The influence of innate immunity, adaptive immunity and diet on intestinal microbiota following Trichuris muris infection

The influence of innate immunity, adaptive immunity and diet on intestinal microbiota following Trichuris muris infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The influence of innate immunity, adaptive immunity and diet on intestinal microbiota following Trichuris muris infection Bridgious Walusimbi, Kelly Hayes, Melissa AE Lawson, Seona Thompson, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7713991/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Trichuris trichiura (whipworm) affects nearly 500 million people globally, causing chronic intestinal inflammation and contributing to malnutrition, growth stunting, and impaired cognitive development especially in children living in low-resource settings. While host immune responses are central to parasite clearance, growing evidence suggests that diet and the gut microbiota may modulate both infection susceptibility and treatment outcomes. However, the mechanisms by which diet influences helminth expulsion, particularly in the context of immune deficiency, remain poorly defined. Methods We used a Trichuris muris infection model to investigate how host immune competence and diet influence worm burden, parasite-specific humoral responses and the gut microbiome. Wild-type (WT), RAG2-deficient (lacking adaptive immunity), and RAG2/γc-deficient (lacking both adaptive and innate lymphoid immunity) mice received either a standard normal diet (ND) or high-fat diet (HFD) and infected with a low dose of T. muris . Worm burdens, parasite specific serum IgG1 and IgG2a/c responses were measured together with profiling of the intestinal microbiota using 16S rRNA gene sequencing and shotgun metagenomics Results Infection of WT mice on a ND with a low dose infection resulted in a chronic infection. Antibody analysis showed a strong parasite-specific IgG2a/c response, consistent with Th1-biased immunity during chronic infection. Notably, WT mice fed a high fat diet (HFD) achieved near-complete parasite clearance, accompanied by elevated IgG1 and reduced IgG2a/c titres, suggesting a diet-induced skewing toward a protective Th2-type response. RAG2-deficient mice and RAG2/γc-deficient mice on a normal diet (ND) also maintained a low dose chronic infection, aligned with the role of immune responses in clearance: no parasite-specific antibodies were detected in either strain as expected. However, RAG2-deficient mice and RAG2/γc-deficient mice fed a HFD exhibited reduced parasite numbers but not complete worm loss as seen in WT mice suggesting a second mechanism of effect. Microbiota composition clustered primarily by genotype and diet, with infection status exerting a more subtle influence. HFD-fed mice exhibited enrichment of several taxa with known roles in mucosal immunity and metabolic regulation, including Bacteroides , Parabacteroides , Faecalibacterium , Blautia , and Lactococcus . Conclusion Diet and host immune competence jointly influence susceptibility to helminth infection and the composition and function of the gut microbiota. A HFD shapes the gut toward a state of resilience, characterized by enhanced Th2-biased immune responses and enrichment of epithelial barrier-supportive microbial taxa. This milieu promotes resistance to T. muris infection, even in partially immunocompromised hosts. These findings underscore the importance of diet in modulating host–microbiota–parasite interactions and highlight the potential for diet or microbiome-based strategies to enhance resistance against intestinal helminths,pending studies that directly test microbiome causality. Immunology Bioinformatics Parasitology Taxonomy microbiota Trichuris muris Diet innate immunity adaptive immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Parasitic helminths such as Trichuris trichiura remain a major public health concern in low- and middle-income countries (LMICs), where they affect over 500 million individuals and contribute substantially to the global burden of neglected tropical diseases [ 1 ]. Chronic infection, particularly in paediatric populations, is associated with impaired linear growth, micronutrient deficiencies, anaemia, and persistent intestinal inflammation [ 2 – 4 ]. These parasitic diseases now increasingly intersect with emergent health challenges in LMICs, where rapid urbanisation and globalisation have driven widespread shifts in dietary patterns, most notably, increased consumption of ultra-processed, high-fat foods [ 5 ]. This transition has coincided with a growing burden of metabolic dysfunction, creating a syndemic landscape in which helminth infections and diet-induced inflammation coexist within the same host [ 6 , 7 ]. While the immunological mechanisms of helminth clearance, especially the requirement for CD4⁺ Th2-polarised responses, are well characterised [ 8 – 10 ], far less is known about how dietary exposures and immune competence jointly modulate host susceptibility, anti-helminth immunity, and gut microbial ecology. Diets rich in saturated fats have been shown to impair intestinal barrier function and drive low-grade systemic inflammation, potentially attenuating effective immune responses to helminth infection and altering microbial composition [ 11 , 12 ]. Moreover, genetic ablation of adaptive or innate immune pathways, including T, B, and innate lymphoid cells (ILCs), can lead to marked dysbiosis [ 13 – 15 ]. However, the extent to which these immunological deficits interact with dietary factors to shape helminth–microbiota dynamics remains poorly defined. To begin to address this gap in knowledge, we used a tractable murine model of Trichuris muris infection, a close immunological and ecological analogue of human T. trichiura , to delineate the individual and combined contributions of host immunity and diet in shaping gut–parasite–microbiota interactions. We utilized three genetically distinct mouse models: wild-type (WT), RAG2 -deficient ( RAG2 ⁻/⁻; lacking T and B cells), and RAG2/γc -deficient ( RAG2 ⁻/⁻ γc ⁻/⁻; lacking T, B, and ILCs) mice, and exposed them to either a normal diet (ND) or high fat diet (HFD) prior to infection. Parasite burden, intestinal microbial composition, and antigen-specific IgG1 and IgG2a/c titres were assessed following infection. Our findings show that both immune competence and diet are important determinants of helminth expulsion and gut microbial structure. Notably, mice deficient in adaptive or innate lymphoid immunity exhibited marked alterations in microbial community assembly, which were further modulated by dietary fat intake. These results highlight the interactive and often synergistic effects of diet and immune status on host–microbe–parasite interactions, with implications for understanding helminth-associated pathophysiology in the context of ongoing nutritional and epidemiological transitions. Methods Animal Experiments and Dietary Intervention Three genetically distinct mouse models were used in this study to investigate the effects of