Wolbachia induces host cell identity changes and determines symbiotic fate in Drosophila

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Wolbachia induces host cell identity changes and determines symbiotic fate in Drosophila Jodie Jacobs , Cade Mirchandani , William E Seligmann , Samuel Sacco , Merly Escalona , View ORCID Profile Richard E Green , View ORCID Profile Shelbi L Russell doi: https://doi.org/10.1101/2025.06.05.658111 Jodie Jacobs 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cade Mirchandani 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site William E Seligmann 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samuel Sacco 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Merly Escalona 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard E Green 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard E Green Shelbi L Russell 1 Department of Biomolecular Engineering, University of California Santa Cruz , Santa Cruz, CA, United States 2 Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shelbi L Russell For correspondence: shelbilrussell{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Preview PDF Summary Many host-associated bacteria influence the differentiation of their eukaryotic host cells. The association between Wolbachia pipientis and Drosophila melanogaster offers a model for understanding how host-microbe gene expression co-evolves. Using Wolbachia -infected Drosophila cell lines, we show that the w Mel strain alters host cell states, inducing novel gene expression programs that diverge from known cell types. Transcriptomic co-expression network analysis identified gene expression modules specific to each cell type and infection state, and revealed that w Mel tailors its gene expression to host context. In macrophage-like host cells, w Mel expresses pathogenic effectors, whereas in neuron-like cells, w Mel upregulates metabolic genes. Micro-C chromatin contact data revealed that many of these infection-induced changes are epigenetically encoded, with w Mel infection conferring reduced chromatin contacts and widespread transcriptional derepression in D. melanogaster . These findings show that the nature of Wolbachia symbiosis—mutualistic or pathogenic—emerges from host cell environments and suggest new paths for engineering host-specific microbial phenotypes. In Brief Wolbachia pipientis reprograms Drosophila cell identity by reshaping host gene expression and chromatin in a cell type-specific manner. Infected cells adopt novel states tailored to w Mel strain gene expression, enabling either mutualism or pathogenesis. These findings advance Wolbachia engineering for targeted host cell interactions and symbiont-driven phenotypes. Download figure Open in new tab Introduction Intracellular symbionts such as Wolbachia pipientis are leveraged as biological control agents around the world to control the reproduction of problematic hosts and suppress the transmission of human diseases 1 , but little is known about how these interactions are regulated at the molecular level. Wolbachia are maternally transmitted through the female germline of their hosts. They are famously known for parasitic reproductive phenotypes that aim to increase the proportion of infected females, regardless of the cost to the host, which include male killing and cytoplasmic incompatibility (CI). The CI mechanism is being used to control mosquito populations around the world 2 . These applications leverage the w Mel strain, that naturally occurs in Drosophila melanogaster populations, to create non-native infections in mosquitoes. Beneficial reproductive manipulations have also been reported for w Mel 3 – 7 , but the mechanistic basis of bacterial-induced host development and cellular differentiation is unknown 8 . Given the diversity and biomedical relevance of Wolbachia , more information is needed about how different strains interact with their host at the cellular and tissue level to ensure the safety and efficacy of future applications. Intracellular bacteria must manipulate host signaling networks to establish a replicative niche without compromising host cell function 9 . For obligate, germline-transmitted symbionts like Wolbachia , maintaining host viability and fertility is essential to their own fitness. Yet, how stable infection persists without overtly harming host cells remains unclear. For example, the w Mel strain slows but does not inhibit host cell division 10 . The downstream effects of Wolbachia infection on host cell phenotypes, tissue functions, and organismal biology are still poorly understood. Like other obligate bacteria—including Mycobacterium, Helicobacter, and Buchnera—some Wolbachia strains appear capable of inducing host cellular differentiation through epigenetic, transcriptional, or translational reprogramming, in a cell type–specific manner 8 . Among its hosts, Wolbachia exhibits a wide range of cellular preferences, or tropisms, that directly influence their impact on host cellular processes. Most arthropod-infecting strains are facultative and exhibit general tropisms, with many somatic as well as germline cells harboring Wolbachia infections. Upon obligate association with a host and the evolution of mutualistic phenotypes, Wolbachia and other endosymbionts tend to evolve highly specific tropisms for the specific cell types that they are involved in differentiating, termed bacteriocytes 11 – 14 . For example, the w Cle strain of Wolbachia evolved to be an obligate mutualist of bed bugs through the requirement for bacterial-mediated b-vitamin synthesis, while also co-evolving the ability to induce the formation of bacteriocytes and restrict their localization to them 15 . We hypothesize that bacteriocyte evolution within the Wolbachia clade is possible due to cell type-specific interactions this adept intracellular bacterium has with its hosts, which feedback on host cell identity. Strains with more general tropisms may represent early evolutionary stages in the convergent evolution of bacteriocytes, provided they alter the epigenetic and transcriptomic state of some of these cells. Remarkably, little is known about how Wolbachia interacts with host gene expression because Wolbachia cannot be grown independently and many studies report minimal or conflicting differential expression patterns due to infection 16 – 21 . The general tissue tropism of most strains likely causes infection-induced gene expression profiles to vary across host cells, limiting the power of in vivo bulk approaches to dissected tissues. Our Wolbachia -infected Drosophila cell culture system provides a way to concentrate the effects of infection spatially - by host cell type - and temporally - by infection and cell state. By sequencing the transcriptomes and chromatin contacts of D. melanogaster cell lines that demonstrate altered morphologies due to infection ( Figure 1A-K ), we reveal that infection by the w Mel strain of Wolbachia induces host cell type-specific changes in gene expression at the transcript level that are hard-coded into the epigenetic landscape by chromatin modifications, and may be induced by w Mel’s differential gene expression. Download figure Open in new tab Figure 1. Wolbachia infection alters Drosophila cell line morphologies and transcriptomic cell states. A-L) Immortalized D. melanogaster A-F) JW18 and G-L) S2 cell lines. A,B,G,H) uninfected and D,E,J,K) infected with w Mel Wolbachia . A,D,G,J) Live cultures imaged at 40x on a tissue culture microscope. B,E,H,K) Fixed and stained cells imaged on an epifluorescent compound microscope. Green=Jupiter-GFP, Blue=DAPI, Red=16S rRNA. Scale bars = 25 µm. C,F,I,L) Schematic representations of the cell lines and their morphologies, colored by cell type and infection state. M) Principal Component Analysis (PCA) revealing distinct transcriptomic profiles by cell line and infection status (orange=JW18, green=S2). N) Hierarchical clustering dendrogram by dissimilarity, demonstrating primary separation by cell line, and secondary separation by Wolbachia infection status. O) Heatmap showing the top four differentially expressed marker genes contributing to the PCA by Wilcoxon Rank Sum p-value, with circle size indicating the fraction of cells expressing each gene. P-Q) UMAP projections of bulk RNA-seq data onto single-cell reference atlases: P) adult Drosophila tissues and Q) Drosophila myeloid-like cells. P’ and Q’) Highlight where our bulk cell transcriptomes mapped to each atlas. Dotted outlines of where the bulk samples mapped, with the points removed, so that the alignment with cell clusters can be seen. Results Through sequencing clonal Wolbachia -infected host cell lines with dual-bulk transcriptome and Micro-C chromatin conformation capture sequencing (outlined in Figure S1, Tables S1-2), we discovered that microbial and eukaryotic gene expression interacts to induce new eukaryotic cell states and to shift the cost/benefit dynamics of Wolbachia infection. The wMel transcriptional response to infecting different host cell types likely underlies many of these changes, and may reflect a response to the nature of the host cell they infect. In the macrophage-like S2 line, w Mel gene expression is highly pathogenic, which is likely a response to being targeted for degradation by the host. In contrast, in the neuroblast-like host cell line, w Mel’s expression profile appears mutualistic and complements host gene expression changes