Non-phosphorylated lamin peptides detected in the loading flowthrough were used to normalize abundances of phosphorylated peptides. As described above, an isotopically labeled synthetic phosphopeptide containing a phosphate on Ser392 was loaded onto the PC as an internal standard.
MS/MS spectra were searched against an in-house lamin A/C database and the human RefSeq database using the Mascot algorithm. Putative lamin A/C phosphopeptide sequences were manually confirmed.
2×105 HFF per well of a 24 well cluster plate were infected at a MOI of 3 (confirmed by back titration) and then incubated at 37°C in DMEM supplemented with 5% FBS. Sixty-nine hours post infection, medium was removed and cells were washed with phosphate-free DMEM (Invitrogen Inc.) and incubated for 1 hour in 0.5 mL phosphate free medium containing 2% FBS, supplemented with 15 µM roscovitine or 1 µM maribavir, both drugs, or no drug. The concentration of DMSO was adjusted to a final concentration of 0.2% (vol/vol) to control for any effects of the carrier. Five hundred microcuries of 32P-labeled orthophosphoric acid (8500–9120 Ci/mmol) was then added in 0.5 mL of phosphate free medium containing 2% FBS and the same drug condition used during the pre-incubation step and left on the cells for 2 h. Lamin A/C and HCMV UL44 were immunoprecipitated as described above and analyzed by SDS-PAGE and autoradiography and western blotting as described above.
HFF were seeded for at 1×105 cells/well on glass coverslips in 24-well dishes and allowed to attach overnight prior to infection at an MOI of 1. At 96 h post infection, cells were fixed in 4% formaldehyde in PBS for 20 min (unless otherwise indicated, all steps were performed at room temperature). Cells were permeabilized with acetone at −20°C for 2 min. Following several washes in PBS, cells were blocked overnight in IF buffer (PBS containing 4% FBS [Sigma]). Primary antibodies were diluted in IF buffer and incubated with fixed cells for 1 h. Lamin A/C goat polyclonal antibody N-18 (Santa Cruz Biotechnology, Inc.) was used at 1∶10 dilution and UL44 mouse monoclonal antibody 10-C50 (Fitzgerald Industries International Inc.) was used at 1∶100. Secondary antibodies (Alexa Fluor 568-rabbit anti-mouse IgG and Alexa 488-chicken anti-goat IgG, Invitrogen) were applied at 1∶1,000. Cells were then washed in IF buffer three times for 5 min per wash, and mounted on slides in Prolong Antifade reagent (Molecular Probes, Inc.). Fluorescence microscopy was performed in the Nikon Imaging Center at Harvard Medical School, using a Nikon TE2000U inverted microscope in conjunction with a PerkinElmer Ultraview spinning disk confocal system equipped with a Hamamatsu Orca ER cooled-CCD camera. Images were acquired using a 60× differential interference contrast oil immersion objective lens and analyzed using Metamorph software from Universal Imaging, Inc. (Downingtown, Pennsylvania). Fisher's exact test was performed using Prism 4 (GraphPad Software, Inc.) for Macintosh.
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10.1371/journal.pcbi.1005332 | Computational investigation of sphingosine kinase 1 (SphK1) and calcium dependent ERK1/2 activation downstream of VEGFR2 in endothelial cells | Vascular endothelial growth factor (VEGF) is a powerful regulator of neovascularization. VEGF binding to its cognate receptor, VEGFR2, activates a number of signaling pathways including ERK1/2. Activation of ERK1/2 is experimentally shown to involve sphingosine kinase 1 (SphK1) activation and its calcium-dependent translocation downstream of ERK1/2. Here we construct a rule-based computational model of signaling downstream of VEGFR2, by including SphK1 and calcium positive feedback mechanisms, and investigate their consequences on ERK1/2 activation. The model predicts the existence of VEGF threshold in ERK1/2 activation that can be continuously tuned by cellular concentrations of SphK1 and sphingosine 1 phosphate (S1P). The computer model also predicts powerful effects of perturbations in plasma and ER calcium pump rates and the current through the CRAC channels on ERK1/2 activation dynamics, highlighting the critical role of intracellular calcium in shaping the pERK1/2 signal. The model is then utilized to simulate anti-angiogenic therapeutic interventions targeting VEGFR2-ERK1/2 axis. Simulations indicate that monotherapies that exclusively target VEGFR2 phosphorylation, VEGF, or VEGFR2 are ineffective in shutting down signaling to ERK1/2. By simulating therapeutic strategies that target multiple nodes of the pathway such as Raf and SphK1, we conclude that combination therapy should be much more effective in blocking VEGF signaling to EKR1/2. The model has important implications for interventions that target signaling pathways in angiogenesis relevant to cancer, vascular diseases, and wound healing.
| Vascular endothelial growth factor (VEGF) signaling is a potent regulator of angiogenesis, the growth and development of new vessels out of a preexisting vascular network. Angiogenesis requires enhanced survival, proliferation, and motility of the vascular endothelial cells. Crucial signaling endpoints in VEGF-mediated angiogenic response include elevation in intracellular calcium and the activation of the proteins ERK1 and 2 (ERK1/2). In this study, we have developed a novel computer model for the activation of ERK1/2 and calcium downstream of VEGF receptor type 2 (VEGFR2). Our model is the first of its kind to incorporate and investigate the consequences of calcium elevation and the role of a cellular lipid modifier known as sphingosine kinase 1 (SphK1). We also utilize the model to simulate therapeutic strategies targeting VEGF signaling to ERK1/2 indicating inefficiency of single therapies known as tyrosine kinase inhibitors (TKI) that target receptor phosphorylation. Computer simulations indicate that combination therapy is essential for effective blockade of this important pathway. Our results have important implications for human diseases such as cancer where plethora of anti-VEGF therapies are currently employed. Overall, our computer model sheds new light on a complex feedback involving SphK1 and calcium that radically alters the response of cells to VEGF.
| Angiogenesis is the growth of new capillaries from the pre-existing vasculature. The process of angiogenesis involves increased proliferation, survival, and migration of the endothelial cells that form the foundation of a developing vascular bed [1]. This process is critically involved in both health and disease [2]. Physiologically, it is involved in placental vascularization during pregnancy and the growth of normal blood vessels during development. Pathological angiogenesis is crucial in vascularizing tumors, a critical step in transition to neoplasm and cancer [3]. Newly formed tumor vasculature also contributes to the process of metastasis by shedding tumor cells into the bloodstream that then travel throughout the body and provide seeds for new tumors in more distant tissues [4,5]. In diseases such as age-related macular degeneration and diabetic macular edema, angiogenesis contributes to the neovascularization of the retina and the leakiness of the ocular blood vessels that may eventually lead to blindness [6]. In other diseases such as peripheral arterial disease, the opposite occurs where the blood capillaries and vessels regress leading to the reduction and, in some cases, total cessation of the blood flow to lower extremities [7]. Left untreated, this condition may require amputation of the regions affected by the lack of blood flow.
Considering the crucial role of angiogenesis in human health and disease it is no wonder that there is deep interest in understanding the mechanisms responsible for regulation and modulation of this phenomenon. Several endothelial cell growth factors have been identified as being critical for priming the endothelial cells to undergo the processes that would eventually lead to the generation of new blood vessels. One critical factor is the vascular endothelial growth factor A (VEGF-A, hereby referred to as VEGF) identified as a potent inducer and regulator of angiogenesis [8]. There are six different human isoforms of VEGF, with VEGF165 being by far the most intensely studied member of the group. VEGF165 sits at the helm of signaling pathways that prominently include VEGF receptor 2 (VEGFR2), VEGF receptor 1 (VEGFR1), and neuropilin-1 and 2 (NRP1 and NRP2) co-receptors. VEGF signaling is initiated by the binding of VEGF to VEGFR2 with subsequent VEGFR2 auto-phosphorylation on several tyrosine residues, leading to pro-angiogenic phenotypes such as increased cell proliferation and motility. Binding of VEGF to VEGFR2 and subsequent dimerization and auto-phosphorylation of at least six tyrosine residues (with Y1175 being the most widely studied) on the receptor leading to the recruitment of various adaptor proteins that transduce the phospho-tyrosine signal to downstream pathways including PI3K/AKT, Nitric Oxide (NO), and ERK1/2 that play crucial roles in determining and regulating vascular function [9]. Of different pathways, ERK1/2 activation has been shown to play a major role in VEGF-induced angiogenesis by inducing endothelial cell proliferation and motility [10–13]. The canonical MAPK pathway that leads to ERK1/2 phosphorylation, involves the recruitment and binding of the adaptor proteins Grb2 and SOS to the phospho-tyrosine sites on the receptors that activate small G protein Ras. Activated Ras then binds and activates Raf with subsequent activation of MEK1/2 and ERK1/2, with the eventual nuclear translocation of activated ERK1/2 [14,15]. While this canonical pathway operates in the activation of ERK1/2 in response to other growth factors such as the fibroblast growth factor (FGF) and epidermal growth factor (EGF), VEGF activation of this cascade seems to be fundamentally different involving the complex feedback mechanism initiated by calcium and ERK1/2-dependent activation of sphingosine kinase 1 (SphK1) [9,11,16,17]. Given the enormous complexity of these signaling pathways, computational models have been developed to aid in the elucidation of basic mechanisms of signal transduction and identify nodes of the pathway that might act as hubs in modulating the strength and duration of the signal. There is a large literature of mathematical models focusing on the quantitative understanding of the canonical MAPK pathway initiated by the activation of ErbB [18,19], EGFR [20,21], and VEGFR2 [22,23]. While ErbB and EGFR do signal via the canonical MAPK pathway to ERK, current experimental data on the VEGFR2 signaling suggest that the signaling is through a mechanism involving SphK1 and calcium [11,24]. Shu et al. were the first to show that the inhibition of SphK1 and PKC completely abolished the pERK1/2 signal [11]. Subsequent evidence suggested that the activation of SphK1 is through phosphorylation by activated ERK1/2 [16]. The mechanism becomes more complicated considering that activated SphK1 needs to be translocated from the cytoplasm to the plasma membrane, and that this is mediated by calcium binding to calcium- and integrin binding protein 1 (CIB1) [25].
Our intention here is to provide the first proof-of-principle simulations for the activation of ERK1/2 by SphK1 and calcium, and investigate the consequences of SphK1 positive feedback on VEGF signal transduction to ERK1/2. In so doing, we also develop a model that includes the major VEGF binding receptors on the cell surface: VEGFR1, VEGFR2, and NRP1. We also include the effect of internalization and degradation of the receptors by considering signaling from separate endocytic and membrane compartments. While internalization has been incorporated in recent computational models of VEGFR2 signaling [22,26], our model is the first to explicitly incorporate multi-complex internalization and signaling to downstream targets such as ERK1/2 and calcium.
Our basic assumption based on the available evidence is that SphK1 signaling is sufficient for activation and sustenance of ERK1/2 downstream of VEGFR2. Moreover, by explicitly incorporating a mechanistic model of cytoplasmic and ER calcium dynamics in the VEGF model here, we highlight the important connections between calcium dynamics and ERK1/2 activation.
Regarding the computational implementation of the model, a radical departure from the existing models of VEGF signaling, is the application of a rule-based modeling approach utilizing the programming language BioNetGen to accurately capture all the species and their interactions in the cell [27,28]. This method has been successfully applied to develop a detailed model of EGF/EGFR signaling taking into account the combinatorial complexity generated by multi-domain protein interactions [29]. BioNetGen automatically generates the biochemical network given the input rules operating on the seed species for domain interactions and phosphorylation reactions. This methodology has the added advantage of including receptor complexes and single or doubly phosphorylated species. While this can be done with conventional modeling where the reaction list is written down manually a priori, rule-based approach generates all the relevant species and bypasses the potential for errors inherent in manual construction of the pathway.
In the rule-based modeling approach, the protein domains and modification sites are explicitly included in the model design process and the rules for the modification and binding are implemented using a programming environment such as BioNetGen (see the supplementary material for the BioNetGen file, the corresponding SBML file, and the list of parameters with their descriptions). The general framework for our rule-based model and the constructed signaling pathway are summarized in Fig 1.
Fig 1A shows different binding sites for the initial receptor species in the model. VEGF contains three binding sites: two that are capable of binding to single binding sites on VEGFR1 and VEGFR2, and the third binding site is located on C-terminal domain that binds NRP1 [30]. VEGFR2 has two binding sites, one for VEGF and the other for ligand-independent coupling with another VEGFR2 or VEGFR1 molecule [31]. Also included, is the essential phosphotyrosine site that can be modified by phosphorylation and de-phosphorylation. VEGFR1 has a binding site for the ligand and a ligand-independent coupling site with VEGFR2 or VEGFR1 as shown. Ligand-independent receptor dimerization of VEGFR2 and VEGFR1 has been observed experimentally and have been incorporated in a recent computational model [32]. VEGFR1 also includes a ligand-independent binding site to NRP1. NRP1 has a single binding site that can competitively bind either VEGF or VEGFR1 [33]. In the absence of the ligand and VEGFR1, NRP1 is assumed to be in monomeric form. VEGF-dependent dimerization of VEGFR2 proceeds by the rule shown in Fig 1B. This rule is capable of generating multi-receptor complexes by including VEGFR2 and NRP1 in the complex. VEGF can also bind to NRP1 binding site directly from the solution. The interaction rule for the binding of VEGFR1 to NRP1 is illustrated by Fig 1C. VEGFR1 and VEGFR2 can also heterodimerize by VEGF according to the scheme shown. Ligand-independent dimerization of the receptors can also occur (Fig 1D). This can generate not only homodimers of VEGFR2/VEGFR2 and VEGFR1/VEGFR1, but also VEGFR1/VEGFR2 heterodimers. Note that all the species interact simultaneously according to the rules that take into account the combinatorial complexity inherent in the interacting multi-domain proteins. VEGFR2 receptors can undergo auto-phosphorylation if they are part of a homodimer complex with the ligand (Fig 1D). We also include internalization of the receptor complexes as shown in Fig 1E. In the model, we also include internalization and degradation of the receptors in the absence of ligand, constrained by the condition that the number of receptors in the absence of ligand remain steady during simulations.
Based on current evidence from the literature combined with the VEGF pathway information from the Reactome database [34,35], a pathway from activated VEGFR2 receptors to ERK1/2 activation is constructed as shown in Fig 1F. According to the experimental evidence, SphK1 is phosphorylated and activated mainly by pERK2 [16]. Direct phosphorylation of SphK1 by active PKC is included in the diagram for completeness and can augment the effects through ERK1/2, but the experimental evidence for direct activation of SphK1 by PKC within the context of VEGF signaling to ERK1/2 is not adequate and thus is not explicitly incorporated in the model [16]. Once activated, SphK1 is translocated to the plasma membrane by the calcium and integrin binding protein 1 (CIB1). CIB1 has a myristoyl switch that is activated upon calcium binding [25]. Calcium/CIB1/phospho-SphK1 complex is translocated to the plasma membrane employing the myristoylated CIB1. SphK1 then phosphorylates its substrate, sphingosine (Sph), generating the diffusible sphingosine 1 phosphate (S1P). S1P then activates Ras in a process conjectured to involve the inhibition of a Ras GTPase activating protein (RasGAP).
A significant addition to the model is the inclusion of a detailed calcium cycling module illustrated in Fig 1G. This module includes the calcium release activated calcium channels (CRAC channels) that are crucial for VEGF-dependent rise in calcium [36,37]. Further details of the model are included in the supplementary section of this paper.
To estimate the parameters for receptor dynamics, the model is fitted to the total VEGFR2 levels using the data from [38,39]. All the relevant parameters are simultaneously fitted to a consistent set of experimental data. The variables computed by the model and the corresponding data are normalized to the maximum values.
After 180 min of VEGF application, 80% of the receptors in the cell are lost as shown in Fig 2A.
We assume that VEGFR1 remains on the cell membrane and does not internalize. This is in accordance with experimental evidence indicating that VEGFR1 internalization requires VEGFR1 phosphorylation [46], and given the weak VEGFR1 auto-phosphorylation under normal conditions, it is reasonable to assume a constant VEGFR1 surface level. To include the effects of NRP1 on VEGFR2 level, Fig 2B shows the result of fitting the total VEGFR2 in the absence of NRP1 (NRP1 = 0 in the model) to the experimental data [39] (red, solid circles) in endothelial cells. In the absence of NRP1, levels of VEGFR2 decline to zero, while the control cell still retains 20% of its VEGFR2 content after 180 min. Surface VEGFR2 levels from the model (Fig 2C, blue) are fitted to two sets of experimental data that have utilized either flow cytometry [40] (solid circles, black) or western blot [38] (solid circles, red). According to the model, the effect of NRP1 association with VEGFR2 is to substantially increase the internalization rate (0.404 s-1 vs. 6.1×10−2 s-1) and the recycling rate to the plasma membrane (0.756 s-1 vs. 1.24×10-3s-1). While in this model the internalized receptors with NRP1 have higher degradation rate than receptors without NRP1 (1.18×10-2s-1 vs. 1.41×10−3 s-1), the combined effect of receptor internalization, recycling, and degradation, is the rapid decline in receptor number in the absence of NRP1 compared to the control (Fig 2B), consistent with the literature. Further, the recycling of the phosphorylated receptors is negligible in the model consistent with data in [39]. To constrain the signaling parameters we use the western blot measurements for pY1175, pPLCγ, and pERK1/2 [41]. We also simultaneously apply the constraint that blocking SphK1 abolishes pERK1/2 consistent with the experimental data in endothelial cells demonstrating that ERK1/2 activation is blocked at t = 10 min following SphK1 inhibition [11]. The phosphorylated pY1175 (normalized by total VEGFR2) from the model is fitted to the experimental data (solid red circles) (Fig 2D). The predicted fractional phosphorylated receptor levels (fraction of total receptors) at the surface and endosomal compartment are shown in Fig 2E and 2F. The surface receptors show rapid transient activation that declines to zero in 10 min. The endosomal signaling is sustained for ~40 min demonstrating that the internalized receptors are the major contributors to the sustenance of the VEGFR2 signal consistent with the current experimental and computational evidence [26,47]. The predicted value for the dephosphorylation of the receptors at the surface is ~150 fold higher than for the internalized receptors. It is instructive to note that this differential signaling from internalized receptors is an emergent property of the model and is not assumed a priori. Phosphorylated PLCγ is fitted to the experimental data (Fig 2G) showing a transient activation and decline of the signal in ~40 min. To find the parameters for calcium cycling module in the model, Fig 2H shows the normalized calcium signal from the model fitted to the normalized experimental data in [36]. The amplitude of the calcium transient was constrained to be within 200–300 nM in accordance with the empirical measurements [48]. The raw calcium transient is shown in Fig 2I with maximum calcium concentration of ~280nM reached in ~1.5 min with duration of ~20 min. While there is variation in the amplitude and duration of the calcium transients in response to different VEGF dosing strategies [49], the simulated calcium output from the model is consistent with the experimental data.
There are several parameters (S1 Table) for the activation of ERK1/2 including the parameters that define the strength of the positive feedback loop from SphK1. The data used to estimate these parameters are the time-course of pERK1/2 [41], the experimental data in human umblical vein endothelial cells (HUVEC) indicating that SphK1 and PKC inhibition block ERK1/2 activation [11], and the VEGF dose-response for ERK1/2 activation in HUVEC [44] and porcine aortic endothelial cells (PAEC) [45]. The fit of pERK1/2 from the model to the data is shown in Fig 2J, along with the constrain that blocking SphK1 blocks pERK1/2 (Fig 2K). PKC inhibition in the model abolishes pERK1/2 consistent with data (Fig 2L). To constrain receptor binding and coupling parameters, the binding curve for VEGF is computed under the condition that there is no receptor internalization (solid blue curve, Fig 2M) and is fitted to the data (red circles, Fig 2M) [42]. The VEGF dose response curve for pVEGFR2 is also constrained (Fig 2N, solid red circles) demonstrating half-maximal activation (EC50) at 30 pM consistent with experimental observation [43]. We were able to constrain the dose response curve for pERK1/2 using the two sets of experimental data in PAEC [45] and HUVEC [44]. The data demonstrated in vitro ERK1/2 activation in response to soluble VEGF at concentrations as low as 0.25 ng/ml (6 pM) in PAEC with ERK1/2 activation saturated at 1 ng/ml (24 pM) (Fig 2O, solid red circles). In HUVEC, the experimental data (Fig 2O, solid black circles) indicated that soluble VEGF is capable of activating ERK1/2 at concentrations as low as 0.5 ng/ml (12 pM). The maximum fractional pERK1/2 from the model is fitted to the data as shown in Fig 2O (solid blue line). The model predicts a threshold behavior in ERK1/2 in response to VEGF (2O, inset). For concentrations of VEGF below 5 pM, the ERK1/2 is incapable of being activated, while for values above 5 pM, there is a gradual increase in maximum pERK1/2 versus VEGF. Two important observations regarding the pERK1/2 dose-response curve are worth noting. First, the EC50 for pERK1/2 is lower than pVEGFR2 (~5pM for pERK1/2 and ~30pM for pVEGFR2). Second, constraining the pERK1/2 VEGF dose response using the available data predicts the existence of a threshold behavior at VEGF concentration of 5 pM that will be explored in more detail later. The threshold behavior (or bi-stable behavior using the language of dynamic systems theory) is expected in systems containing positive-feedback loops [50].
ERK1/2 signals are expected to exhibit wide range of durations and amplitudes [22]. An important aspect of the current study is to identify what parameters or mechanisms determine and modulate the duration and amplitude of the pERK1/2 signal given the positive feedback generated by SphK1 and calcium.
The predicted S1P reaches 1 μM after t~2.3 min and declines to baseline in ~20 min. The activated SphK1 signal is more sustained even after 40 min retaining a ~92 nM concentration (Fig 2P). The predicted curves for active PKC, active Ras, active Raf, and the dose-response curve for active Ras are included in S1 Fig. Active Ras also shows threshold behavior in response to VEGF at 5 pM.
In the next section, we apply global sensitivity analysis to better understand the effect of Sphk1 in shaping the pERK1/2 signal. We will also investigate other parameters modulating the threshold behavior of ERK1/2 activation in response to VEGF.
We next carried out global sensitivity analysis to identify the most sensitive parameters influencing ERK1/2 activation. We utilized partial rank correlation coefficient (PRCC) [51] for this task which determines positive or negative monotonic relationships between the input parameters and the output observable (pERK1/2 in this case). Fig 3A shows the top 15 parameters with positive monotonic relationship with pERK1/2 at t = 15 minutes. PRCC coefficients were computed at 1, 2, 5 and 15 minutes.
We selected t = 15 minutes to identify parameters affecting the decaying and the plateau phase of pERK1/2. The top parameters include either the total protein levels or the parameters determining the kinetic rates. The partial rank correlation coefficient (PRCC) values for parameters with negative correlation to pERK1/2 are shown in Fig 3B. These parameters describe either the off kinetics of binding, the dephosphorylation rate, or the Michaelis-Menten-type constants for the phosphorylation reactions. We next applied the insights gained from sensitivity analysis to investigate the factors determining ERK1/2 response to VEGF ligand. Specifically, we investigated the possibility of modulating the threshold behavior observed by the model (previously illustrated in Fig 2O). Fig 3C shows the maximum value of pERK1/2 (normalized to the total ERK1/2 level) plotted against the concentration of VEGF for various values of total SphK1 concentrations. As illustrated by the figure, maximum pERK1/2 exhibits a threshold response for sufficiently high values of SphK1 concentrations. The threshold value for VEGF here is defined as the concentration of VEGF below which pERK1/2 is zero. The baseline model with 100 nM SphK1 results in a threshold value of 5 pM for VEGF. The threshold level increases gradually as the SphK1 concentration is lowered progressively to 10 nM. The increase in threshold value of VEGF is monotonic as shown in Fig 3D, from 36 pM ([SphK1_0] = 5 nM), to 3 pM ([SphK1_0] = 150 nM). The striking model prediction here is that a single parameter, namely the total SphK1 concentration, can have a significant effect on the sensitivity of the cell to VEGF by setting the threshold for ERK1/2 activation. Total SphK1 concentration is also a strong determinant of the amplitude and duration of pERK1/2 as illustrated by the pERK1/2 versus time curves for various values of the total concentration of SphK1 (Fig 3E). According to the model, SphK1 can modulate both the maximum and duration of the pERK1/2 signal. For sufficiently high SphK1, pERK1/2 signal reaches a plateau with very slow rate of decay (3E, green).
The strength of the SphK1 positive feedback is expected to be influenced by the catalytic rate of SphK1. This parameter was also a top hit in our global sensitivity analysis. The catalytic rate of SphK1 powerfully modulates the threshold for ERK1/2 activation as demonstrated by Fig 3F. The VEGF threshold value monotonically decreases as the catalytic rate increases as shown by Fig 3G. The threshold value of VEGF decreases from 25 pM to 2 pM, as the catalytic rate of SphK1 is increased from 3.7 s-1 to 74.5 s-1. The baseline fitted value of this parameter was kcatSK1 = 37.24 s-1 with the threshold value of VEGF = 5 pM. SphK1 catalytic rate also powerfully modulates the shape of the ERK1/2 activation signal as shown in Fig 3H. Once again, for sufficiently high catalytic rates, pERK1/2 signal reaches a plateau phase with no significant decay.
The responses to variations in total level of Raf are similar and are summarized in S2 Fig. To investigate the effect of a top negatively correlated parameter within the SphK1 feedback on pERK1/2 dynamics, Fig 3 (panels I and J) shows the effect of variations in the dephosphorylation rate of S1P (kdpS1P) on the threshold value of VEGF. Increasing kdpS1P, increases the VEGF threshold from 1 pM (kdpS1P = 0.059 s-1) to 14 pM (kdpS1P = 4.75 s-1). S1P dephosphorylation rate also crucially determines the maximum and duration of pERK1/2 signal (Fig 3K). Fig 3K shows the striking effect of decreasing kdpS1P on pERK1/2 duration and plateau indicating that for sufficiently low values of this parameter, the pERK1/2 signal reaches a steady state with no decay (red and blue curves). Put together, these results demonstrate that ERK1/2 activation in response to VEGF is critically dependent on parameters affecting the SphK1 and S1P feedback downstream of phosphorylated receptors.
Calcium signaling plays a crucial role in the activation of SphK1 by regulating CIB1-dependent SphK1 translocation to the membrane. Sensitivity analysis also identified the rate of membrane calcium pump (see Fig 2B) as being significant. Here we consider perturbations of calcium dynamics and present concrete and experimentally testable predictions of the model. The plasma membrane calcium pump (PMCA) rate significantly perturbs the duration of the ERK1/2 activation and is a sensitive determinant of the plateau phase of the signal as shown in Fig 4A.
Blocking PMCA is predicted to convert a transient pERK1/2 signal into a plateau-phase response (Fig 4A, green to blue). Inhibition of ER calcium pump (SERCA) is considered next. Increasing the pump rate powerfully influences the shape of the pERK1/2 signal (Fig 4B). Similar to PMCA, sufficient inhibition of the SERCA pump can transform a transient pERK1/2 signal into a plateau signal with no decay over time (4B, blue). Increasing the amplitude of the CRAC channel (with baseline fitted value of 1.74x104 μM/s) is also predicted to have a significant effect on the duration of the pERK1/2 signal as demonstrated in Fig 4C. Sufficient increase in CRAC channel current amplitude can lead to a sustained pERK1/2 signal with no decay (Fig 4C, pink and green). We next consider the concentration of the calcium binding protein CIB1 that is critical for SphK1 translocation to the plasma membrane in the model. The concentration of CIB1 is varied and the effects on pERK1/2 are considered. ERK1/2 activation is insensitive to total concentration of CIB1 higher than 0.5 μM (baseline value is 0.5 μM). However, pERK1/2 monotonically decreases as the CIB1 is reduced from 0.5 μM to 0 (Fig 4D). Sample pERK1/2 versus time curves in response to variations in CIB1 concentration are shown in Fig 4E. For sufficiently small values of CIB1 concentrations (5nM), there is no ERK1/2 activation (Fig 4E, blue). We next considered the effect on pERK1/2 of altering the dissociation constant for the binding of CIB1 to SphK1. As shown in Fig 4F, the changes in the binding constant affect maximum pERK1/2. Sample traces are shown in Fig 4G. The change in binding affects both the duration and the amplitude of the pERK1/2 signal. Another important parameter in SphK1 activation is CIB1/SphK1 translocation rate constant from the cytoplasm to the plasma membrane. The maximum pERK1/2 is relatively stable for translocation rates down to 0.1 s-1 (Fig 4H). However, below this value, the maximum pERK1/2 rapidly and monotonically declines to zero. In fact, a lower bound of 0.044 s-1 (time constant of ~22 s) is predicted to be necessary for ERK1/2 activation. Sample traces are also shown in Fig 5I. The translocation time course of CIB1/SphK1 is thus predicted to be an important regulator of the pERK1/2 dynamics in response to VEGF.
Overall, these simulations predict that pERK1/2 shape and dynamics can be strongly modulated by the perturbations in calcium dynamics including the PMCA rate, SERCA pump rate, and the current through the CRAC channels. ERK1/2 activation is also regulated by the concentration of CIB1 protein, the strength of CIB1 binding to SphK1, and the translocation time constant of the activated CIB1 from the cytoplasm to the plasma membrane.
We also carried out simulations to evaluate the effect of receptor-level perturbations on ERK1/2 activation. These included changes in VEGFR2 internalization rate (S3 Fig), dephosphorylation rate (S4 Fig), and the number of receptors (S5 Fig). The main result from these simulations is that ERK1/2 activation is predicted be stable in response to a wide range of changes in parameters that determine receptor dynamics.
Considering signal transduction from VEGFR2 to downstream species, it is tempting to assume that ERK1/2 activation is linearly correlated with the extent of phosphorylated VEGFR2. Our hypothesis was that the effect of SphK1 feedback on ERK1/2 activation would result in a fundamentally different relation between pERK1/2 and pVEGFR2. In fact, we already observed that the model predicted the existence of threshold behavior in ERK1/2 activation in response to VEGF concentration. In this section, we investigate the behavior of pERK1/2 versus pVEGFR2 predicted by the model. As shown in Fig 5A, pERK1/2 is relatively insensitive to changes in the phosphorylation rate down to 98.5% of the baseline phosphorylation rate. Further, pERK1/2 exhibits a threshold behavior in response to receptor phosphorylation. For values larger than 0.64 s-1 (1.5% of the control value), ERK1/2 activation is unscathed. Plotting maximum pERK1/2 versus maximum fractional pVEGFR2 (fraction of total VEGFR2) results in a highly non-linear relation presented in Fig 5B.
The model predicts that there is a threshold fraction of pVEGFR2 below which there is no ERK1/2 activation. This threshold value is ~4.6%, meaning that only 4.6% of the total receptors need be phosphorylated to induce a robust ERK1/2 activation. SphK1-dependent ERK1/2 activation is therefore robust to variations in phosphorylated VEGFR2 population, implying that above 4.6% fractional pVEGFR2, ERK1/2 activation is not fundamentally altered by changes in pVEGFR2 fraction. For instance, reducing pVEGFR2 fraction from 30% to 10%, does not significantly affect ERK1/2 activation and the sole predicted effect is a reduction in maximum pERK1/2 from ~0.23 to ~0.15 (Fig 5B). We next investigated parameters within the SphK1 feedback that might alter this threshold value. As shown in Fig 5C, pVEGFR2 threshold value in ERK1/2 activation is strongly modulated by the concentration of SphK1 in the cell. 50% reduction in SphK1 concentration (baseline value of 0.1 μM), increased the threshold value from 4.6% to ~9%. Increasing SphK1 to 1 μM, reduced the threshold further to 0.8%, implying that under conditions where SphK1 is overexpressed in the cell, less than 1% of the receptors need be phosphorylated for robust ERK1/2 activation. Similar pattern held true for kdpS1P, a parameter determining the dephosphorylation rate of S1P (Fig 5D). Increasing S1P dephosphorylation by 4-fold, increased the threshold value from 4.6% to 15%. Decreasing S1P 10-fold, reduced the threshold value from 4.6% to 0.8%, once again implying that under heightened S1P levels, less than 1% of the receptors are required to be phosphorylated for effective ERK1/2 activation. Threshold behavior is also predicted for the active SphK1 (Fig 5E) and S1P (Fig 5F), implying the existence of intimate connection between SphK1 and ERK1/2 dynamics. The model thus proposes a radical hypothesis that SphK1 feedback endows the cells with the ability to effectively activate ERK1/2 in response to small fraction of active VEGFR2 on the cell and effectively shield downstream ERK1/2 activation from slight perturbations in receptor phosphorylation rate. The model was then utilized to simulate the anti-angiogenic strategies targeting VEGF pathway. Fig 5G simulates the effect of depleting VEGF (with an agent such as bevacizumab). Model demonstrates that VEGF depletion alone is ineffective in blocking ERK1/2 activation. The figure shows that over 99% of the external VEGF need be depleted before any inhibitory effect on pERK1/2 is observed. Similarly, Fig 5H shows the result of inhibiting the phosphorylation rate with a generic tyrosine kinase inhibitor (TKI) such as sunitinib that primarily inhibits receptor phosphorylation. Once again over 99% inhibition is necessary. The model thus predicts that TKIs and antibodies that solely target VEGFR2 autophosphorylation and VEGF are not effective in shutting down signaling to ERK1/2. Depleting of VEGFR2 by an anti-VEGFR2 mAb agent, e.g., ramucirumab is predicted to have a lower threshold of inhibition at 66%, but ERK1/2 activation is robust until the threshold depletion level is achieved (Fig 5I). This level of inhibition might still be very challenging to achieve in an in vivo setting where antibody delivery to tumors is a confounding effect. Moreover, ERK1/2 activation is robust up to 66% VEGFR2 depletion. Next we simulate a combined inhibition of receptor phosphorylation and Raf by a generic small molecule TKI such as sorafenib (Fig 5J). This is much more effective in gradually inhibiting pERK1/2 amplitude until complete inhibition at 78%. This is in contrast with previous cases where drop in ERK1/2 amplitude to zero is very sudden (switch-like) occurring at a threshold inhibition level. Combining sorafenib and ramucirumab is even more effective achieving full blockade of pERK1/2 at 41% inhibition (Fig 5K). Combining sorafenib and ramucirumab with an anti-SphK1 agent further improves the inhibitory potential reducing the threshold for inhibition to 36%. These simulations demonstrate that combination therapy is essential in achieving efficient and sustained (pERK1/2 amplitude decreasing as a function of percent inhibition as in Fig 5J, 5K and 5L) inhibition of VEGF signaling to ERK1/2.
An important prediction of the model is the existence of threshold in VEGF activation of ERK1/2. This implies that there is a critical value of VEGF concentration below which ERK1/2 activation ceases. The EC50 for the activation of VEGFR2 by VEGF is ~30 pM [43]. Soluble VEGF is capable of activating ERK1/2 in concentrations as low as ~12 pM in HUVEC [44] and ~6 pM in PAEC [45]. What emerges from Constraining the model with these experimental data is the existence of VEGF threshold of ~5 pM for ERK1/2 activation. An interesting observation in [44] was that in the absence of the heparin binding domain of fibronectin, the antibody to phosphorylated VEGFR2 did not detect a signal at 1 ng/ml (24 pM), while pERK1/2 signal was still detectable down to VEGF concentrations of 0.5 ng/ml (12pM). One experimentally verifiable prediction of the model is that by overexpressing SphK1, one should be able to increase the lower bound for the activation of soluble VEGF. The pERK1/2 versus pVEGFR2 curve (Fig 5B) is also revealing, demonstrating that robust activation of ERK1/2 is achieved with only 4.6% of the receptors phosphorylated. The small population of phosphorylated receptors required for activation of ERK1/2, might be the reason for the difficulty in detecting phosphorylated receptors at low VEGF concentrations while being able to detect the pERK1/2 signal. The model provides a rationale and motivation for performing further experiments at more physiological concentrations of VEGF, even in the absence of detectable phosphorylated receptor species.
Calcium elevation is essential for angiogenic response to VEGF, and inhibition of VEGF-mediated calcium influx prevents endothelial cell proliferation [48,52,53]. Moreover, strong buffering of cytoplasmic calcium blocks ERK1/2 activation downstream of VEGF [54]. To our knowledge, the computational model developed here is the first of its kinds to incorporate specific calcium cycling mechanisms downstream of VEGFR2, including the CRAC channels that have been experimentally shown to be critical for angiogenesis [36]. The calcium influx through the CRAC channels is predicted to alter the plateau phase and duration of the pERK1/2 signal. Indeed, complete inhibition of CRAC channels abolishes the pERK1/2 plateau phase.
Experimentally, calcium channel inhibition has been explored as a viable target in cancer for inhibiting angiogenesis in solid tumors [55] and ovarian cancer [56]. Moreover, some types of cancer have been shown to down-regulate the expression PMCA and SERCA pumps [57] that are predicted by the model to increase the duration and amplitude of pERK1/2 signal (Fig 4A and 4B). The model predicts that calcium signaling should not be overlooked when investigating the activation of angiogenic pathways by VEGF. Further experimental evidence is needed to elucidate and test the predicted link between calcium dynamics and ERK1/2 activation.
Overall, the model predicts that changes in the amplitude and duration of the calcium transient by interventions such as changes in the activity of SERCA and PMCA pumps, and CRAC channels may modulate VEGF-dependent ERK1/2 signaling. Moreover, any therapeutic agent that interferes with calcium signaling might also have far-reaching effects on VEGF-mediated angiogenesis.
In cancer, the goal is to inhibit angiogenesis and prevent tumor vascularization and growth. Our simulations show that antibodies such as bevacizumab [58,59] that target VEGF would not be effective at shutting down VEGF signaling to ERK1/2 unless 99% inhibition of VEGF is achieved. This high threshold for ERK1/2 inhibition would seriously hinder the applicability of the antibody as an effective anti-angiogenic agent and might explain the limited increase in overall survival (usually less than 6 months) in cancers responding to bevacizumab treatment such as metastatic colorectal cancers [60–62], non-small-cell lung cancer (NSCLC) [63,64], metastatic renal cell carcinoma [65], and ovarian cancer [66]. In metastatic breast cancer, bevacizumab resulted in no improvements in overall survival [67]. While our model has focused on one specific pathway (namely ERK1/2), it does show that even within the context of a single pathway, monotherapy in the form of anti-VEGF antibody would not be effective unless the concentrations are sufficiently high so that 99% of the ligand molecules are sequestered. This is a very stringent requirement in any in vivo setting, especially that we are not even including the very real possibility of developing resistance to anti-VEGF therapies [68]. Similarly, simulating the inhibition of VEGFR2 autophosphorylation by a generic TKI such as sunitinib that primarily inhibits receptor phosphorylation indicates that over 99% inhibition is necessary before signaling to ERK1/2 is compromised. Sunitinib has been approved for use in advanced renal cell carcinoma and increases median overall survival from 21.8 to 26.4 months [69]. What is suggested by our modeling exercise is that the high threshold for inhibition of VEGF-ERK1/2 signaling might explain some of the difficulties in effective inhibition of VEGF-mediated angiogenesis with TKIs in various types of cancers. The model also emphasizes the absolute necessity of developing more efficient TKI drug delivery strategies to enhance local concentration of the drug in tumor microenvironment in order to overcome the inhibition threshold.
An interesting prediction from the model is that compared to other mono-therapies (agents targeting a single node of the VEGF pathway), depleting VEGFR2 with an antibody such as ramucirumab should be more effective, exhibiting lower inhibition threshold (~66%). Once again, ERK1/2 activation is robust up until the threshold level of inhibition is reached. Similar to other agents, ramucirumab has shown limited efficacy in certain types of tumors such as advanced gastric [70] and metastatic advanced non-small-cell lung carcinoma [71].
Our simulations suggests that when it comes to inhibiting VEGF signaling to downstream effectors such as ERK1/2, combination therapy seems to be essential. In fact, according to the model, TKIs such as sorafenib are more effective because they inhibit signaling at both the receptor level (VEGFR2 phosphorylation) and a downstream effector node (Raf). It is indeed interesting to note that sorafenib is the only FDA approved anti-angiogenic agent for hepatocellular carcinoma (HCC) and the only TKI (in the list of inhibitors in clinical trials for HCC) that targets two distinct nodes of the VEGF/VEGFR2 pathway [72].
Simulations demonstrate more effective combination strategies. For example, combining ramucirumab and sorafenib achieves a lower threshold of inhibition (41% according to the model) with rapid decline in maximum pERK1/2 as a function of inhibition. Another example is triple combination involving sorafenib, ramucirumab, and an inhibitor for Sphk1 pathway (such as sphingomab [73]) that is predicted to further improve the inhibitory effects on VEGF-ERK1/2 axis.
In all, the model developed here demonstrates some of the challenges in developing effective anti-angiogenic therapies targeting the VEGF pathway and highlights the need to consider specific pathway dynamics (e.g. threshold behavior) and structure (e.g. positive and negative feedback loops) when evaluating therapeutic interventions. The most clinically relevant prediction from the current model is that even in inhibiting a single pathway involving VEGF signaling to ERK1/2 and in the absence of any consideration of tumor resistance, combination therapeutic strategies seem to be essential.
There are several aspects of the model that can be improved in the future. The model includes only a single phosphorylation site on VEGFR2. The modular structure of BioNetGen allows for additional phosphorylation sites to be included and investigated. These can in principle be readily included and investigated in the future. Another limitation is the simplified description of receptor recycling that includes only two compartments, namely the surface receptors and the receptors within the signaling endosomes. Including Rab specific compartments similar to the model in [26] would significantly increase model complexity and molecular detail.
The calcium cycling module includes a phenomenological description of the current through CRAC channels as a function of calcium concentration within the ER lumen. This model can be improved by including dynamic STIM oligomerization similar to the model in [74]. The model also does not include TRPC calcium channels that are regulated by DAG and are shown to be important in VEGF-mediated angiogenesis [52,75]. As additional data with specific inhibitors of TRPC channels become available, the relation between TRPC signaling and ERK1/2 activation downstream of VEGF would be a fruitful avenue of investigation in the model.
Pertaining to SphK1 signaling, a confounding pathway is the activation of S1P receptors [76,77] (S1PR1 and S1PR2) by the S1P generated downstream of VEGF. Including this receptor would go well beyond the scope of the current study; however, rule-based modeling would indeed be very suitable for studying the interaction between VEGFR2 and S1PRs at the receptor and downstream levels and the future versions of the model can include this important pathway.
The rules for the interaction of the receptors and downstream signaling details are incorporated into the BioNetGen text file and can be accessed with ease and is included in the supplementary material. We have also supplemented the SBML file associated with the model. The binding of VEGF to VEGFR1, VEGFR2, and NRP1 follows standard kinetic schemes similar to previous studies [32,78]. List of parameters with their descriptions is also includes in S1 Table. S2 Table contains the initial values for the seed species in the model. The rules generate 208 species and 932 reactions. The binding of PLCγ to pVEGFR2 and subsequent phosphorylation and dissociation of PLCγ from the receptor is described by a Michaelis-Menten type reaction as follows:
pVEGFR2surface + PLCγ → pVEGFR2surface + pPLCγRate=kpPLCγ[pVEGFR2surface][PLCγ][PLCγ]+KmPLCγ/R2
(1)
pVEGFR2membrane + PLCγ → pVEGFR2membrane + pPLCγRate=kpPLCγ[pVEGFR2membrane][PLCγ][PLCγ]+KmPLCγ/R2
(2)
Note that BioNetGen accounts for all the VEGFR2 species that are phosphorylated. This approach significantly lowers the number of reactions generated by the rules and prevents combinatorial explosion in the model. Another aspect of the model is the generation of active Ras (RasGTP) by S1P. As the precise link between S1P and Ras activation is not entirely clear, we assume a Michaelis-Menten type reaction:
RasGDP+S1P→RasGTP+S1PRate=kS1P/Ras[S1P][S1P]+kmS1P/Ras
(3)
with parameters kS1PRas and Km,S1PRas determining the strength of Ras activation by S1P. More details of calcium cycling and SphK1 activation module are given in the supplementary material.
BioNetGen created the network as a set of reactions and the corresponding ordinary differential equations (ODEs) saved as a C file, readable with MEX functionality in MATLAB (Mathworks, 2015). The set of ODES was numerically integrated using SUNDIAL numerical solver suite [79]. For parameter fitting, we applied a direct search algorithm implemented in the MATLAB function patternsearch as part of the global optimization toolbox. All the pieces of data, including the surface and total receptor levels and downstream activation were fitted simultaneously in MATLAB.
Data from the western blot images were extracted using the software imageJ [80].
Global sensitivity analysis was performed using the partial rank correlation coefficient (PRCC) algorithm described in [51]. The parameter values were randomly chosen from a uniform distribution within a range 0.01 × fitted_value ≤ p ≤ 20 × fitted_value.
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10.1371/journal.ppat.1006126 | Toxoplasma gondii GRA7-Targeted ASC and PLD1 Promote Antibacterial Host Defense via PKCα | Tuberculosis is a global health problem and at least one-third of the world’s population is infected with Mycobacterium tuberculosis (MTB). MTB is a successful pathogen that enhances its own intracellular survival by inhibiting inflammation and arresting phago-lysosomal fusion. We previously demonstrated that Toxoplasma gondii (T. gondii) dense granule antigen (GRA) 7 interacts with TNF receptor-associated factor 6 via Myeloid differentiation primary response gene 88, enabling innate immune responses in macrophages. To extend these studies, we found that GRA7 interacts with host proteins involved in antimicrobial host defense mechanisms as a therapeutic strategy for tuberculosis. Here, we show that protein kinase C (PKC)α-mediated phosphorylation of T. gondii GRA7-I (Ser52) regulates the interaction of GRA7 with PYD domain of apoptosis-associated speck-like protein containing a carboxy-terminal CARD, which is capable of oligomerization and inflammasome activation can lead to antimicrobial defense against MTB. Furthermore, GRA7-III interacted with the PX domain of phospholipase D1, facilitating its enzyme activity, phago-lysosomal maturation, and subsequent antimicrobial activity in a GRA7-III (Ser135) phosphorylation-dependent manner via PKCα. Taken together, these results underscore a previously unrecognized role of GRA7 in modulating antimicrobial host defense mechanism during mycobacterial infection.
| We previously demonstrated that Toxoplasma gondii (T. gondii) dense granule antigen (GRA) 7 interacts with TRAF6 via MyD88, enabling innate immune responses in macrophages and effective protection against T. gondii infection in vivo. However, its exact role and how it regulates host innate immune responses have not been fully explained. Herein, we show that PKCα-mediated phosphorylation of GRA7 is essential for the interaction between GRA7 and ASC or PLD1, which can promote antimicrobial defense against Mycobacterium tuberculosis (MTB). Notably, PKCα specifically phosphorylated Ser52 and Ser135 of GRA7 in vitro and in vivo, indicating that GRA7 is a substrate of PKCα. The N-terminal of GRA7 (GRA7-I) was sufficient for interaction with the PYD domain of ASC, which is capable of ASC oligomerization and inflammasome activation. Furthermore, GRA7-III interacted with the PX domain of PLD1, facilitating its enzyme activity, phago-lysosomal maturation, and subsequent antimicrobial activity in a GRA7 phosphorylation-dependent manner. Interestingly, phosphomimetic mutation in GRA7 overcame the need for PKCα. Collectively, these results provide novel insight into how GRA7 can promote ASC and PLD1 activation in a PKCα-dependent manner as an antimicrobial host defense mechanism.
| Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB) [1]. The World Health Organization reported that in 2014, 9.6 million cases and 1.5 million deaths were globally [2]. Recent developments in TB drug-development strategies (including new and repurposed antimicrobials and host-directed drugs) have produced new regimens to shorten treatment duration, improve outcomes of TB treatment such as, prevent resistance, reduce lung injury by promoting autophagy, antimicrobial peptide production, and other macrophage effector mechanisms, as well as inhibiting mechanisms causing lung inflammation and matrix destruction [1,3–5]. A wide range of candidate host-directed therapies (HDTs)-including new and repurposed drugs, biologics, and cellular therapies-have been proposed to accelerate eradication of infection and overcome the problems associated with current treatment regimens.
Recent studies have revealed the intracellular signaling pathways that govern the outcome of the innate immune response to mycobacteria infection and antibacterial defense [6–11]. First, the NLRP3 inflammasome complex, an intracellular protein complex consisting of the sensor NACHT, LRR and PYD domains-containing protein 3 (NLRP3), the adaptor apoptosis-associated speck-like protein containing a carboxy-terminal CARD (ASC), and pro-caspase-1 regulates IL-1β and IL-18 processing [10–12]. Jayaraman et al. showed that IL-1β directly promotes antimicrobial immunity in murine and human macrophages by regulating TNFR signaling and caspase-3 activation against MTB infection [10]. Verway et al. showed that 1-25-Dihydroxyvitamin D (1,25D) enhances IL-1β signaling from MTB-infected macrophages, inducing antimicrobial peptide DEFB4/HBD2 in primary lung epithelial cells, which in turn helps control MTB [11]. Second, host phospholipids play a critical role in the activation of the antimicrobial innate immune response [13]. Phospholipase D (PLD), which has two isoforms (PLD1 and PLD2) catalyzes the hydrolysis of the membrane phospholipid, phosphatidylcholine, to generate the metabolically active phosphatidic acid (PA) [14]. PLD1 is activated by arf-, ral-, and rho-family GTPases, and protein kinase C (PKC) α, while PLD2 activity is elevated by fatty acids [15]. Interestingly, MTB, unlike the nonpathogenic M. smegmatis, inhibits PLD activation during phagocytosis, a process that is associated with intracellular survival of the pathogen [6]. Garg et al. showed that Natural lysophospholipids promote MTB-induced in vitro PLD-dependent phagolysosome maturation and PLD-dependent intracellular killing of MTB in human macrophages [8] and the type II alveolar epithelial cell line A549 [9]. Third, recent studies have highlighted the role of protein kinases in the biology and pathogenesis of mycobacteria. The members of the PKC-family of proteins are classified into three groups, based on the mechanisms regulating their activation in response to different stimuli [7,16]. Holm et al. showed that PKCα regulates phagocytosis and the biogenesis of phagolysosomes by promoting the interaction of phagosomes with late endosomes and lysosomes [16]. Furthermore, PKCα also plays an important role in the killing of MTB in human macrophages [7]. Collectively, these infection-induced signaling pathways suggest possibilities for the development of novel therapeutic modalities for tuberculosis that target the intracellular signaling pathways permitting the replication of this nefarious pathogen. However, the roles of MTB-infection signal-dependent HDTs involved in host innate immune responses and their regulatory mechanisms have not yet been fully elucidated.
In a previous study, we demonstrated that T. gondii GRA7/MyD88-dependent NF-κB activation is essential for the activation of TNF receptor-associated factor 6 (TRAF6) and ROS generation, and enhances the release of inflammatory mediators. We also found that GRA7 stimulation led to physical and functional associations between GRA7 and TRAF6, resulting in crucial protective efficacy against T. gondii infection in vivo [17]. It remains to be seen whether GRA7 targeting can be used as a therapeutic strategy for infectious diseases. In this study, we further investigated the intracellular regulatory network of T. gondii GRA7-induced ASC, PLD1, and PKCα signaling pathways to help identify novel therapeutic modalities for tuberculosis. We found that the PKCα-mediated phosphorylation of GRA7 was essential for interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB in vitro and in vivo. Our findings demonstrate that GRA7-I and -III play fine-tuning roles in the activation of HDTs and innate immune machineries through direct binding with ASC or PLD1 and may provide a unique opportunity for urgently needed therapeutic interventions against tuberculosis.
All animal experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee of Hanyang University (protocol 2014–0207) and Bioleaders Corporation (Daejeon, Korea, protocol BLS-ABSL3-13-11). All animal experiments were performed in accordance with Korean Food and Drug Administration (KFDA) guidelines.
Cultures of MTB H37Rv (provided by Dr. R. L. Friedman, University of Arizona, Tucson, AZ) were prepared as described previously [1]. The effective concentration of lipopolysaccharide was <50 pg/ml in those experiments, with a bacterium-to-cell ratio of 10:1. For all assays, mid-log phase bacteria (absorbance 0.4) were used. Bacterial strains were divided into 1-ml aliquots and stored at -70°C.
Wild-type C57BL/6 mice were purchased from Orient Bio (Gyeonggi-do, Korea). PKCα-/- (B6;129-Prkcatm1Jmk/J, 009068) and PLD1-/- (B6.Cg-Pld1tm1.1Gbp/J, 028665) mice were obtained from Jackson Laboratory. All animals were maintained in a specific pathogen-free environment. HEK293T cells (ATCC-11268; American Type Culture Collection) were maintained in DMEM (Invitrogen) containing 10% FBS (Invitrogen), sodium pyruvate, nonessential amino acids, penicillin G (100 IU/ml), and streptomycin (100 μg/ml). Human monocytic THP-1 (ATCC TIB-202) cells were grown in RPMI 1640/glutamax supplemented with 10% FBS and treated with 20nM PMA (Sigma-Aldrich) for 24 h to induce their differentiation into macrophage-like cells, followed by washing three times with PBS. Primary bone marrow–derived macrophages (BMDMs) were isolated from C57BL/6 mice and cultured in DMEM for 3–5 d in the presence of M-CSF (R&D Systems, 416-ML), as described previously [12].
For in vitro experiments, cells were infected with MTB for 2–4 h. Then, cells were washed with PBS to remove extracellular bacteria, supplied with fresh medium, and incubated at 37°C for indicated time points. For in vivo experiments, C57BL/6 mice were i.v. injected with MTB (1×106 CFU/mouse). After 3 wks of infection, mice injected intraperitoneally with rGRA7 proteins for 7 consecutive days. After 1 wk of treatment, mice were sacrificed for harvesting of the lungs, spleens, and livers. Mice were maintained in biosafety level 3 laboratory facilities.
CIP (P4978) and DMSO were purchased from Sigma-Aldrich. PKCα (C2-4) inhibitor peptide (17478) was purchased from Cayman Chemical. Flag-PKCα, -β, -δ, and -ξ plasmids were a generous gift from Dr. D. Zhou (Xiamen University, China). The GST-tagged GRA7 and truncated mutant genes were described previously [17]. V5-tagged AC or AU1-PLD1 and truncated mutant genes were cloned into the XbaI and BamHI sites in pcDNA3.0. All constructs were sequenced using an ABI PRISM 377 automatic DNA sequencer to verify 100% correspondence with the original sequence. Specific antibodies against phospho-(Thr147)-PLD1 (3831), phospho-(Ser561)-PLD2 (3834), PLD1 (3832), PLD2 (13904), PKCα (2056), PKCγ (43806), and NLRP4 (12421) were purchased from Cell Signaling Technology. Antibodies specific for actin (I-19), ASC (N-15-R), IL-18 (H-173-Y), TRAF6 (H-274), caspase-1 p10 (M-20), Rab5 (D-11), Rab7 (H-50), LAMP1 (E-5), LAMP2 (H4B4), Tubulin (B-5-1-2), Calnexin (H-70), FACL4 (N-18), VDAC (B-6), His (His17), V5 (C-9), Flag (D-8), and GST (B-14) were purchased from Santa Cruz Biotechnology. AU1 (GTX23402) and PKCβI (A10-F) were purchased from GenenTex and Antibodies-online Inc., respectively. IL-1β (AF-401-NA) and NLRP3 (AG-20B-0014) were from R&D Systems and Adipogen, respectively.
THP-1, 293T, and BMDMs were treated as indicated and processed for analysis by Western blotting, co-immunoprecipitation, and GST pulldown as previously described [17,18].
Immunofluorescence analysis was performed as described previously [1]. The cells were fixed on coverslips with 4% (w/v) paraformaldehyde in PBS and then permeabilized for 10 min using 0.25% (v/v) Triton X-100 in PBS at 25°C. PLD1 or His was detected using a 1/100 dilution of the primary Ab for 1 h at 25°C. After washing, the appropriate fluorescently labeled secondary Abs were incubated for 1 h at 25°C. Slides were examined using laser-scanning confocal microscopy (model LSM 800; Zeiss). For colocalization analysis, the co-distribution of the PLD1 and GRA7 were quantified and validated statistically by Pearson coefficient, as specified by the ZEN 2009 software (version 5.5 SP1; Zeiss).
Peptide arrays were synthesized using the SPOTs synthesis method and spotted onto a derivatized cellulose membrane (Intavis) in the presence of [γ-32P]ATP and calcium as described previously [19,20]. Peptide spot phosphorylation was quantified using phosphoimaging.
For immunohistochemistry of tissue sections, mouse lungs were fixed in 10% formalin and embedded in paraffin. Paraffin sections (4 μm) were cut and stained with hematoxylin and eosin (H&E) [21].
PLD activity was measured using the Amplex Red PLD assay kit (Molecular Probes, A12219) according to the manufacturer’s protocol. The resulting fluorescence was detected using a fluorescence microplate reader at an excitation of 530 nm and an emission of 590 nm.
The recombinant GRA7 protein was described previously [17]. GRA7s from amino acid residues 26–80, 26-80S52A, 26-80S52D, 120-150S135A and 120-150S135D were cloned with an N-terminal 6xHis-tag into the pRSFDuet-1 Vector (Novagen) and induced, harvested, and purified from E. coli expression strain BL-21 DE-3 pLysS, as described previously [17,22], following standard protocols recommended by Novagen.
Supplemental experimental procedures and supplemental references.
All data were analyzed by Student’s t-test with Bonferroni adjustment or ANOVA for multiple comparisons, and are presented as mean ± SD. Grubbs’ test was used for evaluating the outliers. Differences were considered significant at p <0.05.
To establish a role for GRA7 in intracellular signaling pathways as a therapeutic strategy for infectious diseases in macrophages, we investigated whether GRA7 interacts with molecules involved in innate immunity. GRA7 complexes were subjected to co-immunoprecipitation (co-IP) of recombinant GRA7 protein with THP-1 lysates. The purified GRA7 complexes retrieved several endogenous proteins selectively, as identified by mass spectrometry analysis, including PLD1 (124 K), PKCα (76 K), ASC (21 K), and TRAF6 (60 K) (Fig 1A and S1 Fig). Endogenous co-IP showed that GRA7 interacted strongly, although temporarily (from 15 to 60 min), with endogenous PLD1, TRAF6, and ASC but not with PLD2, NLRP3, or NLRC4 after stimulation with rGRA7 in THP-1 cells, and vice versa (Fig 1B, S2A and S2B Fig). As previously reported [17], GRA7 associated with TRAF6, based on their molecular weights and co-IP (Fig 1A–1C, S1 and S2A Figs).
To determine the mechanism by which rGRA7 interact with the intracellular protein, THP-1 cells were preincubated with cytochalasin D, which inhibits actin polymerization. Pretreatment with cytochalasin D completely blocked the phagocytic activities of rGRA7 and it binding with intracellular proteins (S2C and S2D Fig).
Structurally, GRA7 contains a signal sequence, N-terminal domains (I–IV), a transmembrane, and a C-terminal domain (V) (Fig 1C) [17]. In 293T cells, detailed mapping using various mammalian glutathionine S-transferase (GST)-GRA7 fusions and truncated mutants of V5-ASC indicated that the N-terminal I-domain (aa26-80) of GRA7 exhibited only minimal binding affinity to ASC and ASC carrying the N-terminal PYD domain (aa1-91) bound GRA7 as strongly as ASC WT (Fig 1C and 1D). GST pull-down assays using truncated mutants of GST-GRA7 mammalian fusions and AU1-PLD1 showed that the N-terminal III-domain (aa120-150) of GRA7 is required for its interaction with PX (aa81-212) of PLD1 (Fig 1C and 1E and S2E Fig), indicating that the interactions of GRA7 with ASC, PLD1, and TRAF6 are genetically separable (Fig 1). These results show that GRA7 interacts with ASC and PLD1 through its N-terminal I- and III-domains in macrophages, respectively.
In addition to ASC and PLD1 binding, GRA7 also interacted with PKCα. Endogenous co-IP revealed a robust interaction between GRA7 and PKCα, but not PKCβI or PKCγ, after stimulation with rGRA7 in THP-1 cells, and vice versa (Fig 2A).
A large-scale proteomics analysis of the human kinome [23] and computational sequence analysis [24] predicted five PKC phosphorylation residues (S52LR, T121DR, S135FK, T204TR, S209PR) within the GRA7 N-terminal I, III domains, and C-terminal V-domain. To confirm that GRA7 was phosphorylated by PKCα, we used several strategies. First, we performed Phos-tag gel electrophoresis, which involves the use of a Phos-tag biomolecule that specifically binds phosphorylated proteins and retards their migration in the gel [25]. The results showed that GRA7 of wild-type, I-, and III-domains migrated more slowly and produced an ‘up-shifted’ band (as visualized by the Phos-tag labeling system for the analysis of phosphorylation, followed by SDS-PAGE) when co-expressed with PKCα, but when co-expressed with PKCβ, PKCδ, or PKCξ (Fig 2B and S3A Fig). Furthermore, we performed an in vitro phosphorylation assay using purified recombinant PKCα and a non-biased overlapping peptide array covering the entire GRA7 sequence [19,20]. From GRA7, two peptides (49PVDSLRPTNAGVDSK73 and 121TDRKVVPRKSEGKRS135) showed a phosphorylation signal >200 PSL/mm2 (Fig 2C). In contrast, none of the peptides spanning the C-terminal of GRA7 showed a significant phosphorylation signal. GRA7 has serine/threonine residues, and two peptides of GRA7 that were phosphorylated contained three potential phosphorylation sites (Fig 2C and 2D). Interestingly, the specific point mutation forms (IS52A and IIIS135A) of GRA7 markedly decreased phosphorylation in Phos-tag gel electrophoresis, whereas the mutant IIIT121A of GRA7 did not (Fig 2B). These results indicate that PKCα can specifically phosphorylate S52 and S135 residues of GRA7, demonstrating that GRA7 is a substrate of PKCα.
We next investigated whether phosphorylation of S52 and S135 of GRA7 was necessary for binding with ASC and PLD1, respectively. The point mutation (IS52A and IIIS135A) of GRA7 markedly abolished its interaction with ASC and PLD1, suggesting that this interaction is S52- and S135-phosphorylation dependent (Fig 2D and S3B Fig). Furthermore, because phosphomimetic residues (aspartic acid or glutamic acid) do not fully approximate the electronegativity produced by phosphorylation, we employed the strategy of mutating amino acids to overcome the charge differential [20,26]. The GST pull-down assay showed that phosphomimetic mutants of GRA7 strongly bound to ASC and PLD1 compared to GRA7 WT, indicating that mimicking constitutively phosphorylated GRA7 overrode the need for PKCα function in the innate immune pathway. Consistent with the findings shown in Fig 2A–2D, GRA7 interaction with ASC and PLD1 was markedly decreased in BMDMs from PKCα-/- mice, THP-1 from knock down with shRNA specific for PKCα (Fig 2E and S3C Fig), and BMDMs treated with a pharmacological inhibitor of PKCα upon rGRA7 stimulation (S3D Fig). Taken together, these data indicate that PKCα-mediated phosphorylation of GRA7 at Ser52 or Ser135 is essential for interactions between GRA7 and ASC or PLD1, respectively.
To examine the role of T. gondii GRA7-I in innate immune responses of macrophages, we generated bacterially purified His-tagged GRA7-I and its mutant proteins, as described previously [17,22]. The purified rGRA7-I (10 kDa) was confirmed through SDS-PAGE and immunoblotting analysis (Fig 3A). No significant difference compared to vector control observed for rGRA7-induced cytotoxicity in macrophages [17].
We showed previously that rGRA7-induced expression of pro-inflammatory cytokine genes and proteins including IL-1β, in macrophages [17] and NLRP3 inflammasomes involves a multimeric protein complex containing NLRP3 interacting the adaptor ASC and caspase-1 to induce the maturation of IL-1β and IL-18 [10,12]. To investigate the role of GRA7-I in the regulation of inflammasome activation, BMDMs from PKCα+/+ and PKCα-/- mice were stimulated with rGRA7-I and its mutant proteins. In response to rGRA7-I and -WT, PKCα-deficient BMDMs showed significantly attenuated IL-1β and IL-18 production than WT BMDMs, but the phosphomimetic mutant (IS52D)-induced markedly increased secretion of IL-1β and IL-18 (Fig 3B and S4A Fig). Consistent with these results, the caspase-1 activation and IL-1β and IL-18 maturation observed in response to rGRA7-I and -WT proteins were significantly decreased in PKCα-deficient BMDMs, and the constitutively active form (IS52D) of GRA7 ‘rescued’ the PKCα deficiency (Fig 3C). Notably, the PKCα non-phosphorylatable mutant (IS52A) and shRNA-mediated reduction of endogenous ASC expression led to significant attenuation of IL-1β and IL-18 production (Fig 3B and S4B Fig) in an ASC-binding dependent manner.
Next, we determined whether ASC is substantially oligomerized and if the intracellular formation of ASC specks is dependent on GRA7-I interaction. In correlation with secretion of active caspase-1 and IL-1β, PKCα-deficient BMDMs showed markedly attenuated ASC oligomerization and speck formation compared to WT BMDMs, but the phosphomimetic mutant (IS52D) markedly increased both (Fig 3D and 3E and S4C Fig). Further, the intracellular interaction of GRA7 and ASC was confirmed by their co-localization after stimulation with rGRA7. Subcellular fractionation and co-IP analysis showed that GRA7-I associated with ASC and PKCα in the mitochondrial fraction in PKCα+/+ BMDMs. Notably, these binding patterns were increased by the phosphomimetic mutant (IS52D) in BMDMs from PKCα+/+ and PKCα-/- mice (S4D Fig). These data suggest that GRA7-I acts as a positive regulator of ASC-dependent inflammasome activation via PKCα in mitochondria.
IL-1β and IL-18 are cytokines that play crucial roles in host defense and inflammation [11,12]. We first measured caspase-1 activation and maturation of IL-1β and IL-18 induced by rGRA7 and its mutants in MTB-infected macrophages. rGRA7-I treatment increased inflammasome activity in MTB-infected macrophages in a dose, ASC-binding and PKCα phosphorylation-dependent manner. Importantly, treatment with the phosphomimetic mutant (IS52D) of GRA7 markedly amplified inflammasome activity in MTB-infected conditions in BMDMs from PKCα+/+ and PKCα-/- mice (Fig 4A). The PKCα non-phosphorylatable mutant (IS52A) and the shRNA-mediated reduction of endogenous ASC expression led to significant attenuation of caspase-1 activation and maturation of IL-1β and IL-18 (Fig 4A and 4B) in an ASC-binding dependent manner.
IL-1β directly activates MTB–infected macrophages to restrict intracellular bacterial replication [10,27]. We examined whether rGRA7-induced antimicrobial activity was dependent on ASC-dependent inflammasome activation via PKCα in macrophages. The rGRA7-WT and -I-induced antimicrobial responses against MTB were significantly downregulated in BMDMs from PKCα-/- mice and cells transduced with shASC in a dose-dependent manner (Fig 4C and 4D). Notably, the PKCα non-phosphorylatable mutant (IS52A) of GRA7 did not induced antimicrobial responses of MTB, compared with the WT- and rGRA7-I treatment, in BMDMs from PKCα+/+ mice. The phosphomimetic mutant (IS52D) of GRA7 markedly increased antimicrobial responses to MTB in dose-dependent manner, indicating that the constitutively active form (IS52D) of GRA7 partially ‘rescued’ the PKCα deficiency. No significant difference was observed for MTB growth in 7H9 broth with or without rGRA7 (S5 Fig), indicating that ASC-dependent inflammasome-derived IL-1β controls the outcome of MTB infection and is functionally linked via PKCα in macrophages.
To examine the role of T. gondii GRA7-III in innate immune responses by macrophages, we generated bacterially purified His-tagged GRA7-III and its mutant proteins, as described previously [17,22]. The purified rGRA7-III (5 kDa) was confirmed through SDS-PAGE and immunoblotting analysis (Fig 5A).
As GRA7 associates with PLD1 but not PLD2 (Fig 1 and S1 Fig), we sought to determine whether PLD1 activation by GRA7-III was regulated by phosphorylation events in many cellular processes [15,28]. PLD1 activity is regulated by phosphorylation of Thr147 in the PX domain and Ser561 in the negative regulatory loop region of PLD1 by PKCα [15,28]. We first measured rGRA7-III-induced phosphorylation of PLD1 at Thr147 and Ser561 but not the PKCα non-phosphorylatable mutant (IIIS135A) of GRA7 in macrophages. Importantly, the phosphomimetic mutant (IIIS135D) of GRA7 treatment markedly amplified PLD1 activation in BMDMs from PKCα+/+ and PKCα-/- mice (Fig 5B). Consistent with these results, PLD activity was significantly decreased by the PKCα non-phosphorylatable mutant (IIIS135A) and increased by the phosphomimetic mutant (IIIS135D) of GRA7 in BMDMs from PKCα+/+ and PKCα-/- mice (Fig 5C). However, PLD activity was at the basal level in PLD1-/- macrophages with the phosphomimetic mutant, indicating that phosphorylated GRA7-III interacted with activated PLD1 by PKCα and stimulated its enzymatic activity through the phosphorylation of PLD1 Thr147 and Ser561. Further, the intracellular interaction of GRA7 and PLD1 was confirmed by their co-localization after stimulation with rGRA7, as documented by immunostaining and image overlay (Fig 5D and S6 Fig). GRA7-III localized with PLD1 and PKCα in the cytoplasm, appearing as small speckles and punctate spots. Notably, these co-localization patterns were increased by the phosphomimetic mutant (IS135D) in BMDMs from PKCα+/+ and PKCα-/- mice, but not PLD1-/- mice. These data suggest that GRA7-III acts as a positive regulator of PLD1 activation via PKCα in macrophages.
PLD1 activity regulates the actin cytoskeleton, vesicle trafficking for secretion and endocytosis, and receptor signaling. With the emerging concept of dynamic cycling of PLD1 inside the cell, some of the varying reports of localization may be due to differential rates and numbers of vesicles cycling in the cell lines used and thus differential regulation of PLD1 localization [14,15,28]. MTB preferentially infects alveolar macrophages, although mycobacteria allow only early endosome membrane fusion and induce phagosome arrest by selective Rab GTPase recruitment to avoid fusion with late endosomes and lysosomes [29,30]. To investigate the subcellular fractionation of PLD1, we treated MTB-infected BMDMs with rGRA7 and its mutants, and then examined the induction of protein levels of Rab5, Rab7, LAMP1, and LAMP2 regulators of phagosomal maturation in mycobacteria-containing phagosome fractions (phagosome and phago-lysosome) subsequently purified by sucrose-step-gradient-ultra-centrifugations. Interestingly, GRA7-induced MTB-containing phagosomes were recruited to late endosome and lysosome marker Rab7, LAMP1, and LAMP2, indicating that GRA7 facilitates mycobacterial phagosome-lysosome fusion in macrophages in a PKCα- and PLD1-dependent manner (Fig 6A). Furthermore, GRA7 associated with PLD1 in phagosomal fractions in a binding-dependent manner, and the phosphomimetic mutant (IS135D) of GRA7 markedly increased phagosomal trafficking and binding to PLD1, indicating that the constitutively active form (IS135D) of GRA7 ‘rescued’ PKCα deficiency (Fig 6B). Consistently, the viability and growth rate of intracellular MTB decreased following treatment with the phosphomimetic mutant (IS135D) in BMDMs from PKCα+/+ and PKCα-/- mice, but not PLD1-/- mice in dose-dependent manner (Fig 6C and 6D). These results collectively indicate that GRA7 facilitates phagosomal maturation through interactions with PLD1 and thereby, exerts marked control of bacterial killing activity against intracellular mycobacteria in a binding-dependent manner via PKCα.
Drawing on the observation that GRA7-I and -III associate with ASC and PLD1, respectively, and which contributes to antimicrobial defense against MTB in macrophages (S7A Fig), we next evaluated the in vivo efficacy of rGRA7 and its binding mutants in a mouse model of established tuberculosis [31]. MTB-infected mice were given rGRA7 and its mutants, starting three weeks after infection. Mice treated with rGRA7-WT alone, rGRA7-IWT+IIIWT, or rGRA7-IS53D+IIIS135D, but not the binding deficient mutant (rGRA7-IS53A+IIIS135A) had significantly reduced bacillary load in the lung, liver, and spleen, and reduced formation of lung granulomatous lesions in size and number of foci, compared with vector-treated mice (Fig 7A and 7B). Notably, the phosphomimetic mutant (IS53D+IIIS135D) of rGRA7 drastically reduced bacillary load, at a level similar to rGRA7-WT in PKCα+/+ and PKCα-/- mice.
We further investigated the effect of GRA7-III on in vivo host responses to MTB infection. As shown in Fig 7C and 7D, treatment with PLD1-binding domain (IIIWT) and the phosphomimetic mutant (IIIS135D) of rGRA7 markedly increased bacterial killing effects and number of granulomatous foci in PLD1+/+ mice, but not PLD1-/- mice. Treatment with the PLD1-binding deficient mutant (IIIS135A) of rGRA7 had no significant effect on bacterial killing or granulomatous lesions in either PLD1+/+ or PLD1-/- mice, indicated that the anti-mycobacterial effect of GRA7-III acts in a PLD1-binding dependent manner via PKCα in vivo. However, No significant difference was observed for inflammation score in lung (S7B and S7C Fig). The pharmacokinetics of therapeutic rGRA7 proteins were localized in alveolar macrophages was maintained for up to 7 days and gradually cleared until 25 days was studied by the fluorescence of the fluorophore Alexa 488-conjugated with the proteins (S8 Fig). These results unambiguously show that host defenses against MTB infection are substantially affected by GRA7-I and GRA7-III.
The central finding of this study is that the PKCα-mediated phosphorylation of T. gondii GRA7 is essential for the interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB (S9 Fig). Specifically, we found that (1) PKCα specific phosphorylation of Ser52 and Ser135 of GRA7 in vitro and in vivo was functionally required for ASC and PLD1 interactions with GRA7, respectively, (2) GRA7 was a novel substrate of PKCα, (3) the N-terminal of GRA7 (GRA7-I) was sufficient for interaction with the PYD domain of ASC in mitochondria, leading to ASC oligomerization and inflammasome activation, and subsequent antimicrobial activity, (4) GRA7-III interacted with the PX domain of PLD1 in cytosol, facilitating its enzyme activity, phago-lysosomal biogenesis, and subsequent antimicrobial activity, (5) GRA7-I and -III-dependent host protective effects against MTB infection were demonstrated in vivo, and (6) a phosphomimetic mutant that constitutively activated GRA7 ‘rescued’ PKCα deficiency both in vitro and in vivo. Collectively, these observations indicate that T. gondii GRA7-mediated HDTs leading to an antimicrobial response, as a novel host defense mechanism may provide a unique opportunity for urgently needed therapeutic intervention strategies for TB and other infectious diseases.
Although it is well established that dense granule protein GRA7 is important for immunodiagnosis of toxoplasmosis in patients [32,33], new candidates for further effective vaccine development against T. gondii infection is the need [17,34,35]. Recent reports showed that GRA7 is associated with T. gondii ROP5 was required for efficient phosphorylation of Irga6 and additional component of the ROP5/ROP18 kinase complex [22,36] and binding of ROP2 and ROP4 was shown [37] in T. gondii. However, the modulation of host innate immunity by GRA7 in the early phases of infection is critical for the establishment of both the initial invasion and the subsequent maintenance of latent infection is have not been fully elucidated. Growing evidence suggests that host-pathogen interactions have led to the coevolution of toxoplasmosis-causing T. gondii with its host [17,22,38]. GRA7 binds to poly(rC) binding protein 1/PCBP1 along with PCBP2 and hnRNPK, corresponding to the principal cellular poly(rC) binding proteins according to yeast two-hybrid analysis. PCBP1 plays a part in the formation of a sequence-specific α-globin mRNP complex that is associated with the stability of α-globin mRNA [38]. Additionally, GRA7 directly binds to the active dimer of Irga6 in a GTP-dependent manner. The binding of GRA7 to Irga6 led to enhanced polymerization, rapid turnover, and eventual disassembly, which contributed to acute virulence in the mouse [22]. We recently showed that the GRA7-V (aa 201–236) domain led to physical and functional associations with TRAF6. Furthermore, GRA7-V-induced Th1 immune responses and protective efficacy were crucial for T. gondii infection in vivo [17]. In this study, we showed that host cell ASC, PLD1, and PKCα bind to GRA7. The GRA7 protein interacted with a number of host cell proteins including enzymes, and a broad spectrum of structural and functional subcellular organellar proteins revealing a new facet of the role of GRA7 in the regulation of innate host immune responses.
Our results correlate with those of previous studies showing that T. gondii is a novel activator of NLRP1 and NLRP3 inflammasomes by activating caspase-1, an enzyme that mediates cleavage and release of the proinflammatory cytokines IL-1β and IL-18 in vitro and in vivo, thereby establishing a role for these sensors in host resistance to toxoplasmosis [39–41]. Furthermore, Millholland et al. showed that a Gα subunit (Gα)q-coupled host-signaling cascade is required for the egress of T. gondii. Gαq-coupled signaling results in PKC-mediated loss of the host cytoskeletal protein adducin and weakening of the cellular cytoskeleton. This cytoskeletal compromise induces catastrophic Ca2+ influx mediated by the mechanosensitive cation channel TRPC6, which activates host calpain that in turn proteolyzes the host cytoskeleton allowing parasite release [42]. T. gondii induces prostaglandin E2 biosynthesis in macrophages by regulating arachidonic acid production through a Ca2+-dependent pathway and induction of cyclooxygenase-2 expression by a PKC-dependent pathway [43,44]. Reinforcing the feasibility of targeting host proteins as an antiparasitic strategy, mammalian PKC inhibitors demonstrate activity in murine models of toxoplasmosis. In this study, we focused on the role of GRA7-I and -III-dependent innate immunity. Future studies will aim to clarify the precise molecular mechanisms of GRA7 and GRA7-II and -IV-related signaling pathways in inflammatory responses and host defense.
HDTs aim to modulate immune responses in the TB lung [45,46]. Neutralization of pro-inflammatory cytokines such as IL-6, TNF-α, VEGF, and IFN-α/β, as well as anti-inflammatory IL-4, during severe pulmonary disease may help reduce ongoing parenchymal damage in the MTB-infected lung [27,45–47]. Alternatively, suboptimal activation of anti-TB immune responses due to regulatory T cell activity can be reversed by the use of the anti-cancer drug cyclophosphamide. Drugs with anti-TB potential, such as metformin, imatinib, ibuprofen, zileuton, valproic acid, and vorinostat as well as nutraceuticals such as 1,25D, may not only abate the bacterial burden via host-dependent mechanisms, but also fine-tune the immune response to MTB. These drugs increase phagocytosis of extracellular bacteria, improve emergency myeloid response, and increase autophagic and apoptotic killing of bacteria, subsequently editing the T cell response in favor of the host. Immune checkpoint inhibition with blockade of the PD-1/PD-1 ligand 1, CTLA-4/cytotoxic T lymphocyte-associated antigen 4, LAG3/lymphocyte-activation gene 3, and TIM3/T cell immunoglobulin pathways may improve the quality of the cellular immune response to MTB epitopes, as seen in cancer immunotherapy [4,5,45–47]. Our results partially correlate with those of previous studies showing that host-directed immunotherapy with clinically approved drugs that augment prostaglandin E2 level prevents acute mortality of MTB-infected mice. Thus, IL-1 and type I IFNs represent two major counter-regulatory classes of inflammatory cytokines that control the outcome of MTB infection and are functionally linked via eicosanoids [27], and IL-1β either directly or via enhancement by 1,25D promotes antimicrobial immunity against MTB infection [10,11]. Greco et al. showed that PKC-mediated Ca2+ mobilization, PLD activity, and (auto)phagolysosome maturation represent effector processes induced by apoptotic body-like liposomes carrying PA that concur with the intracellular killing of MTB [14]. The MTB-containing phagosomes is involved in arresting phagosome maturation and inhibiting phagolysosome biogenesis [6,8,9], however, rGRA7-induced PKCα regulates phagocytosis, PLD-dependent the biogenesis of phagolysosomes (Rab5 conversion to Rab7) by promoting the interaction of phagosomes with late endosomes and lysosomes, and Rab7 regulated phagosomal acidification, which is important for the killing of MTB in human macrophages [7,16]. Our current observations based on the study of GRA7-III co-localized with PLD1 and PKCα in the cytoplasm (Fig 5D and S6 Fig) have the proposal the localized on phagolysosomes, appearing as speckles and punctate spots, because of an artifact of rGRA7 overexpression. Further studies are needed to localization organelle population.
The rGRA7 have a function of biologicals as potential therapeutics. However, these rGRA7 do not fulfil the requirements of direct anti-mycobacterial agent, which represent feasible alternatives to conventional chemotherapy to TB, due to the still unclear specificity and selectivity does not enable linking the effects of rGRA7s to host immune systems, as well as limitation of animal experimental model, unknown off-target effects, pharmacokinetics, safety data, and their potential feasibility for in vivo proof-of-concept studies. Further analyses are required to find out whether rGRA7s can be translated to the in vivo situation or be observed in the presence of physiological condition to patient with TB.
In conclusion, we provide evidence of a critical role of PKCα-mediated phosphorylation of T. gondii GRA7 in the interaction between GRA7 and ASC or PLD1, which contributes to antimicrobial defense against MTB (S9 Fig). GRA7-I and -III-dependent host protective effects worked against MTB infection in vivo, and a phosphomimetic mutant that constitutively activated GRA7 ‘rescued’ PKCα deficiency. These observations reveal a new role for GRA7 in regulating innate immune responses in host protective immunity. Our findings establish proof of concept for HDT strategies that manipulate host GRA7-mediated immune networks. Further studies are needed to develop more effective GRA7-based potential therapeutic targets and to understand how GRA7 regulates host defense strategies against TB and other infectious diseases.
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10.1371/journal.ppat.1005834 | Latency Entry of Herpes Simplex Virus 1 Is Determined by the Interaction of Its Genome with the Nuclear Environment | Herpes simplex virus 1 (HSV-1) establishes latency in trigeminal ganglia (TG) sensory neurons of infected individuals. The commitment of infected neurons toward the viral lytic or latent transcriptional program is likely to depend on both viral and cellular factors, and to differ among individual neurons. In this study, we used a mouse model of HSV-1 infection to investigate the relationship between viral genomes and the nuclear environment in terms of the establishment of latency. During acute infection, viral genomes show two major patterns: replication compartments or multiple spots distributed in the nucleoplasm (namely “multiple-acute”). Viral genomes in the “multiple-acute” pattern are systematically associated with the promyelocytic leukemia (PML) protein in structures designated viral DNA-containing PML nuclear bodies (vDCP-NBs). To investigate the viral and cellular features that favor the acquisition of the latency-associated viral genome patterns, we infected mouse primary TG neurons from wild type (wt) mice or knock-out mice for type 1 interferon (IFN) receptor with wt or a mutant HSV-1, which is unable to replicate due to the synthesis of a non-functional ICP4, the major virus transactivator. We found that the inability of the virus to initiate the lytic program combined to its inability to synthesize a functional ICP0, are the two viral features leading to the formation of vDCP-NBs. The formation of the “multiple-latency” pattern is favored by the type 1 IFN signaling pathway in the context of neurons infected by a virus able to replicate through the expression of a functional ICP4 but unable to express functional VP16 and ICP0. Analyses of TGs harvested from HSV-1 latently infected humans showed that viral genomes and PML occupy similar nuclear areas in infected neurons, eventually forming vDCP-NB-like structures. Overall our study designates PML protein and PML-NBs to be major cellular components involved in the control of HSV-1 latency, probably during the entire life of an individual.
| Establishment of latency of herpes simplex virus 1 (HSV-1) at the cellular level results from the combination of a series of complex molecular events involving cellular and viral-associated features. HSV-1 establishes latency in trigeminal ganglia (TG) sensory neurons. HSV-1 genomes remain as extrachromosomal DNA; their initial interaction with the nuclear architecture is likely to determine commitment toward the lytic or the latent transcriptional program. Among the major nuclear components that influence the infection process the promyelocytic leukemia (PML) nuclear bodies (NBs) play a major role as nuclear relays of the intrinsic antiviral response. In this study, using infected mice and cultured mouse primary TG neuron models, as well as human TGs, we investigated the interaction between HSV-1 genomes and the nuclear environment in individual neurons. We found that the inability of HSV-1 to initiate a lytic program at the initial stages of infection led to the formation of latency-associated viral DNA-containing PML-NBs (vDCP-NBs), or another pattern if the type 1 interferon pathway was activated prior to infection. vDCP-NB–like structures were also present in neurons of latently infected human TGs, designating PML-NBs as major nuclear components involved in the control of HSV-1 latency for the entire life of an individual.
| Herpes simplex virus 1 (HSV-1) is a neurotropic virus that establishes a life-long latent infection in the trigeminal ganglia (TG) (or Gasserian ganglia) of the infected human host. From time to time the virus asymptomatically or symptomatically reactivates from the latency stage producing epithelial lesions, most of the time on the face but also in the eye, inducing severe pathologies such as keratitis [1]. HSV-1 infection is also associated with pathologies of the central nervous system (CNS), such as encephalitis, especially after primary infection of newborn children with deficiencies in their innate immunity due to genetic alteration of two genes coding proteins involved in the intrinsic antiviral response [2]. In mouse models reproducing latent infection, HSV-1 has also been shown to lead to brain pathologies following reactivation through retrograde transport of the viral particles towards the CNS [3,4].
During latency the virus is in a transcriptionally restricted state. Of the about 80 genes transcribed during lytic infection, only a family of long non-coding RNAs is produced abundantly during latency. These latency associated transcripts (LATs) arise from the transcription of an 8.3 kb primary RNA that is processed in two major LATs of 1.5 kb and 2 kb and several microRNAs with cellular and viral targets [5–12]. The precise role of LATs is a matter of debate; however, a point of convergence among the many studies of LATs is that their initial production would favor the survival of the infected neurons and the coordination of the infectious process towards the latency transcriptional program and reactivation [13–17]. The lytic cycle is the alternative transcriptional program and is characterized by a temporarily regulated transcriptional program, which starts with the expression of immediate early (IE), then early (E), and finally late (L) genes (reviewed in [18,19]). Three proteins favor the onset of the lytic cycle, namely ICP4, ICP0, and VP16. ICP4 is an IE protein and the major viral transactivator that induces the transcription of viral genes of all kinetics [20]. ICP4 is essential for the virus to enter the replication stage and for productive infection [21]. ICP0 is also an IE protein. ICP0 is a RING-finger protein that possesses SUMO-targeted E3 ubiquitin ligase (STUbL) activity [22,23]. ICP0 induces the proteasomal degradation of many cellular proteins, including components of the promyelocytic leukemia nuclear bodies (PML-NBs or ND10), and centromeres [24–29]. As a consequence, ICP0 induces the destabilization of PML-NBs and centromere chromatin, which contributes to creation of a nuclear environment suitable for lytic infection [29–34]. VP16 (α-TIF) is a virion-associated multifunctional protein that transactivates the expression of the five viral IE genes through its interaction with two cellular proteins, HCF-1, a cell cycle regulator and Oct-1, a transcription factor [35–40].
In the viral particle, HSV-1 genome is a 150-kb double stranded naked linear DNA. Upon entry into the nucleus, the viral genome does not integrate in the host cell genome, instead remaining as an extrachromosomal entity. As such, it sustains a process of circularization and associates with chromatin remodeling factors to be chromatinized [41–43]. Chromatinization of the viral genome during latency plays a major regulatory role, and post-translational modifications of histones associated to key viral promoters determines the fate of the latency/reactivation process [39,42,43]. However, latent viral genomes are present in multiple copies within the nucleus of infected neurons in mouse models and human [44–46], and little is known about the molecular determinants that enable one neuron rather than another to sustain reactivation. In contrast, within an individually reactivating neuron, whether some viral genomes are more prone to lead to a complete lytic transcriptional program is unknown. The question is legitimate since in a recent study we reported that the viral genomes were non-randomly distributed in the nucleus of latently infected mouse TG neurons [47]. Latent viral genomes showed two major patterns namely “single” (a single viral genome spot detected in the nucleus) and “multiple-latency” (up to 20–30 spots detected) differentially distributed in the nucleus. The “single” pattern was exclusively associated with the promyelocytic leukemia nuclear bodies (PML-NBs), forming structures known as viral DNA-containing PML-NBs or vDCP-NBs. In the “multiple-latency” pattern some viral genomes co-localized with PML-NBs, while others co-localized with centromeres, or were distributed in the nucleoplasm distal from PML-NBs and centromeres. Importantly, the expression of LATs from individual genomes was observed only for viral genomes neither associated with PML-NBs nor with centromeres. These data highlighted the previously anticipated heterogeneity of HSV-1 latency at the molecular level, and confirmed the major role played by the nuclear environment in the maintenance of latency and probably the reactivation process.
In the present study, using a fluorescent in situ hybridization (FISH) approach combined to immunofluorescence (IF), we investigated the interaction between viral genomes and nuclear proteins within TG neurons of latently infected mice and during the whole process of latency establishment (from 4 to 28 days post infection, dpi). We detected viral genomes in neurons and satellite cells at 4 and 6 dpi, but only in neurons at > 6 dpi. In satellite cells, viral genomes showed only replication compartment (RC) patterns, whereas in neurons both RC and “multiple-acute” patterns were detected. From 4 to 14 dpi both patterns progressively disappeared, and transformed from14dpi onwards to the latency-associated “single” and “multiple-latency” patterns. Expression of two lytic program-associated proteins, ICP4 and ICP27, was detected only in cells with the RC pattern. LAT expression was detected in “multiple-latency” but not “multiple-acute” pattern-containing neurons. Interestingly, at 4 to 8 dpi, a subset of RC-containing neurons showed LAT expression. The “multiple-acute” viral genomes co-localized with PML, Daxx, ATRX, SUMO-1 and SUMO-2/3 proteins in structures similar to vDCP-NBs but with a difference in number per infected neurons (up to 10 vDCP-NBs/neuron at 6 dpi). To gain a better insight into the cellular and viral factors that could lead to the formation of vDCP-NBs or “multiple-latency” patterns, cultures of mouse primary TG neurons from wt mice or knock-out mice for the type I interferon (IFN) receptor were infected with wt or temperature-sensitive (ts) mutant viruses. The results indicates that defects in the onset of the lytic program due to the absence of functional ICP4, combined with the absence of functional ICP0 were the two viral features that led to the formation of vDCP-NBs. In contrast, the type I IFN signaling pathway was required for the formation of a “multiple-latency”-like pattern, demonstrating the essential role of innate immunity in the acquisition of latency-associated viral genome patterns. Finally, immuno-FISH analyses of human TG showed a close spatial distribution between latent HSV-1 genomes and PML protein in neurons, which suggests that, similar to the situation in the mouse model, HSV-1 latency in human is probably tightly linked to the activity of PML-NBs.
In a previous study, we described the distribution of viral genomes in the nucleus of latently infected mouse TG neurons (28 days post-infection, dpi). We found that two major patterns were detectable; i.e., “single” (hereafter S) and “multiple-latency” (hereafter ML). Neurons harboring those patterns differed in LATs expression, with S- and ML-containing neurons being negative and positive, respectively. These viral genome patterns are likely to be among the key features that determine which neurons sustain reactivation. It was thus essential to characterize the nuclear distribution of the viral genomes during the whole process of establishing latency. Mice were infected and TGs were harvested at fixed times (0, 4, 6, 8, 11, 14, 18, 22, and 28 dpi) after inoculation. At 6 dpi, two major viral genome patterns were observed, which we named “replication compartment” (RC) and “multiple-acute” (MA) (Fig 1Ai and 1Aii). Some RC-containing neurons clearly showed annexation of the interchromosomal space (Fig 1Ai), as described previously in cultured cells [48]. The MA was distinguishable from the ML pattern on the basis of the following structural and temporal observations: (i) viral genome spots in the MA pattern were often larger than those in the ML pattern; (ii) neurons with the MA pattern showed up to 10 spots per nucleus, whereas neurons with the ML pattern could contain up to 50 detectable viral genome spots; (iii) viral genomes in the MA pattern co-localized with PML (see Fig 2Avi in this study, and Fig. 5C in [47] for a more precise analysis), forming the previously described “viral DNA-containing PML-NBs (vDCP-NBs, up to 10 per infected neuron) [47], whereas in the ML pattern only one or two spots of viral genome co-localized with PML [47]; (iv) MA pattern is detectable during acute infection and mainly at 6 dpi, whereas ML pattern build up begins from 8 dpi and then persists until latency per se (28 dpi) (Fig 1B).
We analyzed the proportion of neurons with the various viral genome patterns during the whole establishment of latency period from 4 to 28 dpi. Data were collected from two to three mice and are presented as estimations within three percentage ranges (0 to 10%, between 10 and 25%, and > 25%). We could distinguish five major patterns: RC, MA, ML, 4-3-2 spots (4-3-2), and S-single+ (S+) (Fig 1B). RC were visible only during the early stages of acute infection (from 4 to 6–8 dpi), MA appeared from 6 dpi and persisted not beyond 11 dpi, and 4-3-2 was detectable only during a short period between 11 and 14 dpi; ML and S were the two major patterns observed during latency (28 dpi, see [47]) and started to build up from 6–8 dpi for the former and 8–11 dpi for the latter. Similar to S and MA, the 4-3-2 patterns corresponded to vDCP-NBs.
