Skip to main content
  • Loading metrics

Distinct epigenomic and transcriptomic modifications associated with Wolbachia-mediated asexuality

  • Xin Wu ,

    Contributed equally to this work with: Xin Wu, Amelia R. I. Lindsey

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Affiliation School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America

  • Amelia R. I. Lindsey ,

    Contributed equally to this work with: Xin Wu, Amelia R. I. Lindsey

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing (ARIL); (SVY)

    Affiliations Department of Entomology, University of California Riverside, Riverside, California, United States of America, Current Address: Department of Biology, Indiana University, Bloomington, Indiana, United States of America

  • Paramita Chatterjee,

    Roles Investigation, Methodology

    Affiliation School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America

  • John H. Werren,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Biology, University of Rochester, Rochester, New York, United States of America

  • Richard Stouthamer,

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – review & editing

    Affiliation Department of Entomology, University of California Riverside, Riverside, California, United States of America

  • Soojin V. Yi

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing (ARIL); (SVY)

    Affiliation School of Biological Sciences, Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America


Wolbachia are maternally transmitted intracellular bacteria that induce a range of pathogenic and fitness-altering effects on insect and nematode hosts. In parasitoid wasps of the genus Trichogramma, Wolbachia infection induces asexual production of females, thus increasing transmission of Wolbachia. It has been hypothesized that Wolbachia infection accompanies a modification of the host epigenome. However, to date, data on genome-wide epigenomic changes associated with Wolbachia are limited, and are often confounded by background genetic differences. Here, we took sexually reproducing Trichogramma free of Wolbachia and introgressed their genome into a Wolbachia-infected cytoplasm, converting them to Wolbachia-mediated asexuality. Wolbachia was then cured from replicates of these introgressed lines, allowing us to examine the genome-wide effects of wasps newly converted to asexual reproduction while controlling for genetic background. We thus identified gene expression and DNA methylation changes associated with Wolbachia-infection. We found no overlaps between differentially expressed genes and differentially methylated genes, indicating that Wolbachia-infection associated DNA methylation change does not directly modulate levels of gene expression. Furthermore, genes affected by these mechanisms exhibit distinct evolutionary histories. Genes differentially methylated due to the infection tended to be evolutionarily conserved. In contrast, differentially expressed genes were significantly more likely to be unique to the Trichogramma lineage, suggesting host-specific transcriptomic responses to infection. Nevertheless, we identified several novel aspects of Wolbachia-associated DNA methylation changes. Differentially methylated genes included those involved in oocyte development and chromosome segregation. Interestingly, Wolbachia-infection was associated with higher levels of DNA methylation. Additionally, Wolbachia infection reduced overall variability in gene expression, even after accounting for the effect of DNA methylation. We also identified specific cases where alternative exon usage was associated with DNA methylation changes due to Wolbachia infection. These results begin to reveal distinct genes and molecular pathways subject to Wolbachia induced epigenetic modification and/or host responses to Wolbachia-infection.

Author summary

Wolbachia is an extremely common endosymbiotic infection of arthropods and nematodes. One of the reasons why Wolbachia can so successfully infect diverse species is the bacterium’s ability to profoundly alter the reproductive behavior of its host. It has been proposed that Wolbachia may modify host’s epigenetic programs to alter its reproductive behavior. However, it has been difficult to study how epigenetic programs change with Wolbachia infection, due to the confounding effects of genetic backgrounds. Here, we studied host transcriptome and epigenome changes associated with Wolbachia infection in a homogenous genetic background, by carrying out an innovative introgression scheme. By doing so, we show, for the first time, high-resolution molecular consequences of intracellular infection and offer insights into epigenetic and transcriptomic regulation of invertebrates.


Wolbachia is a widespread endosymbiont of arthropods and nematodes, estimated to infect 40 to 60 percent of all insect species [1, 2]. Its success can be attributed to wide ranging effects on host fitness, including reproductive parasitism [3]. For example, in parasitoid wasps of the genus Trichogramma, Wolbachia converts wasp hosts to an asexual mode of reproduction [46]. In uninfected wasps, males develop from haploid, unfertilized eggs, and females only result from eggs that were converted to diploidy via fertilization. When the wasps are infected with Wolbachia, haploid unfertilized eggs are converted to diploidy through a fertilization-independent mechanism, resulting in the asexual production of female offspring. This shift in reproductive mode enables the spread of Wolbachia throughout a population [7]. In some cases, Trichogramma wasps become dependent upon Wolbachia for the production of female offspring. In this scenario, wasps lose the ability to fertilize their eggs, and without Wolbachia-mediated diploidization, no females are produced [7]. This phenomenon is increasingly referred to as “symbiont addiction”: the infection by the symbiont leads to an evolutionary response that creates a dependency on Wolbachia where no such need existed prior to the onset of the symbiotic relationship [8, 9].

While initial discoveries of Wolbachia’s ubiquity and control of host reproduction were made more than two decades ago, molecular details of how Wolbachia manipulates its host to maintain a persistent infection and alter host reproduction still remain relatively poorly understood, and especially so for Wolbachia that induce parthenogenesis. Genes associated with Wolbachia’s prophage are responsible for inducing cytoplasmic incompatibility [1012], and potentially male-killing [13]. However, the Wolbachia strains infecting Trichogramma have no prophage [14, 15], do not contain orthologs of these known reproductive manipulator loci [12], and only have remnants of other prophage genes [15]. While the loci behind parthenogenesis induction have not been described, it is known that in Trichogramma wasps, Wolbachia prevents chromosome segregation during the first mitotic division of unfertilized eggs to create diploid offspring [16].

One of the mechanisms that may have relevance to Wolbachia’s manipulation of the host is epigenetic modification. It has been long thought that Wolbachia may modify host “epigenomes”: heritable chemical modifications of DNA and histones affecting chromatin conformation [17]. For example, in Drosophila simulans, Wolbachia infection is known to affect the chromatin reorganization during spermatogenesis [18]. In a leafhopper species, Wolbachia infection can interfere with the host genome’s imprinting pattern [19]. More recently, Wolbachia infection was shown to alter the expression of host methylation machinery in Aedes aegypti [20, 21], Drosophila melanogaster [22] and Cotesia plutellae [23]. These studies indicate that Wolbachia infection might be accompanied by a systematic modification of the host epigenome, including changes in DNA methylation. However, how Wolbachia affect the epigenome (such as genomic DNA methylation) at fine-scale has been difficult to resolve, due to several problems. First, model insects such as Drosophila and other Diptera (e.g., mosquitoes) harbor little genomic DNA methylation [24]. Second, epigenetic patterns are strongly influenced by genetic background [25, 26], thus it is not straightforward to disentangle the effects of Wolbachia infection and genetic background.

The Trichogramma pretiosum genome harbors a full set of DNA methylation machinery and exhibits genomic cytosine methylation [27], thus making it an excellent system to investigate how the host methylome changes with Wolbachia infection. Trichogramma wasps are geographically widespread, and genetically variable. Consequently, comparative studies of different Trichogramma methylomes would be confounded by genomic differences and each line’s adaptation to its environment. Furthermore, the dependence of fully parthenogenetic Trichogramma on their resident Wolbachia precludes antibiotic curing to create comparable Wolbachia-free lines. In some lines of Trichogramma, there is a complete loss of fertilization. Here, we use a Wolbachia-infected Trichogramma line with a significantly impaired rate of fertilization: females do not fertilize enough eggs to maintain a self-sustaining population in the absence of Wolbachia [28]. However, a low level of egg fertilization allows us to introgress genetic material from a sexual population by back-crossing, eventually replacing the mutations that are responsible for the impaired fertilization rate. The result of this introgression is a line of wasps newly converted to Wolbachia-mediated asexuality, which can also be converted back to sexual reproduction through antibiotic treatment. This scheme allowed us to profile the molecular signatures of Wolbachia infection in Trichogramma hosts in a genetically homogeneous setting. Here, for the first time, we show detailed genome-wide transcriptome and methylome modifications associated with Wolbachia infection.