unique to the JW18-infected state. Intracellular Wolbachia infection alters host cell state, creating novel host cell types Intracellular infection of D. melanogaster cells with the w Mel strain induces significant changes in host cell type ( Figure 1 ). The JW18 cell line undergoes more morphological and behavioral changes due to infection than the S2 cell line ( Figure 1A-K ), displaying reduced cell-to-cell adherence at confluency in the infected compared to uninfected state ( Figure 1A-E ). Principal Component Analysis (PCA) of sample and library-normalized bulk RNA sequencing data revealed that the transcriptomic cell states of each D. melanogaster cell type are highly consistent within a cell line and distinct between cell lines, as illustrated by the tight clustering within groups and clear separation between cell types and infection states in PCA and hierarchical clustering ( Figure 1M,N and S2). The top four principal components (PCs) explained more than 98% of transcriptomic count variance, with PC1 separating cells by type and accounting for 74.73% of variance (see the green versus orange points in Figure 1M ). Infection with Wolbachia significantly shifted the transcriptional composition of the Drosophila cell lines, explaining another 12% of eigenvalue variance in PC2 (see the light versus dark points in Figure 1M and S2A). The third principle component separated the S2 infection states further and explained 10.07% of eigenvalue variance (Figure S2A). Variance among samples was captured by the fourth principle component and explained 1.23% of variance (Figure S2B). Discrete marker genes significantly contributed to the clustering of each cell type and infection group ( Figure 1O ), further supporting the notion that Wolbachia induces novel host cell types. As many of the top marker genes were uncharacterized proteins, we mapped the bulk transcriptomes to three D. melanogaster single cell atlases to assess whether we could identify their closest in vivo cell type analog in Uniform Manifold Approximation and Projection (UMAP) space. The distinction among the cell lines was further supported by UMAP analysis, and suggested that both are related to blood-derived lineages ( Figure 1P-Q’ and Supplemental Results). We projected the in vitro bulk RNA-seq counts onto the comprehensive adult Fly Cell Atlas ( Figure 1P ), adult blood Atlas ( Figure 1Q ), and embryonic-larval Atlas (Figure S3), and found that both host cell type and infection state impacted host cell clustering. While the immortal JW18 and S2 cell lines mapped to adult body and blood cell atlases, they failed to map to the embryonic and larval atlas. This may reflect the terminal differentiation of the in vitro lines, which have been growing continuously in culture for decades 22 , 23 . Both cell types and infection states mapped to poorly defined clusters of the adult Fly Cell Atlas and clusters identified as “body” ( Figure 1P’ ), which is consistent with this atlas being constructed from “bloodless” samples. Given the odd assortment of marker genes, many of which are of unknown function ( Figure 1O ), and the uninfected status of the Drosophila cell atlases (Figure S4), these in vitro cell lines may be novel cell types. Next, we performed differential and co-expression network analyses to better characterize the transcriptomic states of these cell lines and how w Mel infection impacts cellular phenotypes. Concentrating host cell types and infection impacts in vitro enabled us to identify thousands of differentially expressed host genes ( Figure 2 and S5). After quality control and processing, we found cell type corresponded to the differential expression of more than 8,257 genes (p ≤ 0.01 FDR-corrected Wald Test; 8,819 genes at p ≤ 0.05; Figure S5A, Table S3). Infection with w Mel corresponded to the differential expression of 6,538 genes (p ≤ 0.01 FDR-corrected Wald Test; 7,336 genes at p ≤ 0.05; Figure S5B, Table S4). Across both cell types, more genes were upregulated in the infected state than when cells were uninfected, as indicated by the bias in hits on the right side of the volcano plot in Figure S5B. The interaction between cell type and infection was highly significant: 6,567 genes were differentially expressed due to the interaction term (p ≤ 0.01 FDR-corrected Wald Test; 7,395 genes at p ≤ 0.05; Figure S5C, Table S5). Although some of the differentially expressed genes had clear patterns of association with the terms of the model (see the gene plots in Figure S5D-F), the majority had patterns of differential expression associated with the interaction between terms (Figure S5C). Download figure Open in new tab Figure 2. Genes and co-expressed gene networks involved in determining D. melanogaster cell type and the cellular reaction to infection state. A) Linear model fit of WGCNA eigengene modules to cell type, infection, and joint-cell type and infection states (see also Figure S7). The x- and y-axes contain the estimated linear model coefficients for the variables cell type and infection, respectively, for each WGCNA eigengene module. B) Counts of significantly differentially expressed genes per cluster (bottom x-axis and left y-axis). Counts of enriched GO terms and KEGG pathways per D. melanogaster module (top x-axis and right y-axis). Eigengene module evidence associated with C-F) S2 cell type, G-I) JW18 cell type, J-P) the uninfected JW18 cell state, and Q-T) the uninfected state. C,G,J,M,Q) Eigengene value plots for each module. KEGG enrichment network plots for each module (FDR-adjusted p-value<0.05) from E,H,K,N,O) ShinyGO and D,K) clusterProfiler. F,I,L,P,S) GO component enrichment plots for each module, plotting the top 5-10 terms by gene ratio (FDR-adjusted p-value<0.05). R) GO function enrichment plots for each module, plotting the top 5 terms by gene ratio (FDR-adjusted p-value<0.05). T) GO process enrichment plots for each module, plotting the top 5 terms by gene ratio (FDR-adjusted p-value<0.05). Gene set enrichment analysis (GSEA) of the differentially expressed D. melanogaster genes by model condition revealed dozens of functional pathways associated with cell type, infection state, and the interaction between cell type and infection (p ≤ 0.05 permutation test; Fig 2G-I , Tables S6-S11). The JW18 cell type was associated with downregulation of ribosomal and Wnt signalling genes (Tables S6-7). Compared to the S2 cell type, JW18 cells upregulated their sugar metabolism, cell surface mucopolysaccharide (glycosaminoglycan) biosynthesis, DNA replication and repair, and the polycomb repressive complex chromatin modifiers. Infection was associated with the downregulation of bacterial invasion response and fatty acid degradation genes, and the upregulation of purine metabolism (Tables S8-9). Many of these functions were also in the gene set enriched for interactions between cell type and infection (Figure S8 and Tables S10-11), suggesting that some of these pathways were specific to either JW18- w Mel or S2- w Mel. We inferred 13 signed co-expression eigengene modules from the D. melanogaster bulk transcriptomes with weighted gene co-expression network analysis (WGCNA), 11 of which were significantly associated with cell type or infection (p ≤ 0.05, Benjamini–Hochberg FDR-corrected moderated t-test, Figures 2 – 4 and S6-7, Tables S12-13). Most of the genes in these significantly-associated modules were differentially expressed (>95%, Figure 2B and S7E). The Introduction: Intracellular symbionts such as Wolbachia pipientis are leveraged as biological control agents around the world to control the reproduction of problematic hosts and suppress the transmission of human diseases 1 , but little is known about how these interactions are regulated at the molecular level. Wolbachia are maternally transmitted through the female germline of their hosts. They are famously known for parasitic reproductive phenotypes that aim to increase the proportion of infected females, regardless of the cost to the host, which include male killing and cytoplasmic incompatibility (CI). The CI mechanism is being used to control mosquito populations around the world 2 . These applications leverage the w Mel strain, that naturally occurs in Drosophila melanogaster populations, to create non-native infections in mosquitoes. Beneficial reproductive manipulations have also been reported for w Mel 3 – 7 , but the mechanistic basis of bacterial-induced host development and cellular differentiation is unknown 8 . Given the diversity and biomedical relevance of Wolbachia , more information is needed about how different strains interact with their host at the cellular and tissue level to ensure the safety and efficacy of future applications. Intracellular bacteria must manipulate host signaling networks to establish a replicative niche without compromising host cell function 9 . For obligate, germline-transmitted symbionts like Wolbachia , maintaining host viability and fertility is essential to their own fitness. Yet, how stable infection persists without overtly harming host cells remains unclear. For example, the w Mel strain slows but does not inhibit host cell division 10 . The downstream effects of Wolbachia infection on host cell phenotypes, tissue functions, and organismal biology are still poorly understood. Like other obligate bacteria—including Mycobacterium, Helicobacter, and Buchnera—some Wolbachia strains appear capable of inducing host cellular