An intriguing observation was a change in the number of vDCP-NBs per infected neuron from 6 dpi (up to 10 per neuron) to 14–28 dpi (only 1 per neuron), with an intermediate situation consisting in the 4-3-2 pattern (11 to 14 dpi). These data suggested the possibility of fusion of the vDCP-NBs as the process of establishment of latency progressed. To investigate this possibility we used an in vitro model involving infection of human primary fibroblasts with a replication-defective HSV-1 mutant, in1374. This virus does not replicate at 38.5°C and forms vDCP-NBs in human primary fibroblasts and in neurons (see Figs S1A and 4B). Cells were harvested at 6 h to 7 dpi and processed for immuno-FISH to visualize viral genomes and PML. At 6 hpi the number of vDCP-NBs was 1 to 20/nucleus with an average of 5 and an average area of viral spots of 30.5 nm2 (minimum 1.8 nm2, maximum 120.6 nm2). The average number of vDCP-NBs/nucleus decreased over time to 1 to 6 vDCP-NBs/nucleus with an average of 2 at 7 dpi, and an average area of the viral spots of 116.9 nm2 (minimum 19.8 nm2, maximum 279 nm2) (S1B–S1D Fig). Given the experimental conditions used to perform cell infections (see Materials and Methods), it is unlikely that the decrease in viral spot number is due to loss of viral genomes over time due to multiple cell divisions. We counted the number of cells/well at the start and end of each experiment; the results indicated little cell division (S1E Fig). We then determined the total number of viral genomes by quantitative PCR at the start and end of each experiment to rule out an effect of viral genome degradation. Although, we observed a slight (but not significant) loss of viral genomes between 6 and 24 hpi (possibly due to limited cell division at the start of the experiment due to inertia of cells seeded 24 h before the infection), no significant loss of viral genomes was detected (S1F Fig, Student’s t-test). Data obtained in vitro on the amount and size of vDCP-NBs, combined with the in vivo observations, suggested that vDCP-NBs are dynamic structures probably capable of fusion as the establishment of latency progressed. Overall, the in vivo data anticipate changes in the viral genome patterns during the whole process of establishment of latency until the system achieves the physiological, cellular and molecular conditions that enable stable latency to be maintained.
We then determined the presence of HSV-1 genomes in cells other than neurons. TG from mice infected for 4 to 6 dpi were analyzed by immuno-FISH, and neurons were specifically labeled with an antibody recognizing neurofilaments. We detected viral genomes under the RC pattern in neurons and satellite cells (Fig 1Ci and 1Ciii), whereas MA was exclusively detected in neurons (Fig 1Cii). RC- but not MA (or ML, S2 Fig)-containing neurons were positive for ICP4 and ICP27, two of the major proteins of the lytic cycle (Fig 1Di–1Div). This confirmed that during acute infection, neurons positive and negative for productive infection-associated proteins harbored different HSV-1 genome patterns. Overall, these data emphasized the discrepancies in the viral genome nuclear distribution between infected neurons during the whole process of establishment of latency, and the link between these patterns and the capacity of the infected neuron to support a lytic cycle or latency. RC-containing neurons are likely to become productively infected, whereas MA-containing neurons are likely among those that will support latency.
One of the molecular characteristics of latency is a switch in the virus transcriptional program towards the quasi-exclusive expression of an abundant lncRNA known as LAT. We thus analyzed LAT expression in neurons containing the various viral genome patterns. We performed combined RNA/DNA FISH on TG harvested from acutely infected mice. RC-containing neurons were in their vast majority negative for LAT, with the exception of a few (< 1%) (Fig 1Ei and 1Eii). This was not unexpected, as previous studies reported that some latently infected neurons could experience an aborted lytic program [49,50]. MA and S-containing neurons were negative for LAT (Fig 1Eiii and 1Ev; lower neuron), with the exception of rare MA-containing neurons that contained discrete LAT signals juxtaposed to viral genomes (Fig 1Eiv). The only neurons frequently positive for LAT detection were those with the ML and S+ patterns (Fig 1Ev upper neuron and vi) as described previously [47]. Of note is that, with the exception of the few neurons positive for LAT at 4–8 dpi, LAT was readily detectable only from 10–14dpi.
We previously showed that the MA viral genome pattern co-localizes with, PML, Daxx and ATRX, three of the major constituents of the PML-NBs, thereby forming vDCP-NBs [47]. RC-containing neurons lacked the typical PML-NB staining for PML, Daxx and ATRX (Fig 2Ai, 2Aiii and 2Av), unlike those containing the MA pattern, which showed vDCP-NBs (Fig 2Aii, 2Aiv and 2Avi). We noticed an increase in both Daxx and ATRX signals in infected compared to uninfected TGs at 6 dpi (S3 Fig). We then performed WBs on whole TG extracts from uninfected and infected mice to analyze the signal of both proteins (Fig 2B). The model of virus inoculation used (upper left lip, see Materials and Methods) allows the mouse to be heavily infected in the left TG but not in the right TG. WBs were performed on the two TGs (left: infected, right: not infected) of two mice. Similar to what was previously observed for PML [47], we detected an increase in the amount of both proteins in the infected compared to the uninfected TGs. These data showed that during acute infection, and similarly to PML, the overall amount of Daxx and ATRX increased, probably as a result of the antiviral response mediated in the entire TG by type 1 interferons.
Small Ubiquitin MOdifier (SUMO) proteins are also major components of the PML-NBs and are involved in the intrinsic antiviral response against HSV-1 infection in cell cultures [23,51]. We analyzed the involvement of SUMOs in the control of virus infection in TG neurons, by co-detecting SUMOs and viral genomes during the whole process of establishment of latency. SUMO-1 and SUMO-2/3 were found in PML-NBs in uninfected neurons (Fig 2Ci and 2Di). In RC-containing neurons, similar to PML, Daxx, and ATRX, SUMO-1 and SUMO-2/3 did not show the punctate pattern characteristic of their presence in PML-NBs (Fig 2Cii and 2Dii). In MA-containing neurons, SUMO-1 was infrequently (< 20% of infected neurons) found co-localized with not more than one vDCP-NB (Fig 2Ciii), whereas SUMO-2/3 was frequently (> 50% of infected neurons) co-localized with all the vDCP-NBs (Fig 2Diii). Co-localization of SUMO-2/3 with vDCP-NBs persisted until 28 dpi, and SUMO-1 was found to be more systematically co-localized with vDCP-NBs in the S pattern from 14 dpi onwards (Fig 2Civ and 2Div). These data suggest the involvement of SUMO proteins in control of the incoming viral genomes, in accordance with their previously described intrinsic antiviral activity. However, in neurons, the activity and nuclear dynamics of SUMO-1 and SUMO-2/3 could differ with regard to their association with vDCP-NBs.
ICP0 is involved in the proteasomal degradation of several components of the PML-NBs, inducing the destabilization of these nuclear bodies. RC-containing neurons were consistently negative for the presence of PML-NBs. In these neurons, could ICP0 have induced destabilization of the PML-NBs, favoring the lytic cycle? Although we possess several ICP0 antibodies that have been used by us and others in immunocytochemistry, we have not detected an ICP0 signal within infected TG neurons by either IF or immuno-DNA FISH. Indeed, ICP0 in infected cell cultures shows a nuclear punctate pattern that is difficult to distinguish from the nonspecific signal in neurons from TG samples. An indirect way to analyze ICP0 activity in infected nuclei is to detect the disappearance of its cellular substrates. In addition to PML, the centromeric protein A (CENP-A) is another ICP0 substrate, and ICP0 efficiently induces proteasomal degradation of centromeric proteins in mouse cells [28]. TG samples from mice infected for 6 days were processed by immuno-DNA FISH to determine the fate of PML or CENP-A signals in infected neurons. All RC-containing neurons were negative for PML-NBs but positive for the CENP-A signal (Fig 2Fi and 2Fii). Several hypotheses arose from these data: (i) ICP0 is less efficient in inducing the degradation of centromeric proteins than PML in mouse neurons, (ii) ICP0 is synthesized in, but does not reach the nucleus of, infected neurons, (iii) ICP0 is not efficiently synthesized in neurons, (iv) some neurons lack PML-NBs and are more susceptible to lytic infection even in the absence of functional ICP0. Concerning the latter, IF analyses of TG samples from uninfected mice showed that not all neurons contained PML-NBs (Fig 2E). Moreover, a previous study suggested that ICP0 remained in the cytoplasm of HSV-1-infected TG neurons in culture [52]. These data do not exactly fit with our results (see S4Ci Fig), possibly due to the heterogeneity in the type of neurons found in a TG, combined with the use of different methods of purification of neurons in the two studies. Finally, a recent study demonstrated that a neuron-specific microRNA, miR-138, targets ICP0 mRNA, preventing ICP0 synthesis at least in cultured cells [53]. Therefore, our data, together with those of other laboratories suggested that during acute infection the interplay between cellular (including PML-NB-associated proteins) and viral factors is likely to determine the extent of virally-induced modification of the nuclear environment. Depending on the degree of modification, the lytic or latent transcriptional program will then be favored, leading to acquisition of the corresponding viral genome pattern.
To gain further insight into the cellular and molecular features that favor acquisition of the various viral genome patterns, especially vDCP-NBs and ML, which are associated with the latency process, we established mouse primary TG neuron cultures (S4A Fig). We first characterized the PML-NBs in the neurons and found that Daxx, ATRX, SUMOs and PML proteins were detectable in the nuclear bodies in most neurons (S4Bi–S4Biv Fig). We then infected the neuron cultures with HSV-1wt 24H to detect the synthesis of viral IE proteins involved in the onset of lytic infection, such as ICP0, ICP4 and ICP27 (S4Ci–S4Civ Fig). All proteins could be found in the nucleus of the infected neurons. We then performed immuno-FISH on similarly infected neurons to detect simultaneously viral genomes and neuronal or viral markers. As expected, all infected neurons showed RC, and expressed lytic viral proteins as exemplified by specific detection of ICP4, ICP27, or viral proteins (Fig 3Ai–3Aiv). ICP0 could not be detected in these experiments because none of the antibody that we usually use was suitable for our immuno-FISH protocol. We then analyzed the fate of PML-NBs and associated proteins. The majority (about 88%) of infected neurons did not show PML-NBs although some (about 12%) contained PML dots characteristic of PML-NBs (Fig 3D). About half of these infected neurons (Fig 3C) showed co-localization of PML-NB-associated proteins with RC (Fig 3B). This was not unexpected, as previous studies have described the presence of PML in RC of HSV-1-infected cells [54,55]. Because ICP0 is directly involved in the destabilization of PML-NBs in infected non-neuronal cells, we performed infections with a deletion virus unable to express ICP0. Neurons were infected for 24 h with the dl1403 mutant virus and PML-NBs were analyzed. In infected neurons, PML was found both in dots and co-localized with the RC (S5Ai Fig). Dots of PML were frequently located at the edge of and all around the RC (S5Aii Fig). SUMO proteins remained co-localized with PML in the PML dots (S5Avi Fig); however Daxx and ATRX were absent from the remaining PML dots (S5Aiii to S5Av Fig). In infected mice, a virus with deletion of the thymidine kinase (TK) gene is able to replicate more or less efficiently at the site of inoculation but its replication in TG neurons is severely impaired [56,57]. To determine if the absence of TK alone could explain the formation of vDCP-NBs in infected neurons, we infected neurons with a TK mutant HSV-1 virus 17/tBTK- and analyzed the viral genome patterns at 48 hpi. Neurons with RC and without PML-NBs were exclusively observed, likely as a result of ICP0 expression (S5Bi to S5Biv Fig). Taken together, these data showed that, under our experimental conditions, which were compatible with the detection of viral genomes by FISH, if neurons are infected through the cell body and not through the axon as in natural infections, the balance between pro- and antiviral features favors the onset of lytic infection and formation of RC.
Studies performed in infected mice using HSV-1 or pseudorabies virus (PRV), another neurotropic herpesvirus infecting pigs, showed that VP16, which is present in the virion tegument, is inefficiently transported through the axon to the cell body of infected neurons [58,59]. Another study described the distinct regulation of the VP16 promoter (normally a late promoter) in TG neurons, which could be activated early after infection by neuron-specific factors [60]. The stochastic activation of the VP16 promoter in neurons would thus enable the early synthesis of VP16 during acute infection as well as reactivation from latency. This would favor the entry of the virus to the lytic cycle through the activation of IE genes, including ICP4 and ICP0. In that context, the efficiency of the PML-NB–associated intrinsic antiviral response is likely to be inversely proportional to the synthesis of VP16, ICP4 and ICP0, and influence the acquisition of the latency viral genome pattern. To test this hypothesis, we infected neurons with the temperature-sensitive virus, in1374, which inefficiently expressed functional ICP4 at the restrictive temperature of 38.5°C, and lacks functional VP16 and ICP0 due to an insertion of 12 nt in the transactivation domain of the former and to deletion of part of the RING finger of the latter [61–63]. Neurons were first infected at the permissive temperature of 32°C. As expected, the virus showed the same features as HSV-1wt in terms of the formation of RC co-localized with ICP4 (Fig 4Ai and 4Aii), and ICP8, a subunit of the viral DNA replication complex (Fig 4Aiii). PML, Daxx, ATRX, and SUMO signals were similar to those obtained in neurons infected with the ICP0 mutant dl1403, with PML aggregates surrounding the RC and co-localizing with SUMOs, and Daxx and ATRX disappearing from these structures (Figs 4Aiv–4Avii and S3A). We then infected neurons at the restrictive temperature to inactivate ICP4. Under these conditions, viral genomes showed a different pattern and were detected as spots in the nucleus of infected neurons (Fig 4B). The co-detection of PML, Daxx, ATRX and SUMOs showed perfect co-localization of all proteins with the viral genomes, resulting in formation of structures reminiscent of vDCP-NBs (Fig 4Bi–4Biv). Infection with in1330 virus, which contains and expresses a functional VP16 (and hence expresses functional IE proteins ICP27, 22 and 47), also led to the formation of vDCP-NBs at 38.5°C (Fig 4Ci–4Civ). Infections with the tsK virus, which is the parental virus that expresses tsICP4 at 38.5°C and contains functional VP16 and ICP0, exhibited mainly RC (S6 Fig). This is possibly explained by the expression of a fraction of functional ICP4 at 38.5°C that under our experimental conditions of infection of neurons is sufficient to activate the lytic transcriptional program.
If the formation of vDCP-NBs is the default viral genome pattern for a virus unable to replicate in neurons, than infection of mice with a virus deficient in replication in neurons should lead to the exclusive formation of vDCP-NBs. Mice were infected with the TKDM21 mutant virus, which can replicate at the inoculation site, but is unable to replicate in neurons due to a deletion in the TK gene [56,57,64] (S7A and S7B Fig). Mice at 6 dpi were sacrificed and immuno-FISH was performed on TG samples to detect HSV-1 genomes and PML. Complete TGs of four mice were analyzed, and few neurons (48 in total) were detected with a positive signal for the viral genome. The small number of positive neurons may be due to weak replication of the virus in the lip (S7A Fig). This small number of positive neurons hampers precise analysis. However, none of the thousands of neurons analyzed in the four mice showed an RC pattern. All positive neurons showed vDCP-NBs comprising 35 (73%) with a “single” (one spot) pattern, 8 (17%) with two spots, 3 with three spots (6%), and 2 with four spots (4%) (S7C Fig). These data, although obtained from few detectable infected neurons, tend to confirm those obtained in the cultured neurons and suggest that the inability of a virus to start replication in neurons will automatically lead to the formation of vDCP-NBs probably due to the additional absence of ICP0 synthesis due to cellular miR control [53]. Overall, these data demonstrated that the formation of vDCP-NBs resulted from both the failure of the virus to start the lytic program due to the inefficient synthesis of ICP4 (and thus to undergo replication), and the absence of functional ICP0. Although the presence of functional VP16 per se did not directly impact the formation of vDCP-NBs, its stochastic synthesis in neurons during acute infection likely increases the probability of ICP4 synthesis, and thus the start of the lytic program and the formation of RC.
Our previous data showed that latently infected neurons containing vDCP-NBs were deficient in the expression of the 2 kb LAT, and that viral genomes trapped in the vDCP-NBs were unable to synthesize the primary 8.3 kb LAT transcript [47]. These data raise the question of whether the genomes in the vDCP-NBs are permanently silenced, or if they retain the capacity to resume transcription following exposure to stress that could affect the vDCP-NBs. In1374 contains a HCMV-lacZ cassette whose transcription is shut down upon infection of human fibroblasts at 38.5°C and resumes upon treatment with the histone deacetylase inhibitor, trichostatin A (TSA) [65,66]. Similarly, mouse primary TG neurons quiescently infected with a non-replicative HSV-1 virus containing a pCMV-GFP transgene were shown to resume GFP expression upon the addition of TSA [67]. Neurons infected with in1374 for 3 days were treated or not with 2 μM TSA for 24 h at 32°C and RT-qPCR was first performed to quantify LacZ re-expression under these experimental conditions (Fig 4D). Dual RNA-DNA FISH was then performed to detect LacZ transcripts and HSV-1 genomes. Without TSA, viral genomes in the vDCP-NBs showed no sign of LacZ transcription (Fig 4Ei). Addition of TSA led to the observation of three further patterns: (i) LacZ-negative RC-like structures in close proximity to PML-NBs (Fig 4Eii, pattern 1); (ii) RC-like structures juxtaposed with LacZ and PML signals (Fig 4Eiii, pattern 2); and (iii) vDCP-NBs containing LacZ signal (Fig 4Eiv, pattern 3). To determine whether PML-NBs juxtaposed to stress-induced RC are missing Daxx and/or ATRX, similar to the HSV-1 dl1403 (ICP0-)-infected cultured neurons showing RC (see S5A Fig), we analyzed Daxx and ATRX behavior in RC-containing neurons. First, we confirmed that TSA treatment alone did not affect the localization of Daxx and ATRX at the PML-NBs in uninfected neurons (S8A Fig). Unlike PML, the majority of the stress-induced RCs did not juxtapose with Daxx or ATRX in spots, which suggested that the two proteins leave the RC-associated PML-NBs (S8B and S8C Fig). However, some neurons exhibited stress-induced RC in the vicinity of Daxx or ATRX spots, albeit with diffuse Daxx and ATRX signals throughout the nucleoplasm. Moreover, some neurons showed a Daxx signal co-localized with RCs. The two latter most likely reflect transitory situations before the complete disappearance of Daxx and ATRX from RC-associated PML-NBs. These data suggest that upon transcriptional reactivation leading to HSV-1 replication, PML remains in spots juxtaposed to the RC whereas Daxx and ATRX are more labile and tend to leave the RC-associated PML-NB. Daxx and ATRX behavior is in accordance with their previously described mutual contribution to intrinsic antiviral resistance to HSV-1 infection [34].
PML-associated pattern 3 (Fig 4Eiv) was reminiscent of the MA genome-associated discrete LAT signal reported previously (see Fig 1Eiv). TG neurons from 6 to 8 days HSV-1wt–infected mice were analyzed for the expression of LAT together with the detection of viral genomes and PML protein. Rare neurons were indeed positive for a discrete LAT signal associated with vDCP-NBs (Fig 4F). Together, these data showed that: (i) a virus contained in a vDCP-NB is unlikely to be definitively silenced provided that a stimulus sufficient to modify the transcriptional equilibrium and/or the PML-NBs dynamic is applied to the neuron; (ii) at least during the first stages of establishment of latency in mice (6–8 dpi), viruses associated with vDCP-NBs could show some transcription of LAT before being completely silenced during latency per se (28–30 dpi). A previous study performed in cultured cells showed that viral genomes juxtaposed to the PML-NBs were more prone to initiate replication [68]. Other studies suggested that HSV-1 transcription is more likely to occur in the vicinity of PML-NBs [69,70]. Our data, together with those of other groups, show that PML-NBs could have a dual role in viral infection; on the one hand, the capacity to silence incoming viral DNA, and on the other hand, and following appropriate stimuli, to serve as a nuclear platform for virus reactivation, although during this event the protein content of PML-NBs is likely modified in terms of Daxx and ATRX leaving the nuclear body.
Type 1 IFNs are produced very early upon alphaherpesvirus infection, which limits virus replication and spread both in vitro and in vivo [71–74]. IFNα was previously shown to induce a quiescent state of HSV-1 that resembles latency in cultured primary porcine TG neurons [75]. Given the changes in the viral genome patterns observed during acute infection (see Fig 1), we anticipated that type 1 IFN could take part in those changes by preventing the onset of lytic infection even in the presence of functional ICP4, provided that functional VP16 and ICP0 are missing. We infected neurons with in1374 at 32°C in the presence or absence of IFNα. Without IFNα, only RC-containing neurons were observed (Fig 5Ai). Treatment of neurons with IFNα decreased the number of neurons showing the RC pattern, and induced the formation of multiple spots in the nucleus, some of which co-localized with PML-NBs or centromeres, a pattern reminiscent of the ML pattern in vivo (Fig 5Aii and 5B) [47]. Quantification of the effect of IFNα on pattern acquisition (Fig 5C) showed that without IFNα treatment infection with in1374 led to the formation of RC in ~91 (±3.5)% of neurons. IFNα treatment favored the ML-like pattern, with 82 (±1.7)% of neurons showing this pattern. Infection with in1330 or tsK induced the formation of RC in almost all infected neurons (> 98%), irrespective of IFNα addition, consistent with the essential contribution of VP16 and ICP0 to the onset of the lytic cycle. Type I IFNs share the same receptor, type I interferon receptor (IFNAR) [76]. To gain insight into the impact of the IFN signaling pathway on viral genome pattern acquisition, we infected TG neurons harvested from IFNAR KO mice with in1374 at 32°C. Irrespective of treatment with IFNα, nearly all infected neurons (> 98%, two experiments) showed the formation of RC (Fig 5D), whereas infection of wt C57BL/6 neurons yielded results similar to those of OF1-infected neurons. These data confirmed that the type I IFN signaling pathway plays a major role in viral genome pattern acquisition, and favors formation of the ML-like pattern provided that functional VP16 and ICP0 are not produced in infected neurons.
The formation of vDCP-NBs is an important hallmark of HSV-1 latency in mice and likely highlights a close inter-connection at the molecular level between the intrinsic antiviral activity of PML-NBs and the latent viral genomes. To investigate this association in the context of HSV-1 latency in human, we performed analyses of human TGs. The left and right TGs of five patients (Fig 6A) were collected and then processed to analyze the presence of HSV-1 genomes by PCR and possible reactivation by RT-PCR of viral transcripts, and also by immuno-FISH. PCR data showed that all TGs were positive for viral genomes (Fig 6B), and one patient was reactivating HSV-1 at the time of collection (Fig 6C). To avoid any misinterpretation due to reactivation, we further analyzed in priority the TGs that did not show any signs of reactivation at the molecular level. We performed IF using several neuronal markers to correlate the structural analysis of the cells with biochemical markers specific to neurons. Neurons appeared as large cells, usually containing a bright cluster of cytoplasmic autofluorescence due to lipofuscin, and with markedly fainter nuclear DAPI staining than that of satellite cells (Fig 6Di–6Diii). PML-NBs appeared in neurons as large nucleoplasmic aggregates of PML protein (Fig 6E). Detection of the 2 kb LAT showed bright staining throughout the nucleoplasm, irrespective of the probe and stain used (Fig 6F). Samples from TGs were subjected to simultaneous detection of LAT, HSV-1 genomes and PML. Several observations were made in neurons positive for HSV-1 DNA (Fig 6Gi–6Giii): (i) infected neurons unequivocally showed systematic disappearance of the large aggregates of PML-NBs observed in uninfected neurons; (ii) observations at high magnification showed that PML staining was reorganized under a single clustered nuclear signal, most of the time forming a large “doughnut-shaped” structure; (iii) viral genomes were detected as multiple spots clustered in a discrete region of the nucleoplasm; (iv) the clusters of viral genomes constantly co-localized with the PML clusters, and when PML was under the “doughnut-shaped” structure, HSV-1 genomes were localized inside those structures; and (v) all neurons positive for HSV-1 genomes were also positive for LAT. Overall, these data confirmed the close proximity of viral genomes and PML during latency in human TG neurons, and the occasional presence of vDCP-NB-like structures.
The interaction of chromosomal loci with their nuclear environment affects the transcriptional activity of particular genes [77]. Nuclear architecture is thus likely to greatly influence the fate of infection with nuclear-replicating viruses, such as herpesviruses. In this study, we demonstrated that the interaction of latent HSV-1 genomes with the nuclear environment is impacted by the activity of cellular components, such as PML-NBs and type I IFNs, but also by viral features such as the ability of the virus to enter the lytic cycle and to express ICP0. During establishment of HSV-1 latency in the mouse model, viral genomes adopt several nuclear patterns before reaching the two main patterns that are found during stable latency. The nuclear distribution of viral genomes changes greatly until 14 dpi, and stabilizes thereafter. This is in agreement with previous reports of extinction of lytic gene expression, acquisition of chromatin markers, and expression of LAT, which are major molecular features of HSV-1 latency and usually evident by 14 dpi [78–80]. This indicates that the battle between the virus and the infected neurons involves multiple changes in the interaction between the viral genomes and the nuclear environment until reaching a stable situation suitable for both the virus and the host cell.
The RC pattern was found in neurons and non-neuronal cells, whereas vDCP-NBs were found only in neurons. The expression of lytic proteins in infected neurons during acute infection was detectable only in RC-containing neurons. These data, combined with the observation that vDCP-NBs are nuclear structures found during the whole process of establishment of latency until latency per se, suggest that vDCP-NBs play a major role in pushing the virus towards latency. To that extent, the presence in the vDCP-NBs of SUMO proteins, which were shown in cultured cells to participate in intrinsic antiviral resistance to HSV-1 infection [23], strengthens the idea that PML-NBs in general and vDCP-NBs in particular are nuclear relays of the cellular intrinsic antiviral response to HSV-1.
Studies performed in vivo and in vitro showed that VP16 expression likely plays a major role in the onset of the lytic program in neurons [60], and a neuron-specific microRNA, miR-138, targets ICP0 mRNA to prevent its synthesis [53]. Therefore, during the initial infection of neurons, the concomitant absence (or reduced amount below a threshold) of VP16 and ICP0 probably prevents the virus from entering the lytic cycle. Cultured neurons infected with a virus that is unable to express a functional ICP4, and which concomitantly does not express functional ICP0, mimicked the infectious process that in vivo leads to the formation of vDCP-NBs. This demonstrates that as long as the virus is unable to activate the lytic program due to the absence of functional ICP4 (probably linked to the absence of VP16 in vivo), then the only other viral feature required for the formation of vDCP-NBs is the absence of functional ICP0.
Besides the formation of vDCP-NBs, another hallmark of the nuclear distribution of HSV-1 latent genomes is the ML pattern. The ML pattern-containing neurons accumulated in in vivo-infected TGs with a slight delay compared to the vDCP-NBs. In cultured neurons, type I IFNα induced the formation of an ML-like pattern in a non-functional VP16 and ICP0 context. The type I IFN pathway was essential for acquisition of the ML-like pattern because infection of cultured TG neurons prepared from IFNAR KO mice did not result in formation of the ML-like pattern in the presence of IFNα. Two lines of evidences suggest that the ML-like pattern mimics that in vivo; i.e., (i) a subset of viral genomes in the ML-like pattern co-localized with PML, and (ii) some viral genomes co-localized with centromeres. Previous studies showed that the tegument protein VP16 was inefficiently transported from the axon termini to the cell body, which precluded the initiation of lytic infection in neurons [58,59]. However, a subset of neurons supports lytic infection during the early stages of acute infection of mouse TG. This could be due to a change in the kinetics of VP16 expression in neurons whose promoter could acquire IE-like activity in some neurons due to its activation by an as-yet-unknown neuron-specific feature [60]. The absence of VP16 axonal transport, the stochastic regulation of its promoter, and the absence of ICP0 synthesis due to miR-138 activity are likely to lead to a nuclear environment that would favor the establishment of latency through the formation of vDCP-NBs and/or ML patterns, depending on the type I IFN signaling context of the infected neuron. Type I IFNs (IFNα and ß) are major actors in the interplay between the virus, the cell, and the immune response [81]. The IFN response was shown to build up within the infected TG during establishment of latency in mice by both autocrine and paracrine signaling pathways [72]. Previous reports suggested that TGs of infected mice could sustain several waves of virus infection from the site of inoculation at the periphery by means of a positive inter-site “feedback loop” [82]. This suggests that the immune environment of the TG in general, and the neurons in particular, is likely to be different between the first wave of infection and those following. It is thus likely that a virus entering a neuron from a second wave of infection will face a different antiviral environment more prepared to face the virus infection, especially through enhancement of IFN-associated innate immunity. This will inevitably lead to some molecular changes in the nucleus, and the viral genome ML pattern acquisition might reflect these nuclear changes. It is worth mentioning that two of the major components of the PML-NBs, PML and Sp100, are encoded by interferon-stimulated genes (ISGs), which leads to an overall increase in the protein levels following IFN stimulation. Our previous and present data showed that PML, Daxx and ATRX levels increased in the TG during acute infection [47]. Given that PML in the PML-NBs represents only 10% of the total nuclear PML [83], the increase in PML by type I IFN stimulation is likely to increase the “free” pool of PML in the nucleoplasm, promoting its repressor-associated activity. Moreover, the overall increase in Daxx and ATRX, two chaperones of histone H3.3 [84], might result in changes in the chromatinization of the viral genomes, which could be linked to changes in their nuclear distribution. IFNα has also been shown to increase HDACs activity, which favors the acquisition of repressive chromatin marks [85]. It is interesting to note that facultative heterochromatin repressive marks associated with latent viral genomes begin to accumulate on the viral genomes by 7 dpi during acute infection in a mouse ocular model of infection [80]. This roughly corresponds to the time at which ML pattern-containing neurons start to appear in our lip model (Fig 1B). Therefore, ML pattern formation might be tightly linked to the acquisition of specific chromatin-associated marks. We are currently investigating this aspect of viral genome dynamics.
HSV-1 latency, rather than being an inert situation, is a dynamic equilibrium likely generated by multiple attempts by the virus to complete full reactivation, which is repressed most of the time by tight control by the innate and adaptive immune responses [86–89]. Humans latently infected by HSV-1 undergo multiple spontaneous symptomatic, asymptomatic, and aborted reactivations that could be restricted at different stages of the reactivation process. Despite the differences in the physiology and history of infection between a several-year latently infected human and few-week infected mice, we observed a close spatial overlap between viral genome and PML protein signals in HSV-1–infected human TG neurons, similar to what is observed in mouse TG neurons. Viral genomes were detected as multiple spots grouped in a restricted area of the nucleus. PML-NBs systematically disappeared in infected neurons and PML protein accumulated in the same areas as the viral genomes, eventually forming structures reminiscent of vDCP-NBs. These data show that HSV-1 latency/reactivation in human TG neurons is likely to be closely associated with PML protein and PML-NBs activity.
Overall, this study describes the nuclear architecture and nuclear distribution of viral genomes as major determinants of HSV-1 latency. It confirms the close interrelation between PML-NBs and HSV-1 genomes in the establishment of latency through the formation of vDCP-NBs. Finally, it confirms that the spatial organization of HSV-1 genomes and PML is conserved in latently infected neurons in human TG, which indicates PML-NBs to be major HSV-1 genome interactants during latency and probably reactivation.
All procedures involving experimental animals conformed to the ethical standards of the Association for Research in Vision and Ophthalmology (ARVO) Statement for the use of animals in research, and were approved by the local Ethics Committee of the Institute for Integrative Biology of the Cell (I2BC) and the Ethics Committee for Animal Experimentation (CEEA) 59 (Paris I) under the number 2012–0047 and in accordance with European Community Council Directive 86/609/EEC. All animals received unlimited access to food and water.
Human biological samples and associated data were obtained from Cardiobiotec Biobank (CRB-HCL Hospices Civils de Lyon BB-0033-00046). All tissue samples were obtained according to French ethics regulations (specifically, informed consent was obtained from patients for all samples). Cardiobiotec is authorized by the French Ministry of Social Affairs and Health (DC2011-1437), with transfer authorization AC 2013–1867.
The HSV-1 SC16 wild-type (wt) and thymidine kinase (TK) mutant (TKDM21) strains were used for mouse infections and have been characterized previously [64,90]. HSV-1 17 syn + (17+) wt and mutant strains were used for infections of primary mouse TG neuron cultures. HSV-1 mutants 17/tBTK- and dl1403 are deleted in TK and ICP0 genes, respectively [82,91]. The HSV-1 mutant tsK expresses a temperature-sensitive variant of the major viral transcriptional activator ICP4 [92,93]. In1374 expresses a temperature-sensitive variant of the major viral transcriptional activator ICP4 [61], and is derived from in1312, a virus derived from the VP16 insertion mutant in1814 [62], which also carries a deletion/frameshift mutation in the ICP0 open reading frame [63] and contains an HCMV-lacZ reporter cassette inserted into the UL43 gene of in1312 [66]. Virus in1330 is a VP16 rescuant of in1312 [65]. All of these viruses have been used and described previously [65]. All HSV-1 strains were grown in BHK-21 cells (ATCC, CCL-10) and titrated in U2OS cells (ATCC, HTB-96). TsK was grown and titrated at 31°C. Viruses derived from in1312 were grown and titrated at 31°C in the presence of 3 mM hexamethylene bisacetamide [94].
Mice were inoculated and TG processed as described previously [47,90,95,96]. Briefly, 6-week-old inbred female BALB/c mice (Janvier Labs) were inoculated with 106 PFU of virus into the upper-left lip. Mice were sacrificed at the indicated times from 0 to 28 dpi. Frozen sections of mouse TG were prepared as described previously [47,96].
Primary mouse TG neuron cultures were established from BALB/c, OF1, or C57BL/6 wt (Janvier Labs) or IFNAR KO (The Jackson Laboratory) mice, following a procedure described previously [97]. Briefly, 6–8-week-old mice were sacrificed before TG removal. TG were incubated at 37°C for 20 min in papain (25 mg) (Worthington) reconstituted with 5 mL Neurobasal A medium (Invitrogen) and for 20 min in Hank’s balanced salt solution (HBSS) containing dispase (4.67 mg/mL) and collagenase (4 mg/mL) (Sigma) on a rotator, and mechanically dissociated. The cell suspension was layered twice on a five-step OptiPrep (Sigma) gradient, followed by centrifugation for 20 min at 800 g. The lower ends of the centrifuged gradient were transferred to a new tube and washed twice with Neurobasal A medium supplemented with 2% B27 supplement (Invitrogen) and 1% penicillin–streptomycin (PS). Cells were counted and plated on poly-D-lysine (Sigma)- and laminin (Sigma)-coated, eight-well chamber slides (Millipore) at a density of 8,000 cells per well. Neuronal cultures were maintained in complete neuronal medium, consisting of Neurobasal A medium supplemented with 2% B27 supplement, 1% PS, L-glutamine (500 μM), nerve growth factor (NGF; 50 ng/mL, Invitrogen), glial-cell-derived neurotrophic factor (GDNF; 50 ng/mL, PeproTech), and the mitotic inhibitors fluorodeoxyuridine (40 μM, Sigma) and aphidicolin (16.6 μg/mL, Sigma) for the first 3 days. The medium was then replaced with fresh medium without fluorodeoxyuridine and aphidicolin.
Primary human fibroblasts BJ cells (ATCC, CRL-2522) were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum, L-glutamine (1% v/v), 10 IU/mL penicillin, and 100 mg/mL streptomycin. BJ cells stop their division by contact inhibition, therefore to limit their division, cells were seeded until confluence before being infected at a multiplicity of infection (m.o.i.) of 3, and maintained in 2% serum throughout the experiment.
Lips were biopsied at the region of virus inoculation (commissural region) immediately after the animals were euthanized, and TGs were harvested as described in the experimental procedures. Tissues were ground in microtubes containing 250μL of ice-cold PBS. Three rounds of freezing/thawing were applied using liquid nitrogen, and samples were centrifuged and supernatants stored at -80°C until use. Serial dilutions were used to titrate the virus on VERO cells (ATCC, CCL-81).
HSV-1 DNA FISH probes were Cy3 labeled by nick-translation. Cosmids 14, 28 and 56 [98] comprising a total of ~90 kb of the HSV-1 genome were labeled by nick-translation (Invitrogen) with dCTP-Cy3 (GE Healthcare), and stored in 100% formamide (Sigma-Aldrich). The DNA-FISH and immuno-DNA FISH procedures for TG sections have been described previously [47,96]. Briefly, frozen sections were thawed, rehydrated in 1x PBS and permeabilized in 0.5% Triton X-100. Heat based unmasking was performed in 100 mM citrate buffer, sections were post-fixed using a standard methanol/acetic acid procedure, and dried for 10 min at RT. DNA denaturation of section and probe was performed for 5 min at 80°C, and hybridization was carried out overnight at 37°C. Sections were washed 3 x 10 min in 2 x SSC and for 3 x 10 min in 0.2 x SSC at 37°C, and nuclei were stained with Hoechst 33258 (Invitrogen). All sections were mounted under coverslips using Vectashield mounting medium (Vector Laboratories) and stored at 4°C until observation.
For immuno-DNA FISH, frozen sections were treated as described for DNA-FISH up to the antigen-unmasking step. Tissues were then incubated for 24 h with the primary antibody. After three washes, secondary antibody was applied for 1 h. Following immunostaining, the tissues were post-fixed in 1% PFA, and DNA FISH was carried out from the methanol/acetic acid step onward.
The same procedures were used for infected neuronal cultures except that the cells were fixed in PFA 2% before permeabilization.
RNA FISH probe labeling and RNA FISH procedures were performed as described previously [96]. Biotinylated single-strand LAT RNA probe was prepared by in vitro transcription (Ambion) using plasmid pSLAT-2 as a template (gift from S. Efstathiou, University of Cambridge, UK). Biotinylated LacZ probe was prepared from the pCMV-LacZ plasmid (Clontech) using the nick-translation procedure (Invitrogen). Frozen sections were treated as described for DNA FISH up to the antigen-unmasking step using solutions containing 2 mM of the RNAse inhibitor ribonucleoside vanadyl complex. The sections were pre-hybridized in 50% formamide/2 × SSC and hybridized overnight with 60 ng of RNA probe in a 50% formamide buffer at 65°C for LAT and 37°C for LacZ. Sections were washed in 50% formamide/2 × SSC at 65°C, and in 2 × SSC at room temperature. Detection was performed using streptavidin-HRP conjugate, followed by Tyramide Signal Amplification (TSA, Invitrogen) with an Alexa Fluor 350- or 488-conjugated substrate, according to the manufacturer’s guidelines. The DNA-FISH procedure was performed starting from the methanol/acetic acid post-fixation step.
TGs were collected at 6 or 28 dpi and snap-frozen. Frozen tissues were ground, thawed in lysis buffer (10 mM Tris-EDTA, pH 8.0) containing a protease inhibitor cocktail, and briefly sonicated. Protein extracts were homogenized using QiaShredders (Qiagen). Protein concentration was estimated by the Bradford method. Extracted proteins were analyzed by Western blotting using appropriate antibodies.
The following primary antibodies were used:
Mouse Mab anti-mouse PML (mAb3739; Millipore), anti-human PML (clone 5E10; Roel van Driel or clone PG-M3; Santa Cruz), anti-SUMO-1 (clone 5B12; MBL), anti-SUMO-2/3 (clone 1E7; MBL), anti-NF160 (Invitrogen), anti-ßIII tubulin (MAB1637; Millipore), anti-ICP0 (Mab11060), anti-ICP4 (clone 10F1; Virusys), anti-ICP27 (Virusys); rabbit Mab anti-SUMO-1 (clone Y299; Abcam), anti-SUMO-2/3 (Mab4971; Cell Signaling): rabbit polyclonal anti-ATRX (H-300; Santa Cruz Biotechnology), anti-Daxx (M-112; Santa Cruz Biotechnology), anti-NF200 (Pierce), anti-SUMO-1 (4930; Cell Signaling), anti-SUMO-2/3 (ab3742; Abcam), anti-ICP0 (Rab190), anti-VP16 (ab4808; Abcam), and anti-pan-HSV-1 (LSBio) were used. All secondary antibodies were Alexa Fluor-conjugated and were raised in goats (Invitrogen).
Observations and most image collections were performed using an inverted Cell Observer microscope (Zeiss) with a Plan-Apochromat ×100 N.A. 1.4 objective and a CoolSnap HQ2 camera from Molecular Dynamics (Ropper Scientific), or a Zeiss LSM 510 confocal microscope. Raw images were processed using the ImageJ software (NIH).
Detection of LacZ transcripts in in1374-infected cultured neurons was performed using the FastLane Cell cDNA kit (Qiagen) using the following primers: LacZ fwd: 5’ GCAGCAACGAGACGTCA 3’, LacZ rev: 5’ GAAAGCTGGCTACAGGAAG 3’. Detection of HSV-1 genomes in cell and TG extracts was performed using primers targeting: TK fwd: 5’ GGAGGACAGACACATCGACC 3’, rev: 5’ CGAAAGCTGTCCCCAATCCT 3’ and LAT fwd: 5’ CCCACGTACTCCAAGAAGGC 3’, rev: 5’ AGACCCAAGCATAGAGAGCCAG 3’. RT-PCR for the detection of viral lytic mRNAs or LAT in human or mouse TG and cell extracts was performed using primers targeting: ICP0 fwd: 5’ GGT-GTA-CCT-GAT-AGT-GGG-CG 3’, rev: 5’ GCT-GAT-TGC-CCG-TCC-AGA-TA 3’; ICP4 fwd: 5’CGT-GGT-GGT-GCT-GTA-CTC-G 3’, rev: 5’ GCT-CGG-CGG-ACC-ACT-C 3’; ICP27 fwd: 5’ ATG-TGC-ATC-CAC-CAC-AAC-CT 3’, rev: 5’ TCC-TTA-ATG-TCC-GCC-AGA-CG 3’; UL30 fwd: 5’ TGT-TTC-GCG-TGT-GGG-ACA-TA 3’, rev: 5’ TTG-TCC-TTC-AGG-ACG-GCT-TC 3’; VP16 fwd: 5’ TGC-GGG-AGC-TAA-ACC-ACA-TT 3’, rev: 5’ TCC-AAC-TTC-GCC-CGA-ATC-AA 3’; and LAT (see above).
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10.1371/journal.pbio.1002464 | Structure, Regulation, and Inhibition of the Quorum-Sensing Signal Integrator LuxO | In a process called quorum sensing, bacteria communicate with chemical signal molecules called autoinducers to control collective behaviors. In pathogenic vibrios, including Vibrio cholerae, the accumulation of autoinducers triggers repression of genes responsible for virulence factor production and biofilm formation. The vibrio autoinducer molecules bind to transmembrane receptors of the two-component histidine sensor kinase family. Autoinducer binding inactivates the receptors’ kinase activities, leading to dephosphorylation and inhibition of the downstream response regulator LuxO. Here, we report the X-ray structure of LuxO in its unphosphorylated, autoinhibited state. Our structure reveals that LuxO, a bacterial enhancer-binding protein of the AAA+ ATPase superfamily, is inhibited by an unprecedented mechanism in which a linker that connects the catalytic and regulatory receiver domains occupies the ATPase active site. The conformational change that accompanies receiver domain phosphorylation likely disrupts this interaction, providing a mechanistic rationale for LuxO activation. We also determined the crystal structure of the LuxO catalytic domain bound to a broad-spectrum inhibitor. The inhibitor binds in the ATPase active site and recapitulates elements of the natural regulatory mechanism. Remarkably, a single inhibitor molecule may be capable of inhibiting an entire LuxO oligomer.
| Bacteria communicate with one another and orchestrate group behaviors using a process called quorum sensing. The types of behaviors controlled by quorum sensing are typically unproductive when performed by a single bacterium acting alone but become effective when undertaken in unison by the group. Importantly, quorum sensing controls virulence and biofilm formation in many globally important pathogens. Thus, small molecules that modulate quorum sensing could be promising new antimicrobials. Here, we use X-ray crystallography to study LuxO, the central quorum sensing regulator in Vibrio cholerae and related bacteria. We find that LuxO activity is controlled during quorum sensing by a mechanism that entails occlusion of the LuxO active site by a linker region within the protein. The small molecule AzaU also inhibits LuxO and, in so doing, down-regulates V. cholerae virulence. Our studies show that AzaU binds to the LuxO active site, unexpectedly mimicking aspects of the natural regulatory mechanism.
| Quorum sensing is a widespread process of bacterial cell–cell communication that allows bacteria to monitor and respond to fluctuations in cell number and the species composition of bacterial consortia. Quorum sensing relies on the production, release, and subsequent group-wide detection of extracellular signal molecules called autoinducers [1]. Vibrio cholerae, the etiological agent of the disease cholera, possesses multiple quorum-sensing pathways that function in parallel to regulate virulence factor production, biofilm formation, type VI secretion, and competence development, among other behaviors (Fig 1) [2–5]. Modulating quorum sensing might therefore be a strategy for mitigating pathogenicity [3,6–8].
The V. cholerae quorum-sensing receptors are membrane-bound two-component signal transduction proteins [2,3,7]. While each receptor detects a different autoinducer ligand, they all transduce autoinducer binding information to a shared response regulator called LuxO (Fig 1). At low cell density, when ligand is absent, the autoinducer receptors function as kinases and funnel ATP-derived phosphoryl groups to LuxO. Phosphorylated LuxO activates the transcription of genes encoding four small regulatory RNAs, Qrr1-4, which, in turn, control the translation of two key regulatory proteins, AphA and HapR (Fig 1) [9,10]. As a result, both virulence factor production and biofilm formation are activated. At high cell density, the binding of autoinducers to their cognate receptors inhibits receptor kinase activity, leading to the dephosphorylation and inactivation of LuxO. The resulting changes in AphA and HapR levels lead to the down-regulation of virulence factor production and biofilm formation. This counterintuitive pattern of behavior, in which virulence factor production and biofilm formation are inhibited at high cell density, can be understood in terms of the cholera disease itself [11]. Following successful infection, the ensuing diarrhea washes huge numbers of V. cholerae from the human intestine into the environment. Thus, expression of genes for virulence and biofilm formation at low cell density promotes infection, while repression of these genes by quorum-sensing autoinducers at high cell density promotes dissemination [3,12].
The central position of LuxO as the signal integrator in the quorum-sensing cascade controlling V. cholerae pathogenicity makes it an especially promising target for drug discovery. Furthermore, unlike other components of the quorum-sensing circuitry, LuxO is highly conserved in all sequenced vibrio species, including V. parahaemolyticus and V. vulnificus, which, like V. cholerae, are human pathogens [13]. By contrast, LuxO appears to be absent from the genomes of nonvibrio bacteria. Recently, we used high-throughput screening to identify small molecules that activate quorum sensing, and thus repress virulence factor production, in V. cholerae [8]. At least one of the compounds inhibited virulence by acting on LuxO. A more potent derivative, previously called compound 12 and here renamed AzaU, was shown to inhibit virulence factor production in both V. cholerae and V. parahaemolyticus [8].
LuxO belongs to the subfamily of AAA+ ATPases known as bacterial enhancer-binding proteins (bEBPs) [14,15]. Broadly speaking, AAA+ proteins exploit ATP hydrolysis to power mechanical work in processes such as protein unfolding, DNA unwinding, and transcriptional regulation [16]. bEBPs such as LuxO drive the “opening” of σ54-dependent promoters, converting them to transcriptionally activate states [14,15]. They do so by binding to enhancer-like sequences upstream of target promoters and interacting directly with the σ54 subunit of the RNA polymerase holoenzyme. bEBPs are ring-shaped hexamers, or possibly heptamers, in their active states [17]. Within the group I bEBPs, which includes LuxO and the well-studied NtrC proteins, each monomer contains three domains: an N-terminal receiver (R) domain, a
central ATPase (C) domain, and a C-terminal sequence-specific DNA-binding (D) domain [14,15]. In LuxO, as in NtrC proteins, the phosphorylation of an aspartate located within the R-domain activates the C domain for ATP hydrolysis and the opening/activation of σ54-dependent promoters [18,19]. The DNA-binding domains direct LuxO transcriptional activation to the qrr1-4 genes [9].
Group I bEBPs can exhibit either negative or positive phosphorylation-dependent regulation [20–24]. In the negative mode of regulation, the R domain blocks the formation of active bEBP oligomers by stabilizing inactive dimers; these dimers are disrupted by R domain phosphorylation, permitting spontaneous ring assembly driven by the C domains [20,22,23]. In the positive mode of regulation, phosphorylated R domains are needed to drive oligomerization [21]. Here, we present crystal structures and in vitro and in vivo functional studies of LuxO that together reveal what is, to our knowledge, an entirely novel mechanism of AAA+ protein regulation, in which a regulatory segment of the protein prevents substrate ATP binding and hydrolysis by occupying a portion of the enzyme active site. Notably, this intrinsic regulatory mechanism is recapitulated by the small molecule inhibitor AzaU. Finally, our data suggest that a single AzaU inhibitor molecule is capable of inactivating an entire ring, placing constraints on potential mechanisms of AAA+ function.
Previously, we reported that V. harveyi LuxO lacking the R domain is constitutively active in vivo [18]. This result implies that LuxO is negatively regulated by its R domain and that R domain phosphorylation releases this negative regulation. To investigate the mechanism of intrinsic LuxO regulation further, we used X-ray crystallography. Of the seven vibrio LuxO proteins we tested, V. angustum LuxO proved most amenable to structural studies (S1 Fig; S1–S3 Tables). We determined the 1.6 Å resolution crystal structure of a V. angustum LuxO construct lacking the D domain but containing both R and C domains (denoted LuxO-RC) (Fig 2A, S2 Table). The structures of the individual R and C domain are very similar to those of homologs such as NtrC1 (root mean square [rms] deviations of 1.3 Å and 1.7 Å, respectively), although, as discussed below, the relative positioning of the R and C domains is unique. Rather than forming closed rings, V. angustum LuxO monomers in the crystals form continuous helical arrays with six subunits per turn (Fig 2B). We also observed the same helical arrays of LuxO monomers in crystal structures of the V. angustum C domain alone (LuxO-C), either as the apo-protein, with ATP bound, or with the inhibitor AzaU bound (S3 Table; discussed below). Both closed rings and helical arrays of various pitches are common among the known crystal structures of AAA+ ATPases. Presumably, because only a modest alteration in the interaction between neighboring monomers within a flat ring is required to generate a helix, the crystallographically observed arrangements often reflect those that are favored by symmetry considerations and crystal packing forces. Indeed, the monomer–monomer interfaces observed in our LuxO-RC and LuxO-C structures are similar to those observed in NtrC1 [22] and other AAA+ proteins that crystallize in closed-ring arrangements. Sedimentation velocity analytical ultracentrifugation experiments suggest that, in solution, LuxO-RC forms hexamers (discussed below; see Fig 5B).
In the V. angustum LuxO-RC structure, the R domain is nestled between the two lobes of the C domain, forming a discrete interface with each (Fig 2C). Interface I is dominated by an interaction between α-helix 5 (α5) of the R domain and α7 of the C domain. In interface II, α4 and β-strand 5 (β5) of the R domain interact with α13 of the C domain. The R and C domains are connected by a 20-residue linker (denoted R-C linker, residues 123–142). Strikingly, residues 137–142 of the R-C linker occupy a substantial portion of the C domain active site, sterically occluding the binding of the ATP substrate (Fig 2C). The R-C linker is stabilized in the active site by an extensive network of H-bonds, including four with residues within the R domain and three with residues within the C domain (Fig 2D). Gly 141 of the R-C linker appears to be especially important, as it not only makes three key H-bonds using its main chain nitrogen and oxygen atoms but also occupies a position deep within the active site that could not accommodate a larger residue. The positioning we observe for the R domain in relation to the C domain and the insertion of the R-C linker into the catalytic active site have not to our knowledge been observed for any other bEBP or AAA+ protein and thus appear to represent an unprecedented mode of AAA+ protein regulation.
To validate the physiological relevance of the domain and linker interactions that we observe in our structure with respect to the regulation of LuxO activity, we engineered mutations in the highly homologous (66% sequence identity, 78% sequence similarity) V. cholerae LuxO protein (S1 Fig, S4 Table). The mutations were designed to disrupt the R-C interface or, in one case, to change the key R-C linker glycine (Gly 141) and were thereby expected to derepress (i.e., activate) LuxO. LuxO activation is readily assayed using a V. cholerae reporter strain containing a quorum-sensing controlled luciferase operon [8]. This recombinant V. cholerae strain produces light exclusively when LuxO is inactive—that is, at high cell density in the presence of autoinducers (see Fig 1). Mutations that activate LuxO confer the low-cell-density phenotype and cause reduced bioluminescence. As a positive control for LuxO activation, we used an isogenic V. cholerae strain carrying the constitutively active phosphomimetic mutant LuxO D61E (earlier misnamed D47E), which reduces bioluminescence about 100-fold compared to the strain carrying wild-type LuxO (Fig 3).
Interface I was targeted by changing a central residue in the α5–α7 interaction, Val 120 (Ile 113 in V. angustum LuxO), to glutamate. This LuxO V120E mutant exhibited 100-fold reduced bioluminescence compared to wild-type LuxO (Fig 3), signifying strong LuxO activation upon interface I disruption. Interface II was targeted by changing the R domain residue Phe 108 (Phe 101 in V. angustum LuxO) to alanine (F108A) or tryptophan (F108W) or by changing the C domain residue Gly 333 (Gly 329 in V. angustum LuxO) to lysine (G333K). These mutants displayed 10-fold to 100-fold reduced bioluminescence (Fig 3). Taken together, these results strongly support the physiological relevance of the crystallographically defined R-C interfaces. Finally, we note that V. cholerae LuxO Gly 145 (Gly 141 in V. angustum LuxO), whose small size allows the R-C linker to fit into the ATP-binding site (Fig 2D), is conserved in all LuxO proteins but not in the otherwise closely related NtrC proteins (S1 Fig). Changing this LuxO residue to glutamate (G145E), the residue located at this position in NtrC1, reduced bioluminescence 10-fold (Fig 3), validating a direct role for the R-C linker in C domain inhibition. Nonetheless, this residual level of bioluminescence implies that LuxO inhibition has not been completely eliminated, despite the predicted inability of the linker to occupy the ATPase active site. The LuxO-RC crystal structure provides an attractive explanation, showing that the R domain interacts with both lobes of the C domain. These interactions may impede interlobe movements needed for the catalytic cycle.
A prediction of our structure-based model for LuxO regulation is that, at sufficiently high concentrations, a construct containing the R domain and R-C linker might be capable of inhibiting the ATPase activity of the C domain in trans. To lay the groundwork for testing this possibility, we developed an in vitro LuxO ATPase activity assay. Of the vibrio species we evaluated, V. vulnificus LuxO-C (88%/82% sequence similarity/identity with V. cholerae LuxO; S1 Fig) displayed the most robust ATPase activity and was therefore selected for enzyme assays. As expected for an enzyme whose activity relies on oligomerization, the measured rate constant kcat displayed a sigmoidal dependence on LuxO concentration (Fig 4A). Using sedimentation velocity analytical ultracentrifugation, we confirmed that, at the LuxO-C concentration required for full activity, the protein was almost exclusively hexameric (Fig 5A and Materials and Methods). We note, however, that the concentration of LuxO-C required for full activity was nonphysiologically high; this was also the case for the other vibrio LuxO C domains we tested (S1 Table). This result may reflect the absence of the DNA-binding domain; unfortunately, the propensity of full-length LuxO proteins to aggregate has, so far, prevented us from investigating this possibility more fully. Despite this complication we found that, consistent with the negative regulatory role of the R domain on the C domain, LuxO-RC was almost entirely inactive compared to LuxO-C (Fig 4A). Furthermore, the phosphomimetic LuxO-RC mutant D60E (equivalent to V. cholerae LuxO D61E), was substantially activated, whereas a control LuxO-RC D60A mutant that mimics the unphosphorylated form was, like LuxO-RC, inactive. Together, these experiments establish that purified LuxO-RC retains the key regulatory features identified in previous in vivo studies of LuxO.
With a LuxO ATPase assay in hand, we could test whether a construct containing the R domain and R-C linker (“LuxO-R+linker”) was capable of inhibiting the C domain in trans. Indeed, we observed dose-dependent inhibition (Fig 4B). Furthermore, inhibition by the LuxO-R+linker construct was progressively more potent at lower ATP concentrations. These observations are consistent with the idea that the ATP substrate and the R-C linker compete for binding to the C domain active site, as implied by our crystal structures. As controls, we assayed the ability of the D60E phosphomimetic mutant of LuxO-R+linker and of a LuxO-R construct lacking the linker to inhibit LuxO-C in trans. LuxO-R+linker (D60E) displayed only a modest reduction in inhibitory activity relative to LuxO-R+linker (Fig 4C), perhaps because the phosphomimetic mutation fails to fully recapitulate the phosphorylated state (as also suggested by the data in Fig 4A). The absence of the linker in LuxO-R resulted in a larger impairment in inhibitory activity; however, in trans inhibition of the C domain was not abolished entirely (Fig 4C). This result suggests that the R domain itself has a role in inhibiting the C domain, in agreement with the in vivo results for V. cholerae LuxO (G145E) discussed above (Fig 3).
Receiver domains, such as the one in LuxO, are ubiquitous regulatory modules in prokaryotic two-component signaling pathways [25–28]. Despite the remarkable diversity of the effector domains to which they are attached, receiver domains appear universally to adopt the same two stereotypical conformations, “active” and “inactive.” The relative stability of these two conformations is determined by whether or not the key receiver domain aspartate residue (Asp 54/60/61 in V. angustum, V. vulnificus, and V. cholerae, respectively) is phosphorylated or unphosphorylated; phosphorylation strongly favors the active state. The level of aspartate phosphorylation is, in turn, determined by the kinase activities of cognate two-component sensor kinase receptors.
The canonical conformational change associated with receiver domain phosphorylation [27,28] is predicted to have a large effect on residues that, in the LuxO-RC structure, lie in the interface between the R and C domains. The largest of these changes involves interface II (Fig 6A; see also Fig 2C). As an example, we consider the LuxO R domain residue His 84, which contributes to interface II through both salt bridge formation and π-stacking (with the LuxO C domain residues Glu 322 and His 325, respectively; Fig 6A). His 84 is located on the loop connecting β4 and α4 and, in homologous receiver domains, this loop is dramatically repositioned in response to receiver domain phosphorylation. For instance, in the R domain of the close LuxO homolog NtrC1 (for which both unphosphorylated and phosphorylated structures have been reported [22,23]), the corresponding His side chain moves ca. 10 Å upon phosphorylation (compare Fig 6B and 6C). This movement would break the salt bridge and π-stacking interactions that, in unphosphorylated LuxO, stabilize interface II. Additional conformational changes affecting α4 and β5 are expected to dismantle a hydrophobic cluster to which the LuxO R domain contributes Ile 87, Val 91, and Phe 101, further destabilizing interface II. Thus, the paradigmatic conformational change accompanying receiver domain phosphorylation is expected to disrupt the interaction between the LuxO R and C domains, providing a straightforward structural explanation for LuxO activation.
Once the phosphorylated R domain has “undocked” from the C domain, one might predict that the R-C linker would be more susceptible to limited proteolysis. Indeed, LuxO-RC (D60E) is more susceptible to proteolysis than is wild-type LuxO-RC (S2 Fig). Furthermore, wild-type LuxO-RC, the constitutively inactive mutant D60A, and the phosphomimetic mutant D60E all display similar sedimentation velocity ultracentrifugation profiles (Fig 5B–5D) consistent with hexamer formation. We do observe that activation (by D60E and/or BeF3) and/or nucleotide binding causes a broadening of the main peak (Fig 5B and 5D). This result might indicate a mixture of conformational states, or possibly of oligomerization states, that is not observed in the constitutively inactive D60A mutant. Further experiments will be required to determine the origin of this peak broadening.
Previously, we reported the discovery and optimization of a set of 6-thio-5-azauracil compounds that activate vibrio quorum-sensing responses by inhibiting LuxO [8]. The most potent of these, with an EC50 in V. cholerae of 4.1 μM, was a tert-butyl analog previously called compound 12 and here renamed AzaU. To characterize the mechanism of AzaU inhibition of LuxO, we crystallized V. angustum LuxO-C in the absence of ligands (apo), in the presence of ATP, and in the presence of AzaU (S2 and S3 Tables). All three protein structures are highly similar (rms deviation < 0.5 Å). AzaU bound in the LuxO active site, with its modified uracil ring occupying the same region as the adenine ring of the ATP substrate, indicating that AzaU functions as a competitive inhibitor (Fig 7A). Indeed, consistent with this structural inference, ATPase assays using LuxO-C showed that AzaU inhibition is enhanced at lower ATP concentrations (Fig 7B), as expected for a competitive inhibitor. Strikingly, AzaU binds the LuxO active site in a manner that mimics the R-C linker, with both AzaU and the R-C linker forming H-bonds with Arg 316 and Gln 363 (Fig 7C).
In Fig 7D and 7E, the extent of LuxO-C inhibition is presented as a function of inhibitor concentration for AzaU and for a new, more potent inhibitor, CV-133 (EC50 = 1.0 μM). CV-133 closely resembles AzaU and, based on X-ray structural analysis (S3 Table), binds in an almost identical manner (Fig 7C). Sedimentation velocity analytical ultracentrifugation revealed that CV-133-bound LuxO-C is almost exclusively hexameric (Fig 5A), arguing strongly against the possibility that AzaU or CV-133 inhibits LuxO by causing ring dissociation. It is moreover evident from these plots that not every LuxO-C active site (present at 100 μM) needs to be occupied by an inhibitor molecule to obtain quantitative inhibition. Using mathematical modeling, we found that all of our LuxO-C inhibition data (Fig 7, panels B, D, and E) were well fit by a simple, limiting-case model in which a single inhibitor molecule is capable of inhibiting an entire LuxO-C ring (see S1 Modeling for details). Unfortunately, the relatively weak oligomerization of LuxO constructs has prevented us from measuring small molecule binding constants directly, for example, by isothermal titration calorimetry. In place of binding constants, our model therefore contains a single adjustable parameter K, representing the binding affinity of the inhibitor molecule relative to the substrate ATP (K = KaAzaU / KaATP = KdATP / KdAzaU). The fitted K values for AzaU (K = 34 ± 2) and CV-133 (K = 268 ± 186) are roughly consistent with their relative EC50 values, with both comparisons implying that CV-133 binds substantially more tightly than AzaU to the LuxO active site. While we cannot rule out more complex models, all of our data can be well fit using a straightforward model—with a single adjustable parameter—in which only one inhibitor molecule is required to inhibit an entire LuxO hexamer.
Quorum sensing is a chemical communication process that bacteria use to control collective behaviors. Quorum sensing is crucial for virulence in pathogenic vibrios because it controls biofilm formation and virulence factor production [3,12,29]. Curiously, unlike in quorum-sensing bacteria that cause persistent infections, vibrios cause acute diseases, and quorum-sensing autoinducers repress biofilm formation and virulence factor production at high cell density [3]. Presumably, pro-quorum-sensing compounds that lock pathogenic vibrios into the high-cell-density quorum-sensing mode could be explored as therapies [7,8]. Multiple strategies readily come to mind, including agonizing the autoinducer receptors and modulating downstream components. Targeting the receptors could, however, prove difficult because multiple autoinducers are involved and they signal through distinct cognate receptors [2,3]. Thus, a combination strategy that simultaneously influences signaling via all of the receptors could be required. LuxO, by virtue of its key position in the quorum-sensing cascade, functioning downstream of the receptors to integrate sensory information emanating from all of the autoinducer-receptor pairs, is a particularly attractive candidate. Indeed, a ΔluxO V. cholerae strain is severely defective in the production of cholera toxin and the toxin coregulated pilus and is avirulent in a mouse model of infection [12]. Likewise, V. cholerae and other pathogenic vibrios treated with the LuxO inhibitor AzaU do not produce virulence factors [8]. Here, we explore the mechanisms underpinning the modulation of LuxO activity by phosphorylation and by small molecule inhibitors in an effort to forward our basic understanding of quorum-sensing signal transduction and to lay the groundwork for probing the promise of applications of quorum-sensing modulators.
Our studies demonstrate that the unphosphorylated R domain of LuxO inhibits the ATPase activity of the C domain by binding adjacent to the active site and stabilizing the R-C linker in a conformation that sterically occludes a substantial portion of the active site. To the best of our knowledge, this mode of AAA+ ATPase regulation has not previously been reported. Indeed, the closely related NtrC proteins are regulated in a completely different manner, as exemplified by Aquifex aeolicus NtrC1 [22] and Salmonella typhimurium NtrC [21]. NtrC1 in its inactive, unphosphorylated conformation is a homodimer, with both the R and C domains contributing to the intermonomer interface (S3 Fig). R domain phosphorylation drives a conformational change that disrupts the interface between the two R domains, permitting active oligomers to form [23]. NtrC exhibits a positive mode of regulation, in which the phosphorylated R domain is essential for the formation of active oligomers [21].
It has long been known that LuxO uses a negative mode of regulation, since deletion of its R domain is constitutively activating [18]. What is surprising is that LuxO has a unique mechanism of negative regulation not only compared to the AAA+ superfamily in general but compared to the closely related NtrC proteins in particular. In unphosphorylated NtrC1, the R domain helix α5 and the R-C linker form a single, long α-helix (S3 Fig). In unphosphorylated LuxO, by contrast, the R-C linker folds back onto α5, permitting the R domain to pack against the C domain of the same monomer and, in so doing, positioning a segment of the R-C linker so that it occupies the C domain active site. Despite the high homology between LuxO proteins and NtrC proteins, key amino acid differences appear to prevent NtrC proteins from adopting the inactive conformation we observe for LuxO. Indeed, in the two examples we tested (V. cholerae G145E and G333K; see Fig 3 and S1 Fig), exchanging the NtrC1 residue for the corresponding LuxO residue derepressed LuxO. These and a handful of other substitutions appear to be responsible for the drastic alteration in regulatory mechanism between LuxO and its most closely related homologs.
The “front-to-front” dimers formed by unphosphorylated NtrC1 are incompatible with the formation of active rings [22]. By contrast, the autoinhibited LuxO monomers interact back-to-front in a ring-like helical arrangement that closely resembles, except for the occluded ATP binding site, the presumptive active state (Fig 8). This novel regulatory mechanism, by allowing preassembly of inactive oligomers at enhancer-like sequences upstream of σ54-dependent promoters, may confer a competitive advantage in signal transduction dynamics. Phosphorylation of pre-positioned LuxO oligomers could activate transcription of qrr genes and initiate the transition from the high-cell-density signaling state (LuxO inactive) to the low-cell-density signaling state (LuxO active), more rapidly than if LuxO employed an NtrC1 type of regulation. Since the low-cell-density state of LuxO is associated with superior colonization and infection of the host, an especially rapid adaptation might thereby confer a selective evolutionary advantage.
We found that the inhibitor molecules AzaU and CV-133, both derivatives of a small-molecule screening hit [8], function as competitive inhibitors of LuxO (Fig 8). AzaU had previously been reported to be an uncompetitive inhibitor based on Lineweaver-Burk analysis [8]. This analytic method assumes, however, that an inhibitor is capable of inhibiting only the monomer to which it is bound, whereas we now find that a single inhibitor molecule may be capable of inhibiting an entire oligomer. This new finding furthermore implies that there must be communication between LuxO subunits in the process of ATP hydrolysis and that the LuxO hydrolysis mechanism cannot, therefore, be entirely stochastic. Partially concerted mechanisms have been proposed for various AAA+ ATPases and for bEBPs, including NtrC1 [30] and PspF [31]. These intermediate mechanisms assume that monomers are heterogeneously occupied during ATP hydrolysis—some bound to ATP, others to ADP and/or unliganded—which is consistent with structural evidence for asymmetric states [15,17,32]. Nonetheless, it remains unclear how a single AzaU or CV-133 inhibitor molecule inhibits an entire LuxO oligomer. Our crystal structures do not reveal any substantial conformational changes associated with AzaU, CV-133, or ATP binding, possibly because of constraints imposed by the crystal lattice. In particular, the conformation of the GAFTGA loop, implicated in σ54 binding, is unaffected by either inhibitors or ATP, adopting in all cases a conformation similar to that of ADP-bound NtrC1 [17,22,30]. Furthermore, our analytical ultracentrifugation experiments show no evidence of a change in oligomerization state (Fig 5A). Further analysis will therefore be needed to distinguish among possible mechanisms.
Finally, AzaU inhibits V. cholerae LuxO without affecting cell growth, which—because vibrios possess multiple AAA+ ATPases that underpin central biological processes—suggests that AzaU displays high specificity for LuxO. Nonetheless, higher specificity may ultimately be achieved by identifying and optimizing allosteric inhibitors. Alternatively, extending the idea that AzaU recapitulates some of the key interactions that stabilize the R-C linker in the ATPase active site, it may prove possible to engineer potent and selective second-generation inhibitors that capture even more of these interactions.
DNA encoding various LuxO constructs was amplified by PCR from genomic DNA and cloned into pET21b or pET28b for the production of His6-tagged fusion proteins (S1 Table). The His6 tag was placed at the C-terminus for all constructs except the construct containing the R domain and R-C linker of V. vulnificus LuxO used in Fig 4B and 4C; in that case, the tag was located at the N-terminus. For protein purification, Escherichia coli BL21(DE3) cells containing one of the expression plasmids described above were grown at 37°C to OD600 ~ 0.6, followed by induction with 0.1 mM IPTG at 16°C for 16–18 h. Cells, collected by centrifugation at 5,000 g for 15 min, were resuspended in lysis buffer (50 mM Tris-HCl, pH 8.0, 300 mM NaCl, 20% [v/v] glycerol) and lysed using an Emulsiflex-C5 homogenizer (Avestin). The resulting lysates were supplemented with 10 μg/ml DNase (Roche), 4 mM β-mercaptoethanol, and protease inhibitor cocktail tablets (Roche), incubated for 30 min at 4°C, and clarified by centrifugation at 30,000 g for 1 h. The His6-LuxO constructs were purified from the clarified lysates by binding to His60 Ni Superflow resin (ClonTech), which was then washed using lysis buffer containing 20 mM imidazole and 4 mM β-mercaptoethanol. Proteins were eluted using lysis buffer containing 500 mM imidazole and 4 mM β-mercaptoethanol and further purified using a Superdex 200 HR 10/30 column (GE Healthcare) pre-equilibrated in 20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1 mM dithiothreitol (DTT), 10% (v/v) glycerol. All proteins except LuxO-R eluted as broad peaks, showing that different oligomeric states of LuxO were present. After concentration, protein stocks (approximately 15 mg/ml protein) were stored at −80°C.
V. angustum LuxO-C (residues 141–387) crystals were grown at 20°C using the hanging drop vapor diffusion method with a 1∶1 (v/v) mixture of protein (5 mg/ml) and precipitant solution (3.2 M ammonium acetate, 0.1 M HEPES, pH 7.5). LuxO-C:AzaU, LuxO-C:CV-133, and LuxO-C:ATP crystals were grown under the same conditions except for the precipitant solutions used (3.0 M ammonium acetate, 0.1 M Tris-HCl, pH 8.0 for the AzaU complex; 1.4 M ammonium sulfate, 0.1 M HEPES, pH 7.5 for the CV-133 complex; and 0.25 M NaCl, 25% [w/v] PEG3350, 0.1 M HEPES, pH 7.5 for the ATP complex) and the addition of ligand. AzaU and CV-133 were added in 1.2-fold molar excess relative to LuxO-C, whereas ATP was added at 10 mM (without Mg2+ to prevent hydrolysis). Unit cell dimensions for LuxO-C crystals were a = b = 76 Å, c = 82 Å, α = β = 90.0°, γ = 120.0° in space group P61, with one monomer in the asymmetric unit. V. angustum LuxO-RC (residues 1–387, plus a C-terminal His6 tag) was crystallized as above, except that the protein concentration was 6 mg/ml and the precipitant solution contained 2 M ammonium sulfate, 0.1 M sodium acetate, pH 5.5. The size of the LuxO-RC crystals was increased by streak seeding. Unit cell dimensions were a = b = 116 Å, c = 69 Å, α = β = 90.0°, γ = 120.0° in space group P61, with one monomer in the asymmetric unit.
X-ray data for LuxO:AzaU were collected using beamline X29 of the National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory. X-ray data for LuxO-C (native), LuxO-C:ATP, and LuxO-RC were collected using beamline F-1 at the Cornell High-Energy Synchrotron Source (CHESS). Data for LuxO-C:CV-133 were collected using a home source (Rigaku RU-H3R X-ray generator, Rigaku RAXIS-IV++ detector, Xenocs FOX 2D multilayer optics). In each case, crystals were flash-cooled to 100 K and maintained at that temperature for the duration of data collection; as cryoprotectants, we used 20% (v/v) ethylene glycol (LuxO-C) or 15% (v/v) ethylene glycol (LuxO-RC). The structure of LuxO-C:AzaU was determined by the method of molecular replacement using the program PHASER [33] with a model of the catalytic domain from A. aeolicus NtrC1 (PDB entry 1NY5 [22]). The structures of LuxO-C, LuxO-C:ATP, and LuxO-C:CV-133 were obtained by rigid-body refinement from the LuxO-C:AzaU structure. The structure of LuxO-RC was determined by the molecular replacement method using the LuxO-C:AzaU structure as the model for the catalytic domain model and the receiver domain from A. aeolicus NtrC1 (PDB entry 1NY5) as the model for the receiver domain. Structures were manually rebuilt using the program COOT [34] and refined using Phenix.refine [35]. Data collection and structure refinement statistics are provided in S2 and S3 Tables.
The current model of apo-LuxO-C contains residues 141–392, including residues from the C-terminal His6 tag, plus acetate ions from the crystallization solution. The current model of LuxO-C:AzaU contains residues 141–392, including residues from the C-terminal His6 tag, plus acetate ions from the crystallization solution. The current model of LuxO-C:CV-133 contains residues 141–393, including residues from the C-terminal His6 tag, plus sulfate and HEPES ions from the crystallization solution and an ethylene glycol molecule from the cryoprotectant condition. CV-133 is modeled with a partial occupancy of 0.68. The current model of LuxO-C:ATP contains residues 141–393, including residues from the C-terminal His6 tag, a HEPES ion from the crystallization solution, and an ethylene glycol molecule from the cryoprotectant condition. ATP is modeled with a partial occupancy of 0.84. The current model of LuxO-RC contains residues 3–388, including residues from the C-terminal His6 tag, sulfate ions from the crystallization solution, and an ethylene glycol molecule from the cryoprotectant condition. Loops corresponding to residues 124–126 in the R-C linker and residues 221–223 were not placed in the model because of insufficient ordered electron density.
Site-directed mutagenesis of the wild-type V. cholerae luxO gene carried on plasmid pEVS143 was used to introduce the following mutations: F108A, F108W, V120E, G145E, G333A, and G333K. Primers used for the mutagenesis are provided in S4 Table. The LuxO D61A mutation was also introduced into wild-type LuxO and into each of the single mutants listed above. The resulting single and double LuxO-containing mutant plasmids were conjugated into the V. cholerae bioluminescent reporter strain AH261 (V. cholerae ΔluxO ΔlacZ:hapR/pBB1). Plasmid pEVS143 carrying LuxO D61E was introduced into V. cholerae AH261 and used as a positive control.
Overnight cultures from single colonies of the V. cholerae reporter strain carrying the various LuxO alleles were grown in LB medium supplemented with tetracycline (10 mg/ml) and kanamycin (50 mg/ml) at 37°C. Cultures were back-diluted to an OD600 = 0.001 with sterile medium. Following a 6-h incubation at 37°C with shaking, OD600 was measured using a DU800 spectrophotometer. Bioluminescence was measured in triplicate on an Envision Multilabel Reader and normalized to that of the wild-type reporter strain.
ATPase assays were performed using a standard NADH-coupled ATPase assay as described previously [8] with minor modifications: 100 mM K-HEPES, pH 7.4 was used in place of sodium phosphate buffer, and BSA (250 μg/ml) and β-mercaptoethanol (4 mM) were added. All assays were performed at 23°C using a Beckman Coulter DU800 spectrophotometer with a path length of 1.0 cm. In each trial, the absorbance of NADH at 340 nm was measured at 1.5 s intervals for 15 s (or until the NADH was depleted). DMSO was added to a final concentration of 4% in all inhibitor trials.
Sedimentation velocity experiments were performed using a Beckman model ProteomeLab XL-A instrument equipped with an An-Ti60 rotor. Sedimentation of the boundary was measured at 42,000 rpm at 20°C using a step size of 0.003 cm and a delay time of 0 s and collecting between 70–100 scans. Samples were monitored at 250 nm, 280 nm, and 290 nm, with a requirement that the initial absorbance be below 1.5. For samples containing the competitive inhibitor CV-133, samples were also monitored at 335 nm. V. vulnificus LuxO-C and LuxO-RC constructs were dialyzed overnight at 4°C toward 20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 5 mM MgCl2, and 1 mM DTT and stock solutions were diluted in dialysis buffer plus 4% DMSO to reach 60–100 μM monomer concentration. Experiments with CV-133 contained 60 μM LuxO-C and 120 μM CV-133. Experiments with ADP:BeF3 contained 100 μM LuxO-C or LuxO-RC, 150 μM ADP, 750 μM BeSO4, and 3.76 mM NaF. Temperature-corrected partial specific volumes, densities, and viscosities were calculated using Sednterp (v. 1.09) [36]. Model-independent continuous, c(s), distribution analysis for determining sample heterogeneity was performed using Sedfit (v. 14.4d) [37]. Regularization of the distribution by the maximum entropy method was applied with the parameter α constrained to a value of 0.95. Molar masses and diffusion coefficients were determined using DCDT+ (v. 2.4.0) [38].
Pilot experiments using LuxO-C and LuxO-RC (D60E) over a wide range of concentrations (15–230 μM) in 20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1 mM DTT indicated a self-associating system with three components: monomers; intermediates, which are likely dimers; and hexamers. LuxO-C is driven by the presence of CV-133 (a competitive inhibitor) or ADP:BeF3 (a substrate/transition state analog) into a predominantly hexameric state with the major peak at approximately 7.4 S (Fig 5A). The corresponding s(20,w) value coupled with a diffusion coefficient D(20,w) of 3.8 x 10−7 cm2 s-1 gives a mass of 174 ± 2 kDa, in excellent agreement with the theoretical mass of a LuxO-C hexamer (173 kDa). Where monomers and/or other subhexameric species are present, the apparent sedimentation rate can be suppressed by the Johnston-Ogston effect [39], which may in turn account for the lower apparent mobility of LuxO-C in the absence of inhibitor or substrate (Fig 5A). In further pilot experiments, LuxO-RC (D60E) c(s) distributions also displayed a concentration dependence, with a monomer peak at about 4.5 S, a dimer peak at about 6.7 S, and a hexamer peak at about 9.2 S. In the presence of ADP:BeF3, LuxO-RC constructs displayed changes in these distributions consistent with shifts in the equilibrium toward the hexameric state. In all of the experiments shown in Fig 5B–5D, the molar mass (as calculated by DCDT+) of the predominant state fell within the range 262–275 kDa. This value is in excellent agreement with the predicted molecular weight of a LuxO-RC hexamer (269 kDa). We conclude that both inactive and active LuxO-C and LuxO-RC form hexamers in a concentration-dependent manner. The position of this equilibrium can be substantially affected by the presence of ADP:BeF3 and/or CV-133, as observed for LuxO-RC (WT), LuxO-RC (D60E), and LuxO-C (Fig 5).
The synthesis and analysis of AzaU was reported previously [8]. The synthesis and analysis of CV-133 is provided in S1 Methods.
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10.1371/journal.pbio.1002474 | Membrane Tension Acts Through PLD2 and mTORC2 to Limit Actin Network Assembly During Neutrophil Migration | For efficient polarity and migration, cells need to regulate the magnitude and spatial distribution of actin assembly. This process is coordinated by reciprocal interactions between the actin cytoskeleton and mechanical forces. Actin polymerization-based protrusion increases tension in the plasma membrane, which in turn acts as a long-range inhibitor of actin assembly. These interactions form a negative feedback circuit that limits the magnitude of membrane tension in neutrophils and prevents expansion of the existing front and the formation of secondary fronts. It has been suggested that the plasma membrane directly inhibits actin assembly by serving as a physical barrier that opposes protrusion. Here we show that efficient control of actin polymerization-based protrusion requires an additional mechanosensory feedback cascade that indirectly links membrane tension with actin assembly. Specifically, elevated membrane tension acts through phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin nucleation. In the absence of this pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Mathematical modeling suggests roles for both the direct (mechanical) and indirect (biochemical via PLD2 and mTORC2) feedback loops in organizing cell polarity and motility—the indirect loop is better suited to enable competition between fronts, whereas the direct loop helps spatially organize actin nucleation for efficient leading edge formation and cell movement. This circuit is essential for polarity, motility, and the control of membrane tension.
| How cells regulate the size and number of their protrusions for efficient polarity and motility is a fundamental question in cell biology. We recently found that immune cells known as neutrophils use physical forces to regulate this process. Actin polymerization-based protrusion stretches the plasma membrane, and this increased membrane tension acts as a long-range inhibitor of actin-based protrusions elsewhere in the cell. Here we investigate how membrane tension limits protrusion. We demonstrate that the magnitude of actin network assembly in neutrophils is determined by a mechanosensory biochemical cascade that converts increases in membrane tension into decreases in protrusion. Specifically, we show that increasing plasma membrane tension acts through a pathway containing the phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin network assembly. Without this negative feedback pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Mathematical modeling indicates that this feedback circuit is a favorable topology to enable competition between protrusions during neutrophil polarization. Our work shows how biochemical signals, physical forces, and the cytoskeleton can collaborate to generate large-scale cellular organization.
| Cells use multiple mechanisms of spatial signal propagation to orchestrate behaviors like polarity and motility. Well-characterized modes of information propagation include post-translational modifications, nucleotide binding and hydrolysis, and diffusion of signals from one subcellular location to another. More recently, it has become clear that physical forces also play an important role in transmitting and integrating signals in cells and tissues, where they regulate behaviors like differentiation, death, movement, and shape (reviewed in [1,2]). The most well-characterized mode of force transmission is through the cytoskeleton. However, plasma membrane tension also influences diverse cell behaviors ranging from vesicle trafficking to actin assembly [3–6].
Although the magnitude of membrane tension can differ significantly between cell types, tension is tightly controlled within individual cells (reviewed in [7]). When cells experience a change in membrane tension, they revert back to their original membrane tension setpoint within tens of minutes [8,9]. These data suggest that membrane tension is part of a self-regulating system in which cytoskeletal assembly, cell adhesion, and/or membrane trafficking adjust to keep the magnitude of membrane tension constant [3–6,8–10]. However, the mechanistic details of this circuit and how these effectors are coordinated remain unknown.
Actin polymerization-based protrusion is one of the major determinants of membrane tension in motile cells [8,11]. We recently found that membrane tension, in turn, plays a crucial role in regulating actin assembly and cell polarity during neutrophil chemotaxis. Membrane tension acts as a global inhibitor that enables sites of actin assembly to compete with one another [5]. These reciprocal interactions between biochemical signals and physical forces (protrusion increases membrane tension, which decreases protrusion) form a negative feedback circuit that limits the magnitude of membrane tension in neutrophils and prevents expansion of the existing front and formation of secondary fronts. Interfering with this negative feedback circuit impairs cell polarity and motility. For example, increasing membrane tension acts as a long-range inhibitor of leading edge signaling pathways, whereas decreasing membrane tension results in uniform activation of actin assembly [5].
How do motile cells convert increases in membrane tension to decreases in actin assembly? One important mode of regulation is thought to be a direct physical interaction in which the plasma membrane serves as a barrier that opposes protrusion. At the leading edge, where actin density is high, the resistance per actin filament due to membrane tension must be sufficiently small to allow filaments to elongate and generate protrusion. As actin density gradually decreases towards the cell sides, the membrane force per filament increases until polymerization is stalled, resulting in regions that neither protrude nor retract [11–13]. In support of this idea, a front-to-side actin density gradient has been observed in keratocytes [11], and a model consisting entirely of mechanical interactions between the actin cytoskeleton, myosin, and the plasma membrane is sufficient to predict the polarized morphologies of keratocytes [14], as well as the relation between cell shape and speed [11]. But what regulates the magnitude of actin polymerization, and, thus, membrane tension, for efficient motility? And is the purely physical role of the plasma membrane sufficient to prevent the expansion of the existing front and the formation of secondary fronts?
Here we use neutrophils to demonstrate that the magnitude of actin network assembly in chemotactic cells is determined by a mechanosensory biochemical cascade that converts increases in membrane tension into decreases in actin nucleation. Specifically, we demonstrate that increasing plasma membrane tension acts through a pathway containing the phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin network assembly. Without this negative feedback pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Modeling suggests roles for both the direct (mechanical) and indirect (biochemical via PLD2–mTORC2) feedback loops in organizing cell polarity and motility: the direct loop promotes the formation of stable, organized zones of actin nucleation, while the indirect loop facilitates competition among emerging protrusions.
We investigated the hypothesis that an increase in membrane tension inhibits actin network assembly indirectly through a mechanosensory biochemical pathway. To search for possible regulators of this process, we prioritized signaling pathways that are modulated downstream of membrane stretch and also play a role in chemotaxis and cell polarity. Candidates such as stretch-activated calcium channels open downstream of membrane stretch in neutrophils, but calcium transients are dispensable for neutrophil polarity [15].
One attractive candidate that could link membrane stretch to inhibition of actin assembly in neutrophils is mTORC2. This complex is activated downstream of stretch in budding yeast [16] as well as epithelial and vascular smooth mammalian muscle cells [17,18]. Furthermore, mTORC2 knockdown in neutrophils (through a small hairpin RNA [shRNA] of its essential component Rictor) leads to defects in chemotaxis and more uniform accumulation of actin [19], which would be consistent with disruption of an inhibitor of actin network assembly. TORC2 plays a directed role in chemotaxis from neutrophils [19,20] to fibroblasts [21] and is essential for both chemotaxis [22,23] and electrotaxis [24] in Dictyostelium, suggesting that this complex may be a general regulator of directed movement.
mTORC2 is a protein complex comprising the mTOR kinase, the rapamycin-insensitive companion of mTOR (Rictor), and four other proteins [25]. mTORC2 phosphorylates the serine/threonine protein kinase Akt at S473 in mammalian cells in response to a broad range of stimuli [26,27], including neutrophils stimulated with the bacteria-derived chemoattractant peptide formyl-Met-Leu-Phe (fMLP) [19,20]. Several possible inputs and outputs for mTORC2 have been identified [23,28–34], but how mTORC2 is activated downstream of chemoattractant to regulate cell motility is not well understood. We sought to investigate whether the increase in membrane tension that normally occurs downstream of chemoattractant addition [5,35,36] is sufficient to activate mTORC2 in the absence of chemoattractant stimulation (Fig 1A).
To address the effects of an increase in membrane tension on neutrophil signaling, we used neutrophil-like differentiated HL-60 cells, which recapitulate the chemotactic responses of primary blood neutrophils. We first established an experimental framework for manipulating and measuring membrane tension in a manner independent of chemotactic stimulation (Fig 1B–1D). Hypo-osmotic shocks have been extensively used to increase membrane tension by stretching the plasma membrane through osmotically-driven water influx [3,4]. To measure the magnitude of membrane tension changes, we used an atomic force microscope (AFM) to quantify the force needed to extrude a single membrane nanotube (or tether) from the plasma membrane [1,2,37,38]. Resulting force–time curves were fitted with the Kerssemakers algorithm [39] to obtain the tether force. We then estimate the magnitude of membrane tension using the formula [3–6,37]
T=F028Bπ2
with F0 being the tether force measured by AFM and B being the bending rigidity of the membrane, which we assume is to be invariant between the different experimental conditions tested (= 2.7 10−19 Nm [7,37]).
Reducing the osmolarity from 280 mOsm to 140 mOsm by adding distilled water fails to alter the tether force and does not result in a loss of polarity (Fig 1D and S1 Movie). The fact that cells can resist this amount of osmotic pressure without changing cell shape or membrane tension is interesting and suggests the existence of a compensatory mechanism. Nevertheless, the capacity of this mechanism is exceeded following larger changes in osmolarity that lead to loss of polarity. Specifically, further decreasing the osmolarity to 70 mOsm leads to an increase in tether force (Fig 1D) and a loss of polarity (S1 Movie) and results in the activation of mTORC2 (Fig 1E). To rule out indirect effects of osmotic shock on cell polarity and mTORC2 activation, we developed a cell-stretching device to increase membrane tension in a manner independent of osmotic changes. We plated cells on a fibronectin-coated polydimethylsiloxane (PDMS) substrate and performed 40% radial stretch (Fig 1C). To measure the tether force on stretched cells, we designed a cell stretcher that is compatible with AFM-based tether pulling. Membrane tension was elevated in stretched cells compared to cells plated on a pre-stretched membrane, which we used to control for any mechanical effects of the cell substrate (Fig 1D). Similar to hypo-osmotically treated cells, stretched cells also increase phosphorylation of Akt at S473, indicating mTORC2 activation (Fig 1E).
In summary, mechanical stretch of the plasma membrane induced either by hypo-osmotic shock or cell stretching leads to a rapid loss of neutrophil polarity, an increase in membrane tension, and activation of mTORC2.
If mTORC2 is participating in a negative feedback loop to constrain actin network assembly, mTORC2 should not only be activated downstream of membrane stretch, but it should also play a role in inhibiting actin network assembly. To interfere with mTORC2 activity, we used a lentivirus-based shRNA system to stably knock down Rictor, an essential component of mTORC2 (Figs 2A and S1A, mean = 34% of the Rictor protein amount in knockdown compared to control [Ns shRNA] cells, p-value = 0.004).
Consistent with earlier reports, Rictor knockdown (Rictor shRNA) markedly reduced S473-phosphorylation of Akt upon chemoattractant stimulation (S1C Fig; see also [19,20]). Furthermore, Rictor shRNA also blocks the increase in pAkt S473 following 70 mOsm shock, confirming that hypo-osmotic shock leads to an increase in pAkt S473 in an mTORC2-dependent manner (Fig 2B). To corroborate the previously reported role of mTORC2 in neutrophil chemotaxis [19,20] we performed transwell chemotaxis assays. Rictor shRNA cells exhibited severe chemotactic defects (Figs 2C and S1D). This migration defect was not a consequence of defective differentiation, as the neutrophil differentiation marker CD11b was unperturbed by Rictor depletion (S1F Fig).
We previously observed that neutrophils have consolidated propagating zones of SCAR/WAVE2 complex recruitment that closely correspond to zones of membrane protrusion [40]; these focused zones of actin nucleation or wave fronts are likely important to efficiently push the membrane forward in a manner that would be difficult for fragmented nucleation. To investigate the origin of the migration defect observed in Rictor shRNA cells, we analyzed the dynamics of actin nucleation in adherent HL-60 cells by using total internal reflection fluorescence (TIRF) to visualize the neutrophil WAVE2 complex component Hem1. Rictor-depleted cells exhibited larger leading edges, as measured by the percentage area of their basal membrane covered by Hem1-GFP (Fig 2D, S2 and S3 Movies).
Since neutrophils do not require adhesion to a substrate to polarize, we further analyzed the involvement of mTORC2 in adhesion-independent regulation of actin polymerization by quantifying the overall levels of polymerized actin (F-actin) via phalloidin staining of cells that were both stimulated and fixed in suspension. Control (Nonsense, Ns shRNA) cells have an initial burst of actin polymerization upon stimulation, but once the cell is polarized, the amount of F-actin is similar to that of the resting state. In contrast, Rictor shRNA cells exhibited a significantly higher amount of F-actin even 10 min post-stimulation, suggesting a defect in leading edge restriction (Fig 2E). These data suggest that mTORC2 inhibits actin network assembly in a manner that is independent of cell adhesion.
To validate our experiments in suspension (Fig 2B and 2E), we next probed whether membrane tension changes as a function of adhesion strength. By diluting fluorescent fibronectin with a protein blocker, we titrated and measured the surface density of this adhesion molecule (S2A Fig). As expected, the density of fluorescent fibronectin is strongly correlated with the number of adhering cells (S2B Fig) and their migration speed (higher adhesion slows tail retraction and cell advance, providing a 50% decrease in migration speed over the adhesion densities tested [S2C Fig]), so we can be sure we are titrating fibronectin over a sensitive window for the cells. However, we found no change in measure membrane tension across this 10-fold range of fibronectin density (S2C Fig). These data indicate that cell adhesion is not a dominating input to membrane tension in neutrophils.
If the loss of mTORC2 breaks the negative feedback circuit that normally limits actin network assembly and, thus, limits membrane tension during neutrophil chemotaxis, we would expect elevated membrane tension in chemoattractant-stimulated Rictor-depleted cells. Consistent with this hypothesis, the average tether force for control versus mTORC2-depleted cells increased from 38 to 47 pN (Fig 2F). This corresponds to an increase in membrane tension from 69 to 103 μN/m (see above and Section II of S1 Text for details).
Taken together, these data indicate that the mTORC2 complex is activated by membrane stretch, acts as an inhibitor of actin network assembly, and is part of a pathway that establishes the magnitude of membrane tension in chemoattractant-stimulated cells.