Introgression of a sexual nuclear genome into Wolbachia-infected asexual cytoplasm

For these experiments, we used four isofemale lines of Trichogramma pretiosum: two Wolbachia-infected asexual lines (termed “Insectary” and ES865”), and two Wolbachia-free sexually reproducing lines (“CA29” and “CA9”). We introgressed the genome derived from a Wolbachia-free, sexually reproducing Trichogramma line into a cytoplasm derived from a Wolbachia infected, asexually reproducing Trichogramma (CA29 genome into Insectary cytoplasm, and CA9 into ES865) (Fig 1A). Introgression pairs (Insectary X CA29, and ES865 X CA9) were chosen based on the ability to track introgression using a molecular marker (Methods). The fecundity of wasps significantly decreased over each generation of the introgression protocol, consistent with the expectation of cyto-nuclear incompatibilities (Fig 1B; GLM: Insectary: χ2 = 33.701, P < 0.0001; ES865: χ2 = 44.372, P < 0.0001). Across the duration of the introgression, there was no significant change in the sex ratio of offspring produced by Wolbachia-infected virgin females, indicating successful induction of Wolbachia-mediated parthenogenesis in the new backgrounds (Fig 1C; GLM: Insectary: χ2 = 1.527, P = 0.2166; ES865: χ2 = 2.943, P = 0.0862). The ES865 X CA9 introgressed wasps were less fecund than the Insectary X CA29 introgressed wasps, and we were unable to expand the colonies sufficiently for further experimentation. This is consistent with the previously observed disadvantageous cyto-nuclear interactions commonly found in some Trichogramma crosses [46, 16]. Thus, we chose the Insectary X CA29 introgressed lines for transcriptomic and epigenetic analysis. Three independently generated Insectary X CA29 introgression lines were maintained, and individual replicates of each introgressed line were cured of their Wolbachia infections using antibiotics, restoring sexual reproduction. Following curing, virgin females produced only male offspring in the cured introgressed lines, whereas mated females produced both sons and daughters. There is no evidence for any other microbes in these lines of lab-reared Trichogramma [15, 27], so only Wolbachia infection status differs between the cured and infected replicates. Wolbachia infection status in all lines was additionally confirmed with PCR. DNA and RNA were extracted from Wolbachia-infected and cured introgressed lines for whole genome bisulfite sequencing (WGBS) and RNA-Seq (Methods).

Fig 1. Crossing scheme for creating homozygous introgressed isofemale lines and fitness at each introgression generation.

A) After seven generations of introgression, between 95–99% of the Wolbachia-dependent asexual nuclear background was replaced by the sexually reproducing nuclear background. At each generation of the introgression, we used virgin wasps to assay for sex ratio (a proxy for successful Wolbachia-mediated parthenogenesis) and fecundity prior to mating with a sexual male. All offspring were screened for zygosity, allowing for the identification of females produced via fertilization rather than Wolbachia-mediated parthenogenesis. This scheme was performed 3 independent times to generate 3 isofemale lines. B) Wasp fitness and C) efficiency of Wolbachia-mediated parthenogenesis at each generation of introgression. Sex ratio is denoted as the proportion of female offspring.

We utilized the genome sequences of the parental lines (Insectary and CA29) and the WGBS data of the introgressed lines to explicitly map the genomic profiles of introgression. Specifically, using a tool that can identify single nucleotide polymorphism at the genomic level from WGBS data [29], we examined the origin of SNPs in each introgressed line and determined whether they originated from the paternal (introgressed) or maternal (non-introgressed) genomes, and estimated the total percentage of non-introgressed regions in each line (Methods). Based on this approach, two of the three lines (“B” and “C”) showed > 99% introgression (S1 Table). In the third line (line “A”), approximately 5–8% of the genome was of maternal origin, retained from the original asexual maternal line (S1 Table). The reason for the lower level introgression in this line may be that our introgression scheme required tracking a maternal marker through the course of the introgression. This would result in linked material regions being maintained in the introgressed lines. In establishing the final lines, progeny were chosen randomly (rather than again screening for parentage at the A9 locus), as we no longer needed to track fertilization after that point. For controlled comparisons, these non-introgressed regions were excluded from all lines for further transcriptome and methylome analyses. However, including or excluding regions that harbored putatively non-introgressed regions led to similar results (S2 Table), and it should be noted that the genomic make-up is identical between infected and cured versions of the same line.

DNA methylation modification of T. pretiosum associated with Wolbachia

We first assessed genome-wide cytosine methylation profiles of the introgressed lines with and without Wolbachia. There were 106,475 cytosines that were methylated in at least one of the six samples (originating from the three cured and three infected lines), identified using the software Bis-Class [30] and all of which had exceptional sequencing depth (S3 Table). We first identified individual cytosine positions that were significantly differentially methylated, referred to as “differentially methylated positions” (DMPs), between the two lines (Methods, S4 Table). We identified a total of 340 DMPs (FDR-adjusted Q < 0.05), 317 of which were genic and 23 of which were intergenic. Interestingly, we found that 238 (70%) of DMPs had significantly higher levels of fractional methylation (the frequency of methylated reads out of total reads for a specific cytosine) in the infected lines compared to the cured lines (χ2 test, P < 10−15; Fig 2A). The 84 genes harboring 317 genic DMPs were then defined as “differentially methylated genes” (DMGs), whereas the remaining 23 intergenic DMPs were characterized by the nearest annotated gene (S4 Table).

Fig 2. Differential methylation and expression between Wolbachia infected and cured lines.

A) Heatmap of 340 significantly differentially methylated positions (DMPs, found within 84 genes) detected using RADMeth. More sites exhibit increased DNA methylation in the infected lines (239 out of 340 DMPs are hypermethylated in the infected lines compared to cured lines). B) Heatmap of 59 differentially expressed genes, the majority (39 out of 59; χ2, P = 3.13x10-6) of which were up-regulated in the infected lines. C) Gene body fractional methylation and gene expression relationship between DEG, DMG and other genes (genes that are neither DEG or DMG) accompanied by gene body fractional methylation densities of each gene class. Gene body fractional methylation is calculated as the mean fractional methylation of all CpGs within each gene, and shows a bimodal distribution as in other species [46]. Gene body fractional methylation and gene expression are log10 and log2 transformed, respectively, to improve normality. D) Average gene lengths and E) density plot of gene lengths for each gene class. Error bars represent standard deviations.

DMGs contained, on average, 4.5 DMPs (S4 Table). The gene containing the most DMPs had 18 (S1 Fig) despite it not being a particularly long gene (7,439 bp or 74th percentile of all gene lengths; Fig 2D and 2E). This gene, TPRE002596 (homolog of Drosophila CG4896), is implicated in alternative mRNA splicing in Drosophila melanogaster [31]. DMGs also included three genes known to be involved in polarity formation in early development, namely TPRE005355 (homolog of D. melanogaster Rab6, containing 4 DMPs,) TPRE001344 (homolog of D. melanogaster CG7483, containing 10 DMPs), and TPRE000491, harboring 2 DMPs. Rab6 is critical in D. melanogaster actin organization and polarization of the oocyte cytoskeleton [32], and CG7483 is predicted to influence oskar mRNA localization at the posterior pole of oocytes in D. melanogaster [33]. TPRE000491 contains a Toll/interleukin-1 receptor domain, which is involved in dorso-ventral polarity formation in D. melanogaster embryos [34, 35].

Largely due to these three genes, DMGs showed significant gene ontology enrichment for terms relating to embryonic axis specification, embryonic pattern specification, and oocyte anterior/posterior axis specification (S5 Table). Additionally, several other genes containing high numbers of DMPs also had functions relating to embryonic development, despite not being annotated with those specific ontology terms (S6 Table). One of these genes, TPRE012191 (homolog of D. melanogaster Chc), has the second most DMPs of any gene (n = 13) and plays a key role in yolk protein uptake in the developing oocyte [36]. These results align with previous studies suggesting epigenetic modification by Wolbachia [2022, 37], as well as the proposed functional association of Wolbachia with the germline to facilitate transmission [3739].