differentiation through epigenetic, transcriptional, or translational reprogramming, in a cell type–specific manner 8 . Among its hosts, Wolbachia exhibits a wide range of cellular preferences, or tropisms, that directly influence their impact on host cellular processes. Most arthropod-infecting strains are facultative and exhibit general tropisms, with many somatic as well as germline cells harboring Wolbachia infections. Upon obligate association with a host and the evolution of mutualistic phenotypes, Wolbachia and other endosymbionts tend to evolve highly specific tropisms for the specific cell types that they are involved in differentiating, termed bacteriocytes 11 – 14 . For example, the w Cle strain of Wolbachia evolved to be an obligate mutualist of bed bugs through the requirement for bacterial-mediated b-vitamin synthesis, while also co-evolving the ability to induce the formation of bacteriocytes and restrict their localization to them 15 . We hypothesize that bacteriocyte evolution within the Wolbachia clade is possible due to cell type-specific interactions this adept intracellular bacterium has with its hosts, which feedback on host cell identity. Strains with more general tropisms may represent early evolutionary stages in the convergent evolution of bacteriocytes, provided they alter the epigenetic and transcriptomic state of some of these cells. Remarkably, little is known about how Wolbachia interacts with host gene expression because Wolbachia cannot be grown independently and many studies report minimal or conflicting differential expression patterns due to infection 16 – 21 . The general tissue tropism of most strains likely causes infection-induced gene expression profiles to vary across host cells, limiting the power of in vivo bulk approaches to dissected tissues. Our Wolbachia -infected Drosophila cell culture system provides a way to concentrate the effects of infection spatially - by host cell type - and temporally - by infection and cell state. By sequencing the transcriptomes and chromatin contacts of D. melanogaster cell lines that demonstrate altered morphologies due to infection ( Figure 1A-K ), we reveal that infection by the w Mel strain of Wolbachia induces host cell type-specific changes in gene expression at the transcript level that are hard-coded into the epigenetic landscape by chromatin modifications, and may be induced by w Mel’s differential gene expression. Results Through sequencing clonal Wolbachia -infected host cell lines with dual-bulk transcriptome and Micro-C chromatin conformation capture sequencing (outlined in Figure S1, Tables S1-2), we discovered that microbial and eukaryotic gene expression interacts to induce new eukaryotic cell states and to shift the cost/benefit dynamics of Wolbachia infection. The wMel transcriptional response to infecting different host cell types likely underlies many of these changes, and may reflect a response to the nature of the host cell they infect. In the macrophage-like S2 line, w Mel gene expression is highly pathogenic, which is likely a response to being targeted for degradation by the host. In contrast, in the neuroblast-like host cell line, w Mel’s expression profile appears mutualistic and complements host gene expression changes unique to the JW18-infected state. Intracellular Wolbachia infection alters host cell state, creating novel host cell types Intracellular infection of D. melanogaster cells with the w Mel strain induces significant changes in host cell type ( Figure 1 ). The JW18 cell line undergoes more morphological and behavioral changes due to infection than the S2 cell line ( Figure 1A-K ), displaying reduced cell-to-cell adherence at confluency in the infected compared to uninfected state ( Figure 1A-E ). Principal Component Analysis (PCA) of sample and library-normalized bulk RNA sequencing data revealed that the transcriptomic cell states of each D. melanogaster cell type are highly consistent within a cell line and distinct between cell lines, as illustrated by the tight clustering within groups and clear separation between cell types and infection states in PCA and hierarchical clustering ( Figure 1M,N and S2). The top four principal components (PCs) explained more than 98% of transcriptomic count variance, with PC1 separating cells by type and accounting for 74.73% of variance (see the green versus orange points in Figure 1M ). Infection with Wolbachia significantly shifted the transcriptional composition of the Drosophila cell lines, explaining another 12% of eigenvalue variance in PC2 (see the light versus dark points in Figure 1M and S2A). The third principle component separated the S2 infection states further and explained 10.07% of eigenvalue variance (Figure S2A). Variance among samples was captured by the fourth principle component and explained 1.23% of variance (Figure S2B). Discrete marker genes significantly contributed to the clustering of each cell type and infection group ( Figure 1O ), further supporting the notion that Wolbachia induces novel host cell types. As many of the top marker genes were uncharacterized proteins, we mapped the bulk transcriptomes to three D. melanogaster single cell atlases to assess whether we could identify their closest in vivo cell type analog in Uniform Manifold Approximation and Projection (UMAP) space. The distinction among the cell lines was further supported by UMAP analysis, and suggested that both are related to blood-derived lineages ( Figure 1P-Q’ and Supplemental Results). We projected the in vitro bulk RNA-seq counts onto the comprehensive adult Fly Cell Atlas ( Figure 1P ), adult blood Atlas ( Figure 1Q ), and embryonic-larval Atlas (Figure S3), and found that both host cell type and infection state impacted host cell clustering. While the immortal JW18 and S2 cell lines mapped to adult body and blood cell atlases, they failed to map to the embryonic and larval atlas. This may reflect the terminal differentiation of the in vitro lines, which have been growing continuously in culture for decades 22 , 23 . Both cell types and infection states mapped to poorly defined clusters of the adult Fly Cell Atlas and clusters identified as “body” ( Figure 1P’ ), which is consistent with this atlas being constructed from “bloodless” samples. Given the odd assortment of marker genes, many of which are of unknown function ( Figure 1O ), and the uninfected status of the Drosophila cell atlases (Figure S4), these in vitro cell lines may be novel cell types. Next, we performed differential and co-expression network analyses to better characterize the transcriptomic states of these cell lines and how w Mel infection impacts cellular phenotypes. Concentrating host cell types and infection impacts in vitro enabled us to identify thousands of differentially expressed host genes ( Figure 2 and S5). After quality control and processing, we found cell type corresponded to the differential expression of more than 8,257 genes (p ≤ 0.01 FDR-corrected Wald Test; 8,819 genes at p ≤ 0.05; Figure S5A, Table S3). Infection with w Mel corresponded to the differential expression of 6,538 genes (p ≤ 0.01 FDR-corrected Wald Test; 7,336 genes at p ≤ 0.05; Figure S5B, Table S4). Across both cell types, more genes were upregulated in the infected state than when cells were uninfected, as indicated by the bias in hits on the right side of the volcano plot in Figure S5B. The interaction between cell type and infection was highly significant: 6,567 genes were differentially expressed due to the interaction term (p ≤ 0.01 FDR-corrected Wald Test; 7,395 genes at p ≤ 0.05; Figure S5C, Table S5). Although some of the differentially expressed genes had clear patterns of association with the terms of the model (see the gene plots in Figure S5D-F), the majority had patterns of differential expression associated with the interaction between terms (Figure S5C). Gene set enrichment analysis (GSEA) of the differentially expressed D. melanogaster genes by model condition revealed dozens of functional pathways associated with cell type, infection state, and the interaction between cell type and infection (p ≤ 0.05 permutation test; Fig 2G-I , Tables S6-S11). The JW18 cell type was associated with downregulation of ribosomal and Wnt signalling genes (Tables S6-7). Compared to the S2 cell type, JW18 cells upregulated their sugar metabolism, cell surface mucopolysaccharide (glycosaminoglycan) biosynthesis, DNA replication and repair, and the polycomb repressive complex chromatin modifiers. Infection was associated with the downregulation of bacterial invasion response and fatty acid degradation genes, and the upregulation of purine metabolism (Tables S8-9). Many of these functions were also in the gene set enriched for interactions between cell type and infection (Figure S8 and Tables S10-11), suggesting that some of these pathways were specific to either JW18- w Mel or S2- w Mel. We inferred 13 signed co-expression eigengene modules from the D. melanogaster bulk transcriptomes with weighted gene co-expression network analysis (WGCNA), 11 of which were significantly associated with cell type or infection (p ≤ 0.05, Benjamini–Hochberg FDR-corrected moderated t-test, Figures 2 – 4 and S6-7, Tables S12-13). Most of the genes in these significantly-associated modules were differentially expressed (>95%, Figure 2B and S7E). The distinction between S2 and JW18 cell type was most clearly captured by Modules 1 and 2 ( Figure 