In mammalian cells, mTORC2 phosphorylates S473 of Akt in response to receptor tyrosine kinase stimulation, but the signaling pathway that links mTORC2 activation with upstream inputs is not well understood. In Saccharomyces cerevisiae, cell stretch is thought to unfold membrane invaginations known as eisosomes, releasing the TORC2 activator Slm1 [16]. Mammalian cells lack eisosomes and an obvious Slm1 orthologue, but we hypothesized that an analogous mTORC2 activator could be released from membrane invaginations in neutrophils. We searched Biogrid (Biological General Repository for Interaction Datasets, http://thebiogrid.org/) for mTORC2-interacting proteins that are also components of clathrin-coated pits or caveolae and identified PLD2 as a particularly attractive candidate. PLD2 is a phospholipase that converts phosphatidylcholine into choline and phosphatidic acid, which is an activator of the mTor kinase for both mTORC1 and mTORC2 [41,42].
To test the functional relevance of PLD2 in the regulation of mTORC2 activity and actin assembly downstream of membrane stretch, we stably expressed a PLD2 shRNA and verified the knockdown by western blot (Figs 3A and S1B, mean = 39% of the PLD2 protein amount in knockdown compared to control [Ns shRNA] cells). PLD2 knockdown cells showed a reduction in S473-phosphorylation of Akt upon chemoattractant stimulation (S1C Fig) and following a 70 mOsm shock (Fig 3B). These results suggest that PLD2 is necessary for mTORC2 signaling downstream of both chemoattractant and hypo-osmotic shock.
Whether PLD2 has a general role in cell migration is a matter of debate [43–45]. To assess the role for PLD2 in HL-60 chemotaxis, we performed transwell assays on PLD2 shRNA cells and observed a severe chemotactic defect (Figs 3C and S1E). This migration defect is not a consequence of impaired differentiation, as the CD11b differentiation marker is unperturbed by PLD2 knock down (S1F Fig).
To characterize the regulation of actin assembly in PLD2 shRNA cells, we performed TIRF imaging of the WAVE2 complex subunit Hem1 in adherent cells and quantified the amount of F-actin by phalloidin staining of cells that were both stimulated and fixed in suspension. PLD2-depleted cells exhibited both larger wave fronts and a higher amount of polymerized actin following stimulation (Fig 3D and 3E, S2 and S4 Movies). Similar to cells that were knocked down for mTORC2, PLD2 knockdown cells also have higher membrane tension upon stimulation (Fig 3F), with an increase in static tether force from 38 to 52 pN, which corresponds with an increase in membrane tension from 69 to 126 μN/m (see above and Section II of S1 Text for details). As a secondary means of reducing PLD2 activity, we used the pharmacological PLD2 inhibitor VU0285655-1 [46], which produced a similar increase in static tether force from 37 (in the dimethyl sulfoxide [DMSO]-treated control) to 53 pN, which corresponds with an increase in membrane tension from 64 to 132 μN/m (S3A Fig).
Taken together, our observations suggest that PLD2 links membrane tension increases to mTORC2 activation and that this feedback circuit helps to establish the size of the leading edge and the magnitude of membrane tension in chemoattractant-stimulated neutrophils.
In control cells, increasing membrane tension inhibits actin assembly [5]. In the absence of PLD2 or mTORC2, neutrophils show enhanced actin nucleation and a more abundant actin network (Figs 2D, 2E, 3D and 3E), even though their membrane tension is significantly higher than chemoattractant-stimulated control cells ever normally achieve (Figs 2F and 3F). These data suggest that the link between membrane tension and actin assembly may be impaired in the absence of PLD2 and mTORC2. To investigate this possibility, we sought to further increase membrane tension through hypo-osmotic shock to evaluate whether the PLD2 and mTORC2 knockdown cells are defective at converting increases in membrane tension to decreases in actin network assembly (Figs 4 and S4).
We measured membrane tension in individual cells before and after osmotic shock and observed that 70 mOsm hypo-osmotic shock led to a similar elevated level of membrane tension in both control as well as PLD2 and Rictor knockdown cells (Fig 4B). Intriguingly, Nonsense, PLD2, and Rictor shRNA cells achieved the same high level of membrane tension regardless of their initial values of actin assembly or membrane tension. These data suggest that osmotic pressure dominates membrane tension in such circumstances and that a PLD2/mTORC2 independent mechanism sets an upper bound of membrane tension under these conditions.
Cells knocked down for PLD2 or Rictor were defective in stretch-induced inhibition of actin nucleation, as assessed via the loss of the WAVE2 complex component Hem1 from the membrane (Fig 4C, 4D and S4 Fig).
These data indicate that PLD2 and mTORC2 are required to efficiently relay increases in membrane tension to decreases in actin nucleation in neutrophils.
Previous studies suggest that membrane tension reduces actin polymerization directly by providing a barrier to its growth [11]. Our experimental data provide evidence that a separate inhibitory link from PLD2–mTORC2 to the WAVE2 complex is necessary for limiting actin assembly and controlling membrane tension. Here we investigated how these negative feedback links to actin assembly—direct (mechanical via the membrane as a physical barrier) and indirect (biochemical via PLD2–mTORC2 mechanosensory cascade)—might collaborate to regulate cell polarity and motility.
Since not all involved cellular processes are understood in sufficient detail for a complete mechanistic description, we sought to develop a conceptual model that allowed us to understand the contribution of the two different feedbacks. We used a previously developed mathematical model for simulating the propagating spatial dynamics of actin and the Hem-1 component of the WAVE2 complex in a small portion of the membrane in neutrophils [40]. In this previous model, stochastic WAVE2 complex activation—represented by WAVE2 binding to the membrane—is enhanced by self-association. Recruited WAVE2 complex promotes the nucleation of actin filaments, and actin polymerization inhibits further binding of the WAVE2 complex on the membrane via local negative feedback [47].
We then extended the model (Model I in Fig 5A, S5A and S5B Fig) to include feedback from membrane tension (Models II–IV in Fig 5A and S5A Fig). Specifically, we modeled networks in which feedback from membrane tension directly (Model II), indirectly (Model III), or both directly and indirectly (Model IV) inhibit actin polymerization. We additionally analyzed a model with reduced strength of indirect feedback (Model IV* in Fig 5A) to simulate PLD2 and Rictor knockdown experiments (Figs 3 and 4).
We assume that basal membrane tension (before hypo-osmotic shock) is proportional to the total amount of polymerized actin (S5C Fig, see also [8]) and the hypo-osmotic shock-based increase in membrane tension activates the PLD2–mTORC2 pathway in a switch-like manner (S5D Fig and S1 Text). We performed several lines of experiments to test these assumptions.
To verify that membrane tension is proportional to the total amount of polymerized actin, we used low doses of the monomer sequestering drug latrunculin B (50 nM) as a general inhibitor of actin polymerization and the drug CK666 inhibitor to specifically inhibit Arp2/3 complex-mediated actin assembly (100 μM). We quantified the overall levels of polymerized actin (F-actin) via phalloidin staining and showed, as previously reported [48,49], that low doses of latrunculin and CK666 partially blocked chemoattractant-induced actin polymerization (S5E Fig). Latrunculin B produced a profound decrease in the average tether force from 37 pN in control DMSO-treated cells to 11 pN (S5F Fig). This corresponds to a decrease in membrane tension from 64 to 6 μN/m (see above and Section II of S1 Text for details). CK666 produced a more moderate decrease in membrane tension (from 37 pN in control DMSO-treated cells to 23 pN; S5F Fig), corresponding to a decrease in membrane tension from 64 to 25 μN/m. These data suggest that actin polymerization in general, and the Arp2/3 complex in particular, play a significant role in generating membrane tension in neutrophils. Furthermore, for the perturbations tested, membrane tension scales linearly with the amount of actin polymerization (S5C Fig).
To verify that the PLD2-mTORC2 pathway is activated in a switch-like manner, we assayed the activation of this pathway over a range of membrane tension values (S5G Fig). In particular, we used actin polymerization inhibitors to decrease membrane tension (S5F Fig) and hypo-osmotic shock and cell stretching to increase membrane tension (Fig 1D), and we assessed mTORC2 activation by assessing S473-phosphorylation of Akt (Fig 1E). Together, these experiments show that mTORC2 activation responds nonlinearly to increases in membrane tension and suggested this relationship is well described by a Hill coefficient of ~8 (S5G and S5H Fig). We tested this result against curves with fixed, lower Hill coefficients (S5H Fig) and found that the best-fit curve explains the data significantly better than models with Hill coefficient ≤3 (p = 0.03, F test), thus justifying the assumption of the switch-like nature of PLD2-mTORC2 activation.
The SCAR/WAVE2 complex is a critical regulator of actin nucleation and directed migration [50–56], and its spatial and temporal dynamics closely correlate with actin assembly and morphological rearrangements in a wide range of motile cells [57–61], including neutrophils [40]. Therefore, we used the abundance and spatial patterning of WAVE2 as the main readout of the model and as the point of comparison to experimental data (Figs 2D, 3D, 4C and 4D). After a transient period, all models reach a stationary state with only minor stochastic fluctuations (S6A Fig), as they all contain the local negative feedback involving actin’s inhibition of the SCAR/WAVE2 complex. As expected, in the absence of the direct and indirect links from membrane tension to actin assembly (global feedback, Model I in Fig 5A), WAVE2 fails to respond to a simulated hypo-osmotic shock (Fig 5B, simulated by an increase in membrane tension of 80 μN/m, similar to the experiment in Fig 4). The addition of a direct link from membrane tension to actin polymerization (Model II in Fig 5A, S6B Fig) results in an increase in WAVE2 complex recruitment following an increase in tension due to the double-negative feedback via actin. This increase is not consistent with our experimental observations (Fig 4C and 4D). We found that the observed detachment of WAVE2 complex from the plasma membrane upon hypo-osmotic shock is only reproduced in the models containing the PLD2–mTORC2 inhibition of WAVE2 complex recruitment (Models III, IV, IV* in Fig 5A and S6B Fig). Furthermore, a marked WAVE2 complex detachment only occurs if the PLD2–mTORC2 pathway is activated by membrane tension in a switch-like manner (Hill coefficient ≥3, S7A Fig). As in the experiments (Fig 4D), disruption of the indirect feedback (mimicking PLD2 or mTORC2 knockdown) caused a slightly weaker response to osmotic shock (Fig 5B, 50 +/-1% decrease in Model IV versus 41 +/-1% in Model IV* after shock).
We next investigated the spatial organization of WAVE2 before and after osmotic shock (Fig 5C–5E). We quantified WAVE2 spatial patterning by a Wave Index (WI), which is a metric of disorganization calculated from the number of connected components in the spatial organization of WAVE2. A smaller WI indicates a smaller number of connected waves and a higher degree of spatial organization (see Materials and Methods). Analysis of experimental data (S4 Fig) reveals coherent waves before osmotic shock and an increase in the WI upon hypo-osmotic shock, indicating a loss of WAVE2 complex organization (Fig 5D). So an increase in tension not only decreases the amount of WAVE2 complex at the membrane (Fig 4C and 4D), but the portion that remains is also less organized (Fig 5D). These results are qualitatively unchanged for five times higher or lower values of most model parameters (ratio plots in S7B Fig).
Our base model (Model I), which lacks both links from membrane tension to actin, exhibits moderately disorganized waves (WI ≈ 0.2) that predictably do not change upon osmotic shock (Fig 5E). Addition of the direct link from tension to actin polymerization (Model II) resulted in significantly more organized waves (WI ≈ 0.1), but this did not change following osmotic shock (Fig 5E). Both models that contained the inhibitory link from mTORC2 and PLD2 to WAVE2 (Models III and IV) exhibited an increase in WI upon osmotic shock, but only the dual inhibition model (Model IV) had coherent waves before osmotic shock consistent with our experimental data (Figs 2D, 3D and 5C–5E). Furthermore, decreasing the strength of the indirect link (Model IV*) recapitulated the experimental data for PLD2 and mTORC2 knockdowns of delayed kinetics of WAVE2 disappearance following osmotic shock (Figs 4D and 5B) and attenuated increase of WI upon hypo-osmotic shock (Fig 5D and 5E). We conclude that a model that contains both the direct and indirect links from tension to actin assembly is most consistent with our experimental observations.
To analyze the relative impact of both feedbacks on WAVE2 complex spatial organization, we varied the strength of each link individually (S6B Fig). The direct restriction of actin network growth (together with the [local] actin-mediated inhibition of WAVE2) results in a positive link from membrane tension to the WAVE2 complex, resulting in a stabilization of wave fronts; this leads to a higher degree of organization of actin nucleation. In contrast, the PLD2–mTORC2 biochemical mechanosensor provides an indirect inhibitory link to the WAVE2 complex, and this couples tension increases to decreases in the WAVE2 complex on the membrane, potentially forming the basis of competition between sites of actin protrusion. We explicitly test this competition in the following simulations.
Our simulations suggest that the PLD2–mTORC2 link is necessary for membrane tension to influence the magnitude and spatial dynamics of the WAVE2 complex. We hypothesized that this link could play an important role in enabling actin protrusions to cross-inhibit one another. To investigate this possibility, we simulated the response of two spatially separate sites of actin assembly that are linked only by membrane tension (Fig 6A). Thus, actin polymerization and WAVE2 membrane binding are computed separately for each region, and both contribute to cellular membrane tension and subsequent mTORC2 activity.
We analyzed the activity of one protrusion when it grows in isolation (Fig 6A, region 1 alone, black) or following equivalent activation of polymerization in a second region (activity of region 1 [black] followed by activation of region 2 [grey], Fig 6B and 6C, S8 Fig, S6 Movie and S1 Text). Only the models that contain the inhibitory link from PLD2 and mTORC2 to the WAVE2 complex (Models III and IV) exhibit inhibition of WAVE2 complex recruitment and a loss of spatial organization by a secondary front, as can be seen in the higher Wave Index following competition (S8 Fig). The model that contains only the direct link from membrane tension to actin polymerization (Model II) exhibits a potentiation of WAVE2 complex recruitment during competition. This compensatory increase in WAVE2 complex recruitment buffers the tension-mediated decrease in actin polymerization, resulting in a minimal change in overall actin mass for the direct link alone (Fig 6C), making Model II a poor topology for competition. In contrast, the models with the indirect PLD2–mTORC2 link (Models III and IV) are more efficient in competition between fronts, on the level of actin polymer accumulation as well as for the amount and spatial organization of the WAVE2 complex (Figs 6B, 6C and S8).
Taken together, our model suggests that membrane tension plays two complementary roles to regulate actin nucleation and polymerization for cell polarity. The direct (mechanical) restriction of actin polymerization stabilizes the spatial organization of wave fronts while the indirect inhibitory link to the WAVE2 complex (PLD2–mTORC2 based) restricts nucleation and enables competition between protrusions for efficient leading edge formation and movement.
Membrane tension is thought to regulate leading edge dynamics by acting as a direct physical barrier to actin polymerization [11]. Similarly, when neutrophil protrusions are stalled by contacting a physical barrier, the actin nucleation machinery is inhibited [25,40]. We find that membrane tension also acts as an inhibitor of actin network assembly in an indirect manner: increases in plasma membrane tension activate the PLD2–mTORC2 pathway, and these components are essential for efficiently converting increases in membrane tension to decreases in actin nucleation. Our theoretical model suggests that these direct and indirect inhibitions collaborate to ensure the proper number, size, and spatial organization of cellular protrusions (Fig 7).
Upon disruption of the PLD2–mTORC2 pathway, neutrophils exhibit sustained high levels of F-actin in response to chemoattractant, higher membrane tension, and severely defective chemotaxis. Thus, the PLD2–mTORC2 pathway forms a mechanosensory-based negative feedback loop that limits actin nucleation, organizes cell polarity, and limits the magnitude of membrane tension. Our work demonstrates how the plasma membrane integrates physical forces and intracellular signals to organize cell polarity and membrane tension during movement. Furthermore, we place PLD2 and mTORC2 into a new mechanosensory context for eukaryotic chemotaxis.
Using a mechanosensory pathway to regulate actin network assembly could give cells more flexibility in tuning membrane tension and leading edge dynamics than if they were to only rely on direct physical coupling. Our work indicates that membrane tension can affect actin organization through the PLD2–mTORC2 signaling pathway, but, importantly, there are other known (potentially orthologous) signaling inputs into PLD2 and mTORC2 that could affect PLD2 and mTORC2 activation. Global modulation of this pathway could enable different cell types to establish different set points for membrane tension. Local modulation of this pathway could enable different thresholds of actin inhibition for different regions of a cell. Furthermore, membrane tension is lower in the presence of the PLD2–mTORC2 pathway, meaning that the protrusions that are formed have less membrane tension to push against and may be able to extend more efficiently than would be the case in the absence of PLD2 and mTORC2.
It is likely that multiple pathways regulate membrane tension in neutrophils. In response to 140mOsm hypotonic solutions, neutrophils maintain their polarized state and resting membrane tension, indicating that they can compensate for moderate changes in osmotic pressure (Fig 1D). At physiological levels of membrane tension, the mTORC2–PLD2 pathway plays an important role, as actin dynamics and membrane tension are defective in their absence. At high levels of osmotic pressure (by 70 mOsm hypotonic solutions), wild-type, mTORC2, and PLD2 KD cells all reach the same membrane tension limit (Fig 4B), suggesting a PLD2/mTORC2 independent mechanism that sets an upper bound of membrane tension under these conditions.
mTORC2 and PLD2 act downstream of membrane tension to inhibit the WAVE2 complex, but this is not the only means of converting increases in membrane tension to decreases in actin nucleation. An alternative pathway has recently been proposed to link membrane tension to actin assembly via NWASP/WASP modulation [62]. FBP17 is a BAR-containing NWASP/WASP regulator that binds membrane invaginations. Elevated tension releases this protein from the membrane, providing a potential link between increases in membrane tension and decreases in NWASP/WASP-based actin assembly [62]. Whether this pathway plays a role in neutrophil polarity, in which WAVE2 complex-dependent actin assembly dominates over NWASP/WASP-based actin assembly, is not known. Moreover, in neutrophils, the PLD2–mTORC2 axis appears to play a substantial role, as cell polarity, actin assembly, and membrane tension are all significantly defective in its absence. We suggest that multiple mechanisms of responding to changes in membrane tension could regulate different cytoskeletal programs, operate at different thresholds of tension, or dominate in different cell types.
Rictor and PLD2 shRNA cells have higher magnitude of membrane tension following chemoattractant stimulation. This is an unusual phenotype, as chemoattractant-stimulated neutrophils normally have a highly stereotyped value of membrane tension. Even perturbations that affect resting membrane tension (like inhibition of myosin II by blebbistatin) fail to alter membrane tension following chemotactic stimulation [5]. Moreover, in keratocytes, even perturbations that affect the number or size of the leading edge (like myosin phosphatase knockdown) don’t change the value of membrane tension [63].
Our data suggest that for neutrophils, membrane tension regulation operates through a negative feedback circuit involving PLD2 and mTORC2. Actin polymerization increases membrane tension, activating the PLD2–mTORC2 pathway, which in turn inhibits actin nucleation to establish an upper limit on leading edge size and the magnitude of membrane tension.
How might PLD2 connect increases in membrane tension to the activation of the mTORC2 pathway in neutrophils? One possible clue comes from the tension-based regulation of the TORC2 pathway in budding yeast. In S. cerevisiae, membrane invaginations known as eisosomes restrict the activity of Slm1, an activator of TORC2 [16]. Increases in membrane tension liberate Slm1 from eisosomes, freeing it to stimulate TORC2. There are no eisosomes or Slm1 orthologues in mammalian cells, but membrane invaginations and PLD2 could play a functionally similar role. PLD2 interacts with components of the mTORC2 complex and membrane invaginations (clathrin coated pits and caveolae [64,65]) and could be regulated in a similar manner to Slm1. Importantly, there is evidence that membrane reservoirs play an important role in neutrophil polarity and motility [66], and both caveolae and clathrin coated pits have been shown to unfold or arrest upon an increase in membrane tension [3,4]. Future studies should focus on identifying the nature of such membrane reservoirs in mammalian cells and on how PLD2 is regulated downstream of changes in membrane tension.
We observe that knockdown of the mTORC2 subunit Rictor severely inhibits neutrophil polarization and migration following chemoattractant stimulation, and this protein complex is essential to link increases in membrane tension to decreases in WAVE2-mediated actin nucleation. Several reports point to a pivotal role for mTORC2 in regulating neutrophil polarity during chemotaxis [19,20] but it remains unclear if the primary functions of mTORC2 in neutrophil motility depend on the mTor kinase activity or reflect kinase-independent protein interactions of the complex [20].
Upon hypo-osmotic shock, we observe a detachment of WAVE2 complex from the membrane. This may represent an inhibition upstream of the WAVE2 complex, as effects of stretch on Rac activity have been previously reported in neutrophils and other systems [5,67]. Interestingly, genetic loss-of-function perturbations for TORC2 yield a range of cytoskeletal defects in different cellular systems, possibly reflecting the existence of both positive and negative inputs to actin network assembly with different relative strengths in different contexts [23,34,68–71].
Plasma membrane tension impinges on a broad range of cellular events: it determines the leading edge size in neutrophils [5], streamlines polymerization in the direction of movement in Caenorhabditis elegans sperm cell [72], determines the folding state of caveolae [4], orchestrates phagocytosis [73] and cell spreading [74], and regulates the balance between exocytosis and endocytosis in a wide range of cell types [3,6,10,75]. Could the PLD2–mTORC2 pathway be a relevant player in these disparate cellular events?
TORC2 signaling downstream of changes in membrane tension appears to be conserved through evolution from yeast [16] to mammalian cells [17,18]. Furthermore, PLDs are activated in muscle cells after stretch and in erythrocytes upon hypo-osmotic shock [41,76]. Moreover, PLD2 is necessary in early phases of cell spreading [77], when membrane tension also plays a role [78].
An attractive idea is that the PLD2–mTORC2 pathway is part of a homeostatic system for regulating and responding to changes in membrane tension that motile cells have co-opted to regulate polarity and motility. Future studies will focus on testing whether the PLD2–mTORC2 is an integral component of other membrane-tension regulated processes and further elucidating the molecular links from membrane tension to the polarity and motility machinery.
HL-60 cells were grown in RPMI-1640 media with L-glutamine and 25 mM HEPES (10–041-CM, Mediatech) containing: 10% heat-inactivated fetal bovine serum (FBS; Life Technologies, 16140–071), penicillin, and streptomycin (UCSF Cell Culture Facility) at 37°C with 5% CO2 in a humidified incubator. Cell differentiation was initiated by adding 1.5% DMSO (endotoxin-free, hybridoma-tested; D2650, Sigma) to cells in growth media. Cells were used 4–5 d post-differentiation. Each independently-differentiated batch of HL60 cells was considered a biological replicate. For experiments requiring cell starvation, cells were starved for 1 h in RPMI-1640 media with L-glutamine and 25 mM HEPES containing 0.3% bovine serum albumin (BSA, endotoxin-free, fatty acid free; A8806, Sigma).
The shRNA sequences used in this study were:
Nonsense and Rictor shRNA sequences were obtained from [19], and PLD2 shRNA sequence was purchased from Sigma (NM_002663.3-3195s21c1). Each shRNA was cloned into the pMK1200 lentiviral plasmid for shRNA expression [79]. This allowed puromycin selection of positive cells (0.65 μg/ml for 2 wk). Next, cells were sorted for high expressers by fluorescence-activated cell sorting on a FacsAria2 (Beckton-Dickinson), and a bulk-sorted population consisting of fluorescence positive cells was used for each experiment.
HEK293T cells (ATCC) were grown to 70% confluency in a 6-well plate for each lentiviral target and transfected using 1.5 μg lentivirus plasmid, 160 ng VSV-G, and 1.3 μg CMV 8.91 with TransIT-293T transfection reagent (MIR 2705, Mirus Bio) according to manufacturer’s instructions. Viral supernatants were collected 2 d after transfection, passed through a 0.45 μm filter, and concentrated using Lenti-X concentrator (631231, Clontech) according to manufacturer’s instructions. Concentrated virus was used for infection immediately or kept at -80°C for long-term storage. For lentiviral infection, 50–100 μl of each concentrated virus was added directly to 105 undifferentiated HL-60 cells in the presence of 4 μg/ml polybrene. Viral media was replaced with normal growth media 24 h post-infection. Cells were sorted for high expressers by fluorescence-activated cell sorting on a FacsAria2 (Beckton-Dickinson), and a bulk-sorted population consisting of fluorescence-positive cells was used for each experiment.
Differentiation was confirmed with PE-conjugated anti-CDllb Mouse Anti-Human mAb (clone ICRF44; A18675, Life Technologies). In brief, 2*105 undifferentiated or differentiated cells were pelleted and incubated in 7 μl of anti-CD11b antibody on ice for 30 min, washed with ice-cold PBS with 1% FBS, and re-suspended in the same buffer at 106 cells/ml density for analysis. PBS with 1% FBS was used to establish background signal with unstained cells.
For imaging and immunofluorescence experiments, 96-well glass bottom Microwell plates (MatriPlate by Brooks Life Science Systems) were coated for 30 min with 100 μl of 10 μg/ml fibronectin (which we prepared from whole porcine blood) and then washed with PBS. 3*105 cells were plated on each dish in normal growth media and allowed to adhere for 10 min at 37°C with 100 nM formyl-Met-Leu-Phe (fMLP; F3506, Sigma).
For WAVE2 complex de-recruitment from the plasma membrane upon osmotic shock (Fig 4), cells were hypo-osmotically shocked with ddH2O containing 0.2% BSA and 100 nM fMLP.
TIRF images were acquired on a Nikon Ti Eclipse inverted microscope with a 60X Apo TIRF 1.49 NA objective and an electron-multiplying charge-coupled device (EM-CCD) camera (Andor iXon) controlled by NIS-Elements (Nikon, Melville, New York). Sample drift was minimized using an autofocus system (Perfect Focus; Nikon). A 488 nm laser line (200 mW) was supplied from Agilent MLC400. This laser launch uses acousto-optic tunable filters (AOTFs) to control laser output to a single-mode TIRF fiber for imaging. TIRF imaging was performed with 10 mW or less laser power, achieved through AOTF and neutral density-based laser attenuation. NIS-Elements was used for image acquisition.
For WAVE2 complex de-recruitment quantification (Fig 4D), the Hem1-GFP intensity inside the cell was integrated and the background intensity of a non-cell region subtracted using a custom-made MATLAB (MathWorks, R2012a) script. For WAVE2 area measurements (Figs 2D and 3D) a “wave mask” was obtained after applying a 2-pixel Gaussian blur filter. The mask threshold was manually chosen for Nonsense shRNA cells, and the same threshold applied to all Rictor and PLD2 cells imaged on the same day (to account for variability in imaging conditions from day to day) using Fiji. For quantification, the wave mask area was integrated using a custom-made MATLAB script.
All quantifications were normalized to the cell area at each time point. To obtain an accurate “cell mask,” an additional oblique illumination angle image was obtained. The resulting time series was denoised in collaboration with John Sedat, using Priism, a software developed by Jerome Boulanger [80]. Default parameters were chosen for a two-dimensional time series, except for the patch size, which was 3 × 3. The effect of the denoising was to remove speckle noise from the background while minimizing feature loss from the cells, which enabled automatic segmentation of the cell using Fiji.
The source code is available on request to the corresponding authors.
After starvation, cells where either stimulated with 10 nM fMLP or hypo-osmotically shocked with ddH2O containing 0.3% BSA. At appropriate time points (as stated in Figs 2, 3B and S2C), cells were fixed in 4% paraformaldehyde (CytoFix; 554655 BD Biosciences) containing phosphatase inhibitors (40 mM NaF, 20 mM beta-glycerol phosphate [50020, Fluka], and PhosSTOP [4906845001, Roche]) and the protease inhibitor cOmplete, EDTA-free (11873580001, Roche). The samples were spun at 2,000 xg for 5 min to pellet and washed with DPBS. Cells were permeabilized for 30 min at -20°C in ice-cold methanol. Next, cells were washed once in DPBS and blocked in IF buffer (DPBS + 5% FBS + 2 mM EDTA) for 1 hr. Samples were washed 3 × 5 min in IF buffer and incubated for 1 h at room temperature (RT) in primary antibody diluted 1:50 anti-pAktS473 (4060S, Cell Signaling) into IF buffer. Samples were washed 3 × 5 min in IF buffer and incubated for 30 min at RT in fluorescent secondary antibody diluted 1:300 Alexa 488-Donkey-Anti-Rabbit (711-546-152, Jackson Inmuno Research) into IF buffer. After 3 × 5 min washes in IF buffer, cells were analyzed on a FACS LSRII (BD Biosciences). Data analysis and visualization was performed on FlowJo (TreeStar, Ashland, Oregon). For each sample, the median from the Alexa 488-Donkey-Anti-Rabbit Gaussian was obtained in FlowJo and normalized to the corresponding untreated control.
Four cm2 pieces of PDMS were mounted on our custom-made cell stretcher, and a central region was coated with 500 μl of 10 μg/ml fibronectin (which we prepared from whole porcine blood) for 30 min, and then washed with PBS. 6*105 cells were plated on stretched or unstretched PDMS (control cells were plated in pre-stretched PDMS pieces, which we used to control for any mechanical effects of the cell substrate) and allowed to adhere for 10 min at 37°C with 10 nM fMLP (F3506, Sigma). After 15 min of 0% or 40% radial stretch, cells were fixed in 4% paraformaldehyde (CytoFix; 554655 BD Biosciences) containing phosphatase inhibitors (40 mM NaF, 20 mM beta-glycerol phosphate [50020, Fluka] and PhosSTOP [4906845001, Roche]) and the protease inhibitor cOmplete, EDTA-free (11873580001, Roche). Cells were permeabilized with 0.2% triton for 10 min and washed twice with DPBS with inhibitors. Next, cells were blocked in DPBS with inhibitors and 4% BSA for 1 hr. Samples were incubated for 1 h at RT in primary antibody diluted 1:200 anti-pAktS473 (4060S, Cell Signaling) into DPBS with inhibitors. Samples were washed 3 × 5 min in DPBS and incubated for 30 min at RT in fluorescent secondary antibody diluted 1:300 Alexa 488-Donkey-Anti-Rabbit (711-546-152, Jackson Inmuno Research) into DPBS. After 3 × 5 min, PDMS pieces were covered with SlowFade Gold antifade reagent with DAPI (Life Technologies, S36938) and a glass coverslip and sealed with nail polish. Epifluorescence microscopy through the coverslip was performed on a Nikon Eclipse Ti microscope.
5*105 HL-60 cells were stimulated with addition of 10 nM fMLP, fixed with 3.7% paraformaldehyde in intracellular buffer (140 mM KCl, 1 mM MgCl2, 2 mM EGTA, 320 mM sucrose, and 20 mM HEPES with pH 7.5) and incubated on ice for 15 min. After centrifugation at 2,000 xg for 5 min, the cell pellet was re-suspended in intracellular buffer containing 0.2% Triton X-100 and 1:200 Phalloidin-Alexa 488 (Molecular Probes, A12378) and stained for 15 min. Cells were then centrifuged, washed twice, and re-suspended in intracellular buffer before being analyzed on a FACS LSRII (BD Biosciences). Data analysis and visualization was performed on FlowJo (TreeStar, Ashland, Oregon) as follows: For each sample the median from the Phalloidin-Alexa 488 Gaussian was obtained in FlowJo and normalized to the corresponding untreated control.
For latrunculin B, CK666, and VU0285655-1 experiments, cells were preincubated for 10–20 min with either solutions of 10 nM fMLP with 50 nM latrunculin B (Sigma), 100 nM fMLP with 100 μM CK666 (Sigma), or 100 nM fMLP with 4.5 μM VU0285655-1 (Avanti lipids).
Different adhesive substrates were obtained by coating surfaces with a range of % molar mixture of BSA and Fibronectin till saturations for over 1 h. Ten percent of such Human plasma fibronectin (Gibco) was labeled with Alexa Fluor 647 NHS-ester.
Surfaces were then washed twice with DPBS. 5*105 HL-60 cells were plated on each dish in normal growth media and allowed to adhere for 10 min at 37°C. After, cells were washed and imaged in growth media with 10 nM fMLP or membrane tension was measured in RPMI with 2% FBS. Cells were manually tracked/counted using Fiji (image processing package of ImageJ).
For western blots, 106 cells were lysed in 20% ice-cold trichloroacetic acid (TCA). The samples were spun at 20,000xg for 15 minutes to pellet. The sample pellets were washed twice with 0.5% ice-cold TCA and resuspended in Laemmli protein sample buffer (161–0737, BioRad) containing 5% β-mercaptoethanol. Protein bands were separated by SDS-PAGE gel electrophoresis, transferred to nitrocellulose, blocked with Odyssey block, and incubated at 4°C overnight with 1:1000 dilutions of anti-Rictor (Bethyl, A300-459A) or 1:1000 anti-PLD2 (Sigma, WH0005338M1) and 1:2000 anti-Akt (40D4, Cell Signaling). The blot was developed with the fluorescent secondary antibodies Goat anti-Rabbit IRDye 680RD (Licor, 926–68071) and Goat ant-Mouse IRDye 800CW (Licor, 926–32210), and protein bands were imaged using Odyssey Infrared Imaging System (Li-COR, Biosciences).
3*105 HL-60 cells were stained with DiD (V-22887, Life Technologies) and plated on the upper chamber of a 24-well format HTSFluoBlokTM Multiwell Insert System (3 μm pore size; RF351156, BD Falcon) in RPMI without phenol red (Life Technologies, 11835–030) with 2% FBS. Cells were allowed to migrate towards the bottom well containing 10 nM fMLP for 2 hours at 37°C. The migrated cells were measured by fluorescence from the bottom of the insert, while the opaque filter prevented excitation of cells on top of the filter. Analysis was performed with a FlexStation 3 Microplate Reader (Molecular Devices). Each condition was run in triplicate, and the migration index was calculated by dividing the amount of signal in the sample well by the signal in a well in which 300,000 cells were plated in the bottom compartment.
For membrane tension measurements, custom-made chambers were coated for 30 min with 500 μl of 20 μg/ml fibronectin (which we prepared from whole porcine blood), and then washed with PBS. 5*105 HL-60 cells were plated on each dish in normal growth media and allowed to adhere for 10 min at 37°C with 10 nM fMLP (F3506, Sigma). After, cells were washed and probed in RPMI with 2% FBS with 10 nM fMLP (F3506, Sigma) at 30°C.
Olympus BioLevers (k = 60 pN/nm) were calibrated using the thermal noise method and incubated in 2.5 mg/ml Concanavalin A (C5275, Sigma) for 1 h at RT. Before the measurements, cantilevers were rinsed in DPBS.
Cells were located by brightfield imaging, and the cantilever was positioned at any location over the cell for tether measurement. Cells were not used longer than 1 h for data acquisition.
Tethers were pulled using a Bruker Catalyst AFM controlled by custom-made LabVIEW software mounted on an inverted Zeiss fluorescent microscope. Approach velocity was set to 1 μm/s, contact force to 100 pN, contact time to 5–10 s and retraction speed to 10 μm/s. We used the signature of tether breaking in the AFM traces to identify cells that have multiple tethers by visualizing multiple steps in the force trace—these cells with multiple tethers were excluded from analysis. We titrated the productive cantilever/cell interactions to enrich for single tethers by titrating the amount of Concanavilin A used to coat the cantilever, the amount of FBS blocker in the media during tether pulling, and the force clamp time (5–10 s). After a 10 μm tether was pulled, the cantilever position was held constant until it broke. Only tethers that broke in less than 15 s were taken into account as actin polymerized inside of longer-lived tethers. The jump in force during phase II is the result of a viscoelastic response of the cell once the cantilever touches it. Negative and positive forces relate to the angle the cantilever takes, but the sign is arbitrary. By convention, contacting the cell deflects the cantilever towards positive values. Conversely, when the cantilever is pulled downwards by a membrane tether, the values are negative. Resulting force–time curves were analyzed with the Kerssemakers algorithm (Kerssemakers et al., 2006 [39]) kindly provided by Jacob Kerssemakers.
Graphing and statistical analyses were performed using R, Microsoft Excel, and MATLAB.
P-values were calculated using t test after ensuring normality of data with a Shapiro-Wilk test. Otherwise, a non-parametric Mann-Whitney test was used, as it does not assume a particular data distribution.
Boxes in all box plots extend from the 25th to 75th percentiles, with a line at the median. Whiskers extend to ×1.5 IQR (interquartile range) or the max/min data points if they fall within ×1.5 IQR.
All simulations and analysis steps were performed in MATLAB (MathWorks, R2014b). For a detailed description of the model, see S1 Text. All parameter values are listed in S1 Table.
All model variables were initially set to zero except for a point source of membrane-bound WAVE2. We applied periodic boundary conditions on a fixed domain in 2D for the spatial variables, polymerized actin, and membrane-bound WAVE2 complex. Stochastic simulations were performed 20 times, with starting times and times of osmotic shock or onset of competition randomized over a short initial period (2 min) (except for S6A Fig). Quantitative analysis was restricted to time-points after an initial transient or a transient after shock or onset of competition.
To quantify wave patterns of actin nucleation (see Fig 5C–5E), we developed the Wave Index (WI) as follows: At each time point, the spatial pattern (a grayscale image) is filtered by a Gaussian and threshold-segmented by a low-level intensity threshold to obtain the number of connected components in each image, which is normalized by the segmented area. This measure is based on the reasoning that images showing clear wave-like patterns have a high level of connectedness and, thus, a small number of connected components. Since the WI is sensitive to the signal-to-noise quality of the images, in the analysis of model-generated wave patterns (Figs 5E, S6, S7B and S8), we added zero-mean Gaussian noise instead of the filter, to make the WAVE2 signal-to-noise in the simulations more comparable to experimental data.
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10.1371/journal.pcbi.1004610 | From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach | People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.
| When viewing an object, people perceive the object’s shape. Similarly, when grasping the same object, they also perceive its shape. In general, the perceived shape is identical in these two scenarios, illustrating modality invariance, an important type of perceptual constancy. Modality invariance suggests that people infer a modality-independent, conceptual representation that is the same regardless of the modality used to sense the environment. If so, how do people infer modality-independent representations from modality-specific sensory signals? We present a hypothesis about the components that any system will include if it infers modality-independent representations from sensory signals. This hypothesis is instantiated in a computational model that infers object shape representations from visual or haptic (i.e., active touch) signals. The model shows perfect modality invariance—it infers the same shape representations regardless of the sensory modality used to sense objects. The model also provides a highly accurate account of data collected in an experiment in which people judged the similarity of pairs of objects that were viewed, grasped, or both. Conceptually, our research contributes to our understanding of modality invariance. Methodologically, it contributes to cognitive modeling by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.
| While eating breakfast, you might see your coffee mug, grasp your coffee mug, or both. When viewing your mug, your visual system extracts and represents the shape of your mug. Similarly, when grasping your mug, your haptic system also extracts and represents the shape of your mug. Are the representations acquired when viewing your mug distinct from the representations acquired when grasping your mug? If so, these would be modality-specific representations. Or does there exist a level at which the shape representation of your mug is the same regardless of the sensory modality through which the mug is perceived? If so, this would be a modality-independent representation.
Recent experiments on crossmodal transfer of perceptual knowledge suggest that people have multiple representations of object shape and can share information across these representations. For example, if a person is trained to visually categorize a set of objects, this person will often be able to categorize novel objects from the same categories when objects are grasped but not seen [1, 2]. Because knowledge acquired during visual training is used during haptic testing, this finding suggests that neither the learning mechanisms used during training nor the representations acquired during training are exclusively visual. To the contrary, the finding indicates the existence of both visual and haptic object representations as well as the ability to share or transfer knowledge across these representations. Successful categorization of objects regardless of whether the objects are seen or grasped illustrates modality invariance, an important type of perceptual constancy.
What type of learning mechanisms and mental representations might underlie modality invariance? One possible answer is that people are able to abstract over their modality-specific representations in order to acquire modality-independent representations. For instance, people might use modality-specific representations of objects as a foundation for inferring modality-independent representations characterizing objects’ intrinsic properties. To understand the nature of the latter representations, it is important to recognize the distinction between objects’ intrinsic (or “deep”) properties and the sensory (or “surface”) features that these properties give rise to. The shape of an object is a modality-independent intrinsic property. Visual and haptic features are modality-specific sensory cues to the object’s shape arising when the object is viewed or grasped, respectively.
Once acquired, modality-independent representations may underlie modality invariance. For example, they can mediate crossmodal transfer of knowledge. Consider a person who is first trained to visually categorize a set of objects, and then tested with novel objects (from the same set of categories) when the objects are grasped but not seen. During visual training, the person uses his or her visual representation of each object to infer a modality-independent representation characterizing the object’s intrinsic properties, and applies the object’s category label to this representation. When subsequently grasping a novel object on a test trial, the person uses the object’s haptic representation to infer a modality-independent representation of its intrinsic properties. The novel object is judged to be a member of a category if it has similar intrinsic properties to the training objects belonging to that category.
Because modality-independent representations may underlie modality invariance, they would clearly be useful for the purposes of perception and cognition. Importantly, recent behavioral and neurophysiological data indicate their existence in biological organisms. For instance, behavioral and neural evidence support the idea that object features extracted by vision and by touch are integrated into modality-independent object representations that are accessible to memory and higher-level cognition [3–15]. Based on brain imaging (fMRI) data, Taylor et al.[10] argued that posterior superior temporal sulcus (pSTS) extracts pre-semantic, crossmodal perceptual features, whereas perirhinal cortex integrates these features into amodal conceptual representations. Tal and Amedi [15], based on the results of an fMRI adaptation study, claimed that a neural network (including occipital, parietal, and prefrontal regions) showed crossmodal repetition-supression effects, indicating that these regions are involved in visual-haptic representation.
Perhaps the most striking data comes from the work of Quiroga and colleagues who analyzed intracranial recordings from human patients suffering from epilepsy [16, 17]. Based on these analyses, they hypothesized that the medial temporal lobe contains “concept cells”, meaning neurons that are selective for particular persons or objects regardless of how these persons or objects are sensed. For instance, Quiroga et al [16] found a neuron that responded selectively when a person viewed images of the television host Oprah Winfrey, viewed her written name, or heard her spoken name. (To a lesser degree, the neuron also responded to the comedian Whoopi Goldberg.) Another neuron responded selectively when a person saw images of the former Iraqi leader Saddam Hussein, saw his name, or heard his name.
To fully understand modality-independent representations, Cognitive Science and Neuroscience need to develop theories of how these representations are acquired. Such theories would be significant because they would help us understand the relationships between perceptual learning and modality invariance. They would also be significant because they would be early “stepping stones” toward developing an understanding of the larger issue of how sensory knowledge can be abstracted to form conceptual knowledge.
The plan of this paper is as follows. In the Results section, we start by describing a general theoretical framework for how modality-independent representations can be inferred from modality-specific sensory signals. To evaluate the framework, we next describe an instantiation of the framework in the form of a computational model, referred to as the Multisensory-Visual-Haptic (MVH) model, whose goal is to acquire object shape representations from visual and/or haptic signals. Simulation results show that the model learns identical object representations when an object is viewed, grasped, or both. That is, the model’s object percepts are modality invariant. We also evaluate the MVH model by comparing its predictions with human experimental data. We report the results of an experiment in which subjects rated the similarity of pairs of objects, and show that the model provides a very successful account of subjects’ ratings. In the Discussion section, we highlight the contributions of our theoretical framework in general, and of the MVH model in particular, emphasizing its combination of symbolic and statistical approaches to cognitive modeling. Due to this combination, the model is consistent with an emerging “probabilistic language of thought” methodology. The Methods section provides modeling and experimental details.
According to our framework, any system (biological or artificial) that acquires modality-independent representations from sensory signals will include the following three components: (1) a representational language for characterizing modality-independent representations; (2) sensory-specific forward models for mapping from modality-independent representations to sensory signals; and (3) an inference algorithm for inverting sensory-specific forward models—that is, an algorithm for using sensory signals in order to infer modality-independent representations. These three components are discussed in turn.
To better understand and evaluate our framework, we apply it to the perception of object shape via visual and haptic modalities. This application results in the MVH computational model with the three components outlined above.
We have had to make specific implementation choices to instantiate our theoretical framework as a computational model. To us, these choices are both uninteresting and interesting. On the one hand, the implementation choices that we have made are not essential to the framework. Indeed, other reasonable choices could have been made, thereby leading to alternative framework implementations. On the other hand, we believe that some of our choices are important because they contribute to the study of cognitive modeling. In particular, our computational model combines both symbolic and statistical modeling approaches. Because of this combination, the model can be regarded as falling within a recently emerging “probabilistic language of thought” methodology. This contribution is described in the Discussion section.
One of the implementation choices that we made was a choice as to which stimuli we should focus on. Object shape perception via vision and/or haptics is currently an unsolved problem when considered in its full generality. Consequently, we focus on a small subset of objects. We designed 16 novel objects, where the set of object parts was based on a previously existing set of objects known as “Fribbles”. Fribbles are complex, 3-D objects with multiple parts and spatial relations among parts. They have been used in studies of visual [19, 20] and visual-haptic [2] object perception. We used part-based objects because many real-world objects (albeit not all) have a part-based structure. In addition, theories of how people visually recognize part-based objects have received much attention and played important roles in the field of Cognitive Science [21–25].
Each object that we designed is comprised of five parts (the set of possible parts is shown in Fig 1). One part (labeled P0 in Fig 1), a cylindrical body, is common to all objects. The remaining four parts vary from object to object, though they are always located at the same four locations in an object. A particular object is specified by selecting one of two interchangeable parts at each location (4 locations with 2 possible parts per location yields 16 objects). The complete set of objects is shown in Fig 2.
Above, the motivations and merits of our computational model were described based primarily on theoretical grounds. Here, we evaluate the MVH model based on its ability to provide an account of human experimental data. The experiment reported here is related to the experiments of Wallraven, Bülthoff, and colleagues who asked subjects to rate the similarity of pairs of objects when objects were viewed, grasped, or both [57–61]. However, our experiment also includes a crossmodal condition in which subjects rated object similarity when one object was viewed and the other object was grasped.
In brief (experimental details are given in the Methods section), the stimuli were the 16 objects described above (Fig 2). On each trial, a subject observed two objects and judged their similarity on a scale of 1 (low similarity) to 7 (high similarity). The experiment included four conditions referred to as the visual, haptic, crossmodal, and multisensory conditions. Different groups of subjects were assigned to different conditions. In the visual condition, subjects viewed images of two objects on each trial. In the haptic condition, subjects grasped physical copies of two objects (fabricated using 3-D printing) on each trial. In the crossmodal condition, subjects viewed an image of one object and grasped a second object on each trial. Finally, in the multisensory condition, subjects viewed and grasped two objects on each trial.
This paper has studied the problem of learning modality-independent, conceptual representations from modality-specific sensory signals. We hypothesized that any system that can accomplish this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models (i.e., an algorithm for using sensory signals to infer modality-independent representations).
To evaluate our theoretical framework, we instantiated it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic context-free grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit (VTK) and a human hand simulator (GraspIt!) to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. It is worth pointing out that the particular implementational choices we have made in our model are in some sense arbitrary; any model that instantiates our framework will be able to capture modality invariance. Therefore, from this perspective, our particular model in this work should be taken as one concrete example of how modality independent representations can be acquired and used.
Our work in this paper focused on showing how our framework can capture one aspect of multisensory perception, i.e., modality invariance. We take this as an encouraging first step in applying our framework to multisensory perception more generally. We believe other aspects of multisensory perception (such as cue combination, crossmodal transfer of knowledge, and crossmodal recognition) can be easily understood and treated in our framework.
The paper also reported the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and showed that the model provides a very good account of subjects’ ratings. Our experimental results suggest that people extract modality independent shape representations from sensory input and base their judgments of similarity on such representations. The success of our model in accounting for these results are important from two perspectives. First, from a larger perspective, it is significant as a validation of our theoretical framework. Second, it constitutes an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.
Our theoretical framework is closely related to the long standing vision-as-inference [74] approach to visual perception. In this approach, the computational problem of visual perception is formalized as the inversion of a generative process; this generative process specifies how the causes in the world, e.g., objects, give rise to 2D images on the retina. Then, the purpose of the visual system is to invert this generative model to infer the most likely causes, i.e., the explanation, for the observed sensory data. This approach, also called analysis-by-synthesis, has featured prominently both in cognitive science [75, 76] and computer vision [77–79]. Our work here can be seen as the application of this approach to multisensory perception.
Previous research has instantiated our general theoretical framework in other ways. For example, Yildirim and Jacobs [80] developed a latent variable model of multisensory perception. In this model, modality-independent representations are distributed representations over binary latent variables. Sensory-specific forward models map the modality-independent representations to sensory (e.g., visual, auditory, haptic) features. The acquisition of modality-independent representations takes place when a Bayesian inference algorithm (the Indian Buffet Process [63]) uses the sensory features to infer these representations. Advantages of this model include the fact that the dimensionality of the modality-independent representations adapts based on the complexity of the training data set, the model learns its sensory-specific forward models, and the model shows modality invariance. Disadvantages include the fact that the inferred modality-independent representations (distributed representations over latent variables) are difficult to interpret, and the fact that the sensory-specific forward models are restricted to being linear. Perhaps its biggest disadvantage is that it requires a well-chosen set of sensory features in order to perform well on large-scale problems. In the absence of good sensory features, it scales poorly, mostly due to its linear sensory-specific forward models and complex inference algorithm.
As a second example, Yildirim and Jacobs [2] described a model of visual-haptic object shape perception that is a direct precursor to the MVH model described in this paper. Perhaps its biggest difference with the model presented here is that it represents parts as generalized cylinders, and parts connect to each other using a large number of “docking locations”. This strategy for representing object shape provides enormous flexibility, but this flexibility comes at a price. Inference using this model is severely underconstrained. Consequently, the investigators designed a customized (i.e., ad hoc) Bayesian inference algorithm. Despite the use of this algorithm, inference is computationally expensive. That is, like the latent variable model described in the previous paragraph, the model of Yildirim and Jacobs [2] scales poorly.
We believe that the MVH model described in this paper has significant theoretical and practical advantages over alternatives. These arise primarily due to its use of a highly structured implementation of a representational language for characterizing modality-independent representations. In particular, the model combines symbolic and statistical approaches to specify a probabilistic context-free object shape grammar. Due to this shape grammar, the model is able to use a principled inference algorithm that has previously been applied to probabilistic grammars in other domains. We find that inference in the model is often computationally tractable. We are reasonably optimistic that the model (or, rather, appropriately extended versions of the model) will scale well to larger-scale problems. Although important challenges obviously remain, our optimism stems from the fact that shape grammars (much more complex than the one reported here) are regularly used in the Computer Vision and Computer Graphics literatures to address large-scale problems. In addition, due to its principled approach, the model should be easy to extend in the future because relationships between the model and other models in the Cognitive Science and Artificial Intelligence literatures using grammars, such as models of language, are transparent. As a consequence, lessons learned from other models will be easy to borrow for the purpose of developing improved versions of the model described here.
In Cognitive Science, there are many frameworks for cognitive modeling. For example, one school of thought favors symbolic approaches, such as approaches based on grammars, production rules, or logic. An advantage of symbolic approaches is their rich representational expressiveness—they can often characterize a wide variety of entities in a compact and efficient manner. A disadvantage of these approaches is that they are often “brittle” when used in noisy or uncertain environments. An alternative school of thought favors statistical approaches, such as approaches based on neural networks or Bayesian inference. An advantage of statistical approaches is their ability to learn and adapt, and their robustness to noise and uncertainty. Their main disadvantage is that they often require highly structured prior distributions or likelihood functions to work well [81]. Advocates of symbolic and statistical schools of thought have often engaged in heated debates [82–85]. Unfortunately, these debates have not led to a resolution as to which approach is best.
A recently emerging viewpoint in the Cognitive Science literature is that both symbolic and statistical approaches have important merits, and thus it may be best to pursue a hybrid framework taking advantage of each approach’s best aspects [45–48]. This viewpoint is referred to here as a “probabilistic language of thought” approach because it applies probabilistic inference to a representation consisting of symbolic primitives and combinatorial rules [86]. To date, the probabilistic language-of-thought approach has been used almost exclusively in domains that are typically modeled using symbolic methods, such as human language and high-level cognition. A significant contribution of the research presented here is that it develops and applies this approach in the domain of perception, an area whose study is dominated by statistical techniques.
We foresee at least three areas of future research. First, the framework described here sheds light on modality invariance. Future work will need to study whether this framework also sheds light on other aspects of multisensory perception and cognition. For example, can the framework be used to understand why our percepts based on two modalities are often more accurate than our percepts based on a single modality, why training with two modalities is often superior to training with a single modality (even when testing is conducted in unisensory conditions), or why crossmodal transfer of knowledge is often, but not always, successful? Future work will also need to study the applicability of the framework to other sensory domains, such as visual and auditory or auditory and haptic environments. Future work will also need to consider how our framework can be extended to study the acquisition of other types of conceptual knowledge from sensory signals.
Second, future research will need to study the role of forward models in perception and cognition. For example, we have speculated that sensory-specific forward models may be ways of implementing sensory imagery, and thus our framework predicts a role for imagery in multisensory perception. Behavioral, neurophysiological, and computational studies are needed to better understand and evaluate this hypothesis. From a technological perspective, it is advantageous that we live in a “golden age” of forward models. New and improved forward models are frequently being reported in the scientific literature and made available on the world wide web (e.g., physics engines providing approximate simulations of physical systems such as rigid body dynamics or fluid dynamics). These forward models will allow cognitive scientists to study human perception, cognition, and action in much more realistic ways than has previously been possible.
Finally, cognitive scientists often make a distinction between rational models and process models [87]. Rational models (or computational theories [88]) are models of optimal or normative behavior, characterizing the problems that need to be solved in order to generate the behavior as well as their optimal solutions. In contrast, process models (or models at the “representation and algorithm” level of analysis [88]) are models of people’s behaviors, characterizing the mental representations and operations that people use when generating their behavior. Because the MVH model’s inference algorithm is optimal according to Bayesian criteria, and because this algorithm is not psychologically plausible, the model should be regarded as a rational model, not as a process model. Nonetheless, we believe that there are benefits to regarding the MVH model as a rational/process hybrid. Like rational models, the MVH model is based on optimality considerations. However, like process models, it uses psychologically plausible representations and operations (e.g., grammars, forward models).
For readers solely interested in process models, we claim that the MVH model is a good starting point. As pointed out by others [89, 90], the MCMC inference algorithm used by the MVH model can be replaced by approximate inference algorithms (known as particle filter or sequential Monte Carlo algorithms) that are psychologically plausible. Doing so would lead to a so-called “rational process model”, a type of model that is psychologically plausible and also possesses many of the advantages of rational models. Future work will need to study the benefits of extending our framework through the use of psychologially plausible and approximately optimal inference algorithms to create rational process models of human perception.
The experiments were approved by the Research Subjects Review Board of the University of Rochester. All subjects gave informed consent.
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10.1371/journal.pbio.1001234 | The Discovery of New Deep-Sea Hydrothermal Vent Communities in the Southern Ocean and Implications for Biogeography | Since the first discovery of deep-sea hydrothermal vents along the Galápagos Rift in 1977, numerous vent sites and endemic faunal assemblages have been found along mid-ocean ridges and back-arc basins at low to mid latitudes. These discoveries have suggested the existence of separate biogeographic provinces in the Atlantic and the North West Pacific, the existence of a province including the South West Pacific and Indian Ocean, and a separation of the North East Pacific, North East Pacific Rise, and South East Pacific Rise. The Southern Ocean is known to be a region of high deep-sea species diversity and centre of origin for the global deep-sea fauna. It has also been proposed as a gateway connecting hydrothermal vents in different oceans but is little explored because of extreme conditions. Since 2009 we have explored two segments of the East Scotia Ridge (ESR) in the Southern Ocean using a remotely operated vehicle. In each segment we located deep-sea hydrothermal vents hosting high-temperature black smokers up to 382.8°C and diffuse venting. The chemosynthetic ecosystems hosted by these vents are dominated by a new yeti crab (Kiwa n. sp.), stalked barnacles, limpets, peltospiroid gastropods, anemones, and a predatory sea star. Taxa abundant in vent ecosystems in other oceans, including polychaete worms (Siboglinidae), bathymodiolid mussels, and alvinocaridid shrimps, are absent from the ESR vents. These groups, except the Siboglinidae, possess planktotrophic larvae, rare in Antarctic marine invertebrates, suggesting that the environmental conditions of the Southern Ocean may act as a dispersal filter for vent taxa. Evidence from the distinctive fauna, the unique community structure, and multivariate analyses suggest that the Antarctic vent ecosystems represent a new vent biogeographic province. However, multivariate analyses of species present at the ESR and at other deep-sea hydrothermal vents globally indicate that vent biogeography is more complex than previously recognised.
| Deep-sea hydrothermal vents are mainly associated with seafloor spreading at mid-ocean ridges and in basins near volcanic island arcs. They host animals found nowhere else that derive their energy not from the sun but from bacterial oxidation of chemicals in the vent fluids, particularly hydrogen sulphide. Hydrothermal vents and their communities of organisms have become important models for understanding the origins and limits of life as well as evolution of island-like communities in the deep ocean. We describe the fauna associated with high-temperature hydrothermal vents on the East Scotia Ridge, Southern Ocean, to our knowledge the first to be discovered in Antarctic waters. These communities are dominated by a new species of yeti crab, stalked barnacles, limpets and snails, sea anemones, and a predatory seven-armed starfish. Animals commonly found in hydrothermal vents of the Pacific, Atlantic, and Indian Oceans, including giant Riftia tubeworms, annelid worms, vent mussels, vent crabs, and vent shrimps, were not present at the Southern Ocean vents. These discoveries suggest that the environmental conditions of the Southern Ocean may act as a barrier to some vent animals and that the East Scotia Ridge communities form a new biogeographic province with a unique species composition and structure.
| The discovery of hydrothermal vents along the Galápagos Ridge in 1977 [1] led to the identification of chemoautotrophic symbiosis [2] and forced marine biologists to reassess the contribution chemosynthesis makes to marine primary production, particularly in the deep sea, where it supports a high biomass in an otherwise food-limited ecosystem. The existence of life in the extremely harsh conditions of hydrothermal vents has stimulated an increasing research effort on the diversity, ecology, and physiology of vent organisms, as well as new avenues of research into the origins of life on Earth [3] and even into the occurrence of life elsewhere within and outside the solar system. Because of the characteristics of hydrothermal vent communities—in particular the high levels of species endemism, their constraint to discrete habitats separated at different spatial scales and by geological/environmental barriers, their global distribution, and their historical coupling to plate tectonics—they are regarded as unique ecosystems. In particular, ecologists recognise that the unusual characteristics of deep-sea vents compared to other deep-sea habitats, coupled with the ephemeral nature of hydrothermal circulation, have probably had important implications for the composition, diversity, and biogeography of their communities and the dispersal and genetic population structure of vent species [4]–[6].
Several decades of exploration have resulted in the detection of numerous vent sites and faunal assemblages at many mid-ocean ridges and back-arc basins. These discoveries have resulted in an apparent global biogeography of vent organisms with separate provinces in the East Pacific, the North East Pacific, West Pacific back-arc basins, the shallow and deep Atlantic, and the Indian Ocean [7], although a more recent analysis has proposed a single province for the Atlantic, a single province for the North West Pacific, a single province for the South West Pacific and Indian Ocean, and a biogeographic separation of the North East Pacific, North East Pacific Rise, and South East Pacific Rise [8]. These biogeographic provinces are based on sampling undertaken by human-occupied vehicles and remotely operated vehicles (ROVs), and for the most part lie within the tropics and sub-tropics, where deep submergence operations are less limited by prevailing sea conditions than at high latitudes [6],[9]. Weather conditions have constrained the discovery of hydrothermal vents at high latitudes, although there is evidence from water column plumes that vents occur in the Arctic along the Gakkel Ridge [10], the Mohn Ridge, [11] and the Arctic Mid-Ocean Ridge [12], and in the Southern Ocean, in Antarctica, along the East Scotia Ridge (ESR), in the Scotia Sea [13], in the Bransfield Strait, west of the northern Antarctic Peninsula [14],[15], and along the Pacific-Antarctic Ridge [16]. In the Arctic, animal communities have been described at deep-sea hydrothermal vents on the Mohn and Arctic Mid-Ocean Ridges, although only the latter appears to host a high biomass of vent-endemic fauna [11],[12]. Here we show, to our knowledge for the first time, the presence of black smokers, diffuse venting, and associated chemosynthetically driven ecosystems along the ESR, a geographically isolated back-arc spreading centre in the Atlantic sector of the Southern Ocean, Antarctica (Figure 1A). Based on biological observations we also present a re-analysis of the global biogeography of the deep-sea hydrothermal vent fauna, including that of the Antarctic hydrothermal vents.
The Scotia Sea is defined by a loop of shallow banks and islands, known as the Scotia Arc, that extends eastwards from Cape Horn, south of the Falkland Islands (Burdwood Bank, Shag Rocks, and South Georgia), then southwards along the South Sandwich Arc, and westwards along the South Scotia Ridge, including the South Orkney Islands, to the tip of the Antarctic Peninsula near Elephant Island. The western boundary is formed by the Shackleton Fracture Zone. With the exception of these peripheral ridges, the ESR (Figure 1A) and various shallow banks (e.g., Pirie Bank, Bruce Bank), much of the Scotia Sea extends to depths in excess of 3,000 m. West of the ESR, the floor of the Scotia Sea forms part of the Scotia Plate. To the east of the ESR lies the small South Sandwich Plate, beneath which the South American Plate is being subducted at the South Sandwich Trench. To the north, the Scotia Plate abuts the South American Plate at the North Scotia Ridge, while to the south is the Antarctic Plate boundary at the South Scotia Ridge. Both of these are strike-slip plate boundaries [17]. The ESR is ∼500 km long, and spreading was initiated more than 15 million years ago (Mya) [18] and is presently proceeding at an average full spreading rate of ∼70 mm y−1. The ESR consists of nine second-order ridge segments (E1 to E9), separated by non-transform discontinuities [19]. E3 to E8 have well-developed deep rift valleys, but E2 and E9 are characterised by smooth volcanic highs, typical of faster-spreading mid-ocean ridges. An axial magma chamber is known to underlie segment E2 [20], and another is suspected to underlie segment E9 [21]. The southern end of segment E9 is curved to the east because of changes in the stress field as the strike-slip faults separating the South Sandwich and Scotia plates from the Antarctic plate are approached.
The first evidence of hydrothermal activity along the ESR was from data obtained by a light-scattering sensor attached to the Towed Ocean Bottom Instrument (TOBI), a deep-towed sonar system, during a geophysical mapping survey along the ESR in 1999 [13]. Additional evidence was obtained from conductivity–temperature–depth (CTD) profiles and manganese anomalies in water samples collected at depth during that survey. In the austral summer of 2009 we conducted a survey of segments E2 and E9 using a CTD sensor that was continuously raised and lowered in the water column (“tow-yo”), with attached light-scattering sensor and redox potential (Eh) sensors to track hydrothermal plumes and locate potential vent sites to within 100 to 500 m. We then used a lowered camera system, Seabed High Resolution Imaging Platform (SHRIMP), with down-looking and oblique video cameras to survey the seafloor in as systematic a fashion as possible. At E2 we located black smoker chimneys, as well as observing associated fauna, and at E9 we found considerable evidence of diffuse hydrothermal venting, with anemones and stalked barnacles being the dominant megafauna. Because SHRIMP is controllable only in the vertical plane, we withdrew it from the vent sites to prevent unnecessary damage, in accordance with InterRidge guidelines [22]. In the austral summer of 2010 we returned with the ROV Isis and conducted a full and systematic survey of the previously located vent sites at E2 and E9. This was supplemented by additional video analysis using SHRIMP in the austral summer of 2011.
The vent sites at E2 lie just south of the segment axial high (called the Mermaid's Purse [20]), between 56° 5.2′ and 56° 5.4′ S and between 30° 19′ and 30° 19.35′W at ∼2,600 m depth (Figure 1B). Prominent north–south structural fabric to the seafloor defines a series of staircased, terraced features that are divided by west-facing scarps (Figure 1C and 1D). A major steep-sided fissure runs north–south through the centre of the site, between longitude 30° 19.10′W and 30° 19.15′W (Figure 1D). The fissure is filled in places by lobes of pillow basalts, and the main hydrothermal vents are located at the intersection between this main fissure and a west–east striking fault or scarp, consistent with the expected location of active venting on back-arc spreading ridges. Relict (extinct) and actively venting chimneys are resolvable in the high-resolution multibeam bathymetry obtained by the ROV Isis, clustered in a band running approximately northwest–southeast. Numerous volcanic cones and small volcanic craters are also apparent around the vent field. Chimneys of variable morphology were up to 15 m tall and venting clear fluid with a maximum measured temperature of 352.6°C, which formed focused black smokers on contact with cold seawater (Figure 2A). Some of the chimneys have expanded tops with hot vent fluid (>300°C) emanating from the underside (Figure 2B), similar to the flanges found at North East Pacific vents [23]. Diffuse vent flow was observed at a variety of locations, with temperatures varying from 3.5 to 19.9°C, compared with a background temperature of ∼0.0°C. Around the periphery of the active high-temperature vents and diffuse flow sites are microbial mats that form a halo around the venting area at E2 (Figure 2C).
The vent sites at E9 are situated between 60° 02.5′ and 60° 03.00′S and between 29° 59′ and 29° 58.6′W, at ∼2,400 m depth, amongst relatively flat sheet lavas to the north of a major collapse crater (named the Devil's Punchbowl; Figure 1E). The ridge axis is heavily crevassed and fissured, with numerous collapse features, lava drain-back features, and broken pillow lava ridges. Major fissures run north-northwest–south-southeast through the site, breaking up an otherwise flat and unvaried terrain (Figure 1F). Topographic highs in the centre of the study site are possibly dead magma domes, with no hydrothermal activity around these sites. Most active venting appears to lie along one of the smaller fissures, west of a main north–south trending feature. Diffuse flow and black smokers line the feature intermittently, but activity becomes reduced and dies away farther south, towards the “punchbowl” itself. The chimneys were either emitting high-temperature fluids with a maximum temperature of 382.8°C (Ivory Tower; Figure 1F) or had lower temperature diffuse flow between 5 and 19.9°C (Car Wash vent; Figure 1F). Low-temperature diffuse flow was associated with fissures and fine cracks in the sheet lava; the background temperature at E9 varied from −0.11°C to −1.3°C.
A summary of the preliminary chemical and physical data from the vents on both E2 and E9 north and south is given in Table 1. This table also includes data for the closest known vent sites to the ESR in the Atlantic, Indian, and Pacific Oceans, as well as from other hydrothermal vents associated with back-arc basins [24]–[28]. The chemical composition of fluids from E2 is distinct from that at E9, and within E9 there are notable differences in the vent fluid chemistry between vents in the northern part of the site and those in the southern part (Table 1). The chloride (Cl) concentration of fluids from E2 is similar to that of seawater, whereas fluids from E9 have very low levels of Cl and, as a consequence, they have lower concentrations of the major cations such as sodium, and higher concentrations of volatiles including hydrogen sulphide (H2S). This has the potential to impact the energy available for microbial populations at the vent sites, with volatile-dominated systems having higher hydrogen sulphide and hence higher microbial populations [29].
Initial analysis of the data from the seafloor-mounted acoustic doppler current profiler, deployed at E2 and E9 for 7 d each, suggests a semidiurnal north–south tidal flow with velocities between 50 and 100 mm s−1 in the bottom 50 m at E2. The flow in the bottom 50 m at E9 is more complicated, with an underlying semidiurnal tidal flow up to 100 mm s−1 plus an asymmetric west–east–west flow of ∼50 mm s−1.
Examination of 16S rDNA clone libraries from water samples taken within the buoyant vent plumes over E2 and E9 show a highly similar composition of the microbial communities at both sites. Proteobacteria make up 70% of the bacterial community at E2 (66% at E9). Within the proteobacteria, gammaproteobacteria are the dominant group (58% and 55% at E2 and E9, respectively). More than half of the gammaproteobacterial sequences (59% at E2 and 58% at E9) show high similarity (>99%) to bacterial endo- and epi-symbionts of hydrothermal vent fauna from elsewhere. Within the alphaproteobacteria, more than 90% of the sequences fall within the SAR11 clade, a group of ubiquitous heterotrophic bacteria found throughout the oceans. Other numerically abundant sequences in the clone libraries are closely related to Bacteroidetes (12% at E2 and 13% at E9) and Deferribacterales (11% at E2 and 12% at E9). Sequences for the bacterial clone libraries have been deposited in GenBank (http://www.ncbi.nlm.nih.gov/genbank/; accession numbers JN562472–JN562714).
At E2 and E9, the fauna is visually dominated by extensive dense aggregations of a new species of yeti crab, Kiwa n. sp. (Figure 2D and 2E). This species shows sequence divergences for mitochondrial 16S rDNA and nuclear 18S and 28S rDNA of 6.45%, 0.49%, and 1.8%, respectively, when compared with K. hirsuta from the Pacific-Antarctic Ridge (GenBank accession numbers JN628249, JN628250, JN628251). This variation is within the range of congeneric species comparisons for the Anomura [30], and a phylogenetic analysis, using Bayesian inference, of anomuran taxa, indicates that Kiwa n. sp. is the sister taxon of K. hirsuta (Figure S1). Using known substitution rates from geminate species pairs of anomuran crustaceans from either side of the Isthmus of Panama, the 16S data suggest a putative divergence between K. hirsuta and Kiwa n. sp. from the ESR at ∼12.2 Mya (0.53% per million years [31]), although such a preliminary date of divergence is subject to a high level of error. The new species of Kiwa from the ESR has dense mats of two distinct types of setae covering the ventral surface of the body, in contrast to K. hirsuta, which has sparse long setae on the ventral surface and a dense covering of long setae on the pereopods and particularly the chelipeds [9]. Filamentous bacteria were observed attached to the setae, as also seen in K. hirsuta [31]. Macpherson et al. [9] suggested that K. hirsuta is omnivorous, following observations of individuals consuming damaged mussels. However, the presence of sulphur-oxidising bacteria on the setae of this species [32] suggests that K. hirsuta may harvest bacteria as a nutritional source [9], and if this is the case, Kiwa n. sp. from E2 and E9 may also utilise epibiotic bacteria in the same way. At E2 dense aggregations of crabs may be found adjacent to and on chimneys, with large individuals closely associated with the vent orifice. At E9 Kiwa n. sp. was more abundant than at E2, completely covering the seabed in some areas and reaching densities of 600 m−2 (Figure 2E). At some sites this species formed multiple layer aggregations. The distribution of sexes appears to be influenced by distance from vent sources, possibly determined by temperature or vent fluid composition. Males were found closest to vent orifices (Figure 2D), and non-berried females adjacent to the vent but in cooler waters. Berried females and juveniles were associated with low-temperature flow, ∼5°C (as on Car Wash), and at the periphery of vent influence. They had considerably fewer filamentous bacteria on their setae than crabs near or on the chimneys, suggesting that the bacteria rely on the higher temperatures and chemistry in the immediate vicinity of the vent orifice for optimal growth.
Additional common fauna at the sites (Table 2) includes at least five morphospecies of sea anemone, three of which are found in diffuse flow associated with chimneys or sheet and pillow lavas in densities of up to ∼70 m−2 (Figure 3A–3D). These include four putative species of Actinostolidae, a family that includes the anemones Pacmanactis and Marianactis found on deep-sea hydrothermal vents elsewhere. There is also a red anemone that is similar in appearance to Chondrophellia sp. or Hormathia spinosa (personal communication, E. Rodriguez, Division of Invertebrate Zoology, American Museum of Natural History). The most obvious gastropod is an undescribed peltospiroid species (Figures 3B, 3D, and 4), generally found in dense aggregations up to ∼1,000 m−2. A second common gastropod is a limpet of the genus Lepetodrilus (Figure 3D). Phylogenetic analysis of the mitochondrial cytochrome oxidase I gene of this limpet (GenBank accession number JN628254) and a range of other Lepetodrilus species, using Bayesian inference, places the ESR limpet as a sister taxon to L. atlanticus (Figure S2), with a sequence divergence from this species of 5.48%. This level of genetic divergence is consistent with that found between Lepetodrilus species within complexes of sister taxa where interspecific distances of between 3% and 15% have been observed [33]. This new species is ubiquitous in low-temperature diffuse flow, being found on bare rock, sulphides, Kiwa n. sp., peltospiroid gastropods, and stalked barnacles. On the carapace of Kiwa n. sp., a halo of pale colouration surrounding the limpets indicates where Lepetodrilus n. sp. is grazing epizoic microbes. Lepetodrilus species have also been found previously on the carapaces of bythograeid crabs [33], as well as on the shells of vent molluscs and the tubes of siboglinid worms [34]. Not as visually apparent, but abundant in sediment residue in the ROV sample bioboxes from vent and diffuse flow areas at E2 and E9, is a small species of provannid gastropod. Several unidentified octopi were also observed within hydrothermal vent fields at E9 (Figure 3F). The vent fauna also includes dense aggregations of a stalked barnacle morphologically consistent with the genus Vulcanolepas (Figures 2E, 3A, 3B, and 3E). Phylogenetic analyses using Bayesian inference of the histone H3 and 28S rDNA [35] of the ESR Vulcanolepas (GenBank accession numbers JN628252, JN628523) and stalked barnacles from other hydrothermal vents (Figure S3) confirmed that the ESR barnacles were most closely related to, but a distinct species from, V. osheai (divergence of 0.34% and 0.22%, respectively). The latter species was described from the Brothers Caldera, Kermadec Ridge, South West Pacific [36]. The ESR Vulcanolepas occurs at densities of up to ∼750 m−2, particularly at E9 along the broken edge of sheet lava bathed in diffuse vent flow, as well as forming erect, dense colonies on chimneys emitting diffuse flow. Also scattered throughout the vent systems at E2 and E9 are at least three species of the vent pycnogonid Sericosura (Figure 3D), with the larger species Colossendeis cf. concedis and C. cf. elephantis occurring on the peripheral areas of the vents (personal communication, C. Arango, Queensland Museum South Bank). As at other vent sites in the Pacific and Atlantic Oceans [37]–[39], swarms of an unidentified amphipod were observed at E9. Although a variety of echinoderms were found during our observations, only one species, a seven-armed sea star from the family Stichasteridae (personal communication, C. Mah, Smithsonian National Museum of Natural History), appeared to be vent endemic (Figure 3E). This undescribed species was indicative of the proximity of vents in our 2009 observations, and during the 2010 campaign was found both peripherally and in areas of low-temperature diffuse venting. We observed it feeding on vent fauna, especially Kiwa n. sp. and barnacles. Fish were generally uncommon at the vent sites, and the only species that were observed were several species of macrourids on the vent periphery and a zoarcid, several specimens of which were recovered in baited traps at E9.
In order to examine how the fauna at E2 and E9 fit into the current understanding of the biogeography of deep-sea hydrothermal vents, we undertook an analysis of the global dataset on species presence/absence of most of the known hydrothermal vent communities using multivariate regression trees (MRT) after Bachraty et al. [8], but with modifications (see Materials and Methods). The MRT analyses, with cross-validation, produced a series of trees, many of which were only marginally worse than the best predictive tree (Figure 5). The optimal tree size, based on cross-validation error, varied between three and ten provinces for the Bachraty et al. [8] dataset and three and 11 provinces for the Bachraty et al. [8] dataset plus E2 and E9 (combined dataset). The most common optimal trees were the five- and seven-province models for the Bachraty et al. [8] dataset (Text S1; Figure S4A) and an 11-province model for the combined dataset (Figure 6). The six-province model proposed by Bachraty et al. [8] was not found to be the most frequently selected optimal tree. The 11-province model retained the Atlantic and East Pacific clusters but split up the Indo-Pacific province into five smaller clusters (Figure 6). In all iterations of the model (Figures 6 and S4) the sites south of the Easter Microplate in the South Pacific formed a separate cluster from all other East Pacific sites. E2 and E9 form a separate cluster for the optimal 11-province model (and seven-province model; see Figure S4) for the combined dataset, also suggesting that these sites form a new biogeographic province (but see discussion on the MRT method).
Recent investigations of the deep-sea ecosystems of the Southern Ocean have revealed a high proportion of previously undescribed species, many of which are unknown from elsewhere [40]. Particularly notable in this respect are groups of the Isopoda, Ostracoda, Gastropoda, and Nematoda. It has been suggested that Southern Ocean species of these groups are not found outside of the Southern Ocean because they have life histories that are characterised by a low potential for dispersal [40]. Likewise, analyses of the fauna of the shelf and slopes of the islands of the Scotia Arc, as far north as Shag Rocks, suggest that the fauna is largely composed of Antarctic endemics [41]. The finding of a unique vent-endemic fauna within the Southern Ocean is consistent with this pattern of species distribution and is further evidence of the high regional endemism of the Antarctic marine biota. This study also provides the first identification and description, to our knowledge, of high-biomass hydrothermal-vent-endemic chemosynthetic communities in the Southern Ocean. Exploration of deep-sea hydrothermal vents in other sectors of the Southern Ocean, such as the Pacific-Antarctic Ridge [16], are likely to reveal further chemosynthetic communities.
The fauna observed at the vents along the ESR contains none of the dominant vent species normally found at vents along the main mid-ocean ridge systems. The ESR sites are notable for the absence of siboglinid tubeworms, alvinellid polychaetes, vesicomyid clams, bathymodiolid mussels, and alvinocaridid shrimp. In addition, there is an absence of typical predators such as bythograeid crabs. Species found at the ESR vents include anemones, lepetodrilid limpets, provannid gastropods, stalked barnacles, and at least three species of pycnogonids, thus these vents share some faunal elements with communities found at vents associated with back-arc basins in the West and South West Pacific, the mid-ocean ridge in the South East Pacific, and the Mid-Atlantic Ridge. The dominant species at the ESR vents is an anomuran crab of the genus Kiwa, which has congeneric species along the Pacific-Antarctic Ridge and at cold seeps off Costa Rica [9],[42].
Connections among the biogeographic provinces identified over the last ten years are consistent with dispersal of taxa along mid-ocean ridge systems, with vicariance events being related to severance of ridges through subduction or other processes [43]. This connectivity is also consistent with gene-flow studies that have demonstrated significant relationships between measures of genetic differentiation (FST) and whether populations are present on the same ridge segment, are separated by transform faults, or are present on different ridges [6],[44]. However, the biogeographic patterns exhibited by hydrothermal vent communities may also be influenced by larval dispersal on deep-ocean currents that do not follow the line of ridge axes, with or without the aid of evolutionary stepping stones provided by other chemosynthetic ecosystems such as cold seeps and whale falls [6]–[8]. Examples of where such dispersal routes may have been important include the dispersal routes between the eastern Pacific and Mid-Atlantic Ridge, and the eastern Pacific, South Atlantic, and Indian Ocean [7],[8].
Our data from vents at E2 and E9 along the ESR provide three lines of evidence that the fauna at these sites represents a separate and new biogeographic province from those previously described for the global ocean [7],[8]. First, the taxa of the vent fields at E2 and E9 are distinct from those of other provinces at least at the species level (e.g., Kiwa n. sp., Vulcanolepas n. sp., and Lepetodrilus n. sp.). Second, the structure of the assemblages differs from that of other provinces where fauna are shared at higher taxonomic levels. For example, at the nearest vent site where another species of Kiwa has been reported (K. hirsuta; 38°S, Pacific-Antarctic Ridge), that species occurs in the periphery with a reported population density of 0.1–0.2 m−2, and in diffuse venting areas along with other widespread vent fauna, such as Bathymodiolus sp. and bythograeid crabs [9]. In contrast, at the ESR vents, Kiwa n. sp. occurs at high population densities (∼600 m−2) proximal to fluid exits, in the niches usually taken by taxa such as alvinellid polychaetes [45] or aggregations of alvinocaridid shrimp [46]. Also distinct in the assemblages of the ESR vents is the variety of vent-endemic anemones, and the presence of an undescribed seven-arm stichasterid sea star as a predator, and a conspicuous rarity of polychaetes, other than polynoid scale worms. Finally, the MRT analyses of the combined dataset indicate that using the most common optimal tree, E2 and E9 form a separate cluster from other vent provinces. These analyses also indicated that several other areas, especially the eastern Pacific vent sites south of the Easter Microplate, consistently form a separate biogeographic province in a range of optimal trees. This region has been recognised as a biogeographic boundary, known as the Easter Microplate boundary, in several other studies [6].
With regards to the third line of evidence, the MRT results should be interpreted with care. First, the Indian Ocean, South East Pacific Rise, and Antarctic sites are significantly undersampled compared to sites in the northern and central East Pacific Rise, the Mid-Atlantic Ridge, and western Pacific back-arc basins. Second, the species lists presented in Bachraty et al. [8] do not account for many of the cryptic species that have been identified amongst some groups of vent taxa (e.g., Lepetodrilus [33]). Both of these factors introduce significant potential errors into the resolution of biogeographic patterns of the vent fauna using multivariate methods. Notwithstanding these problems, our analysis failed to reproduce the six-province model proposed by Bachraty et al. [8], and we see two major problems with their analysis. The first concerns the stability of the statistical method they used; the second concerns the choice of constraining variables for the cluster analysis. Regarding stability, the MRT method does not give a clear preference to a certain number of provinces, but rather a series of similarly “good” trees. The reason for the choice of the six-province model, given the data of Bachraty et al. [8], is unclear. Breiman et al. [47] recommend picking the smallest tree within one standard error of the minimum tree when there is no clear optimum, which would lead to a model with three provinces for both the Bachraty et al. [8] dataset and the combined dataset used in this study. We chose instead to present models with more than three provinces in this study, based on the results of multiple cross-validation. However, we suspect that the lack of stability of tree size is based on a combination of two things. First, vent biogeographic provinces appear to be hard to resolve based on the current presence/absence data alone. This idea is supported by the marginal differences between a range of preferential trees in the MRT (Figure 5) and by variation in the results across a number of unconstrained agglomerative cluster analyses we undertook whilst exploring the Bachraty et al. [8] and combined datasets for this study (see Text S1 and Figure S5). It is also notable that studies of other deep-sea ecosystems have demonstrated that analyses of species presence/absence can miss significant differences in the structure of marine communities that can be resolved using species abundance or ranked abundance data (e.g., seamounts [48]). Secondly, we think that latitude and longitude are not a sensible choice of constraining variables both from a mathematical and a biological perspective. In the MRT analysis, latitude and longitude are effectively treated as Cartesian coordinates, which is not an appropriate representation of geographic distances on the Earth's surface. This introduces a bias where sites at high latitude appear to be more distant along a latitude circle than sites at low latitude. Furthermore, by encoding the longitude into 0–360° east of Greenwich, Bachraty et al. [8] introduce the implicit assumption that the Atlantic and Indo-Pacific are two extremes of the spatial spectrum, whereas in reality the two are joined around the Cape of Good Hope. Not surprisingly, different representations of longitude yield different MRT results, both in terms of optimal tree size and in the assignment of sites to “provinces” (see Text S1 and Figure S6). Apart from this geographical issue, present-day locations may not be good predictors for vent biogeography as they neither reflect geographic proximity on evolutionary time scales nor take into account other features and processes that are thought to influence deep-sea biogeography. These include factors such as depth, topography, currents, and oceanic fronts, many of which can act as variable dispersal filters [6],[49]–[51].
Overall, our evidence for a separate biogeographic province for the ESR is consistent with the history and present physical environment of the Southern Ocean. The Southern Ocean is separated from the remaining global ocean by the surface-to-seabed Polar Front [52], which is a major barrier to dispersal of fauna to and from Antarctic waters [53]. This region represents a sharp boundary in physical conditions that was established after the initiation of the Antarctic Circumpolar Current and became more extreme at the middle Miocene climate transition (∼13.8 Mya [54]), a time that is close to the initiation of spreading at the ESR. Taxa commonly found in the rest of the world's oceans, such as brachyuran crabs and decapod lobsters, are absent from the Antarctic, and the non-vent marine fauna of the Southern Ocean is highly endemic [40],[55],[56]. Explanations for this have included physiological barriers, an example being the decapod crustaceans, which have an inability to down-regulate blood magnesium levels sufficiently below that of seawater, leading to a loss of activity and eventual death at polar water temperatures [57]. It is also notable that a high proportion of Antarctic marine invertebrates have life histories that include direct or lecithotrophic larval development, although some common species, associated with unstable habitats, exhibit planktotrophy [58]. The reason for this is uncertain, although it is likely to be an adaptation to the extreme seasonality of the Antarctic and poor food supply for large parts of the year [58]. With the exception of the Siboglinidae, the taxa that are absent from the vents of the ESR have planktotrophic larval development (including the alvinocaridid shrimp and vent mussels). It is notable that the deep-sea vent ecosystems recently described from the Arctic also show an absence of vent shrimp and vent mussels [12]. In the Arctic, the niche usually occupied by shrimp in Atlantic vent fields is occupied by an amphipod with chemoautotrophic gill symbionts [12]. The biological filter represented by the Polar Front may thus explain the absence of bythograeid crabs, shrimps of the Alvinocarididae, and other taxa commonly associated with vents.
Current flow south of the Polar Frontal Zone is dominated by the eastward-flowing ACC, and maps of potential vorticity and evidence from ocean tracers of the high southern latitudes give rise to the possibility of larva-mediated dispersal and faunal similarities among the disjunct South East Pacific Rise, Chile Rise, ESR, and southernmost Mid-Atlantic Ridge [7],[59]. Our observations are consistent with this hypothesis in the identification of a new species of Kiwa, other species of which has been found on the Pacific-Antarctic Ridge and on the continental slope of Costa Rica. Other faunal elements may also be shared between the vents on the ESR and those of the South East and South West Pacific. Early investigation of the life history of Kiwa n. sp. from the ESR also suggests that the larvae are brooded and hatch from eggs at a morphologically advanced stage, which is probably not conducive to long-distance dispersal in deep water. However, inferring dispersal capability from life history characteristics should be undertaken with caution, given that life history only partially explains the observed patterns of gene flow for other marine species [60],[61]. A test of the importance of current-mediated dispersal in the evolution of communities at the ESR would be faunal and phylogenetic comparisons of this community with the biota present at the South East Pacific Rise and Chile Rise, along with that at vents along the Antarctic Peninsula in the Bransfield Strait [14].
The discovery of vent biota on the ESR with faunal connections to other southern hemisphere vent systems, including those in both the Pacific and the Atlantic, suggests a more complex picture of vent biogeography than previously considered. A full understanding of the relationships of the fauna of the ESR vents with those elsewhere will only be realised with complete analyses of the fauna collected at 56°S and 60°S at the ESR, and the location and documentation of further hydrothermal vent communities at high latitudes in the Southern Ocean and southern Pacific, Atlantic, and Indian Oceans. Further exploration of high-latitude ridges is critical for a full understanding of the global biogeography of vent ecosystems, given the potential role of the Southern Ocean as a gateway or a barrier between the major ocean ridges and back-arc basins.
Finally, our direct observations of hydrothermal vent fields south of 40°S latitude in the southern hemisphere represent the culmination of a 30-y poleward trend in hydrothermal exploration, which began at low latitudes. However, a seafloor image taken as long ago as 1966 at 2,377 m depth on ESR segment E9 shows a faunal assemblage similar to that which we now identify as associated with hydrothermal vents on this segment [62]. Thus, it appears that a vent community may have been observed but not recognised at high latitudes a decade prior to the original discovery of vent communities in the Galápagos Rift [1]. It is interesting to reflect that if this seafloor assemblage had been investigated in greater detail at that time, the entire history of global-scale hydrothermal exploration could have followed a quite different path.
Two modes of geophysics data acquisition were carried out during the cruises: (1) ship-based geophysical survey and (2) ROV geophysical survey. On both RRS James Clark Ross 224 and RRS James Cook 042, ship-based geophysical data collection consisted of seafloor mapping using hull-mounted Kongsberg-Simrad EM120 multibeam echo sounders, and sub-bottom profiling using a hull-mounted parametric echo sounder. ROV geophysics data collection consisted of high-resolution seafloor mapping using the ROV Isis Simrad SM2000 multibeam echo sounder. Few ship-based surveys were carried out during the RRS James Cook 042 cruise.
The majority of the water samples were collected using a Seabird +911 CTD on a titanium frame with up to 24 externally sprung Niskin bottles. This is a clean system, specifically designed for the sampling of waters with low levels of trace metals and nutrients. The bottles are Teflon lined, with Teflon taps and non-metallic parts; any metallic components are titanium or high-quality stainless steel.
The CTD Carousel Niskin and ROV Mini-Niskin bottles were sampled for (in order): (1) methane (125 ml, poisoned with HgCl for analysis at the National Oceanography Centre, Southampton [NOC]); (2) dissolved inorganic carbon (250 ml, poisoned with HgCl for analysis at NOC); (3) total dissolved organic carbon (20 ml, filtered through a 0.2-µm filter and acidified with HCl for analysis at NOC); (4) trace metals (filtered through a 0.2-µm filter into an 500-ml acid-cleaned LDPE bottle for analysis at NOC); (5) metal speciation (filtered through a 0.2-µm filter into duplicate 250-ml bottles and frozen for analysis at NOC); and (6) siderophores—remaining volume for Mini-Niskin bottles, usually 10 l for large Niskin bottles—filtered and sucked through an Isolute ENV+ column (frozen) for characterisation at NOC. Finally, the filters were all washed for salts with Milli-Q water (pH 8) and stored frozen for analysis at NOC.
Collection of these samples was achieved using titanium (Ti) samplers, equipped with an inductively coupled link (ICL) high-temperature sensor to ensure the collection of high-quality samples. In the case of diffuse flow, or for sampling of friable chimney structures, the Ti samplers were used in conjunction with a specially constructed Ti diffuse sampler, which was used to prevent entrainment of surrounding seawater into the path of the fluid during sampling.
The Ti samplers were cleaned thoroughly before deployment using a solvent flux remover and rinsed with Milli-Q water. All Ti–Ti surfaces were lubricated with Fluorolube. Sample bottles were deployed in pairs, although each bottle had its own nozzle for insertion into the vent orifice (or diffuse flow sampler). Each pair of Ti samplers was coupled to an ICL high-temperature sensor that was located at the tip of the sample nozzles. Pins for firing the bottles were set at a distance of 22–31 mm above the top of the Ti sampler; however, when the pins were set high (31 mm), it proved difficult to couple the ICL temperature probe (for this reason, no temperature was recorded for some samples). The optimal setting for the pins was found to be ∼27 mm.
For optimal sampling of diffuse flow, the diffuse flow sampler was placed over the area to be sampled, and allowed to equilibrate until fluid was observed to be flowing out of the sampler. The nozzles of the Ti samplers were then inserted into the diffuse flow sampler, and the ram was slowly lowered until a reading was obtained on the ICL sensor. Once the temperature reading was considered to be steady, sampling proceeded in the same way as for a focussed fluid.
As soon as the samplers returned to the surface, they were rinsed in Milli-Q water, and the fluid was withdrawn. Separate sub-samples were collected for (1) refractive index, (2) alkalinity, (3) dissolved inorganic carbon and carbon isotopes, (4) pH, (5) gases (including CH4, CO2, and H2), (6) anions and silica, (7) nutrients, (8) dissolved organic carbon, (9) O and H isotopes, and (10) bacteria, in that order. The remainder of the sample was emptied into an acid-cleaned 1-l HDPE bottle for analysis of all other constituents, including cations and the transition metals. Any residue remaining in the bottle was washed in to an acid-clean 30-ml HDPE bottle with Milli-Q water.
Analysis of “time-critical” parameters (e.g., pH) and key indicators of sample quality (e.g., Cl) was carried out onboard. Other constituents were transported back to NOC for analysis over the following 18 mo.
Samples were taken on the RRS James Clark Ross 224 cruise (January–February 2009). Water from within the buoyant vent plume was sampled with a CTD. Two litres of water was filtered through a 0.2-µm pore-size nitrocellulose filter. The filters were frozen at −80°C until further analysis. DNA was extracted from the filters using a phenol/chloroform protocol [63]. The 16S rDNA gene was amplified by PCR using the universal primers 27F and 1492R. PCR conditions were 3 min at 94°C, followed by 30 cycles of 60 s at 94°C, 45 s 50°C, 90 s at 72°C, and a final elongation of 5 min at 72°C. PCR products were cloned into the pCR2.1 vector by TOPO TA cloning (Invitrogen), following the manufacturer's recommendations and plated on LB-ampicillin plates containing X-gal for blue-white screening. White clones were checked for correct insert size by PCR using the plasmid primers M13F and M13R. In total, 285 clones (166 from E2 and 119 from E9) were sequenced from the 3′ end by Sanger sequencing at LGC Genomics. The average sequence length was 885 bp. The sequences were trimmed and quality-control checked with the software package Geneious [64] and subsequently aligned to a reference database (SILVA, version 102 [65]) and identified phylogenetically within ARB [66].
Two equipment arrangements were used to conduct video-graphic surveys during ROV Isis dives. “Horizontal” surveys (surveys of horizontal substratum) were undertaken using a downward-looking Atlas three-chip charge coupled device video camera. The camera housing was mounted to view the seafloor through an aperture cut in the port forward corner of the ROV tool tray. A downward-facing HMI light was similarly mounted through the starboard forward corner of the tool tray. Two parallel lasers, 0.1 m apart, were mounted parallel to the focal axis of the camera to provide scale in images. Footage from the downward-looking Atlas camera was recorded to DVCAM tapes and DVD in the ROV control van. Controls for the Atlas camera (iris, zoom, focus, and colour balance) were adjusted from the ROV control van to obtain the clearest possible images for faunal identification.