Wolbachia infection results in changes in gene expression and exon usage

Next, we assessed the transcriptomic signatures of Wolbachia infection in the introgressed Trichogramma lines. Using a negative binomial generalized linear model method [40], we identified 59 genes that were significantly differentially expressed (DEGs) between Wolbachia-infected and cured lines (FDR-adjusted Q < 0.05)(Fig 2B; S2 Fig). Of these, 45 (76%) genes were up-regulated in the infected lines (χ2 test, P < 10−15), with an average of 4.72-fold change higher expression relative to the cured lines (S4 Table). Genes that were down-regulated in the Wolbachia-infected lines (24%) had an average of 3.47-fold lower expression relative to the cured lines. Interestingly, differentially expressed genes were not significantly overrepresented for any gene ontology terms, partially because the majority of the DEGs had no ontology terms assigned to them at all. Indeed, 35 of the 59 DEGs had previously been classified as lineage-specific in Trichogramma [27], which is a significant enrichment (Fisher’s exact test, P = 1.29X10-7) (S7 Table). The overrepresentation of DEGs with unique evolutionary patterns in the Trichogramma lineage points towards either a host-specific method of manipulation by Wolbachia, or a host response to Wolbachia infection that is unique to Trichogramma.

Next, we examined patterns of differential exon usage associated with Wolbachia infection status, using a method that models exon usage fold changes based on a generalized linear model [41]. We found 1,012 exons spread across 685 genes were differentially used between infected and cured lines, though genes containing differentially used exons were not significantly enriched for any GO terms.

To examine if there was a network-level change associated with Wolbachia infection status, we performed a weighted gene co-expression network analysis (WGCNA) [42] using both cured and infected expression data. This method constructs clusters of correlated genes based on their expression levels and relates these gene clusters, or modules, to external traits [42], such as infection status in this study. We identified a total of 28 modules, in which one module containing 481 genes was significantly enriched for expression changes according to infection status (r = 0.96, P = 0.002). We found no enriched GO terms in this module, though this may in part be due to 160 of the genes having no known annotations. This module includes 44 DEGs, which is a highly significant enrichment (Fisher’s exact test, Odds ratio = 75.84, P < 10−15). The majority of genes in this module are up-regulated in the infected lines (336 out of 481 total genes and 35 out of 44 DEGs, P < 10−15 and 9.82x10-8 respectively, χ2 test). This module also included 2 DMGs, though this overlap was not statistically significant (Fisher’s exact test, Odds ratio = 0.64, P = 0.82).

Differential methylation significantly associates with differential exon usage but not differential gene expression

Next, we investigated relationships between expression and methylation that were correlated with Wolbachia infection. In both the cured and infected lines, there is a clear separation of methylated and unmethylated genes, with methylated genes tending to be more highly expressed than unmethylated genes, regardless of Wolbachia infection status (Fig 2C; S3 Fig). This is consistent with observations in other hymenopterans [4345] and in the majority of invertebrates investigated so far, where methylated genes are more highly and constitutively expressed [46, 47].

We then asked if differentially methylated genes were also differentially expressed, as we might expect changes in DNA methylation to directly modulate gene expression. In contrast, we found that DEGs and DMGs were completely distinct and there was no overlap between them (Fig 2C; S4 Fig). While none of the DEGs were significantly differentially methylated, 32 of the 39 up-regulated DEGs (82%) in the infected lines also had higher (but non-significant) levels of fractional methylation compared to the cured lines. This constitutes a significant bias towards higher levels of methylation in infected lines (χ2 test, P = 5.48x10-8), demonstrating consistency between the epigenomic and transcriptomic changes associated with Wolbachia infection.

We also observed an intriguing pattern with regard to the variability in gene expression, or “transcriptional noise”. Previous studies have proposed that DNA methylation of genic regions, which is often referred to as ‘gene body methylation’, might reduce transcriptional noise [48, 49]. It is notable that we observed hypermethylation of CpGs associated with Wolbachia infection (see above). We thus tested whether transcriptional noise is correlated with Wolbachia infection, by constructing a linear model using transcriptional noise (measured by the coefficient of variation of expression [49]) as the response variable. We included gene body methylation (measured as mean DNA methylation levels of CpGs within each gene), gene expression, gene length, and infection status as explanatory variables (Methods). The results show that each explanatory variable was highly significant, indicating that transcriptional noise is negatively correlated with gene body methylation, gene expression, gene length, as well as Wolbachia infection (Fig 3A). We show, for the first time, that Wolbachia infection is accompanied by a decrease in transcriptional noise (Fig 3B and 3C), even after accounting for the effect of increased DNA methylation.

Fig 3. Transcriptional noise is negatively correlated with gene body methylation, gene expression, gene length, and Wolbachia infection.

A) Summary of multiple linear regression modeling transcriptional noise, represented as the percent coefficient of variation of gene expression. B) Transcriptional noise is lower in infected lines and decreases more sharply with increased gene expression (Regression coefficients for cured and infected lines are -0.83 (P < 10−15) and -0.88 (P < 10−15), respectively). Transcriptional noise and gene expression are log10 and log2 transformed to improve clarity (see Methods). C) Violin plot with an imbedded boxplot demonstrating that transcriptional noise in infected lines is lower than that in cured lines (Student’s t-test, P < 10−15).

Another known impact of DNA methylation is differential exon usage [5053], which could occur due to changes in splicing, or changes in expression levels of specific splice variants. Additionally, three of the genes with the highest numbers of DMPs have predicted functions related to the regulation of splicing (S6 Table). Of the 1,012 exons spread across 685 genes that were significantly differentially expressed between infected and cured lines, only 5 exons contained DMPs. However, given the small number of overall DMPs (340 out of the total 106,475 CpGs analyzed), this represents a statistically significant overlap (Odds ratio = 4.40, Fisher’s exact test, P = 0.0071). Since DNA methylation within one exon can affect splicing of other exons in the same gene [5456], we further examined whether genes harboring differentially used exons also include differentially methylated positions, regardless of whether it was the differentially used exon that directly contained the DMP(s). In other words, we examined the overlap between DMGs and genes with evidence of differential exon usage. Indeed, this overlap was significant (14 genes, Fisher’s exact test, Odds ratio = 3.29, P = 3.14 x 10−4). In comparison, only 3 genes with differentially used exons were DEGs (Odds ratio = 0.87, Fisher’s exact test, P = 0.67). Two examples of the relationship between DMPs and differential exon usage are depicted in Fig 4. Gene TPRE001057, whose D. melanogaster ortholog is involved in autophagy (CG14299), harbors 2 differentially used exons, and one of them (exon 23) harbors 6 DMPs (Fig 4A). Another gene TPRE001061, an ortholog of D. melanogaster Mzt1 involved in male mating behavior, has a total of 4 differentially use exons, where exon 10 contains 8 DMPs (Fig 4B). Interestingly, for both genes, the exons that contain DMPs had higher expression in the infected lines, which is consistent with the higher level of fractional methylation of DMPs in the infected lines. Overall, 614 of the 1,012 (60.1%) differentially used exons had increased usage in the infected lines (χ2 test, P = 10−15).

Fig 4. Representative genes showing differential exon usage between cured and infected Trichogramma.

Boxes filled in purple are differentially used exons. Among the differentially used exons, those that contain DMPs are shown in yellow-filled boxes. A) Gene TPRE001057, ortholog of D. melanogaster CG14299, contains 2 differentially used exons, exons 1 and 23 (exon numbers are shown as integers on the x-axis). The exon 23 contains 6 DMPs is highlighted with a yellow-filled box. B) Gene TPRE010061, ortholog of D. melanogaster Mzt1, contains 4 differentially used exons (exons 5, 7, 8, and 10). The exon 10 has 8 DMPs and shown in yellow-filled box.


Symbiotic microbes are increasingly appreciated as driving forces in evolution [57]. Wolbachia’s widespread presence across arthropods and nematodes, and its primarily maternal transmission, make it a particularly interesting model to study the mechanisms behind infection and host manipulation. The induction of parthenogenesis has been described across several orders of arthropods [3], but is poorly understood, with the lack of traditional insect model organisms infected by parthenogenesis-inducing Wolbachia being a major challenge. Here, we developed an introgression scheme to delineate the global methylome and transcriptome modifications that follow a drastic transition from sexual to asexual reproduction due to the association with Wolbachia. A unique advantage of our introgression scheme and choice of organism is that we can examine molecular changes free from genetic background effects [25, 26] and in an organism with moderate amounts of genic DNA methylation [27].