2A ), although Module 3 was also highly enriched in genes differentially expressed due to cell type ( Figure 2B ). Modules 4-6 and 11 were impacted by both cell type and infection state. Module 11 was highly associated with infection ( Figure 2A ), but more so in JW18 cells than S2 cells ( Figure 2Q ). Modules 9 and 10 most clearly distinguished the uninfected and w Mel-infected states ( Figure 2A,J ), and were relatively enriched in genes differentially expressed due to infection ( Figure 2B ). The D. melanogaster modules that were positively associated with cell type and uninfected cell states contained gene sets that were better annotated by enrichment analysis than those of the other modules. Only Module 3 contained a gene set that was well annotated across all of the databases we tested (consisting of 593 genes, Figure 2B and S7E, and Table S14; the results for each module can be found on Dryad at DOI: 10.5061/dryad.ghx3ffc1g). Modules 1, 2, and 11 also contained gene sets that were well-annotated across most databases. The positive eigengene values for these four modules, 1-3 and 11, indicated that they characterize the pathways expressed in the S2 (ME1), JW18 (ME2), and uninfected JW18 (Module 3 and 11) cell states, respectively ( Figure 2C-P ). Consistent with prior reports that the S2 cell line is macrophage-like, the 2,687 genes in Module 1 were enriched for ribosomal, endocytosis, and vesicle trafficking genes (p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test; Figure 2C-F and S7E). The JW18 cell line is suspected to be neuron-derived 24 , which is consistent with some of Module 2’s enrichment terms, including inositol phosphate metabolism and membrane-bound components involved in apical and tight junctions ( Figure 2G-I ). The JW18 line also displays hematopoietic characteristics ( Figure 1Q-Q’ ), which was reflected in Module 2’s enrichment in Fanconi anemia DNA repair, and apoptosis KEGG pathway terms,which are important for developing neurons, the germline, and hemocytes 25 – 30 . Although Module 2 contained the largest number of genes at 1,952, only a few cell type-specific pathways were significantly enriched ( Figure 2B and Table S14), suggesting that non-canonical pathways may be occurring in these cells. The uninfected and infected cells states were associated with Modules 9 and 10, respectively. Module 9 was associated with both uninfected S2 and JW18 cell states, and contained 294 genes (Figure S7E). It was not significantly enriched in any KEGG pathways, but it was enriched in GO process, function, and component terms for nucleosome binding and chromatin packaging ( Figure 2Q-T ). Module 10 was associated with both infected S2 and JW18 cell states, and contained 229 genes ( Figure 3A-D and S7E). KEGG and GO enrichment analysis indicated that infection-associated Module 10 upregulates the expression of ribosomal genes involved in cytoplasmic translation, as well as translation at the endoplasmic reticulum (p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test). Module 12 was not significantly associated with the model terms, but contained a few DE interaction genes. Pathway enrichment analysis indicated that Module 12 described mitochondrial oxidative phosphorylation (Figure S9). Download figure Open in new tab Figure 3. Drosophila and Wolbachia respond to each other transcriptomically. A-D) Eigengene module evidence associated with the infected host cell state. A) Plot of D. melanogaster sample eigengene values for the infection-associated module 10. B) KEGG enrichment network plot (p ≤ 0.05). C) GO component enrichment plots for each module, plotting the top 5 terms by gene ratio (FDR-adjusted p-value<0.05). D) GO process enrichment plots for each module, plotting the top terms by gene ratio (FDR-adjusted p-value<0.05). E) Volcano plot of DESeq2 differential expression results. F) Plot of the WGCNA models linear fit to host cell type. LogFC measures the direction and magnitude of eigengene expression difference between the two cell types for wMel gene expression. The t variable on the y-axis is the moderated t-statistic from limma::eBayes() for the logFC. JW18-associated wMel Module 3 G) eigengene values, H) KEGG enrichment network plot (p ≤ 0.05), and I) GO process enrichment terms (p ≤ 0.05). Host cell type induces differential wMel Wolbachia gene expression Using our in vitro system to isolate host cell transcriptomic responses, we found that the w Mel strain of Wolbachia exhibits host cell type-specific gene expression profiles ( Figures 3E-I and 4A-G ). Between the two cell type conditions, w Mel differentially expressed 323 of its 1,123 genes (p ≤ 0.01 FDR-corrected Wald Test; 451 genes at p ≤ 0.05; Figure 3E , S12, and S13). More genes were upregulated in the S2 cell type than the JW18 cell type (172 vs 151, Table S15). GSEA analysis of the differentially expressed w Mel genes by model condition revealed nine bacterial functional pathways upregulated due to host cell type (p ≤ 0.05 permutation test; Figure S5G-I, Table S16). Several of these w Mel pathways overlapped with differentially expressed host pathways, including tRNA biosynthesis, glycolysis and other types of carbon metabolism, metabolite synthesis via oxocarboxylic acid metabolism and secondary metabolites, and ribosome biosynthesis. We inferred five signed co-expression modules from the Wolbachia transcriptomes, three of which were significantly associated with the two D. melanogaster cell type conditions (p ≤ 0.05, Benjamini–Hochberg FDR-corrected moderated t-test, Figures 3 – 4 and S13, Tables S17-18). The other two modules appear to be constitutive, and were associated with ABC transporters and other membrane-associated proteins (Figure S14). Only 22-49% of genes in the three significant modules were also differentially expressed (Figure S13E). The genomic locations of the genes in the modules were broadly distributed, with some clustering (Figure S13D). All three significant modules, which contained approximately one to two hundred genes each, were significantly enriched in one or more KEGG pathways (p ≤ 0.05 Benjamini-Hochberg FDR-corrected hypergeometric test, Figures 3H and 4B,E , Table S19 the results for each module can be found on Dryad at DOI: 10.5061/dryad.ghx3ffc1g). JW18-associated wMel Modules 1 and 3 were enriched in informative GO process terms for metabolic processes and ribosomal components, respectively, corroborating the KEGG pathway results (p ≤ 0.05 Benjamini-Hochberg FDR-corrected hypergeometric test, Figure 3G-I and 4D-F ). Complementarity between w Mel Module 3 and D. melanogaster infection Module 10 suggests that infection alters ribosomal translation, especially in the JW18-wMel condition. Module 2 was associated with S2 infection and was enriched in components encoding Wolbachia’s type IV secretion system ( Figure 4A-B and S15) 31 . Download figure Open in new tab Figure 4. Wolbachia gene expression induces compensatory responses specific to host cell type. A-F) Wolbachia eigengene module evidence associated with cell type. A-C) S2-associated w Mel eigengene module 2 A) values, B) KEGG enrichment network, and C) gene count plots for some of the most upregulated wMel genes in S2 cells. D-G) JW18-associated w Mel eigengene module 1 D) values, E) KEGG enrichment network, F) GO process enrichment terms (p ≤ 0.05), and G) gene count plots for some of the most upregulated wMel genes in JW18 cells. D. melanogaster eigengene modules associated with w Mel infection in H-P) S2 cells and in Q-V) JW18 cells. S2- w Mel-associated Drosophila H, K, N) module 8, 7, and 5 eigengene values, I) module 8 KEGG enrichment network, J,L) module 8 and 7 GO function enrichment plots, M,P) module 7 and 5 GO process enrichment plots, and O) module 5 GO component enrichment plots. JW18- w Mel-associated Drosophila Q,T) module 4 and 6 eigengene values, R) module 4 KEGG enrichment network, S,V) module 4 and 6 GO process enrichment plots, and U) module 6 GO component plot. Two alternate feedback loops between host cell type and Wolbachia infection state The remaining D. melanogaster eigengene modules significantly associated with cell type and infection state revealed the pathways through which w Mel alters host cell identities in a cell-specific manner. The w Mel-infected S2 cells were positively associated with eigengene Modules 7 and 8. The KEGG and GO enrichment terms for the genes in these modules suggest that w Mel induces the upregulation of S2 translation components, lipid metabolism, arginine and proline metabolism, and oxidoreductases (p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test; Figure 4H-M ). Infected S2 cells were negatively associated with Module 5, which encoded genes for mitochondrial respiration, endopeptidase activity, and signalling through the hippo and MAPK pathways (p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test; Figure 4N-P ). In stark contrast to the S2 cell line, w Mel-infected JW18 cells expressed genes for growth and lipid metabolism (Module 4 KEGG and GO enrichment p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test; Figure 4Q-S ) and repressed the expression of genes for microtubule-based transport for spindle assembly, chromatid segregation, and transcription factor complexes (Module 6 GO enrichment, p ≤ 0.05, Benjamini-Hochberg FDR-corrected hypergeometric test; Figure 4T-V ). The macrophage-like nature of S2 cells may trigger a pathogenic transcriptional