“Vertical” video-graphic surveys (surveys of vertical substrata such as vent chimneys) were undertaken using the high-definition pilot pan-and-tilt camera of the ROV Isis. For these surveys, this camera was configured to view horizontally forwards from the vehicle, so that its focal axis was perpendicular to vertical substratum surfaces. Two parallel lasers, 0.1 m apart, were mounted parallel to the focal axis of the camera to provide scale in images.
Vertical surveys were undertaken using closed control of the ROV to maintain constant vehicle heading, and Doppler lock to enable movements of the vehicle over precise distances relative to the seafloor. These features enabled the ROV to undertake vertical lines up and down chimneys, offset by fixed horizontal distances, to obtain overlapping video images of the structure from a particular heading. Distance from the vehicle to the structure was kept constant, so that survey lines lay on a flat vertical plane a fixed distance from the structure being surveyed. Camera zoom was set in vertical surveys to achieve image frames approximately 1 m wide, with no adjustments during lines, and images were subsequently mosaicked together from overlapping lines for analysis.
Faunal samples were collected either by suction sampler or by scoop and brought to the surface in ambient seawater. Once on board, samples were immediately transferred to cold water in the controlled temperature laboratory (∼4°C), where individuals were dissected and either frozen or stored in molecular grade ethanol for molecular analysis, frozen for isotope analysis, or fixed in 10% seawater formalin for morphological analysis.
Geographically constrained clustering was performed to investigate the biogeographic placement of the Southern Ocean hydrothermal vents in the global classification scheme proposed by Bachraty et al. [8] using MRT [80]. For this analysis, data were subjected to a Hellinger transformation [81]. Trees were then computed using the “mvpart” package [82] in the R environment for statistical computing [83]. Optimal tree size was investigated by running 1,000 multiple cross-validations on each dataset.
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10.1371/journal.pgen.1000938 | Myeloid Cell-Restricted Insulin Receptor Deficiency Protects Against Obesity-Induced Inflammation and Systemic Insulin Resistance | A major component of obesity-related insulin resistance is the establishment of a chronic inflammatory state with invasion of white adipose tissue by mononuclear cells. This results in the release of pro-inflammatory cytokines, which in turn leads to insulin resistance in target tissues such as skeletal muscle and liver. To determine the role of insulin action in macrophages and monocytes in obesity-associated insulin resistance, we conditionally inactivated the insulin receptor (IR) gene in myeloid lineage cells in mice (IRΔmyel-mice). While these animals exhibit unaltered glucose metabolism on a normal diet, they are protected from the development of obesity-associated insulin resistance upon high fat feeding. Euglycemic, hyperinsulinemic clamp studies demonstrate that this results from decreased basal hepatic glucose production and from increased insulin-stimulated glucose disposal in skeletal muscle. Furthermore, IRΔmyel-mice exhibit decreased concentrations of circulating tumor necrosis factor (TNF) α and thus reduced c-Jun N-terminal kinase (JNK) activity in skeletal muscle upon high fat feeding, reflecting a dramatic reduction of the chronic and systemic low-grade inflammatory state associated with obesity. This is paralleled by a reduced accumulation of macrophages in white adipose tissue due to a pronounced impairment of matrix metalloproteinase (MMP) 9 expression and activity in these cells. These data indicate that insulin action in myeloid cells plays an unexpected, critical role in the regulation of macrophage invasion into white adipose tissue and in the development of obesity-associated insulin resistance.
| Obesity represents a major health burden with steadily increasing incidence. While it is associated with numerous co-morbidities, type 2 diabetes mellitus represents one of the major life-threatening, obesity-related conditions. Over the last years, it has become clear that during the course of obesity development not only does fat mass increase, but also fat composition changes qualitatively, leading to an influx of inflammatory cells, such as macrophages, into adipose tissue. Macrophages in turn secrete inflammatory mediators, which inhibit insulin action in skeletal muscle, liver, and even the central nervous system to ultimately cause insulin-resistant diabetes mellitus. However, the effect of insulin action and resistance in these inflammatory cell types themselves has not been addressed. To this end, we have generated and analyzed mice with inactivation of the insulin receptor specifically in myeloid cell-derived, inflammatory cells. Surprisingly, these animals are protected from the development of obesity-associated deterioration of glucose metabolism, thereby defining insulin action in inflammatory cells as a novel and promising target for therapeutic intervention against obesity-associated diabetes mellitus.
| Obesity in humans and rodents is associated with increased expression of pro-inflammatory cytokines, such as tumor necrosis factor (TNF) α, in white adipose tissue (WAT) [1]–[4]. This results from increased cytokine expression in WAT and more importantly from infiltration of WAT by macrophages [5]–[7]. Elevated concentrations of these cytokines activate the c-Jun N-terminal kinase (JNK)-, nuclear factor (NF) kB- and Jak/Stat/Socs-signaling pathways in metabolic target tissues of insulin action such as skeletal muscle and liver, thereby inhibiting insulin signal transduction [8]–[10]. Inactivation of the inhibitor of NFkB kinase beta (IKK2), the main activator of TNF-α-stimulated NFkB activation in myeloid cells, protects mice from the development of obesity-associated insulin resistance [11]. These findings suggest that macrophages play a key role in the development of obesity-associated insulin resistance and type 2 diabetes. More recently, a critical role in the development of obesity-associated inflammation has also been demonstrated for mast cells and lymphocytes [12], [13].
Early studies indicated that macrophages and monocytes express insulin receptors [14], however, the physiological function of these receptors has been a matter of debate. Macrophages and monocytes have been shown to respond to insulin with increased phagocytosis and glucose metabolism [15] and with increased TNF-α production and inhibition of apoptosis [16], [17]. Additionally, it has been reported that bone marrow-specific deletion of cbl-associated protein (CAP), a downstream molecule of the insulin signaling cascade, protects mice against obesity-induced insulin resistance [18]. To directly address the role of insulin action and resistance in myeloid cells, we generated mice with cell type-specific deletion of the insulin receptor in this lineage (IRΔmyel-mice). We have previously reported that these animals, upon exposure to a high cholesterol diet, exhibit protection from the development of atherosclerosis in the presence of reduced inflammation on an apolipoprotein E (ApoE)-deficient background [19]. However, others reported more complex lesions in the absence of myeloid cell insulin action through activation of the endoplasmatic reticulum (ER) stress pathway on a low-density lipoprotein receptor (LDLR)-deficient background [20]. Nonetheless, these studies did not address the role of insulin action and insulin resistance in myeloid lineage cells under conditions of obesity and obesity-induced inflammation and insulin resistance. To analyze the impact of myeloid cell-restricted insulin resistance on the development of systemic insulin resistance associated with obesity, we characterized glucose metabolism in control- and IRΔmyel-mice receiving either a normal chow diet or a high fat diet.
As previously shown, crossing IRflox/flox-mice with mice expressing the Cre-recombinase under control of the lysozymeM promoter resulted in efficient, myeloid cell-restricted ablation of the insulin receptor [19] (Figure 1A and 1B). Under normal chow diet (NCD), control- and IRΔmyel-mice exhibited indistinguishable weight curves, white adipose tissue mass, body fat content, serum leptin concentrations and serum free fatty acids (FFA) (Figure 1C–1G). When exposed to high fat diet (HFD), control-mice significantly gained weight over animals exposed to NCD, and the degree of weight gain was similar between control- and IRΔmyel-mice (Figure 1C). Moreover, white adipose tissue mass, body fat content, circulating leptin concentrations as an indirect measure of fat mass and serum FFA were significantly elevated in mice exposed to HFD, but indistinguishable between control- and IRΔmyel-mice (Figure 1D–1G). Additionally, food intake, oxygen (O2) consumption and respiratory exchange ratio (RER) were modulated by exposure to HFD but did not show any difference between both genotypes (Figure 1H–1J). Taken together, these results indicate that insulin receptors on myeloid cells are not required for energy homeostasis under NCD and HFD feeding and that myeloid cell-restricted insulin resistance does not affect the development of obesity upon high fat feeding.
To address the role of myeloid cell insulin action on whole body glucose metabolism, we next determined blood glucose and serum insulin concentrations in control- and IRΔmyel-mice. Both parameters were indistinguishable between genotypes under NCD (Figure 2A and 2B). As expected, on HFD, control-mice developed significantly increased blood glucose and serum insulin concentrations suggestive of insulin resistance (Figure 2A and 2B). Glucose and insulin levels did also rise in obese IRΔmyel-mice, but strikingly, this increase was significantly blunted in these mice lacking insulin receptors in myeloid cells (Figure 2A and 2B).
Consistent with this, glucose tolerance was similar in control- and IRΔmyel-mice under NCD and became impaired in control-mice administered a HFD (Figure 2C). In contrast, IRΔmyel-mice receiving the HFD demonstrated only a minimal impairment in glucose tolerance compared to control- or IRΔmyel-mice on NCD (Figure 2C). Similarly, obese IRΔmyel-mice showed significantly higher insulin sensitivity as measured by insulin tolerance test when compared to HFD-fed control-mice, whereas insulin sensitivity was comparable between both groups under NCD (Figure 2D). Taken together, these data reveal that myeloid cell-restricted insulin receptor deficiency leads to striking protection from obesity-induced insulin resistance.
To further define in which tissues myeloid cell-autonomous insulin resistance affects systemic glucose metabolism on HFD, we performed euglycemic, hyperinsulinemic clamps in control- and IRΔmyel-mice after 12 weeks of exposure to HFD. This analysis revealed a significant decrease in basal hepatic glucose production in IRΔmyel- compared to control-mice, while insulin-suppressed HPG (steady state) was similar in both groups (Figure 2E). Accordingly, obese IRΔmyel-mice exhibited a 50% reduction in the hepatic expression of a key enzyme of gluconeogenesis, glucose-6-phosphatase (G6Pase), while expression of phosphoenolpyruvate carboxykinase (Pck1) remained unchanged (Figure 2F).
In addition, insulin-stimulated glucose disposal in skeletal muscle was significantly increased in IRΔmyel- compared to control-mice, whereas insulin-stimulated glucose uptake in brain and adipose tissue remained unaltered under clamp conditions (Figure 2G).
In summary, these experiments indicate that the major improvement in glucose metabolism of obese IRΔmyel-mice results from both increased insulin sensitivity in skeletal muscle and reduced basal hepatic glucose production.
As insulin resistance in response to obesity and high fat feeding has been demonstrated to arise from increased concentrations of local and circulating pro-inflammatory cytokines [21] and from a reduction of circulating adiponectin concentrations [22], [23], we determined these parameters in control- and IRΔmyel-mice. Exposure of control-mice to HFD induced a marked increase of serum TNF-α concentrations compared to animals fed NCD. Strikingly, this obesity-induced increase in TNF-α was completely blunted in IRΔmyel-mice (Figure 3A). Moreover, while high fat feeding significantly reduced the portion of high molecular weight (HMW) adiponectin of total serum adiponectin in control animals, this diet-induced reduction was not observed in IRΔmyel-mice (Figure 3B).
Since increased concentrations of TNF-α have been demonstrated to activate inflammatory signaling cascades critical in the development of insulin resistance in classical insulin target tissues, we next directly investigated the activation of c-Jun N-terminal kinase (JNK) signaling in liver and skeletal muscle of obese control and IRΔmyel-mice. This analysis revealed, that basal JNK activity, as assessed by phosphorylation of c-Jun, was significantly reduced in skeletal muscle and exhibited a trend towards reduction in liver of obese IRΔmyel-mice compared to controls (Figure 3C and 3D).
Furthermore, expression of pro-inflammatory cytokines TNF-α and IL-6 was reduced in skeletal muscle, but not liver of obese IRΔmyel-mice (Figure 3E). However, the number of macrophages, which represent a major source for these cytokines, was unaltered in either tissue as demonstrated by similar expression of the macrophage-specific mRNA F4/80 in both groups of mice (Figure 3E).
Taken together, these experiments demonstrate that myeloid cell-restricted insulin resistance protects from obesity-associated systemic changes in the circulating concentrations of cytokines and adipokines as well as the local activation of JNK in skeletal muscle.
To address whether the observed reduction in systemic, obesity- associated inflammatory response correlates with alterations of the local, obesity-associated infiltration of adipose tissue by macrophages, we next analyzed the expression of F4/80, a specific marker for this cell-type, in WAT of control- and IRΔmyel-mice by quantitative realtime PCR analysis. Compared to NCD, high fat feeding significantly enhanced expression of F4/80 mRNA in adipose tissue of control animals. Strikingly, the diet-induced increase of this marker was almost completely abolished in WAT of IRΔmyel-mice (Figure 4A). This decrease appeared to represent reduced macrophage recruitment, since bone marrow-derived macrophages (BMDM) of IRΔmyel-mice showed unaltered expression of F4/80 mRNA under basal conditions and after treatment with the saturated fatty acid palmitate compared to control cells (Figure S1A). Importantly, among classical insulin target tissues, only adipose tissue showed drastically increased diet-induced expression of F4/80 mRNA, while expression in obese liver and skeletal muscle was not significantly modulated in wildtype animals compared to NCD (Figure S1B).
Furthermore, we performed histological analyses of WAT obtained from obese control- and IRΔmyel-mice. No difference was observable in adipocyte morphology or adipocyte size distribution between control- and IRΔmyel-mice on high fat diet (Figure 4B and 4C), consistent with unaltered obesity development in these animals. However, in line with the reduced mRNA expression of macrophage marker genes, immunohistochemical analysis of infiltrating, activated macrophages demonstrated a striking reduction of Mac-2-positive cells in WAT of diet-induced obese IRΔmyel-mice compared to control-mice (Figure 4D and 4E). Thus, the reduced F4/80 mRNA and Mac-2 antigen expression in WAT in the presence of unaltered macrophage-autonomous marker gene expression clearly provides independent experimental evidence for reduced macrophage recruitment to WAT of IRΔmyel-mice upon high fat feeding.
Since not only macrophages, but also a variety of other immune cells are highly abundant in the obese adipose tissue and contribute to the development of obesity-induced insulin resistance [12], [13], [24], we assessed mRNA expression of different immune cell markers in the stromal vascular (SV) fraction of WAT from obese control- and IRΔmyel-mice. In control mice, markers for macrophages (F4/80), dendritic cells (CD11c), granulocytes (Gr-1), T-lymphocytes (CD3, CD4, CD8) and mast cells (Kit) were highly enriched in the SV fraction compared to adipocytes (Figure 5A). However, in line with the data on whole WAT, we observed a specific reduction of the macrophage marker F4/80, but not of markers for granulocytes, mast cells, dendritic cells or T-lymphocytes, in the SV fraction of IRΔmyel-mice (Figure 5A).
Consistent with the specific reduction of macrophage infiltration, further analysis revealed a decrease in mRNA expression of the cytokine TNF-α and the chemokine CCL3/MIP-1α in SV fraction of IRΔmyel-mice (Figure 5B), indicating reduced inflammation in this compartment. Notably, although TNF-α, interleukin (IL) 1β, interferon (IFN) γ and arginase (Arg) 1 showed higher expression in SV fraction than in adipocytes, IL-6, CCL2/MCP-1, CCL5/Rantes and CXCL5 were equally if not higher expressed by adipocytes compared to SV fraction (Figure 5B).
To verify efficient separation of adipocytes from SV fraction, we analyzed expression of leptin, adiponectin and CD34 in both compartments. As expected, leptin and adiponectin were exclusively expressed in adipocytes, while CD34 expression was highly restricted to the SV fraction (Figure 5C).
Importantly, adiponectin expression was significantly increased in adipocytes from IRΔmyel-mice compared to controls, pointing towards increased insulin sensitivity in these animals (Figure 5C).
Taken together, our data indicate that disruption of the insulin receptor in myeloid cells specifically interferes with the obesity-associated recruitment of macrophages to adipose tissue and ultimately leads to reduced local expression of cytokines and chemokines in WAT.
The observed reduction of adipose tissue macrophage content in obese IRΔmyel-mice, among other factors, might arise from (i) enhanced susceptibility to apoptosis or (ii) reduced invasive capacity of these cells.
To directly address the hypothesis that IR signaling in macrophages might control these processes, we first analyzed the regulation of apoptosis in response to fatty acids to mimic the metabolic environment present upon high fat feeding. To this end, macrophages were isolated from the bone marrow of control- and IRΔmyel-mice, stimulated with palmitate in the absence or presence of insulin and TUNEL assays were performed. Palmitate stimulation profoundly induced apoptosis in control macrophages and insulin significantly reduced the number of TUNEL-positive cells in control cells both in the absence and presence of lipid stimulation (Figure S2A, S2B). However, insulin failed to reduce apoptosis in the IR-deficient macrophages in either the basal or palmitate-stimulated state (Figure S2A, S2B). Furthermore, quantitative realtime PCR analysis suggested that the protective effect of insulin is mediated through stimulation of Bcl-2 mRNA rather than suppression of Bax mRNA expression (Figure S2C, S2D). Nonetheless, this in vitro observation did neither translate into increased numbers of apoptotic macrophages in adipose tissue nor into reduced numbers of circulating monocytes in obese IRΔmyel-mice (data not shown).
Besides control of macrophage survival, an important prerequisite for macrophage invasion into tissues is their ability to express and secrete matrix metalloproteinases (MMPs), which then help to degrade extracellular matrix (ECM) proteins to allow trans-ECM migration. Since it has recently been established that MMP-9 (gelatinase B) plays a critical role in inflammatory macrophage migration [25], we assessed MMP-9 expression and activation in macrophages of control- and IRΔmyel-mice. Peritoneally elicited macrophages were either left untreated (basal) or were stimulated with palmitate and expression of MMP-9 mRNA was determined. Intriguingly, IR-deficient cells exhibited a dramatic reduction of MMP-9 mRNA expression both in the basal state as well as upon stimulation with palmitate (Figure 6A). Importantly, IR disruption in macrophages not only affected MMP-9 expression, but also translated into reduced MMP-9 activity. Thus, zymographical analysis of conditioned media revealed higher MMP-9 activity in those obtained from control macrophages compared to those from IR-deficient cells (Figure 6B).
To verify the in vivo relevance of this cell-autonomous impairment in MMP-9 expression and activation, we first determined serum MMP-9 concentrations in lean and obese control and IRΔmyel-mice. While HFD induced a highly significant increase of circulating MMP-9 in control animals, this increase was less profound in IRΔmyel-mice (Figure 6C). Furthermore, we assessed gelatinolytic activity in WAT lysates of obese control- and IRΔmyel-mice. Strikingly, while MMP-2 activation was unaltered, WAT of IRΔmyel-mice exhibited drastically reduced MMP-9 activation compared to controls, possibly reflecting the reduced accumulation of macrophages in this compartment (Figure 6D and 6E).
To further functionally analyze the effect of IR-deficiency on macrophage migration, we performed transwell migration assays with wildtype BMDM transfected with siRNAs directed against either IR or MMP-9. Compared to a scrambled control siRNA, both oligonucleotides mediated efficient and specific knockdown of their respective target mRNAs without reducing expression of closely related insulin-like growth factor (IGF) 1 receptor and MMP-2 mRNA (Figure 6F). BMDM transfected with the control siRNA showed an approximately 4-fold increase of migrated cells through gelatin-coated membranes in response to MCP-1 compared to the basal level (Figure 6G). However, knockdown of MMP-9 significantly blunted this response and MCP-1 failed to enhance basal migration significantly (Figure 6G). Strikingly, siRNA-mediated ablation of IR reduced macrophage migration capacity to a similar degree as that of MMP-9 (Figure 6G).
Taken together, our experiments reveal that insulin action in macrophages promotes tissue invasion capacity of these cells in vitro and in vivo, thereby critically controlling high fat diet-associated macrophage invasion and activation in WAT upon induction of obesity.
Insulin resistance in metabolically relevant insulin target tissues, such as skeletal muscle, liver, adipose tissue and more recently the brain, represents a well-studied key characteristic during the development of type 2 diabetes mellitus [26]–[30]. Insulin resistance can arise via different mechanisms e.g. mutations in genes encoding insulin signaling components or their reduced expression [31], [32]. However, it has been demonstrated that insulin resistance associated with obesity largely stems from posttranslational modifications of insulin signaling proteins, such as inhibitory serine phosphorylation of the insulin receptor or its downstream signaling mediators [33]. Here, activation of pro-inflammatory signaling cascades, particularly JNK and IKK, have been shown to inhibit insulin action, although to different, tissue-specific extent [8], [34]–[36]. The establishment of a chronic pro-inflammatory state during the course of obesity stems from expression of pro-inflammatory cytokines in adipose tissue, particularly through the recruitment of cells of the innate immune response system to WAT [5], [7]. The critical importance of innate immune response activation during the development of obesity-associated insulin resistance has been highlighted by the phenotype of mice with targeted disruption of the NFκB pathway in myeloid lineage cells, as well as mice deficient for the chemokine receptor CCR2, which both exhibit reduced WAT inflammation and are therefore protected from obesity-induced insulin resistance [11], [37].
While these findings have provided compelling evidence for the immune response pathway to cause insulin resistance in liver, skeletal muscle and adipose tissue, the primary effect of insulin action and insulin resistance in cells of the innate immune system has been poorly investigated and remains controversial. Thus, while there is considerable evidence for a role of inflammation in producing insulin resistance in individuals with type 2 diabetes [38]–[40], it has also been shown that insulin treatment of obese humans can reverse the pro-inflammatory state in macrophages [41], [42], raising a question of which is cause and which is effect. Also, it is not clear if this anti-inflammatory effect is a direct effect of insulin on cells of the immune system or if the reversal of inflammation occurs secondary to normalization of hyperglycemia and other metabolic abnormalities [43]. To further complicate the matter, insulin has been shown to directly increase TNF-α expression in human monocytes, pointing towards a possible direct pro-inflammatory role for insulin in macrophages [16]. Indeed, the latter is consistent with our previous observation that myeloid cell-restricted insulin resistance protects apolipoproteinE-deficient mice from the development of atherosclerosis due to impaired inflammatory response [19]. Alternatively, differential pro- and anti-inflammatory effects of insulin may represent different stages of a process representing acute versus chronic stimulation [44].
The findings of the present study directly demonstrate an unexpected and pivotal role for insulin signal transduction in the control of innate immune cell behavior in the obese state, such that chronic impairment of insulin action in myeloid lineage cells protects from obesity-associated inflammation. This is further supported by the observation that mice with bone marrow-restricted disruption of cbl-associated protein (CAP), a downstream component of insulin action in control of glucose transport, are protected from obesity-associated inflammation and insulin resistance [18]. However, these experiments did not specifically address the role of insulin action, as CAP is a scaffold protein implicated both in insulin signaling and also cytoskeleton regulation [45], [46].
The present results not only reveal clearly that IR-dependent signaling is critical for macrophage recruitment to WAT upon obesity development, they also define at least two potential mechanisms responsible for this phenomenon: increased apoptosis of macrophages and reduced tissue invasion by these cells due to decreased expression of MMP-9.
It is well documented that insulin can protect macrophages from apoptosis induced by numerous stimuli, such as serum/glucose deprivation, lipopolysaccharides and UV-irradiation in vitro [17], [20], [47]. More recently, Senokuchi et al. showed that insulin protects macrophages from ER stress-mediated apoptosis by free cholesterol in the development of atherosclerosis [20], [48]. Consistent with the latter notion, we find that saturated fatty acids, such as palmitate, which are increased in the circulation of obese patients [49], can result in macrophage apoptosis and that this effect is inhibited by insulin in vitro. However, we could not observe increased macrophage apoptosis in adipose tissue or altered numbers of circulating monocytes in obese IRΔmyel-mice, questioning the in vivo relevance of this finding.
Aside from regulation of macrophage apoptosis, we find that insulin promotes expression and activation of MMP-9 in these cells, a protease involved in tissue invasion by macrophages [25]. Furthermore, we could directly demonstrate that siRNA-mediated ablation of MMP-9 drastically impairs macrophage transmigration through a gelatin matrix and that this can be phenocopied by loss of the insulin receptor. Consistent with these results, it has been demonstrated that insulin augments MMP-9 in human monocytes in vitro [50] and that degradation of extracellular matrix (ECM) components by MMP-9 represents a key step during macrophage tissue invasion [51]. Indeed, reduced MMP-9 activity diminishes macrophage trans-ECM migration and protects from local inflammation and inflammation-associated cardiovascular disease [25]. Interestingly, San José et al. have recently demonstrated that insulin activates MMP-9 in murine macrophages in a PI3K/PKC-dependent manner through stimulation of the NADPH oxidase system [52]. Here, the authors propose that insulin-dependent MMP-9 activation might contribute to plaque instability in atherosclerotic lesions. Taking that into account, our results underline the important role of insulin receptor-dependent regulation of MMP-9 in macrophages and further extend it to another hyperinsulinemia-related disease state i.e. the development of obesity-associated inflammation and insulin resistance.
Notably, the LysMCre transgene mediates recombination of loxP-flanked alleles not only in macrophages but also in other myeloid lineage-derived cell types [53]. Therefore, one key question remains why, in our model, the reduction of adipose tissue infiltration is specific for macrophages while marker expression of other immune cell types e.g. granulocytes, T-lymphocytes and mast cells was unchanged. This might be due to the time-dependent fashion in which different subsets of immune cells invade the adipose tissue over the course of obesity. While adipose tissue granulocytes and T-lymphocytes already appear after 7 days and 6 weeks, respectively [13], [24], macrophage numbers do not significantly increase before 12–16 weeks of high fat feeding [7], [13]. Therefore, we cannot exclude that, despite macrophages, adipose tissue numbers of distinct immune cell subsets may be changed at different stages of obesity in our model. Additionally, immune cells invading the adipose tissue, especially macrophages and T-lymphocytes, can be further divided into distinct subtypes which are characterized by differential expression of specific surface markers [13], [54], [55]. Thus, analysis of adipose tissue immune cell populations by fluorescence activated cell sorting (FACS) could potentially yield a higher resolution of adipose tissue inflammatory cell composition than the quantitative realtime PCR analysis performed in this study. Nevertheless, our experiments indicate that protection from diet-induced insulin resistance appears to be primarily paralleled by reduced WAT-macrophage recruitment.
Another question is why reduced macrophage accumulation in obese IRΔmyel-mice is restricted to adipose tissue, while no significant difference of F4/80 expression could be observed in liver and skeletal muscle of these animals. This might be explained by our finding that in wildtype mice the obesity-induced infiltration of macrophages into adipose tissue is several magnitudes higher (∼20-fold vs NCD) than into liver and skeletal muscle (max. 2-fold). Therefore, the effect of general macrophage-autonomous impairment of migration ability may be particularly predominant in adipose tissue compared to other insulin target tissues.
In conclusion, our study directly demonstrates that, despite its positive effects on glucose metabolism in target tissues such as liver, skeletal muscle and WAT, in vivo insulin can also play a deleterious role during the development of the metabolic syndrome by its actions in cells of the innate immune response system. The molecular mechanism of how the insulin receptor signaling pathway affects macrophage function remains to be further defined, but the present study suggests that this may offer a site for pharmacological intervention that could lead to novel therapeutic strategies for metabolic diseases.
All animal procedures were conducted in compliance with protocols approved by local government authorities and were in accordance with NIH guidelines. Mice were housed in groups of 3–5 at 22–24°C in a 12:12 h light/dark cycle with lights on at 6 a.m. Animals were either fed a normal chow diet (Teklad Global Rodent # T.2018.R12; Harlan, Germany) containing 53.5% of carbohydrates, 18.5% of protein, and 5.5% of fat (12% of calories from fat) or from week 4 of age a high fat diet (# C1057; Altromin, Germany) containing 32.7%, 20% and 35.5% of carbohydrates, protein and fat (55.2% of calories from fat), respectively. Water was available ad libitum and food was only withdrawn if required for an experiment. Body weight was measured once a week. Genomic DNA was isolated from tail tips, genotyping was performed by PCR. All experiments on mice were performed at 16 weeks of age.
LysMCre mice were mated with IRlox/lox mice, and a breeding colony was maintained by mating IRlox/lox with LysMCre-IRlox/lox mice. IRlox mice had been backcrossed for at least 5 generations on a C57BL/6 background, and LysMCre mice – initially established on a C57BL6/129sv background – had been backcrossed for 10 generations on a C57BL6 background before intercrossing them with IRlox mice. Only male animals from the same mixed background strain generation were compared to each other. LysMCre mice were genotyped by PCR as previously described [53]. IRlox/lox mice were genotyped by PCR with primers crossing the loxP site as previously described [26].
Body fat content was measured in vivo by nuclear magnetic resonance using a minispec mq7.5 (Bruker Optik, Ettlingen, Germany) as previously described [56].
All measurements were performed in a PhenoMaster System (TSE systems, Bad Homburg, Germany), which allows measurement of metabolic performance. Mice were placed at room temperature (22°C–24°C) in 7.1-l chambers of the PhenoMaster open circuit calorimetry. Mice were allowed to adapt to the chambers for at least 24h. Food and water were provided ad libitum in the appropriate devices and measured by the build-in automated instruments. Parameters of indirect calorimetry and food intake were measured for at least the following 48 hr. Presented data are average values obtained in these recordings.
Glucose tolerance tests were performed after a 16–17h fasting period. After determination of fasted blood glucose levels, each animal received an i.p. injection of 20% glucose (10ml/kg) (DeltaSelect, Germany). Blood Glucose levels were detected after 15, 30, 60 and 120 minutes. Insulin tolerance tests were performed with mice fed ad libitum. After determination of basal blood glucose levels, each animal obtained an i.p. injection of insulin, 0.75U/kg (Actrapid; Novo Nordisk A/S, Denmark), and blood glucose was measured 15, 30 and 60 minutes after insulin injection.
650 pmole siRNA (Silencer Select siRNA negative control, #4390846; Insr, #S68367; MMP9, #S69944; Applied Biosystems, CA, USA) were transferred to a 4-mm cuvette (Bridge, Providence, RI) and incubated for 3 minutes with 4×106 bone marrow-derived macrophages (BMDM) in 100 µL Optimem (Invitrogen, Frederick, MD) before electroporation in a Gene Pulser X cell + CE module (Bio-Rad, Hercules, CA). Pulse conditions were square wave, 1000 V, 2 pulses, and 0.5-ms pulse length. 72–96 hours after electroporation, RNAi efficiency was tested using quantitative realtime PCR and silenced BMDM were used for functional assays.
Blood glucose levels were determined from whole venous blood using an automatic glucose monitor (GlucoMen GlycÓ; A. Menarini Diagnostics, Italy). Leptin, insulin, TNF-α, adiponectin and MMP-9 levels in serum were measured by ELISA using mouse standards according to manufacturer's guidelines (Mouse Leptin ELISA; Crystal Chem, IL, USA / Mouse Insulin ELISA; Crystal Chem, IL, USA / Mouse Adiponectin (HMW & total) ELISA; Alpco, NH, USA / Mouse TNF-α/TNFSF1A and MMP-9 (total) Quantikine ELISA Kit; R&D Systems, Inc., MN, USA). Serum FFAs were determined by colorimetric assay according to manufacturer's guidelines (NEFA kit; Wako chemicals GmbH, Neuss, Germany).
Protein isolation from cells and tissues was performed as previously described [26]. Western blot analysis was performed as previously described [26] with antibodies raised against insulin receptor β subunit (IRβ, catalog # sc-711; Santa Cruz Biotechnology Inc.) and Akt (catalog # 9272; Cell Signaling) as a loading control. SAPK/JNK Kinase assay (catalog # 9810; Cell Signaling Technology Inc.) was performed following the manufacturers instructions. Western blot analysis of total JNK input was performed with an antibody raised against JNK (catalog # 9252; Cell Signaling Technology Inc.). Quantification of changes in optical density was performed with Quantity One (Bio-Rad Laboratories, München, Germany).
Gelatin zymography was performed as previously described [59]. Briefly, cell culture supernatants and tissue extracts were purified from lower molecular weight proteins (<50 kDa) by centrifugation through Microcon YM-50 Centrifugal Filter Units (Millipore, Billerica, MA, USA). 10–40 µg of protein were separated on SDS polyacrylamide gels (containing 0.1 mg/ml gelatine). Gels were renatured in 2.5% Triton X-100 followed by incubation in MMP activation buffer (50 mM Tris-HCl, 5 mM CaCl2, pH 8) at 37°C overnight in a humidified chamber. Gels were stained with 2.5 g/l Coomassie brilliant-blue R-250. Destaining was carried out with 40% (v/v) methanol until the bands appeared clearly.
Animals were sacrificed and epididymal fat pads were removed under sterile conditions. Adipocytes were isolated by collagenase (1 mg/ml) digestion for 45 min at 37°C in DMEM/Ham's F-12 1∶1 (DMEM/F12) containing 1% BSA. Digested tissues were filtered through sterile 150-µm nylon mesh and centrifuged at 250×g for 5 min. The floating fraction consisting of pure isolated adipocytes was then removed and washed three more times before proceeding to experiments. The pellet, representing the stromal vascular fraction containing preadipocytes, macrophages and other cell types, was resuspended in erythrocyte lysis buffer consisting of 154 mM NH4Cl, 10 mM KHCO3, and 0.1 mM EDTA for 10 min.
RNA was isolated from cells and tissues using the Qiagen RNeasy Kit (Qiagen, Germany). The RNA was reversely transcribed with EuroScript Reverse Transcriptase (Eurogentec, Belgium) and amplified using TaqMan Universal PCR-Master Mix, NO AmpErase UNG with TaqMan Assay-on-demand kits (Applied Biosystems, CA, USA). Relative expression of target mRNAs (Adiponectin Mm00456425_m1, Arg1 Mm00475988_m1, Bcl2 Mm00477631_m1, Bax Mm00432050_m1, Ccl2 Mm00441242_m1, Ccl3 Mm00441258, Ccl5 Mm01302428_m1, Cxcl5 Mm00436451_g1, CD11c Mm00498698_m1, CD3 Mm00442746_m1, CD34 Mm00519283_m1, CD4 Mm00442754_m1, CD8 Mm01182108_m1, F4/80 Mm00802530_m1, G6pase Mm839363_m1, Gr-1 Mm00459644_m1, Il1β Mm00434228_m1, Il6 Mm00446190_m1, Ifng Mm00801778_m1, Igf1r Mm00802841_m1, InsR Mm00439693_m1, Kit Mm00445212_m1, Leptin Mm00434759, Mmp2 Mm00439506_m1, Mmp9 Mm00442991_m1, Pck1 Mm00440636_m1, TNFα Mm00443258_m1) was determined using standard curves based on WAT and samples were adjusted for total mRNA content by hypoxanthine guanine phosphoribosyl transferase 1 (Hprt1 Mm00446968_m1) mRNA quantitative PCR. Calculations were performed by a comparative method (2−ΔΔCT). Quantitative PCR was performed on an ABI-PRISM 7900 HT Sequence Detector (Applied Biosystems, Germany). Assays were linear over 4 orders of magnitude.
For assessment of apoptosis in primary macrophages, the DeadEnd™ Fluorometric TUNEL system (Promega Corporation, Madison, WI, USA) was used. The protocol for adherent cells was carried out according to the manufacturer's instructions. Slides were mounted with Vectashield DAPI medium (Vector Laboratories Inc, Burlingame, CA, USA) and analyzed under a fluorescence microscope. Quantification of DAPI- and FITC-positive cells was performed using AxioVision 4.2 (Carl Zeiss MicroImaging GmbH, Oberkochen, Germany).
Chemotaxis of BMDM was quantified using transwell migration assays. Polycarbonate filters (Costar, Corning, 24-well, 8 µm pore size) were coated with gelatin (0.2%, Sigma) for 1h at room temperature or overnight at 4°C. BMDM (2×105 cells in 300 µl IMDM/0,5% FCS) were placed in the upper compartment and subsequently incubated at 37°C/5% CO2 to adhere. After 1h, 100 ng/ml MCP-1 (Peprotech) was added to IMDM/0,5% FCS in the lower compartment. Control assays were performed without chemokine. After incubation for 4 h at 37°C/5% CO2, transmigrated cells were stained with DAPI and nuclei were counted under a fluorescence microscope.
Immunohistochemistry was performed on paraffin sections as previously described [60]. Quantification of adipocyte size and Mac-2-positive area was performed with AxioVision 4.2 (Carl Zeiss MicroImaging GmbH, Oberkochen, Germany).
Data was analyzed for statistical significance using a two-tailed unpaired student's T-Test.
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10.1371/journal.pntd.0003156 | Evaluation of a Rapid Diagnostic Test for Yaws Infection in a Community Surveillance Setting | Yaws is a non-venereal treponemal infection caused by Treponema pallidum ssp. pertenue. The WHO has launched a worldwide control programme, which aims to eradicate yaws by 2020. The development of a rapid diagnostic test (RDT) for serological diagnosis in the isolated communities affected by yaws is a key requirement for the successful implementation of the WHO strategy. We conducted a study to evaluate the utility of the DPP test in screening for yaws, utilizing samples collected as part of a community prevalence survey conducted in the Solomon Islands. 415 serum samples were tested using both traditional syphilis serology (TPPA and quantitative RPR) and the Chembio DPP Syphilis Screen and Confirm RDT. We calculated the sensitivity and specificity of the RDT as compared to gold standard serology. The sensitivity of the RDT against TPPA was 58.5% and the specificity was 97.6%. The sensitivity of the RDT against RPR was 41.7% and the specificity was 95.2%. The sensitivity of the DPP was strongly related to the RPR titre with a sensitivity of 92.0% for an RPR titre of >1/16. Wider access to DPP testing would improve our understanding of worldwide yaws case reporting and the test may play a key role in assessing patients presenting with yaws like lesions in a post-mass drug administration (MDA) setting.
| Yaws is a bacterial infection closely related to syphilis. The WHO has launched a worldwide campaign to eradicate yaws by 2020. If this goal is to be achieved, programme managers and clinical staff will need access to a rapid diagnostic test (RDT) for yaws that can be used in the remote communities where the disease is found. In this study, we present data evaluating one possible RDT for yaws as part of a community survey in the Solomon Islands. The test performed reasonably well—there were some false negatives but few false positives. The performance of the test was best in individuals with more active disease suggesting the test may be most appropriately used for confirming clinically diagnosed cases. These findings should prompt consideration of the use of this RDT as part of worldwide yaws control efforts.
| Yaws is a non-venereal treponemal infection caused by Treponema pallidum ssp. pertenue (T. pertenue) [1] which is currently thought to be endemic in fourteen countries [2]. The emergence of azithromycin as an effective oral agent in the treatment of yaws [3] has prompted renewed calls for a coordinated worldwide programme to eradicate the disease by 2020 [4].
Despite this optimism there are significant barriers still to be overcome. The differential diagnosis of yaws can be broad [5] and serological testing is necessary to help establish a diagnosis. As with syphilis, latent infection occurs, and it is recognized that for every clinical case there may be 5–6 individuals with serological evidence of infection but no clinical manifestations [6], [7]. Failure to adequately treat latent cases was one of the reasons for the failure of previous attempts to eliminate yaws [8]. Detection and treatment of these latent cases will be extremely important if the WHO eradication programme is to achieve its target.
Traditional syphilis serology includes a treponemal specific test, such as the Treponema pallidum particle agglutination assay (TPPA) or the fluorescent treponemal antibody test, and a non-treponemal test, such as the Rapid Plasma Reagin (RPR) or Venereal Disease Research Laboratory (VDRL) assays. The former tests are highly specific but normally remain positive for life following infection. The latter tests are non-specific but the RPR titre more accurately reflects disease activity and falls following successful treatment. Low-titre false-positive RPRs may occur in a number of conditions including acute viral infections, malaria and connective tissue diseases. Testing therefore requires combined treponemal and non-treponemal assays to give a more accurate diagnostic result.
The development of a rapid diagnostic test (RDT) that can be used to improve access to serological diagnosis in the isolated communities affected by yaws has been highlighted as a major research question [9] to be addressed. RDTs allow wider access to diagnostic testing in remote communities where laboratory facilities are not available, with results available at the point of care to inform clinical decision making. As yaws is serologically indistinguishable from syphilis [10], the recent development of syphilis RDTs with high sensitivity and specificity [11] prompts evaluation of their use for the diagnosis of endemic treponemal diseases.
The required role(s) of an RDT may vary depending on the progress of the eradication programme in a given country. The test may have utility to confirm the diagnosis in patients presenting with skin lesions, to detect ongoing transmission of infection after mass drug administration has been conducted, or to conduct community surveillance in areas previously known to be endemic. The target product profiles of the RDTs required in each of these settings are likely to vary.
The Dual Path Platform (DPP) Syphilis Screen and Confirm (Chembio, Medford, NY, USA) provides both a “treponemal” result (analogous to a Treponema Pallidum Particle Agglutination (TPPA) assay (T1 line)) and a “non-treponemal” result (analogous to a qualitative Rapid Plasma Reagin (RPR) assay (T2 line)) [12]. We conducted a study to evaluate the utility of the DPP test in screening for yaws in the general population, utilizing samples collected as part of a community prevalence survey for yaws and trachoma conducted in the Solomon Islands.
This study was embedded in a larger study investigating the epidemiology of yaws in the Solomon Islands. Briefly, we undertook a survey in Western and Choiseul provinces of the Solomon Islands in September and October 2013. Twenty-five clusters were randomly selected in each province. In each cluster, thirty households were visited, and children aged five to fourteen were enrolled in the study. The study team collected information on yaws symptoms, signs and treatment history. Venepuncture was performed and a serum sample was collected from all participants. Sera were kept on wet ice (4°C) in the field and transferred within 5 days of collection to the National Referral Hospital, Honiara, where they were frozen. Samples were shipped on dry-ice to the London School of Hygiene & Tropical Medicine (LSHTM).
We assumed that the prevalence of yaws sero-positivity by the gold standard assay would be 30%. We therefore calculated that a sample size of 415 was required to be 80% confident that the true sensitivity of the DPP, compared to the gold standard, was 85% or greater [13]. For the purposes of this study, simple random sampling was used to select samples to undergo parallel testing with both the DPP kit and traditional serology.
Sera were tested using both the TPPA (Mast Diagnostics, Merseyside UK) and a quantitative RPR (Deben Diagnostics, Sheffield UK) at LSHTM by an operator masked to clinical findings. A second operator, masked to clinical findings and gold standard serology, tested samples using the DPP test kit. Samples for which the control line did not appear were repeated. The manufacturer's instructions were followed for all test kits.
The sensitivity, specificity, positive and negative predictive values of the DPP test kit were calculated using traditional serology as the gold standard. The DPP-T1 line was assessed against TPPA and the DPP-T2 line was assessed against RPR. Secondary analyses estimated these performance characteristics by RPR titre and presence or absence of clinical signs of yaws. Confidence intervals were calculated using robust standard errors to allow for clustering. Findings are reported in line with the STARD checklist for studies of diagnostic accuracy [14].
Written, informed consent was obtained from the head of each household, who was the parent or guardian of children enrolled in the study, and assent was obtained from all children. Ethical approval for the study was granted by the ethics committees of the Ministry of Health and Medical Services in the Solomon Islands, and LSHTM in the UK.
Four hundred and fifteen samples were randomly selected. The median age was 9, and 52.1% of participants were male. Individuals selected for this study did not differ significantly from the larger prevalence survey population with regards to demographic or clinical features (data not shown). Clinical findings consistent with active and healed yaws were found in 19 (4.7%) and 34 (8.2%) respectively of the 415 participants (Table 1 and Figure 1).
123 (29.6%) individuals had a reactive TPPA. 120 (28.9%) individuals had a reactive RPR at any titre. By gold standard serology there were 18 individuals with a false positive RPR (defined as a positive RPR and negative TPPA). All false positive RPRs in our study had an RPR titre of 1∶2. The overall prevalence of true RPR reactivity was therefore 102/415 (24.6%). The distribution of RPR titres in the study population is given in Figure 2.
79 individuals (19.0%) had a positive T1(treponemal) line on the DPP kit. 64 (15.4%) individuals had a positive T2 (non-treponemal) line on the DPP kit. There were ten individuals with a positive T2 line but a negative T1 line. The overall prevalence of dual-positivity was therefore 54/415 (13.0%).
The sensitivity of the T1 line against TPPA was 58.5% and the specificity was 97.6%. The positive predictive value was 91.1% and the negative predictive 84.5%. The sensitivity of the T2 line against RPR was 41.7% and the specificity was 95.2%. The positive predictive value was 78.1% and the negative predictive value was 79.9%. The sensitivity of combined T1 and T2 against combined TPPA and RPR was 47.1% and the specificity was 98.1%. The sensitivity of the DPP was strongly related to the RPR titre (Tables 2 and 3)
In individuals with clinical signs of active yaws the sensitivity of the T1 line, compared to TPPA, was 81.8% and the specificity was 100%. The sensitivity of the T2, compared to RPR, was 55.6% and the specificity was 83.3%. A similar association was seen between T2 sensitivity and RPR titre as in the overall population (data not shown).
In this study, we found that in a community surveillance setting, the sensitivity and specificity of the DPP rapid diagnostic test were markedly lower than in the only previous evaluation of the assay for use in clinically active yaws [15]. There was a strong association between the sensitivity of both the T2 line (against gold standard RPR) and RPR titre and also between the T1 line (against gold standard TPPA) and RPR titre. This finding might suggest that the apparent reduced sensitivity of the test reflects, at least in part, lower antibody titres in this population (where positive serology predominantly reflected asymptomatic latent cases) compared to populations in which the assay has previously been evaluated (where there were greater numbers of patients with active clinical disease), rather than a difference in test characteristics per se. Despite the reduced sensitivity and specificity the positive predictive values of the test remained relatively high, reflecting the high prevalence of treponemal infection in this endemic setting.
An association between the sensitivity of the T2 line and the RPR titre has previously been noted [12], but an association between RPR titre and T1 sensitivity has not been described before. It is possible that, in keeping with its lower pathogenicity compared to syphilis, yaws elicits less vigorous antibody production than does its venereal cousin, affecting both the non-specific (non-treponemal) and specific (treponemal) components of that response. ‘Attenuated yaws’ has previously been described in the Solomon Islands [16] with less florid clinical manifestations than noted elsewhere. Widespread use of antibiotics with treponemocidal activity has been postulated as one possible explanation for this postulated clinical entity. It is conceivable that this phenomenon could also contribute to the predominantly low-titre range of antibody responses seen in this study.
The setting in which this evaluation was carried out varies markedly from previous evaluations of the DPP test. In the largest published study, which evaluated the test in the diagnosis of syphilis, Yin and colleagues [11] evaluated the test in a population of 1,323 individuals presenting to a sexual health clinic in China, and found it to have a sensitivity of approximately 95% against TPPA and 86% against RPR. An association between RPR titre and sensitivity was also found in this study, although the performance of the test was better at titres of 1∶4 and 1∶8 than reported here.
Our study has a number of limitations. First, relatively few individuals tested had clinical evidence of active yaws, reflecting the community surveillance setting in which the test was evaluated. Whilst this limits our ability to comment on the value of the test for the purpose of case-confirmation, the aim of this study was to evaluate the DPP's use in screening whole communities at risk of yaws, and for this context we provide the first published data. Second, testing was performed in a central laboratory facility not in the field. Although the test can be performed rapidly (approximately 15–20 minutes per RDT), field-testing was not practical alongside the other activities being performed in our study, for which teams had to move house-to-house. Evaluations of the test elsewhere show that test performance is unaffected by whether venepuncture or finger-prick samples are used [11]. Further evaluations of the test in the field are warranted.
This study has implications for the use of the DPP RDT in yaws surveillance and control. Whilst the sensitivity of the test was lower than previously reported, the specificity remained high, and the negative and positive predictive values of the test were also high. Our data suggest that the DPP test can be used as part of a community surveillance strategy to identify individuals who are dually sero-positive with high-titre RPRs. These individuals are most likely to represent the major source of ongoing transmission. Identification of communities in which such individuals live is vital to allow adequate community targeted treatment to be undertaken [4]. The performance of the test in individuals with clinical evidence of yaws was better than in those without clinical evidence of disease, an association that is likely to be explained by the higher RPR titres found in individuals with clinically active disease. Because of inadequate access to point-of-care diagnostics, many national yaws surveillance systems only report clinically suspected cases. Wider access to DPP testing could allow a larger proportion of these cases to be evaluated serologically, which would both critically refine understanding of global yaws epidemiology as it evolves towards the eradication endpoint, but also provide a vital clinical aid in the post-MDA setting, where many conditions mimicking yaws will continue to present to health-care facilities. Further evaluation of the DPP in other yaws surveillance settings would be welcomed to provide the basis for guideline development.
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10.1371/journal.ppat.1004924 | Evidence for a Novel Mechanism of Influenza Virus-Induced Type I Interferon Expression by a Defective RNA-Encoded Protein | Influenza A virus (IAV) defective RNAs are generated as byproducts of error-prone viral RNA replication. They are commonly derived from the larger segments of the viral genome and harbor deletions of various sizes resulting in the generation of replication incompatible viral particles. Furthermore, small subgenomic RNAs are known to be strong inducers of pattern recognition receptor RIG-I-dependent type I interferon (IFN) responses. The present study identifies a novel IAV-induced defective RNA derived from the PB2 segment of A/Thailand/1(KAN-1)/2004 (H5N1). It encodes a 10 kDa protein (PB2∆) sharing the N-terminal amino acid sequence of the parental PB2 protein followed by frame shift after internal deletion. PB2∆ induces the expression of IFNβ and IFN-stimulated genes by direct interaction with the cellular adapter protein MAVS, thereby reducing viral replication of IFN-sensitive viruses such as IAV or vesicular stomatitis virus. This induction of IFN is completely independent of the defective RNA itself that usually serves as pathogen-associated pattern and thus does not require the cytoplasmic sensor RIG-I. These data suggest that not only defective RNAs, but also some defective RNA-encoded proteins can act immunostimulatory. In this particular case, the KAN-1-induced defective RNA-encoded protein PB2∆ enhances the overwhelming immune response characteristic for highly pathogenic H5N1 viruses, leading to a more severe phenotype in vivo.
| Error-prone polymerase function of RNA viruses can result in expression of defective RNAs harboring internal deletions of various sizes. Small subgenomic RNAs are strong inducers of the antiviral response by serving as pathogen-associated patterns that are predominantly detected by cellular sensors. Recently, it has been shown that influenza A virus defective RNAs are not only generated upon passages in cell culture, but also in infected humans, indicating that these subgenomic RNAs may also be relevant in infections in vivo. Here, we characterize a novel defective RNA derived from the PB2 segment of a highly pathogenic H5N1 influenza A virus. This RNA encodes a 10 kDa peptide (PB2Δ) which activates type I interferon (IFN) responses through direct interaction with the adapter protein MAVS, a key component of the RIG-I-dependent IFN induction. This is the first time that such a function was described for a defective RNA-encoded protein, a finding that has several important implications with regard to deciphering viral protein functions and options for immunostimulatory approaches. Furthermore, this is an example of how influenza viruses may acquire novel polypeptides with altered functions from its limited genome.
| Influenza A viruses (IAV) belong to the family of Orthomyxoviridae and are characterized by a segmented single-stranded RNA genome with negative orientation. These eight segments encode nine structural and up to seven nonstructural proteins [1–5]. For the production of infectious virus particles at least one copy of each of the segments needs to be packaged into the progeny particle during the virion assembly step [6].
Non-infectious particles exhibiting an incomplete viral genome e.g. due to a large internal deletion within at least one segment, are defined as defective interfering particles (DIPs) [7, 8]. These DIPs need the presence of a helper virus for replication of their defective RNA [9]. The generation of IAV DI RNAs has been linked to the ribonucleoprotein tertiary structure that leads to jumping of the viral polymerase making transitions between adjacent regions of the RNA template. Furthermore, there are indications that polymerase skipping is facilitated by short sequence repeats within viral segments [10]. It has been shown that the competition for viral RNA polymerases and preferential packaging of over-abundant DI RNA segments interferes with replication and packaging of full-length segments of replication competent helper virus [7, 8, 11–13].
IAV DI particles have been observed primarily after multiple passages of viruses in cell culture at high multiplicity of infection or in experimentally infected embryonated chicken eggs [10, 14, 15] and therefore were considered as experimental artifacts. However, subgenomic RNAs were isolated recently in vivo from naso-pharyngeal swabs of human patients [16]. The same study demonstrated that identical in vivo-derived defective RNAs were present in patients linked by direct contact which might suggest efficient co-transmission between them. It is thus reasonable to assume that defective RNAs are common byproducts also in natural infections albeit their function in the biology of influenza viruses and its interactions with the host remains elusive.
Defective RNAs have been shown to act as efficient pathogen-associated molecular patterns (PAMPs) recognized more potently by the sensor molecule retinoic acid-inducible gene-I (RIG-I) than viral genomic segments [17]. This activation of RIG-I leads to the induction of interferon beta (IFNβ) which is one of the key cytokines orchestrating a broadly reactive antiviral program upon infection [18]. Recently, Tapia and colleagues provided evidence that the appearance of defective RNAs coincides with the production of cytokines during IAV infection [19].
It has been shown previously that some subgenomic RNAs harbor open reading frames that might be expressed in the presence of a helper virus or could be in vitro translated into polypeptides [20, 21]. But, so far, a putative biological function of these proteins has never been analyzed, although they might share sequence similarities with their parental proteins and might therefore exhibit related or even different functions. In the past years, the discovery of novel IAV-encoded proteins led to new insights into viral pathogenicity [1–4, 22]. Surprisingly, all viral nonstructural proteins discovered since 2001 have been shown to be nonessential for viral replication [1–4] or even possess antiviral activities under certain conditions [23]. However, several analyses demonstrated that changes in expression levels of these proteins were linked to virulence in vivo [3, 23, 24]. Therefore, defective RNA-encoded proteins may also contribute to the course of infections in vivo and could exemplify a mechanism on how influenza viruses acquire novel polypeptides with altered functions from its limited genome.
The present study describes for the very first time a defective RNA-encoded functional polypeptide, named PB2∆. The subgenomic RNA was identified in the H5N1 strain A/Thailand/KAN-1/2004 (KAN-1). It belongs to the group of defective RNAs derived from the PB2 segment and potently restricts viral replication by a mechanism independent of DI RNA-mediated interference. The present study demonstrates that the defective RNA-encoded polypeptide PB2Δ directly interacts with the mitochondrial antiviral signaling protein (MAVS). In contrast to the effects of PB2-MAVS interaction, this leads to the induction of IFNβ expression, thereby diminishing viral replication. Furthermore, the presence of this particular defective RNA-encoded protein in KAN-1 infection leads to higher expression levels of antiviral acting genes also in vivo, resulting in a more severe disease phenotype.
In recent years, new influenza encoded non-structural proteins were discovered which could be either linked to viral polymerase activity [25] or host cell response [3, 22, 26, 27] and are therefore determinants of viral virulence.
During analysis of a cDNA library generated from total RNAs of A549 cells infected with different IAV subtypes, cDNAs representing a 618 nt-long RNA were identified in H5N1 strain A/Thailand/KAN-1/2004-infected cultures. This RNA has sequence identity to the 5’ and 3’ regions of the viral PB2 gene but lacks 1715 nt from the PB2 internal region. Hence, the PB2Δ RNA retains viral promoter and packaging information required for efficient viral replication (Fig 1A) and thus possesses a structure that is typical for viral defective interfering RNAs [7]. The internal deletion junction site of the PB2∆ RNA occurs at nucleotide position 240 as observed previously for other defective RNAs with junction sites located around positions 200–300 in vitro and in vivo [10, 16]. Furthermore, this RNA harbors characteristic defective RNA sequence motifs as described by Jennings and colleagues (1983) such as the frequent occurrence of the sequences 5’..GAA..3’ and 5’..CAA..3’ near the junction site, the number of adenosine residues varying from 2 to 3. In addition, it has been described that identification of the precise junction is frequently not possible due to local sequence similarities [10]. Here, short repeat sequences at the junction site were also observed, although these nucleotides are not deleted but still present at both ends (5’..AGGAAT/AGGAAT..3’) (Fig 1A). Therefore, it can be concluded that the PB2∆ RNA structurally belongs to the group of viral defective interfering (DI)-like RNAs.
To test whether PB2∆ RNA was expressed also by closely related H5N1 viruses total RNA was isolated from A549 cells infected with A/Thailand/1(KAN-1)/2004, A/Vietnam/1203/2004 or A/Mallard/Bavaria/1/2006. A specific Northern blot probe complementary to the junction site was designed, thus preventing cross-hybridization with the PB2 genomic vRNA under the hybridization conditions applied. The PB2∆ RNA was detectable only in KAN-1-infected cells (Fig 1B), although especially the Vietnam strain shows high sequence similarity and sequence repetition at the junction site within the PB2 gene (S1 Table, NCBI’s Influenza Virus Resource [28] KAN-1: CY111595.1 and Vietnam: HM006756.1). Interestingly, sequence alignments showed that this repeat is present in 53% of all known human and 79% of all avian H5N1 strains compared (S1 and S2 Tables), while all other strains show in general only a single nucleotide substitution. To exclude that the generation of the PB2Δ RNA was a KAN-1 stock-specific event, the ability of de novo PB2Δ RNA synthesis was examined for the H5N1 strains KAN-1 and Vietnam. Undiluted passages of plaque-purified clones from PB2Δ-free virus preparations performed as described by Nayak and colleagues [29] uncovered PB2Δ RNA expression in different passages to various extents for both strains (e.g. KAN-1 clones 8 and 12 as well as Vietnam clones 3 and 7; S3 Table). Although PB2Δ RNA expression is a nonessential event and not specific for KAN-1, this strain seems to exhibit higher PB2Δ expression levels compared to Vietnam virus.
DI dependent interference is characterized by competition for viral polymerases during replication of vRNA into cRNA and preferential packaging of over-abundant DI segments [12, 13]. To test whether PB2∆ RNA can be replicated by the viral polymerase complex, Northern blot hybridization was performed with a specific probe complementary to PB2∆ (+)RNA. As expected, the presence of PB2∆ (+)RNA upon infection with the KAN-1 isolate was confirmed, suggesting that the PB2 DI-like RNA is a substrate for the viral polymerase and is amplified upon viral replication (Fig 1B). This was further verified by strand-specific qRT-PCR, uncovering the existence of all three viral RNA species (S1 Fig).
One further aspect of the interfering activity of defective viral RNAs is the preferential packaging of RNAs which exist at high copy numbers within the cell [12]. The retained 5’ and 3’ regions of the PB2 segment within the PB2∆ RNA suggest that packaging of this smaller RNA fragment into virions is possible. The presence of PB2∆ vRNA in KAN-1 viral particles was confirmed by qRT-PCR (Fig 1C). Total RNA from KAN-1- and mock-infected A549 cells served as controls, showing higher amounts of PB2∆ RNA in infected cells compared to viral particles which might be due to the detection of PB2∆ RNA in negative and positive orientation. To exclude contamination of viral RNA with cellular RNA, the presence of GAPDH mRNA was analyzed, showing no PCR product from total RNA of viral particles (Fig 1C).
Hence, specific expression of PB2∆ DI-like RNA was confirmed and conversion into (+)RNA was shown, arguing that PB2∆ DI-like RNA serves as a substrate for the viral polymerase. Furthermore, it is packaged into viral particles and, thus, possesses all crucial features of defective RNAs owning interfering activity.
To answer the question as to whether the PB2∆ DI-like RNA exhibits interfering activity, a recombinant KAN-1 virus was produced by reverse genetics [30] harboring PB2∆ DI-like RNA as additional segment. This method has the advantage that the artificially introduced RNA predominantly persists over newly originated defective RNAs which are in an inferior position due to copy numbers [11, 31]. Furthermore, the generation of wild type virus allows the comparative analysis with a PB2∆ RNA-free virus.
To investigate whether the PB2∆ DI-like RNA exhibits interfering activity, replication of the recombinant PB2∆ RNA-expressing KAN-1 (rKAN-1 PB2∆) and of wild type virus (rKAN-1 WT) were compared (Fig 1D). Although 9 h p.i. there were no differences in viral replication, infectivity titers were significantly reduced by one log step at all further time points analyzed. Furthermore, rKAN-1 PB2∆ indeed induces the production of non-infectious particles as confirmed by hemagglutination assay (Fig 1E). This method allows the determination of the relative amount of particles in a virus suspension with a predefined number of infectious particles due to the same ability of infectious and non-infectious influenza particles to agglutinate avian erythrocytes. Here, an increased amount of virus particles was observed at all time points analyzed which was elevated by a factor of 4 for rKAN-1 PB2∆ infection compared to wild type virus (Fig 1E). To evaluate whether these differences are due to non-infectious particles induced by the PB2∆ DI-like RNA, whole genomes of both recombinant viruses were sequenced. This analysis revealed no differences in viral segment sequences neither among each other nor to the PB2Δ-expressing KAN-1 isolate, whereas a strong expression of PB2∆ m/cRNA for rKAN-1 PB2∆ was observed (Fig 1F), confirming that the presence of PB2Δ defective RNA indeed induces the production of non-infectious particles.
In recent years, increased attention has been paid to defective RNA-induced interferon expression. The first evidence of an influenza DI RNA inducing IFN expression came from DI 244 which protected wild type mice from lethal infections with heterologous paramyxo- and influenza B virus, but failed in protection of type I IFN receptor-deficient mice [32, 33]. The main PAMP that is sensed by different pattern-recognition receptors (PRRs) to induce the type I IFN response in viral infections is viral RNA. Particularly, detection of the 5’-triphosphate (5’-ppp) structure in viral RNAs by the cytoplasmic helicase RIG-I plays an important role in influenza A virus infection [34]. It has been shown that RIG-I preferentially binds 5’-ppp-containing small genomic segments and subgenomic RNAs [17]. Moreover, defective RNA generation coincides with the production of cytokines during IAV infection [19]. In addition, double-stranded RNAs of more than 30 base pairs in length, often found in snap- or copyback DI RNAs of Paramyxo- and Rhabdoviridae due to long double-stranded regions, also can trigger IFN responses [11]. To analyze whether PB2∆ DI-like RNA affects viral replication via enhancing virus-induced innate immune responses, the expression of IFNβ was determined by qRT-PCR. IFNβ is the most crucial mediator of the innate type I IFN response upon IAV infection [35]. No differences in IFNβ mRNA expression were detectable 8 h p.i. between the two recombinant viruses (Fig 1G), although PB2∆ RNA was expressed in high amounts as early as 2 h p.i. (Fig 1F). Interestingly, 24 h p.i. IFNβ mRNA levels were significantly increased upon infection with rKAN-1 PB2∆, suggesting that the reduced infectivity titers may be in part due to the induction of a strong host cell response.
Internal deletion of nucleotides within influenza segments during the synthesis of defective RNAs in general results in truncated RNAs possessing all important sequences for transcription and translation. Therefore, the presence of an intact open reading frame (ORF) can result in the expression of polypeptides from subgenomic RNAs, as previously described [20, 21]. However, so far there are no studies delineating a potential function of these polypeptides in IAV infection. The PB2∆ RNA comprises the PB2 translational initiation codon followed by a frameshift downstream of the junction site, leading to early termination after 15 additional amino acid codons (Fig 2A). To test whether this potential ORF leads to the generation of a polypeptide from the PB2∆ mRNA, viral protein expression was analyzed. Antibodies directed against the PB2 N-terminus were used, a region that would be identical in both proteins. A distinct protein band around 10 kDa emerged 5 h p.i. that was not observed upon infection with other IAV isolates of various subtypes (Fig 2B). Furthermore, the existence of this protein in KAN-1-infected cells was confirmed by mass spectrometry (S2 Fig). To verify that it is encoded by PB2∆ mRNA, a specific siRNA was designed covering the conserved regions flanking the junction site (Fig 1A) thus limiting interference with viral PB2 RNA. Viral protein expression was analyzed in the presence or absence of PB2∆-specific siRNA (Fig 2C). As expected, the small PB2-like protein encoded by the PB2∆ RNA was down-regulated, whereas the expression of PB2 was not reduced. Interestingly, especially 5 h p.i. there was a slight increase in viral protein expression observable when PB2∆ was knocked down which might suggest a function of the small protein in viral protein expression. Another explanation would be a simultaneous knockdown of PB2∆ v/cRNA leading to a more efficient replication of KAN-1 due to the prevention of non-infectious particles. To analyze whether viral replication is affected by siRNA-mediated knockdown of PB2∆ RNA species, A549 cells were infected with KAN-1 for 8 h (Fig 2D, above). Although the KAN-1 isolate is well adapted to cell culture and replicates efficiently in A549 to high viral titers, there was a slight increase in viral replication observable when PB2∆ was knocked down (Fig 2D, below). Furthermore, expression of PB2Δ protein was compared in rKAN-1 WT and rKAN-1 PB2Δ infection. As expected, the PB2Δ protein was only detectable in rKAN-1 PB2Δ-infected cells concomitant with decreased expression levels of other viral proteins (S3 Fig).
Thus, the viral PB2∆ defective RNA is efficiently transcribed into mRNA that is translated to a truncated 10 kDa protein. The data further suggest that this polypeptide might fulfill an antiviral function in influenza virus replication.
In order to analyze a potential function of the PB2∆ protein, an expression vector containing the PB2∆ ORF was generated. Initially, viral protein expression was analyzed in the presence or absence of PB2∆ in A549 cells infected with KAN-1. Interestingly, overexpression of PB2∆ led to a significant reduction in viral protein expression (Fig 2E). This could be attributed at least in part to the fact that the expression of viral mRNAs such as PB2 and M1 was reduced in the presence of PB2∆ (Fig 2F), although not significant for PB2 and effects on protein levels were more pronounced (Fig 2E).
Due to its function as part of the viral polymerase complex, the IAV protein PB2 has been linked to viral pathogenicity and host adaptation [36, 37]. There are several mutations known that verifiably affect viral polymerase activity and thus have an impact on viral replication [38, 39]. Comparative analysis of the viral proteins PB2 and PB2∆ showed sequence identity within the N-termini (Fig 2A), comprising the binding sites for the viral PB1 protein. Nuclear interaction with PB1 is essential for the generation of an active viral polymerase complex. Therefore, the decreased expression of viral mRNAs might be due to a reduced viral polymerase activity caused by direct interaction of PB2∆ with the polymerase complex. To test this hypothesis, co-immunoprecipitation of the viral polymerase complex was performed by using antibodies specific for the viral nucleoprotein (NP). The polymerase subunits PB1 and PB2 were efficiently co-immunoprecipitated, whereas PB2∆ was only detectable in the input control (Fig 2G). Therefore, reduced levels of viral mRNA expression in the presence of PB2∆ are unlikely to be caused by a modulation of viral polymerase activity induced by direct interaction of PB2∆ with the polymerase complex.
Another more indirect mode of interference with viral protein expression would be the enhancement of the IAV-induced antiviral host cell response. One of the most potent mediators of this response is IFNβ. Beside its role in viral replication, PB2 also participates in the evasion of the antiviral immune response mediated by direct interaction with the mitochondrial antiviral signaling protein (MAVS) [40]. The latter is of particular importance in the RIG-I-mediated signal transduction upon detection of viral RNA which results in the expression of type I IFNs [41–43]. It has been shown that the protein-protein interaction of MAVS and PB2 is mediated by N-terminal amino acids 1–37 of the viral PB2 protein [44] which are present in PB2∆ (Fig 2A). Therefore, an interaction with the cellular MAVS protein affecting IAV-induced IFNβ expression is likely to occur.
To test this hypothesis, HEK293 cells were transfected with plasmids expressing HA-tagged MAVS in combination with expression plasmids for Myc-tagged PB2∆ or Myc-tagged PB2, respectively. 24 h p.t. HA-tagged MAVS was precipitated with HA-specific antibodies and the presence of co-precipitating proteins was analyzed. As expected, co-precipitation with cellular MAVS was confirmed not only for viral PB2 but also for the PB2∆ protein (Fig 3A).
It has been shown that the IAV PB2 protein exhibits an N-terminal mitochondrial-targeting signal, leading to the association of PB2 with mitochondria where it can interact with MAVS which needs mitochondrial localization to fulfill its signaling functions [40, 42, 45]. To analyze whether PB2Δ is also associated with mitochondria and whether this localization is dependent on the presence of MAVS, mitochondria were isolated from MAVS siRNA-transfected A549 cells that were subsequently infected with KAN-1. As expected, knockdown of MAVS led to a reduced mitochondrial localization of PB2Δ that was even more pronounced compared to that of viral PB2 protein (Fig 3B).
To analyze whether this interaction might affect MAVS-induced IFNβ expression, reporter gene assays were performed. A construct containing the luciferase gene under control of the entire IFNβ promoter harboring all transcription factor binding sites responsible for the formation of the IFNβ enhanceosome was used. As expected, empty vector-transfected cells exhibited no luciferase activity when viral proteins were co-expressed (Fig 3C). The presence of the MAVS protein led to a strong induction of the IFNβ promoter activity that was even increased when PB2∆ was co-expressed. In clear contrast, the full-length PB2 protein resulted in a decreased promoter activity, confirming earlier findings [40]. Interestingly, the presence of PB2∆ in PB2/MAVS-expressing cells compensated for the PB2-mediated decrease in IFNβ promoter activity, resulting in the same level of luciferase expression as observed for MAVS/PB2∆-expressing cells. This MAVS-mediated mechanism of antiviral gene expression induced by PB2Δ was confirmed on endogenous levels by siRNA-mediated knockdown of MAVS. In this MAVS deficient situation, infection with a PB2Δ-positive virus led to the same levels of IFNβ and ISG mRNAs as observed in WT virus infection (Fig 3D). Since MAVS does not only participate in IFN induction but leads to the activation of different transcription factors inducing the expression of pro- and anti-inflammatory cytokines [41–43, 46], the expression of IL-6, a primarily NFκB-dependent cytokine, was analyzed in the presence of PB2Δ. As expected, IL-6 mRNA expression was also increased in presence of PB2Δ showing the same dependency on MAVS signaling as observed for IFN and ISGs, suggesting that PB2Δ leads to the activation of the full set of MAVS effector functions.
The results collectively demonstrate that PB2∆ can interact with MAVS, forming a functional complex that induces the expression of IFNβ, ISGs and other cytokines.
It is well known that influenza virus proteins such as the nonstructural proteins NS1 and PB1-F2 or the components of the heterotrimeric polymerase complex modulate IFNβ induction [26, 27, 47, 48], although in an inhibitory manner.
To analyze whether overexpression of PB2∆ protein would interfere with IAV-mediated expression of IFNβ and alter the induction of interferon-stimulated genes (ISGs), KAN-1-induced mRNA levels of different antiviral acting genes were analyzed (Fig 4A). Indeed, overexpression of PB2∆ led to a strong interference with IFNβ mRNA induction that was detectable even in mock-infected cells, however, expression of the cytokine was strongly increased rather than inhibited. According to this, the expression of ISGs such as IP10 was also enhanced.
To gain insight into the role of PB2∆-induced stimulation of the innate immune response in viral replication, infectivity titers were determined in the presence or absence of PB2∆. KAN-1 replication was significantly reduced by more than 10-fold when PB2∆ was overexpressed (Fig 4B). Furthermore, this impaired viral propagation was most likely a consequence of the induction of type I IFN as demonstrated by infection of type I IFN-deficient Vero cells, where PB2∆ expression did not decrease infectivity titers (Fig 4C). This was confirmed by pretreatment of A549 cells with conditioned media of PB2Δ-transfected cells leading to a significant decrease in infectivity titers around 100-fold (Fig 4D). This suppression was recovered by specific knockdown of IFNα/β receptor chain 2 (IFNAR2) in the acceptor cells or enrichment of conditioned media with neutralizing IFNβ antibodies. Efficient interference with PB2Δ-induced type I IFN is demonstrated by reduced STAT1 Tyr701 phosphorylation which led to recovery of viral protein expression (Fig 4E). To analyze whether this PB2∆-dependent IFN induction is sufficient to inhibit replication of other IFN-sensitive viruses as well, A549 cells were infected with vesicular stomatitis virus (VSV), showing significantly reduced replication in the presence of PB2∆ (Fig 4F).
In summary, the defective RNA-encoded protein PB2∆ acts antiviral by the induction of type I IFN and ISGs, thereby inhibiting viral propagation of IFN-sensitive viruses.
The induced cytokine storm during severe influenza infections leads to major morbidity and mortality. A significant association between excessive early cytokine response, immune cell recruitment and poor outcome has been documented for highly pathogenic avian H5N1 virus infections [49]. To investigate the role of PB2Δ in the induction of the innate immune response in vivo, BALB/c mice were infected with 50 pfu rKAN-1 WT in comparison to a PB2Δ-positive virus. Two days post infection, mouse lungs were extracted and total RNA was isolated for qRT-PCR. Fig 5A shows lung ISG mRNA levels normalized to PBS-treated control mice. Although differences in IFNβ mRNA levels between PB2Δ-negative and—positive KAN-1 viruses were not significant at the time point analyzed (S4A Fig), different ISGs such as OAS-1 and IRF7 showed considerably increased expression levels in presence of PB2Δ, while virus replication was not significantly altered at that time (S4B Fig).
Dysregulation of the innate cytokine response indicates disease severity and death during HPAIV infection [49, 50]. To analyze whether PB2Δ-induced ISG expression has an impact on disease severity of KAN-1 infection, BALB/c mice were infected with 50 pfu of wild type or PB2Δ-expressing virus. While in presence of PB2Δ mice lost body weight as early as 4 days post infection, weight loss in WT-infected mice was delayed and was not detectable until day 6 p.i. (Fig 5B, left). This is supported by the survival curves which show the tendency to earlier death of PB2Δ-infected mice, although this result is not significant.
These findings demonstrate that defective RNA-encoded PB2Δ protein-induced amplification of early innate immune responses leads to enhanced disease severity in KAN-1 infection and impressively emphasizes the role defective RNA-encoded proteins could play in viral pathogenicity of IAV.
The generation of IAV defective interfering particles upon serial passages at high multiplicity of infection in vitro has been well described and in recent years there is increasing evidence that influenza defective RNAs are naturally occurring also in vivo [16]. It is still discussed why non-infectious particles are produced upon infection in many or possibly all animal virus systems and defective viruses have been ignored as possible important determinants in the outcome of natural virus infections for years [9]. Generation of non-infectious particles might be induced by the host, thereby restricting viral replication and boosting innate immune responses. One further possibility is that DI particles have evolved as an adaptive measure of the virus, thereby enabling persistent infections to allow further spread in a population, especially in the case of highly pathogenic viruses whose infections are associated with high mortality rates. Furthermore, the non-infectious particle population contains transcriptionally competent gene segments that can be complemented through coinfection [51]. Natural infection of an animal host involves interplay between physically and genetically heterogeneous virions and a mixed collection of wildly different cell types of varying susceptibility in combination with a dynamic and complex immune response. Therefore, genetic variability induced by error-prone polymerases is an evolutionary advantage that is strongly promoted by non-infectious particles [52]. Recently, Saira and colleagues showed that in vivo-derived DI-like RNAs were similar to those generated in vitro and that the presence of identical DI-like RNAs in patients linked by direct contact is compatible with transmission between them [16]. Therefore, a functional role of DI-like RNAs in natural infections seems not unlikely. Here, we investigated the molecular mechanisms by which defective particles, harboring a specific PB2-derived subgenomic RNA that encodes a 10kDa protein, inhibit H5N1 KAN-1 viral replication.
Beside DIP-induced interference mediated by competition for viral polymerases and preferential packaging of over-abundant DI segments [12, 13], recent reports show that subgenomic RNAs can act as potent immune stimulators [17, 19, 32, 33]. It has been observed that the cytoplasmic PRR RIG-I preferentially binds small genomic and subgenomic RNAs [17] and there is evidence that the appearance of defective RNAs coincides with the production of cytokines during IAV infection [19]. Here, overexpression of the defective RNA-encoded protein PB2∆ per se was sufficient to induce transcription of IFNβ mRNA, whereupon subsequent infection led to a further increase in IFNβ expression. In addition, KAN-1-induced expression of IFNβ and subsequently that of ISGs seems to be primarily induced by the PB2∆ protein and not by the detection of the defective RNA itself. This was shown by the recombinant PB2∆-expressing virus which induced increased IFNβ levels only late upon infection although considerable expression of PB2∆ RNA was detectable already 2 h p.i.. These data suggest that not only defective RNA but also some defective RNA-encoded proteins can act immunostimulatory. Furthermore, the PB2Δ protein-mediated IFN induction is completely independent of the detection of subgenomic RNA by the cellular sensor RIG-I but comprises complex formation with the adapter protein MAVS.
MAVS is of primary importance for RIG-I-mediated signal transduction upon sensing of viral PAMPs resulting in the expression of type I IFNs [41–43]. It has been shown that the PB2 protein interferes with IFNβ expression by direct interaction with MAVS via its binding site located within aa 1–37 [44]. It was hypothesized that PB2 binding leads to the inactivation of the MAVS complex by inhibition of intermolecular conformational changes needed for oligomerisation [44]. Here, PB2∆ binding of MAVS per se activates MAVS-mediated IFN induction. This suggests that not the binding of PB2 itself inhibits MAVS signaling but rather leads to a transition of the cellular protein into the active form, whereas the C terminal part of the viral protein interferes with induction of signal transduction. Thus, it is very likely that the C-terminus of PB2 inhibits MAVS oligomerisation or the interaction with downstream acting kinases such as TBK-1, TAK-1 or IKK or different transcription factors like IRF3 or IRF7 which are normally recruited by the adapter protein [41, 43, 46].
Interestingly, there was no interaction of PB2∆ with the viral polymerase complex observable although the binding site within PB2 mediating the interaction with MAVS is also involved in PB1 binding [44, 53]. This might be attributed to the tertiary structure of PB2∆. The binding site for MAVS and PB1 consists of 3 α-helices, of which the first establishes the interaction between PB1 and PB2 [53], whereas in the case of MAVS, helix 3 carries this function [44]. Therefore, specific folding of PB2∆ or steric inhibition by its C-terminus might lead to masking of helix 1, thereby inducing preferential binding of MAVS or even disabling PB1 binding. One further possibility would be the primary mediation of the PB1-PB2 interaction by an alternative PB1 binding site identified within the C-terminus of the PB2 protein [54]. Therefore, the present study reveals that functional analysis of defective RNA-encoded proteins can help to understand the biological characteristics of the parental viral protein and its interactions with viral and/or host factors.
The interaction of MAVS and PB2Δ at mitochondria increases IAV-induced type I IFN expression leading to reduced infectivity titers in the presence of PB2∆. Therefore, the present study links for the first time a biological function to an IAV defective RNA-encoded protein. In addition, PB2∆ is an antiviral acting protein whose action is not restricted to influenza A virus infection, but also limits the replication of other IFN-sensitive viruses as shown by VSV infection. Therefore, this peptide or a low-molecular peptidomimetic thereof might be useful as antiviral agent for IFN-sensitive viruses.
Interestingly, sequence alignments of human and avian H5N1 isolates showed that 53% or 79%, respectively, harbor the short sequence repeat at the junction site as observed for PB2∆ RNA which could be responsible for polymerase skipping, suggesting that these IAV isolates might also evolve a defective RNA similar to KAN-I PB2∆ upon passaging and/or transmission in vivo. This was supported by the finding that serial undiluted passaging of PB2Δ-free propagations of H5N1 strains KAN-1 and Vietnam led to the de novo generation of PB2Δ RNA to various extents. These data highlight different expression patterns of the same RNA within different preparations of the same virus strain and possibly explains different phenotypes of the same virus strain propagated in different laboratories.
Whether there are additional cis- or trans-mutations needed to facilitate generation of these defective RNAs has to be further studied. The apparent late expression of the PB2∆ protein compared to its RNA might suggest that there are other sequences or structures needed that support the translation of PB2∆.
In summary, PB2∆ is a virulence factor in H5N1 KAN-1 infections that restricts viral replication by modulation of the antiviral host gene response. Infections with highly pathogenic avian influenza viruses are characterized by a hyperactivation of the host immune response leading to an excessive expression of proinflammatory cytokines. This so called ‘cytokine burst’ has been discussed to be decisive for disease outcome [49, 50, 55]. Therefore, although PB2∆ is an antiviral acting protein in cell culture, its presence in KAN-1 infections in vivo enhances the induction of innate immune responses and thereby is involved in increased pathogenicity. Graef and colleagues hypothesized that the reduced PB2-induced suppression of MAVS-mediated signal transduction upon infection with avian influenza viruses in comparison to human subtypes might play a role in HPAIV-induced dysregulation of cytokine expression [40]. The adapter protein MAVS is not only essential for the induction of IFNβ but plays a role directly and indirectly in the induction of interferon-stimulated response element (ISRE)-regulated and proinflammatory genes by activation of transcription factors IRF3 (interferon regulatory factor 3) and NF-κB (nuclear factor kappa-light-chain-enhancer of activated B-cells) [41–43, 46]. In this context, it has already been shown that the early suppression of cytokine amplification significantly leads to the protection of mice from lethal IAV infection [56, 57]. Therefore, in context of KAN-1 infection, targeting PB2∆ might be a promising approach to reduce disease manifestation by the suppression of immunopathology.
Overall, the present study provides evidence for the very first time that polypeptides encoded by influenza virus defective RNAs can fulfill biological functions that are associated but must not coincide with the activities of the parental full-length protein. Furthermore, subgenomic RNA-encoded polypeptides seem to play a prominent role in influenza virus pathogenicity, therefore the analysis of their functions leads to deeper understanding of the biology of influenza virus infections and its interactions with the host.
All animal studies were performed in compliance with animal welfare regulations of the German Society for Laboratory Animal Science (GV-SOLAS) and the European Health Law of the Federation of Laboratory Animal Science Associations (FELASA). The protocol was approved by the relevant authorities in Hamburg (Behörde für Gesundheit und Verbraucherschutz) under the license number Az 42/13.
A/Thailand/KAN-1/2004 (H5N1; KAN-1) was used with kind permission from P. Puthavathana (Bangkok, Thailand). Recombinant A/Vietnam/1203/2004 (H5N1; Vietnam) and A/Puerto-Rico/8/34 (H1N1; PR8) were described before [58, 59]. A/FPV/Bratislava/79 (H7N7; fowl plaque virus, FPV) was a kind gift from S. Pleschka (Institute of Virology, Giessen, Germany) and A/Mallard/Bavaria/1/2006 (H5N1, Mallard) was obtained from O. Planz (Interfaculty Institute for Cell Biology, Tübingen, Germany). All experiments and handling of samples containing H5N1 or H7N7 infectious particles were performed in a biological safety level 3 containment. Influenza viruses as well as vesicular stomatitis virus strain Indiana (VSV) were propagated on Madin-Darby canine kidney (MDCKII) cells cultured in minimal essential medium (MEM, PAA Laboratories) containing 10% v/v FCS (Invitrogen) as described elsewhere [25]. Human alveolar epithelial cells (A549), green monkey epithelial cells (Vero) and human embryonic kidney 293 cells (HEK293) were cultured in Dulbecco’s modified eagle medium (DMEM, PAA Laboratories) containing 10% v/v FCS. All cell lines were originally purchased from ATCC and have been passaged in the laboratory. At regular intervals cells are checked for their identity by SNP-profiling (Multiplexion).
The luciferase reporter construct pTATA-IFNβ-luc containing the whole IFNβ enhanceosome upstream of a luciferase gene was a kind gift from J. Hiscott (Vaccine & Gene Therapy Institute of Florida, Port Saint Lucie, Florida, USA). pHW2000-PB2∆ was obtained by reverse transcription of the A/Thailand/KAN-1/2004 PB2∆ DI RNA by using influenza A segment one specific universal 12 primers [60] and subsequent PCR amplification. PB2∆ DI DNA was then cloned into the bidirectional pHW2000 plasmid [30] by using Eco31I restriction enzyme. pcDNA3-6xMyc-PB2∆ was obtained by PCR amplification of the open reading frame of PB2∆ that was subsequently cloned into pcDNA3-6xMyc by using EcoRV and XhoI restriction enzymes. Cloning of the PB2 gene into pcDNA3-6xMyc was performed the same way by Ludmilla Wixler (Institute of Molecular Virology, Muenster, Germany). Primer sequences are included in S4 Table. pCAGGS-HA-MAVS was a kind gift from B. Dauber (Robert Koch Institute, Berlin, Germany).
A set of eight plasmids based on the bidirectional pHW2000 plasmid allowing the rescue of the recombinant wild-type (WT) of influenza A/Thailand/KAN-1/2004 was used with kind permission from J. Stech (Friedrich-Loeffler-Institute, Riems, Germany). The generation of the recombinant viruses was carried out as described elsewhere [25, 30].
Plaque forming units of a given virus suspension were determined by a standard plaque assay as described earlier [61].
Serial 2-fold dilutions of virus supernatants (1x106 infectious particles, according to 1 HA unit of rKAN-1 WT stock) were prepared in V-bottomed microtiter plates in a total volume of 50 μl PBS, the latter also serving as negative control. 50 μl of solution of 1% chicken erythrocytes (Rockland Immunochemicals, Inc) in PBS were added to the wells and microtiter plates were incubated for 30 min at 4°C. Hemagglutination was monitored by photography.
PB2∆ siRNA (5’-AGGAAUAGGAAUGAGAAUA-3’), MAVS siRNA (5’-GCUGAAGACAAGACCUAUA-3’), IFNAR2 siRNA (5’-GAAGCAUAAACCCGAAAUA-3’) and control siRNA (5’-UUCUCCGAACGUGUCACGU-3’) were synthesized by MWG-Biotech AG. Transfection with PB2Δ siRNA was performed with HiPerFect (Qiagen), MAVS and IFNAR2 siRNAs were transfected by the use of Lipofectamine 2000 (Invitrogen) according to the manufacturer’s protocols. Infections were carried out 16–48 h p.t. and conditioned medium experiments 36 h p.t.. Plasmid DNA was transfected using Lipofectamine 2000 (Invitrogen) consistent with manufacturer’s instructions and infections were carried out 24 h p.t..
Cells were lysed in radioimmunoprecipitation assay (RIPA) buffer containing protease and phosphatase inhibitors [61]. Mitochondria were isolated by using the Mitochondria Fractionation Kit from BioVision according to the manufacturer’s protocol. RIPA protein lysates were cleared by centrifugation, lysates were separated by SDS-PAGE and blotted onto nitrocellulose membranes. Antisera directed against ERK2 (C-14) and influenza A PB1 (vK-20) were purchased from Santa Cruz Biotechnology and anti-α-Tubulin from Sigma-Aldrich. Antibody against influenza A PB2 protein was a kind gift of E. Fodor (Sir William Dunn School of Pathology, Oxford, UK [45]. Influenza A M1 and NP antibodies were obtained from AbD Serotec and mouse monoclonal antibody against influenza A NS1 (23–1) was developed at the Institute of Molecular Virology (Muenster, Germany) and can be purchased from Santa Cruz Biotechnology. MAVS (AT107) antibody was obtained from Alexis Biochemicals and anti-Myc (9E10) from ATCC. Antibody against phosphor-STAT1 Tyr701 (clone 14) was purchased from BD Transduction Laboratories. Neutralizing IFNβ antibody was obtained from Abcam and used in a concentration of 4 μg/ml conditioned medium.
For proteome analyses 100 μg of protein mixtures from KAN-1-infected A549 cells were separated on a one-dimensional gel, the lanes were cut into twelve slices each and in-gel digested by trypsin as described previously [62]. Peptide fractions were collected and desalted separately using C18 StageTips [63]. LC-MS/MS analyses were performed on an EasyLC nano-HPLC (Proxeon Biosystems) coupled to an LTQ Orbitrap Elite mass spectrometer (Thermo Scientific) as described previously [64]. Briefly, the peptide mixtures were injected onto the column in HPLC solvent A (0.5% acetic acid) at a flow rate of 500 nl/min and subsequently eluted with an 87-min segmented gradient of 5–90% HPLC solvent B (80% ACN in 0.5% acetic acid). During peptide elution the flow rate was kept constant at 200 nl/min. For proteome analysis the 20 most intense precursor ions were sequentially fragmented by CID in each scan cycle. Sequenced precursor masses were excluded from further selection for 90 s. The target values for the LTQ were 5000 charges (MS/MS) and 106 charges (MS).
The MS data were processed using default parameters of the MaxQuant software (v1.2.2.9) [65]. Extracted peak lists were submitted to database search using the Andromeda search engine [66] to query a target-decoy databases [67] consisting of the uniprot human proteome database (88,692 protein entries, downloaded on the 25th of February 2014), of H5N1 virus database (13 entries, including the sequence of PB2Δ) and 248 commonly observed contaminants. In database search, full tryptic specificity was required and up to two missed cleavages were allowed. Carbamidomethylation of cysteine was set as fixed modification; protein N-terminal acetylation, and oxidation of methionine were set as variable modifications. Mass tolerances were set to 6 ppm at the precursor and 0.5 Da at the fragment ion level, respectively. False discovery rates were set to 5% at peptide, and protein group level and the minimum peptide length was set to five amino acids.
Cells were lysed with RIPA or Triton lysis (TLB) buffer [68] containing protease and phosphatase inhibitors. Lysates were cleared by centrifugation and supernatants were incubated o/n at 4°C with the antibodies indicated coupled to protein A/G-conjugated agarose (Roche). Complexes were washed three times with lysis buffer (5 min overhead shaking, 4°C) and resolved by SDS-PAGE with subsequent electrotransfer onto nitrocellulose membranes.
Transfection of HEK293 with the IFNβ luciferase reporter plasmid (0.25 μg) in combination with the different expression plasmids (1 μg in total) was performed with polyethylenimine (PEI) as described elsewhere [69]. Luciferase assays were carried out 24 h p.t. as previously described [70]. Relative light units were normalized to protein concentrations determined with a standard Bradford assay.
Total RNA from cells was isolated using the RNeasy Kit (Qiagen) according to the manufacturer’s instructions. Lungs from mice were collected at the time points indicated and total RNA was isolated using TRIzol reagent (Invitrogen). TRIzol lysis was performed according to the manufacturer's protocol, introducing a secondary phase separation step as described previously [57].
Three micrograms of total RNA were reverse transcribed with RevertAid H Minus Reverse Transcriptase (MBI Fermentas) and oligo(dT) (MWG-Biotech AG) or random hexamer (Fermentas) primers according to the manufacturer’s protocol.
Strand-specific qRT PCR for distinguishing influenza v/c/mRNAs was performed as described previously [71]. Briefly, 200 ng of total RNA were reverse transcribed with RevertAid Premium Reverse Transcriptase (MBI Fermentas) and 10 pmol of each primer (specific for v/c/mRNAs and Oligo(dT)) according to the manufacturer’s instructions.
The cDNA was used for qRT-PCR, which was performed using a Roche LightCycler 480 and Brilliant SYBR Green III Mastermix (Agilent) according to the manufacturer’s instructions. Primer sequences are included in S4 Table. Relative changes in expression levels (n-fold) were calculated according to the 2-∆∆CT method [72].
Total RNA (10 μg) was separated on 8% (w/v) polyacrylamide [29:1 acrylamide/bisacrylamide], 7 M urea gels and electro-transferred onto positively charged nylon membranes (Roche). Hybridization probes (50 pmol; PB2∆ vRNA: 5’-TGAAAGGAATAGGAATGAGAAT-3’; PB2∆ c/mRNA: 5’-ATTCTCATTCCTATTCCTTTCA-3’) were radioactively labeled using [γ-32P]-ATP (PerkinElmer) and T4 Polynucleotide Kinase (Fermentas) according to the manufacturer’s instructions. Northern blot analysis was performed as described previously [73].
All animal experiments were performed in the BSL-3 animal facility of the Heinrich-Pette-Institute, Leibniz Institute in Hamburg. BALB/c mice were obtained from the Harlan-Winkelmann animal breeding facilities. Eight-week-old mice were anaesthetized by intraperitoneal injection of 100 mg/kg Ketavet and 10 mg/kg Xylazin. Mice were infected by the intranasal route in a 50 μL volume as indicated. Health status of the animals was monitored daily according to the animal protocols approved by the Hamburg authorities.