innate immunity, adaptive immunity and diet on gut microbiota and clearance of Trichuris muris infection. These included WT C57BL/6j immunocompetent mice, C57BL/6j RAG2 −/− deficient (RAG2 KO) mice lacking T and B lymphocytes, and C57BL/6j RAG2 −/− /γc −/− deficient (RAGγc) mice deficient in T cells, B cells, and innate lymphoid cells (ILCs). All mice were bred and housed under specific pathogen-free (SPF) conditions at University of Manchester, with ad libitum access to food and water. Experiments were performed under the regulations of the Home Office Scientific Procedures Act (1986), (Licence P043A082) and were subject to local ethical review by the University of Manchester Animal Welfare and Ethical Review Body (AWERB) and followed ARRIVE 2.0 guidelines. Male and female mice were used. At five weeks of age, mice were randomly assigned to receive either a standard chow diet (ND) or a high fat diet (HFD) (DIO Rodent Purified Diet containing 60% energy from fat, blue, irradiated; Research Diets, Inc.). Diets were maintained throughout the experiment. At week 12, all mice were orally infected with approximately 25 embryonated T. muris eggs. Animals were monitored regularly and euthanized at week 17, corresponding to day 35 post-infection when the infection has reached patency. At necropsy, caecal content, faecal pellets, and serum were collected and immediately stored at -80 o C for subsequent experiments and downstream analyses as shown in Fig. 1 . Worm Burden Quantification Worm burdens were quantified by carefully dissecting the caeca, opening them longitudinally in phosphate-buffered saline (PBS), and counting T. muris worms under a dissecting microscope. Parasite-specific Antibody ELISA Systemic humoral responses to T. muris infection were assessed by quantifying parasite-specific IgG1 and IgG2a/c antibodies in mouse serum using enzyme-linked immunosorbent assay (ELISA). High-binding 96-well plates (NUNC MaxiSorp) were coated overnight at 4°C with T. muris excretory/secretory (E/S) antigen at a concentration of 5 µg/mL in 0.05 M carbonate-bicarbonate buffer (pH 9.6). Plates were washed five times with phosphate-buffered saline containing 0.05% Tween-20 (PBS-T) and blocked with 3% bovine serum albumin (BSA) in PBS for 1 hour at room temperature. Serum samples were initially diluted 1:20 in PBS-T, followed by serial two-fold dilutions. Fifty microliters of diluted serum were added per well and incubated for 1 hour at room temperature. After washing, 50 µL of biotinylated rat anti-mouse IgG1 (Bio-Rad MCA336B, 1:2000) or IgG2a/c (BD 553388, 1:1000) diluted in PBS-T were added to the appropriate wells and incubated for 1 hour. Plates were washed again and incubated with 75 µL of streptavidin–horseradish peroxidase (HRP) (Roche, 1:1000 dilution) for 1 hour at room temperature. Colour development was achieved using ABTS substrate, freshly prepared by mixing 1 mL ABTS stock (0.5 g ABTS in 50 mL citrate buffer) with 9 mL citrate buffer and 1 µL of 30% hydrogen peroxide. One hundred microliters of substrate were added to each well, and absorbance was read at 405 nm (reference 490 nm) using a VersaMax microplate reader (Molecular Devices, UK). Antibody responses were plotted as optical density (OD) values across serial serum dilutions. Microbiome Sample Preparation and Sequencing Microbial DNA was extracted from faecal samples using the QIAamp DNA Stool Mini Kit (Qiagen) according to the manufacturer’s instructions. The V3–V4 hypervariable region of the 16S rRNA gene was amplified using the Illumina 16S Metagenomic Sequencing Library Preparation protocol (Part # 15044223 Rev. B). In brief, the first PCR used universal primers targeting the V3–V4 region (forward primer: 341F 5’-CCTACGGGNGGCWGCAG-3’; reverse primer: 806R 5’-GACTACHVGGGTATCTAATCC-3’) with Illumina overhang adapters. PCR cycling conditions were as follows: initial denaturation at 95°C for 3 minutes, followed by 25 cycles of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 30 seconds, and a final extension at 72°C for 5 minutes. PCR amplicons were cleaned using AMPure XP magnetic beads (Beckman Coulter). Index PCR was performed using Nextera XT dual indices under the following conditions: initial denaturation at 95°C for 3 minutes, 8 cycles of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 30 seconds, and a final extension at 72°C for 5 minutes. Libraries were purified again with AMPure XP beads, quantified using Qubit dsDNA HS Assay (Thermo Fisher Scientific), and normalized to a final concentration of 4 nM. Libraries were pooled, denatured, and sequenced on an Illumina MiSeq platform using a 2 × 300 bp paired-end MiSeq Reagent Kit v3 (600-cycle) at the Bioinformatics Core Facility, University of Manchester. Caecal contents were subjected to shotgun metagenomic sequencing. DNA was extracted using standardized protocols, and libraries were prepared and sequenced by Transnetyx (UK; https://www.transnetyx.com ) using Illumina short-read technology. Quality control, host DNA removal, and taxonomic profiling were performed using standard bioinformatic pipelines. Microbiome Bioinformatics Processing Raw demultiplexed FASTQ files were processed using QIIME2 (version 2023.2). Primers were removed using Cutadapt (version 4.4) with default parameters. Reads were denoised, quality filtered, merged, and checked for chimeras using DADA2 (implemented within QIIME2) to generate amplicon sequence variants (ASVs) with high-resolution taxonomic specificity. ASVs were classified against the Greengenes 13.8 reference database using a Naive Bayes classifier. To standardize sampling depth, rarefaction was performed to 20,000 sequences per sample. Microbial alpha diversity was assessed using the Shannon diversity index and observed richness.Beta diversity was calculated using Bray–Curtis dissimilarity matrices to assess differences in overall microbial community composition between groups. These differences were visualized using Principal Coordinate Analysis (PCoA) plots, which display the major axes of variation in microbial profiles across samples. Statistical significance of group-level differences in beta diversity was tested using permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. To identify microbial taxa differentially abundant across experimental groups, we performed differential abundance (DA) testing using the DESeq2 method implemented within the phyloseq and DESeq2 packages in R. A Wald test was used to estimate log₂ fold changes in microbial abundance between groups. Resulting p-values were corrected for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure. Taxa with an FDR-adjusted p < 0.05 were considered statistically significant.To visualize differentially abundant taxa, we generated volcano plots (log₂ fold change vs. –log₁₀ FDR-adjusted p-value) and waterfall plots. In the latter, we ranked significantly differentially abundant taxa by descending absolute log₂ fold change, using a cutoff of |log₂ fold change| ≥ 1 to highlight the most strongly altered microbes. Statistical Analysis Statistical analyses