response from Wolbachia that induces a retaliatory host response. S2 cells upregulate the expression of endocytosis and vesicle coat genes ( Figures 2C-F and S12A, Tables S3 and S13), indicating that they behave like hematopoietic-derived macrophages 32 – 34 , and may be actively degrading w Mel in vitro . Consistent with this, we observed that S2 cells rapidly clear many of the first w Mel to enter in de novo experimental infections (Figure S10). In response to S2-mediated degradation, w Mel upregulates the expression of binary fission genes, like the DNA translocase FtsK and the septal ring protein RlpA ( Figure 4C , Tables S15 and S18). The upregulation of capsid and subtilisin genes may be a pathogenic response from wMel or it may reflect a de-repressed, stress response. Other upregulated wMel genes in S2 cells include AAA-ATPases and conjugal transfer TrbC/type IV secretion proteins ( Figure 4B , Tables S15 and S18), which may be used to secrete effectors, such as the upregulated ankyrin repeat domain proteins, into the host cytoplasm. These activities are likely pathogenic to S2 cells 35 . S2 cells mount a response to pathogenic w Mel gene expression that may offset costs to the host cell. We previously showed that wMel titers are similar between S2 and JW18 cells 10 , which implies that w Mel effectively compensates for their rate of lysis in S2 cells by upregulating binary fission. This likely requires extra nutritional supplementation from the host, which may explain the S2 cell line’s upregulation of arginine and proline metabolism when infected with w Mel ( Figure 4I , Table S15, S18). While arginine is an essential amino acid for w Mel 36 , it is only conditionally essential for D. melanogaster 37 , i.e., flies keep up with arginine demand except during demanding periods, such as embryogenesis. The S2 cell line’s stress responses appear modulated by w Mel infection: signal transduction via RNA and lipid binding is upregulated ( Figure 4L,M , Table S5,S13), while mitochondrial respiratory complexes, hippo and MAPK signaling, and the proteasome endopeptidase complex are down-regulated ( Figure 4O,P , Tables S5,S13). Given the signals of stress and pathogenesis w Mel expresses in S2 cells ( Figure 3E and 4A-C ), intracellular infection in this host cell type likely invokes an immune response that has to be suppressed for w Mel’s persistence. The neuron-like nature of JW18 cells may present a hospitable environment for w Mel and may promote a more mutualistic-type of infection than seen in S2 cells. Consistent with their neuroblast-derived cytological properties 38 , JW18 cells express neuronal pathways such as inositol phosphate metabolism for intracellular signaling and calcium regulation 39 , 40 ( Figure 2G-P ). This cell line does not appear to target w Mel for degradation, permitting w Mel to differentially express a suite of genes that may promote its intracellular persistence. In particular, wMel expressed metabolic genes for flavin biosynthesis and turnover, a peroxiredoxin that may mitigate infection-induced free-radical damage, and many transfer RNA (tRNA) genes for translation ( Figures 4E-G and S12B). Upregulated flavin biosynthesis is consistent with Wolbachia’s demonstrated capacity for nutritional benefit 41 , and may explain how w Mel mitigates the costs of its presence in host cells. The abundance of host metabolic pathway genes upregulated in JW18- w Mel-associated Module 4 supports the idea that w Mel compliments host metabolism ( Figure 4R ). The w Mel strain of Wolbachia induces large shifts in the transcriptomic state of their host cells, which are likely encoded epigenetically. Uninfected D. melanogaster cells, both JW18 and S2, upregulate chromatin, nucleosome, and histone modifiers ( Figure 2R-T ), suggesting that infection alters chromatin compaction. Consistent with the morphological changes induced by w Mel infection in JW18 cells ( Figure 1A-L ) and downregulation of chromatin modifiers in infected cells, histone and chromatin modifiers are specifically down regulated in w Mel-infected JW18 cells (see Module 11 in Figure M-P). Furthermore, several modules contain genes that are clustered along particular regions of the genome (Figure S11), suggesting their transcription may be co-regulated. Infection-associated changes are epigenetically encoded in JW18 cells Infection with w Mel alters JW18 chromatin packaging to globally reduce the number of contacts ( Figures 5A-L and S16-17, Table S20), consistent with more differential upregulation than downregulation due to infection in the transcriptomic dataset (Figure S5B). Comparing the w Mel-infected and uninfected JW18 chromatin states revealed 24,196 differential contact loops (p ≤ 0.05, Laplace distribution test with Benjamini-Hochberg FDR correction), 17,409 of which were negative contact loop changes (more open) relative to uninfected cells, and 6,787 were positive (more closed) (Supplemental Table S20). As some genes had multiple differential contacts, the total count by gene was also informative: 5,385 genes experienced differential chromatin contacts due to infection, with 3,523 genes having fewer contacts in the infected state and 1,861 genes having more contacts. Infection was associated with decreased contact probability locally across all three autosomes and the X chromosome (Figure S17G-L). Given that the JW18 cells are male 38 and infection with w Mel reduced the probability of chromatin contact across nearly the entire X chromosome ( Figure 5A ), Wolbachia may interact with dosage compensation in vitro . However, JW18 cells are tetraploid 38 , so male upregulation of the X may not occur as it does in vivo . Download figure Open in new tab Figure 5. Genomic browser views of the Micro-C results for two representative WGCNA module co-localized regions showing multiple data tracks. Regions shown: A-F) Chromosome 3L:0.0-5.0Mb and G-L) Chromosome 2L: 6.0-9.0Mb. A,G) Top two tracks: Chromatin contact heatmaps (green intensity) for JW18 w Mel-infected and JW18 uninfected cells. Orange outlines highlight significant (q < 0.1) topologically associated domains (TADs), pink outlines indicate significant contact differences (q < 0.05) between uninfected and w Mel-infected JW18 cells.B,H) Middle tracks: Colored dots representing genes from identified eigengene modules. Black lines indicate regions where module genes are statistically enriched, compared to the background gene density. Module-colored vertical dashed lines mark the boundaries of enriched regions. Next two tracks: Contact probability differences between C,I) infected (dark green) and D,J) uninfected cells (light green). E,K), RNA-seq coverage differences. Bottom track: D. melanogaster genome mappability (blue). The chromatin locations where differentially expressed module genes co-localized (Figure S11) were found to have differential contacts in JW18- w Mel versus JW18 uninfected cells ( Figures 5M,N , and S20). Genes in Modules 4 and 10, the JW18- w Mel and infection-associated modules, respectively, were enriched along an approximately three megabase tract of chromosome arm 3L. This region, which is upregulated in infected states, also had more open chromatin in infected JW18 cells, as evidenced by higher Micro-C coverage and probability of contact for the JW18-uninfected condition ( Figure 5A-F ). Module 3 was associated with uninfected JW18 cells ( Figure 2J ), and its gene set was enriched along chromosomal arm 3L (Figure S11). Consistent with this eight megabase region being repressed in the infected state, chromatin contact probability was higher in the w Mel-infected state than the uninfected state along this region (Figure S17B). Module 2 was associated with both JW18 uninfected and infected cell states, and many of its genes co-localized to regions of complex chromatin regulation, including an approximately three megabase region of the X chromosome (Figure S17A) and a one and a half megabase region of chromosomal arm 3R (Figure S17C). Although the overall trend was for w Mel infection to open D. melanogaster chromatin, some regions were closed due to infection. Module 11, which was positively associated with the JW18 uninfected state and negatively associated with JW18- w Mel infection ( Figure 2M ), and consisted of genes enriched along chromosomal arm 2L (Figure S11), was more likely to be open in the uninfected than w Mel-infected state ( Figure 5G-L ). Pathway enrichment analysis indicated that Module 11 encodes genes for glycolysis, gluconeogenesis, and nucleotide excision repair ( Figure 2N,O ). Module 6 was also negatively associated with the w Mel-infected JW18 cell state ( Figure 4T ), and this repression was reflected in the condensed JW18- w Mel chromatin state along the region of chromosomal arm 3R, where many Module 6 genes are located (Figure S17D). GO analysis suggested a mechanism for infection-induced epigenetic regulation via Modules 11 and 6: w Mel infection down-regulates JW18 histone and other chromatin modifications ( Figure 2P ), leading to reduced chromatin contacts globally (Figure S17A-L). These results suggest that stable JW18 cellular infection with w Mel involves the derepression of hundreds of normally repressed host genes that enable the persistence of the bacteria, and indicates that w Mel does not present as a pathogen in these host cells. The persistence of many intracellular pathogens requires the repression of host processes