Using this approach, we demonstrate clear changes of epigenomes and transcriptomes. Differentially expressed genes following Wolbachia infection tended to be unmethylated and are enriched for those with unique evolutionary patterns in the Trichogramma lineage [27], pointing to a lineage-specific response to Wolbachia infection in this group of insects. Previous studies have determined that the Wolbachia strains infecting Trichogramma wasps form a unique monophyletic clade within the Wolbachia phylogeny [58]. This pattern is relatively unusual, as Wolbachia undergoes host-switching at a relatively high rate for an intracellular, maternally transmitted reproductive symbiont [5861]. If these Wolbachia strains have mechanisms for facilitating infection that specifically target processes unique to Trichogramma, it would explain why there have been no horizontal transfers back out of Trichogramma hosts. Given that there are limited functional predictions available for these lineage specific genes, reverse genetics approaches will be particularly powerful for elucidating their role in the maintenance of and/or the response to Wolbachia infection and the induction of parthenogenesis.

We found 84 genes that were differentially methylated between infected and cured Trichogramma wasps. This number is on par with that associated with Wolbachia infection in Aedes mosquitoes (63 genes in A. aegypti [20]) and that associated with viral infection in honey bee [62], indicating that approximately 1–2% of analyzed genes change DNA methylation in response to pathogenic infection in these species. Interestingly, the number of DNA methylation changes associated with human diseases tended to be in much smaller proportions (less than 0.5%, e.g., [6365]). In contrast to differentially expressed genes, differentially methylated genes are evolutionarily conserved genes and included those involved in processes such as oogenesis, meiosis, and cell division, which is consistent with the proposed role of Wolbachia in inducing parthenogenesis via disruption of chromosome segregation during oocyte mitotic divisions [15]. The list of differentially methylated genes includes those involved in core eukaryotic processes, and thus potentially could affect both host chromosome segregation and transmission of Wolbachia to daughter cells.

While we show there is no direct correspondence between differential DNA methylation and differential gene expression, our study offers novel insight into how these two processes might be linked. We show that Wolbachia infection increases DNA methylation in Trichogramma. Wolbachia infection was similarly associated with increase in DNA methylation in Drosophila testis [37] and virus-infected honey bees [62]. In addition, Wolbachia infection is significantly negatively associated with transcriptional noise, suggesting that the presence of Wolbachia reduces the variability in gene expression, after accounting for the overall increase in methylation. However, it is also possible that the observed methylation changes may be a result of changes in physiology associated with Wolbachia infection and not necessarily the cause of any Wolbachia-mediated changes to the host. Though further experiments with larger numbers of samples will be necessary to validate and explain this observation, it provides another possible avenue of research for exploring the wide-ranging effects of Wolbachia infection. Moreover, we show that exons that are differentially used according to Wolbachia-infection status often occurred in the same genes that contained differentially methylated cytosines. Even though our current data are insufficient to explicitly identify alternatively spliced transcripts (due to the lack of detailed annotations), these results indicate that differential DNA methylation may regulate the isoforms of genes that are used, thus favoring different splice variants at certain loci. Our results provide a direct support to a link between differential DNA methylation and differential exon usage in insects [51, 62, 66], by controlling for genetic background effects. However, we note that only a small portion of differentially methylated positions appear to be associated with the differential usage of exons. Additionally, it is of note that our data were generated by pooling of individuals, which was necessary given the minute size of Trichogramma. While each biological replicate was clonal and thus the genomes are identical baring any mutations, there may be heterogeneity in the methylation signature (perhaps due to fluctuations in Wolbachia titer) that masks some links between methylation patterns and transcriptomic signatures, such as exon usage. Additional studies using more targeted approaches with single individuals or specific tissues will be useful for resolving these links.

Another unique feature of our experimental set-up is the use of introgression to generate genetically identical lines with and without Wolbachia. While this was required here (as the original Wolbachia-infected lines could not be cured of Wolbachia due to mutations in the wasp genome), there are likely distinct signatures of the infection in these introgressed lines (that now have Wolbachia associated with a different genomic background) as compared to the naturally infected lines. While the introgression was controlled for (i.e., both infected and cured lines had the same combination of nuclear and mitochondrial genomes), the signatures of infection in the “new” association may be different than Wolbachia-host associations that are long co-adapted. Comparing “new” vs co-adapted Wolbachia associations may be a useful tool in the future to identify how Wolbachia and host evolve after the onset of a new infection. There are many previous studies that identify genotype-by-genotype effects in Wolbachia-host associations (for a review, see [67]), so identifying commonalities and differences between associations will be useful for understanding how Wolbachia manipulates diverse hosts.

Our results will direct mechanistic investigations focused on the induction of symbiont-mediated parthenogenesis, and further our understanding of the relationship between epigenetics and gene expression in arthropods. While there are not currently any genetic tools for Trichogramma or Wolbachia, there is the potential to use reverse genetics (e.g., RNAi) in Trichogramma, or surrogate systems (e.g., Nasonia, Drosophila, yeast). Knockdown of target gene expression in Trichogramma will be useful in interrogating the contribution of particular proteins to Wolbachia establishment and reproductive manipulation. Expression of Wolbachia genes in surrogate systems is also a powerful tool for identifying bacterial proteins that may interface with host biology [10, 68].

Materials and methods

Trichogramma lines and rearing

Trichogramma pretiosum colonies are maintained in 12 x 75 mm glass culture tubes plugged with cotton, and incubated at 24°C with a light:dark cycle of 16:8 hours. Every 11 days, colonies are given fresh honey and egg cards to parasitize made of UV irradiated Ephestia kuehniella eggs (Beneficial Insectary, Guelph, Canada) adhered to card stock by double-sided tape. Four different isofemale lines were used in experiments (Insectary, ES865, CA9, and CA29). The “Insectary” line was initiated by a single female collected from the Puira Valley of Peru, and has been kept in an insectary since 1966 [15]. The “ES865” line was initiated by a single female collected in Oahu, Hawaii, USA in 2011 that emerged from a Lycaenid egg on Crotalaria assamica. Both the Insectary and ES865 lines are infected with parthenogenesis-inducing Wolbachia, as witnessed by the production of female offspring from virgin mothers and confirmed molecularly by PCR [69]. Additionally, all attempts at establishing sexually reproducing lines (using rifampicin) have failed (as seen in [70]). Males are produced, but self-sustaining, sexually reproducing lines have not been possible. The “CA9” line and the “CA29” are both highly inbred, sexually reproducing lines that each originated from a different sibling-mated female collected in Irvine, California, USA in 2008. The females emerged from Manduca sexta eggs collected from tomato plants. These lines are not infected with Wolbachia: virgin females produce only male offspring. Species identification and Wolbachia infection status were confirmed using molecular methods, as described previously [28, 7173].

Introgression of Wolbachia into sexual backgrounds

We introgressed the sexual CA9 nuclear genome into the asexual ES865 cytoplasm, and the sexual CA29 nuclear genome into the asexual Insectary cytoplasm (Fig 1A). These pairs were chosen based on the ability to differentiate them at the A9 microsatellite locus, a size-diagnostic amplicon routinely used for identifying unique isofemale lines of this wasp [71]. For example, the Insectary line has an A9 microsatellite of 312 bp in length, and the CA29 line has an A9 microsatellite of 220 bp in length–allowing us verify fertilization during introgression: a critical step because Wolbachia induces diploidization of unfertilized eggs, and thus female offspring are produced from both fertilized and unfertilized eggs [16]. To perform the introgression, virgin females from the “cytoplasm-donating line” were mated to virgin males of the “nuclear-genome-donating line”. This cross resulted in female offspring that are either heterozygous (due to fertilization), or homozygous (due to Wolbachia-mediated gamete duplication). These females, with undetermined zygosity, were isolated as virgins and allowed to parasitize egg cards for 24 hours, allowing us to determine how efficiently Wolbachia induces parthenogenesis (as inferred by sex ratio of the resulting offspring). After 24 hours of reproduction as virgins, wasps were mated to virgin males from the appropriate “nuclear-genome-donating line” (either CA9 or CA29), and then allowed to parasitize a separate egg card for the next 24 hours. After these two reproductive periods, the mated females were removed from their egg card, and used for DNA extraction and A9 microsatellite PCR assays, as described previously [71]. At each step of the introgression, we identified mothers who were heterozygous (indicating she was produced via fertilization), and then isolated virgin females originating from her “mated” egg card. These females, again with undetermined zygosity, were handled in the same manner as her mother: 24 hours on an egg card as a virgin to check sex ratio and fecundity, followed by mating and 24 hours on a fresh egg card to continue the introgression (Fig 1B).