were performed using R (version 4.1.3). Group comparisons for worm burden and alpha diversity indices were conducted using one-way ANOVA or Kruskal-Wallis tests, depending on data distribution. Two-way ANOVA was used to compare parasite-specific antibody responses across serum dilution series. For all tests, a p-value < 0.05 was considered statistically significant. Results We analyzed fecal samples from a total of 49 mice , stratified by immune genotype (WT, RAG2-deficient [RAG-KO], and RAG2 -/- /γc -/- deficient [RAGγc]), dietary exposure (normal chow [ND]vs. high-fat diet [HFD]), and T. muris infection status (infected vs. naïve), as shown in table 1. Among infected groups, mice included WT_N (n = 7), WT_H (n = 4), RAG_N (n = 7), RAG_H (n = 7), RAGγc _N (n = 7), and RAGγc _H (n = 8) . In addition, uninfected (naive) control groups comprised WT_N (n = 5) and RAG_N (n = 4) mice maintained on a ND. Table 1. Mouse group characteristics by genetic background, diet, and infection status. Immune Phenotype Diet Group (DIET_mousetype) Number of Mice T. muris Infection Status WT ND WT_N 7 Infected WT HFD WT_H 4 Infected RAG-KO ND RAG_N 7 Infected RAG-KO HFD RAG_H 7 Infected RAGγc-KO ND RAGγc_N 7 Infected RAGγc-KO HFD RAGγc_H 8 Infected WT ND WT_N 5 Naive RAG-KO ND RAG_N 4 Naive WT –intact immunity, RAG-KO – RAG knockout mice (no adaptive immunity), RAGγc -KO – RAG2/γc-deficient mice (deficient in innate and adaptive immunity). HFD- High Fat Diet. ND – Normal diet. Host Immune Competence and Diet Influence Trichuris muris Worm Burden To evaluate how host immune competence and dietary environment influence parasite clearance, we assessed the impact of host immune genotype and diet on T. muris worm burden across different mouse models (Figure 2). Under a normal diet (ND), all three genotypes – wild-type (WT_N), RAG-KO (RAG_N), and RAGγc-KO (RAGγc _N ) – harboured low worm burdens, with no significant differences between groups despite slight variation in mean worm counts (WT_N: 14.6 worms, 95% CI 11.2–18.0; RAG_N: 21.0 worms, 95% CI 19.6–22.4; RAGγc _N : 14.7 worms, 95% CI 11.6–17.8). This indicates that under normal dietary conditions, and low dose infection, host immune status alone does not strongly influence the establishment of chronic infection. In contrast, worm burdens were markedly reduced under HFD. WT mice on HFD (WT_H) showed complete parasite clearance across all individuals while RAG-KO and RAGγc _N KO mice exhibited substantial but incomplete clearance of worms (RAG_H: 2.1 worms, 95% CI 0.9–3.4; RAGγc_H: 4.5 worms, 95% CI 1.0–8.0) A Kruskal-Wallis test confirmed significant differences in worm burdens across groups (p = 5.8 × 10⁻⁵). These results demonstrate that the interaction of host immune competence and diet is essential for parasite clearance, with HFD exposure strongly reducing worm burden across genotypes and immunity, but complete parasite clearance occurring only in immune-competent mice. Parasite-specific IgG1 and IgG2a/c responses show diet-dependent immune polarization Given the differences in worm burden, we next assessed parasite-specific IgG1 and IgG2a/c responses as proxy serological markers of Type 2 and Type 1 immunity respectively (Figure 3). This allowed us to determine whether impaired parasite expulsion associatedwith altered antibody production across genotypes and dietary exposures. As expected, WT mice on a normal diet (WT_ND) with low-dose chronic infection developed a mixed response, characterized by detectable IgG1 and a predominant IgG2a/c signal, consistent with Type 1-skewed immunity in the context of persistent infection. In contrast, WT mice on a high-fat diet (WT_H) that expelled their parasites mounted a strong IgG1 response with only weak IgG2a/c production, reflecting a shift toward Type 2 immunity associated with effective clearance. As expected, RAG-KO and RAG2/γc-deficient mice failed to generate detectable parasite-specific antibodies, confirming their lack of adaptive immunity. Naive mice showed no measurable parasite specific antibody responses. These results demonstrate that dietary environment modulates the balance of Type 1 versus Type 2 immunity in WT mice, in line with the patterns reported by Fujinka et al [16]. Relative abundance of the gut microbiota across mouse genotypes Figures 2 and 3 establish key host phenotypes in response to Trichuris muris infection, namely parasite burden and systemic humoral immunity across mouse genotypes and dietary exposures. While these outcomes highlight immunological differences in worm clearance and antibody production, they do not reveal how such host-intrinsic and diet influence the gut microbiota itself, which may be both a modulator and a target of helminth-driven immunomodulation. To address this, we next present the overall taxonomic composition of the gut microbiota across individual mice (Figure 4). This stacked taxonomy bar plot provides a detailed summary of the microbial community structure at the genus level, enabling visual comparison of dominant and subdominant taxa across genotypes. WT mice fed on a ND, relative to those on HFD, displayed higher relative abundances of taxa such as Muribaculaceae, Dubosiella and Lactobacillus . Immune-deficient genotypes (RAGγc-KO and RAG-KO) on ND compared to their counterparts on HFD, exhibited increased representation of genera such as Escherichia–Shigella , suggestive of dysbiosis. Dietary exposure further modulated microbial profiles: HFD was generally associated with expansion of Lactobacillaceae , Blautia , and Alistipes , Bacteriodes across genotypes. Notably, several taxa such as Faecalibaculum , Parabacteroides , and Romboutsia varied in abundance in a genotype-specific manner, highlighting the interactive effects of host immunity and diet in shaping gut microbial communities. Gut microbiota composition stratifed by diet alone is shown in supplementary figure 1. Gut microbiota composition is shaped by diet and immune competence more than infection status Having established distinct taxonomic signatures across mouse genotypes and diets (Figure. 