like innate immunity, apoptosis, or cell clearance pathways 9 . This is clearly not necessary for stable JW18 cell line infection by w Mel. Discussion Despite the diverse range of Wolbachia tropisms and phenotypes, remarkably little could be gleaned about how Wolbachia interacts with specific host cell types until now. Through concentrating the impact of Wolbachia infection on clonal D. melanogaster cell types in an in vitro cell culture system, we show that host cell type determines the nature of Wolbachia gene expression, pathogenic or mutualistic, which shapes downstream host gene expression. This feedback loop results in the alteration of host cell state epigenetically and transcriptomically. These results suggest that Wolbachia -host interactions likely vary on a host cell type-specific basis and negative interaction feedback loops may be common in nature. By integrating host and the microbe transcriptomic signals, our results provide explanatory scenarios for the cell type-by-infection signal for both organisms, and suggest that w Mel may be pathogenic in certain contexts. Our bulk transcriptomic data showed that the w Mel strain of Wolbachia responds differently to the various host cell types they inhabit, and their responses can induce host cells to differentiate into new states that may be either pathogenic or mutualistic, depending on the starting host cell type. S2 cells appear to provoke a stress response from w Mel that is consistent with a high rate of intracellular bacterial lysis in this host cell type. While the S2 cells appear to be able to keep up with the negative response they invoke, perhaps other hosts cannot, which could explain the low natural infection rate of some host species, e.g., Aedes aegypti mosquitoes 42 . Infection with w Mel altered JW18 cell morphology ( Figure 1A-F ), transcriptomes ( Figures 1 - 4 ), and epigenomes ( Figures 5 -6), but does not invoke pathogenesis, providing compelling evidence that the w Mel strain can induce the differentiation of a new beneficial cell type. Upon infection with w Mel, the JW18 cells become less adherent to each other, and the flask, consistent with altered glycerolipid, glycerophospholipid, and sphingolipid metabolism encoded by JW18- w Mel-associated transcriptomic Module 4 ( Figure 4R ). Infected JW18 cells also downregulate their mitotic cell cycle machinery ( Figure 4V ), consistent with the lowered rates of cell division we previously quantified in vitro 10 . In JW18 cells, w Mel upregulates riboflavin biosynthesis genes, tRNAs, and oxidoreductases ( Figure 4E-G ), suggesting that they may nutritionally supplement the cell and offset the free-radicals their metabolism is likely to generate. We characterize the altered cell state that w Mel invokes JW18 to enter as novel compared to the uninfected state because the 524 genes in eigengene Module 4 associated with w Mel-infected JW18 cells match only a few KEGG and GO pathways ( Figure 2B and S7 and Table S14). These results underscore the need for systems and approaches that allow us to simultaneously disentangle the effects of cell type, developmental state, and infection state on both host and bacterial gene expression. The clonal Wolbachia -infected cell lines we used here enabled us to show that different host cell types induce different transcriptomic responses from Wolbachia , which feedback to stimulate infection-by-cell type specific responses from the host cell. While our approach enabled us to discover unknown aspects of this association, the departure of the in vitro cell state from the in vivo state, demonstrated by the unknown combination of marker genes expressed by both the S2 and JW18 cells ( Figure 1O ), suggests that Wolbachia and other endosymbiosis researchers will benefit from the development of dual single cell transcriptomics approaches. Biological control strategies that leverage w Mel and other strains of Wolbachia will use these results to explore whether pathogenic feedback loops inhibit the establishment of infections in non-native hosts. Alternatively, mutualistic feedback loops may be necessary in some fraction of host cells for infection persistence. By understanding the range of infection phenotypes across non-native hosts, we will be enabled to identify, select, and engineer strains to have minimal pathogenic impacts. Ultimately, we hope to engineer the enrichment of certain strains in specific host cell types. This will enable Wolbachia researchers to increase the breadth of biological control applications that are possible across hosts and habitats. Declaration of Interests Richard E Green holds patents for the methods behind the Micro-C chromatin conformation capture kit and he holds a financial stake in the company who sells these kits, Dovetail Genomics. No other authors have any financial interests or patents to declare. None of the authors were board or advisory committee members or paid consultants related to this study or journal. Author Contributions Conceptualization: SLR Data production: SLR, SS, WS, and JJ Analysis: SLR, JJ, CM, and ME Resources: SLR and RG Funding Acquisition: SLR Figures: SLR and JJ Writing: SLR and JJ Supervision: SLR and RG STAR Methods KEY RESOURCES TABLE View this table: View inline View popup Resource availability Further information and requests for resources and reagents should be directed to and will be fulfilled by Shelbi Russell ( shelbilrussell{at}gmail.com ). Materials availability This study did not generate new unique reagents. All cell lines can be obtained from the corresponding authors of the original publications 10 , 24 , 78 . Data and code availability All scripts are available from our Github pages: https://github.com/shelbirussell/Dual_bulk_RNAseq_analysis-Jacobs_et_al.git https://github.com/jodiejacobs/wolbachia_induced_differentiation.git All transcriptomic and Micro-C short read data can be found under NCBI BioProject PRJNA1240446. Experimental Model and Subject Details Drosophila cell culture We maintained Wolbachia w Mel-infected and uninfected immortalized Drosophila melanogaster cell lines S2 78 and JW18 24 in 4 mL of media composed of Shields and Sang M3 Insect Medium (MilliporeSigma S3652) supplemented with 10% v/v Fetal Bovine Serum (FBS, ThermoFisher A3160502) in plug-seal T-25 flasks (Corning 430168) in a refrigerated incubator set to 23°C 10 . Cell cultures were maintained on a seven-day schedule, splitting at a 1:2 ratio for w Mel-infected lines and 1:6 to 1:4 ratio for uninfected lines. For passaging, we removed adherent cells by scraping the flask surface with a sterile bent Pasteur pipette (Fisher, 1367820D) after aspirating the media. Cells were then resuspended in fresh media, transferred at the appropriate ratio to new T25 flasks, and the volume adjusted to 4 mL with fresh media. Validation of Wolbachia infections To validate Wolbachia infections in our cell culture lines, we employed methods previously described in 10 . Briefly, infections were confirmed using fluorescence in situ hybridization (FISH) with DNA oligonucleotide probes targeting the Wolbachia 16S ribosomal RNA. Cells were fixed in paraformaldehyde, subjected to a series of hybridization and washing steps, counterstained with DAPI, and imaged either mounted in Vectashield mounting media on a Leica Widefield epifluorescent microscope or imaged in 1xPBS in a 6-well dish on a Leica DMi8 epifluorescent microscope. For quantitative assessment of Wolbachia infection by genomic titer, we collected 1.5 mL of cell culture and performed Tn5 Illumina library preparation following protocols detailed in 10 , 79 . Method and Analysis Details Transcriptomics Sample collection and sequencing We collected immortalized Drosophila melanogaster cell culture cells stably infected with Wolbachia from confluent cultures grown at 23°C. For each sample, 1.2 mL (at ~2e6 cells/mL) of cells were pelleted by centrifugation at 16,000xg for 10 minutes at 4°C. Following supernatant removal, we promptly transferred the cell pellets to −80°C for storage. Frozen cell pellets were shipped on dry ice to Genewiz Azenta Life Sciences for RNA extraction, cDNA synthesis, Illumina library preparation, and Illumina sequencing. The cell pellets were lysed, ribosomal RNA sequences were depleted with sequential eukaryotic and bacterial rRNA depletion kits (Qiagen FastSelect), and the remaining RNAs used as templates for cDNA synthesis using random hexamers. Illumina dual-indexed libraries were made from these cDNAs and sequenced as 2×150bp reads on a NovaSeq. Read processing and pseudoalignment We processed and analyzed RNAseq datasets for unsupervised clustering and differential expression analyses using standard computational approaches and custom parsing scripts. We trimmed adapter fragments from the demultiplexed RNAseq reads with Trimmomatic (v 0.39) 56 . We quantified transcripts by pseudoalignment with Kallisto 58 . To obtain alignments against the full, non-redundant host-symbiont transcriptome, we merged the NCBI RefSeq assemblies for the wMel reference genome CDSs and RNAs from genomic (accession GCF_000008025.1) and the D. melanogaster reference genome RNAs from genomic (accession GCF_000001215.4; Release_6_plus_ISO1_MT) as in 3 . Simultaneous mapping to both genomes was performed to avoid cross-species mismapping 80 . This reference transcriptome was indexed at a k-mer length of 31 in Kallisto (version 0.45.1) 58 and reads were pseudoaligned against this reference