After seven generations of introgression, we initiated three introgressed isofemale lines, each derived from a parallel, independent introgression, and grew up the lines over a period of three generations, following standard rearing protocols detailed above. After three generations, the lines were split, and one replicate was cured with rifiampicin, as described previously [4]. The antibiotic treatment protocol takes three generations to complete. We verified successful curing of the Wolbachia infection using protocols described above. Finally, after antibiotic treatment was complete, we allowed wasps to recover from the antibiotics for three generations prior to sequencing.

Sex ratio and fecundity were tracked over the course of the introgression to monitor the level of parthenogenesis induced by Wolbachia (sex ratio), and any potential cyto-nuclear incompatibilities caused by interactions between the cytoplasm (mitochondria and Wolbachia) and the nucleus (fecundity, as measured by the number of offspring that emerged as adults). Sex ratio and fecundity measurements were taken from virgin wasps after exposure to an egg card for 24 hours, as described above. Variation in fecundity or sex ratio was assessed with a generalized linear model, including “generation” as a fixed effect, and using either a Gaussian or binomial error distribution, respectively. Separate GLMs were run for the ES865 and Insectary datasets. Analyses were carried out in R version 3.5.0 [74], using the ‘stats’ package. The introgressed lines from the ES865 X CA9 wasps had very low fecundity, and we were unable to expand the colonies sufficiently for further experimentation. Thus, the CA29 X Insectary introgressed lines were chosen for further experimentation.

Nucleotide extractions

To assess signatures of genome-wide cytosine methylation and gene expression, we extracted DNA and RNA from pools of T. pretiosum females. Newly emerged wasps (less than 48 hours post emergence and having had no exposure to a fresh egg card) were collected in a fresh culture vial and placed on a cooling pad for sex sorting, as determined by antennal morphology. Only females were found in the Wolbachia-infected lines, while the cured lines contained both males and females. Approximately 500 females (each female is ~0.3 mm long [75]) were collected for each biological replicate (total of six: three infected, three cured), and flash frozen in liquid nitrogen. That pool was homogenized, then split evenly between DNA and RNA extractions with Qiagen DNeasy and RNeasy kits, respectively, according to manufacturer’s instructions, and including the appropriate on-column RNase or DNase digestion to remove RNA and DNA contamination. The final number of extractions, each of which was made into a library (see below), was 12: DNA and RNA samples for three biological replicates each of infected and cured lines.

Transcriptome sequencing

Strand specific RNA-Seq libraries were prepared by NovoGene (, Chula Vista, CA) using a standard eukaryotic preparation workflow. Briefly, mRNA was enriched for using oligo(dT) beads, and then fragmented randomly by adding fragmentation buffer. cDNA was synthesized by using mRNA template and random hexamers, after which a custom second-strand synthesis buffer (Illumina), dNTPs, RNase H and DNA polymerase I were added to initiate second-strand synthesis. After a series of terminal repair, A ligation and sequencing adaptor ligation, the double-stranded cDNA library was completed through size selection and PCR enrichment. The final library quality and quantity was assessed using an Agilent 2100 Bioanalyzer, and Qubit 2.0, respectively [76, 77]. Libraries were multiplexed and sequenced on an Illumina HiSeq with paired-end 150 base pair reads. This produced between 30–42 million paired-end reads per sample.

Whole-genome sequencing

Genomic libraries were made following a modified version of an Illumina sequencer compatible protocol [78]. The extracted DNA was fragmented by S-series focused ultrasonicator (Covaris) using the 200bp-target peak size protocol. Fragmented DNA was then size selected (200bp-600bp) with an Agencourt AMPure XP bead-based (Beckman Coulter Cat. No. A63880) size selection protocol [78]. The DNA End repair step was performed with End-It DNA end repair kit (Epicentre, Cat. No. ER81050. After the end repair step, A-tailing (NEB, cat. No. M0202) and ligation steps were performed to ligate methylated adaptors.

Whole genome bisulfite sequencing

We followed a previously published protocol [78] to perform whole genome bisulfite sequencing (WGBS). Briefly, bisulfite treatment of genomic DNA was performed using the MethylCode Bisulfite Conversion Kit (Life technologies). Purified genomic DNA was treated with CT conversion reagent in a thermocycler for 10 minutes at 98°C, followed by 2.5 hours at 64°C. As a control for bisulfite conversion, 10ng of unmethylated lambda phage DNA (Promega, Cat. No. D1501) was added to the 1μg of input DNA. Bisulfite-treated DNA fragments remain single-stranded as they are no longer complementary. Low-cycle (4–8) PCR amplification was performed with Kapa HiFi Uracil Hotstart polymerase enzyme (KAPA Biosystems, cat. No. KK2801) which can tolerate Uracil residues. The final library fragments contain thymines and cytosines in place of the original unmethylated cytosine and methylated cytosines, respectively. The methylome libraries were diluted and loaded onto Illumina HiSeq X system for sequencing using 150bp paired-end reads. We received 100–200 million paired-end reads per sample. Bisulfite conversion efficiencies are all greater than 99.8% (S3 Table).

Creating alternative reference genome

We used the GATK best practices pipeline [79] to discover SNPs in the CA29 line and incorporated them into the original Trichogramma genome (the Insectary line) [27] to create an alternate reference genome for subsequent RNA-Seq and WGBS alignment. Briefly, CA29 reads were aligned to the Tpre_1.0 reference genome using BWA-MEM with option–M enabled. Duplicate reads were marked with Picard tools and INDELs were realigned to eliminate mapping artifacts with GATK. Base recalibration was performed with 3 rounds of bootstrapping with the resulting variants used as substitutes for the gold standard variants in the HaplotypeCaller SNP calling algorithm. The final lists of SNPs were substituted into the original Tpre_1.0 reference using GATK’s built-in FastaAlternateReferenceMaker tool.

Transcriptomic data analysis

Reads were cleaned of adaptor sequence and quality filtered with Trimmomatic v.0.35 [80], using a sliding window of 4 bases and cutting when the average quality dropped below 20, employing a minimum read length of 40 bases. Cleaned reads were mapped to the CA29 alternate T. pretiosum reference genome (see above) [27] with tophat2 v. 2.2.1 [81], and mappings to coding regions were quantified with HTSeq’s ‘count’ feature [82]. Differential expression was assessed with DESeq2 [40], including infection status (either Wolbachia positive or Wolbachia negative) as a factor. DESeq2 and graphics were run in R version 3.3.2 [74]. Gene expression was measured by normalizing counts based on library sizes using the “estimateSizeFactors” function from the DESeq2 [40] package.

To assess expression differences at the exon level, we utilized the DEXseq [41] package. Differences in the level of gene expression between DEGs, DMGs, and genes that fell into neither of those categories were assessed with a one-way ANOVA using log-transformed gene expression values, and Tukey’s post hoc tests. Exon-level reads were counted using the alignments from our RNA-seq analysis and normalized using the “estimateSizeFactors” function from DESeq2 [40]. Exon usage was modeled with the following generalized linear model: ~ sample + exon + infection status:exon. Exonic regions were considered to be significant at a FDR-adjusted [83] level of 0.05.

Transcriptional noise analysis

Transcriptional noise was measured as the percent coefficient of variation of gene expression [49] and used as the response variable in the following multiple linear regression model: log10(transcriptional noise) ~ gene body methylation + log2(gene expression) + log10(gene length) + Wolbachia infection status. Gene body fractional methylation was calculated as the mean fractional methylation of all CpGs within each gene [49] and gene expression was measured as described above. Analysis was performed in R version 3.3.2 [74] using the default ‘lm’ function.

WGBS data processing

Raw reads were assessed for quality using FastQC and trimmed and filtered for quality and adapter sequences using Trim Galore! [84]. Bismark was then used to align reads to the CA29 alternate T. pretiosum reference genome (see above) with parameters—score_min L,0,-0.4 [85]. Additionally, we aligned our reads to the unmethylated lambda genome (GenBank Accession: J02459.1) to estimate the bisulfite conversion efficiency. Alignments were deduplicated and C and T counts for CpGs on plus and minus strands were combined using custom scripts ( and classified as either “Methylated” or “Unmethylated” using Bis-Class for downstream analyses [30]. Detailed information regarding read numbers, alignment efficiency, bisulfite conversion rates and other methylation statistics can be found in S3 Table.