4), we next sought to understand how these compositional differences relate to broader microbial community structure. To this end, we profiled overall microbiota composition using 16S rRNA gene sequencing and visualized community-level differences using principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity. This approach enabled us to assess how gut microbial diversity clusters across immune genotypes, diets, and infection states. WT mice on a ND (WT_N) formed a tightly clustered group, indicating a stable and consistent microbial community under immunocompetent conditions and conventional feeding. In contrast, WT mice on a HFD (WT_H) clustered separately, demonstrating that diet alone induces substantial shifts in microbiota composition. Similar separation was observed between RAG knockout (RAG_N vs. RAG_H) and RAG2/γc-deficient (RAGγc_N vs. RAGγc_H) groups, further underscoring the impact of dietary exposure across different immune backgrounds (Figure 5A). When stratified by infection status (Figure 5B), microbial profiles remained predominantly clustered by genotype and diet, with minimal separation between infected and uninfected mice within each dietary group. This suggests that while T. muris infection may influence the microbiome at a more specific taxonomic or functional level, its impact on overall microbial community structure is modest compared to the strong effects of immune status and dietary composition. PERMANOVA analysis confirmed that the combination of diet and immune competence (mouse_Diet) significantly explained variation in gut microbiota profiles (R² = 38.6%, p = 0.001). These results collectively indicate that immune competence and dietary exposure are the dominant factors shaping gut microbial communities in this model, while T. muris infection exerts subtler or more localized effects on the microbiome. Gut microbiota profiles, assessed by principal coordinate analysis (PCoA) using Bray-Curtis dissimilarity, revealed that microbial community structure clustered distinctly by mouse genotype and dietary exposure. WT, RAG2/γc-deficient, and RAG-deficient mice each formed separate clusters, with diet further stratifying microbiota profiles within each genotype. PERMANOVA analysis confirmed significant associations between microbiota composition, immune genotype, and diet (R² = 0.39, p = 0.001). Validation of 16S data using shotgun metagenomics To extend these findings and achieve greater taxonomic resolution, we performed shotgun metagenomic sequencing on caecal samples. This enabled deeper characterization of microbial shifts observed in 16S analysis and confirmed the dominant roles of host genotype and diet in shaping the microbiome. In the principal coordinate analysis (PCoA) plot presented in Figure 6A, where samples are stratified by mouse genotype RAG2/γc-deficient , Wild type and RAG2-deficient, gut microbiota profiles clustered distinctly according to immune competence. Wild-type (WT) mice formed a relatively tight cluster, indicating a stable and consistent gut microbiota structure under intact immune conditions. In contrast, RAG2-deficient, which lack adaptive immunity, clustered separately, reflecting a unique microbial community shaped by their immunodeficiency. RAG2/γc-deficient mice, with further impaired immune function, also formed their own distinct cluster, demonstrating that increasing degrees of immune impairment strongly influence gut microbiota composition. These observations confirm that host immune architecture is a major determinant of microbiome structure and a contribution of both adaptive and innate lymphoid cells, even when using high-resolution shotgun metagenomic profiling. In Figure 6B, where samples are stratified by both dietary exposure and infection status, diet emerged as the dominant factor shaping microbial community structure. Mice fed HFD consistently clustered away from those on ND across all immune genotypes, indicating that dietary composition has a strong and consistent impact on the gut microbiota. Infection status did not significantly separate samples within the same diet-genotype groups, supporting earlier findings from 16S rRNA analysis that T. muris infection induces more subtle, taxon-specific microbiome changes rather than large-scale community shifts. Interestingly, naïve (uninfected) mice on HFD often overlapped with their infected counterparts, further suggesting that diet exerts a stronger influence on microbiome structure than infection in this experimental context. Differential impact of high-fat diet on gut microbiota across immune genotypes We then sought to disentangle the independent effect of diet by comparing microbiota composition between high-fat and ND conditions within each genotype, in the absence of infection. This clarified how diet alone alters microbial ecology across different immune backgrounds. We performed differential abundance analysis to compare the gut microbiota profiles of mice fed on a HFD versus a ND within each immune genotype. This stratified approach allowed us to isolate the specific influence of diet within distinct immune backgrounds. Wild-Type Mice (WT) In WT mice, the volcano plot (Figure 7A) revealed a substantial number of significantly differentially abundant taxa between the HFD (10) and ND (11) groups. Key taxa such as Akkermansia , Muribaculum , and Parasutterella were enriched in the ND group, while Lactococcus and Clostridium sensu stricto 1 were significantly enriched in the HFD group (Figure 7B), indicating that HFD reshaped the gut microbial landscape in immunocompetent hosts. RAG-Deficient Mice (RAG) In severely immune-compromised RAG-deficient mice dietary impact was also evident as seen in Figure 7C where HFD-fed mice had 14 enriched taxa compared to 8 taxa that were more enriched in the ND-fed mice. HFD-fed RAG-KO mice exhibited enrichment of taxa such as Alloprevotella , Lactococcus , Blautia and Eubacterium coprostanoligenes group, while Lachnospiraceae NK4A136 and Eubacterium xylanophilum were more abundant in the ND group (Figure 7D). The pronounced microbial shifts in this group suggest that diet can still modulate microbial communities even when key immune components are absent. RAG2/γc-deficient Mice (RAGγc-KO) The volcano plot Figure 7E displays the number and statistical significance of differentially abundant bacterial genera between RAG2/γc-deficient mice fed a HFD (21) versus a ND (11). Genera with significant differences in abundance are highlighted based on adjusted p-value and fold change. The accompanying waterfall plot Figure 7F illustrates the direction and magnitude of change for selected genera, with enrichment of Romboutsia , Acetatifactor , Bacteriodes , and Lactococcus in HFD-fed mice, and higher levels of Escherichia–Shigella , Clostridia vadinBB60 , and Parasutterella in mice on a ND. Shared and Unique Microbial Signatures Across Genotypes The Venn diagram (Figure 7G) summarizing differentially abundant taxa across genotypes revealed that only eight microbial taxa were consistently affected by diet across all mouse types. In contrast, a substantial number of taxa were unique to each genotype, with 21 taxa specific to WT mice, 32 unique to RAG2/γc-deficient mice, and 22 unique to RAG-KO mice. These findings indicate that while some dietary effects on the microbiome are conserved across immune backgrounds, the majority of microbial responses to diet are strongly genotype-specific. In WT mice, HFD feeding was associated with an enrichment of Lactococcus and Clostridium sensu stricto 1 , while Akkermansia , Muribaculum , and Parasutterella were more abundant in those on a ND. In RAG2/γc-deficient mice, Romboutsia , Acetatifactor , and Lactococcus were enriched under HFD conditions, whereas Escherichia-Shigella and Clostridia vadinBB60 dominated in the ND group. Similarly, in RAG-KO mice, Alloprevotella , Lactococcus , and Eubacterium coprostanoligenes group were enriched under HFD, while Lachnospiraceae NK4A136 and Eubacterium xylanophilum were more abundant in mice fed a ND. These results underscore the