with the “kallisto quant” command and default parameters. Host and symbionts have distinct transcriptome distributions 81 , necessitating the separation of the two transcriptomes prior to transcript normalization and quantification in DESeq2 55 , which we performed with a custom script. Transcriptome normalization and quantification We imported the subset D. melanogaster Kallisto transcriptome alignments into R with Tximport 82 . We estimated gene-level normalized counts by mapping transcript-level abundances to gene IDs (see Table S21). Using DEseq2’s DEseqDataSetFromTximport, we calculated the gene-level offset that corrects for average transcript length across samples with design = ~cell type + infection + cell type:infection. For each transcriptome, we filtered out low-count and low-coverage genes across samples by requiring all six samples of each cell type and infection state to have a minimum read count of 10, 20, or 70, for differential expression, bulk-to-single cell clustering, or weighted gene co-expression network analysis (WGCNA), respectively. This gene count matrix was exported as a .tsv file for bulk-to-single cell clustering. Bulk to Single Cell Clustering To analyze the effects of immortalization and Wolbachia infections on host cell identity, we performed two unsupervised clustering analyses of the pseudoaligned gene counts output from Kallisto. We imported the gene count matrix .tsv file from DEseq2 into Scanpy 83 as an AnnData object. The sample by gene matrix was filtered to retain samples with at least 200 genes expressed and genes expressed in at least three samples. Counts based normalization was performed to a target sum of 1e4 gene counts per sample. We computed the neighborhood graph of cells using the sc.pp.neighbors function with 10 nearest neighbors and 20 principal components. Clustering was performed using the Leiden algorithm (sc.tl.leiden) to assign cells to discrete clusters. To better understand the global connectivity structure of the data, we constructed a partition-based graph abstraction (PAGA) using the sc.tl.paga function, followed by visualization of the PAGA graph using UMAP initialized with PAGA positions (sc.tl.umap). UMAP plots were generated to visualize the clustering structure, with cells colored by Leiden clusters. We log-transformed the counts using the sc.pp.log1p function and performed principal component analysis (PCA) using the sc.tl.pca function, with the ARPACK solver, to reduce the dimensionality of the dataset. We extracted the first two principal components (PCs) and calculated the variance ratio for each PC to assess the variance explained by each component. To further explore differences between clusters, we computed a distance matrix of the bulk data and performed hierarchical clustering to visualize these differences using single-linkage clustering. We visualized the hierarchical relationships between clusters with the scipy.cluster.hierarchy.dendrogram function. We calculated descriptive statistics, including the mean, median, standard deviation, and range of distances, to quantify the variability between clusters. To identify marker genes for each cluster, we performed differential expression analysis using the Wilcoxon rank-sum test (sc.tl.rank_genes_groups), chosen to account for the non-normal distribution of bulk RNA sequencing data. We compared our dataset to three reference atlases: the Fly Cell Atlas, specifically the 10X VSN All (Stringent) dataset without blood, which contains whole transcriptome data from 5-day-old D. melanogaster nuclei generated using the 10X Genomics platform 84 ; an Embryo Atlas, which provides temporal resolution of Drosophila embryonic development 85 ; and a Myeloid Blood Cell Atlas, which offers detailed characterization of Drosophila blood cell lineages 75 . For each atlas, we imported the data into Scanpy 83 and filtered the datasets: cells expressing fewer than 200 genes were filtered out, and genes expressed in fewer than three cells were removed using the sc.pp.filter_cells and sc.pp.filter_genes functions. The filtered data was normalized to a target sum of 1e4 counts per cell (sc.pp.normalize_total). To ensure balanced representation of cell types for reference mapping, we implemented a subsampling strategy where each cell type was represented by an equal number of cells, determined by the size of the smallest cell type group. This was accomplished using custom subsampling functions that randomly selected cells from each category while maintaining the overall cell type distribution. To integrate our bulk RNA-seq data with each single-cell reference atlas, we performed batch correction to account for technical differences between the datasets. We first subset both datasets to include only shared genes, which ensured comparable feature spaces. The datasets were then concatenated with appropriate batch labeling (bulk_adata.obs[‘dataset’] = ‘Bulk’, ref_adata.obs[‘dataset’] = ‘Single-cell’). We applied the BBKNN algorithm 52 for batch effect correction with the ‘dataset’ variable as the batch key. For classification of bulk samples, we implemented a k-nearest neighbors (kNN) approach that assigned cell type labels based on proximity in the reduced dimensional space. The optimal k value was determined algorithmically as the minimum of: 1) the square root of the reference cell count and 2) 10% of the smallest cell type class, with a minimum threshold of 5 neighbors. This approach balanced statistical power with the risk of overfitting. For each bulk sample, we identified its k nearest reference cells and assigned a cell type based on a distance-weighted voting system where closer neighbors had greater influence on the classification outcome. To assess classification confidence, we calculated a weighted score based on the proportion of neighbors belonging to the assigned cell type, with distances used as weights. We also implemented a permutation test (1000 iterations) to calculate p-values for each assignment, where labels were randomly shuffled to determine the likelihood of obtaining the observed classification by chance. For visualization, we computed principal components using the sc.tl.pca function with the ARPACK solver, followed by neighborhood graph construction (sc.pp.neighbors) using 15 neighbors and 30 principal components. UMAP dimensionality reduction was applied to the integrated data (sc.tl.umap), allowing visualization of bulk samples within the context of reference cell types. We generated enhanced visualizations highlighting both experimental conditions (JW18 uninfected, JW18 w Mel, S2 uninfected, S2 w Mel) and predicted cell type assignments across each of the three reference atlases. Differential expression Following short-read processing and pseudoalignment, we filtered out transcripts with less than 10 reads across all replicates of each condition. This resulted in the detection of 10,839 expressed transcripts, out of 35,344 transcripts in the D. melanogaster genome and 1,122 expressed transcripts, out of 1,286 transcripts in the w Mel genome. We performed Wald tests to detect differential expression while accounting for multiple testing by calculating FDR/Benjamini-Hochberg p-value corrections in DESeq2 55 . For these tests, we modeled the impact of the experimental conditions on the Drosophila transcriptomes as a function of cell line, infection, and the interaction between genotype and infection (~cell line + infection + cell line*infection). Wolbachia transcriptomes were tested for an impact of cell type on expression (~cell line). Gene set enrichment analysis (GSEA) For each set of differentially expressed genes, we imported the DEseq results into R, ranked the expressed genes by stat value, from high to low order, and performed GSEA with the clusterProfiler package 70 . The stat value was selected because it incorporates information from both the adjusted-p value and the log2-fold change value. We performed KEGG enrichment analysis on the DE hits with gseKEGG (organism = “dme”, minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05). After mapping the FLYBASECGs ids to the ENTREZIDs with the “org.Dm.eg.db” database, we performed GO enrichment analysis with gseGO (minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05). Weighted gene co-expression network analysis (WGCNA) For each transcriptome, we filtered counts to only include genes with greater than 70 reads across all replicates of each condition, resulting in 8,322 expressed D. melanogaster transcripts and 763 w Mel transcripts, which were then normalized for composition and transformed with the regularized log function, rlog. We used the pickSoftThreshold R function (Horvath and Dong 2008) to find the best fit scale free topology value for network construction, which was 18 for D. melanogaster and eight for w Mel. Using the blockwiseModules function in R (Zhang and Horvath 2005), we clustered genes and samples hierarchically using signed networks and signed TOMtypes, Pearson correlation, and automated block-wise clustering to group associated genes based on their expression patterns into eigengene modules. Clustering resulted in 13 D. melanogaster modules and five w Mel modules. We tested the eigengene modules for association with the terms of the differential expression model ~ cell type + infection + cell type * infection by linear regression with limma::lmFit in R (Newville et al. 2014). We applied empirical Bayes, limma::eBayes, to