Genomic mapping of introgression from bisulfite sequencing data

In order to identify paternal (introgressed) and maternal (non-introgressed) genomic regions present in each of our introgressed isofemale lines, we mapped WGBS reads of each sample to both the maternal and paternal genomes separately. We then used BS-SNPer [29], a tool that can identify polymorphisms from bisulfite-sequencing data, on each maternal and paternal alignment and used stringent parameters to filter for high confidence SNPs. We required putative SNPs to be homozygous, have at least 10X sequencing depth, and a sequencing quality score of at least 30 to pass the filtering step. We determined the allelic origin of each SNP by comparing the base call to the original SNP list generated between maternal and paternal genomes (see Creating alternative reference genome section above). Maternal SNPs occurred in clusters, as evidenced by the short distance between adjacent SNPs (S1 Table and S4 Fig). We identified putatively non-introgressed regions as those starting from a maternal SNP, and added the next maternal SNP within a certain threshold distance of the previous maternal SNP until a paternal SNP was reached. We removed all regions putatively non-introgressed using a 10kb threshold from all analyses.

Gene ontology analyses

Gene Ontology (GO) terms were mapped to the coding sequences using Blast2GO [86] after blastp searches against the NCBI non-redundant database, and interpro scans with the Blast2GO software, the same as done previously for T. pretiosum [27]. BiNGO [87] was used to identify significantly overrepresented GO terms in sets of differentially expressed or differentially methylated genes, using all genes in the genome as the background. Testing was performed with hypergeometric tests and Benjamini & Hochberg FDR correction, at a corrected significance level of 0.05 [83]. In addition to using GO terms to classify genes, the previously published orthology data was used to determine whether genes were specific to the Trichogramma lineage [27]. Trichogramma-specific genes are as previously described, and include those that are: A) truly unique to the Trichogramma lineage, B) genes that have orthologs in other hymenopteran taxa but evolved rapidly in Trichogramma such that orthology was difficult to detect, and/or C) genes that are members of gene families that expanded significantly in copy number in the Trichogramma lineage, resulting in one more Trichogramma-specific paralogs not present in other hymenopterans.

Differential methylation analysis

To improve the statistical power to detect differential DNA methylation, we followed a suggested protocol [88]. First, we selected 106,475 CpG sites that were methylated according to Bis-Class [30] in at least one of the six samples. Next, we used a beta-binomial regression method to identify individual CpGs that were differentially methylated between infected and cured Trichogramma lines. We refer to these individual CpGs as ‘differentially methylated positions (DMPs)’. The RADMeth (logit link) package [89] was applied using default parameters and adjusted for multiple testing using the option -bins 1:200:200. This method utilizes a beta-binomial regression model and has been shown to excel at its ability to detect differential DNA methylation [89]. DMPs were corrected for multiple testing at an FDR-adjusted [83] level of 0.05, which roughly corresponds to P = 5 x 10−5.

Weighted correlation network analysis (WGCNA)

To detect clusters of genes that are highly correlated in their expression, WGCNA was performed using library normalized counts assembled via the HTSeq [82] package from the transcriptome analysis step and using infection status as the sole external factor. Normalized counts were calculated by dividing the total count by the size factor obtained using the “estimateSizeFactors” function from the DESeq2 [40] package. Module detection was done using a soft thresholding power β of 16, a minimum module size of 20, and default parameters for the rest of the options (S5 Fig).

Supporting information

S1 Fig. Distribution of the number of DMPs per DMG.

DMGs have on average 4.5 DMPs and up to 18 DMPs.


S2 Fig. Volcano plot of all genes based on their log2 fold-change and adjusted P-values.

Differentially expressed genes were classified at an adjusted P-value of < 0.05.


S3 Fig.

Methylated genes (>0.004 gene body methylation) exhibit higher gene expression than unmethylated genes (<0.004 gene body methylation) in both A) cured and B) infected wasps. Variability of gene expression is reduced in methylated genes when measuring gene body methylation by the number of methylated sites in C) cured and D) infected wasps.


S4 Fig. Density plots of distances between adjacent maternal SNPs for samples in line A, both of which contain a large number of maternal SNPs.

Samples are labeled using a 2-letter system–the first letter indicates the line (‘A’, ‘B’, and ‘C’) while the second letter indicates the infection status (‘i’ for infected and ‘c’ for cured). 89.5% of adjacent maternal SNPs are within 2kb of each other while 98.8% are within 10kb of each other.


S5 Fig. Scale-free fit index and mean connectivity as a function of the soft-thresholding power β.

β was chosen based on when the value of when R2 begins to flatten near a relatively high value (indicated by the red line at R2 = 0.80).


S1 Table. Estimated number and size (in bp) of non-introgressed regions in each sample based on two threshold distances allowed between maternal SNPs (Materials and methods).

Samples are labeled using a 2-letter system–the first letter indicates the line (‘A’, ‘B’, and ‘C’) while the second letter indicates the infection status (‘i’ for infected and ‘c’ for cured).


S2 Table. Comparison of significant findings before and after removing genes and mCGs from putative non-introgressed regions in line A (10kb threshold; see S3 Table).


S3 Table. Summary statistics of BS-seq alignment and basic CpG coverage and methylation information.

Samples are labeled using a 2-letter system–the first letter indicates the line (‘A’, ‘B’, and ‘C’) while the second letter indicates the infection status (‘i’ for infected and ‘c’ for cured).


S4 Table. Detailed results of the various differential methylation, expression, and exon usage analyses as well as lists of gene names significant from each.


S5 Table. Top ten significant biological processes GO terms for DMGs based on FDR values.


S6 Table. List of the top 10 genes ordered by the number of DMPs contained and their corresponding Drosophila melanogaster ortholog and functional annotation.


S7 Table. Trichogramma lineage-specific differentially expressed genes.



The authors would like to thank Robert J. Schmitz and Nicholas A. Rhor for their protocol and training on bisulfite sequencing library preparation.