complex and immune-genotype-specific nature of dietary impacts on gut microbiome composition. Gut microbiota profiles cluster by host immune background and diet Having defined the baseline effects of diet, we next examined how infection interacts with diet and immune status to modulate microbial community structure. This analysis enabled us to determine whether infection amplifies or overrides diet-induced differences.We compared microbiota profiles between WT and RAG2/γc-deficient (RAGγc-KO) mice following T. muris infection under both ND and HFD conditions. Principal coordinate analysis (PCoA) of gut microbiota composition revealed distinct clustering by both genotype and diet among T. muris –infected mice (Figure 8A). RAG2/γc-deficient and wild-type mice formed clearly separated clusters, and within each genotype, HFD and ND induced marked shifts in microbial community structure. These findings demonstrate that both immune competence and dietary exposure independently contribute to shaping the gut microbiota, even in the context of helminth infection. To unravel the specific contribution of host immune status from dietary influence, we next focused on T. muris –infected mice maintained on a ND (Figure 8B). Comparing RAGγc-deficient and wild-type mice under this controlled dietary condition revealed strong genotype-dependent clustering. This indicates that the absence of adaptive immune cells together with innate lymphoid cells is sufficient to drive significant alterations in microbial community structure. This focused comparison isolates the immunological effect observed in Figure 8A and establishes immune competence as a primary determinant of microbiota composition. Finally, to test whether this immune-driven separation persists under HFD diet, we compared RAGγc-deficient and wild-type mice fed a HFD (Figure 8C). Again, PCoA revealed robust genotype-dependent separation, mirroring the pattern observed under ND. This confirms that loss of the common γ chain—and the consequent absence of adaptive and innate lymphoid cells—consistently reshapes the gut microbiota across dietary contexts. Taken together, these three analyses establish that while both diet and immune status shape the gut microbiome, immune competence exerts a dominant and reproducible influence, regardless of dietary environment. To understand the taxonomic drivers of these observed compositional shifts, we performed differential abundance analysis within each dietary context. Under ND, WT mice were enriched for taxa typically associated with gut health, including Bacteroides , Turicibacter , and Romboutsia (Figure 8D). In contrast, RAGγc-deficient mice exhibited higher abundances of potentially dysbiotic genera such as Faecalibaculum , Candidatus Arthromitus , Escherichia–Shigella , and unclassified members of the Gastranaerophilales order,suggesting that the absence of adaptive and innate lymphoid compartments disrupts microbial homeostasis. Under HFD conditions, genotype-specific microbial signatures remained evident (Figure 8E). RAGγc-deficient mice were enriched in taxa such as Acetatifactor , Rikenella , and Candidatus Saccharimonas , whereas WT mice maintained a microbial community dominated by Blautia , Turicibacter , Dubosiella , and Helicobacter . These shifts indicate that HFD exacerbates genotype-driven microbial divergence, potentially amplifying maladaptive microbiota configurations in immunodeficient hosts. Diet and infection independently and jointly shape microbial diversity in wild-type mice To dissect the effects of infection and diet on microbial diversity, we performed principal coordinate analysis (PCoA) of gut microbiota composition across WT mice under varying dietary and infection conditions. We included four distinct PCoA comparisons, each capturing a specific intersection of diet (ND vs. HFD) and infection status ( T. muris –infected vs. uninfected), allowing us to systematically assess the contribution of each factor to microbial community structure. Under uninfected conditions, comparison of WT mice on ND versus HFD revealed clear separation in microbiota profiles (Figure 9A). Axis 1 and axis 2 accounted for 28.0% and 16.9% of the variance, respectively, indicating that dietary composition alone is sufficient to drive substantial alterations in the gut microbiota, even in the absence of infection. In the presence of infection, microbial divergence between dietary groups became more pronounced. Infected WT mice fed a HFD clustered distinctly from those on a ND (Figure 9B), with axis 1 and axis 2 explaining 37.6% and 22.6% of the variance, respectively. These results suggest that infection interacts with dietary background to induce greater microbiome restructuring. We next examined the effect of infection within each dietary context. Among mice maintained on a ND, infected and uninfected WT mice showed moderate separation in community composition (Figure 9C; axis 1: 24.8%, axis 2: 16.5%), indicating that infection alters the microbiota, but within a relatively stable nutritional environment. In contrast, the HFD amplified these infection-associated shifts. WT mice on HFD showed a trend toward separation between infected and uninfected groups (Figure 9D), with axis 1 and axis 2 capturing 36.5% and 20.9% of the variance, respectively,consistent with a combined effect of diet and infection, although the clustering was less distinct than under ND conditions.. To identify specific microbial taxa altered by T. muris infection, we performed differential abundance analysis comparing infected (WT_N_INF) and uninfected (WT_N_UNINF) wild-type mice maintained on a ND (Figure 9E). The analysis revealed significant compositional shifts, with infection associated with both enrichment and depletion of distinct bacterial genera. Infected mice exhibited increased relative abundances of genera such as Marvinbryantia , Roseburia , Bacteroides , and Erysipelatoclostridium , suggesting a restructuring of the gut ecosystem in response to helminth exposure. Conversely, uninfected mice harbored higher levels of taxa including Gastranaerophilales, Bilophila, Anaerotruncus, Ruminococcus, Desulfovibrio, Intestinimonas, and Alloprevotella. These findings indicate that even in the absence of dietary perturbation, helminth infection drives targeted microbial remodeling, potentially through immune-mediated or niche-altering mechanisms. When interpreted alongside diversity and clustering analyses (Figure 9 A–D), these taxon-level changes provide insight into how infection selectively modulates gut microbial communities beyond the overall shifts in composition. In contrast to the infection-driven microbial shifts observed under normal dietary conditions, differential abundance analysis revealed no significantly altered genera between infected and uninfected WT mice fed a HFD. Our interpretation is that the high-fat diet produces such a dominant shift in overall community structure that any infection-related changes are comparatively subtle and fall below detection in differential abundance testing. We observed consistent reductions in microbial alpha diversity associated with HFD, immune deficiency, and T. muris infection. These effects were evident across gut compartments and sequencing platforms, including 16S rRNA and shotgun metagenomics (Supplementary Figures. 