smooth standard errors. Enrichment Analysis We performed gene ontology (GO) enrichment analysis on the DE genes in each category to identify pathways that are overrepresented compared to the background transcriptome (i.e., the full sets of expressed transcripts). Lists of genes comprising each eigengene module were compared with the background expressed reference transcriptome sets of D. melanogaster and w Mel gene IDs with clusterProfiler 69 , 70 , 86 and ShinyGO 87 . D. melanogaster gene sets were also analyzed with the Drosophila -specific tool, PANGEA 60 . Drosophila cell atlas Wolbachia titer estimation We investigated the Wolbachia infection status of three Drosophila cell atlas datasets (Fly Cell Atlas, Embryo Cell Atlas, and Myeloid Cell Atlas), by comparing against known infected and uninfected scRNAseq controls obtained from testis data in SRA: PRJNA788731 88 . Reads were first processed with Trimmomatic (v0.39) to remove adapter sequences and low-quality bases prior to alignment. We aligned transcriptomic data from each atlas to a combined reference genome containing both Drosophila melanogaster and Wolbachia w Mel sequences using STAR (v2.7.9a). For the cell atlas datasets, we generated subsampled data using seqtk (v1.4; https://github.com/lh3/seqtk ), randomly sampling 1,000,000 reads from each SRA accession to ensure consistent depth across samples. We quantified gene expression using featureCounts from the Subread package. We extracted reads mapping to ribosomal RNA genes using established reference lists for both host and symbiont. To estimate infection status of the scRNAseq atlases, we normalized the ratio of w Mel ribosomal RNA (rRNA) read counts to D. melanogaster rRNA counts for each sample. This approach takes advantage of mis-priming events of the poly-T primers used in scRNAseq to regions of ribosomal RNA enriched in adenine. We normalized read counts by transcript length to account for gene size differences. We employed Fisher’s exact test on contingency tables of normalized read counts to compare each test sample against known infected and uninfected controls. A sample was classified as “uninfected” if it had an rRNA ratio profile that was not significantly different from the uninfected control, but significantly different from the infected control (p < 0.05). Conversely, a sample was classified as “infected” if the pattern was reversed. Samples with consistent signals from both controls were labeled as “inconclusive,” with the final determination based on the relative similarity of their ratios to each control. We implemented a bootstrap analysis to estimate 95% confidence intervals for w Mel/ D. melanogaster rRNA ratios. The distribution of rRNA read counts normalized by transcript length was visualized using scatter plots with logarithmic scaling with custom Python scripts. We also generated bar plots displaying sample ratios with their associated 95% confidence intervals to facilitate direct comparison between samples. Micro-C Chromatin Capture To understand shifts in chromatin structure within infected cell lines we conducted a chromatin capture assay using the Dovetail Genomics Micro-C kit (Dovetail #: 21006). Micro-C is a chromatin conformation capture technique developed in 2015 derived from the original 3C method developed in 2002 89 . The unique MNase used in Micro-C allows for high-resolution analysis of chromatin contacts while maintaining the ability to identify long-range interactions 90 , 91 . These interactions include interactions within 1kb, such as enhancer-promoter loops, and long-range interactions, such as topologically associated domains (TADs) which can span lengths greater than 1Mb 91 , 92 . This broad range makes the Micro-C method ideal for characterizing Wolbachia infections as the effects on chromatin structure have not previously been identified. We performed a Micro-C chromatin capture assay using the materials and methods from Dovetail’s Micro-C Kit (Dovetail #: 21006) followed by an Illumina library preparation. Unless specified all materials were obtained from Dovetail Genomics. Cell culture and Crosslinking We collected immortalized D. melanogaster cell culture cells stably infected with Wolbachia from confluent cultures grown at 23°C. For each sample, 1 mL (at ~2e6 cells/mL) of cells were pelleted by centrifugation at 3,000xg for 5 minutes at 4°C. The supernatant was then removed and we resuspended cells in 1X PBS. We then repeated the centrifugation step and removed the PBS. Following supernatant removal, we promptly transferred the cell pellets to −80°C for overnight storage. We performed a dual crosslinking protocol with formaldehyde and DSG (disuccinimidyl glutarate) to fix protein-DNA and protein-protein interactions. Cell pellets were thawed on at room temperature and resuspended in 1mL 1X PBS, 0.3 mM DSG each followed by a 10 minute rotation at room temperature on a Digestion and Lysis The pelleted material was resuspended in 50 μL of freshly prepared 1X Nuclease Digest Buffer and 0.5 μL of MNase Enzyme mix to fragment the crosslinked chromatin. Following crosslinking, we proceeded with the Dovetail Micro-C chromatin capture assay kit (Dovetail #: 21006) protocol and materials. We performed a shallow sequencing run to determine the quality of the libraries (Table S1) before full-depth sequencing. Paired-end Illumina reads were generated following the NEBNext Illumina library protocol. Libraries were sequenced on a NovaSeq S4 2×150bp lane by Fulgent Genetics. The demultiplexed sequencing reads underwent initial processing with Trimmomatic (v0.39) to processes paired-end Illumina sequencing data with Phred+64 quality encoding, trim Illumina adapters, perform quality trimming by removing leading and trailing bases with Phred scores below 3, and discard reads shorter than 36 bases 56 . We processed these trimmed reads following recommendations from Dovetail Genomics ( https://micro-c.readthedocs.io/ ). Briefly, the reads were aligned to the D. melanogaster genome (dmell-all-r6.46) with the Burrows-Wheeler Alignment tool (BWA, v0.7.17) 43 using the BWA-MEM algorithm with a minimum quality score of 0 without pairing. The alignment results were parsed using pairtools, aiming to identify ligation junctions with a minimum quality score of 40 and a walks policy of 5 unique to report the 5’-most unique alignment on each side. Pairtools was also used to eliminate optical duplicates and produce read pairs for subsequent analysis 45 . Following this, pairix was used to index the paired reads, and cooler (v 0.9.3) 47 was employed to construct chromatin contact maps with a bin size of 1kb. Contact maps were balanced at multiple resolutions with the cooler zoomify –balance function. Default settings were used unless otherwise specified. Mustache (v1.0.1) 93 was run to pinpoint chromatin loops, revealing insights into the three-dimensional organization of the host genome. We identified specific regions in the Drosophila melanogaster genome characterized by heightened chromatin contacts, referred to as topologically associated domains (TADs), using Mustache diff to identify TADs in each condition with an fdr adjusted p-value (q-value) < 0.05 and enriched loops with a q-value < 0.1 at resolutions of 1kb, 16kb, and 128kb and a sparsity threshold of 0.8. The output generated by Mustache was then converted from a bedpe file to a bed6 file before visualization through CoolBox ( Figure 5 A-L ). To identify genetic elements situated within the enriched TADs, we filtered the output bed6 files with the D. melanogaster genome annotation file (gtf), accessed from Genbank (GCF_000001215). The chromatin contact map was visualized using the Python tool CoolBox. Regions with significant changes in chromatin contacts were highlighted in differential plots ( Figure 5 A-L ). Chromatin loops identified by Mustache were overlaid onto the contact difference map, and genes located within those loops associated with Eigengene modules were plotted. Acknowledgements We thank the UCSC Life Sciences Microscopy Center (RRID:SCR_021135) and Ben Abrams for training and microscope maintenance. We thank Russ Corbett-Detig for his comments and feedback throughout this project. This work was supported by UC Santa Cruz startup funds, NIH R00GM135583 and R35GM157189 to SLR, and NIH T32HG012344 to JJ. 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Share Wolbachia induces host cell identity changes and determines symbiotic fate in Drosophila Jodie Jacobs , Cade Mirchandani , William E Seligmann , Samuel Sacco , Merly Escalona , Richard E Green , Shelbi L Russell bioRxiv 2025.06.05.658111; doi: https://doi.org/10.1101/2025.06.05.658111 Share This Article: Copy Citation Tools Wolbachia induces host cell identity changes and determines symbiotic fate in Drosophila Jodie Jacobs , Cade Mirchandani , William E Seligmann , Samuel Sacco , Merly Escalona , Richard E Green , Shelbi L Russell bioRxiv 2025.06.05.658111; doi: https://doi.org/10.1101/2025.06.05.658111 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cell Biology Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17635) Bioengineering (13859) Bioinformatics (41846) Biophysics (21401) Cancer Biology (18534) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24285) Genetics (15582) Genomics (22463) Immunology (17700) Microbiology (40298) Molecular Biology (17141) Neuroscience (88424) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)