  1. 1. Zug R, Hammerstein P. Still a host of hosts for Wolbachia: Analysis of recent data suggests that 40% of terrestrial arthropod species are infected. PLoS One. 2012;7(6). pmid:22685581
  2. 2. Hilgenboecker K, Hammerstein P, Schlattmann P, Telschow A, Werren JH. How many species are infected with Wolbachia?—A statistical analysis of current data. Fems Microbiology Letters. 2008;281(2):215–20. pmid:18312577
  3. 3. Werren JH, Baldo L, Clark ME. Wolbachia: master manipulators of invertebrate biology. Nat Rev Micro. 2008;6(10):741–51. pmid:18794912
  4. 4. Stouthamer R, Luck RF, Hamilton WD. Antibiotics cause parthenogenetic Trichogramma (Hymenoptera, Trichogrammatidae) to revert to sex. Proceedings of the National Academy of Sciences. 1990;87(7):2424–7 pmid:11607070
  5. 5. Stouthamer R, Breeuwer JAJ, Luck RF, Werren JH. Molecular-identification of microorganisms associated with parthenogenesis. Nature. 1993;361(6407):66–8. pmid:7538198
  6. 6. Stouthamer R, Werren JH. Microbes associated with parthenogenesis in wasps of the genus Trichogramma. Journal of Invertebrate Pathology. 1993;61(1):6–9.
  7. 7. Stouthamer R, Russell JE, Vavre F, Nunney L. Intragenomic conflict in populations infected by Parthenogenesis Inducing Wolbachia ends with irreversible loss of sexual reproduction. BMC Evolutionary Biology. 2010;10:12. pmid:20667099
  8. 8. Bennett GM, Moran NA. Heritable symbiosis: The advantages and perils of an evolutionary rabbit hole. Proceedings of the National Academy of Sciences. 2015;112(33):10169–76. pmid:25713367.
  9. 9. Sullivan W. Wolbachia, bottled water, and the dark side of symbiosis. Molecular Biology of the Cell. 2017;28(18):2343–6. pmid:28855327
  10. 10. Beckmann JF, Ronau JA, Hochstrasser M. A Wolbachia deubiquitylating enzyme induces cytoplasmic incompatibility. Nat Microbiol. 2017;2:17007. Epub 2017/03/02. pmid:28248294.
  11. 11. LePage DP, Metcalf JA, Bordenstein SR, On J, Perlmutter JI, Shropshire JD, et al. Prophage WO genes recapitulate and enhance Wolbachia-induced cytoplasmic incompatibility. Nature. 2017;543(7644):243–7. Epub 2017/02/28. pmid:28241146.
  12. 12. Lindsey ARI, Rice DW, Bordenstein SR, Brooks AW, Bordenstein SR, Newton ILG. Evolutionary Genetics of Cytoplasmic Incompatibility Genes cifA and cifB in Prophage WO of Wolbachia. Genome Biol Evol. 2018;10(2):434–51. Epub 2018/01/20. pmid:29351633.
  13. 13. Perlmutter JI, Bordenstein SR, Unckless RL, LePage DP, Metcalf JA, Hill T, et al. The phage gene wmk is a candidate for male killing by a bacterial endosymbiont. PLoS Pathog. 2019;15(9):e1007936. Epub 2019/09/11. pmid:31504075 applications of wmk in arthropods.
  14. 14. Gavotte L, Henri H, Stouthamer R, Charif D, Charlat S, Bouletreau M, et al. A Survey of the bacteriophage WO in the endosymbiotic bacteria Wolbachia. Mol Biol Evol. 2007;24(2):427–35. Epub 2006/11/11. pmid:17095536.
  15. 15. Lindsey ARI, Werren JH, Richards S, Stouthamer R. Comparative genomics of a parthenogenesis-inducing Wolbachia symbiont. G3: Genes|Genomes|Genetics. 2016;6(7):2113–23. Epub 2016/05/20. pmid:27194801.
  16. 16. Stouthamer R, Kazmer DJ. Cytogenetics of microbe-associated parthenogenesis and its consequences for gene flow in Trichogramma wasps. Heredity. 1994;73:317–27.
  17. 17. Bernstein BE, Meissner A, Lander ES. The mammalian epigenome. Cell. 2007;128(4):669–81. pmid:17320505.
  18. 18. Harris HL, Braig HR. Sperm chromatin remodelling and Wolbachia-induced cytoplasmic incompatibility in Drosophila. Biochem Cell Biol. 2003;81(3):229–40. pmid:12897857.
  19. 19. Negri I, Franchini A, Gonella E, Daffonchio D, Mazzoglio PJ, Mandrioli M, et al. Unravelling the Wolbachia evolutionary role: the reprogramming of the host genomic imprinting. Proc Biol Sci. 2009;276(1666):2485–91. pmid:19364731.
  20. 20. Ye YH, Woolfit M, Huttley GA, Rances E, Caragata EP, Popovici J, et al. Infection with a Virulent Strain of Wolbachia Disrupts Genome Wide-Patterns of Cytosine Methylation in the Mosquito Aedes aegypti. PLoS One. 2013;8(6):e66482. pmid:23840485.
  21. 21. Zhang G, Hussain M, O’Neill SL, Asgari S. Wolbachia uses a host microRNA to regulate transcripts of a methyltransferase, contributing to dengue virus inhibition in Aedes aegypti. Proc Natl Acad Sci U S A. 2013;110(25):10276–81. pmid:23733960.
  22. 22. Bhattacharya T, Newton ILG, Hardy RW. Wolbachia elevates host methyltransferase expression to block an RNA virus early during infection. PLoS Pathog. 2017;13(6):e1006427. pmid:28617844.
  23. 23. Kumar S, Kim Y. An endoparasitoid wasp influences host DNA methylation. Sci Rep. 2017;7:43287. Epub 2017/02/24. pmid:28230192.
  24. 24. Bewick AJ, Vogel KJ, Moore AJ, Schmitz RJ. Evolution of DNA Methylation across Insects. Mol Biol Evol. 2017;34(3):654–65. Epub 2016/12/28. pmid:28025279.
  25. 25. Keller TE, Lasky JR, Yi SV. The multivariate association between genomewide DNA methylation and climate across the range of Arabidopsis thaliana. Mol Ecol. 2016;25(8):1823–37. pmid:26850505.
  26. 26. Yi SV. Insights into Epigenome Evolution from Animal and Plant Methylomes. Genome Biol Evol. 2017;9(11):3189–201. pmid:29036466.
  27. 27. Lindsey ARI, Kelkar YD, Wu X, Sun D, Martinson EO, Yan Z, et al. Comparative genomics of the miniature wasp and pest control agent Trichogramma pretiosum. BMC Biology. 2018;16(1):54. pmid:29776407
  28. 28. Lindsey ARI, Stouthamer R. Penetrance of symbiont-mediated parthenogenesis is driven by reproductive rate in a parasitoid wasp. PeerJ. 2017;5:e3505. pmid:28663939.
  29. 29. Gao S, Zou D, Mao L, Liu H, Song P, Chen Y, et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics. 2015;31(24):4006–8. pmid:26319221.
  30. 30. Huh I, Yang X, Park T, Yi SV. Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information. BMC Genomics. 2014;15:608. Epub 2014/07/20. pmid:25037738.
  31. 31. Lasko P. The drosophila melanogaster genome: translation factors and RNA binding proteins. J Cell Biol. 2000;150(2):F51–6. pmid:10908586.
  32. 32. Coutelis JB, Ephrussi A. Rab6 mediates membrane organization and determinant localization during Drosophila oogenesis. Development. 2007;134(7):1419–30. pmid:17329360.
  33. 33. Palacios IM, Gatfield D, St Johnston D, Izaurralde E. An eIF4AIII-containing complex required for mRNA localization and nonsense-mediated mRNA decay. Nature. 2004;427(6976):753–7. pmid:14973490.
  34. 34. Medzhitov R, Preston-Hurlburt P, Janeway CA Jr. A human homologue of the Drosophila Toll protein signals activation of adaptive immunity. Nature. 1997;388(6640):394–7. pmid:9237759.
  35. 35. Sun H, Towb P, Chiem DN, Foster BA, Wasserman SA. Regulated assembly of the Toll signaling complex drives Drosophila dorsoventral patterning. EMBO J. 2004;23(1):100–10. pmid:14685264.
  36. 36. Richard DS, Gilbert M, Crum B, Hollinshead DM, Schelble S, Scheswohl D. Yolk protein endocytosis by oocytes in Drosophila melanogaster: immunofluorescent localization of clathrin, adaptin and the yolk protein receptor. J Insect Physiol. 2001;47(7):715–23. pmid:11356418.
  37. 37. LePage DP, Jernigan KK, Bordenstein SR. The relative importance of DNA methylation and Dnmt2-mediated epigenetic regulation on Wolbachia densities and cytoplasmic incompatibility. PeerJ. 2014;2:e678. pmid:25538866.
  38. 38. Serbus LR, Sullivan W. A cellular basis for Wolbachia recruitment to the host germline. PLoS Pathog. 2007;3(12):e190. pmid:18085821.
  39. 39. Newton IL, Savytskyy O, Sheehan KB. Wolbachia utilize host actin for efficient maternal transmission in Drosophila melanogaster. PLoS Pathog. 2015;11(4):e1004798. pmid:25906062.
  40. 40. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12):550. pmid:25516281
  41. 41. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22(10):2008–17. pmid:22722343.
  42. 42. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. pmid:19114008.