2–4). Genotype-, diet-, and infection-specific comparisons further revealed that immune-compromised mice, particularly under dietary and infectious stress, exhibited the greatest losses in alpha microbial diversity. Diet and infection influence microbial functional potential Finally, to explore the functional implications of the observed compositional changes, we predicted microbial metabolic pathways in infected wild-type mice (Figure 10). Functional pathway analysis using PICRUSt2 provided insight into how dietary environment modulates microbiome function during infection. Infected WT mice on a HFD showed significant enrichment of amino acid degradation pathways, butanoate fermentation, fatty acid β-oxidation, and aromatic compound degradation that coincided with their enhanced resistance to T. muris infection. Conversely, infected WT mice on a ND, which were more susceptible to T. muris infection, exhibited higher abundance of pathways involved in carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism. These shifts suggest that HFD reprograms the microbiome's metabolic capacity in infected hosts, potentially impacting host energy balance and immune responses. Functional pathway predictions using PICRUSt2 revealed distinct microbial metabolic signatures driven by diet and infection. Infected WT mice on HFD exhibited enrichment of pathways related to amino acid degradation, butanoate fermentation, fatty acid β-oxidation, and aromatic compound degradation. Conversely, infected WT mice on a ND had greater abundance of pathways involved in carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism. These findings suggest that HFD exposure alters the metabolic capacity of the gut microbiome, potentially impacting host energy balance, immune responses, and helminth-host interactions. Discussion Our study demonstrates that host immune competence and diet interact to shape the outcomes of Trichuris muris infection and gut microbiota composition. Across mouse models of varying immune capability, we observed that adaptive immunity plays a critical role in parasite expulsion and both innate and adaptive immunity play key roles in the structuring of gut microbial communities, with the diet further modulating these effects. To evaluate how host immune competence and diet influence T. muris expulsion, we compared worm burdens and parasite-specific antibody responses across genotypes and dietary conditions. RAG-KO mice, which lack adaptive immunity, and RAGγc -KO mice which lack adaptive and innate lymphoid cells unsurprisingly harbored persistent full worm burdens from a low dose infection, consistent with the essential role of immunity in parasite control [8]. WT mice on a normal diet also failed to expel worms from a low dose infection as reflected by worm recovery at day 35 and establishing chronic infection as previously shown [8]. WT mice on a ND (WT_N) showed robust parasite-specific antibody responses, particularly IgG2a/c consistent with a Th1-skewed profile. In the context of T. muris , such IgG2a/c-dominated responses are characteristic of chronic infection [17, 18]. The expulsion of worms in WT mice on a HFD (WT_HFD) was accompanied by elevated IgG1 titres and reduced IgG2a/c levels, suggesting a diet-induced shift toward a Th2-skewed immune response. Our findings are consistent with recent work by Funjika et al. [16], who showed that provision of a high-fat diet enabled WT mice to expel worms, supporting the idea that dietary modulation can unlock protective immune responses and overcome the chronicity otherwise observed in WT animals under normal diet. Their study showed that dietary fat primes CD4⁺ T cells to upregulate the IL-33 receptor ST2, leading to amplified Th2 cytokine production upon IL-33 stimulation. Together, these data support the idea that dietary lipids can reshape immune responses toward a Th2-dominant profile, enhancing protection against T. muris infection. Our observations further suggested that the gut microbiota might contribute to or be modulated by the observed variation in parasite clearance across the dfferent diets and immunocompetence models. Given the established role of the microbiome in shaping immunity [19] and helminth susceptibility [15, 20, 21], as well as the effect of HFD on the gut microbiome [22], we hypothesized that differences in microbial composition could impact the divergent resistance phenotypes observed here. Our findings show that both host immune competence and dietary exposure are important in shaping the gut microbiota, exceeding the impact of T. muris infection on microbial community structure. When compared to RAGγc-KO mice on ND, WT mice on a ND showed consistent immunocompetent-associated microbiota characterized by high relative abundances of genera such as Bacteroides and Lactobacillus , while immune-deficient mice showed signatures of dysbiosis, including enrichment of Escherichia–Shigella and Candidatus Saccharimonas . HFD further disrupted microbial composition across all genotypes, increasing taxa such as Blautia and Alistipes . These observations align with previous work showing that host genotype and diet independently and interactively shape gut microbiota composition [23-25], with immune deficiency often predisposing to dysbiotic shifts [26, 27] and HFD promoting inflammation-associated taxa [22]. In support of our findings, recent work by Le and colleagues demonstrated that dietary cholesterol directly modulates the gut microbiome, selectively enriching microbial taxa such as Bacteroides and Parabacteroides in both mice and humans [22]. These cholesterol-interacting microbes also exhibited enriched bile acid–like and sulfotransferase-like gene functions, underpinning their metabolic adaptation to lipid-rich environments. Our observation of Bacteroides and Parabacteroides enrichment in HFD-fed mice therefore likely shows similar microbial sensing and metabolism of dietary lipids, which may contribute to a gut milieu that modulates host immunity and helminth susceptibility through microbiota–lipid–host interactions. The enhanced resistance to T. muris observed in HFD–fed mice may be partly driven by diet-induced shifts in gut microbial composition. Several taxa enriched in these mice including Faecalibacterium , Blautia , Lactococcus , Parabacteroides , and Bacteroides have established roles in regulating mucosal immunity [28, 29], promoting epithelial barrier integrity [30], and shaping anti-inflammatory responses [29, 31, 32]. For instance, Faecalibacterium prausnitzii and Blautia spp. are prominent butyrate producers, known to reinforce barrier function and promote regulatory T cell differentiation via SCFA-mediated pathways [28, 29]. Similarly, Lactococcus lactis has been linked to enhanced mucosal IgA production and modulation of CD4⁺ T cell responses [29, 