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10.1371/journal.pgen.1006790 | Specification and spatial arrangement of cells in the germline stem cell niche of the Drosophila ovary depend on the Maf transcription factor Traffic jam | Germline stem cells in the Drosophila ovary are maintained by a somatic niche. The niche is structurally and functionally complex and contains four cell types, the escort, cap, and terminal filament cells and the newly identified transition cell. We find that the large Maf transcription factor Traffic jam (Tj) is essential for determining niche cell fates and architecture, enabling each niche in the ovary to support a normal complement of 2–3 germline stem cells. In particular, we focused on the question of how cap cells form. Cap cells express Tj and are considered the key component of a mature germline stem cell niche. We conclude that Tj controls the specification of cap cells, as the complete loss of Tj function caused the development of additional terminal filament cells at the expense of cap cells, and terminal filament cells developed cap cell characteristics when induced to express Tj. Further, we propose that Tj controls the morphogenetic behavior of cap cells as they adopted the shape and spatial organization of terminal filament cells but otherwise appeared to retain their fate when Tj expression was only partially reduced. Our data indicate that Tj contributes to the establishment of germline stem cells by promoting the cap cell fate, and controls the stem cell-carrying capacity of the niche by regulating niche architecture. Analysis of the interactions between Tj and the Notch (N) pathway indicates that Tj and N have distinct functions in the cap cell specification program. We propose that formation of cap cells depends on the combined activities of Tj and the N pathway, with Tj promoting the cap cell fate by blocking the terminal filament cell fate, and N supporting cap cells by preventing the escort cell fate and/or controlling the number of cap cell precursors.
| Establishment and maintenance of stem cells often depends on associated niche cells. The germline stem cell niche of the Drosophila ovary has been a long-standing model for the analysis of the interactions between stem cells and niche cells. Surprisingly little is known, however, about the mechanisms that pattern this niche, leading to the specification of different niche cell types and to their distinct arrangement around the stem cells. The observation that Tj is expressed at different levels in the different cell types of the niche motivated us to ask what contribution this transcription factor makes to the formation of the niche. Our data suggest that Tj activity is needed for the presence of escort cells and for the correct specification of cap cells but appears to be dispensable for the formation of terminal filament cells in the germline stem cell niche. Moreover, our analysis indicates that the establishment of the cap cell fate depends on the cooperation between Tj and the N signaling pathway. We conclude that Tj regulates the germline stem cell carrying capacity of the niche by controlling the fate and the spatial arrangement of niche cells.
| Stem cells retain the capacity for development in differentiated organisms, which is important for tissue growth, homeostasis and regeneration, and for long-term reproductive capability. Stem cells are often associated with a specialized microenvironment, a niche that is essential for the formation, maintenance, and self-renewal of stem cells by preventing cell differentiation and controlling rate and mode of cell division [1,2]. The niche for the germline stem cells (GSCs) in Drosophila serves as an important model for the analysis of interactions between niche and stem cells [1,3–5]. The astounding fecundity of Drosophila females that can lay dozens of eggs per day over several weeks depends on approximately 100 GSCs that are sustained by 40 stem cell niches. To understand the formation and maintenance of these GSCs, it is important to understand how stem cell niches form and how they function.
The GSC niche of the Drosophila ovary consists of three somatic cell types: cap cells, escort cells, and terminal filament (TF) cells (Fig 1A). GSCs are anchored to cap cells by DE-cadherin-mediated adhesion and require close proximity to cap cells to retain stem cell character [6–8]. Cap cells secrete the BMP homolog Decapentaplegic (Dpp), activating the TGFß signaling pathway in adjacent GSCs [9], which leads to the repression of the germline differentiation factor Bag-of-Marbles (Bam) [10,11]. Through Hedgehog (Hh) signaling, cap cells also appear to stimulate escort cells to secrete Dpp [12]. The combined pool of Dpp from cap and escort cells, together with mechanisms that concentrate Dpp in the extracellular space around GSCs [13], promotes the maintenance of 2–3 GSCs, whereas the adjacent GSC daughter cells that have lost the contact to cap cells will enter differentiation as cystoblasts [3,4]. In contrast, TFs are not in direct contact with GSCs but serve important functions in the development and probably also in the maintenance and function of GSC niches [14].
Formation of GSC niches begins with the progressive assembly of TFs by cell intercalation during the 3rd larval instar [15–17]. The process of TF cell specification is not understood but might start in 2nd instar when the first TF precursor cells appear to leave the cell cycle [18,19]. TF morphogenesis depends on the Bric à brac transcriptional regulators that control the differentiation of TF cells and their ability to form cell stacks [15,16,20], and involves the Ecdysone Receptor (EcR) [21,22], Engrailed [23], Cofilin [24], and Ran-binding protein M (RanBPM) [25]. The number of TFs that form at the larval stage determine the number of GSC niches at the adult stage [26–28], and are regulated by several signaling pathways that control cell division and timing of cell differentiation in the larval ovary, including the EcR [22], Hippo and Jak/Stat [27,28], Insulin [29] and Activin pathways [19]. Despite the recent advance in elucidating mechanisms that control the number of GSC niches and the temporal window in which they form [14], relatively little is known about the origin and specification of the somatic cell types of the GSC niche.
Notably, the origin and specification of cap cells, the main component of an active GSC niche is little understood. Cap cells (also called germarial tip cells) are first seen at the base of completed TFs at the transition from the 3rd larval instar to prepupal stage [16,17]. They appear to derive from the interstitial cells (also called intermingled cells) of the larval ovary that are maintained by Hh signaling from TFs [14, 30]. The formation of cap cells is accompanied by the establishment of GSCs [17]. The N pathway contributes to the development of cap cells [3]. A strongly increased number of functionally active cap cells per niche form in response to overexpression of the N ligand Delta (Dl) in germline or somatic cells, or the constitutive activation of N in somatic gonadal cells [8,22,31]. The ability of N to induce additional cap cells seems to depend on EcR signaling [22]. Loss of Dl or N in the germline had no effect on cap cells. However, loss of N in cap cell progenitors or Dl in TF cells caused a decrease in the number of cap cells [8,32]. A current model suggests that Dl signaling from basal-most TF cells to adjacent somatic cells together with Dl signaling between cap cells allows for a full complement of cap cells to form [8,32]. Furthermore, N protects cap cells from age-dependent loss as long as its activity is maintained by the Insulin receptor [32,33]. The Jak/Stat pathway, which operates downstream or in parallel to the N pathway in the niche [34], is not required for cap cell formation [34,35]. As cap cells were reduced in number but never completely missing when the N pathway components were compromised [8,31,32], the question remains whether N signaling is the only factor that is important for cap cell formation. Furthermore, no factor that operates downstream of N has been identified that is crucial for cap cell formation.
Here, we find that Traffic jam (Tj) is both required for cap cell specification and for the morphogenetic behavior of cap cells, enabling them to form a properly organized niche that can accommodate 2–3 GSCs. Tj is a large Maf transcription factor that belongs to the bZip protein family [36]. Its four mammalian homologs control differentiation of several cell types and are associated with various forms of cancer [37–39]. Tj is essential for normal ovary and testis development [36,40–42], and is only expressed in somatic cells of the gonad [36,43,44]. Interestingly, Tj is present in cap cells and escort cells but not in TFs [36]. We show that Tj is essential for the formation of the GSC niche. First, Tj regulates the behavior of cap cells, enabling them to form a cell cluster instead of a cell stack, which appears to be important for the formation of a normal-sized GSC niche with the capacity to support more than one GSC. Second, cap cells adopt the fate of TF cells in the absence of Tj function, and TF cells develop cap cell-like features when forced to express Tj, indicating that Tj specifies the cap cell fate. Genetic interactions suggest that Tj and N are required together for cap cell formation, but have different functions in this process. For somatic gonadal cells to adopt the cap cell fate, we propose that Tj has to be present to inhibit the TF cell fate and N has to be present to prevent the escort cell fate and/or produce the correct number of cap cell precursors.
To understand the defects in the stem cell niche of tj mutant ovaries, we reviewed the organization of the wild-type GSC niche, confirming and extending previous observations. The three somatic cell types of the GSC niche could be distinguished based on their position, cell and nuclear shape, and marker expression (Fig 1A–1D; S1 Table) [3,4,45]. The TF is a stack of disc-shaped cells (Fig 1A and 1B) [46]. The cap cell cluster at the tip of the germarium was either centered (Fig 1B) or formed an asymmetric streak that was attached to the base of a TF (Fig 1C) [6,47]. Cap cells had a rounded shape and were tightly packed in a cluster, with their nuclei in close proximity (Fig 1A–1D). Nuclei of escort cells had an angular (often triangular) appearance, and were bigger and more widely spaced than cap cell nuclei (Fig 1D). The anterior and posterior location of cap cells and escort cells, respectively, in relation to GSCs, produced a prominent gap between cap and escort cell nuclei (Fig 1A and 1D). GSCs made extensive contact to cap cells by forming a Bezel set-like rim of plasma membrane around each cap cell (Fig 1A and 1D–1F') [48]. We found that a GSC usually forms at least one prominent cellular protrusion toward cap cells, which distinguishes it from cystoblasts (Fig 1D, 1E' and 1F' arrowheads). These protrusions were seen with germline-specific markers that either label the cytoplasm (Vasa; Fig 1D) or the plasma membrane (nos-Gal4 UAS-Gap43-mEos; Fig 1E and 1F'). GSC protrusions were visible at various stages of the cell cycle as indicated by changes in the position of the spectrosome organelle (Fig 1E and 1F') [49]. The described morphological features helped identify cell types in the ovarian stem cell niche in addition to molecular markers.
Despite their different morphologies, cap cells have several markers in common with TF cells and some markers with escort cells (Fig 1B and 1D; S1 Table). Very few markers have been identified that seem to be specific for just one of these three cell types, but several markers showed differences in expression level (Fig 1B and 1D; S1 Table) [3,4,45]. Tj is expressed in cap cells and escort cells, which are located within the germarium and in contact to germline cells, but is not detected in TF cells, which form a stalk outside of the germarium (Fig 1B–1D) [36]. In addition, the cell that connects the cap cell cluster with the TF also contains Tj although at a considerably lower level than adjacent cap cells. We named this cell, which is disc-shaped similar to TF cells and aligned with TF cells, 'Transition cell' (Fig 1A and 1D). It might correspond to one of the basal cells of the TF that have been mentioned previously [47].
In each ovariole of a wild-type ovary, a bab-lacZ positive TF and cap cell cluster are followed by a string of follicles (Fig 2A). Adult ovaries from tjeo2/tjeo2 null mutant females (tjnull) lack germaria and follicles, and appear to mostly consist of TFs and ovariole sheath tissue (Fig 2B) [36,40]. Although TFs were seen properly oriented and enveloped by ovariole sheaths in some tj mutant ovaries, they were often not fully separated from each other, forming a tangled mass, or protruded from the ovary and adhered to extra-ovarian fat body tissue (Fig 2B).
Strikingly, the TFs appeared substantially longer in tjnull than in wild-type ovaries (Fig 2A and 2B). Instead of containing an average of 8 disc-shaped cells as in wild type ovaries (Fig 2C) [15], tjnull ovaries had TFs that contained on average 15 disc-shaped cells (Fig 2C). Moreover, cap cell clusters were not detected. To determine whether there is a connection between the larger stalks and the absence of cap cells, we used tjz4735, a genetic null allele that produces non-functional but detectable Tj protein to visualize cap cells [36]. The analysis of pupal tjz4735/tjeo2 ovaries showed that Tj-positive cells, which were never seen outside the germarium in wild type (Fig 2D), formed the basal portion of the TFs in mutant ovaries and were disc-shaped similar to normal TF cells (Fig 2E). The Tj-positive cells were often organized in a single file following the Tj-negative TF cells, although some stalks were found to branch or to form knob-like structures (Fig 2F). We conclude that cap cells form a TF-like stalk in the absence of tj function.
A similar niche defect was observed in a hypomorphic tj mutant. We isolated a very weak hypomorphic tj allele, tj39, through mobilization of tj-Gal4. It contains a P element fragment just upstream of the tj transcription unit and does not affect the tj coding region (S1A and S1B Fig; see Materials and methods). Although tj39 homozygous females had normally looking and functional ovaries, tj39 caused sub-fertility in trans to the tjeo2 null allele. tj39 produces full-length Tj protein, whereas tjeo2 produces a truncated isoform that is predicted to lack the DNA binding and leucine zipper domains due to a premature stop codon (S1C Fig) [36]. The amount of full-length Tj in tj39/tjeo2 ovaries was reduced to 40–50% of the wild-type value, whereas it was only reduced to approximately 70% in tjeo2/+ ovaries (S1C and S1C' Fig). Hypomorphic tj39/tjeo2 (tjhypo) ovaries had proper ovarioles with a germarium and developing follicles, but developed unusually pear-shaped germaria with age and had abnormal interfollicular stalks (see S3C and S3D Fig). Notably, tjhypo ovaries displayed abnormally long TFs that included Tj-positive cells (Fig 2C and 2G–2I). In some cases, all Tj-positive cells anterior to GSCs were integrated into the TF (Fig 2H and 2J). More frequently, while most Tj-positive cells were part of the TF a few remained clustered at the tip of the germarium (Fig 2I and 2J), explaining the smaller cell number in TFs of tjhypo compared with tjnull mutant ovaries (Fig 2C). Moreover, stalk-forming Tj-positive cells were often disc-shaped and arranged in a single row similar to normal TF cells, and even clustered Tj-positive cells often appeared flatter in shape than regular cap cells (Fig 2H and 2I). The range in cellular behavior suggests that these Tj-positive cells have a hybrid character, having gained TF cell characteristics and lost cap cell features to a variable degree. The hypomorphic tj mutant phenotype supports the notion that Tj is important for niche organization, enabling cap cells to form a cluster inside the germarium where they can contact GSCs.
If additional TF cells form at the expense of cap cells, as our data suggest, one would expect the number of cells in the TF of tjnull ovaries to equal the sum of TF cells and cap cells in wild-type ovaries. Indeed, those numbers were similar when we counted the cells of individual stalks using the markers B1-lacZ and Lamin C (LamC) that both label TF and cap cells but not escort cells (Fig 2K, S1 Table). In tjhypo ovaries, a combination of the markers LamC, labeling TF and cap cells, and Tj, labeling cap and escort cells, allowed us to clearly distinguish all three cell types. The number of cap cells in our controls was similar to previous reports (Fig 2L) [6,8,32]. A minor increase in the number of cap cells in tjhypo mutant ovaries was observed in two out of three genetic backgrounds, with an average of 6.5–8.3 cap cells in tjhypo mutants compared with 5.8–7 cap cells in controls (Fig 2L). However, there was no significant difference in the number of TF cells or in the combined number of TF and cap cells between control and tjhypo ovarioles (Fig 2L). The total number of stalk-forming cells was lower than the combined count of TF and cap cells in tjhypo ovarioles, which was expected as not all cap cells become part of the stalk in tjhypo ovaries (Fig 2J). Taken together, our quantitative analysis indicates that the number of anterior niche cells remained unaffected in tj mutant ovaries, suggesting that Tj regulates the fate of niche cells but not their numbers.
Cap cells adopted the shape and morphogenetic behavior of TF cells in tj mutants. To determine whether a reduction of Tj causes indeed a change in cell fate, we used several markers that differ in their expression in the two cell types (S1 Table). In tjhypo ovaries, cells in the upper portion of the terminal stalks expressed low levels of Bab2 and background levels of 1444-lacZ similar to wild-type TF cells (Fig 3A and 3B). In contrast, the lower portion of the tjhypo mutant stalks expressed high levels of Bab2 and 1444-lacZ (Fig 3B), which is typical of wild-type cap cells (Fig 3A). Furthermore, the markers LamC and B1-lacZ, which stained TF cells more intensely than cap cells in wild type (S2A Fig), showed a stronger signal in the upper than in the lower portion of tjhypo mutant stalks (S2B Fig). This indicates that the additional stalk cells retain the expression profile of cap cells despite the dramatic change in morphology. However, LB27-lacZ, a TF-specific marker that is expressed in a complementary pattern to Tj in wild type (S2E Fig), was sometimes seen at reduced levels in the stalk-forming Tj-positive cap cells in tjhypo ovarioles, (S2F Fig), pointing toward a potential defect in cell specification.
To further investigate the function of Tj in cap cell specification, we evaluated the expression of markers in tjnull ovaries, using different allelic combinations, including tjeo2, tjz4735, and a newly generated transcriptional null mutation, tjDf1 (see Materials and methods, S1A Fig). The absence of the cap cell-specific marker 1444-lacZ (Fig 3C and 3D), the weak signal of Bab2 (Fig 3C–3F), the strong signals of LamC (Fig 3E–3G and S2C and S2D Fig) and B1-lacZ (S2C and S2D Fig), and in particular the presence of the TF-specific marker LB27-lacZ (Fig 3E–3G and S2E and S2G Fig) throughout the elongated stalk of tjnull mutants are all indicative of a shift in cell fate. This expression profile is consistent with the TF cell-like disc-shaped morphology and stalk-forming behavior, and we therefore conclude that additional TF cells form at the expense of cap cells in the absence of Tj function.
To test whether the effect of Tj depletion on the fate of cap cells is cell-autonomous, we induced tjnull mutant cell clones in the GSC niche during the larval stage. We focused on germaria that contained mutant anterior niche cells (cap and/or TF cells) but did not contain mutant escort cells close to cap cells to separate the tj loss-of-function effect on cap cells from that on escort cells (S2H and S2I Fig). Use of the tjz4735 allele allowed us to distinguish between regular TF cells and transformed cap cells, as the latter expressed Tj. In cases of mosaic cap cell groups, tj homozygous mutant cells usually looked like TF cells and had become part of the TF, whereas the non-mutant cap cells were rounded and clustered posterior to the mutant cells in the germarium (Fig 3H and 3I and S2H and S2I Fig). The abnormal behavior of tj mutant cap cells was independent of whether the neighboring bona fide TF cell was a tj mutant cell (GFP negative, Fig 3H) or a control cell (GFP positive; Fig 3I). Our clonal analysis shows that Tj is cell-autonomously required for cap cell morphology and behavior.
To determine whether presumptive cap cells are abnormally specified at the time of their origin or are not able to maintain the cap cell fate when Tj is depleted, we looked at developing ovaries at the stage of cap cell formation. Cap cells develop gradually during the late 3rd instar larval and early prepupal stage following the formation of TFs [17]. Already at the prepupal stage, the TFs were longer in tjnull ovaries than in wild-type ovaries, consisting of an increased number of cells that were aligned in a single file (Fig 3J and 3K). The expression of niche markers at the prepupal stage is different from the adult stage (S2 Table). All cells of the mutant stalks showed prominent LamC staining in contrast to control prepupal ovaries, where this marker was not detected in cap cells and only weakly expressed in basal TF cells (Fig 3J–3M; S2 Table). Moreover, 1444-lacZ, which was co-expressed with Tj in cap cells of prepupal wild-type ovaries (although not yet seen in escort cells), was not detected in the Tj-positive stalk cells of tjnull ovaries (Fig 3L and 3M). This shows that the defects in cap cell specification already develop at the time of niche formation. Taken together, our data indicate that all the anterior niche cells adopt a TF cell fate in the absence of Tj function, implicating Tj as a crucial factor for cap cell specification.
As Tj is required for cap cell specification, we asked whether expression of Tj in anterior niche cells would be sufficient to induce the cap cell fate. We induced Tj-expressing cells in TFs in early 3rd instar, before TFs begin to form by cell intercalation [15,16], and analyzed mosaic TFs in adult ovaries. The frequency of mosaic TFs was similar in ovaries with clonal expression of either Tj or GFP, suggesting that Tj expression did not affect the survival of TF cells (S3 Table). Strikingly, Tj-positive TF cells expressed high levels of 1444-lacZ and low levels of LamC similar to wild-type cap cells (Fig 4A and 4B). In addition, the expression of LB27-lacZ was strongly reduced in Tj-positive TF cells compared with control TF cells, although not completely abolished (Fig 4C–4E). Thus, expression of Tj in TFs resulted in ectopic expression of a cap cell marker and partial suppression of a TF cell marker.
Despite the changes in marker expression, Tj-expressing TF cells remained in the TF (Fig 4A–4C), and even formed a stack of aligned cells when all TF cells expressed Tj (Fig 4D and 4E). However, Tj-expressing TF cells appeared rounder than their control neighbours (Fig 4B and 4C). Cell shape analysis confirmed that Tj-expressing TF cells have a significantly increased height and decreased width compared to control TF cells (Table 1), which is consistent with a rounder, more cap cell-like morphology. Our data indicate that Tj induces TF cells to adopt molecular and morphological characteristics of cap cells.
The spread-out cluster of cap cells in a wild-type germarium provides a large contact surface for anchorage of GSCs [25]. In tjhypo ovarioles, however, most cap cells are recruited into the TF, and few remain in the germarium, potentially limiting their availability to GSCs. In cases where all cap cells form a single file stalk, only the basal-most cap cell would offer a physical GSC anchor point. Therefore, we asked whether the abnormal organization of cap cells in tjhypo ovarioles might affect the number or maintenance of GSCs. We noticed that the germaria of tjhypo ovaries were often unusually narrow (Fig 5A and 5B), harbouring only 1–2 GSCs (Fig 5B, 5C' and 5D) in contrast to 2–3 GSCs in wild-type germaria (Fig 5A and 5D) [6,50]. As Tj was found to be neither expressed nor required in the germline [36], the reduction in GSC numbers is likely caused by the observed defects in the stem cell niche of tj mutants.
Only a single GSC was present when all cap cells had joined the TF in tjhypo ovarioles (Fig 5B). The number of GSCs per mutant ovariole increased with the number of cap cells that remained in the germarium (Fig 5C, 5C' and 5E). The majority of ovarioles with 2–3 cap cells within the germarium had still only one GSC, whereas those with four and six cap cells in the germarium had usually two and three GSCs, respectively (Fig 5E). Therefore, the number of GSCs in tjhypo mutant ovarioles correlates with the number of cap cells that remain in the germarium instead of the total number of cap cells.
These data are consistent with an approximate 2:1 ratio of cap cells to GSCs that has been observed for wild-type ovarioles [6,8], the finding that GSCs require direct contact to cap cells [7,8,22,51], and the observation that GSCs partially envelop more than one cap cell with cytoplasmic extensions in wild type (Fig 5F). If, however, a contact to more than one cap cell is required to support a GSC, how could any GSC exist if all cap cells are arranged in a stalk. Interestingly, we discovered that in 79% of those cases (n = 14), the GSC produced a long cellular protrusion that reached far into the TF of a tjhypo ovariole, allowing it to contact at least two cap cells (Fig 5B and 5G). In comparison, when two or more than two cap cells remained in the germarium, the frequency of unusually long GSC protrusions was only 42% (n = 12) and 0% (n = 11), respectively.
To determine whether the cells that we identified as GSCs based on morphological criteria are bona-fide GSCs, we analyzed the activity of the Dpp signaling pathway by probing for the presence of phosphorylated Mothers against dpp (pMad), the effector of this pathway [52]. In wild-type germaria, nuclear pMad identifies GSCs (Fig 5H) [11]. Similarly, in tjhypo germaria, nuclear pMad was restricted to germline cells that abutted cap cells and had the morphology of GSCs (Fig 5I). In most tjhypo ovarioles, only a single germline cell was positive for pMad, consistent with a reduced number of GSCs. The staining intensity of nuclear pMad was comparable between GSCs of wild-type (n = 23) and mutant germaria (n = 25). Consistent with this finding, bam expression, which is repressed by pMad to prevent differentiation of GSCs [10,11], was not detected in GSCs but was seen in differentiating germline cysts in both tjhypo and wild-type germaria (Fig 5J and 5K). Together, this suggests that Dpp signaling from niche cells is active and confirms the presence of GSCs in the tjhypo mutant.
An aging experiment that assessed whether reduced expression of Tj affects the maintenance of GSCs showed that the number of GSCs remained stable over a period of three weeks in wild-type and tjhypo mutant ovarioles (Fig 5D). Presence of germaria and rows of follicles of successive developmental stages in 2–22 day-old tjhypo ovaries similar to wild-type ovarioles confirms the proper maintenance of GSCs (S3A–S3D Fig). Absence of bam-GFP expression (S3E and S3F Fig) and presence of nuclear pMad in the anterior-most germline cells that contact cap cells (S3G and S3H Fig) are consistent with the conclusion that GSCs, although smaller in number, are maintained normally in tjhypo mutant ovaries.
As cap cells are considered essential for GSC establishment and maintenance [3,4], and our analysis indicates that cap cell specification depends on Tj, we expected a loss of GSCs in tjnull mutant ovaries. In ovaries of wild-type prepupae, the anterior-most row of germline cells next to the newly formed niches represented GSCs as indicated by the presence of nuclear pMad (Fig 6A) [11,17]. In tjnull prepupal ovaries (n = 15), 68% of the TFs were not associated with any germline cells (orphan TFs). The remaining TFs captured usually no more than one germline cell. Surprisingly, we found that some of the TF-associated germline cells displayed nuclear pMad (Fig 6B), although their number was very low, with a mean of 2.3 nuclear pMad-positive cells in a tjnull ovary (n = 17) compared with 13.6 in a wild-type prepupal ovary (n = 11). As orphan TFs in tj mutant ovaries might potentially result from the abnormal distribution of germ cells and interstitial cells [36], we asked whether the number of pMad-positive cells per occupied TFs was different from wild type. Taking into account that even in wild-type prepupal ovaries only a third of niche-associated germline cells were positive for nuclear pMad, that only 32% of the mutant TFs were occupied by a germline cell, and that the mean number of TFs was reduced by 20% in tjnull ovaries (15 and 19 TFs in tjnull (n = 18) and wild-type ovaries (n = 17), respectively), we calculated a 33% reduction of nuclear pMad-positive germline cells per occupied TF in mutant ovaries (0.48 and 0.72 nuclear pMad-positive cells per occupied TF in tjnull and wild-type ovaries, respectively). Notably, bam-GFP, which was absent in GSCs of wild-type ovaries was detected in some of the TF-associated germline cells in tjnull ovaries (Fig 6C and 6D'), suggesting entrance into differentiation [11,17].
To follow the fate of germline cells in tjnull mutant ovaries, we analyzed ovaries at the pupal and adult stage. We had previously reported that more than 25% of tjnull ovaries from young adult females were devoid of germline cells [36]. Already at the mid pupal stage, when the germaria had matured in wild-type ovaries, and displayed a largely expanded germline cell population (Fig 6E), tjnull ovaries contained only a few small and scattered germline cells or cell clusters and pMad staining was drastically reduced (Fig 6F), suggesting a rapid loss of germline cells during the pupal period. The bam-GFP signal that revealed early germline cysts in control ovarioles (Fig 6G and 6G') ranged from non-detectable to prominent in the remaining germline clusters in tjnull ovaries (Fig 6H and 6H'). In adult ovaries, where 96% of the few remaining germline cell clusters (n = 45; 10 ovaries) were associated with TFs (Fig 6I and 6J), pMad-positive cells were rare, and only present in 9.5% of the clusters (n = 21; 18 ovaries). Some clusters consisted of individual germline cells, as indicated by spectrosomes (Fig 6J and 6J'), others had undergone transit amplification with incomplete cytokinesis, displaying branched fusomes [36]. Taken together, we infer that the complete loss of Tj activity severely compromises GSC establishment and maintenance.
To determine whether the reduction/loss of germline cells in tj mutant ovaries is responsible for the recruitment of cap cells into the TF we analyzed the behavior of cap cells in tudor and oskar maternal effect mutants (tudmat and oskmat, respectively) that lack germline cells [53,54]. The number of cap cells was reduced in tudmat ovaries (average of 3.7 cap cells; n = 49) compared with wild type (average of 7.7 cap cells; n = 11; p<0.0001). This indicates that cap cells can form in the absence of a germline, although their number is reduced, which is consistent with previous reports [8,51,55]. Importantly, however, the number of TF cells was not increased but rather slightly reduced in tudmat ovaries (average of 7.1 TF cells; n = 44) compared with wild type (average of 8 TF cells; n = 11; p<0.01), indicating that the reduced number of cap cells is not caused by a change from cap cell to TF cell fate. Furthermore, the remaining cap cells had a rounded shape, were organized into a cluster that resided within the germarium, and expressed Tj similar to wild-type cap cells (Fig 7A and 7B). Similarly, cap cells were organized as a cluster in oskmat mutant ovaries. These findings indicate that the number but not the morphology or spatial arrangement of cap cells depends on the presence of GSCs.
Mutants with reduced N activity displayed a decrease in the number of cap cells [8,31,32]. Similarly, knocking down N by tj-Gal4-driven expression of UAS-NRNAi, which caused a typical N loss-of-function phenotype with fused follicles (Fig 8A) [56], reduced the average number of cap cells per germarium from a normal complement of 6 down to 2 (Fig 8B and 8J). Accordingly, the average number of GSCs dropped from 3 in control germaria to 0.8 in NRNAi germaria (Fig 8J). When all cap cells were missing, GSCs were absent, and escort cells were misplaced to the tip of the germarium, where they made contact with the TF and a differentiating germline cyst (Fig 8C). Although the number of cap cells was reduced, the number of TF cells was normal in N depleted ovarioles (Fig 8J), indicating that the loss of cap cells is not due to a cap cell to TF cell fate change. This shows that the tj and N loss-of function phenotypes are different.
As both Tj and N are required for cap cell formation, we asked if and how their functions might be related. First, we investigated whether their expression is dependent on each other. To determine whether the expression of Tj depends on N signaling we checked Tj expression in NRNAi ovaries. However, we could not separate a direct effect on Tj from an effect on cap cells. Remaining cap cells in NRNAi ovaries always expressed Tj, and at an apparently normal level (Fig 8B). That existing cap cells remained in the germarium and were not recruited into TFs also suggests that the expression level of Tj is not affected. Although this argues against Tj being a downstream target of the N pathway, it cannot be excluded that remaining N activity in NRNAi ovaries can enable the formation of a few cap cells with full expression of Tj.
To test whether Tj influences the expression of N signaling components, we evaluated the expression pattern of N, its ligand Dl, and its target and effector Enhancer of split (E(spl)) in tjnull ovaries at the prepupal stage when anterior niches have formed (S4 Fig). Dl staining was much stronger in TFs than in cap cells of control ovaries (S4A Fig) [8]. Dl staining in the upper half of TFs in tjnull ovaries was as robust as in controls. Interestingly, however, Dl staining in the lower portion of the stalk, which is composed of transformed cap cells in tjnull ovaries, was as weak as in cap cells of controls (S4B Fig). Thus, in contrast to all other tested markers, Dl expression appears not to have changed in the transformed cap cells, indicating that Dl expression in niche cells is not regulated by Tj. Expression of N protein and an E(spl) expression reporter (E(spl)mß-CD2), which can be used to detect activity of the N pathway in niche cells [8], appeared to be rather homogeneous throughout the anterior niche cells of tj mutant ovaries similar to wild-type ovaries (S4C and S4D Fig). Thus, Tj appears not to affect the activity of the N pathway in cap cells. Taken together, our expression analysis is consistent with Tj acting downstream or in parallel to the N pathway in the formation of cap cells.
To further analyze the relationship between Tj and N, we looked for genetic interactions by changing their expression level either in the same or opposite direction. Overexpressing Tj in its endogenous pattern, including cap and escort cells, by driving expression of UAS-tj with tj-Gal4 did not cause any obvious defects in the stem cell niche (Fig 8K), although it led to defects at later stages of oogenesis. The NRNAi phenotype prevailed in the presence of increased Tj expression (Fig 8D–8F and 8J), suggesting that Tj cannot rescue cap cells in the absence of N.
To achieve a double knockdown of Tj and N, we used a UAS-tjRNAi transgene that strongly reduces Tj expression [57]. When driven with tj-Gal4, the tjRNAi knockdown was variable but consistently strong and frequently caused a phenotype that was indistinguishable from a tjnull ovary phenotype (Fig 8N). tj N double-knockdown ovaries largely resembled tjnull ovaries, with TFs and ovariole sheaths remaining and all other cell types drastically reduced or missing (Fig 8G). Not surprisingly, cap cells were absent (Fig 8J). Interestingly, however, tjRNAi NRNAi ovaries did not have the extended TFs that are the hallmark of tj mutant ovaries (Fig 8G). With an average of 9.1 cells, TFs of tjRNAi NRNAi ovaries were considerably shorter than those of tjRNAi ovaries, which had an average of 16 cells similar to tj mutant ovaries (compare Fig 8G with 8N and 8Q). Although the number of TF cells per stack was highly variable in tjRNAi NRNAi ovaries (Fig 8J), 42% of the TFs had more than 8 cells per stack and were therefore longer than those of control ovaries (average of 7.8 cells), resembling more the combined number of TF and cap cells in the niches of NRNAi ovaries (average of 9.9 cells) (Fig 8J). All cells in these elongated stalks of tjRNAi NRNAi ovaries had a TF cell identity based on morphology and marker expression (Fig 8H and 8I‴). We propose that cap cells that remained after strong reduction of N expression acquired the TF cell fate due to the loss of Tj. Further analysis of ovaries with tj N double-knockdown revealed that the total number of TF stalks was strongly reduced, with an average of 6.2 (n = 12), compared to 16.1 in NRNAi ovaries (n = 14) and 19.3 in tjRNAi ovaries (n = 9), and that most ovaries contained several unusually short TFs with less than 7 TF cells. This suggests, that loss of Tj and/or N does not only affect cap cell formation but that their combined loss affects TF cell formation as well.
Next, we investigated the effects of increased or decreased Tj expression on the phenotype caused by Nintra, which constitutively activates N signaling. Overexpression of Tj did not appear to affect the number of cap cells (Fig 8K), whereas driving UAS-Nintra with tj-Gal4 led to a large increase of cap cells (Fig 8L), similar to what had been reported previously for Nintra expression with another somatic driver [8]. Co-expression of Nintra and transgenic Tj resulted in the same phenotype (Fig 8M). For both genotypes, we observed two different types of germaria. Some germaria contained a cluster of undifferentiated germ cells that resembled GSCs (Fig 8O), consistent with a previous report [8], whereas other germaria were devoid of germ cells despite a large aggregate of cap cells (Fig 8L and 8M).
We were particularly interested in the phenotype of Nintra tjRNAi ovaries. If N determines the number of cap cells while Tj controls their identity, one might expect that all Nintra-induced additional cap cells become TF cells in the absence of Tj, causing even longer TFs than when Tj alone is lost. The phenotype of Nintra tjRNAi ovaries was variable, consistent with the variability in the individual tjRNAi and Nintra phenotypes but the defects were considerably more severe (Fig 8N–8P). Most Nintra tjRNAi ovaries were extremely small, not connected to an oviduct and often attached to the gut, and seemed to consist largely of TFs and a few germline cells embedded in fatbody (Fig 8P). In contrast to tjRNAi or Nintra ovaries, the epithelial sheaths were missing and TFs were located side-by-side (Fig 8N–8P). In the most severe cases, Nintra tjRNAi ovaries consisted only of TFs (Fig 8P). To account for the two UAS-constructs in Nintra tjRNAi ovaries, we co-expressed a second UAS-construct together with tjRNAi or Nintra in our controls, which expressed GFP (Fig 8Q and 8R). This did not appear to influence the mutant phenotype but showed that tj-Gal4 is active in the lower half of the extended TFs in tjRNAi ovaries (Fig 8Q), and is excluded from the TF but present in cap cells of Nintra ovarioles (Fig 8R), as expected. Notably, TFs in Nintra tjRNAi ovaries were always considerably longer than in Nintra ovaries (Fig 8O, 8P and 8R–8T), although they were on average slightly shorter than in tjRNAi ovaries (Fig 8N, 8P and 8T). Fig 8S shows a rare example of a particularly long TF in a Nintra tjRNAi ovary. Taken together, these findings suggest that the tjRNAi niche phenotype is epistatic to the Nintra niche phenotype. The effects on cap cells in response to alterations in Tj and N expression are summarized in Fig 8U.
Loss of Tj has a profound negative effect on the establishment, number, and maintenance of GSCs. Effects of Tj on the germline were previously shown to be indirect as Tj is neither expressed nor cell-autonomously required in the germline [36]. Therefore, we propose that the dramatic change in the structure of the somatic niche affects GSCs when Tj function is compromised. An inverse causal relationship, where a reduced number of GSCs would trigger the somatic niche defects was ruled out by showing that cap cells can still look and behave normally in the absence of any germ cells. We conclude that Tj controls GSCs indirectly by controlling somatic cell fate and cell arrangement in the stem cell niche.
By controlling the morphology and behavior of the cap cells, Tj regulates the GSC-carrying capacity of the niche. When Tj expression was moderately reduced, the number of GSCs per niche was reduced, with the remaining GSC properly maintained over several weeks. The decrease of GSCs per niche correlated with a decrease of cap cells in the germarium. Two cap cells were on average required to sustain one GSC, similar to what has been proposed for a wild-type ovary [6,8]. Our data indicate that the reduced niche capacity is due to a reduction in the available contact surface between cap cells and GSCs. Tj-depleted cap cells that convert from forming a cluster inside the germarium to forming a stalk outside the germarium minimize their availability for GSC attachment. A connection between the GSC-cap cell contact area and niche capacity is similarly reflected in the increased number of GSCs that accompanies an increase in cap cell size due to loss of RanBPM [25]. Here, we show that the spatial arrangement of the cap cells has a crucial impact on the number of stem cells per niche.
When Tj function was completely abolished, the number of GSCs was drastically reduced, as expected in the absence of cap cells. The very few pMad-positive GSC-like cells in tj mutant prepupal ovaries were always associated with a TF, suggesting that TFs might temporarily provide enough Dpp to activate Mad in a few germline cells, consistent with the finding that Dpp is expressed in TFs at the late larval stage [17,58]. This is not sufficient, however, to maintain GSCs and adult ovaries rarely contain pMad-positive germline cells. This is in agreement with the finding that Dpp is not detected in adult TFs [6], and corroborates that cap cells are required for GSC maintenance. In addition, the rapid loss of the entire germ cell pool in Tj-depleted ovaries during the pupal stage might be precipitated by loss or defects in escort cells. Escort cell precursors are not properly intermingled with germ cells at the larval stage and differentiated escort cells appear to be missing in adult ovaries that lack Tj [36]. As escort cells are crucial for germ cell differentiation [59–62], the defect in escort cell differentiation could be responsible for the demise of the germline in tj mutants.
We discovered that GSCs have broad cellular protrusions, which they use to reach and tightly ensheath the accessible surface of cap cells. In wild type, relatively short protrusions are sufficient to make extensive contact with more than one cap cell. However, when cap cells formed a stalk, GSCs were often observed to produce unusually long extensions that allowed them not only to contact the immediate cap cell neighbor but also a more distantly located cap cell. This suggests that GSCs respond to a chemotactic signal from cap cells and send protrusions toward this signal. It remains to be investigated whether this is a response to Dpp signaling or signaling through another pathway. The importance of cellular protrusions in signaling events in the stem cell niche has recently come to light with the discovery of nanotubes that mediate Dpp signaling between GSCs and hub cells in the Drosophila testes [63], and cytonemes that contribute to Hh signaling from cap to escort cells in the ovary [12].
Our analysis shows that Tj is required for the specification of cap cells. In the absence of Tj function, additional TF cells form at the expense of cap cells, resulting in unusually long TFs while the cap cell fate is not established. Whereas the formation of cap cell precursors appears not to require Tj, this transcription factor is essential for the ability of these precursors to take on the cap cell fate and to prevent the TF cell fate that is otherwise adopted as a default state. The following findings support this conclusion: (i) In the absence of Tj function, cap cells were missing while additional cells that displayed TF cell-characteristic morphology, behavior and marker expression were integrated into the TF. The number of additional TF cells was comparable to the normal number of cap cells. (ii) Prospective cap cells cell-autonomously adopted a TF-specific morphology and behavior in the absence of functional Tj. (iii) A hypomorphic tj mutant provided direct evidence for the incorporation of cap cells into TFs, forming the basal portion of these stalks. (iv) Ectopic expression of Tj in TF cells caused a change toward cap cell-typical marker expression and morphology. Together, these data demonstrate that Tj promotes cap cell specification.
The expression pattern of Tj supports the notion that Tj has a function in cap cells but not in TF cells. Tj is continuously expressed in cap cells [36; this study]. Tj is also present in the anterior interstitial cells of the larval ovary [36,44], which are thought to develop into cap cells [14]. In contrast, Tj is neither detected in the cell population that gives rise to TFs during 3rd larval instar, nor in differentiated TFs [28,36]. Interestingly, we found that even in the absence of Tj function, the tj gene remains differentially expressed in the anterior niche, being inactive in regular TF cells but active in the additional TF cells, which form the apical and basal portion of a TF, respectively. This differential expression of Tj indicates that a regionally or temporally regulated mechanism operates upstream of Tj that initiates differences in anterior niche cells. Although it is conspicuous that Tj expression from 3rd instar onwards is restricted to cells that are in direct contact with germline cells, which includes cap cells but excludes TF cells, it has previously been shown that Tj expression is not dependent on the germline [36,43]. This suggests that a soma-specific mechanism is responsible for the differential expression of Tj in anterior niche cells. Interestingly, a recent study uncovered the importance of Hh signaling from TFs to neighboring interstitial cells in the larval ovary and proposes that tj is a direct target of the Hh signaling pathway [30].
Our findings suggest the presence of a new cell type in the GSC niche that we named 'transition cell' as it is located between the cap cell cluster and the TF, connecting these two structures of the niche. Notably, the one or occasionally two transition cells have the morphology of TF cells and align with neighboring TF cells despite displaying a cap cell-like marker profile that includes the expression of Tj—although Tj expression is substantially lower than in cap cells. Interestingly, cap cells from ovaries with reduced Tj expression (tjhypo) similarly displayed a TF cell-like morphology and behavior while their expression profile remained cap cell-like. A similar, although weaker effect was noted in a tj hemizygous condition, suggesting that Tj function is haplo-insufficient in cap cells. Thus, when Tj levels are reduced, cap cells adopt very similar molecular and morphogenetic properties as the transition cell in a wild-type niche, and might have adopted this cell fate.
Together, our findings indicate that Tj has an important role in the establishment of three cell types in the GSC niche: TF cells, transition cells, and cap cells. As lack of Tj function seems to cause a transformation of cap and transition cells into TF cells, and a mild reduction of Tj a cap to transition cell transformation, we propose that different Tj expression levels establish different cell fates and morphogenetic traits. We propose that a high concentration of Tj leads to the formation of cap cells and a lower concentration to the formation of the transition cell, whereas absence of Tj is required for the formation of TF cells (Fig 9A). This model implies that different levels of Tj have different effects on target genes. We predict that Tj has at least one target gene that only responds to high levels of Tj and that specifically controls the morphogenetic behavior of cap cells, allowing them to adopt a round morphology and organize into a cell cluster. Whether this relates to an effect of Tj on the expression of adhesion molecules as observed in other gonadal tissues [36,42,57,64, 30] awaits further analysis.
Our study identifies Tj as essential for cap cell formation. In addition, this process depends on the N pathway [8,31,32]. Therefore, we wondered how the functions of Tj and N in cap cell formation relate to each other (Fig 9). A comparison between the loss and gain-of-function phenotypes suggests that Tj and N have different functions in the establishment of cap cells. In the absence of Tj function, cap cell precursor cells are present but take on the fate of TF cells, whereas depletion of N leads to a loss of cap cells but does not cause the formation of additional TF cells. Ectopic activation of N can induce a strong increase in the number of cap cells, whereas overexpression of Tj did not appear to affect the number of cap cells. Therefore, both factors are important for cap cell formation but contribute differently to this process. The questions then are: What is the respective contribution of Tj and N to cap cell formation, and how are their functions related?
The function of N in cap cell formation is still not fully understood. Our observation that depletion of N reduces the number of cap cells confirms previous findings [8,32,65]. However, neither in our nor any previously published experiments were cap cells lost completely when the N pathway was compromised, and it remains therefore unclear whether N is de facto essential for cap cell formation or primarily functions in regulating the size of the cap cell pool. Interestingly, evidence amounts to a function of the N pathway in a decision between the cap cell and escort cell fate: First, Dl signal from TF cells activates the N pathway in adjacent interstitial cells, inducing them as cap cells, whereas the remaining interstitial cells are thought to develop into escort cells [8,32]. Second, escort cells expressing activated N can develop into cap cells [31,65]. Third, when we used tj-Gal4 to express active N in interstitial cells, the number of cap cells dramatically increased while the escort cell region became smaller, and some germaria seemed to lack escort cells all together. These germaria also lacked germline cells, although a larger pool of cap cells was expected to increase the number of GSCs [8,31]. However, the absence of germline cells is consistent with an absence of escort cells, as escort cells have been shown to be important for maintaining the germline [60]. Together, these observations support the hypothesis that N is involved in a cap cell versus escort cell fate decision, and suggest that the N pathway might promote the formation of cap cells by inhibiting the escort cell fate.
To determine how the functions of Tj and N depend on each other we looked for genetic interactions. The N pathway seems to be still functional in tj mutants. First, the expression of N and Dl appeared unaffected and E(spl) was activated in the additional TF cells (= transformed cap cells) similarly to normal cap cells. Second, the formation of additional TF cells in the absence of Tj depended on the presence of N, as only very few additional TF cells formed in a N compromised background. These findings indicate that the N pathway is still active in cap cell precursors when Tj function is abolished. This together with the observation that constitutively active N cannot suppress the tj mutant phenotype suggests that Tj does not act upstream of N in regulating cap cell fate.
Therefore, we asked whether Tj might operate downstream of N. We did not detect a loss of Tj upon N depletion, and this together with the finding that Tj is expressed in all interstitial cells, and not only in those that receive Dl signaling argues against a requirement of N signaling for tj expression. If at all, one would expect tj to be negatively regulated by N as cap cells express a lower level of Tj than escort cells. The maintenance of somatic cell types in N mutant ovaries that are lost in tj mutant ovaries, including the escort cells is also not consistent with a linear relationship. Nevertheless, the ability of Tj to promote the formation of cap cells appears to depend on the activity of the N pathway in cap cell precursors. Again, this is suggested by the finding that when N and Tj were both compromised, the number of additional TF cells were much smaller than when N was fully active. Therefore, we propose that N activity sets aside a pool of percursor cells that in the presence of Tj take on the cap cell fate, and in its absence the TF fate (Fig 9B).
Similar to the ovary, N is important for the formation of the GSC niche (= hub) in the Drosophila testis [66,67]. Interestingly, N contributes to hub cell specification by downregulating the expression level of Tj [42]. Not only is the hub still present in tj mutant testes [36] but additionally, ectopic hub cells form in the absence of Tj [42]. Thus, Tj seems to have opposing functions in testes and ovaries, suppressing the niche cell fate in the testis [42], while promoting it in the ovary.
The interplay between Tj and N seems not restricted to the cap cell fate in the ovary. Whereas neither factor alone is required for TF cell formation, as TF cells formed normally in the absence of either Tj or N, the combined loss of Tj and N led to a strong reduction in the number of TFs and number of TF cells within stalks. This suggests that their combined action is already required at an earlier stage of ovary development, when Tj is still expressed in all somatic cells of the ovary [44]. Moreover, Tj knockdown combined with expression of activated N caused TF cells to be the only cell type remaining of the ovary, indicating that several cell types in the ovary require proper input from both factors. Taken together, our findings support a model, in which both Tj and N operate together to promote the cap cell fate but have separate functions. We propose that Tj and N promote the cap cell fate by blocking the TF cell fate and escort cell fate, respectively, and that the combined actions of Tj and the N pathway are required to establish the cap cell fate (Fig 9C).
tjeo2 (amorphic) [36,40], tjz4735 (amorphic) [36,41], tj39 (hypomorphic, see below), tj-Gal4 (hypomorphic, see below), tjDf1 (molecular null, see below), and UAS-tjRNAi [57] (NIG-Fly Stock Center] were used for tj loss-of-function analysis. UAS-tj1(2) and UAS-tj6(3) (UAS-tj; full-length tj coding sequence and 3'UTR) [57] were used for ectopic and overexpression of Tj. UAS-N754.BF (UAS-Nintra on 3rd) [68] and P{TRIP.JF02959}attP2 (UAS-NRNAi) [69] (Bloomington Drosophila Stock Center (BDSC)] were used for N loss- and gain-of-function experiments. tud1 and tudb45 [70] (gift from M. Van Doren), and osk301 [54] were used to generate flies without a germline. We used tj-Gal4 (P{GawB}NP1624) [71,72] (Kyoto Stock Center) to drive expression in cap and escort cells and their larval progenitors, and the FLPout cassette [73] (BDSC) for clonal expression in TF cells. tjz4735 mutant cell clones were induced by mitotic recombination using hs-FLP1 and FRT40A (BDSC). UAS-GFP.S65T and Ubi-GFP (BDSC) were used as clonal cell markers. nos-Gal4 (BDSC) was used to drive UAS-GAP43-mEos (BDSC) in germline cells. The enhancer reporter lines babA128 (bab-lacZ) [15,74], P{PZ}1444 (1444-lacZ) [55], P{A92}LB27 (LB27-lacZ) [16], P{lacW}B1-93F (B1-lacZ) [15,56], and bamP702-GFP (bam-GFP) [75] were used as cell type specific markers. E(spl)mß-CD2 was used to monitor activity of the N pathway [8,76] (gift from D. Drummond-Barbosa). We generated the following recombinant chromosomes: tjeo2 1444-lacZ, UAS-tj1(2) 1444-lacZ, and UAS-tj1(2) UAS-GFP for functional analysis of tj, and UAS-tj6(3) UAS-NRNAi, UAS-tj6(3) UAS-Nintra, UAS-tjRNAi UAS-NRNAi, UAS-tjRNAi UAS-Nintra, and tj-Gal4 UAS-GFPnls to test genetic interactions between tj and N. Oregon R, w, or y w were used as a genetic background. The copy number of all genetic markers, such as enhancer reporters was identical between control and experimental animals.
tjDf1, a transcriptional null mutation is a genomic deletion of 13.3 kb (2L:19464294–19477599), beginning 286 bp upstream of the tj start codon and ending 9.8 kb downstream of the tj transcription unit (S1A Fig). This mutation deletes the complete coding and 3' UTR sequences of tj, and three predicted RNA coding genes. tjDf1 was generated by FLP-mediated recombination between the FRT elements of the transposable elements P{XP}d06467 and PBac{WH}f02713 [77,78] (Exelixis Collection at the Harvard Medical School), using the technique described by Parks et al. [79]. We screened for recombinant flies by eye color as recombinant chromosomes containing a deletion were expected to carry two mini-white genes. Recombination was confirmed by PCR analysis, using genomic DNA from homozygous tjDf1 flies. Primer pair AGCGAATGGTGGCGTTCGAGCTC—ACCACCTTATGTTATTTCATCAT confirmed the presence of the 3' end of P{XP}d06467, primer pair CCTCGATATACAGACCGATAA—AGCCAAATGAACTGCCCGCT the presence of the 3' end of PBac{WH}f02713, and primer pair GACCTTTGAAACCACCCACTAAC—GTGGTGTGCGTAAGTCTGAGC the absence of tj-specific sequences. tjDf1 is homozygous viable, but both female and male sterile.
tj39, a weak hypomorphic allele was generated in a P element excision mutagenesis, using tj-Gal4 (P{GawB}NP1624), which is located in the 5' UTR of tj, 0.7 kb upstream of the translation start site [72], as a starter line. tj39 caused strongly reduced fertility in trans to tjeo2 (approximately 20% of the fertility of the tjeo2/+ control), whereas 29 other excision mutations were fully fertile in trans to tjeo2. PCR analysis, using genomic DNA of homozygous mutant tj39 flies and four primer (P) pairs (P2: GCTCTTGCACAGTGGTCGAG—P1: ACCACCTTATGTTATTTCATCAT, P1: ACCACCTTATGTTATTTCATCAT—P3: GTGTCGTTTATGGTGGGATC, and P2: GCTCTTGCACAGTGGTCGAG—P4: GAACTCCTGTTGGAAACGTG showed that the genomic sequences flanking the insertion site are still present and revealed a partially excised P element (the 3' end is still present). Sequencing the PCR-amplified tj coding region, using primers described in Li et al. [36], confirmed that the tj open reading frame is intact, suggesting that the remaining P element impairs tj expression at the transcriptional or translational level. Subsequent tests revealed that tj-Gal4 itself is a weak hypomorphic allele of tj, causing a similar phenotype in trans to a tj null allele as its derivative tj39. tj39 tested positively for Gal4 activity.
Flies were raised and maintained at 25°C on standard Drosophila medium supplemented with yeast pellets. Ovaries were extracted from 1–4 hour old prepupae, two-day-old pupae, or 1–2 day old yeast-fed adult females, which had been kept in the company of males unless indicated otherwise. Staging, dissection, and processing of prepupal and pupal ovaries were done as described in Godt and Laski [15]. For the aging experiment, female flies were collected and separated from males within 24 hours of eclosure, and were transferred every day to a new food vial (supplemented with yeast pellets) until they were dissected 2, 7, 14, and 22 days after eclosure. All experiments were independently repeated at least twice.
Clonal analysis: (1) tj mutant cap cell clones were induced in y w hsFlp1/+; tjz4735 FRT40A/ P{Ubi-GFP.D}33 P{Ubi-GFP.D}38 FRT40A larvae by three 2-hour heat shocks at 37°C during early to mid 3rd instar (at 72–74, 82–84, and 90–92 hours after egg deposition). Animals were reared at 25°C to adulthood and ovaries dissected from 2-day old females. (2) To generate Tj-expressing cell clones in TFs we used the following genotypes: y w hsFlp1/+; UAS-tj1(2) /+; Act5C>CD2>Gal4/+, or y w hsFlp1/+; UAS-tj1(2) 1444-lacZ /+; Act5C>CD2>Gal4/+, or y w hsFlp1/+; UAS-tj1(2)/+; Act5C>CD2>Gal4/LB27-lacZ, or y w hsFlp1/+; UAS-tj1(2) UAS-GFP/+; Act5C>CD2>Gal4/+. Flies of the genotype y w hsFlp1/+; UAS-GFP/+; Act5C>CD2>Gal4/+ were used as a control. Early 3rd instar larvae (72 +/-1.5 hours at 25°C after egg deposition) were heat shocked at 37°C for 11 minutes, cooled down to 25°C for 10 minutes in a water bath, and reared at 25°C to adulthood. Ectopic expression of Tj caused a relatively high degree of lethality in larvae and pupae, and ovaries were extracted from escaper flies. To measure the height and width of a TF cell, a line through the center of the cell was drawn along the anterior-posterior axis and perpendicular to it, respectively, and ßPS integrin was used to recognize the plasma membrane.
The following primary antibodies were used: guinea-pig anti-Tj (G5 or GP6, 1:5000) [57], rat anti-Bab2 (R10, 1:3000; or R7, 1:2000) [20], rabbit anti-Vasa (1:2000) [80], rabbit anti-Vasa (d-260, 1:500; Santa Cruz Biotechnology), chicken anti-Vasa (1:5000; gift from K. Howard and M. Van Doren), rabbit anti-α-spectrin (#254, 1:1000; gift from D. Branton), mouse anti-LamC (LC28.26, 1:50), mouse anti-Hts (1B1, 1:5), mouse anti-N (C17.9C6, 1:5; C458.2H, 1:5), mouse anti-Dl (C594.9B, 1:5), mouse anti-Engrailed (4D9, 1:5), and mouse anti-ßPS integrin (CF.6G11, 1:10) (Developmental Studies Hybridoma Bank), rabbit anti-pMad (PS1, 1:250; gift from T. Tabata) [81], rabbit anti-pMad (pSmad1/5, 41D10, 1:100; Cell Signalling), rabbit anti-ß-galactosidase (1:1500; MP Biomedicals), and rabbit anti-GFP (1:100; BD Biosciences). Secondary antibodies (1:400) were conjugated either to Cy3, Cy5 (Jackson Immuno Research Laboratories), Alexa-405, Alexa-555, Alexa-488, or Alexa-647 (Molecular Probes, Life Technologies). Ovaries were mounted in Vectashield (Vector Laboratories).
All imaging was done with a 40x/1.4 Plan-Apo objective, using confocal laser scanning microscopes LSM510 (Carl Zeiss Microscopy) and Leica TCS SP8 (Leica Microsystems) at RT. A zoom factor of 4–5 was used to image individual stem cell niches. Images represent either individual confocal sections or projections of 2–3 sections that were chosen from Z-stacks (1 μm intervals), which were routinely acquired of all studied germaria. Image analysis, cell counts and cell shape measurements were done by evaluating Z-stacks, using the LSM 5 Image Browser and (Carl Zeiss Microscopy) and Leica LAS X software (Leica Microsystems). Images were processed with Adobe Photoshop and Illustrator CS5 and CS6 (Adobe Software).
Unpaired, two-tailed Student’s t-tests or one-way ANOVA tests were used for statistical analysis. Prism 6 (GraphPad Software) was used for statistical tests, and Prism 6 and Illustrator CS6 for the generation of graphs.
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10.1371/journal.pntd.0006933 | CYP-mediated permethrin resistance in Aedes aegypti and evidence for trans-regulation | Aedes aegypti poses a serious risk to human health due to its wide global distribution, high vector competence for several arboviruses, frequent human biting, and ability to thrive in urban environments. Pyrethroid insecticides remain the primary means of controlling adult A. aegypti populations during disease outbreaks. As a result of decades of use, pyrethroid resistance is a global problem. Cytochrome P450 monooxygenase (CYP)-mediated detoxification is one of the primary mechanisms of pyrethroid resistance. However, the specific CYP(s) responsible for resistance have not been unequivocally determined. We introgressed the resistance alleles from the resistant A. aegypti strain, Singapore (SP), into the genetic background of the susceptible ROCK strain. The resulting strain (CKR) was congenic to ROCK. Our primary goal was to determine which CYPs in SP are linked to resistance. To do this, we first determined which CYPs overexpressed in SP are also overexpressed in CKR, with the assumption that only the CYPs linked to resistance will be overexpressed in CKR relative to ROCK. Next, we determined whether any of the overexpressed CYPs were genetically linked to resistance (cis-regulated) or not (trans-regulated). We found that CYP6BB2, CYP6Z8, CYP9M5 and CYP9M6 were overexpressed in SP as well as in CKR. Based on the genomic sequences and polymorphisms of five single copy CYPs (CYP4C50, 6BB2, 6F2, 6F3 and 6Z8) in each strain, none of these genes were linked to resistance, except for CYP6BB2, which was partially linked to the resistance locus. Hence, overexpression of these four CYPs is due to a trans-regulatory factor(s). Knowledge on the specific CYPs and their regulators involved in resistance is critical for resistance management strategies because it aids in the development of new control chemicals, provides information on potential environmental modulators of resistance, and allows for the detection of resistance markers before resistance becomes fixed in the population.
| Cytochrome P450 monooxygenases (CYPs) are one of the most important mechanism of insecticide resistance in mosquitoes. These CYP enzymes break down insecticides into non-toxic forms that can be readily excreted. An increase of CYP-mediated detoxification is commonly found in pyrethroid resistant Aedes aegypti, however the link between specific CYPs and the resistance loci have not been clearly established for this species. In this study, we measured the expression levels of nine candidate CYPs in two strains highly resistant to permethrin: SP, a field collected strain that was selected with permethrin for high levels of resistance and CKR, a strain that contains the resistance mechanisms from SP, but that is congenic (i.e. has the genetic background) to the insecticide susceptible strain, ROCK. We found seven overexpressed CYPs in SP and four in CKR, confirming their involvement in resistance. Next, we sequenced the CYP genes (with the exception of the duplicated ones) to determine if the genes themselves are located in the resistance locus (meaning their expression is cis-regulated) or not (meaning their expression is trans-regulated). We found no reduced polymorphisms in any of the resistant strain (SP) CYPs, suggesting that the overexpression of these CYPs (and thus CYP-mediated resistance) is trans-regulated.
| Aedes aegypti is an important pest capable of transmitting four important human disease viruses: dengue, yellow fever, chikungunya, and Zika. Dengue, for example, causes morbidity and mortality in 141 countries across the tropical and subtropical regions of the world and is estimated to be a risk to over 50% of the world’s population [1]. Yellow fever is an endemic disease in the tropical regions of Africa and South America with a recently rising number of cases in Brazil [2,3]. Chikungunya is a disease new to the Americas as of 2013 [4] that often causes debilitating joint pains in addition to flu-like symptoms. Zika was introduced to the Americas in 2015 [5] and has generated great concerns due to its association with birth defects and Guillain-Barré syndrome [6]. Given that A. aegypti has a wide global distribution, high vector competence for several arboviruses, frequently bites humans and thrives in urban environments, it poses a serious risk to human health.
Insecticides are still the primary means to control A. aegypti in endemic areas. More specifically, pyrethroids are the most widely used class of insecticides for control of adult A. aegypti [7] in the past three decades. As a result of this continued use, pyrethroid resistance in A. aegypti is a global problem [8].
Cytochrome P450 monooxygenase (CYP)-mediated detoxification is one of the primary mechanisms of pyrethroid resistance in mosquitoes. CYPs are a large family of enzymes that metabolize both endogenous substrates and xenobiotics, such as insecticides. A. aegypti have approximately 160 CYP genes [9]. Several studies have directly (e.g. in vivo and/or in vitro metabolism) or indirectly (reduction in resistance with the CYP inhibitor piperonyl butoxide (PBO)) implicated CYPs as a mechanism of pyrethroid resistance in A. aegypti [8]. Elucidating the specific CYP(s) responsible for resistance is challenging because of the large number of CYPs and because CYP-mediated resistance can be due to overexpression of a CYP or to a mutation in the open reading frame of a CYP [10]. However, identifying these CYPs is extremely important to manage resistance because it allows us to detect resistance markers and stop insecticide use before resistance becomes fixed in the population [11]. Knowledge of the specific CYPs may also aid in the development of new insecticides and resistance inhibitors as well as allow us to better understand the influence of environmental xenobiotics in the development of insecticide resistance [12].
Most studies done to identify the CYPs responsible for resistance in A. aegypti have looked at changes in expression levels using unrelated strains [9,13–16]. However, when strains of different origins are used, it is not possible to determine the exact relationship between the overexpressed genes and insecticide resistance, because CYP expression can vary for reasons unrelated to insecticide resistance. For example, CYP9M9 was overexpressed in the SBE strain, relative to the BORA strain [13], even though both were susceptible strains.
Increased transcription of a CYP resulting in resistance could be due to a change in the regulatory region of the CYP [17], to a change in a CYP regulatory protein, or an increase in the copy numbers of the CYP through gene amplification [18,19]. These processes of increasing CYP expression would give different outcomes. First, a mutation in a specific CYP that leads to increased expression (cis-regulation) would be expected to show a specific increase in only that CYP, and the resistance would map to that CYP. In contrast, if resistance is due to a mutation in a gene regulatory protein (trans-regulation), there could potentially be multiple CYPs whose expression are elevated in the resistant strain, even if only one of them is responsible for the resistance. In addition, the resistance locus would not map to the CYP that is overexpressed. There are now several cases where CYP overexpression is found in insecticide resistant strains, and the overexpression is due to trans-regulation of the CYP. Examples include CYP6D1 in Musca domestica [20], 6A2 and 6A8 in Drosophila melanogaster [21], 6BJa/b, 6BJ1, 9Z25, and 9Z29 in Leptinotarsa decemlineata, and 4G7, 4G14 and 6BQ in Tribolium castaneum [22–24]. Gene amplification could also lead to increased expression of a single CYP or multiple CYP genes occurring in tandem depending on the length of the duplicated region. In this case, the resistance locus could map either to one or more of the duplicate CYPs.
CYPs as a group are rapidly evolving genes [25] and are frequently polymorphic within and between strains [26,27]. When a mutation causing resistance occurs and is under high selection pressure such that the resistance allele becomes fixed in the strain or population, the region near this mutation will have decreased amounts of polymorphisms relative to the rest of the genome [28–35]. Thus, reduced abundance of single nucleotide polymorphisms (SNPs) are useful to detect resistance loci [31–33]. The footprint of the region around the resistance locus will decrease in time, as recombination introduces back variation, but this will be a slow process. Furthermore, if a mutation in a CYP causes resistance, we would expect the CYP to have a single unique allele in the resistant strain, but be polymorphic in susceptible strains.
One of the best-characterized pyrethroid resistant strains of A. aegypti is Singapore (SP). SP developed a 1650-fold resistance to permethrin (relative to the susceptible SMK strain) after 10 generations of selection [19]. Pyrethroid resistance in SP is due to CYP-mediated detoxification and target site insensitivity (V1016G+S989P mutations in the voltage sensitive sodium channel [Vssc]). CYP-mediated resistance was unambiguously demonstrated in SP through in vitro and in vivo metabolism experiments and by PBO suppression of the resistance. Nine CYP genes (CYP9M6, 9M5, 9M4, 6Z8, 6Z7, 6F3, 6F2, 6BB2 and 4C50) in SP were overexpressed >3-fold relative to the susceptible SMK strain [19]. Overexpression of four of these (CYP6Z7, 9M4, 9M5 and 9M6) was due, in part, to gene amplification. The genetic linkage of these CYPs, or the genetic linage of their overexpression, relative to resistance has not been investigated.
In order to understand which CYPs in SP map to the resistance locus, we introgressed the resistance from SP into the genetic background of the susceptible ROCK strain resulting in CYP+KDR:ROCK (CKR), a resistant strain congenic to ROCK. We then asked two questions. First, which CYPs overexpressed in SP are also overexpressed in CKR relative to ROCK? Second, are any of the overexpressed CYP genes cis- (map to a resistance locus) or trans-regulated (do not map to a resistance locus)?
Two parental strains of A. aegypti were used: Rockefeller (ROCK), an insecticide-susceptible strain which originated from the Caribbean [36] and has been reared without exposure to insecticides for several decades, and Singapore (SP), a pyrethroid resistant strain in which the mechanisms of resistance have been well studied [19]. SP is resistant to permethrin due to two mutations in Vssc, V1016G+S989P (referred to as kdr), and CYP-mediated detoxification, but not by hydrolases or decreased cuticular penetration [19]. A third strain, CYP+KDR:ROCK (CKR), was isolated from crossing ROCK with SP followed by four backcrosses and permethrin selections. CKR is congenic to ROCK, but resistant to pyrethroids due to CYP-mediated resistance and to Vssc mutations S989P+V1016G. The procedure for isolating CKR is illustrated in Fig 1. In short, unmated ROCK females were crossed en masse with SP males. Unmated F1 females were backcrossed with ROCK males and unmated BC1 females were selected with a permethrin dose that killed at least 60%. BC1 females that survived were backcrossed to ROCK males. This process was repeated for the BC2 and BC3 generations, again using doses of permethrin that gave approximately 60% mortality. To ensure that we retained all the resistance alleles, both male and unmated female BC3 were selected with permethrin (~60% kill) and crossed with each other prior to backcrossing to ROCK again. At BC4 both males and unmated females were selected with permethrin (~60% kill) and reared en masse. Males and unmated females from the following three generations were selected with permethrin (~60% kill) and reared en masse. At BC4F4, kdr homozygosity was confirmed by allele-specific polymerase chain reaction (ASPCR), (n = 190) following our established protocol [37]. The resultant strain was named CKR (Fig 1).
Mosquitoes were reared at 27˚C (± 1˚C) with 70–80% relative humidity, and a photoperiod of 14L:10D. Females were blood fed using membrane-covered water-jacketed glass feeders with cow blood (Owasco Meat Co., Moravia, NY). Adults were maintained on 10% sugar water in cages approximately 35 x 25 x 25 cm holding ≤ 1000 mosquitoes. Larvae (~400–600) were reared in 27.5 x 21.5 x 7.5 cm containers with 1 L distilled water and fed Cichlid Gold fish food pellets (Hikari, Hayward, CA) (ground pellets for 1st instar and medium size pellets for 2nd to 4th instars). Food pellets were given daily as needed.
Adult bioassays were done by topical application using 3- to 7-day-old mated females. Permethrin (99.5% pure, 24.1% cis, 75.8% trans, Chem Service) and piperonyl butoxide (PBO) (90%, Sigma-Aldrich) were diluted in acetone (VWR, Radnor, PA, USA) for the bioassays. Mosquitoes were briefly anesthetized with CO2 and held on ice. A 0.22 μL drop of permethrin in acetone was applied to the thorax of each mosquito using a Hamilton PB-600 repeating dispenser equipped with a 10-μL syringe. Controls were treated with acetone only. At least five doses were used per bioassay with at least three giving mortality values between 0 and 100% and each containing 20 mosquitoes. Mosquitoes were given a cotton ball saturated with distilled water and held at 25˚C. A minimum of four replicates over at least two days and two cages were done per strain. Mortality was defined as mosquitoes that were ataxic after 24 h. Probit analysis [38], as adapted to personal computer use [39] using Abbott’s [40] correction for control mortality, was used to calculate the LD50 and the 95% confidence intervals (CI). All of the bioassay data fit a line (chi-square test). Resistance ratios (RR) were calculated by dividing the LD50 of the resistant strain (SP or CKR) by the LD50 of ROCK. Significant differences were determined by calculating the RRs for the minimum and maximum LD50 values based on the 95% CI. If the minimum and maximum RR values did not overlap, they were deemed significantly different. Bioassays using the synergist PBO was performed as described above, except that 2.5 μg PBO (maximum sublethal dose for the ROCK strain) was applied to each mosquito 2 h prior to permethrin application. For this, the mosquitoes were anesthetized on ice twice, once for PBO and once for permethrin application. Two controls were run: double acetone and an acetone plus PBO application.
Ten 5–7 days old mated female mosquitos were pooled into 2 mL micro tubes (Starstedt AG & Co., Nümbrecht, Germany) containing 500 μL of TRIzol reagent (Invitrogen, Carlsbad, CA, USA) per replicate and four replicate tubes were prepared. The mosquitos were pulverized at 4.5 m/s for 20 s with an MP FastPrep 24 bead beater (MP-Biomedicals, Santa Ana, CA, USA). The RNA content was extracted following Invitrogen’s TRIzol reagent protocol. The concentration of RNA was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) and then diluted to 10 μg/50μL with nuclease-free water to standardize the concentration between the tubes. DNA was removed by DNase treatment (TURBO DNA-free kit, Invitrogen) following the manufacturer’s instructions. Complete digestion of DNA was confirmed by lack of PCR amplification of the 5’ UTR of CYP6Z7 (S1 Table) determined by visual inspection on an ethidium bromide-stained 1% agarose gel.
Complementary DNA (cDNA) was synthesized with 1 μg of total RNA per reaction using the Promega GoScript Reverse Transcription System kit (Promega, Madison, WI, USA) and random primers per the manufacturer’s instructions. The cDNA pools were then diluted 1:5 using nuclease-free water before use in real time quantitative polymerase chain reaction (RT-qPCR).
RT-qPCR plates were set up with three cDNA biological replicates and two technical replicates of each biological replicate. Two strains were compared at a time; first ROCK and SP, then ROCK and CKR. For each strain comparison, the nine CYPs were run along with two internal control genes, ribosomal protein S3 (RPS3) and eukaryotic translation elongation factor 1-alpha (EF1α). Plates were spun down at 2100 RPM for 1 minute to ensure the liquid had reached the bottom of the wells. The reaction volume (20 μL) contained 10 μL of 2 × iQTM SYBR Green SuperMix, 7.4 μL of nuclease-free water, 0.8 μL of 10 μM of each specific primer (S1 Table), and 1 μL of first-strand cDNA template. The qPCR was performed in a CFX ConnectReal-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) thermocycler with the following program: an initial denaturation and enzyme activation at 95°C for 10 min followed by 40 cycles of denaturation at 95°C for 10 s, annealing at 60°C for 10 s with a plate read, and extension at 72°C for 10 s. An automatic dissociation step cycle was added for melting curve analysis.
Relative quantification analysis was performed using the amplification efficiency-corrected ΔΔCt method [41]. The change in Ct value of each strain between the target CYP gene and the reference gene (RPS3 or EF1α) represents a ΔCt value, while the change in ΔCt value of a CYP between the susceptible strain (ROCK) and a resistant strain (SP or CKR) represents a ΔΔCt, or fold-expression difference value. This is synonymous to, and will be referred to as, an R/S value in this paper. Amplification efficiency for each gene and strain was determined using LinRegPCR with a 20% exclusion of outliers from the median value, along with a manual correction of the window of linearity to fit the straight continuous set of data points in the log-linear phase of the amplification plots [42]. Data were normalized to the two endogenous controls for both strain comparisons. Multiple t-tests were conducted to determine the significance of the R/S ratios.
Genomic DNA was extracted using two methods; 1) an isopropanol precipitation method from whole bodies of pooled mosquitoes, and 2) an alkali extraction from the hind legs of individual mosquitoes. The isopropanol extraction was conducted as follows: eight whole mosquitoes were placed in 2 mL tubes (Starstedt Inc., Nümbrecht, Germany) containing ten 2.3-mm diameter zirconia/silica beads (BioSpec Products, Bartlesville, OK, USA) and 400 μl Buffer A (100 mM Tris-HCl, pH 8.0, 100 mM EDTA, 100 mM NaCl, 0.5% SDS, ddH2O). Samples were homogenized and 800 μl of 4.3 M LiCl and 1.4 M KOAc solution was added followed by centrifugation (14,100 x g for 10 min) and collection of supernatant. Next, 570 μl isopropanol was added, mixed, centrifuged (14,100 x g for 10 min), and supernatant removed to isolate DNA pellet. Tubes containing the pellet were centrifuged (14,100 x g for 30 sec) once more with 500 μl of 70% EtOH. The supernatant was removed, then the DNA pellet was dried and resuspended in ddH2O. The alkali extraction method was conducted as follows: legs of individual mosquitoes were placed in individual wells of a 96-well PCR plate (BioRad, Hercules, CA, USA) containing three 2.3-mm diameter zirconia/silica beads and 10 μl 0.2 M NaOH per well. The leg samples were beaten for 1–2 min on a vortex mixer at maximum speed and then incubated for 10 min at 70˚C. Ten μL of neutralization buffer (360 mM Tris-HCl, pH 8.0 and 10 mM EDTA) and 80 μL ddH2O were then added to each well.
PCR was carried out using 2 μl template gDNA, 10 μl PrimeSTAR GXL Buffer (Takara Bio Inc., Shiga, Japan), 4 μl dNTP Mixture, 1 μl PrimeSTAR GXL DNA Polymerase, 2 μl forward and reverse primer mix (S1 Table, S1 Fig), 31 μl ddH20 and the following thermocycler conditions: 95˚C for 3 min, 37 x (98˚C for 10 sec, 60˚C for 15 sec, 68˚C for 3 min) and 68˚C for 10 min.
Our goal was to examine the polymorphisms in the CYPs that are overexpressed in the SP strain [19] to look for markers or mutations that can link the CYPs to resistance. To do this we sequenced CYP gDNA from pools of mosquitoes from the SP and ROCK strains. If polymorphisms were found in the pools, eight additional mosquitos were sequenced individually. If no polymorphism were found in the pools, this process was repeated until we had high confidence in the polymorphisms in each strain. For genes where SNPs were found, the SP sequences were compared to ROCK sequences to check for any reliable and unique SNPs in SP. We then sequenced from CKR any CYP with a reliable marker to examine if it was inherited from the ROCK or SP parent strain (i.e. linked to resistance or not).
ROCK and SP gDNA were sequenced and aligned for each of the single copy CYPs (4C50, 6BB2, 6F2, 6F3, and 6Z8) that are overexpressed in the SP strain. Four of the nine overexpressed CYPs found by Kasai et al. [19] could not be used to search for SNPs due to multiple gene copies; these were CYP6Z7, 9M4, 9M5 and 9M6. CYP sequences were determined by Sanger sequencing using PCR products treated with ExoSAP (Thermo Fisher Scientific, Waltham, MA, USA) and sequenced at the Cornell University Biotechnology Resource Center (BRC). Sequence alignments were carried out on DNASTAR’s Lasergene software, EditSeq and SeqMan Pro (Madison, WI). SNPs were searched for using the SNP Report feature in SeqMan Pro and confirmed by visually inspecting the alignments and chromatograms.
CKR and SP were both resistant to permethrin (110- and 360-fold respectively) relative to ROCK (Table 1). The resistance was PBO suppressible in both CKR and SP, lowering the RR to 77- and 70-fold, respectively, confirming CYP-mediated resistance in both strains. The 3-fold difference in the resistance ratio (RR) between the CKR and SP strains suggests some minor mechanism of resistance was lost during the isolation of the CKR strain.
To determine if overexpression was genetically linked to permethrin resistance, CYP expression was quantified in both the SP and CKR strains relative to ROCK. CYP6BB2, 6Z8, 9M5 and 9M6 were overexpressed in both SP and CKR indicating that their overexpression is linked to resistance. Seven CYPs are significantly overexpressed in the SP strain relative to ROCK: CYP6BB2, 6F2, 6F3, 6Z7, 6Z8, 9M5 and 9M6 (Fig 2). The level of increased expression in SP was 169 for CYP9M6 (p = 7.0 x 10−6), 54 for CYP9M5 (p = 6.6 x 10−6), 7.5 for CYP6BB2 (p = 0.01), 5.2 for CYP6Z7 (p = 0.01), 3.8 for CYP6Z8 (p = 2.2 x 10−4), 1.7 for CYP6F3 (p = 0.03), and 1.4 for CYP6Z2 (p = 0.03). For CKR relative to ROCK, only CYP6BB2, CYP6Z8, CYP9M5 and CYP9M6 were significantly overexpressed (Fig 2). The fold- change in expression (R/S) in CKR were 76 for CYP9M6 (p = 0.05), 21 for CYP9M5 (p = 0.02), 6.9 for CYP6BB2 (p = 2.8 x 10−3), and 1.5 for CYP6Z8 (p = 0.03). When comparing the two resistant strains, SP had a higher level of expression of CYP6F2 (p = 4.0 x 10−3), 6F3 (p = 3.0 x 10−3), 6Z7 (p = 0.01), 6Z8 (p = 4.8 x 10−4), CYP9M5 (p = 1.5 x 10−3), and CYP9M6 (p = 0.02) compared to CKR. On average, the expression in SP was about 2.4-fold greater than seen for CKR. CYP4C50, 6BB2 and 9M4, and were not significantly different between SP and CKR (Fig 2).
The basal CYP transcription levels (ΔCt values) were also investigated for each strain relative to RPS3 and EF1α. Expression levels of all nine CYPs were readily detectable (S2 Fig, S3 Fig) and similar between both endogenous controls.
Genomic DNA of five single copy CYPs (4C50, 6BB2, 6F2, 6F3, 6Z8) were sequenced from both the ROCK and SP strains (see S1 Fig for diagram of genes and approximate primer locations). GenBank accession numbers for consensus CYP sequences are listed in S2 Table. These CYPs were selected because they are overexpressed in SP, but not duplicated [19]. SP had more SNPs than ROCK in CYP4C50, 6F2, 6F3 and 6Z8 (Table 2). The frequency of polymorphisms per kilobase (kb) ranged from 23–53 in SP and from 18–36 in ROCK. However, there were no strain-specific polymorphisms (i.e. neither a unique nucleotide between strains at a non-polymorphic site, nor a SNP in which both nucleotides differed between strains). This is not what would be expected for a gene at a resistance locus and leads us to conclude that the CYP4C50, 6F2, 6F3 and 6Z8 genes are not linked to resistance, even though their increased expression was (see above). In contrast to these four CYPs, there were many less SNPs detected in CYP6BB2, which had 0.08 and zero SNPs per kb in ROCK and SP, respectively (Table 2). CYP6BB2 had a unique, strain-specific synonymous polymorphism (thymine in ROCK and cytosine in SP at position 1595) that was homozygous in both strains. This allowed us to test if CYP6BB2 was linked to resistance by sequencing this CYP from the CKR strain. We found seven individuals homozygous for the SP allele, 10 heterozygotes, and four homozygous for the ROCK allele in the CKR strain. This indicates a partial genetic linkage of CYP6BB2 and the resistance locus. Based on the methods used to isolate the CKR strain, a measurement of the linkage was not possible.
Our bioassay results generated compelling data that we had isolated a strain (CKR) that had CYP-mediated resistance and kdr from the SP strain. Most of the resistance in SP was recaptured in the isolation of the CKR strain, although the 3-fold lower permethrin resistance in CKR (110-fold) compared to SP (360-fold) reveals that some resistance alleles may have been lost in the selection process. This can happen if the resistance factor is recessive and/or has a high fitness cost [43]. Interestingly, the RR value to SP is nearly 5-fold lower than that reported in Kasai et al. 2014. There are at least three possible explanations for this. First, different batches of permethrin were used and this is known to alter the expression of resistance [44]. Second, different susceptible strains were used and this can cause differences in levels of resistance reported [45]. Third, the SP strain may have lost some resistance while being maintained in the lab since it was received in 2014. Bioassays with PBO, reduced the RR to 77- and 70-fold in CKR and SP respectively, confirming the involvement of CYP-mediated resistance in both strains. The suppression of resistance with PBO was incomplete (kdr alone confers 40-fold resistance [37]) as is commonly seen in strains with CYP-mediated resistance [19,46]. Both CKR and SP are homozygous for the S989P+V1016G Vssc mutations.
Our study both validates previous work [19] and provides new information about the basis of CYP-mediated resistance in SP. Consistent with what was previously reported [19], we find elevated expression of CYP6BB2, 6Z7, 6Z8, 9M5 and 9M6 in SP. Given that we used a different susceptible strain in this study, and still found increased expression of these CYPs in SP, strengthens the hypothesis that these CYPs are involved in resistance. Further, our data provides evidence that the overexpression of four CYPs in the SP strain are genetically linked to resistance: CYP6BB2, 6Z8, 9M5 and 9M6. For CYP9M5 and 9M6 (but not 6BB2 or 6Z8), this is due in part to gene duplication [19].
How many different transcriptional regulation genes might be involved in insecticide resistance is an important, but unanswered question. Thus far, several different transcription factors have been implicated in insecticide resistance, including Gfi-1 in M. domestica [17,47] cap n collar C (CncC) and muscle aponeurosis fibromatosis (Maf) family transcription factors in Tribolium castaneum [24]. Identification of the mutation responsible for the increased expression of CYPs in the SP strain would expand our knowledge about this important evolutionary process and would provide a means by which the population genetics of this resistance could be studied. There is clearly some evolutionary plasticity in CYP-mediated resistance [48], but identification of the transcriptional regulatory factors, the mutations that cause CYP overexpression and the geographic frequency of these mutations are needed before we will start to have a satisfactory understanding of this important mechanism of resistance.
CYP genes are generally highly polymorphic. For example, in Anopheles gambiae CYPs have an average SNP frequency of 1 every 26 bp compared to the 1 every 34 bp genome average [26]. Determining the genetic diversity of A. aegypti has proven to be a challenging task due to the large genome size and high percentage of repetitive transposable elements [49]. One study found the average SNP frequency in the A. aegypti genome to be 12 per kb, however estimates of average nucleotide diversity (π) have varied greatly, ranging from about 0.001 to 0.015 [50] [51,52]. We found that the CYPs we studied (with the exception of 6BB2) had a frequency of polymorphisms similar to the average reported for An. gambiae [26] with an average SNP frequency of 1 per 36 bp in ROCK and 1 per 26 bp in SP. However, CYP6BB2 also had little variation (only two SNPs in the 2430 bp sequenced) in ROCK. The low level of polymorphism in CYP6BB2 appears more to do with the stains we used, rather than the gene per se, as wild A. aegypti populations from Uganda and Senegal had 164 CYP6BB2 SNPs, from Mexico there were 45 SNPs, and in populations from Sri Lanka there were no SNPs [50].
Overall, our results suggest that CYP-mediated resistance in SP is due to a trans-regulatory factor(s) that is capable of increasing the expression of multiple CYPs. The overexpression of four CYPs (CYP6BB2, 6Z8, 9M5 and 9M6) were linked to resistance. However, sequencing of the five single-copy CYPs that were found to be overexpressed in the SP strain, revealed that none of them showed expected signs of being at the resistance locus, except for CYP6BB2 which showed partial linkage to a resistance locus. Given that three of the CYPs have multiple copies in SP precluded us from being able to evaluate their linkage to resistance. Hopefully more sequence information will become available for these amplicons in the future which would allow for testing of linkage. Based on these results and other studies [20–24,53–55], it appears that trans-regulation of CYP expression may be a common mechanism of insecticide resistance.
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10.1371/journal.pntd.0002978 | Transmission and Control of Plasmodium knowlesi: A Mathematical Modelling Study | Plasmodium knowlesi is now recognised as a leading cause of malaria in Malaysia. As humans come into increasing contact with the reservoir host (long-tailed macaques) as a consequence of deforestation, assessing the potential for a shift from zoonotic to sustained P. knowlesi transmission between humans is critical.
A multi-host, multi-site transmission model was developed, taking into account the three areas (forest, farm, and village) where transmission is thought to occur. Latin hypercube sampling of model parameters was used to identify parameter sets consistent with possible prevalence in macaques and humans inferred from observed data. We then explore the consequences of increasing human-macaque contact in the farm, the likely impact of rapid treatment, and the use of long-lasting insecticide-treated nets (LLINs) in preventing wider spread of this emerging infection.
Identified model parameters were consistent with transmission being sustained by the macaques with spill over infections into the human population and with high overall basic reproduction numbers (up to 2267). The extent to which macaques forage in the farms had a non-linear relationship with human infection prevalence, the highest prevalence occurring when macaques forage in the farms but return frequently to the forest where they experience higher contact with vectors and hence sustain transmission. Only one of 1,046 parameter sets was consistent with sustained human-to-human transmission in the absence of macaques, although with a low human reproduction number (R0H = 1.04). Simulations showed LLINs and rapid treatment provide personal protection to humans with maximal estimated reductions in human prevalence of 42% and 95%, respectively.
This model simulates conditions where P. knowlesi transmission may occur and the potential impact of control measures. Predictions suggest that conventional control measures are sufficient at reducing the risk of infection in humans, but they must be actively implemented if P. knowlesi is to be controlled.
| Plasmodium knowlesi is a malaria of macaques which is now recognised as a leading cause of human malaria in Malaysia. Although current human infections are a result of human-macaque contact, there is a potential for P. knowlesi to be transmitted solely among humans. The authors developed a multi-host, multi-site transmission model to assess the likelihood of this happening due to increased human-macaque contact as a consequence of deforestation, population growth, and land-use change. How effective currently available malaria control measures were against P. knowlesi was also an important issue that was explored using the model. Although the model predicts that conventional control measures will be sufficient against P. knowlesi, with the push to eliminate malaria by the end of 2015, it is crucial to be aware of zoonotic malarias which may undermine such efforts.
| Despite advances in the control and treatment of malaria more than half the world's population remain at risk of infection and disease. Of the estimated 216 million episodes of disease occurring worldwide in 2010, 13% were estimated to occur in South-East Asia [1]. In 2004, large numbers of malaria cases previously diagnosed as Plasmodium malariae in the Malaysian Borneo were discovered to be due to the simian Plasmodium knowlesi malaria [2]. P. knowlesi is a zoonotic malaria of macaques transmitted by the Anopheles leucosphyrus group of mosquitoes in South East Asia, and is increasingly recognised as a human malaria as incidence among humans continues to increase [3], [4]. It is difficult to determine whether the increase in reported P. knowlesi cases is genuine or a product of previous misdiagnosis as P. malariae. However the significant increase in the total number and proportion of malaria patients aged 50 years and above, an age group over-represented among genuine P. knowlesi patients suggests that there has been a true increase in P. knowlesi cases, especially as this coincides with reduced transmission of P. falciparum and P. vivax [4]. Although only recently confirmed in Malaysian Borneo, there is further evidence that P. knowlesi is much more widespread than previously thought with sporadic cases reported in China, Thailand, Myanmar and other neighbouring countries [5]–[9].
As an emerging infection that could become a major public health threat, it is critical to understand the extent to which transmission is maintained by the simian host population and whether wider spread of infections outside the traditional forested areas is likely. To date, the identified human P. knowlesi cases are mostly reported from individuals who have a history of exposure through proximity or travel to forest environments [10], supporting the premise that P. knowlesi is primarily zoonotic, with incidental human infections when humans encroach on non-human primate habitats at the forest-fringe. In addition, although Anopheles latens (a member of the An. leucosphyrus group and one of the main vectors in Malaysia) will feed on both non-human primates (NHPs) and humans, it is primarily a forest-feeder, and macaques found in human settlements have a lower prevalence of infection compared to their wild counterparts [5].
P. knowlesi was not considered an important cause of human malaria in the 1960s when vectors were typically found in the primary forest which covered much of Malaysia. However, with the extensive population growth of the last decades, humans encroach on large expanses of natural P. knowlesi transmission causing further habitat disruption and destruction. In response, NHPs have moved towards the forest fringes and mosquito vectors are increasingly found in human habitats [11]. Therefore, the increasing overlap between macaque, human, and vector habitats may in part explain the recent rise in P. knowlesi cases as humans are increasingly exposed to the vector and host, together increasing the probability of successful cross-species transmission. As an increasing number of P. knowlesi cases are reported from traditionally malaria-free areas, and with the push to eliminate malaria by the end of 2015, it is crucial to be aware of zoonotic malarias which may undermine such efforts. As such there is an urgent need to investigate appropriate treatment and prevention strategies [4], [11].
Here we develop a mathematical model for the transmission cycle of P. knowlesi incorporating the human, macaque and vector hosts allowing for human-vector-human transmission which has been demonstrated under laboratory conditions [12]. Although human-vector-human transmission has yet to be definitively documented in the wild, autochthonous cases reported in the Philippines [13], and the familial clustering of cases reported from a wide age distribution in Sabah, Malaysia are suggestive of peri-domestic transmission and point toward potential human-mosquito-human transmission [14]. Using parameters derived from the literature we estimate the extent to which infection is sustained by the different host populations and hence the potential for a shift from zoonotic to sustained transmission in the human population if human-macaque contact increases as a consequence of deforestation. We then use the model to assess the likely impact of rapid treatment and the use of insecticide-treated bed nets in preventing wider spread of this emerging infection.
We extended a previous multi-host model for P. knowlesi transmission, which incorporated transmission between macaques, mosquitoes and humans [15], and then extended this further by accounting for three characteristic geographical sites (forest (J), farm (F) and village (V)) in which exposure to infection and transmission can occur. Here we define the forest as dense rainforest where macaques primarily reside, the farm area as an area on the forest-fringe that has been cleared for agricultural use where workers are present during the day, and the village as very small rural communities where humans live. A schematic of the model is shown in Figure 1 and full mathematical details are provided in the Supporting Information. Humans and macaques are assumed to move between locations (village-farm-forest and farm-forest respectively) whilst the vector population is stratified by each location. Each host (human, macaque, and vector) can be in one of two states – susceptible or infected – and hence we track the proportion of infected humans (IH), infected macaques (IM), and infected vectors in the forest (IVJ), farm (IVF) and village (IVV). A recovered compartment was excluded for macaques since infection is chronic, and for humans since data on immunity against P. knowlesi are not available.
We assume that humans and macaques are immediately infected and infectious following a mosquito bite [16], [17]. Parasitaemia peaks at day 8 after infection in humans, and falls rapidly to low levels by day 13 after infection. Thus we assumed a recovery rate of 1/14 days for humans [17]. We allow a delay in the vector transition from susceptible to infected state of 10 days to represent the extrinsic incubation period of the parasite [18]. Humans become infected at a rate λH which depends on (i) the rate at which vectors blood feed (accounting for human and macaque population sizes, the biting preference of the vector, and a reduction in biting rates on the farm to account for the absence of both humans and macaques in the evening when mosquitoes are most active [19]); (ii) the probability of transmission from mosquito to human/macaque per infectious bite; and (iii) the proportion of vectors infected in each location. Similarly vectors become infected in each of the three locations (forest, farm and village) at rates λVJ, λVF and λVV respectively, which depend on their frequency of biting, the proportion of bites in each location taken on humans versus macaques and the prevalence of infection in humans or macaques. Since our model is not temporal, we have used the median vector biting rates from the literature [19]–[23]. Table 1 contains details of the parameters and values used and additional parameter values are given in the Supplementary Material (Table S1). To assess the potential for sustained transmission in the absence of the macaque population we also calculate the component of the basic reproduction number for a human-vector system, R0H. Full details are given in the Supporting Information.
The total human population was fixed and distributed with 5%, 30% and 65% for the forest, farm, and village respectively. These percentages were chosen to reflect the proportion of time that an individual might spend in each location since our model does not explicitly include human movement. These values were chosen based on the Malaysian National Census and the average population density in corresponding areas [24]. Despite spending most of their time in the forest, macaques have been observed to encroach on farm land whilst foraging for food. The proportion of the total macaque population in daily contact with the farm was unknown. Furthermore there were very limited data available to inform the transmission probabilities between vectors and macaques, and vectors and humans, in addition to the infectious period among macaques.
In order to account for uncertainty in the parameters describing transmission of P. knowlesi between humans, macaques, and mosquitoes, and the unknown duration of infection in macaques, we undertook a model validation step using Latin hypercube sampling to obtain sets of these unknown parameters that were consistent with the possible prevalence of infection in humans and that in macaques. Due to its zoonotic nature, infection prevalence of Plasmodium knowlesi in humans in South East Asia is very low with an estimated annual incidence of 1% (95% CI: 0.4–1.7%) in southern Vietnam [25], 0.3% in Cambodia [26], and 0.65% in Thailand. In contrast, macaque P. knowlesi infection prevalence in the wild is extremely high at over 90%. Vythilingam et al., compared P. knowlesi infection among urban and forest macaques in Malaysia and found that, while urban macaques were infection free, forest macaques had a prevalence of 97% [27]. Tan et al., also found a prevalence of 87% in Sarawak among long-tailed macaques [28]. Since there are still very limited data on the true burden of P. knowlesi infection in humans and given that P. knowlesi is now the leading cause of malaria in Malayisan Borneo accounting for 87% of malaria admissions in Sabah [29], we allowed the upper limit of human infections to be high to reflect the possible range of prevalence using molecular detection tools [30], [31]. We therefore chose target ranges of 0–5% prevalence in humans and 80–100% prevalence in macaques for model validation. 50,000 parameter sets were selected using Latin hypercube sampling, and only those sets that resulted in infection prevalence within the target ranges were retained.
We considered the potential impact of two interventions on transmission – the provision of long lasting insecticidal nets and hammocks (LLINs, LLIHs) and more rapid treatment of human infections. To incorporate the former we adapted an approach previously described for models of P. falciparum transmission [32], [33]. Full mathematical details are given in the Supporting Information. In brief, the presence of a net reduces the biting rate on humans by providing direct protection to the individuals using a net; has a repellency effect which acts to increase the proportion of bites taken on other hosts and to increase the gonotrophic cycle length due to additional time spent searching for blood meals; and finally increases mosquito mortality due to the killing effect of the insecticide. Importantly, under this model, LLINs/LLIHs will affect the vector populations in the forest, farm and village differently, as there will be different numbers of humans sleeping under nets in each setting.