  43. 43. Lyko F, Foret S, Kucharski R, Wolf S, Falckenhayn C, Maleszka R. The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol. 2010;8(11):e1000506. Epub 2010/11/13. pmid:21072239.
  44. 44. Wang X, Wheeler D, Avery A, Rago A, Choi JH, Colbourne JK, et al. Function and evolution of DNA methylation in Nasonia vitripennis. PLoS Genet. 2013;9(10):e1003872. pmid:24130511.
  45. 45. Zeng J, Yi SV. DNA methylation and genome evolution in honeybee: gene length, expression, functional enrichment covary with the evolutionary signature of DNA methylation. Genome Biol Evol. 2010;2:770–80. pmid:20924039.
  46. 46. Sarda S, Zeng J, Hunt BG, Yi SV. The evolution of invertebrate gene body methylation. Mol Biol Evol. 2012;29(8):1907–16. pmid:22328716.
  47. 47. Keller TE, Han P, Yi SV. Evolutionary Transition of Promoter and Gene Body DNA Methylation across Invertebrate-Vertebrate Boundary. Mol Biol Evol. 2015;submitted.
  48. 48. Bird AP. Gene number, noise reduction and biological complexity. Trends Genet. 1995;11(3):94–100. pmid:7732579.
  49. 49. Huh I, Zeng J, Park T, Yi SV. DNA methylation and transcriptional noise. Epigenetics Chromatin. 2013;6(1):9. pmid:23618007.
  50. 50. Ding XL, Yang X, Liang G, Wang K. Isoform switching and exon skipping induced by the DNA methylation inhibitor 5-Aza-2'-deoxycytidine. Sci Rep. 2016;6:24545. pmid:27090213.
  51. 51. Arsenault SV, Hunt BG, Rehan SM. The effect of maternal care on gene expression and DNA methylation in a subsocial bee. Nat Commun. 2018;9(1):3468. pmid:30150650.
  52. 52. Li S, Zhang J, Huang S, He X. Genome-wide analysis reveals that exon methylation facilitates its selective usage in the human transcriptome. Brief Bioinform. 2018;19(5):754–64. pmid:28334074.
  53. 53. Park J, Peng Z, Zeng J, Elango N, Park T, Wheeler D, et al. Comparative analyses of DNA methylation and sequence evolution using Nasonia genomes. Mol Biol Evol. 2011;28:3345–54. pmid:21693438
  54. 54. Flores K, Wolschin F, Corneveaux JJ, Allen AN, Huentelman MJ, Amdam GV. Genome-wide association between DNA methylation and alternative splicing in an invertebrate. BMC Genomics. 2012;13:480. pmid:22978521.
  55. 55. Foret S, Kucharski R, Pellegrini M, Feng S, Jacobsen SE, Robinson GE, et al. DNA methylation dynamics, metabolic fluxes, gene splicing, and alternative phenotypes in honey bees. Proc Natl Acad Sci U S A. 2012;109(13):4968–73. pmid:22416128.
  56. 56. Lev Maor G, Yearim A, Ast G. The alternative role of DNA methylation in splicing regulation. Trends Genet. 2015;31(5):274–80. pmid:25837375.
  57. 57. Moran NA. Symbiosis. Curr Biol. 2006;16(20):R866–71. pmid:17055966.
  58. 58. Werren JH, Zhang W, Guo LR. Evolution and phylogeny of Wolbachia: reproductive parasites of arthropods. Proc Biol Sci. 1995;261(1360):55–63. pmid:7644549.
  59. 59. Raychoudhury R, Baldo L, Oliveira DC, Werren JH. Modes of acquisition of Wolbachia: horizontal transfer, hybrid introgression, and codivergence in the Nasonia species complex. Evolution. 2009;63(1):165–83. pmid:18826448.
  60. 60. O’Neill SL, Giordano R, Colbert AM, Karr TL, Robertson HM. 16S rRNA phylogenetic analysis of the bacterial endosymbionts associated with cytoplasmic incompatibility in insects. Proc Natl Acad Sci U S A. 1992;89(7):2699–702. Epub 1992/04/01. pmid:1557375.
  61. 61. Turelli M, Cooper BS, Richardson KM, Ginsberg PS, Peckenpaugh B, Antelope CX, et al. Rapid Global Spread of wRi-like Wolbachia across Multiple Drosophila. Curr Biol. 2018;28(6):963–71.e8. Epub 2018/03/13. pmid:29526588.
  62. 62. Galbraith DA, Yang X, Nino EL, Yi S, Grozinger C. Parallel epigenomic and transcriptomic responses to viral infection in honey bees (Apis mellifera). PLoS Pathog. 2015;11(3):e1004713. Epub 2015/03/27. pmid:25811620.
  63. 63. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142–7. pmid:23334450.
  64. 64. Dayeh T, Volkov P, Salo S, Hall E, Nilsson E, Olsson AH, et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 2014;10(3):e1004160. pmid:24603685.
  65. 65. Mendizabal I, Berto S, Usui N, Toriumi K, Chatterjee P, Douglas C, et al. Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol. 2019;20(1):135. pmid:31288836.
  66. 66. Li-Byarlay H, Li Y, Stroud H, Feng S, Newman TC, Kaneda M, et al. RNA interference knockdown of DNA methyl-transferase 3 affects gene alternative splicing in the honey bee. Proc Natl Acad Sci U S A. 2013;110(31):12750–5. pmid:23852726.
  67. 67. Lindsey ARI, Bhattacharya T, Newton ILG, Hardy RW. Conflict in the Intracellular Lives of Endosymbionts and Viruses: A Mechanistic Look at Wolbachia-Mediated Pathogen-blocking. Viruses. 2018;10(4). pmid:29561780.
  68. 68. Rice DW, Sheehan KB, Newton ILG. Large-Scale Identification of Wolbachia pipientis Effectors. Genome Biol Evol. 2017;9(7):1925–37. Epub 2017/09/01. pmid:28854601.
  69. 69. Werren JH, Windsor DM. Wolbachia infection frequencies in insects: evidence of a global equilibrium? Proc Biol Sci. 2000;267(1450):1277–85. pmid:10972121.
  70. 70. Russell JE, Stouthamer R. The genetics and evolution of obligate reproductive parasitism in Trichogramma pretiosum infected with parthenogenesis-inducing Wolbachia. Heredity. 2011;106(1):58–67. pmid:20442735
  71. 71. Lindsey ARI, Stouthamer R. The effects of outbreeding on a parasitoid wasp fixed for infection with a parthenogenesis-inducing Wolbachia symbiont. Heredity. 2017. pmid:28902190
  72. 72. Stouthamer R, Hu JG, van Kan F, Platner GR, Pinto JD. The utility of internally transcribed spacer 2 DNA sequences of the nuclear ribosomal gene for distinguishing sibling species of Trichogramma. Biocontrol. 1999;43(4):421–40.
  73. 73. Werren JH, Windsor D, Guo LR. Distribution of Wolbachia among neotropical arthropods. Proceedings of the Royal Society B: Biological Sciences. 1995;262(1364):197–204.
  74. 74. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria: URL; 2014.
  75. 75. Greenberg S, Nordlund DA, Wu Z. Influence of Rearing Host on Adult Size and Ovipositional Behavior of Mass Produced FemaleTrichogramma minutumRiley andTrichogramma pretiosumRiley (Hymenoptera: Trichogrammatidae). Biological Control. 1998;11(1):43–8.
  76. 76. Panaro NJ, Yuen PK, Sakazume T, Fortina P, Kricka LJ, Wilding P. Evaluation of DNA fragment sizing and quantification by the Agilent 2100 Bioanalyzer. Clin Chem. 2000;46(11):1851–3. pmid:11067828
  77. 77. Mardis E, McCombie WR. Library Quantification: Fluorometric Quantitation of Double-Stranded or Single-Stranded DNA Samples Using the Qubit System. Cold Spring Harb Protoc. 2017;2017(6):pdb prot094730. pmid:27803271.
  78. 78. Urich MA, Nery JR, Lister R, Schmitz RJ, Ecker JR. MethylC-seq library preparation for base-resolution whole-genome bisulfite sequencing. Nat Protoc. 2015;10(3):475–83. pmid:25692984.
  79. 79. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43:1101–33. pmid:25431634.
  80. 80. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. pmid:24695404
  81. 81. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg S. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology. 2013;14(4):R36. pmid:23618408
  82. 82. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. pmid:25260700
  83. 83. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate—a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met. 1995;57(1):289–300.
  84. 84. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17(1):10–2.
  85. 85. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27(11):1571–2. Epub 2011/04/16. pmid:21493656.
  86. 86. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6. pmid:16081474.
  87. 87. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–9. Epub 2005/06/24. pmid:15972284.
  88. 88. Huh I, Wu X, Park T, Yi SV. Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species. Brief Bioinform. 2017. pmid:28981571.
  89. 89. Dolzhenko E, Smith AD. Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinformatics. 2014;15:215. pmid:24962134.