30], while Parabacteroides spp. have demonstrated the capacity to reduce systemic inflammation and support gut homeostasis in metabolic contexts [32, 33]. These microbial changes may provide a mechanistic link between diet, immune system, and resistance to T. muris infection. By promoting a mucosal environment that favors type 2 immunity while maintaining epithelial integrity and suppressing low-grade inflammation, the HFD-associated microbiota may inadvertently enhance the host’s capacity to expel helminths. The enrichment of Alistipes and Bacteroides , typically associated with metabolic inflammation [31, 34], may also contribute to controlled immune priming without overt pathology. Thus, rather than uniformly impairing immunity, HFD may alter microbial communities in a way that enhances specific protective responses, thereby highlighting the nuanced role of the microbiome in diet–immune–parasite interactions. Notably, T. muris infection did not produce global shifts in microbiota structure, suggesting that T. muris infection effects may be taxon-specific as reported in ealier research [35, 36]. This limited community-wide restructuring could reflect the immunomodulatory strategies of chronic T. muris infection, which often stabilize host–microbe interactions rather than provoke widespread dysbiosis. Alternatively, infection-induced changes may be more pronounced at the functional gene or metabolite level features not captured by 16S-based taxonomy. Future metagenomic or metabolomic profiling could help resolve whether helminth–microbiome interactions unfold through targeted alterations in metabolic pathways or microbe–immune links. Immune-deficient RAG knockout (RAG) and RAG2/γc-deficient mice, as expected, did not expel their worms irrespective of dietary exposure, though HFD further influenced microbial shifts in these groups. As such, even in the absence of adaptive and innate immunity, the microbiome remained responsive to dietary changes, reinforcing the potent influence of diet in shaping microbial ecosystems independent of immune surveillance. Given the pronounced resistance to T. muris infection observed in HFD–fed wild-type mice, we examined how infection status influenced gut microbial composition within this group compared to their ND counterparts. In WT mice on a ND, several taxa were differentially abundant between infected and uninfected mice. Specifically, microbes such as Marvinbryantia , Roseburia , Bilophila , and Romboutsia that have been previously associated with mucosal immune responses [37-40], bile acid metabolism[41, 42], and inflammation [37], were more abundant in infected mice, suggesting that they either respond to or contribute to the immune responses during T. muris infection. Conversely, Bilophila , Anaerotruncus , and Desulfovibrio were enriched in uninfected mice, and have been implicated in maintaining mucosal barrier integrity and modulating host immunity [43-48], suggesting a potential protective or immunoregulatory role in the absence of infection. Their depletion in infected mice may reflect a shift toward a more inflammation-prone or permissive microbial state that facilitates colonization by T. muris infection. When we conducted the same analysis in WT mice fed a HFD, no differentially abundant taxa were detected between infected and uninfected groups. This lack of microbial changes may reflect a pre-existing HFD-driven microbial structure that resists further reshaping upon infection. This may underpin the enhanced resistance to infection observed in this group, potentially through pre-activation of the innate immunity or altered metabolite production. Comparing infected WT mice on a HFD versus those on a ND was critical to uncover how diet shapes microbial functional capacity during infection. The enrichment of amino acid degradation, butanoate fermentation, fatty acid β-oxidation, and aromatic compound degradation pathways in infected mice fed on HFD suggests a microbiome adapted for energy-dense substrate utilization and anti-inflammatory metabolite production [49-52]. Butanoate, for instance, is known to support epithelial barrier integrity and modulate immune responses, potentially reinforcing Th2 polarization [53-55]. These functional changes align with the enrichment of butyrate-producing taxa such as Faecalibacterium and Blautia , as well as Parabacteroides and Lactococcus , which have established roles in epithelial protection and immune modulation. In contrast, infected WT mice on a ND exhibited greater enrichment of carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism pathways, indicative of a microbiome oriented toward rapid bacterial growth and mucosal carbohydrate processing [56]. Such a profile may be less conducive to the immunoregulatory or protective effects observed under HFD, potentially contributing to the increased susceptibility observed in these animals. To advance our understanding of host resistance to helminth infections, future studies should prioritize mechanistic approaches of microbiota–immunity interactions. Identifying causal microbial taxa and their metabolites, and elucidating how these shape immune responses to intestinal parasites, will be critical to move beyond correlative associations. Gnotobiotic mouse models would offer a tractable system to validate the candidate microbes, and enable direct testing of their roles in immune modulation and parasite clearance. Complementary studies in helminth-endemic human populations are essential to determine whether similar diet–microbiota–immunity relationships are conserved in natural settings, and to identify population-specific microbial signatures or dietary patterns associated with susceptibility or resilience. Ultimately, these insights may inform the development of microbiome-targeted or dietary interventions, such as prebiotics, probiotics, or precision microbiota engineering to bolster host resistance against intestinal helminths. Conclusion Our findings reveal a complex interplay between host immunity, diet, and the gut microbiota in determining resistance to Trichuris muris infection. While adaptive immunity is essential for parasite expulsion, a HFD may enhance protective responses by shifting the microbiota toward taxa and functional pathways associated with epithelial integrity and immune modulation. The enrichment of butanoate fermentation and fatty acid β-oxidation pathways, alongside increases in butyrate-producing and immunoregulatory taxa, suggests that microbial metabolism may actively reinforce type 2 immunity and barrier function in the gut. Even in the absence of adaptive immunity, diet retained a strong influence over microbial community structure and function, underpinning the importance of diet in shaping the gut microbiome profiles. Declarations Ethics statement All mouse experiments complied with the United Kingdom Animals (Scientific Procedures) Act 1986 and were conducted under Home Office project licence P043A3082, following approval by the institutional Animal Welfare and Ethical Review Body. Acknowledgement We are