We considered the impact that different levels of LLINs/LLIHs coverage and usage may have in reducing P. knowlesi infection in humans, the human reproductive number, and the basic reproduction number. LLIN/LLIH usage in the farm was set to 0 since there is no evidence of net usage in this area. We do however assume that insecticide-treated hammocks (LLIHs) can be used in the forest [34], [35]. As a baseline scenario coverage (defined as the proportion of individuals in the population who always sleep under a net) was set at 80% in both the village and the forest based on a study in peninsular Malaysia [19].
To explore the impact of rapid treatment (a recovery rate of 1/5 days as opposed to 1/14 days) we varied the coverage from 0–100% and assumed that this would result in clearance of the parasites and hence reduce the duration of infection in the human host. This would then prevent onward transmission among humans and have a knock-on effect on the basic reproduction number for human infections.
Of the 50,000 parameter sets tested, 1,046 were compatible with the specified boundaries of 0–5% and 80–100% infection prevalence in humans and macaques respectively (Table 2). All of these scenarios were consistent with duration of infection in macaques of at least 1 year. In addition, the transmission probability from vectors to humans (CVH) was less than 0.5 in these scenarios, a condition that was required to match the low prevalence of infection in humans. The uncertainty in the other transmission probabilities could not be reduced by these constraints. In further sensitivity analyses, the human infection prevalence was found to be most affected by transmission probabilities directly involving humans (human-vector or vector-human transmission coefficients) (Figure S2). If human transmission coefficients are low, macaque transmission coefficients can be relatively high (allowing sustained transmission with an overall R0>1) yet still result in infection prevalence of less than 5% among humans. As expected, R0H which describes human infection events with a human origin was only dependent on the parameters describing transmission between humans and mosquitoes indicating that these are the key parameters that would facilitate a shift towards human-human transmission, and independent of macaque-mosquito transmission.
All of the parameter sets were indicative of sustained macaque-to-macaque infection with human infections being driven by the high infection prevalence in the macaque population. Furthermore, of the 1,046 parameter sets that were consistent with the set target ranges, we only identified 1 scenario which was consistent with R0H>1 (Table 2). This scenario had R0H and overall R0 values of 1.04 and 2.0 respectively. This scenario had a high vector-to-human and human-to-vector transmission probability (CVH = 0.14, CHV = 0.62 respectively) but a very small macaque-to-vector transmission probability (CMV = 0.001). This single scenario where R0H was greater than one had extreme values and such a low CMV is highly unlikely given the high infection prevalence observed in macaques. This suggests that sustained human-human transmission is possible but unlikely particularly in the absence of macaques. Macaques infect more than six times as many mosquitoes as humans indicating that the human contribution to the overall system is currently small.
Both human infection prevalence and the overall R0 depend on the proportion of macaques that spend time in the farm, with both these quantities reaching a peak when just over half of the macaque population are in the farm (Figure 2). When the majority of macaques remain in the forest, there is minimal overlap between areas where humans and macaques are active, and human infection prevalence stays low. As a greater proportion of the total macaque population are present in the farm, humans are increasingly infected. However when the ratio of macaques in the forest to farm reaches a certain threshold there is a switch to a situation of low infection prevalence in macaques as the high infection rates macaques experience in the forest are not maintained, and hence we observe a corresponding decrease in human prevalence.
An increased LLIN/LLIH coverage in the village and forest is predicted to decrease human infection prevalence. At 100% coverage prevalence drops by approximately 40% due to the combined direct impact of personal protection and the indirect impacts of vector killing, repellency, and a longer gonotrophic cycle (Figure 3a). The low infection prevalence still observed with some plausible parameter sets at 100% coverage is due to infection in the farm, where LLINs/LLIHs are not assumed to be used, and also the small proportion of individuals not using a net even when they are available. The human component of the reproduction number under control (R0H_C) decreases as expected with increasing LLIN/LLIH coverage (Figure 3b), with coverage greater than 5% required to reduce R0H_C to less than 1 in the single scenario in which R0H was greater than 1 (represented by the pale pink area in Figure 3b and as described in Table 2).
As expected, human infection prevalence decreases rapidly with increasing coverage of access to rapid treatment where the infectious period in humans is three times shorter than when rapid treatment is not available (Figure 4). Since rapid treatment decreases the infectious period in humans, clearance of the parasite brings overall human infection prevalence down. Additionally, if all infected individuals were treated promptly, there would be minimal onward transmission from humans to vectors and hence a lower risk of infection from vectors to humans in the villages and farms.
Our model examines how P. knowlesi transmission may depend on different mixing patterns between humans and the primary host, long-tailed macaques, in different ecological settings. The model suggests that macaques sustain P. knowlesi transmission with minimal or no self-sustaining transmission between humans and vectors in the absence of macaques. However there is potential for this to change if macaque mixing patterns change in the farm (at the forest-fringe) with the highest infection prevalence among humans occurring when macaques forage in the farms but return sufficiently frequently to the forest where they experience higher contact with vectors and hence sustain transmission.
The model suggests that the majority of transmission is sustained and driven by the macaque population. This result is supported by data that show that patients presenting at hospitals infected with P. knowlesi were mostly subsistence farmers whose work took them into the forest or plantations associated with forest on a regular basis, or individuals who travelled through at risk areas [36]. As such, among the population at risk, the majority of infections occur in men aged 20–29 years [10]. However, P. knowlesi is now the most common cause of malaria in Sabah, Malaysia and true numbers of human infections may be missed by passive case detection at facilities. Recent studies have shown that all ages and both sexes are susceptible to infection with cases also reported in Malaysian children [29], and Vietnamese children [37], [38]. Additionally, familial clustering of cases has been demonstrated indicating transmission is probably now occurring peri-domestically contrary to previous reports, and that this may be linked to deforestation and/or land-use change in these environments [14].
The vector species that have been implicated in the transmission of P. knowlesi are numerous and the dynamics of many of these are poorly understood [23], [39]. Therefore additional data on the exact vector species present in the different ecological zones; forest canopy, forest ground level, farm, and village and their respective bionomic data including extrinsic incubation periods could be used to improve the model. P. knowlesi has not yet been reported beyond the range of the An. leucosphyrus group which are predominantly forest mosquitoes, occasionally found at forest fringes and open areas where presumably incidental human infection occurs. Experimentally, however, the entire Leucosphyrus group, comprised of 20 species, can transmit P. knowlesi under favourable conditions [16]. Thus it is probable that the current restriction of P. knowlesi to a vector which prefers the forest fringe habitat rather than a completely anthropophilic one has limited the emergence of P. knowlesi as a fully human malaria parasite and public health threat [5]. The likelihood is that where multiple vectors exist, such as in the Malaysian Borneo, they occupy distinct environmental niches with mosquito trapping likely to be logistically demanding [40]. The extent to which variations in species-specific host blood meal choice and susceptibility to plasmodial infection influence transmission dynamics is not known.
Even if infection becomes more prevalent in the human population and the domestic environment, it is the individuals who spend time in proximity to areas where macaques are also active, the farm or forest, who will remain at most risk of zoonotic P. knowlesi infection. Thus control measures directed to these at-risk areas and populations would be beneficial as a whole. Our simulations showed that with 100% LLIN/LLIH coverage in the village and the forest, human infection prevalence can be reduced by up to 42%. Studies looking at the effectiveness of bed nets on P. falciparum have reported overall protective effectiveness of 17%–54% [41], [42]. We have assumed that insecticide-treated hammocks (LLIH) can be used in the forest and that they are as effective as LLINs [35]. Magris et al., found that LLIHs could reduce parasitaemia by 83% among the Yamomami people in Southern Venezuela [43]. Other studies have found reduction in malaria prevalence was 1.6 times greater when LLIHs were included in the intervention, with a 46% (95% CI: 35–55%) reduction in biting rates against Anopheles minimus in forested villages in Cambodia [35], [44]. Since the majority of infection is maintained in the forest by macaques, individuals who frequent these at risk areas should be made aware of the risks and encouraged to use LLIHs and other preventative options such as repellents as an easy and effective method of protection. However we did not find any impact on macaque infection prevalence with the use of LLIHs in the forest. The use of bed nets in the village will also become increasingly beneficial if human-human transmission becomes more frequent.
We found that rapid treatment of infected individuals to be the most effective in reducing infection prevalence among humans with a 95% reduction if every case is treated quickly (or within 5 days in our model). P. knowlesi has a rapid 24 hour erythrocytic cycle, and can result in severe and fatal infections if diagnosis and treatment are not prompt [3], [45]. Current observations show that P. knowlesi patients with uncomplicated malaria respond well to standard schizonticidal drugs with good prognosis and recovery after administration, with no relapse as P. knowlesi does not form dormant liver stages [16], [46]. There is no evidence for chloroquine-resistant P. knowlesi and as such chloroquine represents an inexpensive and highly effective therapy for uncomplicated P. knowlesi infections [47]. Additionally, since the majority of transmission is sustained by macaques, treatment of humans would not exert any substantial drug pressure.
As demonstrated in the model validation step, there are wide ranges of parameter values that are consistent with our current understanding of P. knowlesi from the limited data available. The upper bound of the overall R0 of 2267 was due to the extreme values of macaque to vector (CMV) and vector to macaque (CVM) transmission probabilities of 0.97 and 0.98 respectively in combination with a 10 year infectious period in macaques. Without detailed bionomic studies and empiric quantification of the natural history of P. knowlesi, it is impossible to reduce the uncertainties surrounding these values. Furthermore the role of super-infection has not yet been documented but it is plausible to assume that infection in macaques is more dynamic than the chronic infection assumed here. Additionally there are several other limitations to the model structure. In conventional malaria models individuals will move from susceptible to a pre-infectious compartment to take into account the latent period, around 9 −12 days from experimental studies in humans [12], rather than straight to an ‘infected’ compartment as set up here. Although the vector populations have been set up to incorporate the extrinsic incubation period crucial to any malaria model, it is assumed that humans and macaques are infectious immediately upon infection. Experimental observations suggest that although P. knowlesi produces gametocytes in mammalian hosts more rapidly than Plasmodium falciparum, they still take approximately 48 hours to develop and mature [16], [17]. Thus the addition of a pre-infectious period for humans and macaques would make the model more robust.
This model has assumed a constant seasonality in terms of vector and macaque densities. In many settings seasonality is a key factor in malaria transmission intensity where rainfall influences vector breeding and density. Seasonality is evident in the peak of P. knowlesi notifications in June in Sabah, Malaysia [4]. Seasonal fluctuations in the abundance and availability of different food types in the forest and on farms will also affect the behavior and density of macaques that move between these areas to forage and roost, and therefore any cross-species transmission that occurs may depend on the time of year.
Finally this model has been constructed based on the conditions observed in Malaysia and particularly in Sabah; thus predictions derived here may not be applicable to P. knowlesi infections elsewhere. With P. knowlesi cases being reported from several countries throughout South East Asia including Thailand [48], Singapore [49], [50], Indonesia [6], Vietnam [37], Myanmar [51], Cambodia [26], and the Philippines [13], both environmental conditions, demographics, and vector species involved are likely to be considerably different.
In summary, our results show that sustained human-vector-human transmission is unlikely to be occurring at present. However, as environmental change continues, there is the potential for the prevalence of P. knowlesi to increase and to become a significant public health problem. Our results highlight the need for sustained control and awareness of this zoonotic malaria particularly as Malaysia enters the pre-elimination stage for other malaria species.
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10.1371/journal.ppat.1006753 | Long-term persistence and function of hematopoietic stem cell-derived chimeric antigen receptor T cells in a nonhuman primate model of HIV/AIDS | Chimeric Antigen Receptor (CAR) T-cells have emerged as a powerful immunotherapy for various forms of cancer and show promise in treating HIV-1 infection. However, significant limitations are persistence and whether peripheral T cell-based products can respond to malignant or infected cells that may reappear months or years after treatment remains unclear. Hematopoietic Stem/Progenitor Cells (HSPCs) are capable of long-term engraftment and have the potential to overcome these limitations. Here, we report the use of a protective CD4 chimeric antigen receptor (C46CD4CAR) to redirect HSPC-derived T-cells against simian/human immunodeficiency virus (SHIV) infection in pigtail macaques. CAR-containing cells persisted for more than 2 years without any measurable toxicity and were capable of multilineage engraftment. Combination antiretroviral therapy (cART) treatment followed by cART withdrawal resulted in lower viral rebound in CAR animals relative to controls, and demonstrated an immune memory-like response. We found CAR-expressing cells in multiple lymphoid tissues, decreased tissue-associated SHIV RNA levels, and substantially higher CD4/CD8 ratios in the gut as compared to controls. These results show that HSPC-derived CAR T-cells are capable of long-term engraftment and immune surveillance. This study demonstrates for the first time the safety and feasibility of HSPC-based CAR therapy in a large animal preclinical model.
| Hematopoietic Stem/Progenitor Cell (HSPC) based gene therapy can be used to treat many infectious and genetic diseases. Here, we used an HSPC-based approach to redirect and enhance host immunity against HIV-1. We engineered HSPCs to carry chimeric antigen receptor (CAR) genes that detect and destroy HIV-infected cells. CAR therapy has shown huge potential in the treatment of cancer, but has only been applied in peripheral blood T-cells. HSPC-based CAR therapy has several benefits over T cell gene therapy, as it allows for normal T cell development, selection, and persistence of the engineered cells for the lifetime of the patient. We used a CAR molecule that hijacks the essential interaction between the virus and the cell surface molecule CD4 to redirect HSPC-derived T-cells against infected cells. We observed >2 years of stable production of CAR-expressing cells without any adverse events, and wide distribution of these cells in lymphoid tissues and gastrointestinal tract, which are major anatomic sites for HIV replication and persistence in suppressed patients. Most importantly, HSPC-derived CAR T-cells functionally responded to infected cells. This study demonstrates for the first time the safety and feasibility of HSPC based therapy utilizing an HIV-specific CAR for suppressed HIV infection.
| HIV-1 specific cytotoxic T lymphocytes mount a key immune response to HIV and are crucial for the control of viremia and the elimination of HIV infected cells. Previous studies have shown that a chimeric antigen receptor containing the CD4 molecule linked to the signaling domain of the T cell receptor ζ chain (CD4CAR) can be used to redirect peripheral T cells to target HIV infected cells[1]. CD4 CAR modified T cells can recognize and respond to HIV gp120 envelope protein on infected cells and can effectively kill HIV infected cells and limit HIV replication in vitro. Early clinical trials with CD4CAR modified T cells were shown to be safe but had limited antiviral efficacy[2,3]. The lack of in vivo functionality of the transferred T cells may have been due to suboptimal T-cell handling and expansion, or because CD4CAR-expressing CD8+ T-cells were susceptible to HIV infection and elimination[1,2]. Protection of the CD4CAR modified cells from viral entry is essential in order to ensure T-cell functionality and survival[4,5].
Hematopoietic Stem/progenitor Cell (HSPC) -based gene therapy has several advantages over T cell adoptive therapy. First, the regenerative nature of HSPC provides a lifelong supply of engineered T cells against antigen-expressing target cells, which is key to achieve long term immune surveillance and a functional cure of HIV infection. Secondly, modified cells undergo normal T cell differentiation and selection, eliminating potentially self-reactive T cells and increasing the potential for the development of immunological memory[6–8]. Our previous studies using humanized mice demonstrated that HSPCs modified with a protective CD4CAR resulted in successful differentiation of CD4CAR expressing T cells and significant suppression of HIV replication, suggesting a high degree of feasibility in redirecting immunity with an HSPC-based approach[4].
The nonhuman primate model is an ideal preclinical surrogate for the development of cure strategies in HIV+ patients. A series of well-characterized HIV-like viruses are available to recapitulate acute, chronic, and cART-suppressed infection [9,10]. Furthermore, use of a large animal model facilitates the detailed measurement of viral reservoirs in peripheral blood and in tissues. We and others have used macaque models extensively to evaluate autologous HSPC transplantation to combat a number of human diseases, including HIV infection. We have previously demonstrated that infection with the highly CCR5-tropic, HIV-enveloped simian/human immunodeficiency virus SHIV-1157ipd3N4 (“SHIV-C”) resembles suppressed infection in patients, including suppression by cART, rebound following cART withdrawal, and seeding of viral reservoirs in tissues [11,12]. Further, we have shown that autologous HSPCs can be modified to resist infection, for example via CCR5 gene editing [13], or expression of a potent inhibitor of HIV/SHIV fusion, the enfuvirtide-related peptide C46[14]. We have optimized these experiments in pigtail macaques (M. nemestrina), which carry a TRIM5 genotype that is permissive to lentivirus-mediated gene therapy approaches [15]. In short, our nonhuman primate model recapitulates virological and immune facets of HIV infection in patients, and facilitates evaluation of gene therapy-based HIV cure strategies.
Here, we asked whether HIV/SHIV-specific immunity could be engendered in HSPCs and their progeny, via modification of autologous HSPCs with a C46CD4CAR-expressing lentivirus vector. Modified cells were evaluated in vitro, and in SHIV-infected nonhuman primates.
Our lentivirus constructs contain a CD4 based CAR (CD4CAR) that is composed of the human CD4 extracellular and transmembrane domain linked to the human CD3ζ signaling domain (4). CD4 is the primary receptor for HIV, hence to protect CD4CAR expressing T cells from viral infection, we co-expressed the C46 fusion inhibitor in the CAR-containing vector (C46CD4CAR)(Fig 1A) [16]. We also generated a control vector that contains C46 and a truncated form of CD4CAR that lacks the signaling domain of CD3 ζ (C46CD4CARΔzeta) (Fig 1A). Expression of CD4CAR (without C46) resulted in increased HIV infection of Jurkat T cells (35.8% HIV+, as compared to 12% for unmodified cells) (Fig 1B). However, expression of C46CD4CAR blocked HIV infection (0.21% HIV+ cells), indicating that C46 protected gene modified cells from HIV infection. We next tested if the CD4CAR molecule can functionally respond to antigen in pigtail macaque T cells. We transduced pigtail macaque T cells with control C46CD4CARΔzeta or C46CD4CAR vector, then stimulated the cells with either uninfected or HIV-infected cells that expressed HIV envelope. C46CD4CAR transduced pigtail macaque T cells produced IL-2 and IFNγ in response to stimulation, indicating that the CD4CAR molecule is functional in the NHP cells (Fig 1C). In contrast, control cells that expressed C46CD4CARΔzeta did not respond to HIV infected cells. These data show that C46CD4CAR cells are protected against HIV infection and respond functionally and specifically to HIV antigen in NHP cells in vitro.
To examine the effects of the CD4CAR in vivo, four male juvenile pigtail macaques were transplanted with autologous HSPCs that were transduced with lentivirus expressing C46CD4CAR (“CAR1” and “CAR2”) or C46CD4CARΔzeta (“Control 1” and “Control 2”). As shown in S1 Fig, percent lentivirus marking from each animal’s HSPC infusion product ranged from 4.65% to 40% in colony forming assays. After HSPC transplant, recovery kinetics of total white blood cells, platelets, neutrophils, and lymphocytes in both control and CAR animals were normal [14,17] (S2 Fig). We detected stable gene marking of PBMCs from all animals prior to SHIV challenge (Fig 2A). In addition, we were able to detect C46CD4CAR or C46CD4CARΔzeta modified cells in peripheral blood by using an anti-human CD4 antibody clone (13B8.2) that detects human, but not pigtail macaque CD4 (Fig 2B). Because this antibody will only label the human CD4CAR in our animals, we refer to CAR+ cells as “huCD4+”. We found that 0.1% to 1.25% of CD45+ peripheral blood leukocytes from control or CAR animals were huCD4+ (Fig 2C). Importantly, huCD4+ CAR cells from both control and CD4CAR animals differentiated into multiple hematopoietic lineages, including T cells (CD45+CD3+), NK cells (CD3−CD2+NKG2A+)[18], B cells (CD45+CD3−CD20+) and monocytes and macrophages (CD3−CD20−CD14+) (Fig 2D). These results show that autologous transplantation of C46CD4CAR-transduced HSPC is safe and well tolerated, and results in stable, multilineage engraftment with typical kinetics of hematopoietic recovery.
To study the effect of C46CD4CAR transplantation on SHIV replication, animals were infected with SHIV-C for ~24 weeks followed by 28 weeks of combination antiretroviral therapy (cART) and subsequent cART withdrawal. At least 12 weeks after cART cessation, animals were then sacrificed for necropsy (Fig 3A). Both CD4CAR and control animals had slightly higher plasma viral loads prior to cART, and did not achieve full virus suppression following cART (Fig 3B). This was likely due to residual immune suppression from the transplant procedure [11,19]. The CAR1 animal had approximately 1 log higher viral load as compared to control animals during acute and chronic SHIV infection, while the CAR2 animal, in which more than 1% of PBMCs were modified with C46CD4CAR prior to SHIV infection, had lower peak viremia during acute infection and showed progressively decreasing viral loads prior to cART (Fig 3B). While CAR 1 and control animals appear to have reached set points after 4 weeks of SHIV infection, CAR 2 animal demonstrated a trend of continuous reduction of viral load throughout primary infection (Fig 3B). Interestingly, when we compared average viral load after cART withdrawal to average viral load during primary infection (week 2 to week 22), we found that both CAR containing animals had lower average rebound viremia (1.4–2.11 log lower than primary setpoint) as compared to the control animals (0.4–0.8 log lower than primary setpoint) (Fig 3C). These findings are consistent with a model in which C46CD4CAR cells are capable of establishing virus-specific immune memory and responding to recrudescent SHIV viremia.
We next measured antigen-dependent responses in C46CD4CAR and control animals by monitoring CAR gene marking as a function of SHIV plasma viremia. Lentivirus-marked cells were readily detectable by Taqman in CAR and control animals over the course of our nearly two-year study (Fig 4A). Interestingly, CAR animals, but not controls, showed increased gene marking in the periphery at multiple time points. These were coincident with increases in SHIV viremia, notably during primary infection and viral rebound after cART withdrawal (Fig 3B). To investigate further, we used flow cytometry to stain for huCD4+ PBMCs at multiple time points following SHIV infection. Consistent with Taqman-based gene marking data, we found that huCD4+ cells from CAR animals, but not control animals, expanded upon SHIV infection and post-cART withdrawal viral rebound (Fig 4B–4E). This confirms that functional C46CD4CAR cells require intact CD3ζ signaling in order to expand in response to SHIV antigen. Furthermore, we observed an increase of C46CD4CAR+ cells during acute and chronic SHIV infection (Fig 4D and 4E), reminiscent of a primary immune response to infection. After the cessation of cART treatment, the percentage of CD4CAR+ cells again increased rapidly, mimicking a memory response. The CAR2 animal, which had higher gene marking prior to SHIV infection (Fig 2A), contained as many as 10% and 12.6% huCD4+ PBMCs during primary untreated infection and after cART withdrawal, respectively. We also observed expansion in percentage of huCD4+ cells among T cells (S3A Fig) and in huCD4+ T cell numbers (S3B Fig). The expansion of CD4CAR+ cells is primarily driven by CAR expressing T cells as shown in Sup Fig 3A and 3B. During viral rebound, the 4–10 fold higher levels of CAR marking in C46CD4CAR animals relative to controls was consistent with the 1.5–2 log decrease in rebound viremia relative to primary infection in these animals (Fig 3C). These data suggest that CAR-marked cells engraft long term and are capable of antigen-specific expansion months or years after transplantation.
Our in vitro data suggest that expansion of C46CD4CAR cells is specific to HIV/SHIV antigen (Fig 1). To examine how CAR modified cells respond to SHIV replication in vivo, we monitored the naïve, effector, and memory phenotypes of CAR T cells longitudinally in our transplanted animals following SHIV infection. Prior to SHIV infection, huCD4+ (CAR+) and unmarked T cells (CAR-) shared similar percentages of naïve (CD28+CD95−), effector (CD28−CD95+) and memory (CD28+CD95+) subsets (Fig 5A). Strikingly, huCD4+ cells became predominantly effector T cells after SHIV infection, consistent with a response to SHIV antigen. During cART-dependent viral suppression, when the percentage of huCD4+ T cells contracted, we found that most displayed a naïve or memory phenotype. After cART withdrawal, huCD4+ T cells again displayed a predominant effector phenotype. Antigen-dependent increases in the percentage of effector cells were observed in C46CD4CAR animals, but not in CD4CARΔzeta controls (Fig 5B–5E).
To investigate if CAR+ effector cells can mediate specific killing of HIV Env expressing cells, we performed ex vivo killing assays using PBMCs from CAR or control animals during primary SHIV infection prior to cART treatment and after cART withdrawal (S4A Fig). We used U1 cells stimulated to express HIV envelope as targets. Notably, this human cell line lacks rhesus MHC molecules, and therefore should only be killed following CAR-dependent recognition of the HIV envelope. While PBMCs from control animals failed to mediate specific killing of Env+ U1 cells, PBMCs from CAR animals effectively mediated specific killing of Env+ U1 cells over control U1 cells (S4B and S4C Fig). Interestingly, we observed a trend of an increase in IFNγ production of cryopreserved T cells from CAR animals in response to SHIV peptide pool stimulation, suggesting a potential synergy between CAR T cells and anti-SHIV natural T cell response (S5A–S5C Fig). However, we did not observe a clear difference in production of natural anti-SHIV or anti-HIV Env antibody (S6A and S6B Fig). These findings are consistent with our previous data (Figs 1 and 3), demonstrating that C46CD4CAR HSPC-derived cells generate a long-lived, functional response to SHIV antigen. Previous CD19 CAR T cell therapy has been associated with cytokine release syndrome, and the principle cytokines elevated in patients treated with CD19 CAR T cells were IFNγ and IL-6[20].
To monitor toxicity associated with the CD4CAR HSPCs treatment, we measured plasma cytokine levels from transplanted CAR and control animals blood collected prior to SHIV challenge, during untreated SHIV infection, during cART treatment and after cART withdrawal (S7A Fig). As shown in S7B Fig, most proinflammatory cytokines, including IFNγ, IL-1β, IL-2, IL-6, MIP1β, MIP1α, MCP1 have mostly undetectable levels or no clear difference between control and CAR animals (S7C Fig). We observed lower sCD40L levels for CAR control animals during untreated SHIV infection and after cART, which may be a result of reduced SHIV-mediated inflammation in CAR animals. Overall, we observed no toxicity associated with the CAR transplant as compared to control animals.
We extended our analysis of C46CD4CAR cells by examining trafficking to multiple tissue sites, including those that have been characterized as viral reservoirs[21–23]. Both C46CD4CAR and C46CD4CARΔzeta cells were found in multiple lymphoid tissues, including various lymph nodes, gut, and bone marrow (S8 Fig). Similar to PBMCs, CAR animals had higher percentage of huCD4+ cells among T cells in various tissues as compared to control animals(S8A Fig). As with huCD4+ PBMC, tissue-associated CAR cells were multilineage, including CD4+ and CD8+ T cells, NK cells, and macrophages/monocytes. There were no obvious differences in cell composition between C46CD4CAR and C46CD4CARΔzeta modified cells (S8B–S8E Fig).
To examine the ability of C46CD4CAR cells to protect against SHIV-dependent depletion of CD4+ cells in the gut, biopsies were taken from the GI tract (colon or duodenum/jejunum) before SHIV infection and after cART withdrawal, and analyzed by flow cytometry. Control animals displayed a profound loss of CD4+ cells, both in terms of CD4+CD3+ T cell percentage (Fig 6A and S9A Fig) and CD4/8 ratio (Fig 6B and S9B Fig). Strikingly, CD4+ T-cell percentage and CD4/8 ratio were substantially higher in C46CD4CAR animals following cART withdrawal as compared to the control animals, suggesting that functional CAR cells contributed to protection of immune homeostasis in this compartment. Furthermore CD4+ effector memory T-cells (CD3+CD4+CCR7−CD45RA−), which are major target cells of HIV infection, were also protected in the gut of C46CD4CAR animals (Fig 6C and S9C Fig).
At necropsy, gene marking in lymphoid tissues (including spleen, mesenteric lymph nodes, axillary lymph nodes, inguinal lymph nodes, and submandibular lymph nodes) and gut (including duodenum, jejunum, ileum, cecum, colon, and rectum) was significantly higher in CAR animals relative to controls (Fig 6D). Interestingly, SHIV RNA measurements in these tissues showed that CAR containing animals had substantially lower viral loads (Fig 6E). Although we did not observe significant differences in gene marking in the brain (including hippocampus, basal ganglia, thalamus, parietal cortex, and cerebellum) between CAR and control animals, SHIV RNA measurements were also lower in this compartment in CAR animals, relative to controls (Fig 6D and 6E). In particular, the CAR2 animal had dramatically lower SHIV mRNA (4–5 logs) across all lymphoid tissues as compared to control animals (S10 Fig). Collectively, these results demonstrate that C46CD4CAR cells in tissues are capable of long term, multilineage engraftment, and are protected against SHIV replication, consistent with our observations in peripheral blood.
The seminal case study for HIV cure/remission is the Berlin patient, who received an allogeneic, HLA-matched HSPC transplant from a donor homozygous for CCR5Δ32[24], and has stimulated the search for HSPC-based cure approaches. Allogeneic HSPC transplantation without CCR5Δ32-protected donor cells in 2 HIV+ recipients initially resulted in undetectable HIV-1 after patients achieved full donor chimerism; this was likely due to a “graft versus reservoir” effect in which donor lymphocytes destroyed latently infected host cells. Ultimately, this intervention failed to eradicate latently infected cells, which rebounded after cART cessation[25]. These studies indicated that a combinatorial approach, rendering the blood and immune system resistant to infection and at the same time harnessing the immune system to attack infected cells, would be required. Numerous studies have used various gene therapy and gene editing approaches to genetically modify autologous stem cells, rendering them resistant to HIV infection[13,14,26–30]. Our nonhuman primate model of suppressed HIV infection is highly relevant to HIV cure studies in humans, utilizing a virus that is suppressed by a clinically relevant cART regimen, establishment of viral persistence in secondary lymphoid tissues, and rebound of viral replication following cART withdrawal[11,12]. We have previously shown that C46-expressing T-cells are protected against CCR5- and CXCR4-tropic viruses, and support a more robust immune response against infected cells in vivo [14,16]. Here, we demonstrate that HIV/SHIV-specific CAR cells possess strong antiviral activity even at low levels. These cells should act as sentinels, generating a robust immune response to reactivated infected cells months or years after they are introduced, without requiring expression in a high percentage of immune cells.
The most novel aspect of our approach is the generation of CAR cells from autologous HSPCs. Stem cell-based expression of CARs contributes a long-lived source of these cells, capable of providing lifelong immune surveillance against recrudescent virus. In contrast, adoptive transfer of CAR-modified T-cell products must overcome barriers including immune exhaustion, limited trafficking to tissues, and lack of functionality at these sites [31,32]. Furthermore, although CD4CAR T-cells persist long term in patients [33], it is unclear whether persisting cells are capable of responding to increased antigen loads, for example during cART treatment interruption. Here, we show for the first time in a clinically relevant large animal model, that autologous transplantation with a CAR-modified HSPC is safe and can be used to redirect long-term anti-viral immunity. We observed multilineage engraftment of autologous, gene-modified cells that persisted for almost 2 years. Moreover, we found that CAR-expressing cells expanded in response to SHIV infection in an antigen-driven fashion, and differentiated into effector cells in a CD3ζ domain-dependent manner. Intriguingly, we found that engineered CAR cells contracted during cART treatment during lower levels of antigen expression, followed by a rapid expansion after cART withdrawal, mimicking a memory response. As a result, CAR animals had decreased viremia during post-cART viral rebound, as compared to control animals. More importantly, we were able to detect CAR cells in multiple lymphoid tissues, including gut-associated lymphoid tissues. CD4+ T-cells at these sites facilitate viral replication, are significantly depleted during untreated infection, and are slow to regenerate during cART treatment[34]. We found significantly lowered SHIV mRNA in lymph nodes, gut and brain from C46CD4CAR animals as compared to control animals. Strikingly, both CAR animals showed substantially improved CD4/CD8 ratios and higher percentages of CD4+ and CD4+ effector memory cells in the gut after cART withdrawal, suggesting CD4+ T-cell protection was C46CD4CAR-dependent.
A small percentage of CAR-modified cells appeared to be sufficient to redirect an effective immune response against SHIV-infected cells in our study. For both CAR animals, we observed robust expansion of C46CD4CAR cells after SHIV infection and cART withdrawal, which was dependent on CD3ζ signaling. The immediate, memory-like response for CAR cells after cART withdrawal from both CAR animals likely contributed to improved control of SHIV viremia and CD4 protection in the gut as compared to control animals. Many of the same optimization parameters used in T-cell-based CAR products are also applicable to HSPC-based CAR cells. For example, 2nd and 3rd generation chimeric antigen receptors with co-stimulatory molecules such as 41BB and/or CD28 [35] may further boost the primary and secondary responses of HSPC-derived CAR T cells, although the impact of these modifications on thymopoiesis has not yet been tested. Another novel aspect of our HSPC-based approach is the generation of CAR cells in lineages other than T-cells (Figs 2 and S3). The contribution of CAR-expressing cells other than T-cells in our model remains to be determined. While CAR expressing natural killer cells can contribute to clearance of infected cells[36,37], C46CD4CAR is likely not functional in other cell types due to the lack of signaling pathway for CD3ζ[38]. If necessary, cell type-specific expression of the chimeric antigen receptor from transduced HSPCs may further improve efficacy and safety.
Our studies clearly demonstrate the potential of using CAR gene therapy in HSPCs to redirect anti-HIV immunity against HIV-1 infection. These results set the stage for future attempts to eradicate viral infection and provide more effective immune surveillance for HIV, using optimized CAR vectors and combinatorial approaches, for example with latency reversing agents and/or additive immunotherapies. Importantly, these findings have broad implications beyond HIV: additional preclinical studies should be performed to explore HSPC-expressed CARs against other infectious diseases and cancer in greater detail.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (”The Guide”), and was approved by the Institutional Animal Care and Use Committees of the Fred Hutchinson Cancer Research Center and University of Washington, Protocol # 3235–01. All animals were housed at and included in standard monitoring procedures prescribed by the Washington National Primate Research Center (WaNPRC). This included at least twice-daily observation by animal technicians for basic husbandry parameters (e.g., food intake, activity, stool consistency, overall appearance) as well as daily observation by a veterinary technician and/or veterinarian. Animals were housed in cages approved by “The Guide” and in accordance with Animal Welfare Act regulations. Animals were fed twice daily, and were fasted for up to 14 hours prior to sedation. Environmental enrichment included grouping in compound, large activity, or run-through connected cages, perches, toys, food treats, and foraging activities. If a clinical abnormality was noted, standard WaNPRC procedures were followed to notify the veterinary staff for evaluation and determination for admission as a clinical case. Animals were sedated by administration of Ketamine HCl and/or Telazol and supportive agents prior to all procedures. Following sedation, animals were monitored according to WaNPRC standard protocols. WaNPRC surgical support staff are trained and experienced in the administration of anesthetics and have monitoring equipment available to assist, including monitors of heart rate, respiration, blood pressure, temperature, and blood oxygenation. Monitors supplied readily and easily interpretable alerts, including audible alarms, LCD readouts, etc. For minor procedures, the presence or absence of deep pain was tested by the toe-pinch reflex. The absence of response (leg flexion) to this test indicates adequate anesthesia for this procedure. Similar parameters were used in cases of general anesthesia, including the loss of palpebral reflexes (eye blink). Analgesics were provided as prescribed by the Clinical Veterinary staff for at least 48 hours after the procedures, and could be extended at the discretion of the clinical veterinarian, based on clinical signs. Decisions to euthanize animals were made in close consultation with veterinary staff, and were performed in accordance with guidelines as established by the American Veterinary Medical Association Panel on Euthanasia (2013). Prior to euthanasia, animals were first rendered unconscious by administration of ketamine HCl.
Four juvenile male pigtail macaques were transplanted with autologous, lentivirus modified HSPCs as previously described [14]. In short, animals were mobilized with granulocyte-colony stimulating factor (G-CSF) and stem cell factor (SCF) for 4 days prior to collection of large volume bone marrow aspirates and bead-based positive selection of CD34+ cells. Over a 48-hour ex vivo culture period, cells were transduced twice with the lentiviruses indicated in Fig 1 at a multiplicity of infection (MOI) of 5 (CAR 1) or 10 (CAR 2, Control 1, Control 2). During HSPC transduction ex vivo, animals received a myeloablative conditioning regimen consisting of a fractionated dose of 1020 cGy total body irradiation. Following conditioning, the HSPC product was infused back into the autologous animal. A small aliquot of the infused cell product was plated in Colony Gel Medium (Reach Bio, Seattle, WA) and analyzed as previously described[14,39]. Individual colonies and total genomic DNA (gDNA) isolated at the indicated post-transplant time points were measured by gel-based and Taqman-based PCR methods, respectively, as previously described [14,17].
Animals were allowed to recover for approximately 200 days prior to infection with SHIV-C, which was administered to animals via the intravenous challenge route as previously described [11]. Combination antiretroviral therapy consisted of 20 mg/kg Tenofovir and 40 mg/kg FTC dosed 1X/day subcutaneous, and 150mg Raltegravir, dosed 2X/day oral with food. Plasma viral loads, peripheral T-cell counts, longitudinal tissue surgeries, and necropsy tissue collections were conducted as previously described [12,14]. Taqman-based peripheral blood measurements were performed from gDNA isolated from total leukocytes. Total RNA and gDNA from tissue samples were isolated using a Precellys 24 homogenizer and CK28-R hard tissue homogenizing beads (Bertin Corp.) as previously described [12]. Normalized SHIV RNA copy number in tissue was calculated by normalizing SHIV RNA copy number to the crossing threshold of macaque RNase P subunit p30 RNA. PCR-based assays for SHIV were designed not to detect HIV-based lentiviral vectors and average viral loads and log reduction in viral load were calculated as described previously [12]. Briefly, average viral loads following cART withdrawal were calculated by averaging each measurement from the first detection of recrudescent plasma viremia to the final measurement taken at necropsy (11–13 weekly data points). Average viral loads before cART were calculated by averaging plasma viremia over the same number of weekly data points from primary infection, beginning with the first detection of virus following intravenous SHIV challenge.
To detect huCD4+ CAR modified cells, PBMCs and tissue necropsy samples were stained with the following antibodies: anti-human specific CD4 antibody (for detection and analysis of CD4CAR modified cells; Beckman Coulter, clone 13B8.2), anti-NHP CD45 (BD Biosciences, clone D058-1283), anti-CD4 (eBiosciences, clone OKT4), anti-CD8 (eBiosciences, clone SK1), anti-CD20 (eBiosciences, clone 2H7), anti-NK2Ga (Beckman Coulter, clone A60797), anti-CD14 (Beckman Coulter, clone IM2707U), anti-CD95 (BD Biosciences, clone DX2), anti-CD28 (BD Biosciences, clone D28.2), and anti-CD3 (BD Biosciences, clone SP34-2). Fluorophore conjugates included FITC, PE, PE-Cy5, PE-Cy7, alexa700, V500, efluor450, APC, and APC-efluor780.
To test C46CD4CAR cell functions in NHP cells, NHPPBMCs were purified from healthy pigtail macaque blood and stimulated with bead bound anti-CD3 (BD Biosciences, clone SP34-2) and anti-CD28 (BD Biosciences, clone D282.2) for 3 days. Afterwards, cells were transduced with either C46CD4CAR or C46CD4CARΔzeta lentivirus. 2 days after transduction, cells were co-incubated with either T1 cells or HIV-infected Sup-T1 cells (AIDSreagent) for 16 hours, followed by 6 hours of GolgiPlug treatment. Afterwards cells were first surface-stained with anti-CD3, anti-human CD4 (for detection of CD4CAR transduced cells) and then intracellular-stained with anti-IFNγ (eBiosciences, clone 4S B3), anti-IL-2 (BD Biosciences, clone MQ1-17H12) and analyzed by flow cytometry.
To measure natural T cell response to SHIV infection, PBMCs were isolated from transplanted pigtail macaque peripheral blood collected during primary SHIV infection prior to cART treatment, and after cART withdrawal and cryopreserved. Total PBMCs were then thawed and stimulated with either no stimulation or SIVmac Gag, Pol, tat, nef, vif and HIV Env peptide pool overnight, followed with 6 hours of GolgiPlug (BD biosciences) incubation. Afterwards, cells were harvested and stained with anti-NHP CD3, CD4, CD8, huCD4 and intracellular IFNγ.
Jurkat cells (clone E6-1, AIDSreagent) were either untransduced or transduced with 2MOI CD4CAR (without C46) or C46CD4CAR for 2 days and infected with HIV-1NL4.3 (100 ng p24/106 cells) for 3 days. Afterwards, cells were intracellularly stained with anti-Gag (clone KC57) and analyzed by flow cytometry.
PBMCs were isolated from transplanted pigtail macaque peripheral blood collected during primary SHIV infection prior to cART treatment, and after cART withdrawal. Total PBMC were co-incubated with unstimulated U1 (HIV latent cell line, Env-) or PMA activated U1 (HIV Env+) at 3:1, 10:1 and 20:1 ratios for 10 hours. In order to quantify target killing, target cells were pre-stained with celltrace Vioblue (ThermoFisher Scientific) prior to co-incubation, and percent killing was calculated as the percent loss of live celltrace Vioblue+ target cells after co-incubation with effector PBMC.
Plasma was isolated from transplanted pigtail macaque peripheral blood collected during primary SHIV infection prior to cART treatment, and after cART withdrawal and frozen at -80. After necropsy, plasma was thawed and 25ul (max) was used to carry out Milliplex none-human-primate multiplex assay (EMD Millipore) for detection of proinflammatory cytokines IFNγ, IL-1β, IL-2, IL-6, MCP-1, MIP1β, MIP1α, sCD40L, TNFα. Detection range of cytokines are between 2.44 to 10,000 pg/ml. Data shown was average of 2 replicate wells.
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10.1371/journal.pgen.1005102 | Zinc Finger Independent Genome-Wide Binding of Sp2 Potentiates Recruitment of Histone-Fold Protein Nf-y Distinguishing It from Sp1 and Sp3 | Transcription factors are grouped into families based on sequence similarity within functional domains, particularly DNA-binding domains. The Specificity proteins Sp1, Sp2 and Sp3 are paradigmatic of closely related transcription factors. They share amino-terminal glutamine-rich regions and a conserved carboxy-terminal zinc finger domain that can bind to GC rich motifs in vitro. All three Sp proteins are ubiquitously expressed; yet they carry out unique functions in vivo raising the question of how specificity is achieved. Crucially, it is unknown whether they bind to distinct genomic sites and, if so, how binding site selection is accomplished. In this study, we have examined the genomic binding patterns of Sp1, Sp2 and Sp3 in mouse embryonic fibroblasts by ChIP-seq. Sp1 and Sp3 essentially occupy the same promoters and localize to GC boxes. The genomic binding pattern of Sp2 is different; Sp2 primarily localizes at CCAAT motifs. Consistently, re-expression of Sp2 and Sp3 mutants in corresponding knockout MEFs revealed strikingly different modes of genomic binding site selection. Most significantly, while the zinc fingers dictate genomic binding of Sp3, they are completely dispensable for binding of Sp2. Instead, the glutamine-rich amino-terminal region is sufficient for recruitment of Sp2 to its target promoters in vivo. We have identified the trimeric histone-fold CCAAT box binding transcription factor Nf-y as the major partner for Sp2-chromatin interaction. Nf-y is critical for recruitment of Sp2 to co-occupied regulatory elements. Equally, Sp2 potentiates binding of Nf-y to shared sites indicating the existence of an extensive Sp2-Nf-y interaction network. Our results unveil strikingly different recruitment mechanisms of Sp1/Sp2/Sp3 transcription factor members uncovering an unexpected layer of complexity in their binding to chromatin in vivo.
| A major question in eukaryotic gene regulation is how transcription factors with similar structural features elicit specific biological responses. We used the three transcription factors Sp1, Sp2 and Sp3 as a paradigm for investigating this question. All three proteins are ubiquitously expressed, and they share glutamine-rich domains as well as a conserved bona fide zinc finger DNA binding domain. Yet, each of the three proteins carries out unique functions in vivo, and each is absolutely essential for mouse development. By genome-wide binding analysis, we found that Sp1 and Sp3 on the one hand, and Sp2 on the other hand engage completely different protein domains for their genomic binding site selection. Most strikingly, the zinc finger domain of Sp2 is dispensable for recruitment to its target sites in vivo. Moreover, we provide strong evidence that the histone-fold protein Nf-y is necessary for recruitment of Sp2. Conversely, Sp2 potentiates Nf-y binding showing that binding of Sp2 and Nf-y to shared sites is mutually dependent. Our findings uncover an unexpected mechanistic diversity in promoter recognition by seemingly similar transcription factors. This work has broader implications for our understanding of how members of other multi-protein transcription factor families could achieve specificity.
| Eukaryotic transcription factors are grouped into families based on their common structural features. Prototypical zinc finger-containing transcription factors are the evolutionary conserved Specificity proteins/Krüppel-like factors (Sps/Klfs) (reviewed in [1,2,3,4]) that share three consecutive C2H2-type zinc fingers in their C-terminal moiety. Mammals have nine different Sp factors (Sp1 to Sp9), which can be grouped into two subclasses based on structural features outside of the zinc finger domain [5]. The Sp1 to Sp4 subclass is characterized by glutamine-rich domains that have been shown to act as transactivation domains in Sp1, Sp3 and Sp4. Sp1, Sp2 and Sp3 are ubiquitously expressed whereas expression of Sp4 is largely restricted to neuronal cells [1,6].
Despite their structural similarities and broad co-expression there seems to be little functional overlap between Sp1, Sp2 and Sp3 [7,8,9]. Briefly, Sp1null as well as Sp2null embryos are severely growth-retarded and die before embryonic day 10 [7,9]. Conditional inactivation of Sp2 in neuronal stem cells and neuronal progenitor cells resulted in impaired proliferation and disrupted neurogenesis in embryonic and postnatal brain [10]. In homozygous Sp2 transgenic mice terminally differentiated keratinocytes are depleted and the animals die within two weeks after birth again underlining the physiological importance of Sp2 [11]. Moreover, a genome-wide screen for cell division genes identified Sp2 as a gene essential for proper mitosis in HeLa cells [12]. Finally, Sp3null mice develop until birth but are not viable due to manifold defects including impaired lung, cardiac, bone and red blood cell development [8,13,14,15].
At the molecular level, the functional properties of Sp1 and Sp3 are well characterized. Particularly, numerous publications reported binding of Sp1 and Sp3 to the GC box (GGGGCGGGG) and related motifs in vitro. In contrast, Sp2 has largely escaped attention since its initial discovery [16] likely because no DNA-binding activity of full-length Sp2 is detectable by the electrophoretic mobility shift assay [17,18], and because Sp2 has little activation capacity on promoters that are regulated by Sp1 or Sp3 in reporter gene assays [19].
We have recently determined the genome-wide occupancy of Sp2 in mouse embryonic fibroblasts (MEFs) and in HEK293 cells, and have found that Sp2 occupies numerous proximal promoters of essential genes [18]. Bioinformatics analysis of the Sp2 binding sites identified CCAAT- and GC boxes as prevalent motifs in these promoters. On this basis and taking into account the similarity of Sp2 with other Sp factors, we concluded that Sp2 is recruited to its sites in chromatin by binding to the GC box in vivo. However, in the global chromatin context, it remains a largely unanswered question whether the very similar transcription factors Sp1, Sp2 and Sp3 are bound to the same promoters in vivo and whether regions outside the bona fide DNA-binding domain contribute to their binding site selection.
To address this important question, we have compared the genome-wide chromatin occupancy of Sp1, Sp2 and Sp3 with each other in mouse embryonic fibroblasts. We found that Sp1 and Sp3 essentially occupy the same promoters and localize to GC boxes. In marked contrast, Sp2 predominantly localizes at CCAAT motifs. By re-expression of various Sp2 and Sp3 mutants in corresponding Sp2 and Sp3 knockout (Sp2ko and Sp3ko) MEFs, we found that the zinc finger region mediates chromatin binding of Sp3. Unexpectedly, the bona fide zinc finger DNA-binding domain is completely dispensable for binding of Sp2. Rather, it is exclusively the glutamine-rich N-terminal domain, which mediates recruitment of Sp2 to its genomic sites. We further show that Sp2 colocalizes with the trimeric CCAAT-binding transcription factor Nf-y at a large fraction of Sp2 binding sites. We provide evidence that Nf-y is necessary for recruitment of Sp2 and suggest that, in turn, Sp2 potentiates Nf-y binding to shared sites, since binding of Nf-y to sites that are also bound by Sp2 is attenuated in Sp2ko MEFs. Therefore, we have discovered that the seemingly similar transcription factors Sp1/Sp3 and Sp2 utilize completely different modes of genomic binding site selection and shed light on the previously enigmatic properties of Sp2.
Recently, we have identified genomic binding sites of the transcription factor Sp2 in MEFs [18]. To identify the binding sites for the related transcription factors Sp1 and Sp3, and to elucidate the potential overlap with Sp2, we performed ChIP-seq analysis with the same cells. Two different antibodies for each factor that do not cross-react with other Sp family members were used ([8,18] and S1–S2 Figs). An Sp3 ChIP using Sp3ko MEFs served as a control for the selection of Sp3-specific peaks; and an IgG ChIP for the selection of Sp1-specific peaks, as Sp1ko MEFs are not viable.
We identified 5589 Sp1 and 4041 Sp3 peaks as overlapping across two sets of samples using two different antibodies for each factor (Fig. 1A). Comparing the Sp1 and Sp3 peaks of all four ChIP-seq data sets revealed 3597 high-confidence sites that are bound by Sp1 as well as by Sp3 (Fig. 1B). The large majority of these sites (~93%) are located close to the 5´-end (+/- 500 bp) of annotated transcripts (Fig. 1C). The comparison of the Sp1- and Sp3 ChIP-seq data sets also revealed a fraction of ~700 Sp1-specific peaks and a few (81) Sp3-specific peaks (Fig. 1B). The majority of these sites represent peaks with relatively low tag counts just above the threshold used for peak selection. Another fraction of the potential Sp1-specific peaks is also found in the Sp3 ChIP-seq data sets. However, these peaks were removed from the classified Sp3 list because a peak appeared also in Sp3ko MEFs. The latter observation highlights the great benefit of using knockout cells as ChIP controls. Taken together, we are not convinced about the reliability of the apparently specific Sp1- or Sp3 binding sites. Rather, our ChIP-seq results support the notion that Sp1 and Sp3 essentially occupy the same promoters in vivo. Combined binding of Sp1 and Sp3 to the same promoters is consistent with the phenotype of Sp1/Sp3 compound heterozygous mice. These mice are not viable suggesting that a critical threshold of Sp1 and Sp3 activity is required for normal embryonic development and for proper regulation of common target genes [20].
We next compared the Sp1/Sp3 binding sites with those of Sp2 [18]. Although there is a large overlap, we were able to distinguish, with high confidence, Sp1/Sp3-specific as well as Sp2-specific binding sites (Fig. 1D). Genome browser snapshots of representative shared Sp1/Sp2/Sp3, Sp1/Sp3-specific, and Sp2-specific binding sites are shown in Figs. 1E and S2. We also probed a panel of selected target promoters by conventional ChIP-qPCR analysis, and confirmed binding of all three Sp-factors to common promoters such as the L3mbtl2, Nxt1, Bin3 and Dhfr promoter, Sp1/Sp3-specific binding to the Raf1, Calcoco1, Kdelr2 and the Grb2 upstream promoter, and Sp2-specific binding to the Oxr1, Plcl1, and the Nfyc and Grb2 downstream promoters (Fig. 1F), the latter containing alternative promoters that are either occupied by Sp1/Sp3 or Sp2. Of note, consistent with our previous observation [18], Sp1 and Sp3 binding to several promoters is reduced in Sp2ko MEFs, which is likely due to their lower expression levels in these cells [18].
We performed an unsupervised de novo sequence motif analysis at Sp1/Sp3 and Sp2 binding sites. The top motif at the Sp1/Sp3 peaks matches well-known in vitro Sp1/Sp3 binding sites with the GGGCGGG core sequence (GC box). The second enriched motif is “CCAAT”, which is a binding site for the transcription factor Nf-y. Essentially, the same two motifs are found at the Sp2 binding sites [18]. However, at the Sp2 binding sites, the CCAAT motif is much more prevalent than the GC box motif (Fig. 2A). Moreover, only the GC box motif but not the CCAAT motif is enriched at sites that are bound by Sp1 and Sp3 but not by Sp2. Conversely, only the CCAAT motif but not the GC box is enriched at sites that are bound by Sp2 but not by Sp1 and Sp3 (Fig. 2A). To extract positional information for the GC and CCAAT motifs within Sp1/Sp3 and Sp2 peaks, we performed a central motif enrichment analysis (CMEA) [21]. This analysis revealed that the GC box motif is enriched at the peak centers of the Sp1/Sp3 binding sites, whereas the CCAAT box motif is found at flanking regions showing a multimodal shape (Fig. 2B). A strikingly different picture emerges at the Sp2 binding sites. Most significantly, the GC box motif is barely enriched at the Sp2 peak centers, whereas the CCAAT box motif exhibits a centrally enriched, symmetrical bimodal distribution with a mean distance of ~35 bp (Fig. 2B).
Intrigued by the nicely shaped distribution of the CCAAT motifs at the Sp2 binding sites, we examined the distribution of the CCAAT motif in greater detail. We found that ~70% of the top Sp2 binding sites but only ~23% of the Sp1/Sp3 sites contain two, or more, perfect CCAAT motifs. Moreover, ~65% of the Sp2-specific sites but only ~8% of the Sp1/Sp3-specific sites contain more than one CCAAT motif (Fig. 2C). Finally, many Sp2-specific promoters (~40%) but only a few Sp1/3-specific promoters (2.3%) contain two CCAAT/ATTGG motifs that are located within a distance of 30 to 50 nucleotides (Fig. 2D). Thus it appears that a large fraction of the Sp2 binding sites is characterized by tandem CCAAT motifs. The CCAAT motif is a binding site for the transcription factor Nf-y. Since Nf-y is also found at promoters that contain imperfect CCAAT motifs [22], the number of Sp2 binding sites with tandem arranged CCAAT motifs (e.g. Nf-y binding sites, see below) could be even higher.
Finally, we also determined the binding sites of Sp1 in HEK293 cells and compared the motifs at the Sp1 binding sites with those at the Sp2 binding sites [18]. Similar to mouse cells, the GC box is the prevalent motif at the Sp1 binding sites, whereas the CCAAT box is the prevalent motif at the Sp2 binding sites (S3 Fig). In summary, the comparison of the genomic Sp1/Sp3 and Sp2 binding sites revealed markedly different sequence motif distributions indicating that binding of Sp2 to chromatin is distinct from Sp1/Sp3, and questions our previous conclusion that Sp2 is recruited to its target promoters in vivo via binding to the GC box [18].
Intrigued by the finding that Sp1 and Sp3 are present at GC boxes, whereas Sp2 is located primarily at CCAAT motifs, we sought to identify the protein domains that are responsible for the differences in binding site selection in vivo. We focused on Sp2 and Sp3 because corresponding knockout MEFs are available [7,23] that have been proven to be useful for rescue experiments [18,24,25].
We stably expressed Flag-tagged full-length Sp2 and Sp3, and deletion mutants thereof in corresponding knockout MEFs. All proteins are expressed at levels similar to endogenous Sp2 and Sp3 with the exception of the zinc finger domains that are expressed at a higher level (Fig. 3A-B). ChIP-qPCR analysis of selected target promoters including promoters that are bound by Sp2 as well as by Sp3 (Nxt1, Sp1 and Sp2), and promoters that are preferentially bound by either Sp2 (Gas2l3 and Osbp) or Sp3 (Calcoco1 and Raf1) revealed specific binding of re-expressed full-length Sp2 and the full-length Sp3 isoforms (Sp3li and Sp3si) (Fig. 3C-D). The Sp3ZF fragment lacking the entire N-terminal part is also bound at the Sp3 target promoters showing that the Sp3 zinc finger domain is sufficient for binding. As expected, the Sp3NT mutant lacking the DNA-binding domain is not bound at any of these promoters (Fig. 3D). The picture, which emerged from the analysis of the Sp2 mutants is completely different. The Sp2ZF fragment lacking the N-terminal part is not bound at any of the Sp2 target promoters (Fig. 3C). Strikingly, the Sp2NT mutant expressing the entire N-terminal part but lacking all three zinc fingers is bound at the Nxt1, Sp1, Sp2, Gas2l3 and Osbp promoters almost as strongly as full-length Sp2 (Fig. 3C). Thus, the canonical DNA-binding domain of Sp2 is dispensable, and the N-terminal part sufficient for binding to these promoters in vivo. This result suggests that Sp2 is recruited to its target promoters indirectly, likely involving protein-protein interactions (see Discussion chapter for further details).
To further substantiate the conclusion that Sp2 is recruited to its genomic sites by the N-terminal region rather than by the C-terminal zinc finger domain, we performed ChIP-seq using Sp2ko MEFs re-expressing Flag-tagged versions of full-length Sp2 (Sp2FL), the Sp2 N-terminal part (Sp2NT) or the Sp2 zinc finger domain (Sp2ZF; see scheme in Fig. 3A). Although the Flag ChIP was less efficient in this particular experiment, we identified more than seven hundred highly reliable Sp2FL and Sp2NT binding sites but only ten potential Sp2ZF sites (Fig. 4A). Comparison of these binding sites with those of endogenous Sp2 in wild type MEFs revealed that >99% of the Sp2FL and >95% of the Sp2NT sites, but remarkably none of the Sp2ZF sites correspond to native Sp2 binding sites (Fig. 4A). The correlation of normalized tag counts at individual Flag-Sp2FL and Flag-Sp2NT peaks (Fig. 4B) shows that full-length Sp2 and the zinc finger-deficient Sp2NT mutant bind to chromatin with similar strength. Of note, the sites bound by the Sp2NT moiety correspond to the strongest native Sp2 peaks (Fig. 4C). Representative genome browser snapshots of Sp2FL and Sp2NT peaks in comparison with native Sp2 peaks are shown in Fig. 4D. Taken together, these striking results show convincingly that the zinc finger domain is dispensable for genomic binding of Sp2 to its target promoters. Recruitment of Sp2 in vivo by its N-terminal region is not limited to just a few selected target sites, but is a general feature of Sp2-targeting to chromatin (Fig. 4E).
To map protein regions of Sp2 that are essential for chromatin binding in vivo, we stably re-expressed a series of N-terminal Sp2 deletion mutants (Fig. 5A) in Sp2ko MEFs and tested their binding to selected loci. Western blotting and immunohistochemistry control experiments showed that all mutants are expressed at similar levels and are present in the nucleus (Fig. 5B-C). Deletion of 27 N-terminal amino acids does not affect binding of Sp2, whereas deletion of 93 N-terminal amino acids completely abolishes binding (Fig. 5D). The Sp2 mutant lacking 49 N-terminal amino acids shows some residual binding to the Sp2 and Osbp promoters. We conclude that the Sp2 sequence between amino acids 28 and 93 is essential for binding of Sp2 in vivo. This region contains the Sp-box (33-SPLALLAATCSKIG-46), a hallmark of the Sp transcription factor family members [1]. Whether the Sp-box is directly involved in recruitment of Sp2 remains to be established. Finally, we also rescued Sp2ko MEFs with a C-terminal deletion mutant (1–506 mutant in Fig. 5A) that lacks the zinc finger domain and, in addition, the buttonhead box. The buttonhead box is a cysteine-rich motif (CxCPnC) of unknown function, and it is also a hallmark of Sp factors. The Sp2 1–506 mutant binds to the Sp2, Osbp and Amd1 promoters as efficiently as full-length Sp2 demonstrating that the buttonhead box is also dispensable for binding of Sp2 to its sites in vivo.
We wanted to know whether binding of the Sp2NT fragment could affect expression of target genes. At first, we tested whether it has the capacity to activate transcription. We fused the Sp2NT fragment to the Gal4-DNA-binding domain and performed reporter gene assays. The Gal4-Sp2NT fusion protein activated a Gal4-responsive 5xUAS-luciferase reporter as efficiently as a corresponding Gal4-Sp1NT fusion protein (Fig. 6A) showing that the N-terminal part of Sp2 has an activation function.
Next, we tested expression of a selection of Sp2 target genes in wild type, Sp2ko, and Sp2ko MEFs re-expressing Sp2 mutants. Compared to wild type MEFs, Nlk transcript levels are markedly lower in Sp2ko MEFs (20% of wt). Nearly wild type Nlk mRNA levels are restored in Sp2ko MEFs expressing either full-length Sp2 or the Sp2NT mutant, but not in cells expressing the Sp2ZF mutant (Fig. 6B). This result indicates that the loss of Sp2 leads to downregulation of Nlk transcription, and that re-expression of the Sp2 N-terminal fragment is sufficient to rescue Nlk expression. Nevertheless, the expression levels of many other Sp2 target genes were not significantly affected in Sp2ko MEFs [18]. Inspection of annotated ensemble transcripts revealed that many Sp2 target genes (65%) have alternative transcriptional start sites. Moreover, many of the Sp2 binding sites overlap with Sp1/Sp3 binding sites (Fig. 1). Therefore, we reasoned that alternative initiation sites might hide transcriptional initiation driven by Sp2. We chose the Grb2 and the Oxr1 genes to test this possibility. Both genes contain an upstream promoter bound by Sp1 and Sp3 but not by Sp2 and a downstream promoter bound by Sp2 but not by Sp1 and Sp3 (Fig. 6C). We analyzed levels of Grb2 and Oxr1 transcripts with primer pairs that detect all transcripts, and with primer pairs that detect specifically the transcripts initiated at the Sp2-specific downstream promoters. Compared to wild type MEFs, the total transcript levels of the Grb2 and Oxr1 genes are largely unaffected in Sp2ko MEFs. In contrast, the downstream promoter-specific transcript levels are significantly lower in Sp2ko MEFs; and they are at least partially restored in Sp2ko MEFs expressing full-length Sp2 or the Sp2NT mutant (Fig. 6C). In summary, these results indicate that the N-terminal region of Sp2 is sufficient for activation of a subset of genes. However, they do not exclude that the zinc finger domain of Sp2 is essential for activation of other genes.
The CCAAT box is the binding site of the ubiquitous transcription factor nuclear factor y (Nf-y, previously also termed Cbf for CCAAT-binding factor; reviewed in [26]). Nf-y is a heterotrimeric protein composed of the three subunits Nf-ya, Nf-yb and Nf-yc. The Nf-ya subunit confers base-specific recognition of the CCAAT sequence, whereas the basic surface of the histone-fold Nf-yb/Nf-yc dimer makes strong contacts with the negatively charged DNA sugar-phosphate backbone [27]. All three Nf-y subunits are necessary for binding to the CCAAT box. Based on its histone-like properties, Nf-y is considered as an architectural promoter organizer that keeps a promoter free of nucleosomes [27].
The presence of Sp2 at CCAAT boxes rather than at the GC box prompted us to ask whether Nf-y is also present at these sites, and whether genomic binding of Nf-y and Sp2 impinge on each other. We performed ChIP-seq of Nf-ya, Nf-yb and Nf-yc with chromatin from wild type and Sp2ko MEFs (Fig. 7A). We obtained a higher number of Nf-yb sites with respect to Nf-ya and Nf-yc sites, which very likely does not reflect the occurrence of Nf-yb-specific target loci, but a better ChIP efficiency of the Nf-yb antibody. This assumption is supported by the significantly higher average reads per peak obtained for Nf-yb as well as by the higher enrichment values obtained in ChIP-qPCR experiments (see below). Comparison of the Sp2 and the Nf-y ChIP-seq data sets revealed that 84% of the Sp2 target sites are also bound by Nf-y (Fig. 7B). Moreover, the strength of Nf-y- and Sp2 binding correlates with each other; in other words, sites with high Nf-y subunit tag counts have also high Sp2 tag counts (Fig. 7C). Nevertheless, a subset of sites is clearly bound exclusively by Sp2 or exclusively by Nf-y. Examples of such sites are the Fanci and the Taf1c promoters that are bound by Sp2 but not by Nf-y, and the Atxn3 and the Wapal promoters that are bound by Nf-y but not by Sp2 (Fig. 7D).
We compared sites that are bound by Sp2 as well as by Nf-y with sites that are bound only by Sp2 or only by Nf-y. MEME reported the CCAAT and the GC motif as the top motifs at sites that are co-bound by Sp2 and Nf-y as well as at sites that are bound by Nf-y but not by Sp2 (Fig. 7E). Consistent with the analysis of all Sp2 ChIP-seq peaks, the CCAAT motif shows a symmetric bimodal distribution at shared Sp2/Nf-y sites. The CCAAT motif at the Nf-y-specific sites does not show this pronounced bimodal distribution but is largely centrally enriched (Fig. 7F). The prevalent motifs at sites bound by Sp2 but not by Nf-y are the GGAAG motif, a binding site for the Ets family members Gabp and Elk4, and the GC box (Fig. 7E). At these sites, the GC box appears to be centrally enriched and flanked by the GGAAG motif (Fig. 7F).
The occurrence of a centrally enriched GC box made it possible that the zinc finger domain of Sp2 could be essential for binding to these sites. Notably, these sites are weak binding sites (S4 Fig) that were not detected in our Flag-Sp2FL and Flag-Sp2NT ChIP-seq experiments shown in Fig. 4. Therefore, we tested binding of Sp2 mutants to this class of promoters by conventional ChIP-qPCR. Full-length Sp2 as well as the Sp2NT fragment are bound to the Fanci and the Taf1c promoters (S4 Fig). We conclude that the zinc finger region of Sp2 is also dispensable for binding to sites that are not bound by Nf-y.
Finally, we determined the overlap of Sp1/Sp3, Sp2 and Nf-y sites. Approximately 45% of the Sp1/Sp3 sites are also occupied by Nf-y (Fig. 7G). Importantly, the vast majority of the Sp1/Sp3 binding sites that are not bound by Nf-y are also not bound by Sp2 (1825 of 1967; 93%) (Fig. 7G). These sites represent high tag count Sp1/Sp3 binding sites that are enriched of multiple GC boxes and, as expected, do not contain CCAAT motifs (Fig. 7H). Thus, the occurrence of GC boxes and the presence of Sp1/Sp3 are not sufficient to recruit Sp2. Of note, the prevalent, centrally enriched motif at the few Sp2 binding sites that are also bound by Sp1/3 but not by Nf-y (142 sites in Fig. 7G) is the GGAAG motif and not the GC box (Fig. 7H). These findings have also implications concerning the binding of Sp2 to promoters that contain both, GC and CCAAT boxes and are bound by Sp1/Sp3 and by Nf-y (Figs. 2, 7G). At these promoters, the resolution of the ChIP-seq peaks does in most cases not allow to distinguish whether Sp2 is located at a GC box or at a nearby CCAAT box. Given that Sp2 is bound to a large fraction of promoters that are bound by Nf-y lacking Sp1/Sp3 (1081 sites; 42%), it is tempting to conclude that the CCAAT box (i.e. Nf-y) and not the GC box (i.e. Sp1/Sp3) is the decisive sequence for the recruitment of Sp2 to promoters bound by Nf-y and by Sp1/Sp3.
Having established the overlap of Sp2 and Nf-y target sites, we tested whether Sp2 and Nf-y simultaneously associate with chromatin, and performed sequential ChIP experiments (re-ChIP). The eluate from Sp2 antibody chromatin precipitation was subjected to precipitation with Nf-yb antibodies, and vice versa. Re-ChIPs detected all selected promoters bound by Sp2 and Nf-y (Sp1, Sp2, Osbp, Amd1, Nxt1 and Nipal3) independently of the immunoprecipitation order (Fig. 8A), but not the Sp2-specific Fanci, the Nf-y-specific Atxn3 or the Sp1/Sp3-specific Raf1 promoter. This result demonstrates that Sp2 and Nf-y co-occupy their shared binding sites in the context of chromatin.
Co-occupancy of Sp2 and Nf-y at shared target promoters led us to ask whether Nf-y is necessary for recruitment of Sp2 to these sites. We knocked down all three Nf-y subunits individually by RNAi (Fig. 8B), and subsequently analyzed binding of Nf-y and Sp2 to a panel of target promoters. These promoters include those that are co-bound by Nf-y and all three Sp factors (Sp1, Osbp, Amd1 and Dcnt4), promoters that are co-bound by Nf-y and Sp2 but not by Sp1 and Sp3 (Oxr1 and Plcl1), and promoters that are bound by all three Sp factors but not by Nf-y (Fanci and Taf1c) (Fig. 8C). Lower expression of any of the Nf-y subunits resulted in attenuated binding of Sp2 to all promoters that are co-bound by Nf-y and Sp2. Importantly, the reduction of Sp2 binding is particularly strong at the Plcl1 and Oxr1 promoters, which are not bound by Sp1 and Sp3. This result strongly suggests that reduced binding of Sp2 is not an indirect effect of reduced Sp1 levels in Nf-ya and Nf-yc depleted cells (Fig. 8B, panel 4, lanes 2 and 4) but directly caused by attenuated Nf-y binding. Binding of Sp2 to the Fanci and Taf1c promoters, which are not co-bound by Nf-y, was not affected upon depletion of Nf-yb and Nf-yc. Reduced binding of Sp2 to these two promoters upon Nf-ya knockdown is likely due to lower Sp2 levels in Nf-ya knockdown cells (see Fig. 8B, panel 3, lane 2). Taken together, these results strongly suggest that the presence of Nf-y is necessary for recruitment of Sp2 to shared sites.
Given the dependency of Sp2 on Nf-y, we wanted to know whether Sp2 does affect binding of Nf-y at these shared sites. We compared occupancy of Nf-y in wild type MEFs with occupancy in Sp2ko MEFs. Expression of Nf-y is similar in both cell types (Fig. 9A). By calculating the ratio of normalized Nf-y ChIP-seq tag counts in wt and in Sp2ko MEFs (wt/Sp2ko), we found attenuated Nf-y occupancy in Sp2ko MEFs at many promoters that are co-bound by Sp2 in wild type MEFs (Fig. 9B, top panel). Attenuated Nf-y binding in Sp2ko cells is particularly strong at promoters that are bound in wild type MEFs by Sp2 but not by Sp1/3 such as the Nrxn2, Grb2, Nipal3, Plcl1 or the Oxr1 promoters (Figs. 9C-D, S5). This demonstrates that binding of Nf-y to these promoters depends on the presence of Sp2. Importantly, Nf-y binding remains largely unchanged or is even slightly potentiated at sites that are not co-bound by Sp2 (Fig. 9B, bottom panel) including promoters that are also bound by Sp1/Sp3 such as the Atxn3, Mxi1, and Pdcd4 promoters (Figs. 9C-D, S6).
Given that genomic Nf-y binding is attenuated in Sp2ko MEFs at loci that are co-bound by Sp2, it is to be expected that re-expression of Sp2 potentiates Nf-y binding at these sites. Indeed, we found stronger Nf-y binding in rescued MEFs, specifically at loci that are re-occupied by Sp2FL or by Sp2NT (Sp2, Osbp, Amd1, Nipal3 and Nlk) (Fig. 9E). Binding of Nf-y at these promoters remained unchanged in cells expressing the Sp2ZF fragment. Finally, binding of Nf-y to the Atnx3 promoter, which is not an Sp2 target, is similar in Sp2FL, Sp2NT and Sp2ZF expressing cells (Fig. 9E). Taken together, these results strongly support the conclusion that Sp2 potentiates binding of Nf-y at shared sites.
In this study, we have elucidated the mode of genomic binding site selection of the zinc finger transcription factors Sp1, Sp2 and Sp3. A key finding is that Sp1/3 and Sp2 bind to chromatin by distinct mechanisms. Consistent with numerous in vitro DNA-binding studies, Sp1 and Sp3 localize to GC boxes and essentially occupy the same promoters. Unexpectedly, Sp2 primarily localizes at CCAAT motifs, often arranged in tandem with a mean distance of 35 bp (Fig. 2). In line with their different binding site selection, re-expression of Sp2 and Sp3 mutants in corresponding Sp2ko and Sp3ko MEFs revealed that different protein domains mediate binding to their sites in chromatin. As expected, the C-terminal zinc finger domain is essential and largely sufficient for binding of Sp3 to its target promoters. Although not formally tested, we consider it as very likely that the zinc finger domain of Sp1 also mediates binding to chromatin. Intriguingly, the zinc finger domain of Sp2 is fully dispensable for binding in vivo. Instead, binding of Sp2 to its target promoters is mediated by the N-terminal glutamine-rich part, providing the essential clue for the different binding site selection of Sp2, as compared to Sp1 and Sp3.
Interestingly, Sp2 targeted mice that do not express the zinc finger domain but aberrantly express an RNA encoding the N-terminal region display a markedly less severe phenotype than Sp2null mice [7]. Although expression remains to be established at the protein level, binding of the Sp2 N-terminal region to chromatin shown in this report provides a rational explanation for the different phenotypes of these mice.
Sequence comparisons of the N-terminal region of Sp2 with the corresponding regions of Sp1 and Sp3 reveal major differences of their amino acid composition. Most significantly, in addition to the frequent occurrence of glutamine residues, the N-terminal domains of Sp1 and Sp3 are rich in acidic amino acids, whereas the N-terminal domain of Sp2 is very rich in basic amino acids (S7 Fig). Thus, the general assignment of the N-terminal domains of Sp1, Sp2 and Sp3 as glutamine-rich domains is misleading. Importantly, the presence of multiple positive charged lysines in the N-terminal region of Sp2 raises the possibility of non-specific interactions with the negatively charged sugar-phosphate backbone of the DNA.
Binding sites of Sp1 and Sp2 have also been identified in K562 cells by the ENCODE consortium [28]. Since Sp1 and Sp2 binding sites largely overlap, it was concluded that Sp1 and Sp2 binding motifs (i.e. GC boxes) are indistinguishable [29]. We believe that this interpretation of the ChIP-seq data results from the structural similarity of Sp1 and Sp2, and from Sp2´s capacity to bind GC rich oligonucleotides in vitro [18,19]. Admittedly, we also fell into this trap when we initially analyzed our Sp2 ChIP-seq data [18]. These misinterpretations emphasize the limitations of genomics data integration and highlight the importance of performing additional detailed experimental analysis.
The data presented here revealed fundamental different modes of binding site selection of Sp1/Sp3 and of Sp2, and also allow different interpretations of the Sp1-Nf-y and Sp2-Nf-y interactions. Sp1 and Sp3 ChIP-seq peaks locate to GC boxes that are often accompanied by Nf-y binding sites (see Fig. 2) strongly suggesting that Sp1/Sp3 and Nf-y co-bind to neighboring sites in the genome. This is consistent with earlier reports showing synergistic activation by Sp1 and Nf-y and a direct interaction between Sp1 (and Sp3) and Nf-ya [30,31,32]. In contrast, the majority of Sp2 locates at CCAAT boxes and binding is independent of the presence of a nearby GC box, and, most importantly, independent of the zinc finger domain.
The localization of Sp2 at CCAAT motifs led us to detail the interaction network between Sp2 and Nf-y on a genome-wide scale. The arrangement of CCAAT motifs in regions that are bound by both factors differ from sites that are bound by Nf-y but not by Sp2. Sp2-Nf-y interactions occur predominantly at tandem CCAAT boxes, whereas single CCCAT boxes are mostly bound by Nf-y but not by Sp2. Utilizing Sp2ko cells lacking any Sp2 DNA-binding activity, and an RNAi approach to interfere with Nf-y binding, we have explored the interaction between Nf-y and Sp2 recruitment in an unbiased manner. Genomic binding analysis revealed a widespread attenuation of Nf-y loading in cells lacking Sp2. Vice versa, reduced binding of Nf-y led to attenuation of Sp2 binding. Importantly, the reduction of Sp2 binding is specific to elements that are also bound by Nf-y, thereby providing support for a direct effect of Nf-y at shared sites. Given that multiple CCAAT boxes in a promoter region are simultaneously occupied by Nf-y, the particular placement of Nf-y complexes might be essential for direct or indirect interactions with Sp2. In line with this, we observed a general agreement between the strength of the Sp2 ChIP-seq signals and the number of CCAAT motifs. Particularly, the strongest Sp2 peaks contain several pairs of CCAAT motifs.
In a recent publication it was shown that Nf-y promotes binding of pluripotency transcription factors such as Oct4 or Sox2 at enhancers in murine ES cells by facilitating a permissive chromatin conformation [33]. Nf-y´s role in promoting the binding of Sp2 could be by a similar mechanism. However, unlike the pluripotency factors, Sp2 also potentiates binding of Nf-y at sites of co-localization. Whether Sp2 facilitates recruitment of Nf-y or whether it stabilizes binding of Nf-y at shared sites remains elusive at this stage.
The mode of interaction between different transcription factors has been classically categorized into DNA-binding dependent (co-binding) and DNA-binding independent, whereby one transcription factor binds to another that, in turn, binds to DNA (tethering). Tethering to chromatin has been reported for several transcription factors including recruitment of the glucocorticoid receptor by AP1 [34,35] or STAT3 [36] and the estrogen receptor alpha by Runx1 [37]. Likely, tethering mechanisms are also involved in the recruitment of SCL/TAL1 [38] and KLF3 [39] to a subset of their binding sites in vivo. The sequence adjacent to CCAAT boxes at which Sp2 is located is highly variable and does not contain a particular sequence motif. Consistently, the N-terminal region of Sp2 does not contain an obvious structural motif that might interact with a specific DNA sequence. It can be proposed that Nf-y binds to the CCAAT box through base-specific contacts. Sp2 would then be recruited by interaction with Nf-y or with additional factors. However, this simple tethering model does not explain the attenuated binding of Nf-y in Sp2ko cells at shared sites (Fig. 9), and the exceptionally large Sp2 ChIP-seq peaks and high ChIP-qPCR values (more than 10% of input on highly occupied promoters; see Figs. 3, 5, 9) particularly as formaldehyde preferentially crosslinks proteins to DNA. Therefore, we envisage an alternative mechanism. Potentially, promoters bound by Nf-y, particularly those bound by two or more Nf-y complexes, adopt a particular conformation that allows the basic N-terminal region of Sp2 to interact, directly or indirectly, with two Nf-y complexes simultaneously, and additionally with the DNA backbone in a sequence-independent manner (Fig. 10). Such a scenario would explain the mutual dependency of Sp2 and Nf-y binding to common sites.
Finally, a small fraction of the Sp2 binding sites does not contain CCAAT boxes and are not co-occupied by Nf-y. Nevertheless, binding of Sp2 to these sites is also mediated by the N-terminal region suggesting that binding of Sp2 to these sites is by a similar mechanism. De novo motif discovery at these sites revealed enrichment of the Ets transcription factor-binding motif GGAAG. Future work aims to identify the Ets factor that occupies these sites. A promising candidate is the widely expressed heterodimeric Ets transcription factor Gabp [40] that binds as a GABPalpha2beta2 heterotetramer complex to DNA containing two tandem GGAAG sites [41].
In conclusion, the data provided in this report challenge the prevailing view that the transcription factors Sp1/Sp3 and Sp2 regulate transcription by binding to similar promoter elements via their zinc finger domains. Instead, Sp1/Sp3 and Sp2 have distinctive binding landscapes, and their modes of genomic binding site selection are completely different. Collectively, our findings uncover strikingly different recruitment mechanisms of very similar transcription factors, and add another crucial level of detail to the current model of transcription factor binding to chromatin.
For Western blotting and ChIP of Sp1, Sp2 and Sp3 in MEFs, we used homemade rabbit antibodies [7,42] affinity-purified against the respective recombinant Sp factor. Anti-Sp3 antibodies (Santa Cruz, sc-644) were used for the Western blot shown in Fig. 3B, and anti-Sp2 antibodies (Santa Cruz, sc-643) for ChIP-seq of Sp2 in HEK293 cells. Additional antibodies: Anti-Nf-ya (Santa Cruz, sc-10779), anti-Nf-yb (Genespin, PAb001), anti-Nf-yc (Santa Cruz, sc-7715-R), anti-Flag M2 (Sigma, F3165), anti-Tubulin (Millipore, MAB3408).
Retroviral expression plasmids for 3xFlag-Sp2 and 3xFlag-Sp3 mutants were generated by restriction cloning of PCR fragments into a pBABE3xFlag-puro plasmid. Primer sequences used for PCR can be found in S1 Table. The production of virus stocks, infection of MEFs and the selection of transduced Sp2ko and Sp3ko MEFs were as described [7].
MEFs were cultured in a 1:1 mixture of Dulbecco’s modified Eagle’s medium—high Glucose (PAA) and HAM’s F-10 (PAA) supplemented with 10% (v/v) fetal calf serum (PAA) and 1% Penicillin-Streptomycin. Wild type and Sp3ko MEFs, and MEFs with floxed Sp2 alleles (Sp2fl/fl) were isolated from E13.5 embryos using standard methods and subsequently immortalized by serial passages. Sp2ko MEFs were obtained by retroviral transduction of pBABE-Cre-neo as described [7]. Sp2ko MEFs have severely impaired proliferation rates [7]. Cells that escaped growth inhibition over time were used for rescue experiments.
Immunofluorescence and microscopy was performed as described [43]. In brief, 5x104 MEF cells expressing 3xFlag-Sp2 mutants were grown on coverslips in 6-well plates overnight. Cells were fixed in 4% PFA/PBS for 25 min, permeabilized in 0.2% TritonX-100/PBS for 20 min, and blocked with 3% BSA/PBS for 1 hr. Incubation of the anti-Flag M2 antibody (Sigma, F3165, 1:800 dilution) was for 1 hr at RT. Secondary antibody incubation (anti-mouse AlexaFluor568, Invitrogen, A10037, 1:500 dilution) was performed for 1 hr at RT in the dark. After a final washing step, coverslips were mounted onto glass slides using Vectashield mounting medium with DAPI (Vector Laboratories, Inc.).
For RNAi-mediated depletion of mouse Nf-ya, Nf-yb and Nf-yc, pools of four On-target plus siRNAs (GE Dharmacon) were used (LU-065522, LU-046072, LU-060374). The siGenome non-targeting siRNA #1 (D-001210–01) was used as unspecific siRNA control. Wild type MEFs on 15 cm plates were transfected with 24 nM siRNA using Oligofectamine (Invitrogen). Three days post-transfection 2x106 cells were replated, and transfected a second time. Additional three days later, cells were collected and cross-linked chromatin was prepared. To monitor knockdown efficiency at the protein level, a chromatin sample was incubated with an equal amount of 2xLaemmli buffer at 100°C for one hour and subsequently analyzed by western blotting. To monitor knockdown efficiency at the transcript level, RNA was isolated using the RNeasy Mini kit (Qiagen) and expression of Nf-ya, Nf-yb and Nf-yc was analyzed by RT-qPCR.
Expression analysis by quantitative RT-qPCR was performed as described in [44] using gene-specific primers (S1 Table).
ChIP experiments were performed as described [18] using the One Day ChIP kit (Diagenode). For a sequential ChIP of Sp2 and Nf-yb, the precipitated material of a standard ChIP was eluted twice from the beads with 100 mM NaHCO3, 1% SDS, 10 mM DTT for 30 min at 37°C. Eluates were diluted 1:50 with ChIP buffer and subsequently subjected to a second ChIP in accordance with the One Day ChIP kit manual performing an overnight antibody incubation at 4°C. Enrichment was calculated relative to the input of the first ChIP. Primer sequences for ChIP-qPCRs are listed in S1 Table.
For ChIP-seq experiments, four to six individual ChIPs were pooled, and precipitated DNA was purified on QIAquick columns (Qiagen). Five nanograms of DNA were used for indexed next generation sequencing library preparation using the MicroPlex library preparation kit (Diagenode) in accordance with the manufacturer’s instructions. Library purification was performed with AMPure magnetic beads (Beckman Coulter) as described in the MicroPlex kit manual. Libraries were quantified on a Bioanalyzer (Agilent Technologies) and subsequently sequenced on an Illumina HiSeq1500 platform, rapid-run mode, single-read 50 bp (TruSeq Rapid SR Cluster Kit—HS, TruSeq Rapid SBS Kit—HS—50 cycle) according to manufacturer´s instructions. An overview of the various ChIP-seq results is shown in S2 Table.
Raw ChIP-seq data were aligned to the mouse genome assembly mm10 or the human genome assembly hg19 using Subread 1.3.3-p3 [45]. Reads were filtered to have at most 5 mismatches to the reference, indels up to 5 bp and to occur at exactly one position in the genome. Peak calling was performed using MACS 1.4 [46] with default parameters. Raw and normalized (to 1 million uniquely aligned reads) read counts were annotated, and peaks were filtered to have at least 30 raw reads and a signal to background (IgG, Sp2ko or Sp3ko) normalized read ratio of at least 3. Further analysis of peaks such as association with transcripts in the vicinity, classification of genomic position and Venn diagram generation was performed using a custom Python based pipeline. Genome annotation data from Ensembl revision 74 [47] was used. Position and characteristics of ChIP-seq peaks are listed in S3 Table. Venn diagrams were calculated by building the union of the datasets involved, and assigning each union-peak to a region by requiring at least a one-basepair overlap with the input regions. De novo motif search including central motif analysis [21] was performed with MEME-ChIP version 4.9.1 [48] using sequences surrounding peak summits (+/- 150 bp). Elongating reads by 200 bp, and determining the position of highest overlap defined summits.
Raw sequencing data were deposited at ArrayExpress under accession number E-MTAB-2970.
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10.1371/journal.pbio.1001482 | Reciprocal Regulation of Protein Synthesis and Carbon Metabolism for Thylakoid Membrane Biogenesis | Metabolic control of gene expression coordinates the levels of specific gene products to meet cellular demand for their activities. This control can be exerted by metabolites acting as regulatory signals and/or a class of metabolic enzymes with dual functions as regulators of gene expression. However, little is known about how metabolic signals affect the balance between enzymatic and regulatory roles of these dual functional proteins. We previously described the RNA binding activity of a 63 kDa chloroplast protein from Chlamydomonas reinhardtii, which has been implicated in expression of the psbA mRNA, encoding the D1 protein of photosystem II. Here, we identify this factor as dihydrolipoamide acetyltransferase (DLA2), a subunit of the chloroplast pyruvate dehydrogenase complex (cpPDC), which is known to provide acetyl-CoA for fatty acid synthesis. Analyses of RNAi lines revealed that DLA2 is involved in the synthesis of both D1 and acetyl-CoA. Gel filtration analyses demonstrated an RNP complex containing DLA2 and the chloroplast psbA mRNA specifically in cells metabolizing acetate. An intrinsic RNA binding activity of DLA2 was confirmed by in vitro RNA binding assays. Results of fluorescence microscopy and subcellular fractionation experiments support a role of DLA2 in acetate-dependent localization of the psbA mRNA to a translation zone within the chloroplast. Reciprocally, the activity of the cpPDC was specifically affected by binding of psbA mRNA. Beyond that, in silico analysis and in vitro RNA binding studies using recombinant proteins support the possibility that RNA binding is an ancient feature of dihydrolipoamide acetyltransferases. Our results suggest a regulatory function of DLA2 in response to growth on reduced carbon energy sources. This raises the intriguing possibility that this regulation functions to coordinate the synthesis of lipids and proteins for the biogenesis of photosynthetic membranes.
| Metabolic control of gene expression coordinates the levels of specific gene products to meet cellular demand for their activities. This control can be exerted by metabolites acting as regulatory signals on a class of metabolic enzymes with dual functions as regulators of gene expression. However, little is known about how metabolic signals affect the balance between enzymatic and regulatory roles of these proteins. Here, we report an example of a protein with dual functions in gene expression and carbon metabolism. The chloroplast pyruvate dehydrogenase complex is well-known to produce activated di-carbon precursors for fatty acid, which is required for lipid synthesis. Our results show that a subunit of this enzyme forms ribonucleoprotein particles and influences chloroplast mRNA translation. Conversely, RNA binding affects pyruvate dehydrogenase (metabolic) activity. These findings offer insight into how intracellular metabolic signaling and gene expression are reciprocally regulated during membrane biogenesis. In addition, our results suggest that these dual roles of the protein might exist in evolutionary distant organisms ranging from cyanobacteria to humans.
| Accumulating evidence suggests that metabolism and gene expression are tightly linked. For instance, changes in metabolite levels affect protein modification, for example by acetylation or N-glycosylation, which in turn influences signal transduction and gene expression [1]–[3]. In line with this, several metabolic enzymes functioning in diverse pathways were found to possess unexpected RNA-binding properties by which they are proposed to regulate gene expression and other cellular processes (reviewed in [4],[5]). Often these proteins represent key enzymes of metabolic pathways, which make them particularly suitable to coordinate distinct biochemical pathways in response to changes in metabolism.
In eukaryotic organisms, photosynthesis is performed in endosymbiotically acquired organelles, the chloroplasts. Within chloroplasts, the light-driven reactions of photosynthesis take place in thylakoid membranes, which represent a highly organized system of lipid membranes and embedded multisubunit protein complexes. These complexes include photosystem I (PSI) and photosystem II (PSII), the cytochrome b6f complex, and the chloroplastic ATP synthase.
The biogenesis of thylakoid membranes requires the synthesis of both lipids and proteins. Major lipids include two glycolipids, monogalactosyl diacylglycerol (MGDG) and digalactosyl diacylglycerol (DGDG), the synthesis of which necessitates acetyl-CoA for fatty acid production within the chloroplast (reviewed in [6],[7]). This acetyl-CoA is mainly generated from pyruvate by the chloroplast pyruvate dehydrogenase complex (cpPDC), which—like its mitochondrial counterpart (mtPDC)—is a megadalton complex consisting of multiple copies of three subunits; a pyruvate dehydrogenase (E1), a dihydrolipoamide acetyltransferase (DLA, E2), and a dihydrolipoyl dehydrogenase (E3) [8]–[11]. The decarboxylation of pyruvate by cpPDC is a central reaction in chloroplast carbon metabolism and is regulated by light, Mg2+, and feedback inhibition by acetyl-CoA and NADH (for reviews, see [9],[11]).
The biogenesis of thylakoid membranes also necessitates the synthesis of polypeptides and their assembly into multisubunit complexes. Since their origin as a cyanobacterial endosymbiont, chloroplasts have retained downsized genomes and gene expression systems. However, most chloroplast proteins are encoded by the nuclear genome, synthesized in the cytosol, and then imported into the chloroplast. Therefore, the synthesis of thylakoid membrane protein complexes requires an intracellular coordination, which is mainly mediated via nucleus-encoded factors acting at all levels of chloroplast gene expression (for a recent overview, see [12]). Particularly chloroplast translation initiation has been considered to play a key role in determining the levels of photosynthesis-related proteins (for recent overviews, see [12]–[14]). Moreover, targeting of mRNAs to specific subcellular sites for localized translation contributes a further level of regulation of chloroplast gene expression [15],[16]. These targeting mechanisms are thought to involve membrane-associated RNA binding proteins (RBPs) that tether respective mRNAs to specific membrane regions within the chloroplast [16],[17].
The unicellular green alga Chlamydomonas reinhardtii is an established model organism for the analysis of biogenesis and gene expression in chloroplasts [18]. Moreover, it has the potential to be an ideal model system to study metabolic regulation of gene expression because it can adjust its metabolism to different energy sources. Unlike plants and animals, which derive energy exclusively from light and reduced carbon, respectively, C. reinhardtii can use both sources. It can exclusively use light energy in photoautotrophic growth, acetate in heterotrophic growth, or a combination of both in mixotrophic growth. The C. reinhardtii psbA mRNA is probably one of the most studied models of translational regulation in chloroplasts. The de novo assembly of PSII requires the synthesis of all subunits of this multimeric complex, including the psbA gene product, the D1 subunit, and its assembly partner D2. This thylakoid membrane protein synthesis is localized to a specialized membrane region called the T (translation)-zone surrounding the pyrenoid, a spherical body and the primary site of CO2 assimilation in the chloroplasts of most algae [19],[20]. However, under higher intensity light, D1 is damaged. Under these conditions, the so-called damage-repair cycle replaces degraded D1 proteins by newly synthesized ones (reviewed in [21]). Interestingly, repair synthesis of the PSII reaction center protein D1 is not localized to the T-zone but distributed over stroma-exposed thylakoid membranes indicating a strict spatial separation from the PSII de novo synthesis machinery [20].
Using in vitro RNA-binding assays, we previously identified an RBP of 63 kDa, RBP63, in C. reinhardtii, which is associated with stromal thylakoids and preferentially binds to an A-rich element within the 5′ UTR of the psbA mRNA [17]. This A-rich element is important for psbA translation [22]. Based on these findings, RBP63 was suggested to fulfill a role in membrane targeting and translation of the psbA mRNA [17].
Here, we report the identification and further characterization of RBP63, which surprisingly turned out to be the dihydrolipoamide acetyltransferase subunit, DLA2, of the cpPDC. Therefore, DLA2 might represent another example of a metabolic enzyme with an alternate function as an RBP involved in gene expression. Our data support a role of DLA2 in the localized translation of the psbA mRNA and confirm its known enzymatic role in acetyl Co-A synthesis. Moreover, the binding of this subunit to RNA might not be restricted to C. reinhardtii but appears to be a more general feature of dihydrolipoamide acetyltransferases, including the mitochondrial forms. Taken together, these results lead us to a new concept for the coordination of chloroplast translation and carbon metabolism that involves a cross-talk between protein and lipid synthesis in thylakoid membrane biogenesis.
RBP63 was purified from detergent-solubilized thylakoid membranes of C. reinhardtii by two sequential affinity chromatography steps, using first heparin and then poly(A). RBP63 was monitored in the fractions on the basis of its psbA mRNA binding activity with a UV-cross-linking assay (Figure 1). We observed the highest RNA binding activity in the fraction eluted with 0.5 M KCl from poly(A)-Sepharose (Figure 1B, pA E500). SDS-PAGE and subsequent Coomassie Blue staining revealed that this fraction contained predominately the 63 kDa protein as well as minor amounts of proteins with different sizes (Figure 1B, pA E500). For the identification of the gene encoding RBP63, the excised 63 kDa protein species was then subjected to proteolytic digestion and subsequent analysis by mass spectrometry. Surprisingly, four different peptides mapped to DLA (DLA2), the E2 subunit of the cpPDC (Figure 2).
Blast searches of the C. reinhardtii nuclear genome using the DLA2 amino acid sequence identified two homologues: DLA1 and DLA3. However, the mass spectrometrically identified peptides unambiguously assigned the purified protein to DLA2 (Figure 2). Moreover, in silico analysis of the N-terminal region of DLA2 by TargetP [23] revealed properties of a 30 aa N-terminal chloroplast transit peptide, while DLA1 and DLA3 were predicted to localize to mitochondria, and thus are likely to represent putative E2 subunits of the mitochondrial PDC. Mitochondrial localization of DLA1 and DLA3 is further supported by their identification during mass spectrometrical determination of the C. reinhardtii mitochondrial proteome [24],[25].
To establish the gene structure of DLA2, we sequenced the EST clone MXL069g06 (BP097085) from the Kazusa DNA Research Institute. The clone included the full-length DLA2 cDNA, whose sequence corresponds to the gene model au5.g10333_t1 (chromosome 3:1419737–1423311) annotated in JGI v4 (Joint Genome Institute; http://genome.jgi-psf.org/Chlre4/Chlre4.home.html). The 2,174 nt DLA2 transcript contains six exons, comprising 1,485 nt, flanked by untranslated regions of 38 and 651 nt at its 5′ and 3′ ends, respectively. In silico analysis of the genome sequence and DNA hybridizations confirm that DLA2 is a single copy gene (Figure S1). The predicted protein has 494 amino acids with a molecular mass of 49.8 kDa. The discrepancy between the predicted molecular weight of DLA2 (49.8 kDa) and the apparent MW determined from SDS-PAGE (63 kDa, Figure S7) represents a size anomaly observed for all PDC E2 subunits analyzed to date and has been explained by frequently occurring turn-inducing and charged amino acid residues within the interdomain linker regions of these proteins [26]–[29]. Consistently, the same phenomenon was found with recombinant His-DLA2 protein (see below).
In agreement with α-proteobacterial and cyanobacterial origins of mitochondrial and chloroplast E2 subunits, respectively, DLA2 shows an overall amino acid sequence identity of more than 50% to the E2 subunit from the cyanobacterium Synechocystis sp. PCC 6803 and the chloroplast E2 orthologue from the vascular plant Arabidopsis thaliana (also named LTA2, At3g25860). In contrast, DLA2 shows only an identity of ∼30% to DLA1, DLA3, and mitochondrial orthologues from A. thaliana, S. cerevisiae, and H. sapiens [30]. The DLA2 sequence exhibits conserved regions for lipoamide attachment, E3 subunit binding, and a C-terminal 2-oxoacid dehydrogenase catalytic domain (Figure 2). The lipoamide attachment site and the catalytic domain show a relatively high conservation between the different organisms, whereas the E3 binding region is more variable. C. reinhardtii DLA2 contains a single predicted lipoamide attachment site, similar to the Synechocystis sp. PCC 6803 protein and the chloroplast E2 subunit from A. thaliana, whereas DLA1 and DLA3 exhibit two lipoyl binding sites similar to the mitochondrial enzymes from A. thaliana and H. sapiens (Figure 2).
To verify the predicted chloroplast localization and exclude an additional targeting of DLA2 to mitochondria, we first carried out subcellular fractionation detecting DLA2 with an antiserum raised against the recombinant protein. The antibody detected a protein with an apparent molecular weight of 63 kDa in whole cell extracts (Figure S7). Subsequently, the comparisons of the level of DLA2 in subcellular fractions revealed it to be localized to chloroplasts, with approximately similar levels in thylakoid membranes versus the soluble stromal compartment (Figure 3A). As is often seen for C. reinhardtii, the chloroplast and thylakoid fractions were contaminated with mitochondria as judged by following the mitochondrial alternative oxidase (AOX; Figure 3A [31],[32]). However, the most significant result of this analysis was that no DLA2-specific signal was detected in the mitochondrial fraction, confirming that DLA2 does not form part of the mitochondrial PDC. In addition, this result reveals that a cross-reactivity of the antibody to the putative mitochondrial isoforms, DLA1 and DLA3, is improbable.
As a second independent approach to determine the intracellular localization(s) of DLA2, a DLA2–GFP fusion protein was expressed in C. reinhardtii (Figure 2). The DLA2–GFP signal was detected in the chloroplast, which was revealed by chlorophyll autofluorescence (Figure 3B). By contrast, when only GFP was expressed from the same expression vector, its signal was detected primarily in the central nuclear-cytosolic region.
Because DLA2 (RBP63) was previously characterized as a psbA mRNA binding protein, we tested whether DLA2 forms part of a HMW ribonucleoprotein (RNP) complex. Detergent-solubilized thylakoid membranes were prepared from wild-type cells cultured under photoautotrophic (light, no acetate), mixotrophic (light, with acetate), or heterotrophic (no light, with acetate) conditions and then subjected to size exclusion chromatography (SEC, [33]). By following the elution pattern of DLA2, we verified that it forms part of a HMW complex in a size range between 700 kDa and more than 2,800 kDa under each of the conditions (Figure 4). Peak fractions of eluted DLA2 slightly varied between samples from light versus dark grown cells. Under photoautotrophic and mixotrophic growth conditions, the DLA2 complex was more than 2,800 kDa, as indicated by a peak of elution in fraction 1 (Figure 4A,C). In cells grown heterotrophically, the peak fractions were moderately shifted toward smaller molecular sizes (i.e., fraction 2) (Figure 4B). This suggests a light-dependent formation of these different DLA2 HMW complexes. To investigate whether the observed HMW complexes contain RNA, solubilized thylakoid membranes were treated with RNase prior to SEC analysis. Intriguingly, the detected complexes revealed a growth condition-dependent RNase sensitivity: only under mixotrophic conditions did the complex shift toward lower molecular weight fractions upon RNase treatment (Figure 4C, peak of elution in fraction 4). In contrast, no such size shifts were observed for material from cells grown under photoautotrophic or heterotrophic growth conditions (Figure 4A,B). This strongly suggests that DLA2 forms a HMW–RNP complex in an acetate and light-dependent manner.
As localization studies revealed an almost equal distribution of DLA2 in the stroma and the membrane fraction (Figure 3A), we also investigated the size and RNase sensitivity of stromal DLA2 complexes of the wild-type grown under different conditions by SEC (Figure S2). In contrast to what was seen for the membrane-bound DLA2 complex (Figure 4A–C), we did not observe significant RNase sensitivity of stromal DLA2 complexes under any condition.
To obtain an indication of which of the gel filtration fractions contained active PDH complexes and additionally investigate if it is the active PDH complex itself that binds RNA or an alternative RNP complex, we performed cpPDC enzyme tests on the SEC fractions by measuring the reduction of NAD+ spectrophotometrically. These assays were performed on stroma or thylakoid SEC fractions from mixotrophically grown wild-type cells. The results revealed cpPDC activity mainly in the highest molecular weight fraction as expected (Figure S3). Approximately 10% of the activity measured in fraction 1 was detected in fraction 2 in a size range of ∼1.2–1.7 MDa, whereas no significant cpPDC activity was detectable in fractions containing smaller complexes. Moreover, no changes in the elution profile of cpPDC activity were observed upon RNase treatment even though significant amounts of DLA2 proteins were detected in lower molecular weight fractions 4 and 5. Therefore, it is probably not the active cpPDC that binds RNA (Figure S3A).
To test whether the RNA bound by the DLA2 complex is the psbA mRNA, solubilized thylakoid membranes from the chloroplast psbA deletion mutant FuD7 (grown under mixotrophic conditions) were subjected to SEC (Figure 4D, [34]). With this mutant, the DLA2 complex was mainly detected in fraction 4, resembling the elution pattern of RNase-treated DLA2 complexes from mixotrophically grown wild-type cells. Reduced amounts of HMW complexes were detected in fractions 1+2 as compared to the WT, indicating that the lack of psbA mRNA in a mutant background reduces the DLA2 complex size. Therefore, this result supports the psbA mRNA as being the RNA component of the DLA2 RNP complex.
We cannot exclude DLA2 binding to RNAs other than the psbA message. However, another mixotrophically grown PSII mutant (nac2–26) tested in this analysis, which lacks the psbD mRNA, was not affected in DLA2 complex formation (Figure 4D [35],[36]). In conclusion, the data revealed a psbA mRNA-dependent DLA2 RNP complex under mixotrophic growth conditions.
To further verify a specific interaction of DLA2 and the psbA mRNA in vivo, RNA co-immunoprecipitations (co-IPs) using solubilized thylakoids from mixotrophically grown wild-type cells were performed (Figure S4). The immunoprecipitate obtained with the αDLA2 antiserum contained the psbA mRNA, but not the mRNAs of rbcL or atpB, chloroplast genes encoding the large subunit of Rubisco and the beta subunit of ATP synthase, respectively. This confirms the formation of a specific psbA mRNA/DLA2 complex in vivo (Figure S4B). In agreement with the results of SEC analysis, no psbA mRNA was co-immunoprecipitated from cells grown under heterotrophic conditions (Figures 4B and S4B). It should be noted that this IP was relatively inefficient because the DLA2 antiserum only weakly recognizes the native protein (Figure S4A).
As a next step, we further substantiated the RNA binding capacity of DLA2 by testing the hexahistidine-tagged recombinant protein (His-DLA2) for in vitro RNA binding activity (Figure 5). In UV cross-linking experiments with various 32P-labeled RNAs, His-DLA2 showed a clear intrinsic RNA binding activity for all probes tested (e.g., the 5′ UTRs of psbA, psbD—encoding the PSII reaction center protein D2—and rbcL) (Figure 5A). We next tested the specificity of RNA binding by applying RNA competition assays. Therefore, the UV cross-linking assays were performed in the presence of increasing concentrations of homologous or heterologous unlabeled RNA probes as competitors (Figure 5B). In contrast to what was previously reported for the native DLA2 protein, His-DLA2 exhibited no specificity for the psbA RNA ([17], Figure 5B). This is probably due to a lack of other components of the DLA2–RNP complex that facilitate recognition of the psbA 5′ UTR by DLA2 in vivo. Such a behavior would resemble other chloroplast RBPs, like RBP40 from C. reinhardtii, which—as an isolated protein—unspecifically recognizes any RNA. Only as part of a complex with its cognate binding partner Nac2 does RBP40 have binding specificity to the psbD mRNA [37].
However, even though the protein appeared to bind nonspecifically to the psbA mRNA in vitro, we determined its equilibrium binding constant (KD) for the psbA 5′ UTR to put DLA2 on a comparable basis with other known RBPs (Figure 5C). To this end, we applied an independent filter binding assay according to Ostersetzer et al. [38], which does not involve a UV cross-linking step. The obtained KD value of ≈51 nM is in line with those determined for other RBPs (for examples, see [38]–[40]).
To further elucidate the role of DLA2 in psbA expression and acetyl-CoA production, DLA2–RNAi lines were generated [41]. Of ca. 800 transformants, ca. 100 clones survived the selection procedure. Three of these lines, namely iDLA2-1, -2, and -3, exhibited the highest reductions of DLA2 level, which were determined to be 5% (±2), 10% (±4), and 6% (±2)%, respectively, relative to the DLA2 level in the recipient strain transformed with the empty vector. These strains maintained the DLA2 deficiency for over 2 years under selection for the RNAi-induced phenotype.
To explore the phenotypic consequences of DLA2 deficiency in these RNAi lines, their growth rates were monitored under photoautotrophic, mixotrophic, and heterotrophic conditions (Figure 6). Growth of RNAi lines was only slightly affected under photoautotrophic and not at all affected under heterotrophic conditions. However, under mixotrophic conditions, severe growth retardation was observed for the DLA2 knock-down strains as compared to the wild-type. Therefore, DLA2 seems to be required for wild-type growth rate specifically under the condition associated with the formation of an RNP complex (Figure 4).
To determine whether DLA2 is required for psbA expression and accumulation of PSII, D2 protein levels were measured in the DLA2–RNAi lines by immunoblot analyses. It was previously shown in C. reinhardtii that D1 and D2 accumulate in a 1∶1 stoichiometry, whereas unassembled proteins are rapidly degraded [42]–[44]. Therefore, D2 can be used as a proxy measure of D1 accumulation. Interestingly, we observed a light- and acetate-dependent accumulation of PSII in the DLA2–RNAi lines (Figure 7A). In contrast, under photoautotrophic growth conditions, PSII accumulation was higher in the RNAi lines as compared to the wild-type. In contrast, in lines cultured mixotrophically under the same light conditions or heterotrophically in the dark, RNAi-mediated DLA2 deficiency leads to slightly reduced PSII levels. Other protein complexes in the chloroplast were not affected in the RNAi lines, as judged by parallel monitoring of steady-state levels of the large subunit of Rubisco and cytochrome b6 (Figure 7A).
To test whether altered psbA mRNA levels or D1 protein synthesis rates are responsible for the observed changes of the levels of D1 protein and, consequently, PSII accumulation, we performed Northern blots as well as 35S protein pulse labeling assays (Figure S5, Figure 7B). No alterations in psbA transcript levels were observed in iDLA2 lines as compared to the wild-type from cells grown under photoautotrophic, mixotrophic, or heterotrophic growth conditions (Figure S5). However, whereas only minor effects on D1 protein accumulation were observed, D1 synthesis rates were clearly altered in the iDLA2 lines relative to in the wild-type strain (Figure 7). For example, higher D1 protein synthesis rates in the iDLA2 lines were detected under phototautotrophic conditions, whereas D1 synthesis rates were reduced under mixo- and heterotrophic growth conditions, respectively (Figure 7B). The less obvious effect of DLA2 deficiency on D1 protein accumulation under mixotrophic and heterotrophic conditions as compared to D1 protein synthesis might be explained by posttranslational stabilization effects counteracting a reduced translation rate.
As another means of testing whether DLA2 plays a role in psbA translation, we asked whether it is associated with a “chloroplast translation membrane” (CTM). This membrane subfraction was identified as a privileged location of translation in the C. reinhardtii chloroplast. CTMs are characterized by their higher density as compared to thylakoids and their association with the translation machinery including the translational regulator RBP40 [45]. Accordingly, membranes from wild-type cells from mixotrophic conditions were separated on the basis of density by floatation from a 2.5 M sucrose cushion into a 0.5–2.2 M sucrose gradient during isopycnic ultracentrifugation. Most thylakoid membranes were detected in the intermediate-density fractions 3 and 4, as revealed by their high chlorophyll concentrations (Figure 8A, left panel). Progressively less thylakoid membrane was present in fractions with increasing density (fractions 5–8). The CTM marker RBP40 was detected in fractions 3–8, as reported previously [45]. Immunodetection of DLA2 revealed that its concentrations were highest in lanes 3–6 and progressively decreased in the denser fractions 7–8. The presence of DLA2 in fractions 3 and 4 was inconclusive because this is consistent with DLA2 association with thylakoid membranes, CTM, or both membrane types. However, DLA2's sustained high levels in lanes 5 and 6 (i.e., in fractions in which thylakoid membrane levels progressively decrease) indicate that DLA2 is associated with a nonthylakoid membrane such as the CTM in these fractions (Figure 8A, left panel). That the RBP40 distribution extends to the bottom of the gradient (lanes 7 and 8) indicates that the DLA2 is not associated with the densest CTMs to detectable extent.
To determine whether the DLA2 that cofractionates with CTM (lanes 5 and 6) in membranes from mixotrophically cultured cells (Figure 8A, left panel) could be relevant to its role in psbA translation, we carried out the same analysis on membranes from cells cultured photoautotrophically, which lack the DLA2 RNP complex (Figure 4A). In this experiment, DLA2 more closely co-fractionated with the thylakoid membranes (Figure 8A, right panel, fractions 3–5). The density distribution of CTM was not dramatically altered relative to mixotrophically cultured cells, based on the distribution of RBP40. Together, these results substantiate the proposed acetate-dependent role of DLA2 in psbA translation.
It was proposed previously that DLA2 binds the psbA mRNA to localize it for translation and cotranslational membrane insertion of D1 [17]. To test this possibility, we asked whether DLA2 is required to localize the psbA mRNA to a specific, spatially defined, “translation zone” (T-zone) in the C. reinhardtii chloroplast, which is believed to be a privileged subcompartment for protein synthesis and to contain the CTM [16],[20]. The T-zone was originally defined by results of confocal microscopy, which revealed in the outer perimeter of the pyrenoid by the colocalization of the psbA mRNA, chloroplast ribosome subunits, and the PSII translation factor RBP40 (RB38) [20]. The pyrenoid serves as a cytological landmark for the T-zone because it is large (1–2 µm in diameter) and located in the same position in every chloroplast (Figure 8B). To determine whether DLA2 localizes the psbA mRNA to the T-zone, we first asked whether this localization is altered in the most severe RNAi line: iDLA2-1. When we visualized the suborganellar distribution the psbA mRNA by fluorescence in situ hybridization (FISH) in the recipient strain used for RNAi, WT-NE, we found that the psbA mRNA was localized to the T-zone in 78% of cells (Figure 8B). In the representative WT-NE cell, the green psbA FISH signal is concentrated in distinct regions adjacent to the pyrenoid, which is seen in the accompanying phase contrast image. These cells were from the mixotrophic growth conditions in which DLA2/psbA mRNA complex formation was detected (Figure 4C). In contrast, when iDLA2-1 cells were examined, only 30% showed this psbA mRNA localization pattern, a greater than 2-fold reduction relative to WT-NE. In the representative iDLA2-1 cell, the psbA FISH signal is more dispersed and not concentrated in distinct regions adjacent to the pyrenoid (Figure 8B). Together, these results reveal that DLA2 is involved in psbA mRNA localization to the T-zone and that the sustained psbA localization in 30% of these cells probably reflects the residual DLA2 in this RNAi line (i.e., 5% of the wild-type level, Figure 7A), the activity of a partially redundant DLA2-independent localization mechanism, or both.
As another means of determining whether DLA2 localizes the psbA mRNA, we asked whether they colocalize in the T-zone. When the WT-NE cells were immunofluorescence (IF) stained for DLA2, 65% showed the DLA2 IF signal near the pyrenoid (Figure 8B). However, these analyses were hampered by the dispersed IF signal, reflecting the difficulty of specifically detecting the RNA-binding form of DLA2 amidst the “background” IF signal from DLA2 of cpPDC, which is most probably nonlocalized based on its detection as soluble and membrane-bound forms (Figures 3A and S3). To reveal a pool of DLA2 involved in psbA mRNA localization, amidst the nonlocalized signal, we used the program Colocalization Finder (ImageJ) to display signals of maximal intensity from both DLA2 and the psbA mRNA [20]. Our prediction was that the strongest signals from each should be colocalized for translation. As shown in Figure 8B, clusters of maximal colocalized signals from DLA2 and the psbA mRNA were seen in the T-zone in 60% of the cells analyzed (and are labeled white in the right-hand most image). Moreover, this pattern requires DLA2 because parallel analyses of the most severe knock-down line, iDLA2-1, revealed that only 8% of these cells showed the colocalization with DLA2 in the T-zone (Figure 8B). Therefore, these in situ results and the biochemical evidence that DLA2 is associated with the CTM (Figure 8A) support our hypothesis that DLA2 is required for the localization of the psbA mRNA to the T-zone and, thereby, targets newly synthesized D1 protein to this PSII biogenesis center.
cpPDC catalyzes the oxidative decarboxylation of pyruvate to acetyl-CoA for chloroplast fatty acid synthesis with the concomitant generation of NADH [9],[11]. Thus, to test whether DLA2 is an active subunit of the cpPDC, enzyme activity assays were performed on protein extracts of photoautotrophically grown DLA2–RNAi lines by measuring the reduction of NAD+. As shown in Figure 9, cpPDC activity in extracts of the three DLA2–RNAi lines was reduced to approximately 15%–25% of the enzyme activity measured with the wild-type used for RNAi. The level of cpPDC activity thus correlated with the level of DLA2 protein in the RNAi lines (compare Figure 7A and Figure 9). We therefore conclude that DLA2 represents an active component of the chloroplast PDH complex. However, reduced enzyme activity did not lead to dramatic changes in overall lipid accumulation as assayed by thin layer chromatography (Figure S6). This might be explained by sufficiently high residual cpPDC levels in the investigated RNAi lines that still mediate efficient production of acetyl-CoA for fatty acid synthesis.
In order to explore possible signals for the transition of the enzymatic form of DLA2 to its RNA-binding form for psbA translation, we asked whether cpPDC activity is altered by interaction of DLA2 with the psbA mRNA. This idea is supported by bioinformatical analysis of the DLA2 amino acid sequence. By using the RNABindR software to predict possible RNA-binding residues within the amino acid sequence of DLA2 [46], we detected a putative RNA-binding region that overlaps the E3 subunit attachment site (aa 191–216, compare Figure 2 and Figure S9A). Hence, a competitive binding of either E3 or psbA mRNA to this DLA2 site might be involved in regulation. Since binding of E3 is required for cpPDC function, the lack of psbA mRNA should then lead to increased cpPDC activity. We therefore decided to analyze cpPDC activity in the absence of psbA mRNA.
Data from higher plants have revealed an activation of the complex by light, due to an increase of stromal pH and Mg2+ concentration (reviewed in [11]). A similar light-dependent activation of cpPDC could be confirmed for C. reinhardtii wild-type as indicated by an increase of activity of ca. 30% of light versus dark grown cells (Figure 10A). This light activation depends on electron transport from PSII to PSI, as indicated by clearly reduced cpPDC activity upon treatment of cells with DCMU, a chemical that specifically blocks the electron flow from PSII (Figure 10A). Next, we tested cpPDC activity in the PSII mutants FuD7 and nac2–26 specifically lacking either the psbA or the psbD mRNA, respectively. Consistent with a requirement for photosynthetic electron flow, both mutants exhibited reduced cpPDC activities as compared to the wild-type. However, in the FuD7 mutant, this reduction (66% of wild-type level) was less pronounced than in nac2-26 (47% of wild-type level; Figure 10A). This might suggest that the psbA mRNA, which is still present in nac2–26 but lacking in FuD7, has an additional inhibitory effect on cpPDC activity.
To further test this, we measured the cpPDC activity in extracts of light grown wild-type and FuD7 cells after incubation with in vitro transcribed RNAs derived from the 5′ UTRs of the psbA or rbcL mRNAs (Figure 10B). Whereas we detected only minor changes of cpPDC activity in the wild-type upon addition of exogenous psbA or rbcL RNAs, a dose-dependent reduction of enzyme activity was obtained for the FuD7 mutant after incubation with psbA, but not with rbcL RNA. Addition of 450 pmol psbA RNA thereby significantly (p<0.05) reduced the activity of FuD7 cpPDC to approximately 48% of the activity measured in the wild-type. Therefore, the activity level measured for nac2–26 strongly resembles that measured for FuD7 in the presence of 450 pmol RNA, suggesting that the observed differences between nac2–26 and FuD7 are due to the absence of psbA mRNA in FuD7 (Figure 10A,B). The marginal reduction of cpPDC activity upon addition of psbA mRNA to wild-type extracts might indicate a saturation of DLA2 with psbA mRNA under these conditions (Figure 10B). Taken together, our data strongly suggest that cpPDC activity is specifically affected by the presence of psbA mRNA, presumably via its binding to the E3 attachment site of DLA2.
The dihydrolipoamide acetyltransferase component of the PDC represents an evolutionary and functionally conserved gene family from prokaryotes to eukaryotes [9],[30],[47]. But does this also hold for its ability to bind RNA and fulfill a possible role in gene expression? To gain first insights, we predicted the possible RNA binding sites within the amino acid sequences of E2 subunits from phylogenetically distant organisms (i.e., human, yeast, and a cyanobacterium) (Figure S9). Similar to C. reinhardtii DLA2, all analyzed proteins revealed a putative RNA binding domain within the proposed E3 attachment site. Consequently, we tested recombinant E2 versions from these organisms for their ability to bind RNA in vitro. Therefore, E2 subunits were heterologously expressed in Escherichia coli as hexahistidine-tagged fusion proteins and purified on Ni-NTA Sepharose (Figure 11A). Even though the recombinant proteins were markedly enriched after purification, especially the preparation of the E2 subunit from human (Hs-E2) revealed contaminations with proteins in a size range of 30–45 kDa. These most likely represent Hs-E2 degradation products because they are specifically recognized by an anti-hexahistidine antibody (unpublished data, Figure 11A). As controls for the subsequent RNA binding assay, we also included PratA, which is an unrelated his-tagged protein not described to possess any RNA binding activity [48]. As a positive control, we used the RNA binding protein RBP40, which was already shown to unspecifically bind to RNA in vitro [49],[50]. Since no specific RNA targets of the different E2 subunits are known, psbA 5′ UTR RNA was used to detect general binding activity (Figure 11B). Interestingly, all tested E2 proteins showed a binding to the RNA probe applied in the UV cross-linking assay, suggesting that RNA binding is an intrinsic capacity of all E2 subunits, even of those from mitochondria and prokaryotes. However, the binding of the mitochondrial proteins, especially of the human protein, to the psbA RNA appeared to be weaker as compared to the recombinant proteins of the green lineage.
Our finding that RBP63 is DLA2 reveals another example of a metabolic enzyme with a dual function as an RBP. Some of these proteins, including enzymes involved in carbon and fatty acid metabolism, were shown to directly interact with nucleic acids and have been proposed to influence transcription or translation (reviewed in [4],[5]). However, the physiological significance of RNA binding by metabolic proteins is mostly unclear and might be a remnant of the “RNA-world” [51]. In this context, our results are important because they reveal a physiological function of an alternate RNA-binding form of a metabolic enzyme: that is, DLA2 as an RBP in psbA translation. Our results also suggest a model in which DLA2 adjusts psbA expression in response to available sources of energy.
After mass spectrometrical identification of DLA2, we confirmed its in silico predicted chloroplast localization (Figure 3). Results of enzyme activity assays of DLA2 knock-down lines demonstrated DLA2 to be a functional subunit of the cpPDC (Figures 8 and 9). SEC analyses revealed a psbA mRNA-specific DLA2 complex of ca. 700 kDa only in cells cultured mixotrophically (i.e., in the presence of exogenous acetate and light) (Figure 4). In addition, a large >2.8 MDa, RNase-insensitive, DLA2 complex is likely to represent the functional cpPDC (Figures 4 and S3). This complex accumulated predominantly in cells cultured in photoautotrophic conditions and only to a lower level in mixotrophically or heterotrophically grown cells. Which of these complexes is formed, therefore, seems to be dependent on whether light or acetate is available as an energy source. Under photoautotrophic conditions, C. reinhardtii chloroplasts depend on the production of acetyl-CoA via cpPDC. Therefore, it is likely that most of DLA2 is in complexes of >2.8 MDa. In contrast, under mixotrophic conditions acetate can be converted into acetyl-CoA by acetyl-CoA synthetase (ACS) and/or by the acetate kinase (ACK)/phosphate acetyltransferase (PAT) system [10]. Accumulating acetyl-CoA then signals substrate availability for fatty acid synthesis, which might cause a product-inhibition of the cpPDC and/or acetylation of its subunits [2],[3]. This, we propose, leads to its partial disassembly, thereby stimulating the light-dependent binding of the psbA mRNA to DLA2 and, consequently, the formation of the smaller DLA2 RNP complex (Figure 12). How this light-regulation might be exerted on DLA2 remains to be shown.
The alternate function of DLA2 reported here may be conserved in higher plants. Several hints suggest that cpPDC subunits are present in complexes other than fully assembled cpPDC in higher plants. The analysis of HMW complexes from A. thaliana chloroplasts by SEC revealed the chloroplast E2 subunit (LTA2, At3g25860) as a component of two chloroplast complexes, one of >5 MDa and another one of 1–2 MDa [52]. The former most likely represents a fully assembled cpPDC, whereas the latter did not cofractionate with the E1 and E3 subunits. Instead, most E2 coeluted with ribosomal proteins and RNA binding proteins. Likewise, the A. thaliana chloroplast E2 subunit has been reported to co-immunoprecipitate with CSP41b, a protein involved in chloroplast RNA metabolism [53]–[55]. Further indications for E2 subunits possessing an additional function besides being a cpPDC subunit are derived from A. thaliana T-DNA insertion lines. Assuming that all three cpPDC enzyme components are necessary for its functionality, it is surprising that T-DNA insertions in E2 genes lead to embryo lethality in the homozygous state, whereas the E3 subunit is dispensable, thereby indicating an essential function of E2 distinct from its role in the cpPDC [56]–[58]. Similarly, an extensive PCR-based search for a DLA2 insertion mutant in an indexed library of C. reinhardtii failed, suggesting that DLA2 also is essential in green algae (Bohne, Grossman, and Nickelsen, unpublished results).
Consistent with a functional role of the DLA2–RNP complex, the DLA2–RNAi lines exhibited growth retardation and reduced D1 synthesis rates under mixotrophic conditions (Figures 6 and 7). Based on results reported here and previously, we propose a role of DLA2 in localizing the psbA mRNA to the T-zone for its translation and the synthesis of D1 in de novo PSII biogenesis. First, the existence of mRNA localization factors in the T-zone was suggested by a previous demonstration that the psbA mRNA localizes there in the absence of the nascent D1 polypeptide and, hence, any localization information in the polypeptide sequence [16]. Second, we show here that DLA2 is associated with membranes that are distinct from thylakoid membranes in density and within the density range of CTM, a biogenic membrane that is probably localized in the T-zone (Figure 8A, left image, [45]). DLA2 is also associated with stromal thylakoid membranes, another proposed location of psbA translation [17]. Third, DLA2 functions in the localization of the psbA mRNA in the T-zone (Figure 8C). Together, these results support a model in which the RNA-binding form of DLA2 tethers the psbA mRNA to CTM in the T-Zone for translation and cotranslational membrane insertion of D1 for its incorporation into assembling PSII complexes (Figure 12).
While the phenotypes of the DLA2–RNAi lines suggest a role in psbA translation under mixotrophic conditions, the effects of DLA2 silencing under photoautotrophic conditions appear more pleiotropic. Although PSII levels increase as compared to the wild-type (Figure 7), growth rates remained unaltered despite the requirements for efficient photosynthesis under these conditions (Figure 6). This might be due to counteracting growth limitations caused by reduced cpPDC activity in the absence of acetate. Moreover, this raises the question: What is the molecular basis of the observed differences in the rates of PSII subunit synthesis in the DLA2–RNAi lines versus the wild-type in photoautotrophic and heterotrophic conditions (i.e., in the absence of DLA2–RNP complex formation)? Evaluation of the protein pulse-labeling data in Figure 7B revealed that D2 synthesis also increased and decreased concomitantly with D1 synthesis in DLA2–RNAi lines under photoautotrophic and heterotrophic growth, again suggesting more pleiotropic effects (Figure S8). In contrast under mixotrophic conditions when DLA2–RNP complex formation occurs, only D1 synthesis but not D2 synthesis was affected, confirming a psbA-specific function of DLA2 in the presence of light and acetate (Figure S8). The translational regulation of psbA mRNA is complex and has been shown to involve many factors in addition to DLA2 [59]. Nevertheless, based on the presented data, DLA2 appears to be specifically required for adjusting chloroplast gene expression when high levels of reduced carbon energy sources are available to the cell (Figure 12).
Little is known about the regulation of cpPDC activity in green algae. DLA2 is constitutively expressed in C. reinhardtii under different light conditions, and thus, it is likely that its enzymatic activity is regulated at the posttranslational level (Figure S7). One mechanism to regulate cpPDC activity has been attributed to product inhibition by NADH and acetyl-CoA [11]. Additionally, cpPDC activity is stimulated in the light by an increase of stromal pH as well as Mg2+ concentration due to enhanced photosynthetic electron flow (Figure 10A, reviewed in [11]). However, the prediction of a putative RNA binding site overlapping the E3 attachment site within the DLA2 primary amino acid sequence raised the possibility of competitive binding of either E3 or the psbA mRNA to this region and, therefore, a further level of cpPDC activity regulation. This idea is supported by the observation that cpPDC activity in the psbA deletion mutant FuD7 is higher than in the PSII mutant nac2–26 (Figure 10). This is likely due to the lacking inhibitory effect of the psbA mRNA in FuD7, which can be recovered by the addition of exogenous psbA 5′ UTR RNA (Figure 10A,B). This inhibitory effect of psbA RNA on cpPDC activity in FuD7 might suggest a continuous assembly and disassembly of the PDH complex, as it is difficult to imagine how otherwise psbA should be able to access the DLA2 subunit, which was reported to be located in the inner core of the enzymatic complex [60]. Disassembled DLA2 could bind RNA and might then be inhibited from re-association with other PDC subunits. Alternatively, the structure of the cpPDC might be different from those known from mitochondrial or bacterial PDCs. This is supported by sedimentation analyses that show that cpPDC forms less defined complexes as compared to mitochondrial or bacterial PDCs [61]. It was speculated that this is due to dissociation of one or more of the component enzymes or association with other enzymes [61].
Taken together, the data indicate a reciprocal light and acetate-dependent regulation of D1 and cpPDC activity, which might guarantee a coordination of protein and lipid synthesis in the course of thylakoid membrane biogenesis. A linkage between carbon and protein metabolism is additionally supported by earlier studies that revealed that chloroplast protein synthesis, including that of D1, can be induced in cells in the dark phase of a diurnal regime after addition of acetate to the medium [62].
Our data demonstrate that recombinant E2 proteins from C. reinhardtii, human, yeast, and a cyanobacterium all have an intrinsic unspecific RNA binding ability in vitro (Figures 5 and 11). This might point to a very ancient feature of dihydrolipoamide acetyltransferases as RBPs, which is likely to have occurred even before the separation of mitochondrial and chloroplastic homologues. It should be noted, however, that the binding of the human E2 subunit to the provided plant-specific mRNA was very weak as compared to the other tested E2 subunits, and the significance of its RNA binding capacity requires further investigations.
Further indications that RNA binding by E2 subunits is conserved are provided by the human mitochondrial E2 subunit of the mtPDC (Hs-E2). Only recently Hs-E2 has been found to interact with the transcription factor STAT5 (Signal Transducer and Activator of Transcription 5), which was demonstrated to bind mitochondrial DNA [63]. Furthermore, Hs-E2 and STAT5 are translocated to the nucleus under certain conditions, where Hs-E2 is thought to function as a co-activator in STAT5-dependent nuclear gene expression [64]. This raises the possibility that regulation of gene expression by E2 subunits occurs in diverse phylogenetic contexts.
How the RNA binding ability might have been acquired is uncertain, but one might speculate that this occurred before the complex composition of PDC evolved. The ancient E2 enzyme might have had the capacity to bind NAD+, a role that is now performed by the E3 subunit of PDC to reoxidize E2-bound dihydrolipoamide produced during catalysis. This is indicated by prediction of a typical NAD+/FAD+ binding site known as the Rossmann fold within the E3 binding region of many E2 subunits including C. reinhardtii DLA2 (Figure S10, [65]). Interestingly, the Rossmann fold, which is typically described as a functional NAD+ binding domain, is highly conserved among dehydrogenases and has been reported to be involved in binding of RNA molecules by metabolic enzymes [5],[66]. One well-studied example is the human glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which is described to specifically bind to AU-rich elements within the 3′ UTRs of several mRNAs via its Rossmann fold domain [67],[68]. A wide variety of physiological functions has been attributed to this enzyme–RNA interaction including regulation of mRNA stability, degradation, and/or translation; tRNA and mRNA transport; and as an RNA chaperone (reviewed in [4],[5]). As reported in this study for DLA2, Nagy et al. [67] observed a decreased enzymatic activity of GAPDH upon RNA binding in vitro, which also might indicate a reciprocal regulation between metabolic enzyme activity and RNA-binding (Figure 10B). In conclusion, the available data suggest a complex and coordinated interplay between metabolic pathways and gene expression in the chloroplast.
As the wild-type C. reinhardtii strain, we used CC-406, which has a defective cell wall to allow chloroplast isolation. The chloroplast PSII mutants were FuD7, in which the psbA genes have been deleted [34], and a nac2 mutant, which carries nac2–26 [35] and lacks a stability factor for the psbD mRNA. Strains were maintained on 0.8% agar-solidified Tris/acetate/phosphate (TAP) medium [69] at 25°C under constant light (30 µE/m2/s) if not indicated otherwise. Liquid cultures were grown under agitation at 25°C to a density of ∼2×106 cells/mL in TAP medium containing 1% sorbitol (TAPS medium) for mixotrophic and heterotrophic growth. Photoautotrophic growth was in high-salt minimal (HSM) medium [69]. Light conditions were as indicated: moderate light (30 µE/m−2/s−1), high light (200 µE/m2/s), or darkness. For GFP import studies and the generation of RNAi lines, the UVM4 expression strain described by Neupert et al. [70] was used.
Chloroplasts from cell wall–deficient strains carrying the cw15 mutation were isolated from a discontinuous Percoll gradient (45% to 75%) as described previously [71]. To remove stromal proteins, isolated chloroplasts were osmotically lysed in hypotonic buffer (10 mM tricine/KOH, pH 7.8, 10 mM EDTA, and 5 mM 2-mercaptoethanol) by repeated pipetting. Membrane material was pelleted by ultracentrifugation for 30 min at 100,000 g through a 1 M sucrose cushion in hypotonic buffer in a SW40 rotor (Beckman). Pellets were solubilized in equal volumes of 1.8 M sucrose in hypotonic buffer and floated using an upper 1.3 M sucrose cushion and an additional hypotonic layer (180 min, 4°C, 100,000 g). Thylakoid membranes were taken from the interphase, resuspended in twice the volume of hypotonic buffer, and pelleted again (30 min, 4°C, 100,000 g). The pellet was then lysed in Brij buffer (20 mM tricine/KOH, pH 7.8, 0.12 mM KCl, 0.4 mM EDTA, 10 mM β-mercaptoethanol, 1% Brij-35). After centrifugation (60 min, 4°C, 100,000 g) an aliquot of the resulting supernatant containing solubilized proteins was then applied to a 5 mL heparin–Sepharose 4B (GE Healthcare) column equilibrated with Buffer I (50 mM KCl, 10 mM tricine/KOH, pH 7.8, 10 mM EDTA, and 5 mM 2-mercaptoethanol). Bound proteins were eluted using a discontinuous salt gradient (150 mM, 500 mM, and 1 M KCl in buffer I) after washing the sample with 4 CV of buffer I. Proteins eluting at 150 mM KCl were desalted using Amicon Ultra centrifugal filtration devices (Millipore) with a 10 kDa molecular mass cutoff according to the manufacturer's instructions. The protein solution (in Buffer I) was then applied to a 2 mL poly(A)–Sepharose 4B (GE Healthcare) column equilibrated with buffer I. The column was washed with 4 volumes of buffer I, and bound proteins were eluted with a discontinuous salt gradient (150 mM, 500 mM, and 1 M KCl in buffer I). Prior to use in UV cross-linking assays, all protein fractions were dialyzed against RNA binding buffer (30 mM Tris-HCl, pH 7.0, 50 mM KCl, 5 mM MgCl2, and 5 mM 2-mercaptoethanol). Protein concentrations were determined using the Bradford assay (Bio-Rad). For mass spectrometric peptide sequencing, RBP63-containing gel pieces were treated with trypsin (Promega), and the resulting peptides were analyzed on a Q-TOF2 mass spectrometer (Micromass) as described [72].
A fusion protein containing glutathione-S-transferase (GST) and the C-terminal region of the DLA2 protein was used as an antigen for production of a polyclonal rabbit antiserum. For generation of the plasmid expression vector, a DNA fragment encoding the amino acids 391–488 of DLA2 was amplified from genomic DNA by PCR with the primers FWD63BamHI (5′-GGATCCGACCTGGTCAAGCGCGCTCG-3′) and REV63SalI (5′-GTCGACGTTCTCAATCACAGCCTTGA-3′). Attached restriction sites are underlined. The fragment was inserted into the expression vector pGEX4T1 (GE Healthcare) via the BamHI and SalI restriction sites. Overexpression and purification of the DLA2–GST fusion protein in the E. coli strain BL21 were performed according to the manufacturer's protocol using glutathione–Sepharose 4B (GE Healthcare). A polyclonal antiserum was produced by immunizing rabbits with this protein fraction (Biogenes).
For analysis of HMW complexes, chloroplasts isolated from cw15 strains according to Zerges and Rochaix [71] were lysed in nonreducing hypotonic solution (10 mM EDTA, 10 mM tricine-KOH, pH 7.8, and Roche Complete Mini protease inhibitors). Crude thylakoid membranes were separated from soluble proteins by centrifugation on a 1 M sucrose cushion (100,000× g, 30 min, 4°C). The supernatant of this centrifugation step was defined as stromal proteins. Pellets were solubilized in equal volumes of lysis buffer (120 mM KCl, 0.4 mM EDTA, 0.1% Triton X-100, 20 mM tricine, pH 7.8), and insoluble particles were removed by an additional sucrose cushion step. If RNase treatment was required, samples were incubated with 400 U RNase One (Promega)/mg protein for 60 min at 4°C before application to the gel filtration column. Gel filtration samples were loaded through an online filter onto a Superose 6 10/300 GL column (GE Healthcare), and elution was performed at 4°C with buffer containing 50 mM KCl, 2.5 mM EDTA, 5 mM ε-aminocaproic acid, 0.1% Triton X-100, and 20 mM tricine-KOH, pH 7.8, at a flow of 0.3 mL/min using an ÄKTApurifier 10 system (GE Healthcare). Aliquots of each elution fraction were subjected to immunoblotting.
For protein isolation, cells were placed into 20 mL of liquid TAPS medium and grown under indicated light conditions on a rotary shaker (125 rpm) to mid-log phase (∼5×106–1×107 cells/mL). Cells were harvested by centrifugation and lysed under pipetting in a buffer containing 20 mM KCl, 20 mM tricine, pH 7.8, 0.4 mM EDTA, 5 mM β-mercaptoethanol, and 1% Triton X-100. For cell fractionation, chloroplasts were isolated as described above. Mitochondria were basically prepared according to [73]. Immunoblot analysis was performed using standard procedures. The procedures for subcellular fractionation experiments shown in Figure 8A were described previously [45]. Protein concentrations were determined using the BCA (Pierce) or the Bradford (C. Roth) assay following the manufacturer's instructions.
A codon-adapted CrGFP has been integrated as NdeI/EcoRI into the PsaD expression vector [74],[75]. This expression cassette was then inserted into the pBC1 vector as XhoI/XbaI, which contains the APHVIII resistance gene under control of the constitutively active HSP70/RBCS2 promoter regions [76],[77] to result in pBC1-CrGFP. For DLA2–GFP import studies, the coding sequence of the N-terminal amino acids 1–114 including the predicted transit peptide and the complete lipoyl attachment site was PCR-amplified from genomic DNA with the primer pair RBP63 fw (5′-AACATATGCAGGCCACGACCCG-3′)/RBP63 rv (5′-AACATATGCTCGTTGGCGTTTTCGGCCAC-3′), introducing 5′ and 3′ NdeI sites (restriction sites underlined). The NdeI fragment was then inserted into pBC1–CrGFP to result in pBC1–TP–DLA2–CrGFP. This construct was transformed into UVM4, and positive transformants were selected on TAP plates supplemented with 10 µg/mL paromomycin. As a control for cytosolic CrGFP expression, the pBC1–CrGFP vector was directly transformed into UVM4. GFP fluorescence of transformed cells was observed with a confocal laser scanning system (Zeiss LSM 51 Meta).
For expression of recombinant DLA2 protein from C. reinhardtii, a 1,389 bp fragment encoding the C-terminal amino acids 32–494 was PCR-amplified from EST clone MXL069g06 (Kazusa DNA Research Institute) using the primer pair RBP63–pQEfwBamHI (5′-aaggatccAACGCGGTCAAGGATG-3′)/RBP63-pQErevSalI (5′- gtcgacTTAGAACAGCAGCTGGTCGG-3′) and inserted into the plasmid pQE30 (Qiagen) via the BamHI/SalI restriction sites to yield the plasmid pQE–DLA2. Expression was accomplished in E. coli M15 cells (Stratagene). Cells were grown to an OD600 of 0.5–0.6, and protein expression induced by addition of IPTG to a final concentration of 1 mM followed by growth at 37°C for 3 h. The recombinant protein was purified according to the GE Healthcare protocol for purification of histidine-tagged recombinant proteins under native conditions using Ni Sepharose 6 Fast Flow (GE Healthcare). Primers used for cloning of other His-E2 fusion proteins excluding predicted signal and transit peptides are as follows: Synechocystis sp. 6803 Fw (5′-aaggatccATTTACGACATTTTCATGCC-3′)/Rev (5′-gtcgacGTCAAAGACTGGGCATTC-3′), Saccharomyces cerevisiae Fw (5′-ggatccCCAGAGCACACCATTATTG-3′)/Rev (5′-gtcgacTCACAATAGCATTTCCAAAGG-3′), and Homo sapiens Fw (5′-ggatccCCGCATCAGAAGGTTCCATTG-3′)/Rev (5′-gtcgacAGTGTGACCTGGGAGAGTTTA-3′). For cloning of the expression vector for cyanobacterial PratA (slr2048), the CDS (aa 38–383) was amplified using the following primers: Ss_PratA_fw01 (5′-ctaggatccAATCTTCCTGACGTTACCC-3′)/Ss_PratA_rv01 (5′-ctactgcagTTAGAGATTATCCAGCTTTTCTTGG-3′) and cloned via BamHI/PstI sites into the vector pET28b SUMO-Ser. The full length CDS of RBP40 was amplified with primers BamHI–RBP40 (5′-aaggatccATGCTGACCTTGAGACGTGC-3′)/RB38DN44revSalI (5′-ttgtcgacCTAGTAGCGGGCGCCC-3′) and cloned into the vector pQE30 via BamHI/SalI sites.
Expression and purification were as described above with some minor changes: Expression for Hs-E2 was performed overnight at 17°C (0.5 mM IPTG), Syn-E2 for 5 h at 25°C, Sc-E2 for 5 h at 18°C, and RBP40 for 5 h at 30°C. PratA was expressed in BL21 DE3 cells overnight at 12°C. Concentrations of recombinant proteins were determined along with a BSA dilution series.
In vitro synthesis of RNA and UV cross-linking experiments were basically performed as described by Zerges and Rochaix [71]. DNA templates for the in vitro synthesis of rbcL, psbD, and psbA leader RNA probes were generated by PCR using the following primers: T7rbcL5 (5′-gtaatacgactcactatagggTATGCTCGACTGATAAGAC-3′)/rbcL3 (5′-CTGCTTTAGTTTCTGTTTGTGGAACC-3′); T7psbD5 (5′-gtaatacgactcactatagggCCACAATGATTAAAATTAAA-3′)/psbDUTR3 (5′-ACCGATCGCAATTGTCAT-3′); and T7psbA5 (5′-gtaatacgactcactatagggTACCATGCTTTTAATAGAAG-3′)/2054-psbA (5′-GATCCATGG TCATATGTTAATTTTTTTAAAG-3′). Each template contained the promoter of the T7 RNA polymerase (written in lowercase letters in the fw-primer sequence) fused to the 5′ end of the described fragments. A total of 0.5 µg of the PCR products were transcribed in vitro by T7 RNA Polymerase (Fermentas) in a 20 µl reaction in the presence of 20 U RNase inhibitor (Fermentas), 40 µCi of α-32P-UTP (3,000 Ci/mmol; Hartmann Analytic), 30 µM nonradiolabeled UTP, and 0.5 mM each of ATP, CTP, and GTP according to the manufacturer's protocol. We added 1 U of RNase-free DNase (Promega), and the reaction was incubated for an additional 15 min at 37°C. Unincorporated nucleotides were removed using a MicroSpin S-200 HR column (GE Healthcare). The reactions were extracted once with phenol-chloroform and ethanol precipitated in the presence of ammonium. Binding reactions (20 µl) were performed at RT for 5 min and contained 20 mM HEPES/KOH, pH 7.8, 5 mM MgCl2, 60 mM KCl, and 200 ng or 10 ng protein. Each reaction contained 50–100 kcpm of 32P-RNA probe. For competition experiments, protein and RNA probe were used in equimolar amounts or in 5-fold to 200-fold excess of cold RNA. Radiolabeled RNA and nonlabeled competitors were mixed prior to the addition of proteins in competition experiments. Quantification of competitor RNAs was performed by measuring the incorporation of low levels of radioactivity into transcripts. Subsequent exposure to a 254 nm UV irradiation of 1 J/cm2 using a Stratalinker UV cross-linker (Stratagene) covalently cross-linked the RNA probe and bound proteins. After irradiation, the nonbound 32P-RNA probes were digested by treatment with 10 U RNase One (Promega) for 20 min at 37°C. Samples were fractionated by SDS-PAGE and analyzed by autoradiography or phosphorimaging.
The KD was determined as described by Ostersetzer et al. [38]. Increasing amounts of recombinant DLA2 protein were incubated for 15 min at RT with in vitro transcribed 32P-labeled psbA mRNA (6.7 pM) in 30 µl reactions in the same binding buffer used for UV cross-linking assays. Subsequently, the reactions were filtered through stacked nitrocellulose (Reprobe nitrocellulose plus, 0.45 µm; Applichem) and nylon membranes (Nylon plus, 0.45 µm; C. Roth) using a dot blot apparatus (Minifold SRC96, Schleicher & Schuell). The membranes were washed once with 100 µl of binding buffer, dried, and subjected to phosphorimaging and quantitation with AlphaEase software (Alpha Innotech Corporation).
To create DLA2-deficient mutants of C. reinhardtii, we used the RNAi system previously described by Rohr et al. [41]. For the generation of an inverted repeat construct specific for DLA2 RNA, a 400 bp fragment corresponding to the last exon and part of the 3′ UTR of DLA2 was amplified by PCR using genomic DNA as a template with the primers 5/63 SHE 5′-GTCGACAAGCTTGAATTCCAACTGGGCTCA-3′ and 3/63 400 B 5′-GGATCCGCTAACCCTGCAGCCCACCT-3′, which add SalI, HindIII, EcoRI, or BamHI restriction sites, respectively (restriction sites underlined). A longer 600 bp fragment containing an additional 200 bp of the 3′ UTR that functioned as a spacer for the inverted repeat was amplified using the primers 5/63 SHE and 3/63 600 B (BamHI) 5′-GGATCCGGCATTCAAGCCACCCTGCT-3′. These two fragments were ligated and cloned as an inverted repeat (with the central spacer) into the EcoRI site of the vector NE537 [41]. The UVM4 strain was transformed with the resulting construct, kept for 2 d in liquid culture (TAP +1.5 mM L-tryptophan) in dim light, and then plated on TAP plates containing 5 µg/mL paromomycin and 1.5 mM L-tryptophan [70]. C. reinhardtii cells transformed with the empty NE537 vector served as a control (WT-NE). At intervals of 2 wk, colonies were transferred to TAP plates containing 5 and then 10 µM 5-fluoroindole (5-FI). Plates were kept in low light (∼10 µE/m2/s−1) under a yellow foil (Q-MAX 010 medium yellow, Multi-Lite, Hamburg, Germany), which filters out wavelengths of light between 400 and 470 nm to inhibit photodegradation of tryptophan and 5-FI.
FISH and IF were performed according to Uniacke et al. [78]. The psbA FISH probes were labeled with Alexa Flour 488, and the IF staining involved Alexa Fluor 568 conjugated anti-rabbit secondary antibody (Invitrogen). Images were captured on a Leica DMI6000B microscope (Leica Microsystems) using a 40×/0.75 objective, a Hamamatsu OrcaR2 camera, and Volocity acquisition software (Perkin Elmer). For each condition, ≥20 cells were observed.
Chlamydomonas liquid cultures were grown in TAPS or HSM medium to a density of ∼1–2×106 cells/mL, pelleted by centrifugation (10 min, 4°C, 1,000× g), resuspended in the same medium in which all sulfur-containing ingredients were replaced by the respective chloride salts (TAPS-S/HSM-S), and incubated for 16 h at 23°C in the light. Cells were pelleted, washed, and resuspended in TAPS-S/-T or HSM-S/-T (lacking both sulfur salts and trace elements), respectively, and grown under indicated light conditions for 2 h. Cells were then washed again and resuspended in TAPS-S/-T or HSM-S/-T to a concentration of 80 µg chlorophyll per mL. Aliquots (225 µl) of the cell suspension were incubated with cycloheximide (10 µg/mL) for 10 min. Subsequently, 100 µCi H235SO4 (Hartmann Analytic) was added to each, followed by incubation for 15 min in the light as before. After centrifugation, sedimented cells were frozen in liquid nitrogen. Cells were resuspended in 10 mM HEPES-KOH, pH 7.5, 10 mM EDTA in the presence of CompleteMini protease inhibitors (Roche) and disrupted by sonication (30 s, RT). The homogenate was then centrifuged at 20,000 g for 30 min. The pellet was resuspended in 10 mM HEPES-KOH, pH 7.5, 10 mM EDTA. Samples were fractionated by electrophoresis on 16% SDS-polyacrylamide gels containing 8 M urea. Radioactive protein signals were detected on the dried gel by phosphorimaging. Significance of difference between the mean D1/AtpA/B signal ratio for each RNAi line and WT-NE (100%) was determined with a one-sample t test (p<0.05).
Cells were lysed in a buffer containing 25 mM MgCl2, 100 mM tricine (pH 8.0), and 0.1% Triton X-100 at 4°C by sonication. Insoluble material was removed by centrifugation (10 min, 4°C, 10,000 g). cpPDC activity was measured photometrically at 23°C using a Pharmacia Biotech Ultrospec 3000 spectrophotometer. The assay was based on that described by Qi et al. [79] and performed under conditions that favor the activity of the cpPDC over mtPDC (high Mg2+ concentration, alkaline pH; [61],[80]).
The reaction mixture used contained 0.1 mM TPP, 5 mM MgCl2, 2 mM NAD+, 0.1 mM CoA, 3 mM cysteine, 0.05% Triton X-100, 0.1 M tricine (pH 8.0), and 200 µg of proteins in a final volume of 0.990 mL. Reactions were initiated by the addition of 1 µmol sodium pyruvate in a volume of 10 µl, and the change in absorbance at 340 nm caused by NADH production was followed for 2 min. If the influence of DCMU was tested, it was added to a final concentration of 20 µM 3 h prior to cell harvest. An equivalent volume of ethanol, the solvent for the DCMU stock, was used instead of DCMU for control strains. For pre-incubation of cell lysates with psbA or rbcL mRNAs, 5′ UTRs were in vitro transcribed as described above. Lysates were prepared as described above in the presence of 250 U/mL RNase inhibitor (Fermentas). Protein samples (200 µg) were incubated with 150 or 450 pmol RNA in a final volume of 130 µl for 10 min at RT prior to the PDC activity assay. Unpaired two-sample t tests were used to determine whether the mean activity under each condition is significantly different from that in the absence of RNA (p<0.05).
If cpPDC activity tests were performed with size exclusion fractions, SEC elution was performed in 60 mM KCl, 5 mM MgCl2, and 100 mM tricine-KOH, pH 7.8, and 0.05%. We used 300 µl of 1.2 ml fractions for the assay as described above.
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10.1371/journal.pntd.0005996 | Characterization of the catalytic center of the Ebola virus L polymerase | Ebola virus (EBOV) causes a severe hemorrhagic fever in humans and non-human primates. While no licensed therapeutics are available, recently there has been tremendous progress in developing antivirals. Targeting the ribonucleoprotein complex (RNP) proteins, which facilitate genome replication and transcription, and particularly the polymerase L, is a promising antiviral approach since these processes are essential for the virus life cycle. However, until now little is known about L in terms of its structure and function, and in particular the catalytic center of the RNA-dependent RNA polymerase (RdRp) of L, which is one of the most promising molecular targets, has never been experimentally characterized.
Using multiple sequence alignments with other negative sense single-stranded RNA viruses we identified the putative catalytic center of the EBOV RdRp. An L protein with mutations in this center was then generated and characterized using various life cycle modelling systems. These systems are based on minigenomes, i.e. miniature versions of the viral genome, in which the viral genes are exchanged against a reporter gene. When such minigenomes are coexpressed with RNP proteins in mammalian cells, the RNP proteins recognize them as authentic templates for replication and transcription, resulting in reporter activity reflecting these processes. Replication-competent minigenome systems indicated that our L catalytic domain mutant was impaired in genome replication and/or transcription, and by using replication-deficient minigenome systems, as well as a novel RT-qPCR-based genome replication assay, we showed that it indeed no longer supported either of these processes. However, it still showed similar expression to wild-type L, and retained its ability to be incorporated into inclusion bodies, which are the sites of EBOV genome replication.
We have experimentally defined the catalytic center of the EBOV RdRp, and thus a promising antiviral target regulating an essential aspect of the EBOV life cycle.
| Ebola viruses cause severe hemorrhagic fevers, and were responsible for the devastating Ebola virus disease epidemic in West Africa from 2013 to 2016. While a number of experimental therapeutics against these viruses target the viral polymerase, there are still significant gaps in our knowledge regarding this essential viral protein. In particular, until now no experimental evidence has been provided identifying the catalytic center of the viral RNA-dependent RNA polymerase, which is absolutely essential for the virus life cycle due to its role in replicating and transcribing the viral negative-sense RNA genome. Based on a comparison to related negative-sense RNA viruses from other virus families we identified a putative catalytic center within the Ebola virus polymerase, and provide the experimental evidence that the Ebola virus polymerase indeed utilizes a classical GDNQ motif for both genome replication and transcription. This finding not only increases our knowledge regarding the molecular biology of Ebola viruses, but also defines a molecular target for the development of antivirals against this deadly virus.
| Ebola virus (EBOV) is a member of the genus Ebolavirus in the family of Filoviridae, and the causative agent of a severe hemorrhagic fever called Ebola virus disease (EVD) with case fatality rates of up to 90% [1]. While outbreaks are usually of comparatively small scale, the recent EVD epidemic in West Africa involved more than 28,000 cases with more than 11,000 deaths [2], highlighting the urgent need for effective countermeasures against this virus. Significant progress has been made in recent years on the development of such countermeasures, with experimental vaccines showing promise in phase III clinical trials [3]. Similarly, a number of experimental therapeutics are under development, many of which target the viral polymerase L (reviewed in [4,5]). This viral protein acts in concert with the other viral ribonucleoprotein complex (RNP) proteins, the nucleoprotein NP, the polymerase cofactor VP35, and the transcriptional activator VP30, to facilitate replication of the negative sense RNA genome of EBOV, as well as its transcription into viral mRNAs [6].
Despite its central role in the virus life cycle, relatively little is known about the L protein both in terms of its structure and in terms of functional details, which might in part be due to its large size and the fact that no specific antibodies are available, making biochemical studies of this protein challenging. Thus, much of what we know about L has been elucidated using reverse genetics-based life cycle modelling systems [7]. The most basic of these systems is the minigenome system [6]. Here a miniature version of the viral genome (a so-called minigenome), in which all viral open reading frames have been removed and replaced by a reporter gene, but in which the non-coding terminal leader and trailer regions are retained, is expressed together with the RNP proteins in mammalian cells. These RNP proteins recognize the minigenome as an authentic viral template based on its leader and trailer regions, and replicate and transcribe it resulting in reporter activity levels that mirror these steps in the viral life cycle. As a modification of this classical monocistronic minigenome system, a so-called replication-deficient minigenome system has also recently been developed, which utilizes a minigenome with a deletion in the antigenomic replication promotor [8]. This system allows genome transcription to be investigated in isolation, something that is not possible in the classical system, where reporter activity is always the product of both transcription and genome replication (which amplifies the number of genomic templates available for transcription). Further, with the tetracistronic transcription and replication-competent virus-like particle (trVLP) system another life-cycle modelling system has been developed. This system utilizes a minigenome that encodes not only a reporter, but also the viral proteins VP40, GP1,2, and VP24, which are responsible for virus particle morphogenesis and budding, entry and fusion, and proper nucleocapsid assembly, respectively [9,10]. In this system minigenome replication and transcription in so-called producer (p0) cells not only leads to reporter activity, but also to the formation of trVLPs, which package minigenomes-containing nucleocapsid-like structures and can infect target (p1) cells. Using this system it is possible to model virtually the complete virus life cycle outside of a high containment laboratory. In all such minigenome-based systems an important consideration is to control for effects on plasmid-driven gene expression. This is usually done by including a control plasmid that encodes for another reporter, e.g. Firefly luciferase. This allows these cell-based assays to be normalized for well-to-well differences in transfection efficacy, cell density, or experimental effects, e.g. differences in cytotoxicity of tested drugs. Similarly, the extent to which virus RNP complex-specific genome replication and transcription are occurring is typically assessed by omitting the polymerase L.
In the past, minigenome systems have been used to demonstrate the role of L in replication and transcription [6], as well as to investigate the functional interactions of L with VP35 [11]. Indeed, this interaction has been shown to be crucial for genome replication and/or transcription, and a further study revealed an additional interaction of L with VP30 [12]. In that same study, we also identified a flexible linker region in L that is tolerant to insertions, and by subsequently fusing mCherry into this region we were able to characterize the intracellular fate of this protein, and show that it localizes in so-called inclusion bodies, which are formed in virus-infected cells and act as sites of genome replication [13]. However, fundamental aspects of L have still not been investigated, and for example the catalytic center of the RNA-dependent RNA-polymerase (RdRp) has not been experimentally determined, even though this information would be of great importance for drug development efforts [5]. In contrast, for other negative sense RNA viruses this catalytic center is well defined and involves a GDNQ motif, which based on bioinformatics analysis has been proposed to also be present and functional in the filovirus polymerase [14,15]. This motif represents a variant of the GDD motif found in RdRps of other viruses [16], and sits in a deep channel of the polymerase [17], where it complexes two metal ions essential for polymerase function [18]. Here, we provide the first experimental evidence that this motif is indeed essential for both virus genome replication and transcription, providing further insight into the molecular biology of filoviruses, and defining an important molecular target for the development of antiviral compounds against this deadly virus.
Filovirus reference sequences (NC_014373, NC_016144, NC_001608, NC_004161, NC_006432, NC_014372, NC_002549) [19] as well as reference sequences for RSV (NC_001803) and VSV (NC_001560) were obtained from GenBank. Sequences were imported into Geneious v10.0.9 (Biomatters), the L open reading frames were translated, and a multiple sequence alignment was performed using the ClustalW algorithm and the BLOSUM substitution matrix series. For calculating similarities in the pairwise distance analysis, amino acids were deemed similar if exchanges between those amino acids reached or exceeded a threshold of 0 in a BLOSUM62 matrix.
HEK 293T (human embryonic kidney; Collection of Cell Lines in Veterinary Medicine CCLV-RIE 1018) cells were maintained in Dulbecco´s modified Eagle’s minimum essential medium (DMEM; ThermoFisher Scientific) supplemented with 10% fetal bovine serum (FBS; Biochrom), and 100 U/ml penicillin and 100 μg/ml streptomycin (P/S; ThermoFisher Scientific). Huh7 cells (human hepatoma cells; Collection of Cell Lines in Veterinary Medicine CCLV-RIE 1079) were cultured in a 1:1 mix of Ham’s nutrient mixture F-12 (ThermoFisher Scientific) and Iscove's modified Dulbecco's medium (IMDM; ThermoFisher Scientific) supplemented with 10% FBS, 100 U/ml penicillin and 100 μg/ml streptomycin. All cells were grown at 37°C with 5% CO2.
pCAGGS expression plasmids for NP, VP35, VP30, L, L-mCherry, T7, firefly luciferase, Tim1, and replication-competent and -deficient monocistronic minigenomes as well as the tetracistronic minigenome have been previously described [8,13]. A GFP-expressing minigenome was generated by deleting the luciferase open reading frame (ORF) from a luciferase-expressing minigenome and replacing it with the eGFP ORF using conventional PCR techniques and cloning with type IIS restriction enzymes. Primers and details of the cloning strategy are available upon request. Mutation of the EBOV-L gene (specifically A13805C, A13807G, A13808C, A13811C, with all positions relative to the full length EBOV genome) was performed by a combination of conventional and overlap extension PCR methods. To this end, first two separate touchdown PCRs using IProof polymerase (Biorad) and pCAGGS-L as template with the primers 5’-CCCGGGGCGGCCGCAAATG-3’ and 5’-CACCCATCACAGCTGAGCGTAACTTAAAAC as well as 5’-GTTTTAAGTTACGCTCAGCTGTGATGGGTGCCGCTGCGTGCATTACTGTTTTATC-3’ and 5’-GTTTGCCGAGTGTTAACTGTCCAAGG-3’ were performed. PCR-products were digested with DpnI (New England Biolabs, NEB), and a second PCR was performed using the two PCR products as template, and the primers 5’-CCCGGGGCGGCCGCAAATG-3’ and 5’-GTTTGCCGAGTGTTAACTGTCCAAGG-3’. The final PCR-product was cloned via NotI (NEB) and HpaI (NEB) into pCAGGS-L. To generate the mutant L fused to mCherry the region of pCAGGS-Lmut with the mutation was subcloned into pCAGGS-L-mCherry using the restriction enzymes HpaI and NotI. All plasmids were sequence confirmed by Sanger-sequencing.
Minigenome assays were performed as previously described [8], with slight modifications. HEK 293T cells were seeded into 12 well plates, and transfected at a confluency of about 50% using 3 μl Transit LT1 (Mirus) per μg DNA with expression plasmids encoding NP (62.5 ng), VP35 (62.5 ng), VP30 (37.5 ng), T7-polymerase (125 ng), firefly luciferase (12.5 ng), L or Lmut (500 ng) or an equivalent amount of empty vector in the -L control, and a replication-competent minigenome (transcription and replication assay) or a replication-deficient monocistronic minigenome (transcription assay) (125 ng) with Renilla luciferase as the reporter. At 24 hours post transfection (p.t.), medium was exchanged against 2 ml of DMEM supplemented with 5% FBS and P/S, and after 48 hours p.t. luciferase activity was measured. To this end the supernatant was removed from the cells, 200 μl 1x Lysis Juice (PJK) was added to the cells, and after 10 minutes incubation at room temperature the lysate was removed and cell debris spun down 3 minutes at 10,000 x g. Then 40 μl clarified lysate was added to 40 μl Beetle Juice (PJK) or 40 μl Renilla Glo Juice (PJK) in black opaque 96-well plates, and luminescence was measured using an Infinite F200 PRO (Tecan) multimode reader with an integration time of 1 sec. Renilla luciferase activities were normalized to Firefly luciferase activities.
To assess genome replication in isolation, a modified transcription and replication-competent virus-like particle (trVLP) assay [10] was combined with a newly developed RT-qPCR. HEK 293T producer cells (p0) were transfected with expression plasmids for NP, VP35, VP30, the T7-polymerase, L, and a tetracistronic minigenome to generate trVLPs for infection of HEK 293T target cells (p1) as previously described [10]. Target p1 cells in 12-well format were pre-transfected with pCAGGS-NP (62.5 ng), pCAGGS-VP35 (62.5 ng), pCAGGS-Tim1 (125 ng), as well as pCAGGS-L or pCAGGS-Lmut (500 ng). 24 hours p.t. these p1 cells were infected with 1.5 ml of clarified (5 minutes at 800 x g and room temperature), pooled p0 supernatant containing trVLPs. In order to do so, trVLP-containing supernatant was added to p1 cells, and the cells were centrifuged for 10 minutes at 1,000 x g, and then incubated at 37°C for 1 hour. After that, the inoculum was exchanged against 2 ml DMEM with 5% FBS and P/S. No VP30 was expressed in these p1 cells, so that only genome replication but not transcription could take place [6]. 48 hours post infection cells were lysed and RNA was isolated using the NucleoSpin RNA kit (Macherey-Nagel) following the manufacturer’s instructions. An additional DNA digestion step was performed using the turbo DNA-free kit (ThermoFisher Scientific) after RNA purification following the manufacturer’s instructions. RNA samples were then quantified by real-time RT-qPCR using the AgPath-ID One Step RT-PCR kit (Applied Biosystems), with EBOV_IGR: 5’-6FAM-CAATAGCCAATACCAAACACCTCCTCCACAGCTTG-BHQ1-3’ as probe, and the primers EBOV_IGR-fwd2 5’-TCACAATCTACCTCTTGAAACAAGAAC-3’ and EBOV_IGR-rev2 5’-CATGACTTACTAATGATCTCTTAAAATATTAAG-3’ in 3 technical replicates, the results of which were averaged. To allow absolute quantification of copy numbers an RNA standard was prepared by in vitro transcription of the tetracistronic minigenome using the TranscriptAid T7 High Yield Transcription kit (ThermoFisher Scientific) following the manufacturer’s instructions, and quantified using a P-class P330 nanophotometer (IMPLEN). 105, 107, and 109 RNA copies were used as standards in the real-time RT-qPCR.
For western blot analysis Huh7 cells were seeded into 12 well plates and transfected as described above for the replication and transcription minigenome assay, with pCAGGS-L-mCherry, pCAGGS-Lmut-mCherry, or empty vector (-L control) in place of pCAGGS-L. After 24 hours p.t. the medium was changed to 1 ml medium supplemented with 5% FBS and P/S. The cells were lysed after 48 hours p. t. in 1x SDS sample buffer (10% glycerol, 5% 2-mercaptoethanol, 2% SDS, 37.5 mM Tris-HCl, 2.5 μg/ml bromphenol blue), incubated at 95°C for 5 minutes, and lysates were analyzed by SDS-PAGE and western blotting as previously described [20] using anti-mCherry (Biozol: 1:1000) and anti-actin (Sigma-Aldrich: 1:2000) primary antibodies and a peroxidase conjugated goat-anti-mouse secondary antibody (Diavona: 1:10000).
For localization studies, Huh7 cells in 4-well μ-slides (Ibidi) were transfected with the same plasmids as for the western blot analysis but using half the amount of plasmid per well and a GFP-expressing minigenome. Additionally, 125 ng pmTurquoise2-H2A, which was a gift from Dorus Gadella (Addgene plasmid # 36207)[21], was cotransfected in order to label cell nuclei. Cells were visualized by spinning disc live cell microscopy using a Leica DMi8 with a Yokogawa CSU-W1 confocal scanning head, an Andor iXon Ultra 888 EMCCD camera, and 445 nm, 488 nm, and 561 nm laser lines. All images were taken using identical laser and camera settings for each wavelength.
Paired two-tailed t-tests were performed using the GraphPad online QuickCalc (https://www.graphpad.com).
For many negative sense RNA polymerases the catalytic center of their RdRp has been well defined, and incorporates a GDNQ motif [4]. Therefore, in order to identify the putative catalytic center of the EBOV polymerase, a multiple sequence alignment was performed between L open reading frames (ORFs) obtained from reference sequences for all filoviruses (the ebolaviruses EBOV, Sudan virus (SUDV), Bundibugyo virus (BDBV), Reston virus (RESTV), and Taï Forest virus (TAVF), the marburgvirus Marburg virus (MARV), and the cuevavirus Lloviu virus (LLOV)) [19], as well as Respiratory Syncytial Virus (RSV) and Vesicular Stomatitis Virus (VSV). The alignment showed a very high degree of conservation among the filovirus polymerases (Fig 1A), with the ebolavirus polymerases showing 90 to 94% sequence similarity and 73 to 84% sequence identity to each other, 79 to 80% similarity and 54 to 56% identity to LLOV L, and 70% similarity and 44% identity to MARV L. LLOV L showed a higher similarity and identity to the ebolaviruses polymerases (79 to 80% similarity and 54 to 56% identity) than to MARV L (68% similarity and 43% identity), consistent with previous reports regarding the phylogenetic relationships between these genera [22]. As expected, the similarity to RSV and VSV was much lower, with 45 to 46% similarity and 15 to 16% identity between ebolaviruses and RSV, and 42 to 43% similarity and 13% identity between ebolaviruses and VSV. Nevertheless, a conserved GDNQ motif could easily be identified (Fig 1B) at positions 741–744 of the EBOV polymerase sequence (position 815–818 in the multiple sequence alignment), and was identical in all analyzed sequences.
After having identified a putative catalytic center within the EBOV L, we generated expression plasmids in which this motif was mutated by substitution of the DNQ sequence to 3 alanine residues. In order to assess whether these mutations affected expression of the protein, we performed western blot analysis after transient expression in 293T cells. Since no L-specific antibodies are available, we instead used L versions in which the fluorescent tag mCherry had been inserted into a flexible linker region, which we have previously shown tolerates insertions well without dramatically impacting protein function [13]. By western blotting we did not observe any significant differences (p = 0.708) in expression level (Fig 2A and 2B) between L with an intact GDNQ motif and a mutated GAAA motif.
In order to further assess whether there were changes to the intracellular localization of mutated L, we again used mCherry-tagged variants of L in combination with an eGFP-expressing minigenome. As expected based on previous studies, L-mCherry with an intact GDNQ motif localized into punctate structures that most likely represent early inclusion bodies (Fig 2C) [13]. Similar structures were also observed in the presence of L-mCherry with an abrogated putative catalytic domain. However, unlike the situation with untagged wild-type L, or L-mCherry with an intact GDNQ motif, we did not observe any GFP reporter activity with the L GAAA mutant, strongly suggesting a lack of activity of this mutant in transcription and/or replication.
Given the strong impact of the GDNQ motif on reporter expression in context of the GFP-encoding minigenome, we next sought to quantify this impact using a Renilla luciferase-expressing minigenome, which allows easier quantification and more sensitive detection of reporter activity. To this end, we first performed classical minigenome assays, which measure both genome replication and transcription at the same time, but do not distinguish between these two steps. In this system, when using the L mutant with the abrogated GDNQ motif we observed a complete loss of reporter activity (i.e. >1000 fold reduction) with signals being reduced down to the background levels observed also in the complete absence of L, clearly indicating that this motif is absolutely essential for EBOV genome replication, transcription, or both of these processes (Fig 3A) (-L vs. +L: p = 0.001; +L vs. +Lmut: p = 0.003). When looking at the control Firefly luciferase, it became apparent that there was significantly less Firefly signal in the -L control than in the +L sample (p = 0.019). However, this difference was only about 2.4 fold, and did not contribute appreciably to the difference in the Renilla reporter signal, which was several orders of magnitude larger (i.e. 1433 fold).
In order to distinguish whether genome replication or transcription or both processes were impaired by mutation of the GDNQ motif, we next used a replication-deficient minigenome [8]. In these experiments absolute reporter levels were considerably lower, also in the positive control using wild-type L, compared to the reporter activity in the replication-competent minigenome (Fig 3B), reflecting the strong contribution of minigenome replication to overall reporter activity in this system (due to amplification of the vRNA templates available to serve as templates for transcription). However, again reporter activity in the presence of the mutated L was 67 fold lower than in the presence of wild-type L, which represents the background level for the assay (-L vs. +L: p = 0.005; +L vs. +Lmut: p = 0.002). This clearly indicates that the GDNQ mutation is essential for viral transcription, independent of any contribution from effects on viral replication. Again the Firefly signal appeared lower in the -L control compared to the +L sample; however, in this series of experiments this difference was not statistically significant.
Finally, we also wanted to assess an independent impact of the GDNQ motif on genome replication. To this end, we developed a novel replication assay by combining a tetracistronic transcription and replication competent virus-like particle (trVLP) assay [10] with an RT-qPCR. To this end, a tetracistronic minigenome encoding VP40, GP1,2, and VP24, in addition to a reporter, was expressed in p0 producer cells in the presence of the RNP proteins. This resulted in the formation of trVLPs that contain copies of the minigenome encapsidated in nucleocapsid-like structures. These trVLPs were then used to infect p1 target cells, which had been pretransfected with expression-plasmids for NP, VP35, the EBOV adhesion factor Tim1, and either wild-type L, or L with a mutated GDNQ motif. VP30 was intentionally omitted in p1 cells, since this protein has been shown to be an essential transcriptional activator, but not required for replication [6,8]. Total RNA from these p1 cells was harvested 2 days after infection, and subjected to an RT-qPCR assay targeting the intergenic (i.e. non-transcribed) region between the GP gene and the VP24 gene in the minigenome. Again, we saw a significant (-L vs. +L: p = 0.048; +L vs. +Lmut: p = 0.011) reduction in vRNA/cRNA accumulation down to background levels when using L containing the mutated GDNQ motif, indicating that this motif is also required for genome replication.
The EBOV polymerase is the target for a number of potential antivirals such as favipiravir (T705) [23], BCX4430 [24], GS-5734 [25], and β-D-N4-hydroxycytidine [26]. Further, it has been the target for a number of high-throughput drug screens [27–29], which generally exploit minigenome systems to allow rapid and easy modelling of the EBOV life cycle without the need for a high containment facility [30]. However, despite its central role in the virus life cycle, structural and functional data for this protein remains scarce.
When the protein sequences of negative sense RNA virus polymerases of Rhabdo- and Paramyxoviruses were first published [31], it quickly became clear that they share highly conserved regions that we now know to correspond to the RdRp, the polyribonucleotidyltransferase (PRNTase), and the methyltransferase (MTase) domains (reviewed in [32]), and for the Rhabdovirus VSV the structure of these domains has been solved at the atomic level [17]. The same conserved regions have since been tentatively identified based on sequence comparisons in other negative-sense RNA viruses including EBOV and MARV [33,34]. Further, while only limited crystal structure information is available for negative sense RNA virus polymerases, bioinformatics-based structural predictions suggest that the filovirus polymerase has a similar structure than polymerases from viruses for which a structure is known [14,15]. Experimental evidence of such a similar structure and experimental identification of molecular targets within the polymerase can help in rational drug design, as well as provide important insight in the mechanisms of action of compounds targeting L.
This conservation of sequence and structure was the basis for the identification of flexible linker sites that allowed insertion of peptide tags as well as fluorescent proteins into the EBOV polymerase, with little impact on its expression, localization or function [12,13], similar to previous studies involving the polymerases of paramyxoviruses [35,36]. Further, this assumption formed the basis for the multiple sequence alignment used in the present study to search for a putative catalytic center of the EBOV polymerase. Using this alignment, a GDNQ motif in the RdRp was readily identified, consistent with predictions by Cong et al., who have suggested that D742 is a catalytic site in the filovirus polymerase [14]. After mutating the GDNQ motif, our functional results using luciferase-encoding minigenomes showed very clearly that this motif is required for genome replication and/or transcription of EBOV, and that this mutation completely abolishes transcription (based on the results of the replication-deficient minigenome system) and potentially both of these processes, resulting in reporter levels that are identical to samples completely lacking viral polymerase, and corresponding to the background noise of the luminometer (about 102 RLU). Similar results were observed using GFP as a reporter, where in cells expressing L-mCherry with a mutated catalytic domain no GFP signal was observed. This was in contrast to cells expressing wild-type L-mCherry, where a strong GFP signal was readily observed, corresponding to robust minigenome transcription and replication (albeit not in all cells, since in addition to L the other RNP proteins, as well as the minigenome, all have to be expressed in the same cell).
In order to show definitively that genome replication is also abolished, we developed a replication assay by combining RT-qPCR technology and the recently published tetracistronic trVLP system. This approach has the advantage that neither minigenome-encoding plasmid DNA nor initial T7-transcribed and naked minigenome RNA is present in the p1 cells analyzed, since the source of the minigenome in those cells is infecting trVLPs which have packaged minigenome RNA-containing nucleocapsid like-structures [10]. Further, the target of the RT-qPCR is the VP30/VP24 intergenic region. In the tetracistronic minigenome this sequence is located between the GP1,2 ORF and the VP24 ORF (in contrast, in the full-length EBOV the GP and VP24 genes are separated by the VP30 gene, so that no native GP/VP24 gene junction exists). This region harbors the longest non-transcribed sequence in the EBOV genome with a length of 144 nt [37]. This approach allowed us to exclude detection of mRNA, rather than cRNA/vRNA, despite the use of a one-step RT-PCR (i.e. instead of a strand-specific two-step RT-PCR to target vRNA specifically). Additionally, we further exclude the erroneous detection of mRNA in this system by omitting expression of the transcriptional activator VP30 in p1 cells, as this protein has been shown to be required for transcription, but not for genome replication [6,8].
While, as with all point mutations, there is always the concern that the introduced mutations might negatively affect protein folding, we believe this not to be the case in this instance for two reasons: First, the mutated mCherry-tagged polymerase is readily recruited into inclusion bodies similar to those observed previously in cells infected with a recombinant EBOV expressing L-mCherry at early time points after infection, indicating that it still has to be able to interact with the other RNP proteins. Secondly, we have previously shown that an interaction with VP35 is required for stable expression of L, and that in the absence of VP35 L cannot be detected in significant amounts by western blotting [13]. The interaction domain between VP35 and L has been mapped to the amino acids 280 and 370, which are located in the RdRp domain of L [11]. Since we do not see any differences in the expression level of our mutated L, we have to conclude that this mutant remains able to interact with VP35, and that, therefore, the RdRp domain which harbors the mutation is not grossly misfolded.
On a technical note, analysis of the Firefly control luciferase in this study showed signals for this reporter that were lower in the -L controls than in the +L samples (regardless of whether L was functional or not). This phenomenon is most likely due to the fact that it is common good practice to include empty vector in samples where plasmids are omitted for experimental reasons (e.g. in -L controls), in order to equalize the transfected plasmid mass. However, given the size of the L expression plasmid (11.6 kB) vs. the empty vector (4.8 kB), this means that in terms of absolute numbers many more empty plasmids than L expression plasmids are transfected, which may lead to a reduction in gene expression from the other co-transfected plasmids. This effect can skew the results of minigenome assays, since reporter luciferase activity values are normalized to these control luciferase values, thus artificially inflating -L control values. While this effect is small compared to the very large dynamic range of EBOV minigenome assays (which in our hands is about 3 log10), particularly for high-throughput assays were a large dynamic range is required and this control luciferase is essential to normalize for well-to-well variations, this situation is less than ideal. In contrast, when using the mutated L version, no differences in plasmid-based gene expression (i.e. Firefly controls) are observed, while genome replication and transcription are completely abolished. Thus, this mutant represents a superior control compared to the -L control, particularly in context of high-throughput assays. This is of particular importance as high throughput-screens under BSL4 conditions, which are necessary for work with infectious EBOV, are significantly more complex and cost-intensive than similar screens under BSL2-conditions, providing a strong incentive for the use of EBOV minigenome and other life cycle modelling systems for drug screening purposes [30]. Further, the development and use of similar catalytically inactive polymerase mutants in the place of conventional -L controls may represent a technical improvement for other minigenome systems (e.g. for other viruses) that may demonstrate more modest dynamic ranges and thus be more significantly impacted by such effects.
Overall, we have experimentally confirmed the catalytic center of the RdRp of the EBOV polymerase, which represents a promising target for the development of antivirals. This work provides a basis for future studies aimed at inhibiting the activity of this protein, which is absolutely crucial for the virus life cycle, as well as providing technical advancements in the tools available for high-throughput screening applications.
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10.1371/journal.ppat.1005790 | Virus Infection of Plants Alters Pollinator Preference: A Payback for Susceptible Hosts? | Plant volatiles play important roles in attraction of certain pollinators and in host location by herbivorous insects. Virus infection induces changes in plant volatile emission profiles, and this can make plants more attractive to insect herbivores, such as aphids, that act as viral vectors. However, it is unknown if virus-induced alterations in volatile production affect plant-pollinator interactions. We found that volatiles emitted by cucumber mosaic virus (CMV)-infected tomato (Solanum lycopersicum) and Arabidopsis thaliana plants altered the foraging behaviour of bumblebees (Bombus terrestris). Virus-induced quantitative and qualitative changes in blends of volatile organic compounds emitted by tomato plants were identified by gas chromatography-coupled mass spectrometry. Experiments with a CMV mutant unable to express the 2b RNA silencing suppressor protein and with Arabidopsis silencing mutants implicate microRNAs in regulating emission of pollinator-perceivable volatiles. In tomato, CMV infection made plants emit volatiles attractive to bumblebees. Bumblebees pollinate tomato by ‘buzzing’ (sonicating) the flowers, which releases pollen and enhances self-fertilization and seed production as well as pollen export. Without buzz-pollination, CMV infection decreased seed yield, but when flowers of mock-inoculated and CMV-infected plants were buzz-pollinated, the increased seed yield for CMV-infected plants was similar to that for mock-inoculated plants. Increased pollinator preference can potentially increase plant reproductive success in two ways: i) as female parents, by increasing the probability that ovules are fertilized; ii) as male parents, by increasing pollen export. Mathematical modeling suggested that over a wide range of conditions in the wild, these increases to the number of offspring of infected susceptible plants resulting from increased pollinator preference could outweigh underlying strong selection pressures favoring pathogen resistance, allowing genes for disease susceptibility to persist in plant populations. We speculate that enhanced pollinator service for infected individuals in wild plant populations might provide mutual benefits to the virus and its susceptible hosts.
| Cucumber mosaic virus, an important pathogen of tomato, causes plants to emit volatile chemicals that attract bumblebees. Bumblebees are important tomato pollinators, but do not transmit this virus. We propose that under natural conditions, helping host reproduction by encouraging bee visitation might represent a ‘payback’ by the virus to susceptible hosts. Although tomato flowers can give rise to seed through self-fertilization, bumblebee-mediated ‘buzz-pollination’ enhances this, increasing the number of seeds produced per fruit. Buzz-pollination further favors reproductive success of a plant by facilitating pollen export. Mathematical modeling suggests that if self-fertilization by infected plants, as well as pollen transfer from these plants (cross-fertilization) to surrounding plants is increased, this might favor reproduction of susceptible over that of resistant plants. This raises the possibility that under natural conditions some viruses might enhance competitiveness of susceptible plants and inhibit the emergence of resistant plant strains. We speculate that it may be in a virus’ interest to pay back a susceptible host by enhancing its attractiveness to pollinators, which will likely increase fertilization rates and the dissemination of susceptible plant pollen and may compensate for a decreased yield of seeds on the virus-infected plants.
| Insects pollinate many plant species, including several major crops [1]. Bees are the single most important insect pollinator group and can be a limiting factor for the success of plant reproduction [1–3]. Consequently, there is strong inter- and intra-specific competition among plants for the attention of pollinators [2, 3]. With respect to insect-pollinated crops, pollinator visitation (or artificial pollination) is required to obtain maximal seed and fruit production [4, 5]. Consequently, pollination facilitates higher yields even when a crop plant is self-compatible [4, 5]. Tomato (Solanum lycopersicum) provides a good example of the relationship between pollination and yield. Bumblebees are important pollinators of tomato and other Solanum species that utilize an unusual pollination system called ‘buzz-pollination’ [6]. Buzz-pollinated flowers provide excess pollen as a reward to foraging bumblebees that feed it to their developing larvae [6]. Although domesticated tomato is to a large extent ‘self-fertilizing’, buzz-pollination by bumblebees or by manual application of mechanical vibration ‘wands’ is required for maximal seed production, which in turn promotes increased fruit yield (see [5] and references therein).
Cucumber mosaic virus (CMV), one of the major viral pathogens of tomato, is a positive-sense RNA virus that encodes five proteins including the 2b protein, which is a viral suppressor of RNA silencing (VSR) [7, 8]. Bees do not transmit CMV but the virus is vectored by several aphid species [7, 8]. Virus infection causes dramatic changes in plant host metabolism (reviewed in [9]). CMV-induced metabolic changes include qualitative and quantitative alterations in the emission of volatile compounds and in certain host species this makes infected hosts more attractive to aphid vectors [10, 11].
It is not known if the virus-induced alterations in host volatile emission that influence aphid behavior can also affect plant-pollinator interactions. Most bee-plant interaction studies have focussed on the effects of visual cues. Therefore, the influences of floral and non-floral volatiles on bee-mediated pollination are less well understood [12–14]. In contrast, the floral odors that attract moth pollinators have been more extensively researched [15–17]. In this study we determined that CMV infection induced changes in olfactory cues emitted by Arabidopsis thaliana (hereafter referred to as Arabidopsis) and tomato plants in ways that could be perceived by the bumblebee Bombus terrestris, and confirmed in tomato that this was associated with quantitative and qualitative changes in the blend of plant-emitted volatile organic compounds (VOCs). We also elucidated a role for the host microRNA (miRNA) pathway in regulating the emission of bee-perceivable olfactory cues. Our data indicated that bumblebees possess an innate preference for olfactory signals emitted by CMV-infected tomato plants and we mathematically modeled what the possible wider implications of this might be if a similar preference occurred in wild host plants under natural conditions.
In ‘free-choice’ assays, bumblebees encountered flight arenas containing ten tomato plants (five plants/treatment) concealed within towers designed to allow odors to diffuse out but prevent the bees from seeing or touching the plants (Fig 1A). Cups that were placed on top of towers hiding plants of both treatment groups offered bumblebees the identical ‘incentive’ of a 30% sucrose solution. Nonetheless, when presented with mock-inoculated and CMV-infected tomato plants, bumblebees preferred to visit the towers that were hiding infected plants (Fig 1B) (S1 Table). Bumblebees showed similar preferences for flowering and non-flowering CMV-infected plants, indicating that leaves were the main source of attractive volatiles (Fig 1B). Bumblebees also displayed a preference for CMV-infected tomato plants over plants infected with CMVΔ2b, a viral mutant lacking the gene for the 2b VSR (Fig 1B), a factor that also influences CMV-plant-aphid interactions [18,19].
The results obtained in free-choice assays with tomato plants infected with CMVΔ2b suggested that the 2b protein, which is a VSR, may be exerting effects on the metabolism of plant volatiles by interfering with host small RNA pathways. The model plant Arabidopsis is the best higher plant system to use to investigate the effects of small RNA pathways. However, whilst Arabidopsis plants emit potentially pollinator-influencing volatiles, this species is not bee-pollinated [20]. Consistent with this, bumblebees showed no significant difference in preference for volatiles emitted by CMV-infected versus mock-inoculated Arabidopsis plants in free-choice assays (Fig 1B).
An alternative approach to investigate the ability of bees to recognise differences in olfactory or other stimuli is to set up a differential conditioning or ‘learning curve’ assay [14,21]. A differential conditioning assay can reveal whether bees can perceive cues that would not normally induce any behavioural responses and that could not be studied in free-choice assays. In our differential conditioning assays, cups on towers offered bumblebees either a 30% sucrose solution ‘reward’ for choosing one treatment group or a ‘punishment’ (0.12% quinine) for choosing the other group [14,21]. Bumblebees cannot distinguish quinine from sucrose except by taste [22]. Thus, increasing frequency of visits to sucrose-offering towers over the course of an experiment indicated that bees have learned to use plant odor as a cue to identify and avoid drinking from cups placed on towers offering quinine solutions. In these assays, a steep learning curve shows that bumblebees can easily distinguish between two treatment groups, and indicates that the volatile blends are likely to be qualitatively and/or quantitatively very distinct, whereas less steep curves indicate that differences between blends are less marked, and that bees find it more difficult to learn to distinguish between them based on odor. An illustration of the power of this approach is shown in Fig 2 (S2 Table). Although bumblebees displayed an innate preference for volatiles emitted by CMV-infected tomato plants in free choice assays (Fig 1A), they could be trained by differential conditioning to overcome their innate preference and instead preferentially visit mock-inoculated tomato plants and avoid CMV-infected plants (Fig 2A).
Although we had observed that bumblebees had no innate preference for, or aversion to, volatiles emitted by Arabidopsis plants (Fig 1B), differential conditioning assays revealed that the insects could recognize differences between volatiles emitted by Arabidopsis plants that had been mock-inoculated and by plants that were infected with CMV (Fig 3A) (S2 Table). Bumblebees could also distinguish between CMV-infected and CMVΔ2b-infected Arabidopsis plants (Fig 3B). Hence, although they exhibit no innate behavioural response to the volatile blends emitted by Arabidopsis plants, differential conditioning assays showed that bumblebees could perceive differences in volatiles emitted by these plants. This meant that differential conditioning assays could permit further dissection of the mechanisms underlying CMV-induced changes in volatile emission using Arabidopsis as a model system.
Bumblebees could learn to differentiate transgenic plants constitutively expressing the 2b VSR from non-transgenic plants (Fig 3C) and from control-transgenic plants that were expressing an untranslatable 2b transcript (Fig 3D). However, the insects displayed less ability to learn to distinguish mock-inoculated from CMVΔ2b-infected plants (Fig 3E). Comparison of the learning curves in Fig 3A versus Fig 3E by logistic regression (see Methods) indicated that bumblebees were better at distinguishing mock-inoculated plants from CMV-infected plants than from CMVΔ2b-infected plants (χ2(1) = 40.17, p < 0.0001). Bees could not be trained to differentiate non-transgenic plants from control-transgenic plants expressing a non-translatable 2b transcript (Fig 3F).
The results with CMVΔ2b suggested that the 2b VSR plays an important role in altering the emission of bee-perceivable olfactory cues emitted by tomato and Arabidopsis plants (Figs 1A and 3E). However, CMVΔ2b accumulates to lower levels in plants than wild-type CMV and in previous work it was found that viral titer, as well as the presence of the 2b protein, plays a role in modification of the interactions of Arabidopsis with aphids [19]. Hence, it was conceivable that differences in virus titer might affect the emission of bee-perceivable volatiles by plants infected by CMV or CMVΔ2b and explain why the bees found it difficult to distinguish CMVΔ2b-infected plants from mock-inoculated plants. However, it is known that CMVΔ2b accumulates to levels comparable to those of wild type CMV in Arabidopsis plants carrying mutations in the genes encoding the Dicer-like (DCL) endoribonucleases DCL2 and DCL4, which are important factors in antiviral silencing [19]. Therefore, we examined the ability of bumblebees to learn to distinguish between volatile blends emitted by CMVΔ2b-infected and mock-inoculated dcl2/4 double mutant plants (Fig 3G). The resulting learning curve (Fig 3G) was not significantly different from that obtained using wild-type plants that had been mock-inoculated or infected with CMVΔ2b (Fig 3E) (χ2(1) = 0.66, p = 0.42), indicating that an increase in CMVΔ2b titer did not enhance bee learning. Although we cannot rule out a role for other CMV gene products, the results indicate that the 2b VSR is the most significant viral factor conditioning changes in the emission of bee-perceivable volatiles.
One of the host molecules that interact with the 2b VSR is the Argonaute 1 (AGO1) ‘slicer’ protein. AGO1 is required for silencing directed both by short-interfering RNAs (which can be generated de novo) and by miRNAs, which are generated by a specific host endoribonuclease (DCL1) from miRNA precursor transcripts encoded by nuclear genes [23,24]. In differential conditioning assays, bumblebees were able to learn to distinguish between volatiles emitted by wild-type plants versus those emitted by ago1 mutant plants (Fig 3H) and those emitted by dcl1 mutant Arabidopsis plants (Fig 3I). However, bumblebees showed little or no ability to learn to distinguish between volatile blends emitted by ago1 and dcl1 mutant plants, indicating that the volatile blends emitted by plants of these two mutant lines were very similar (Fig 3J). Thus, the miRNA-directed silencing pathway regulates the emission of bee-perceivable volatile compounds. Double mutant dcl2/4 plants are unable to generate CMV-derived short-interfering RNAs but are not affected in miRNA biogenesis. In CMV-infected dcl2/4 plants a higher proportion of the 2b protein is available to bind AGO1 and inhibit its miRNA-directed activity [19], which is likely to enhance virus-induced changes in emission of bee-perceivable volatiles. In line with this, bumblebees were able to learn to distinguish between volatiles emitted by CMV-infected wild-type and dcl2/4 double mutant Arabidopsis plants (Fig 3K). As an additional control we showed that bumblebees could not learn to distinguish between volatiles emitted by mock-inoculated plants covered by towers offering sucrose rewards or quinine punishments (Fig 3L).
The responses of bumblebees to CMV-infected tomato plants that were hidden from the insects indicated that changes in the emission of volatiles were affecting bee behavior and were responsible for the innate preference of these insects for CMV-infected plants (Fig 1B). To confirm that CMV infection caused changes in the emission of VOCs, tomato plant headspace volatiles were collected and analysed by gas chromatography coupled to mass spectrometry (GC-MS). VOCs were collected from non-flowering mock-inoculated plants, plants infected with CMV-Fny and plants infected with the 2b gene deletion mutant of CMV-Fny, CMVΔ2b. The emitted VOCs were distinct from each other when compared by principal component (PC) analysis on the relative intensity of ions (over 75 Da in size) within the samples (Fig 4A). PC1 explained 80.3% of the variation and discriminated between samples from mock-inoculated and CMV-infected plants, whereas PC2 discriminated between samples from mock-inoculated and CMVΔ2b-infected plants (Fig 4A). Thus, the VOC blend emitted by CMV-infected tomato plants was more distinct from that released by mock-inoculated plants than it was from the volatiles emitted by CMVΔ2b-infected plants. Nevertheless, VOC emission by CMVΔ2b-infected tomato plants was distinct from either mock-inoculated plants or CMV-infected plant VOC emission (Fig 4A), despite this mutant virus accumulating to markedly lower levels than CMV (S1 Fig).
Although CMV-infected plants were smaller than either mock-inoculated or CMVΔ2b-infected plants, the emission of the combined volatiles on a whole plant basis was similar between mock-inoculated and CMV-infected plants (Fig 4B). Indeed, expressing the emission of the combined VOCs by mass of tissue revealed that CMV-infected plants released greater quantities of volatiles compared to mock-inoculated and CMVΔ2b-infected plants (Fig 4C). Thus, despite being stunted, CMV-infected plants generated a greater total quantity of VOC than either mock-inoculated or CMVΔ2b-infected tomato plants.
Identification by GC-MS of the most abundant VOC by g dry weight of tomato plant tissue showed that terpenoids dominated the profile, with α-pinene, 2-carene, p-cymene, β-phellandrene and the sesquiterpene (E)-caryophyllene being apparent (Fig 4D and 4E). CMV infection caused quantitative changes in the profile of these VOCs; α-pinene and p-cymene emission increased markedly, whereas 2-carene and β-phellandrene did not, and (E)-caryophyllene almost disappeared from the profile (Fig 4E). Isomeric composition was not further determined than that stated here. When VOC emission was compared on a whole plant basis, α-pinene and p-cymene emission rates from CMV-infected plants appeared similar to mock-inoculated or CMVΔ2b-infected plants, while 2-carene and β-phellandrene levels appeared to be lower (although this was not statistically significant in a one-way ANOVA: Fig 4D). Bumblebees of a closely related species (B. impatiens) are known to be repelled by β-phellandrene and 2-carene [25]. Thus, lower emission values of these VOCs from CMV-infected plants may explain why bumblebees displayed an innate preference for CMV-infected tomato plants over mock-inoculated plants in free choice assays (Fig 1B). The VOC profiles of mock-inoculated and CMVΔ2b-infected plants were similar, although not identical (Fig 4A), and this could explain the bees’ lack of preference in free choice assays (Fig 1B).
Domesticated tomato plants are often said to be self-fertilizing. However, optimal self-fertilization requires sonication of the flower to release pollen from the anthers onto the stigma, which is provided either by buzz-pollination from a bee (typically a bumblebee) or simulated buzz-pollination using mechanical vibration [5]. This is illustrated in Fig 5A, which shows how mechanical buzz-pollination of flowers increased seed production by around a third. Seed production by tomato was very dramatically decreased in plants infected with CMV-Fny to less than 10% of the yield in mock-inoculated plants (Fig 5A). Remarkably, artificial buzz-pollination of flowers of CMV-infected plants rescued seed production to a significant degree with seed numbers reaching approximately half the level seen for non-buzzed flowers of mock-inoculated plants and about 6- to 7-fold greater than the number of seeds produced in non-buzzed, CMV-infected plants. The difference in seed yield between mock-inoculated and CMV-infected plants that had been vibrated was less marked than between non-buzzed, mock-inoculated and CMV-infected plants (Fig 5A).
Although CMV-infected plants produced fewer seeds, the mass of individual seeds was unaffected by infection and was not affected whether or not flowers were vibrated (Fig 5B). Additionally, the number of flowers produced by CMV-infected plants was similar to the number produced by mock-inoculated plants, and tomato flower morphology was also not markedly altered by infection (S2 Fig). Overall plant growth was stunted by CMV infection (S2 Fig) but, interestingly, virus infection appeared to accelerate the appearance of flowers by a small but statistically significant degree (S2 Fig). A recent report indicated that flowers of squash (Cucurbita pepo) plants infected with the potyvirus zucchini yellow mosaic virus yielded decreased quantities of pollen [26]. However, we found no significant differences in the quantity or viability of pollen released from mock-inoculated and CMV-infected tomato flowers (S3 Fig).
We investigated the effects of CMV infection on bumblebee-mediated pollination under glasshouse conditions in which the insects could see and interact with flowers (Fig 6). A European CMV isolate, PV0187, which is 99% identical in RNA sequence to CMV-Fny and which encodes a 2b VSR that is identical in amino acid sequence to that of CMV-Fny (S4 Fig), was used for these experiments in order to comply with UK quarantine and containment regulations. CMV-PV0187 had similar effects on growth of tomato plants as CMV-Fny (S5 Fig) and volatiles emitted by tomato plants infected with CMV-PV0187 were attractive to bumblebees in the free choice assay (Fig 1B).
When CMV-infected and mock-inoculated tomato plants were exposed to bumblebees, a higher proportion of the insects made their initial floral visits to CMV-infected plants and spent longer sonicating the flowers of CMV-infected plants (S6 Fig). As had been seen for artificial buzz-pollination (Fig 5), when bumblebees buzzed flowers, seed yield was increased (Fig 6). For CMV-infected plants, when bees did not visit flowers or where flowers were on plants not exposed to bees (untouched plants), the seed yield was significantly decreased (Fig 6). However, although CMV infection decreased seed number in fruits derived from unvisited flowers, buzz-pollination by bumblebees negated this effect; indeed, bee-pollinated flowers on CMV-infected plants yielded fruit that contained seed numbers similar to those found in fruit that developed from bee-pollinated flowers on mock-inoculated plants (Fig 6). The results imply that there was greater buzzing activity on flowers of CMV-infected plants (S6 Fig), resulting in a greater amount of seed production.
We have seen that under controlled conditions CMV infection made tomato plants more attractive to bumblebees (Fig 1B). We also found that although infected plants yielded fewer seeds, simulated buzz-pollination could to some extent rescue seed production (Fig 5A) and when bees were allowed access to CMV-infected plants they caused a greater increase in seed production per fruit compared to simulated buzz-pollination (Fig 6). The results obtained with this domesticated plant under controlled conditions prompted us to wonder what would be the consequences for a wild buzz-pollinated plant growing under natural conditions, if virus infection resulted in greater pollinator visitation and/or seed production and whether this might result in any benefits for the host plant or the virus.
To investigate this idea further we developed a mathematical model to test whether increased pollinator service to virus-infected plants could allow genes for virus susceptibility to persist in a host plant population, despite the significant fitness cost of infection for plants as female (seed producing) parents (cf. Figs 5 and 6). Our model tracks the long-term dynamics of the interaction between resistant and susceptible phenotypes in a population of annual plants (see also Materials and Methods). We focused on resistance as a dominantly inherited trait and attached no fitness penalty to the presence of resistance, which is a conservative approach given that recessive resistance is a commonly observed antiviral defense mechanism and that resistance may incur fitness costs in the absence of infection [27]. We assume infected susceptible plants produce fewer seeds, with the parameter δ controlling the proportionate number of viable seeds produced per fertilized ovary on a virus-infected plant. However, we also assume that, all other things being equal, an individual visit by a pollinator is ν times more likely to be to a flower on an infected versus an uninfected plant. This pollinator bias makes infected plants more likely to reproduce as both male (pollen donor) and female (seed producing) parents, potentially out-weighing the deleterious effect of infection on seed production.
We focus initially on the trade-off between pollinator bias (ν) and reduction in seed production (δ), for different levels of pollinator service (which we parameterize via γ, the mean number of pollinator visits per flower over the plant’s reproductive season). In indicative examples of both low (γ = 0.25) and high (γ = 2.5) pollination regimes, (ν, δ) parameter space can be divided into three regions: resistance takes over in the long-term, susceptibility takes over in the long-term, or resistant and susceptible plants coexist (Fig 7A and 7B). For both values of γ, at high values of ν and δ (i.e. if infected plants are strongly preferred by pollinators but do not suffer a great reduction in seed production), then genes conferring susceptibility will take over in the plant population. For low values of ν and δ the situation is reversed, and resistance is favored. At intermediate values of ν and δ, resistant and susceptible plants coexist.
For fixed baseline values of ν = 3.0 and δ = 0.5, the proportion of susceptible alleles in the population first increases then decreases as the level of pollinator service (γ) is increased (Fig 7C). At very low values of γ, although virus-infected plants benefit from additional pollinator service on both male and female sides, the vast majority of fertilizations do not involve pollinator visits (instead being via self-pollination). The cost to susceptible plants of reduced seed production as female parents is therefore more important than increased pollinator visitation, and so virus resistance takes over. As γ is increased, the proportion of fertilizations caused by pollinators goes up, which allows the benefits to virus-infected plants on both male and female sides to outweigh the cost of infection, and so the genes for susceptibility are favored. As γ is increased still further, the benefit on the female side becomes smaller (since pollinator visitation is not limiting and almost all ovules are fertilized), but on the male side proportionately more pollen still comes from infected plants. For these values of the parameters, alleles conferring virus susceptibility persist in the plant population, but at reduced density. The maximum density of susceptible genotype plants is therefore realised at intermediate pollinator densities.
The broad pattern of a rise then fall in the proportion of plants carrying the susceptible allele is repeated for a range of values of the proportion of susceptible plants that are infected (i.e. the parameter α in our model: Fig 7D). However, for our default parameterization at low levels of infection the eventual fall with increasing pollinator levels is not apparent, and susceptible plants exclude resistant plants even for very high values of γ.
A full sensitivity scan around default parameter values ν = 3.0, δ = 0.5, γ = 1.0, σ = 0.5, φ = 0.75 and α = 0.5, (Fig 8; S7 Fig) shows the behaviour of the model over large regions of parameter space. The susceptible genotype is able to persist under many combinations of parameters. Our model therefore suggests preferential visitation of infected plants by pollinators could in principle provide a robust mechanism allowing susceptible genotype plants to be retained in the host population for a wide range of conditions.
Infection with CMV altered the volatile profile of tomato plants and made them more attractive to bumblebees, indicating that these insects possess an innate preference for the blend of volatile compounds emitted by CMV-infected tomato. Although bumblebees showed no innate preference for CMV-infected or mock-inoculated Arabidopsis plants, differential conditioning experiments showed that bumblebees were able to perceive alterations in volatiles emitted by these plants. Experiments with the 2b gene deletion mutant virus, CMVΔ2b, in tomato and Arabidopsis, and with 2b-transgenic and ago1 and dcl1 mutant Arabidopsis plants, implicate small RNA pathways in the regulation of the production of bee-perceivable volatile compounds.
The inability of bees to learn to effectively distinguish between volatiles emitted by ago1 and dcl1 mutant plants causes us to conclude that miRNAs are the predominant class of small RNAs involved in regulating the metabolism of bee-perceivable compounds. The rationale for this conclusion is that AGO1, a target for the CMV 2b VSR, utilizes both short-interfering RNAs and miRNAs to guide RNA cleavage, while DCL1 is involved in miRNA biogenesis but is not involved in production of short-interfering RNAs (see refs. [23, 24] and references therein). As far as we are aware, an effect of miRNAs on plant volatile production (presumably through regulation of stability or translation of specific plant mRNAs) has not been previously reported. The work also indicates that olfactory signals emitted by non-floral tissue may have a more important effect than previously thought in plant-bee interactions and may play roles in bee attraction, presumably at longer ranges than visual features such as the optical or tactile qualities of flowers. Thus, foliar volatile signals may affect bee choices or synergize with and reinforce visual floral cues, as has been seen with floral odors [28, 29].
How do changes in the output of volatiles increase the attractiveness of CMV-infected plants for bumblebees? Much of the existing bee perception literature is focused on the effects of visual stimuli (e.g. color and other optical properties of flowers [14]), whereas the effects of olfactory stimuli have been relatively neglected. But it is known, for example, that the VOC output from flowers decreases after they have been pollinated [12]. Pollination can also trigger qualitative changes in the volatile blend. For instance, following pollination by bees, blueberry (Vaccinium corymbosum) flowers emit an increased proportion of their volatiles as (E)-caryophyllene [30]. It is thought that decreased volatile emission by pollinated flowers decreases their saliency to bees and prevents damage from over-visitation [12] and a similar explanation was offered by Rodriguez-Saona and colleagues [30] to explain the post-visit increase in (E)-caryophyllene emission. In the case of tomato plants infected with CMV, it may be that the virus is both ‘turning up the volume’ of plant volatile emission (making these plants more apparent to the bumblebees) whilst ‘tuning’ volatile blend composition so as to diminish levels of a signal ((E)-caryophyllene), that at higher levels might indicate a previous bee visitation, and levels of β-phellandrene and 2-carene that might discourage visitation [25].
When the bumblebees were allowed access to flowering tomato plants under glasshouse conditions we found that buzz-pollination by bumblebees was more effective at enhancing seed yield on CMV-infected plants. This result suggests that additional foliar or floral cues, for example visual or tactile stimuli, do not negate the effects on the bees of CMV-induced changes in volatile emission.
It is possible that our findings may have implications for transmission of viruses vectored by bees. However, pollinators transmit very few viruses and CMV is not one of them (discussed on page 522 in reference [31]). Nevertheless, is it possible that a virus that is not bee transmitted gains some advantage by re-paying a susceptible host by altering its volatile cues to attract pollinators? In our mathematical model it was assumed that a hypothetical population of wild plants included some hosts that possessed genetic resistance to the virus. It might then be assumed that pathogen-imposed selection pressure would favor the takeover of the plant population by any plants possessing one or more resistance genes. This outcome, causing a decrease in the population, or even the extinction, of susceptible plants would clearly not be beneficial either for the pathogen or for the susceptible hosts. However, our mathematical model shows that in the case where pollinators show increased bias towards pathogen-infected plants, the increased reproductive success of infected plants means that the outcome might be different. Thus, it is plausible that if the attractiveness of infected plants to pollinators is increased, this might inhibit or negate the selective advantage of resistant individuals and prevent them from taking over the population (represented conceptually in Fig 9). We also noted that CMV infection accelerated the appearance of flowers in tomato. If such an effect occurred in a wild plant population, it is conceivable that this may give infected, susceptible plants a further advantage over resistant or uninfected plants in the competition for limited pollinator services. Indeed, there are examples in which earlier flowering increases pollination and enhances yield (for example in the oil crop plant Echium plantagineum)[32]. However, the relationship between flowering time and pollination is complex and there may be environments in which it is more advantageous for plants to flower in a concerted fashion. However, in certain contexts earlier flowering may result in flowers being produced before pollinators are available (reviewed in [33]).
At this stage, it may be imprudent and premature to propose that increased pollinator attraction to infected, susceptible hosts represents some sort of specific viral strategy to inhibit selection for resistance, and there are difficulties in envisaging how this might initially arise. However, it seems plausible to suggest that in principle increased pollinator attraction to virus-infected plants could favor the persistence of susceptible plants in the environment and this could be seen as payback or compensation to the host. It is worth noting that other forms of payback by viruses to their hosts have been observed in a number of systems. This has led to the suggestion that our general view of viruses has been overly colored by their pathogenic properties and that we should view them as symbionts in the classical sense (viz. on a spectrum that ranges from parasitic to mutualistic [34]). For plant viruses it has been shown that virus infection can enhance the endurance of susceptible host plants to drought or in one case to cold [35, 36] and that plants of several species were protected from herbivory by virus infection [37–40]. It has been suggested that resistance to drought is a conditional phenotype that could act as a payback to the host. In the case of CMV-induced drought resistance in Arabidopsis and other plants [35, 36] and in the present study, in which CMV enhances a tomato plant’s attractiveness to bumblebees, we may be seeing examples of ‘extended phenotypes’. An extended phenotype emerges from the action of a parasite gene when it alters the phenotype of a host; potentially to the benefit of the parasite [41]. In both examples, drought resistance in Arabidopsis [36] and pollinator attraction in tomato (the present study), the parasite gene controlling these extended phenotypes is the CMV 2b gene. A potential result of these extended phenotypes would be to increase the odds of continued survival of susceptible host plant populations, which would be beneficial to both host and pathogen.
Our mathematical modeling results indicated that, for the areas of the parameter space that are most salient to our experimental findings, the most likely outcome of long-term selection would be coexistence of resistant and susceptible genotypes, i.e. the long-term maintenance of R gene polymorphisms. Several mechanisms have been proposed that could contribute to the maintenance of balanced R gene polymorphisms such as the ratio of costs versus benefits of resistance, and diffuse interactions between hosts and attackers [27,42,43]. Our data suggest that the enhanced attraction of pollinators to infected susceptible plants might add to these mechanisms and contribute to the long-term maintenance of R gene polymorphisms in insect-pollinated species.
Production of many important crops depends on bee-facilitated pollination. Worryingly, bee populations are endangered by disease, environmental change [44,45] and, more controversially, by anthropogenic factors [46]. To mitigate the ensuing loss of pollination activity requires among other things a deeper understanding of the mechanisms shaping bee-plant interactions. Our data show that non-floral plant volatiles can be perceived by bumblebees and affect their behaviour and that emission by plants of bee-perceivable compounds is regulated in part by miRNA activity. This information may be useful in developing strategies to increase pollinator services for crops under conditions of cultivation, as well as for a better understanding of the interplay of plant pathogens, wild plants and pollinators under natural conditions.
Plants used were Arabidopsis thaliana (Heynh.) accession Col-0 and Solanum lycopersicum (L.) cv. Moneymaker (Suttons Seeds Ltd., Paignton, UK). Plants were grown in a growth chamber at 22°C in M3 compost (Levingtons Ltd., Northampton, UK). Tomato and Arabidopsis plants were grown under 16hr light/8hr dark and 8hr light/16hr dark photoperiods, respectively. Fny-CMV [47], Fny-CMVΔ2b [48], the 2b-transgenic plant line 2.30F [49], and the dcl1-9, dcl2/4, and ago1-25 mutant plant lines have been described elsewhere [19,50,51]. CMV isolate PV0187 was obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ, www.dsmz.de). RNAs1, 2 and 3 of CMV isolate PV0187 were sequenced and submitted to GenBank under accession numbers KP165580, KP165581, and KP165582, respectively. Inoculations were carried out at the seedling stage and were performed as described previously [49]. Plants were used in experiments when the virus had spread systemically and infection was confirmed routinely by double-antibody sandwich enzyme-linked immunosorbent assays (BioReba, Reinach, Switzerland). Quantification of CMV and CMVΔ2b RNA accumulation was carried out as previously described [52]. Leaf tissue from systemically infected leaves was harvested at 10 and 18 dpi. Total RNA for reverse transcription coupled polymerase chain reaction analysis was extracted using an RNeasy Plant Kit (Qiagen) and treated with TURBO-DNase (Ambion) according to the manufacturers’ instructions. Reverse transcription was carried out with 0.5 μg total RNA using Goscript (Promega) with random hexamer primers according to the manufacturer's instructions. Following the reaction, cDNA was diluted 1/10 for subsequent use. Semi-quantitative PCR was performed using Biomix Red (Bioline) and products were separated electrophoretically on a 1.5% agarose gel. Reverse transcription coupled to quantitative polymerase chain reaction analysis was performed using SYBR Green JumpStart Taq ReadyMix (Sigma) in 15 μl reactions according to the manufacturer's instructions. Reactions were performed in triplicate. Primers described in [52] were designed against the conserved 3’ non-translated regions of the CMV genomic RNAs and the stable transcript elongation factor 1 alpha (EF1α) was used as the reference RNA. Data were analyzed using LinRegPCR to give Ct values. Relative viral RNA accumulation was calculated using ΔΔCt methodology, incorporating the EF1α transcript to control for variation in loading [53].
Bombus terrestris (L.) colonies (obtained from Syngenta-Bioline, Leicester, UK and Koppert Biological Systems, Berkel en Rodenrijs, The Netherlands) were connected by gated transparent tubing to flight arenas with the dimensions 72 x 104 x 30 cm [22] containing 11 cm tall feeding towers (to conceal plants) formed from black card sitting within ‘Aracon’ bases (Lehle, Roundrock, TX), roofed by plastic mesh supporting a microcentrifuge tube lid (Fig 1A) containing sucrose solution. Tower height was selected because bumblebees cannot effectively resolve visual cues beyond 10 cm [54]. Seven days prior to carrying out conditioning or free choice assays bees were allowed to feed on sucrose solution from cups placed on empty towers for three days to familiarize them with the arena. Foraging bees were marked on the thorax with water-soluble paint and used once.
Initially, cups on towers offered 30% sucrose, conditioning bees to associate towers with a reward. For differential conditioning and free-choice experiments, five plants per treatment group were individually covered by towers. For differential conditioning experiments, towers hiding plants from one treatment group provided 0.3 ml quinine hemisulfate (0.12%), whilst the others offered 0.3 ml of 30% sucrose. Individual foraging bumblebees were released into the arena and allowed to forage until satiated. Aborts following landing or hovering over towers offering quinine or drinking on towers offering sucrose were scored as correct choices. Between foraging bouts, towers were re-arranged randomly to inhibit spatial learning and meshes cleaned (30% ethanol) to remove scent marks. One hundred choices for each bee tested for each pair-wise comparison were recorded. In free-choice preference assays towers covering plants from both treatment groups offered equal sucrose rewards and only the first feeding choice was recorded.
The learning curve data were analysed using binomial logistic regression [55]. The experimental protocol did not record individual choices made by the bees, but instead the number of ‘correct’ choices made by each bee was grouped into sets of 10 successive choices for ease of scoring. Exploratory analyses suggested no pronounced differences between individual bees within treatment groups, and so we fitted the following fixed effect model to these data
bij~Bin(10,pi),log(pi1−pi)=α0+α1(i−0.5),
where bij is the number of correct choices made by the jth bee in its ith set of ten choices, pi is the probability of choosing correctly in each successive batch of ten choices, and where α0 and α1 are the parameters to be estimated. We used Hosmer-Lemeshow tests to assess model goodness-of-fit [56]: in all cases there was no evidence for lack-of-fit. We therefore went on to assess whether the parameter α1 was different to zero via a likelihood ratio test against the simpler nested model with α1 fixed to be zero [57]. Since the parameter α1 controls how the (logit) of the probability of making a correct choice pi increases with i, positive values of α1 correspond to the bees ‘learning’ over time.
Any systematic differences in the rate at which bees learn between pairs of experiments was assessed by simultaneously fitting a single regression model to the results of both experiments, allowing the probabilities of making a correct choice to depend on the experiment via
log(pi(E)1−pi(E))=α0+(α1+α2E)(i−0.5),
in which E is an indicator variable which is equal to zero for the first experiment, and equal to one in the second experiment. A value α2 ≠ 0 corresponds to bees learning at a different rate in the different experiments: again, this was tested via a likelihood ratio test against the simpler nested model in which α2 was fixed to be zero.
Artificial buzz-pollination was carried out using an electrically actuated toothbrush (‘Oral-B’: Proctor and Gamble, Cincinnati, USA). Mean seed mass was obtained by dividing the mass of seeds by the total seed number for a total of five fruits per plant, with three plants per treatment group. Pollen viability was assessed by staining with fluorescein diacetate [58] and pollen grains viewed under blue light and bright field using an epi-fluorescent microscope (DMRXA, Zeiss) connected to a digital camera (DFC425, Zeiss).
For bumblebee pollination experiments two-week-old tomato seedlings were inoculated with CMV (isolate PV0187) or mock-inoculated and grown in a controlled environment room for 4 weeks. At this time, the plants began flowering and were transferred to a glasshouse. Two weeks later single bumblebees (released from a small flight arena) were allowed to buzz pollinate flowers on three mock-inoculated and three CMV-infected tomato plants within a larger flight arena (125 x 370 x 90cm, H x W x D) constructed from nylon netting (S8 Fig) (JoTech-Insectopia Ltd., Austrey, UK). Two inflorescences of two to three flowers per plant were left accessible to the bee (any more inflorescences were covered with a paper bag). When each bee had made 10 visits to flowers (or had ceased pollinating), any buzz-pollinated flowers were labeled with a jeweler’s tag and all plants that had been visited by the bee were removed from the arena and replaced with another. A new bee was then released from the small arena into the larger arena containing plants. In total, 8 bees freely pollinated flowers from 17 mock-inoculated and 14 CMV-infected tomato plants. Bumblebee visitation to mock-inoculated versus CMV-infected plants was noted and, using a stopwatch, the duration of flower sonication was recorded for each bee. The plants were left in the greenhouse for a further 8 weeks to allow fruits to develop. Further flower development on the plants was permitted.
To release seeds, fruits were harvested individually into 60 ml screw-cap pots and left to ferment for 1–2 weeks before washing and counting. Fruits were either from flowers that were not buzz-pollinated by a bumblebee (fruit from flowers not visited by bee) or from flowers that were buzz-pollinated (fruit from bee-pollinated flowers). A further category of fruit was from flowers that were not buzz-pollinated, but were adjacent to fruit from buzz-pollinated flowers (fruit from flowers adjacent to bee-pollinated flowers). Fruits were also harvested from eight mock-inoculated and eight CMV-infected plants that were not exposed to bees in the flight arena, but had otherwise experienced the same growth conditions as the plants used in the bee pollination experiment (fruit from untouched plants).
Headspace volatiles were collected from tomato plants (4 weeks-old) by dynamic headspace trapping over a period of 24 hours onto Porapak Q filters [50 mg, 60/80 mesh size, Supelco (Sigma-Aldrich)] as described by Beale and colleagues [59]. The tomato plants were contained in a 1.0 liter bell jar clamped to two semi-circular metal plates with a hole in the center to accommodate the stem. Charcoal-filtered air was pumped in at the bottom of the container at a rate of 750 ml.min-1 and drawn out through the Porapak Q filter at the top, at a rate of 700 ml.min-1. Leaf fresh weight and dry weight were measured to enable normalization of the volatile abundance. Trapped organic chemicals were eluted from the Porapak Q filter with diethyl ether for analysis by gas chromatography coupled to mass spectrometry (GC-MS). For initial investigation of volatiles by principal component analysis, volatiles were separated on a capillary GC column (TG-SQC, 15 m by 0.25mm; film thickness, Thermo Scientific, UK). The injection volume (splitless) was 1μl, the injector temperature was 200°C, and helium was used as the carrier gas at a constant flow rate of 2.6 ml min−1 in an oven maintained at 30°C for 5 minutes and then programmed at 15°C.min-1 to 230°C. The column was directly coupled to a mass spectrometer (ISQ LT, Thermo Scientific, UK) with a MS transfer line temperature of 240°C. Ionization was by electron impact with an ion source temperature of 250°C in positive ionization. Mass ions were detected between 30 and 650 m/z. Data were collected using Xcalibur software (Thermo Scientific). Principal component analysis on the mass spectra was performed with MetaboAnalyst 2.0 [60] using binned m/z and per cent total ion count (%TIC) values.
Confirmation of identities of specific organic compounds comprising the blends emitted by mock-inoculated and virus-infected plants was carried out by re-analysis of trapped organic compounds using a Thermo-Finnigan Trace GC directly coupled to a mass spectrometer (MAT-95 XP, Thermo-Finnigan, Bremen, Germany) equipped with a cold on-column injector. Two microliters of collected volatiles were separated on an HP1 capillary gas chromatography column (50 m x 0.32 mm I.D.) in an oven maintained at 30°C for 5 min and then programmed at 5°C.min-1 to 250°C [61]. The carrier gas was helium. Ionization was by electron impact at 70 eV at 220°C. Compounds were identified by comparison of spectra with mass spectral databases (National Institute of Standards and Technology: http://www.nist.gov/), as well as by co-injection with authentic standards on a Hewlett-Packard 6890 gas chromatograph with two different columns of different polarity (HP1 and DB-WAX).
Our model tracks the interaction over evolutionary time between virus resistant and virus susceptible phenotypes in a population of diploid annual plants. The plant population size is assumed to be large and to remain constant over generations. Since CMV is a broad host-range pathogen, we can reasonably make the simplifying assumption that within-generation pathogen prevalence is not affected by the density of resistance in the focal host plant species. The proportion of susceptible plants that become virus infected in each generation is therefore held constant as a parameter (α) in our model. We model resistance as controlled by a single bi-allelic locus, with resistant (R) and susceptible (r) forms, and we assume R is dominant.
We assume infected plants produce fewer seeds, with the parameter δ controlling the proportionate number of viable seeds produced per ovary on a virus-infected plant. We additionally assume that virus resistance carries no fitness penalty when compared to uninfected susceptible hosts. If the reduction in seed number were the only consequence of virus infection, resistance would certainly fix in the plant population under such a conservative assumption on the cost of virus resistance for the plant. However, we also assume that increased attractiveness to pollinators means infected plants are more likely to reproduce, as both male (pollen donor) and female (seed producing) parents.
In particular, we assume the pollinator density remains constant over generations, and that this pollinator density leads to an average of γ pollinator visits per flower averaged over all plants over the entire reproductive season. We assume that flowers visited by pollinators will certainly be pollinated: by cross-pollination (proportion φ) or by self-pollination (proportion 1 – φ). Self-pollination after a visit by a pollinator can be due to either geitonogamous pollen transfer from flowers on the same plant, or via autogamous buzz-pollination (cf. Figs 5 and 6). A proportion σ of the remaining ovules in flowers that are not visited by pollinators also go on to self-pollinate. The potential selective benefit to virus-infected plants is caused by pollinator preference. We assume that an individual pollinator is ν times more likely to visit a flower on an infected vs. an uninfected plant than would be expected by chance alone. This potentially increases female (seed producing) fitness by making ovules on infected plants more likely to be fertilized, and male (pollen donor) fitness by increasing rates of pollen transfer from infected plants.
Given these assumptions, our model tracks the proportion of the plant population in generation n with genotype RR, Rr or rr, which we denote by xn, yn and zn, respectively. The equations linking populations over generations are
xn+1=ζn(ϵR(xn+yn4)+κR(βRR+βRr2)(xn+yn2)),yn+1=ζn(ϵRyn2+κR(βRr2+βrr)xn+κRyn2+κr(βRR+βRr2)zn),zn+1=ζn(ϵRyn4+ϵrzn+(βRr2+βrr)(κRyn2+κrzn)),
in which
η=11+(ν−1)αzn,βRR=ηxn,βRr=ηyn,βrr=η(1+(ν−1)α)zn,ω−=1−e−γη,ω+=1−e−νγη,θ−=(1−ω−)σ+ω−(1−ϕ),μ−=ω−ϕ,θ+=(1−ω+)σ+ω+(1−ϕ),μ+=ω+ϕ,ϵR=θ−,κR=μ−,ϵr=αδθ++(1−α)θ−,κr=αδμ++(1−α)μ−
and where ζn is chosen in each generation to ensure xn+1 + yn+1 + zn+1 = 1. A full derivation of the model showing how it follows from the underlying assumptions is given in S1 Text.
The majority of the results presented in the main text are relative to our default parameterization of the model. By default we take the following parameter values: ν = 3.0, δ = 0.5, γ = 1.0, σ = 0.25, φ = 0.75 and = 0.75. However, as described above, we perform a full two-way sensitivity analysis of pairs of parameters around these default values (Fig 8) to test the robustness of our results to our choice of parameterization.
The behaviour of the model can most easily be characterised in terms of which genotypes persist in the long-term. This classification follows from a stability analysis of the susceptible-free (i.e. xn = 1, yn = zn = 0) and resistance-free (i.e. xn = yn = 0, zn = 1) equilibria. Since we are working in discrete time, an equilibrium is stable if the magnitude of the largest Eigenvalue of the Jacobian matrix evaluated at the equilibrium is less than unity [62]. If neither equilibrium is stable then both susceptible and resistant plants are able to invade a population consisting almost exclusively of the other when rare, and so the genotypes are predicted to coexist. If only the susceptible-free equilibrium is stable, then resistance dominates. If only the resistance-free equilibrium is stable, then susceptibility dominates. But if both equilibria are stable, then the long term outcome depends on the initial densities of each genotype.
Extensive numerical simulations of the model were performed to verify that local stability analyses could be used to infer the long-term outcome for all initial conditions. In particular we tested 10,000 combinations of parameters and initial conditions (1,000 sets of randomly-chosen parameters, each simulated starting from 10 independent and randomly-selected sets of initial conditions). In all cases the outcome after 10,000 generations of the model matched that predicted by the (purely local) stability analysis described above. We also performed a number of individual tests for pairs of sets of parameters chosen to cross stability boundaries: the stability analysis predicted behaviour in full simulations of the model in the large number of cases we tested.
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10.1371/journal.pntd.0007251 | A little goes a long way: Weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens | Zoonotic pathogens such as Ebola and rabies pose a major health risk to humans. One proven approach to minimizing the impact of a pathogen relies on reducing its prevalence within animal reservoir populations using mass vaccination. However, two major challenges remain for vaccination programs that target free-ranging animal populations. First, limited or challenging access to wild hosts, and second, expenses associated with purchasing and distributing the vaccine. Together, these challenges constrain a campaign’s ability to maintain adequate levels of immunity in the host population for an extended period of time. Transmissible vaccines could lessen these constraints, improving our ability to both establish and maintain herd immunity in free-ranging animal populations. Because the extent to which vaccine transmission could augment current wildlife vaccination campaigns is unknown, we develop and parameterize a mathematical model that describes long-term mass vaccination campaigns in the US that target rabies in wildlife. The model is used to investigate the ability of a weakly transmissible vaccine to (1) increase vaccine coverage in campaigns that fail to immunize at levels required for herd immunity, and (2) decrease the expense of campaigns that achieve herd immunity. When parameterized to efforts that target rabies in raccoons using vaccine baits, our model indicates that, with current vaccination efforts, a vaccine that transmits to even one additional host per vaccinated individual could sufficiently augment US efforts to preempt the spread of the rabies virus. Higher levels of transmission are needed, however, when spatial heterogeneities associated with flight-line vaccination are incorporated into the model. In addition to augmenting deficient campaigns, our results show that weak vaccine transmission can reduce the costs of vaccination campaigns that are successful in attaining herd immunity.
| Zoonotic pathogens pose a significant health risk to humans. Mass vaccination programs have shown promise for controlling zoonoses in reservoir populations and, in turn, lessening the health burden posed to neighboring human populations. Despite some significant successes, major logistical challenges remain for programs that seek to establish and maintain herd immunity in free-ranging animal populations. Specifically, limited host access and costs associated with vaccine distribution may hinder efforts to vaccinate a host population and preempt spillover of a zoonotic pathogen. We use mathematical models, parameterized with data from campaigns in the US that target rabies in wildlife, to illustrate how transmissible vaccines can overcome these challenges. Specifically, we find levels of vaccine transmission necessary to boost vaccination efforts that seek to preempt the spread of rabies, and also predict the cost savings that could be realized with a transmissible vaccine.
| Zoonotic pathogens represent a global threat to human welfare. Rabies circulating in domestic dogs in Asia and Africa, for example, results in 59,000 human deaths each year [1]. Ebola, a disease that circulates in non-human primates and bats, killed over 11,000 people during the 2014 outbreak [2]. In addition to the continual threat posed by zoonotic pathogens that occasionally spill over into human populations, zoonoses function as a major source of new infectious diseases in humans [3, 4]. Over 60% of emerging infectious diseases in humans originated as zoonotic pathogens, and recent studies predict that new harmful zoonoses are most likely to originate in geographical hotspots where health infrastructure is poorest [3]. Given these global risks, the ability to vaccinate free-ranging animal populations against dangerous zoonotic pathogens remains an essential goal for safeguarding human populations against future infectious diseases.
Free-ranging animal populations present challenges to mass vaccination. The ultimate goal of any vaccination campaign is to establish herd immunity against a targeted pathogen, that is, to vaccinate a proportion of the population that is sufficient to preclude the pathogen’s spread. In the US, various free-ranging mammalian populations, including coyotes, gray fox, and raccoons, still act as potential or active reservoirs for multiple variants of the rabies virus [5]. These wildlife pose a serious health risk to humans or domestic pets that come into contact with a rabid animal. However, achieving herd immunity in these populations requires that vaccine be distributed across thousands of square kilometers [5, 6]. Because of the inaccessibility of wildlife hosts, Oral Rabies Vaccine (ORV) baits, distributed by aircraft, have been the primary means of vaccinating animal populations that are spread across large tracts of land [7, 8]. ORV bait programs have been crucial in lowering the incidence of raccoon rabies in the US and Canada, and played a fundamental role in eliminating canine rabies from difficult-to-access populations such as coyotes and foxes [9, 10].
Though proven effective in some cases, ORV programs highlight challenges that long-term wildlife vaccination campaigns must overcome. In North America, raccoons serve as the primary reservoir of the raccoon variant of the rabies virus. In order to mitigate the risk of transmission to humans, the US and Canadian governments have organized intense vaccination efforts since the 1990s, with the goal of preventing the westward spread of raccoon rabies across the Appalachian mountains, as well as the northward spread of the virus into Canada [11, 12]. However, low rates of seroconversion in raccoons, and bait competition with non-targeted hosts, together prevent vaccine coverage from exceeding the herd immunity threshold [13–15]. In turn, despite decades of ongoing vaccination effort, the rabies virus still occasionally breaches vaccination barriers meant to contain it [5, 16, 17]. For other wildlife reservoirs, such as coyotes and gray fox, ORV programs in the US are successful at establishing and maintaining herd immunity [9]. However, to ensure that the rabies virus cannot re-invade, these programs may need to be maintained for decades before the risk of rabies re-infection has passed. These challenges highlight the need for cost-effective ways to immunize populations that are difficult to access.
Transmissible vaccines are a promising new technology that, when paired with oral vaccine technology, could transform our ability to vaccinate wildlife populations. Transmissible viral vaccines are engineered to transmit between hosts, inoculating hosts they infect. Vaccine transmission supplements direct vaccination efforts and increases vaccine coverage. To date, transmissible vaccines have been explored for zoonotic pathogens such as Ebola in non-human primates [18] and Hantavirus in deer mice [19], and have been suggested as a possibility for rabies [20]. Although transmissible vaccines that target human pathogens are still in the early stages of development, a transmissible vaccine targeting myxoma and rabbit hemorrhagic fever has been both developed and tested in European rabbits. Studies of the rabbit vaccine demonstrated relatively high levels of transmission in caged rabbit populations, and in field trials, the vaccine was shown to immunize a substantial portion of a rabbit population through horizontal transmission [21, 22].
In addition to this promising empirical work, theoretical models of transmissible vaccines suggest that low levels of transmission can dramatically increase the level of vaccine coverage in a well-mixed host population [23–25]. However, little is known about the extent to which weak vaccine transmission might augment campaigns that target a geographically widespread, free-ranging animal population in which host interactions are spatially localized. The extent to which the vaccine transmits is encapsulated in the basic reproduction number, notated R0,v, that describes the average number of secondary vaccine infections caused by one vaccine-infected individual in a susceptible population. Weakly transmissible vaccines, defined as vaccines with R0,v < 1, are particularly desirable as they have a reduced likelihood of vaccine evolution, which reduces the risk of vaccine reversion, as well as competition between the vector and vaccine [23, 26].
We use a mathematical modeling framework, based on the SIR (Susceptible-Infected-Recovered) infection model, to quantify the benefits imparted by vaccine transmission on long-term ORV-style vaccination campaigns that target wildlife in the US. Our focal questions are: (1) can weak levels of vaccine transmission augment campaigns in the US that fail to establish herd immunity in raccoon populations? (2) to what extent can vaccine transmission reduce the costs of maintaining herd immunity in ORV programs that are successful? We address these questions using mathematical models parameterized with data from historical campaigns that targeted raccoons, coyotes and gray fox in the US.
We model a population of animal hosts that are regularly vaccinated with a transmissible vaccine bait to preempt the establishment of rabies. We assume that the rabies virus has not yet infected the host population, so any immunity that exists in the population is a result of vaccination. The modeling framework uses differential equations to model the effects of host demography, vaccine transmission, and attributes of the vaccination campaign to predict the fraction of the population that is vaccinated at steady state (i.e. seroprevalence). We use two versions of a single underlying mathematical model. The first model ignores any spatial heterogeneities that might exist in the distribution of immune hosts as a result of vaccination. The second model incorporates the spatial challenges associated with vaccination programs that distribute vaccine along lines in the environment (i.e. flight-lines).
We start with a model that describes a well-mixed host population. The model tracks the densities of hosts that are susceptible to rabies infection (S), hosts that are currently infected with a transmissible vaccine (Iv), and hosts that have recovered from vaccine infection (V). In the model, new susceptible hosts are born at constant rate b, and all hosts die at per-capita rate d. Vaccination of susceptible hosts occurs in one of two ways. The first is through direct consumption of a vaccine bait containing a transmissible vaccine, which occurs with per-capita rate σ. Upon consumption of the bait, susceptible hosts become infected with the vaccine virus. We assume that, simultaneously, exposure to the rabies antigen that the vaccine carries prompts a host immune response that results in lifelong immunity to the rabies virus. Alternatively, susceptible hosts can become vaccinated through infectious contact with another host that is infected with the vaccine. The rate at which such contacts occur will depend on attributes of the vector virus from which the vaccine is made and the rate at which hosts experience infectious contact with each other. We assume that vaccine-infected hosts transmit the vaccine to susceptible hosts at frequency-dependent rate β v S ( t ) I v ( t ) S ( t ) + I v ( t ) + V ( t ). Vaccine-infected hosts clear the infection at per-capita rate δv, and transition into a vaccine-recovered class (V). After recovering from infection with the vaccine, the host is immune to subsequent vaccine infection, as well as infection with the rabies virus. These biological assumptions lead to the following system of differential equations:
d S d t = b - σ S - d S - β v S I v S + I v + V d I v d t = σ S - ( d + δ v ) I v + β v S I v S + I v + V d V d t = δ v I v - d V (1)
Many ongoing rabies campaigns utilize aircraft or cars to distribute vaccines into geographically widespread wildlife populations. In these scenarios, the vaccine is distributed along lines in the environment. In order to ensure an even distribution of vaccines, the flight-line spacing must be chosen with the home range of the host animal in mind [27]. Choosing a flight-line spacing that is too large relative the animal’s home range, for example, will cause gaps in seroprevalence between flight-lines. We modify System (1) to investigate how vaccine transmission addresses these unique spatial challenges associated with flight-line vaccination. The resulting model tracks the same classes as System (1), however each state variable is a one-dimensional spatial density described by a partial differential equation. For each host class, we use a diffusion term with diffusion coefficient k to model the movement of a host throughout its lifetime (S1 Appendix). In the model, flight-lines are spaced at intervals of width 2L, and the vaccination rate σ is normally distributed around flight-line positions according to 2Lf(x)σ. Here, f(x) is a normal distribution that is truncated to the interval [−L, L] with standard deviation ξ; the factor 2L ensures that the mean density of vaccine effort is independent of the flight-line spacing that is chosen (more details in S2 Appendix). Now, vaccine infection is a spatially localized process, so that an infected host at location x can only infect susceptible hosts that are also at location x. The resulting system is
∂ S ∂ t = k ∂ 2 S ∂ x 2 + b - 2 L f ( x ) σ S - d S - β v S I v S + I v + V ∂ I v ∂ t = k ∂ 2 I v ∂ x 2 + 2 L f ( x ) σ S - ( d + δ v ) I v + β v S I v S + I v + V ∂ V ∂ t = k ∂ 2 V ∂ x 2 + δ v I v - d V (2)
We also use variations of Systems (1) and (2) to model campaigns that use a nontransmissible vaccine. For these simulations, the Iv class is omitted, βv is set to 0, and directly vaccinated susceptible hosts transition into the V class.
We use data from the USDA to parameterize our models. Each year, the USDA compiles a “National Rabies Management Summary Report” that provides an overview of the previous year’s vaccination efforts, including where vaccination campaigns were carried out, types of wildlife that are vaccinated, and the number of vaccine baits used. In addition, these reports document the seroprevalence that was measured in follow-up population surveys. All data were retrieved from summary reports posted on the USDA website for the years 2006–2010 [28].
If campaigns occur only rarely, the effective vaccination rate σ is zero, and the host population relies on vaccine transmission to distribute the vaccine. In this case, our nonspatial model reduces to a classic SIR infection model. Local stability analysis of our model indicates that if a small number of vaccine-infected individuals are introduced into an otherwise susceptible population, the density of seropositive hosts will increase when R0,v > 1, and comprise a fraction
ϕ = 1 - 1 R 0 , v (3)
of the host population at steady state (S3 Appendix). Here, R0,v is the so-called basic reproduction number of the vaccine, defined as the number of secondary vaccine infections caused by one infected individual in an otherwise susceptible population (R 0 , v = β v d + δ v, parameters defined in Table 1). Eq (3) implies that, if the goal of a campaign is to maintain seroprevalence in the host population at a level ϕ, the vaccine used must transmit at a level
R 0 , v = 1 1 - ϕ . (4)
To understand the extent to which vaccine transmission can augment long-term campaigns when regular vaccination is possible, we find steady states of System (1) with σ > 0. Stability analysis indicates that with constant vaccination, the seroprevalence of System (1) approaches a level ϕ described by the expression
ϕ = d ( 1 - R 0 , v ) - σ + ( d R 0 , v + d + σ ) 2 - 4 d 2 R 0 , v 2 d R 0 , v (5)
(S3 Appendix). Eq (5) shows that the long-term effect of vaccine transmission on seroprevalence is again encapsulated in the vaccine’s R0,v. Furthermore, for a fixed value of R0,v, the steady state benefit from transmission does not depend on the length of time over which these secondary infections occur, which is given by 1 δ v.
To find the level of vaccine transmission that is necessary to augment real-world campaigns, we parameterize σ in Eq (5) to a range of seroprevalence outcomes from USDA vaccination campaigns applied to raccoons. Between 2006–2010, follow-up seroprevalence surveys reported average seroprevalence that varied from a minimum of 0.29 in 2006, to a high of 0.37 in 2010. Interpreted as steady state seroprevalence levels, and assuming that raccoons live for 2.5 years, these values of ϕ imply a range of vaccination rates 0.17 < σ < 0.24 yr−1(S1 Appendix).
We use our spatial model to understand how heterogeneities in vaccine distribution affect the benefits of a transmissible vaccine. To this end, we numerically solve for steady state solutions of System (2) on the interval [−L, L], with Neumann boundary conditions that describe the aggregate effects of many repeating flight-lines. We simulate high and low values of spatial heterogeneity in the distribution of vaccines by adjusting ξ, and we use values of the diffusion coefficient k to simulate small (1 km2) and large (10 km2) host home ranges. This variability in home range is chosen to reflect the variability that is found in raccoons in peri-urban and rural environments (details in S1 Appendix).
We nondimensionalize System (2) to better understand the potential for vaccine transmission to smooth spatial heterogeneities in population seroprevalence. Nondimensionalization is an analytical technique that summarizes the effects of a model’s parameters into unitless parameter combinations (S2 Appendix). Our analyses show that spatial heterogeneities are encapsulated in two nondimensional parameters. ξ ^ describes the level of spatial heterogeneity in the distribution of vaccination effort around each flight-line location, scaled relative to one-half of the flight-line spacing. κ is referred to as scaled dispersal, and describes the capacity for spatial heterogeneities in seroprevalence to persist as a function of host home range, the duration of vaccine infection, and the spacing of flight-lines in the environment:
κ = k ( d + δ v ) L 2 ξ ^ = ξ L (6)
Motivated by transmissible vaccine designs with a long duration of infection, we investigate how vaccines with slow recovery rates (i.e. small δv) might augment the spatial lows that are predicted by our model. We parameterize our model to the yearly averaged seroprevalence levels that were realized in campaigns targeting raccoons. Next, we use a root-solving method to determine the minimal amount of vaccine transmission, R0,v, that is necessary to achieve herd immunity. In these simulations, we consider a population protected from rabies when the minimum of the spatial seroprevalence is raised to the herd immunity threshold ϕ = 0.5 (details in S2 Appendix). All numerical analysis is performed in the statistical language R [29].
In populations where a traditional oral vaccination campaign can achieve herd immunity (e.g., coyotes and gray fox), the use of a weakly transmissible vaccine could result in large reductions in program costs. To quantify the savings that might be realized by using a transmissible vaccine, we use the spatially homogeneous model, described by System (1), to find the fractional reduction in the rate of vaccination that is required to sustain herd immunity at level ϕ in a host population. In doing so, we use the fact that a fractional reduction in the vaccination rate is equivalent to a fractional reduction in the rate at which vaccine baits must be deposited (S2 Appendix). Furthermore, if bait depletion by other animals can be ignored, a continual vaccination rate σ relates to the number of vaccines distributed per year, ρ, by
σ = ρ ( b d )- 1 . (7)
Here, b d is the steady state density of the host population.
If a nontransmissible vaccine is used to maintain seroprevalence at level ϕ, the rate of vaccination must exceed
σ N T * = d ϕ 1 - ϕ (8)
By solving Eq (5) for σ, we find that a transmissible vaccine can achieve the same seroprevalence with
σ T * = d ϕ 1 - ϕ ( 1 - R 0 , v ( 1 - ϕ ) ) (9)
(S3 Appendix). With Eqs (8) and (9), we calculate the fractional reduction in the rate of vaccination that is required for sustained herd immunity,
f σ = 1 - σ T * σ N T * = R 0 , v ( 1 - ϕ ) . (10)
Note that the population density b d is not present in the fractional reduction calculation, and need not be estimated.
We use Eq (10) to calculate the theoretical reduction in bait costs that would have been possible in past campaigns if a transmissible vaccine with R0,v = 0.9 was used. To parameterize ϕ, we use seroprevalence outcomes in campaigns that targeted coyotes and gray fox between 2006–2010. Next, we multiply the calculated reductions by the total number of vaccine baits that were used, and the cost per vaccine bait. For this calculation, we assume that the per-unit cost of the transmissible vaccine bait is the same as a nontransmissible bait, and later evaluate how the anticipated savings might differ if the transmissible vaccine is more expensive. Accounting for inflation, and using vaccine bait costs that were reported for similar campaigns [30], we estimate a current value of $2.12 per bait (details in S1 Appendix).
In addition to the expenses associated with the number of vaccine baits that are required, campaigns must also acquire, maintain, and man aircraft that distribute baits. To better understand the cost reductions that are possible in such programs, we define a function that incorporates both the expenses from the use of aircraft (e.g. wages, maintenance, fuel), and the purchase of vaccine baits. To this end, we assume the vaccinated region A is an ℓ × w km2 rectangle. Given that flight-lines are arranged along either the ℓ or w direction and spaced at intervals of 2L, the linear flight distance required to vaccinate the region A grows according to A 2 L km. Defining Cf as the cost per linear kilometer of flight, the total flight costs of vaccinating the area A scale with flight-line spacing as C f A 2 L. The expenses from the purchase of vaccine baits are given by the product C b σ ( b d ) A, where Cb is the cost per bait, and σ ( b d ) A is the number of vaccine baits required per year to achieve an effective vaccination rate σ when population density is b d (S2 Appendix). Combining flight and bait costs, and dividing by the area of region A gives a per km2 cost of
C = C f 1 2 L + C b b d σ . (11)
To estimate the cost reduction that is possible in flight-line vaccination campaigns, we use a numerical solver to find the pairing of vaccination rate σ*, and flight-line spacing 2L* km, that minimizes Eq (11) while maintaining seroprevalence at level ϕ = 0.5. To convert the optimal strategy into a dollar amount, we use the same baseline vaccine bait cost as before (Cb = 2.12), and a flight-line cost of Cf = 18.16 km−1. We vary Cb to better understand how sensitive the cost reductions are to the cost markups that might apply to transmissible vaccines. The value of Cf is derived using averaged flight costs reported for campaigns in Ohio, and multiplying by the standard flight-line spacing (0.5 km) to convert to cost per linear kilometer of flight (S1 Appendix). We choose host densities of b d = 1 , 10 , 100 km−2 to simulate the wide range of densities found in raccoons.
In order to gauge the sensitivity in the cost reductions that are predicted by our model, we also calculate the cost reductions that occur when assumptions of the model are changed. The Baseline model simulates a vaccine with a 1 month infectious period, R0,v = 1, and a desired seroprevalence of ϕ = 0.5. The “Lagged Immunity” and “Temporary Immunity” variants are obtained by changing the equations of the Baseline model. In the Lagged Immunity variant, hosts are not immune to rabies until they have fully recovered from vaccine infection. In the Temporary Immunity variant, rabies-immunity wanes after a period of one year. All other variants are obtained by changing parameter values. More details can be found in the S2 Appendix.
In the absence of repeated vaccinations, a single campaign could in theory preempt the establishment of rabies if R0,v is sufficiently large. Standard epidemiological theory implies that to achieve seroprevalence ϕ, the vaccine must transmit at level
R 0 , v = 1 1 - ϕ .
This expression implies that 1.7 < R0,v < 2.5 is required to achieve the seroprevalence that successfully preempted the reinvasion of rabies into wild canines (0.4 < ϕ < 0.6, [9]). Similarly, R0,v ≈ 2 is required to achieve the recommended seroprevalence in raccoons (ϕ ≈ 0.5, [31, 32]). Because it is currently unknown whether these levels of vaccine transmission are feasible or will ever be deemed safe to implement in free-ranging animal populations, we next evaluate the extent to which vaccine transmission can augment ongoing campaigns that regularly vaccinate the host population.
If spatial heterogeneities are ignored, our model predicts that weak vaccine transmission could be effective at augmenting US campaigns that target raccoons but do not achieve the desired herd immunity threshold of ϕ = 0.5. When parameterized to vaccination outcomes reported in National Rabies Management Summary Reports between 2006–2010, our model suggests that a vaccine with 0.85 < R0,v < 1.18 would augment the range of seroprevalence averages to that required for herd immunity (Fig 1). This implies that even weakly transmitting vaccines, i.e. those that do not transmit sufficiently to remain endemic in the population, might substantially benefit campaigns that seek to establish herd immunity in raccoon populations.
When spatial heterogeneities are incorporated, elevating the minimal seroprevalence to the herd immunity threshold can require substantially higher levels of vaccine transmission. Both host movement and vaccine bait heterogeneity influence the amount of vaccine transmission that is necessary to raise seroprevalence levels above the 0.5 herd immunity threshold (Fig 2). Our model predicts that hosts with small home ranges (∼1 km2) are most likely to be affected by heterogeneities in vaccine coverage when the distribution of vaccine is spatially clustered along flight-lines. In these populations, seroprevalence falls below the herd immunity threshold even when vaccine transmission is relatively high, R0,v = 1.5. As a result, portions of the population remain unprotected from pathogen invasion (Fig 2).
A nondimsionalization of our model reveals that the parameter combination
κ = k ( d + δ v ) L 2
determines the extent to which spatial heterogeneities in seroprevalence persist at steady state. Small values of κ describe scenarios where flight-line spacing is too large, relative to host dispersal, to significantly smooth out heterogeneities in seroprevalence. One way to overcome these heterogeneities is to increase vaccine transmission via R0,v. However, augmenting the spatial lows in seroprevalence requires relatively high levels of vaccine transmission (R0,v > 1). Specifically, when scaled dispersal is small, κ ≈ 10−2, and the steady state distribution of baits is relatively clustered around each flight-line, increasing vaccine transmission from no transmission, R0,v = 0, to modest transmission, R0,v = 1, fails to substantially augment the minimal seroprevalence in the spatially explicit model (Fig 3). This demonstrates that weak transmission has a limited effect on augmenting seroprevalence lows that result from a heterogeneous bait distribution.
The expression for κ implies that vaccines with longer infectious periods might be beneficial for overcoming spatial heterogeneities in vaccine coverage. For fixed R0,v, increasing the duration of vaccine infection increases the scaled dispersal parameter κ, which, in turn, smooths out spatial heterogeneities in the seroprevalence profile. In a host with a 1 km2 home range, our results indicate that establishing herd immunity requires R0,v ≈ 2 when vaccine infection lasts 1 month, but only R0,v ≈ 1.5 if the duration of infection is lifelong (Fig 4). However, our results also indicate that weak levels of vaccine transmission, even when paired with a longer duration of infection, will likely be ineffective at augmenting seroprevalence levels in raccoons with small home ranges. In contrast, for populations with larger home ranges, the required levels of vaccine transmission are similar to those predicted by the spatially homogeneous model, regardless of the duration of infection (Fig 4).
Our model provides broad estimates of the cost-savings that might be possible in campaigns that use transmissible vaccines. Assuming a homogeneous host population, the fractional reduction in the rate at which vaccine baits need to be distributed, while maintaining herd immunity at level ϕ, is
f σ = R 0 , v ( 1 - ϕ ) .
The expression for fσ implies that, in campaigns that seek to maintain herd immunity at a level ϕ = 0.5 in wildlife, a weakly transmitting vaccine with R0,v = 0.5 would reduce the number of vaccine baits required each year by 25% (Fig 5). Evaluating fσ with R0,v = 1 shows that the maximal reduction in baits that is provided by weak transmission is 50%.
The cost-savings that are predicted by fσ can be substantial. Between 2006 and 2010, vaccination efforts of the US Wildlife Services that targeted coyote and gray fox populations distributed approximately 2 million baits every year. In gray fox populations, these efforts resulted in an average seroprevalence of 0.69. fσ implies that the corresponding reduction due to a vaccine with R0,v = 0.9 is 27.9%. Given that 1.53 million baits were distributed each year, we calculate that by using a transmissible vaccine, the same seroprevalence could be achieved with 430,000 fewer baits per year. In coyotes, the average seroprevalence was 0.55, which implies that a 40.5% reduction in the number of baits is possible with R0,v = 0.9. This reduction would bring the number of baits required each year down from 571,000 to 340,000. Using a cost per vaccine bait reported in other USDA campaigns, the total cost-savings on vaccine baits associated with transmission at R0,v = 0.9 is $1.4 million each year (S1 Appendix [30]). Here, the savings due to vaccine transmission account for 32% of the estimated $4.4 million total bait costs.
When the costs of aerial bait delivery are incorporated into our model, our results suggest that a transmissible vaccine can reduce the costs of vaccination programs in two ways: by reducing the spatial density of flight-lines that are necessary to ensure even coverage, and by reducing the rate at which vaccine baits need to be distributed (Fig 6). Parameterized with the aircraft and vaccine bait costs of Ohio campaigns between 1997 and 2000, our model finds the optimal flight-line spacing and vaccination rate that minimizes the costs of maintaining seroprevalence at 0.5. Though we do not prove it, numerical explorations suggest that the optimal combination of vaccination rate and flight-line spacing is unique (S1 Fig). Compared to the strategy using a vaccine that does not transmit, the effect of vaccine transmission on the optimal strategy is to reduce the vaccination rate, and to a lesser extent, decrease the total flight distance needed to distribute the vaccine by widening the flight-line spacing (Fig 6). Our results imply that the primary role of vaccine transmission is to reduce the quantity of vaccine that needs to be distributed along each flight-line, as opposed to changing how the vaccine baits are distributed spatially (Fig 6).
Our results imply that the cost-savings associated with vaccine transmission will be greatest in high density populations (Fig 7). The fractional reduction in costs predicted by the spatial model is always less than the savings predicted by the homogeneous model. This is due to the limited effect that vaccine transmission has on easing the flight-costs of vaccination. However, in campaigns that target host populations at high densities, the cost reductions that result are similar to those predicted by the homogeneous model, where the sole cost is the purchase of vaccine. In campaigns that target hosts at low densities, the flight costs comprise a greater proportion of the total costs. In this case, a vaccine with a long duration of infection can provide a greater reduction in total costs. For a modestly transmissible vaccine with R0,v = 1, and moderate raccoon densities of 10 km−2, the net reduction in cost is about 20% when home range is small, and between 20-30% for larger home ranges, depending on the duration of infection.
Next, we investigate how our model’s prediction of the cost reduction due to vaccine transmission changes with different model assumptions. One important factor that will influence the anticipated cost savings is the price of vaccine baits that contain a transmissible vaccine virus, compared to the price of conventional, nontransmissible baits. Our model shows that, for a vaccine that transmits at a level R0,v = 0.5, up to a 30% increase in vaccine bait cost can be tolerated and still reduce the overall costs of the campaign. If the vaccine transmits at R0,v = 1, an increase of 89% is allowable (Fig 8).
Table 2 summarizes how modifying other assumptions of the baseline model changes the cost-reduction that is provided by vaccine transmission. When parameterized to a vaccine with R0,v = 1 and a host with a 10 km2 home range, our baseline model predicts a 22% reduction of the summed aircraft and vaccine unit costs. Similar reductions occur when hosts that are vaccinated with a transmissible vaccine do not gain rabies immunity until after they recover from vaccine infection (“Lagged immunity” variant), or when the underlying distribution of vaccines is tightly clustered. Even greater reductions are predicted when limitations of the host cause rabies-immunity to wane after an average period of one year (“Temporary immunity” variant), or if vaccine transmission occurs throughout a host’s lifespan (“Lifelong vaccine infection” variant). However, this reduction is only 16% if high levels of seroprevalence are necessary for herd immunity, or if the transmissible vaccine is 25% more expensive than the nontransmissible vaccine.
Oral vaccine technology has proven effective at vaccinating widely dispersed animal populations, and in turn protecting wildlife against zoonoses that harm humans [9, 10]. At the same time, however, ongoing campaigns against rabies in North America have identified challenges that must be overcome for wildlife vaccination to become a broadly applicable tool for controlling human pathogens in wildlife reservoir populations. Specifically, the difficulties associated with delivering the vaccine to widely distributed wildlife populations, and the costs of manufacturing sufficient quantities of vaccine, constrain the extent to which herd immunity can be established, and the length of time herd immunity can be maintained [13, 32]. When paired with oral vaccination technology, our mathematical results indicate that a vaccine with the capacity for weak transmission could help to overcome these challenges, facilitating robust and cost-effective control of zoonotic pathogens.
Historically, ORV campaigns have struggled to establish herd immunity in raccoon populations, due to low rates of seroconversion in raccoons that consume baits, as well as competition from non-targeted species that consume vaccine baits [13–15]. When parameterized by the range in seroprevalences achieved in raccoons by USDA campaigns, our results show that weak levels of vaccine transmission (R0,v < 1) are capable of increasing seroprevalence levels to those required for herd immunity.
Moreover, for vaccination efforts that maintain herd immunity in North American wildlife, the costs associated with the purchase of vaccine baits can be decreased by up to 50% by a weakly transmissible vaccine. Because this estimate does not incorporate the costs of aerial delivery, these reductions are upper bounds on the total cost-savings that are possible with weak transmission. Even so, these estimates are relevant because the purchase of vaccine baits typically constitutes the majority of the total costs of oral vaccination programs [33]. When the costs of distributing baits with aircraft are incorporated, the cost-reductions predicted by our model are more modest, but still substantial. Our results indicate that weak transmission can cut between 20-30% of the total costs associated with protecting a reservoir population like raccoons against rabies.
In addition to the number of secondary infections, described by the vaccine R0,v, the duration of vaccine infection can influence the overall effectiveness of weak vaccine transmission. Our model predicts that for fixed vaccine R0,v, vaccines with longer periods of infection result in a greater reduction in the overall costs of a vaccination campaign. In hosts with large home ranges and low population density, a vaccine with a long infectious period can cut an additional 7% of the costs of vaccination, relative to the savings that result when a transmissible vaccine with a short infectious period is used. Currently, herpesviruses such as cytomegalovirus are being considered as vaccine vectors due to their relatively low virulence, natural occurrence in mammalian populations, apparent ability to reinfect hosts, and host specificity [20]. Though the epidemiological details of cytomegaloviruses are still being uncovered, in human, simian, and mouse models these viruses are broadly characterized by weak levels of infection that sometimes persist for life [34, 35]. As such, transmissible vaccines that are vectored by cytomegalovirus may allow greater reductions in costs, especially in hosts typified by large home ranges, and in populations with high host densities.
Our model also clarifies the role of vaccine transmission in augmenting oral vaccination campaigns. The self-disseminating properties of transmissible vaccines will amplify the effect that a fixed number of vaccine baits has on a population’s seroprevalence, and reduce the rate at which vaccine baits need to be deposited in order to maintain herd immunity. Our results also indicate that weak transmission is relatively ineffective at augmenting spatial minimums in seroprevalence that might arise because of flight-line style vaccination. In light of this, weak transmission may hold the most promise in campaigns that achieve an even distribution of vaccine baits, but are unable to establish herd immunity due to the challenges associated with limited host access.
Although insightful, our model simplifies several potentially important aspects of the vaccination process. Most obviously, our results are based upon model predictions at steady state. In contrast, population seroprevalence will fluctuate in time due to seasonal birth pulses that introduce new susceptibles into the host population, pulse-style vaccination campaigns, and waning immunity. In addition, accurately predicting the time-dependent outcome for a transmissible vaccine will require more flexible models that are able to incorporate short-term host movements, details of the vaccine’s design, as well as details of how hosts interact. Our model assumes that transmission is frequency-dependent, meaning that the number of infectious interactions does not scale with population size. Because our results stem from model behavior at steady state, similar outcomes would be expected for density-dependent transmission [36]. In contrast, how transmission scales with population size will be important in non steady state models, especially for wildlife populations that experience annual fluctuations due to seasonal reproduction [37].
The need for more detailed models will become greater as future empirical studies clarify the potential for engineering transmissible vaccines. Agent-based simulations, for example, could be used to incorporate contact structure of raccoon populations in different environments, stochasticity in the infection process, and predict at a fine scale the spread of different raccoon vectors. This undertaking will also require accurate details of the vaccination program, including details of how vaccines are distributed in the environment. Our spatially explicit results assume a fixed location of flight-lines in the environment. Consequently, the flight-line vaccination model presented here describes a worst-case scenario where the campaign relies entirely on host movement to avoid heterogeneities in vaccine coverage. This assumption might be appropriate, however, for campaigns that distribute baits from vehicles along roadways.
Our results focus on preemptive vaccination scenarios in which the pathogen has not yet spread within the host population. More generally, transmissible vaccines will need to function in populations that have pre-existing immunity from the pathogen. This might be an important limitation, for example, in targeting bat reservoirs of Ebola and rabies [20]. In terrestrial carnivores, however, rabies is typically not an immunizing infection. As a result, competition between the pathogen and the vaccine is minimal in the scenarios we model.
We have focused on quantifying the benefits of vaccine transmission for rabies circulating within North American wildlife because long-running oral vaccination campaigns provide opportunities for estimating key model parameters. However, our results suggest that transmissible vaccines could be used in other wildlife reservoirs for which parenteral vaccination is not possible. These results motivate the continued investigation of transmissible vaccines that target zoonotic diseases.
Technology that engineers vaccine transmission may never be deemed safe for use in humans, but empirical studies have shown its efficacy and safety in non-human animals [20–22]. The need for better vaccine technology, particularly in the control of zoonotic pathogens in free-ranging animal populations, is apparent from ongoing campaigns in the US. A primary concern for the anticipated use of transmissible vaccines is the extent to which vaccine transmission can be engineered. Our results, combined with the capacity of oral vaccine campaigns to distribute vaccine to free-ranging host populations, demonstrate that weak vaccine transmission should be explored as a means of augmenting campaigns that do not achieve seroprevalence levels that are required for herd immunity. Namely, our results imply that weak vaccine transmission could bolster ongoing rabies campaigns that target raccoons, yet fail to establish herd immunity. More generally, though empirical research into the engineering of transmissible vaccines is still in its early stages, our results indicate that even weak levels of vaccine transmission could play a major role in the global control of infectious disease.
|
10.1371/journal.pntd.0001021 | Comparative Gene Expression Analysis throughout the Life Cycle of
Leishmania braziliensis: Diversity of Expression Profiles
among Clinical Isolates | Most of the Leishmania genome is reported to be
constitutively expressed during the life cycle of the parasite, with a few
regulated genes. Inter-species comparative transcriptomics evidenced a low
number of species-specific differences related to differentially distributed
genes or the differential regulation of conserved genes. It is of uppermost
importance to ensure that the observed differences are indeed
species-specific and not simply specific of the strains selected for
representing the species. The relevance of this concern is illustrated by
current study.
We selected 5 clinical isolates of L. braziliensis
characterized by their diversity of clinical and in vitro
phenotypes. Real-time quantitative PCR was performed on promastigote and
amastigote life stages to assess gene expression profiles at seven time
points covering the whole life cycle. We tested 12 genes encoding proteins
with roles in transport, thiol-based redox metabolism, cellular reduction,
RNA poly(A)-tail metabolism, cytoskeleton function and ribosomal function.
The general trend of expression profiles showed that regulation of gene
expression essentially occurs around the stationary phase of promastigotes.
However, the genes involved in this phenomenon appeared to vary
significantly among the isolates considered.
Our results clearly illustrate the unique character of each isolate in terms
of gene expression dynamics. Results obtained on an individual strain are
not necessarily representative of a given species. Therefore, extreme care
should be taken when comparing the profiles of different species and
extrapolating functional differences between them.
| Leishmania is a group of parasites (Protozoa, Trypanosomatidae)
responsible for a wide spectrum of clinical forms. Among the factors explaining
this phenotypic polymorphism, parasite features are important contributors. One
approach to identify them consists in characterizing the gene expression
profiles throughout the life cycle. In a recent study, the transcriptome of 3
Leishmania species was compared and this revealed
species-specific differences, albeit in a low number. A key issue, however, is
to ensure that the observed differences are indeed species-specific and not
specific of the strains selected for representing the species. In order to
illustrate the relevance of this concern, we analyzed here the gene expression
profiles of 5 clinical isolates of L. braziliensis at seven
time points of the life cycle. Our results clearly illustrate the unique
character of each isolate in terms of gene expression dynamics: one
Leishmania strain is not necessarily representative of a
given species.
| Leishmania are digenic Protozoan parasites endemic worldwide and
causing a spectrum of diseases in humans collectively referred to as leishmaniasis.
As part of their life cycle, Leishmania alternate between the
alimentary tract of the sandfly vector (where they grow as extracellular flagellated
promastigotes and differentiate into infective non-dividing metacyclic forms) and
the phagolysosome of the vertebrate host macrophages (where parasites differentiate
into aflagellated replicative amastigotes). Important morphological and biochemical
changes underlie the differentiations involved in the life cycle and are most likely
the result of regulated changes in gene expression in response to environmental
signals (e.g. temperature change and pH shift) [1], [2], [3]. A recent study assessing gene
expression profiles throughout the life cycle of a single strain of L.
infantum showed that most of the Leishmania genome is
constitutively expressed and that consequently, regulated genes are a minority [4]. Interestingly,
most significant events of regulation were shown to occur during promastigote
development, with a dramatic down-regulation during the transition from
promastigotes to amastigotes, hereby supporting the hypothesis of pre-adaptation of
the parasite to intracellular survival in the macrophage [4].
In parallel to these longitudinal studies of gene expression throughout the life
cycle of a given strain, another critical dimension needs to be explored, i.e. the
gene expression diversity within natural populations of Leishmania.
Indeed, these parasites successfully colonized a large range of hosts in various
ecological niches, hereby determining different transmission types in which humans
play a variable role (from dead-end host to reservoir). Not surprisingly, the
parasites are characterized by high phenotypic diversity: infectivity, tissue
tropism, clinical pattern or drug susceptibility, among others. This polymorphism is
reflected at the taxonomic level, with more than 20 described species. A first
comparative transcriptomic study throughout the life cycle of 3 strains belonging to
the species L. major, L. infantum and L.
braziliensis was recently reported: this provided in the 3 species a
similar picture of a mostly conservative gene expression, and a minority of
species-specific differences related to differentially distributed genes or the
differential regulation of conserved genes, either of which are subject to
translational and/or post-translational controls [5]. This type of research being
undertaken among other reasons for a better understanding of the differences in
virulence and pathogenicity of the respective species, it is of utmost importance to
ensure that the observed differences are indeed species-specific or simply specific
to the strains selected as representative of the species.
In a previous study targeting a limited number of genes, we analyzed the expression
profile of 21 L. braziliensis clinical isolates during in
vitro promastigote growth [6]. A fraction of genes were
up-regulated during differentiation, but the set of regulated genes varied between
isolates, providing a picture of intra-species gene expression mosaic. We aimed here
to continue our assessment of gene expression diversity within a single species
(L. braziliensis) by extending it to the whole life cycle of
the parasite. This type of study is complicated by the extreme sensitivity of the
amastigote stage to disturbance of the intracellular parasite's environment
during harvesting, hence extreme care is needed to ‘freeze’ gene
expression levels instantly [7]. Our previous work also demonstrated the importance of
time-course analyses to better observe the dynamics of gene expression changes
during in vitro growth and to allow more reliable comparisons
between different strains [6], [7]. These considerations currently impede high-throughput
experiments involving the clinically relevant amastigote stage. We surveyed here 5
clinical isolates of L. braziliensis, characterized by their
diversity of clinical and in vitro phenotypes. We applied a
standardized and reproducible biological protocol [6], [7] and used real-time
quantitative PCR to assess gene expression profiles at seven time points covering
the whole life cycle. We tested 12 genes encoding proteins with roles in transport,
thiol-based redox metabolism, cellular reduction, RNA poly(A)-tail metabolism and
housekeeping functions.
The five parasite isolates used here were obtained from patients with cutaneous
leishmaniasis at the Instituto de Medicina Tropical A. von Humboldt, Lima, Peru.
Protocol and informed consent were approved by the Research Ethic Committees of
Universidad Peruana Cayetano Heredia (Lima, Peru) and Institute of Tropical
Medicine (Antwerp, Belgium). Written, informed consent was obtained from all
participating subjects or their legal guardians. Animal experimentation
concerned only the use of peritoneal macrophages obtained from mice. Our animal
protocol adhered to the guidelines at the Universidad Peruana Cayetano Heredia,
Lima, Peru and was in agreement with the Peruvian and Belgian regulations for
the protection and welfare of laboratory animals. Mouse care and experimental
procedures were performed under approval of the Ethic Committee of the
Universidad Peruana Cayetano Heredia as well as the Animal Ethic Committee of
the Institute of Tropical Medicine Antwerp (PAR-018/2).
The five parasite isolates were obtained from Peruvian patients with confirmed
cutaneous leishmaniasis and originating from 4 regions of the country (Table 1). The selected
isolates were previously characterized as L. braziliensis by
PCR-RFLP assays targeting a range of markers [8], but we retyped them here
to obtain a more precise perception of their genetic diversity. Therefore, we
sequenced part of the coding region of the hsp70 genes, a locus
recently shown to be phylogenetically very informative [9] and more and more used for
species identification of Neotropical Leishmania species [10]–[12].
Sequencing concerned the isolates of this study [GenBank accession numbers:
FR715986 (isolate PER002), FR715987 (isolate PER006), FR715988 (isolate PER182),
FR715989 (isolate PER104), FR715990 (isolate PER163)] and the additional
Peruvian L. braziliensis reference strain LEM2222 (http://www.parasitologie.univ-montp1.fr/cnrl.htm; GenBank
accession number: FR715991). The sequences were aligned with reference strains
recently published by our group [9] and available GenBank entries, and compared by
Neighbor-Joining analysis of p-distances, using the software package MEGA4
(http://www.megasoftware.net/). Data on the in
vitro SbV and SbIII susceptibility of the
studied isolates, as tested by the intracellular amastigote-macrophage model
[8], and
of the clinical treatment outcome of respective patients, were available (see
Table 1 for summary of
isolates' features). Isolates were used here for gene expression analysis
within a maximum of 25 in vitro passages post-isolation from
patients.
The in vitro conditions for promastigote generation have been
described previously [6]. Briefly, growth curves were initiated by inoculating
3×106 parasites/mL in 5 mL medium 199 (M199; Sigma)
containing 20% heat-inactivated fetal bovine serum (FBS; Lonza
Bioscience), 25 mM Hepes (pH 7.4), 100 units/mL penicillin and 100 µg/mL
streptomycin (Lonza). Two independently grown cultures and corresponding
harvests at 24 h (early-log phase), 72 h (late-log phase), 120 h
(early-stationary phase) and 168 h (late-stationary phase) time points were
performed in parallel for each isolate (biological replicates).
Intracellular amastigotes were obtained after infection with axenic
amastigote-like forms, as this was reported to enhance infectivity in species of
L. (Viannia) subgenus [13]. Briefly, stationary-phase
promastigotes were incubated in M199-20% FBS, at pH 5.5 and 34°C,
during 96 h [14]. Following this incubation, parasites were washed,
resuspended in M199-pH7.4–10% FBS (pre-warmed at 32°C), and
then used to infect murine peritoneal macrophages (collected from BALB/c mice)
at a parasite to macrophage ratio of 10∶1. Infected cultures were
incubated for 4 h at 32°C in a humidified 5%
CO2/95% air environment, then washed with pre-warmed medium to
remove free extracellular parasites, and further incubated for 3 days.
Intracellular amastigotes were co-harvested with macrophages at 24 h, 48 h and
72 h post-infection using a protocol that has shown to ‘freeze’
L. donovani gene expression levels instantly [7].
Infection rates were monitored using control plates, as described elsewhere
[15]; up
to 200 cells were counted in order to determine the percentage of infected
macrophages and the average number of amastigotes by infected macrophages. The
whole experiment was done in 2 biological replicates.
RNA sampling protocols, RNA isolation and quality control were performed as
described elsewhere [16]. First strand cDNA was synthesized from 150 ng total
RNA of promastigote samples and from 1 µg total RNA of mixed
amastigote-macrophage samples, using a 18mer oligo(dT) and Transcriptor Reverse
Transcriptase, according to the manufacturer's instructions (Roche Applied
Science). The resulting cDNA was diluted 10-fold with DEPC-treated water
(Ambion) for further use.
From the cDNA diluted samples, 2 µL was used as template in 25 µL
SYBR Green-based quantitative PCR (qPCR) reactions on the iCycler (Bio-Rad), as
previously described [16], with the only modification that amplification was
done for 34 cycles [6]. We analyzed 12 Leishmania-specific
genes, with predicted function in transport (LbAQP1,
MRPA), thiol-based redox metabolism (GSH1,
GSH2, ODC, TRYR),
cellular reduction (ACR2, TDR1), RNA
poly(A)-tail metabolism (PABP, PAP14),
cytoskeleton function (Actin) and ribosomal function
(S8). Primer sequences, parameters and reproducibility of
the respective qPCR assays have been described previously [6].
Analysis was performed on duplicate biological samples that were each assayed in
triplicate. The arithmetic average threshold cycle (Ct) was used for data
analysis. The Ct values of each qPCR run were imported as Excel files into
qBasePlus 1.3 (Biogazelle NV, Zulte, Belgium), a software
for real-time PCR data analysis based on the geNorm method [17] and qBase technology
[18].
Three genes (ACR2, GSH2,
PAP14) showed the most stable expression through parasite
life stages in our sample panel (geNorm stability mean M-value and mean
coefficient of variation lower than 0.45 and 20%, respectively) and data
were normalized to their geometric mean. The use of multiple control genes
allows more accurate and reliable normalization of gene expression data [17].
The linear component of the variability of the expression level of each gene
during in vitro growth was modelled independently for each
isolate using linear regression. The main predictor was the development time of
the parasite during in vitro promastigote or intracellular
amastigote growth/differentiation. The time point 24 h was considered as the
baseline time in the regression models, in order to prevent negative values for
gene expression levels, a biological impossibility. The constant terms of the
regression models were used as measure of the baseline expression of a given
gene, with the criterion of significance that the constant terms differed by at
least 2-fold from the lowest constant term in the series.
In addition, the fold change (FC) of gene expression of (i) stationary
versus logarithmic phase promastigotes (time points 120 h
and 168 h versus 24 h and 72 h of the growth curves,
respectively); (ii) late-differentiating phase amastigotes
versus early-differentiating phase amastigotes (time points
72 h versus 24 h and 48 h post-infection macrophages,
respectively); and (iii) stationary phase promastigotes (time points 120 h and
168 h of the growth curves) versus early-differentiating phase
amastigotes (time points 24 h and 48 h post-infection macrophages) was
determined for each parasite isolate. FC were considered significant if they
satisfied a 2-fold cutoff and P<0.01, as recommended
elsewhere [19]. All analyses were performed using the statistical
software Stata 10 (StataCorp).
Out of the 5 isolates, 4 (PER002, -006, -104 and -182) clustered in a
well-supported group (bootstrap value of 92%) constituted by several
reference strains of the L. braziliensis complex (Fig. 1). The fifth isolate,
PER163, has 100% sequence identity with the MLEE typed L.
braziliensis reference strain LEM2222, and they cluster with a
bootstrap support of 78%. Even though PER163 has more sequence similarity
(99.64%) with the main L. braziliensis group than with
L. naiffi (99.49%), it clusters with the latter
without bootstrap support.
A series of parameters were measured during the growth of promastigotes and the
macrophage infection by amastigotes for phenotypic comparison of the 5 isolates.
At the promastigote level, our observations led us to the empirical description
of 3 main phenotypes (Table
1): (i) slow growth, but high density at stationary phase in PER163
and PER006, (ii) rapid growth and high density at stationary phase in PER104 and
PER182, and (iii) slow growth and lower density at stationary phase in PER002.
At the amastigote level, 2 main phenotypes were observed (Table 1): (i) high percentage of infected
macrophages and high number of amastigotes per macrophage in PER163 and PER104,
and (ii) low values for both parameters in PER002, PER006 and PER182.
We obtained highly reproducible gene expression measurements between biological
replicates, in both the promastigote and intracellular amastigote developmental
time series (Fig. 2, 3, 4), thereby confirming that parasites were
manipulated in a standardized way. Visual inspection of the graphs revealed
notable differences between life stages in general and between genes according
individuals. For instance, LbAQP1 showed in PER163 and PER006 a
clear up-regulation during promastigote development, followed by a 5-fold
down-regulation during the transition to amastigotes (Fig. 2). In sharp contrast,
TRYR showed a similar expression during the 4 time points
of promastigote growth curve in PER163, while in PER006 a clear up-regulation
was observed (Fig. 3). A
last example concerns the expression of Actin shown to be
down-regulated from promastigote to amastigote in PER163 and PER006, whereas it
appeared to be clearly constitutive and at relative low levels in PER182 through
the 7 time points analyzed here (Fig. 4).
In order to summarize and better visualize the gene expression profiles during
the life cycle, we examined 5 parameters in the 5 isolates: the baseline
expression levels (given by the constant term of the linear regression models)
in (1) promastigotes and (2) amastigotes, and the fold change in mRNA abundance
(3) from logarithmic to stationary phase promastigotes (further called FC-PRO),
(4) during the transition from stationary phase promastigotes to
early-differentiating phase amastigotes (further called FC-PRO-AMA), and (5)
from early- to late-differentiating phase intracellular amastigotes (further
called FC-AMA). Results are schematized in Figures 5 and 6.
First, when analyzing all parameters together, we identified 3 genes that were
constitutively expressed during the whole life cycle and in the 5 isolates:
ACR2, GSH2 and PAP14.
Secondly, analysis of the baseline expression in promastigotes (Fig. 5A) discriminated 2
groups of parasites: (i) PER002 and PER182, which showed the lowest baseline
levels for the 12 genes studied here and (ii) PER163, PER006 and PER104 which
showed significantly higher baseline levels for 2 to 5 genes (out of the
following set: Actin, LbAQP1,
MRPA, PABP, S8 and
TRYR). Thirdly, analysis of the baseline expression in
amastigotes (Fig. 5B) also
discriminated 2 groups of parasites: (i) PER163 and PER006 showed the lowest
baseline for the 12 genes at the amastigote stage, which is in sharp contrast to
promastigote data, and (ii) PER002, PER104 and PER182 which showed significantly
higher baseline expression of 4 to 6 genes (out of the following set:
Actin, LbAQP1, GSH1,
ODC, PABP, TDR1 and
TRYR). Fourthly, up-regulation of gene expression during
promastigote differentiation (FC-PRO ≥2, Fig. 6A) was observed for up to 3 genes out
of the following set: LbAQP1, GSH1 and
TRYR. As did other parameters, the FC-PRO also
discriminated PER163 and PER006 from other isolates, as the former showed the
lowest number of up-regulated genes. Fifthly, down-regulation during the
promastigote-amastigote transition (FC-PRO-AMA ≥2, Fig. 6B) was observed in all isolates, but to
a very different extent: (i) involving 7 and 8 genes in PER163 and PER006
respectively, (ii) 3 genes in PER002 and (iii) one gene only in PER104 and
PER182. Sixthly, and in stark contrast to previous parameters, we did not
observe up-regulation of any gene in any isolate during amastigote development
(FC-AMA <2, Fig. 6C).
Most of the Leishmania genome is reported to be expressed
constitutively [4], [5], a small fraction of it only (estimated at 5,7% in
L. infantum and 9% in L. braziliensis;
[4], [5]) being
regulated during the life cycle. In our attempt to explore the diversity of gene
expression profiles within a single species -L. braziliensis in our
case- we thus focused our analysis on a set of genes that were more prone to show
variation in gene expression in L. braziliensis when assaying
promastigotes [6].
Overall, the fine monitoring of the expression of these 12 genes over seven time
points of the promastigote and amastigote stages supported previous data on the
dynamics of up-/down-regulation during the life cycle. When considering the present
sample of isolates as a single pool and looking at the general trend of the
expression profiles, regulation appeared to be concentrated around the stationary
phases of promastigotes: (i) up-regulation during promastigote growth (3 genes in
total), (ii) down-regulation during the promastigote-amastigote transition (9 genes
in total) and (iii) no gene showing any up-regulation during the period of
monitoring (24 to 72 hours) of amastigote growth. Several reports on the expression
profile of the intracellular amastigote stage compared with logarithmic or
stationary phase promastigotes or both in several Leishmania spp.
showed a highly significant predominance of down-regulated over up-regulated genes
in amastigotes [4], [20], [21]. Altogether, our results are in agreement with the
previously formulated hypothesis that Leishmania are pre-adapted
for intracellular survival [4], [5].
However, when considering each isolate separately, our results clearly illustrate the
unique character of the different parasite populations in terms of gene expression
dynamics. Indeed, major differences were observed between the isolates studied here.
This concerned essentially the baseline expression levels in promastigotes and
amastigotes and the degree of down-regulation during the transition from
promastigotes to amastigotes. In terms of similarity of gene expression patterns, 2
groups of parasites could be distinguished: PER163 and PER006 on one hand, and
PER002, PER104 and PER182 on the other hand. This grouping was not dependent of the
genetic relationships among isolates as inferred from the Hsp70
sequences (Fig. 1) nor confirmed
by other genetic markers (MLMT and AFLP, unpublished results). Strikingly, the
isolates PER163 and PER006, which have a higher baseline expression level for
several genes in promastigotes than the other isolates and the highest number of
down-regulated genes during the transition from promastigotes to amastigotes, were
the 2 isolates showing the lowest constitutive levels of baseline expression in
amastigotes: the coherence between the different parameters studied here further
supports the validity of our methodology. Reciprocally, isolates PER002, PER104 and
PER182 call the attention by their low baseline expression levels in promastigotes,
the few down-regulated genes during the transition from promastigotes to
amastigotes, and the higher level of baseline expression in intracellular
amastigotes compared to the isolates PER163 and PER006.
Gene expression diversity in the present sample is not a surprise, considering the
phenotype diversity of the analyzed L. braziliensis isolates:
growth rate at promastigote stage, in vitro infectivity for
macrophages, in vitro susceptibility to SbV and
SbIII or the treatment outcome in patients. The choice of genes
likely also played a role in the observed diversity, as they encode proteins with
roles in transport, thiol-based redox metabolism, cellular reduction and RNA
metabolism, and were previously shown to undergo significant changes in expression
in some of the phenotypes present here [7], [22]. However, our aim was not to
search any correlation between the gene expression patterns and the main phenotypes
observed. This would require another experimental set-up, including (i) a larger
sample size, (ii) a broader coverage of the transcriptome and (iii) complementary
studies at other ‘omic levels. Proteomic approaches are very relevant,
considering the importance of post-transcriptional regulation in Trypanosomatids
[23]. But,
even more important, the metabolome should be explored, because of its closest
position to the phenotype [24].
In conclusion, our results show that the gene expression pattern of a strain is not
necessarily representative of a given species. They remind us of an essential
feature of natural populations of Leishmania: diversity. We
recommend to keep this dimension in mind in any future work and to take extreme care
when comparing the profiles of different species represented by single strains and
extrapolating functional differences between them.
The accession numbers for the genes analyzed in this study are as annotated at
L. braziliensis GeneDB database (http://www.genedb.org/Homepage/Lbraziliensis).
LbAQP1 (GeneDB ID LbrM.31.0020) encodes an
aquaglyceroporin. MRPA (LbrM.23.0280) codes for an ABC-thiol
transporter. GSH1 (LbrM.18.1700) encodes a putative
gamma-glutamylcysteine synthetase (γ-GCS). GSH2
(LbrM.14.0880) codes for a putative glutathione synthetase (GS).
ODC (LbrM.12.0300) codes for a putative ornithine
decarboxylase. TRYR (LbrM.05.0350) encodes trypanothione
reductase. ACR2 (LbrM.32.2980) encodes a putative
SbV/AsV reductase according to the orthologous
ACR2 sequence in L. major (GeneDB ID
LmjF.32.2740; GenBank accession number AY567836.1). TDR1
(LbrM.31.0550) encodes a thiol-dependent reductase 1. PABP
(LbrM.30.2560) encodes a putative RNA-binding protein. PAP14
(LbrM.14.1350) codes for a putative poly(A) polymerase. Actin
(LbrM.04.1250) codes for the actin protein. S8 (LbrM.24.2160)
encodes a putative 40S ribosomal protein S8.
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Subsets and Splits