grateful to Assoc. Prof. Gyaviira Nkurunungi and Mr Alfred Ssekagiri for their expert comments on the manuscript. The authors acknowledge the Uganda Virus Research Institute High-Performance Computing (UVRI-HPC) facility for providing the computational resources used in this study. Funding BW was partially supported by GCRF collaborative Grant (R120442) from the Royal Society awarded to Professors Richard Grencis and Alison Elliott; he is also partially funded by the National Institute for Health Research (NIHR) under its Global Health Research Group on Vaccines for Vulnerable People in Africa (VAnguard) (Grant Reference Number: NIHR134531), using UK aid from the UK Government to support global health research. This project has also been supported by Wellcome Trust Investigator Award Z10661/Z/18 /Z awarded to Professor Richard Grencis and the Wellcome Centre for Cell Matrix Research Grant 088785/Z/09/Z.The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Government. BW is based at the MRC/UVRI and LSHTM Uganda Research Unit which is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement. 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BioRender\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/4fef1579e55429852f6b1316.png"},{"id":92478956,"identity":"fe4681c0-24be-4e3d-a2fa-64c2997511ff","added_by":"auto","created_at":"2025-09-30 07:35:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eWorm burden across mouse genotypes and dietary groups following T. muris infection\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Boxplots illustrate parasite counts in infected wild-type, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eRAG-KO\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e , and RAGγc -KO mice fed either a ND or HFD. Mice on a ND displayed higher worm burdens, particularly \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eRAG-KO\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e , while those fed a HFD exhibited marked reductions in parasite load, with wild-type HFD mice showing complete parasite clearance. The Kruskal-Wallis test demonstrated significant differences in worm burden across groups (p = 5.8 × 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e⁻\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e⁵).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/872f2a21b4750687a2dc36a8.png"},{"id":92480786,"identity":"5c0e25e9-2810-4287-972e-02a6cb97989b","added_by":"auto","created_at":"2025-09-30 07:43:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":641577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eParasite-specific IgG1 and IgG2a/c antibody responses across immune genotypes and dietary exposures.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n(A) Optical density (OD) readings for parasite-specific IgG1 across serial serum dilutions in wild-type mice on ND (WT_ND), WT mice on HFD (WT_HFD), RAG_KO, RAGγc_KO, and naïve wild-type controls.\u003cbr\u003e\n(B) Corresponding OD readings for parasite-specific IgG2a/c across the same groups.\u003cbr\u003e\nWT_HFD mice exhibited the strongest IgG1 responses. WT_ND mice showed highest IgG2a/c antibody titres, while RAG2_KO and RAGγc _KO mice, which lack adaptive immunity, failed to mount detectable responses. Naïve mice served as uninfected controls.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/9dde4771fd8c329eef457aed.png"},{"id":92478968,"identity":"7cbefc9a-824e-4e06-adab-e86c7a177978","added_by":"auto","created_at":"2025-09-30 07:35:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6637064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGut microbiota composition across mouse genotypes.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\nStacked bar plots representing the relative abundance of bacterial genera across individual fecal samples from Trichuris muris–infected and uninfected mice. Taxa are shown at the genus level, with taxonomy annotated down to species where available. Each bar represents one sample, and colors correspond to distinct bacterial taxa, as indicated in the legend on the right. Mice are grouped by genotype and diet type e.g., wild-type (WT_N), RAG2-deficient (RAG_H), RAG2/γc-deficient (RAGγc_H), and individual mouse IDs are shown on the x-axis. Prominent genera include Lactobacillus\u003c/em\u003e, \u003cem\u003eBacteroidesand Blautia. Microbial diversity and composition vary across host genotypes, with observable shifts in dominant taxa between groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/d205450deaa398a328a1956d.png"},{"id":92481523,"identity":"01411f3d-1d14-40a8-a019-f960e55fa131","added_by":"auto","created_at":"2025-09-30 07:51:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":615806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrincipal coordinate analysis (PCoA) of gut microbiota profiles stratified by mouse genotype, diet, and infection status.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e PCoA based on Bray-Curtis dissimilarity shows distinct clustering of microbial communities driven by mouse genotype and dietary exposure. WT_N = wild-type mice on ND; WT_H = WT mice on HFD; RAG_N = RAG2-deficient mice on ND; RAG_H = RAG2-deficient on HFD; RAGγc_N = RAG2/γc-deficient mice on ND; RAGγc_H = RAG2/γc-deficient mice on HFD. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePCoA plot further stratified by infection status. Clustering remained largely defined by genotype and diet, with limited separation between infected and uninfected mice within each group. PERMANOVA output: Mouse genotype and diet explained a significant proportion of microbiota variance (R² = 0.38595, p = 0.001), highlighting the dominant role of these factors in shaping gut microbial composition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/682642b5c47bed9243956d5e.png"},{"id":92481524,"identity":"a9e8aa63-7d7e-41a5-81fe-7eac6f455b83","added_by":"auto","created_at":"2025-09-30 07:51:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":755569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrincipal coordinate analysis (PCoA) of gut microbiota profiles based on shotgun metagenomic sequencing.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Samples colored by mouse genotype (RAGγc_KO, WT, RAG_KO) demonstrate distinct clustering driven by immune competence, indicating that host immune architecture is a primary determinant of microbiota structure.WT samples form two sub-clusters (upper left and upper right). Panel B reveals that this split corresponds largely to diet, indicating that while immune status is a major determinant of microbiota structure, dietary differences within WT mice also create distinct community profiles. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eRAG mice\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (lacking adaptive immunity) cluster separately, consistent with a unique microbial community structure shaped by their immunodeficiency. RAG2/γc-deficient (further impaired immune function) also form their own distinct cluster, showing that the degree of immune impairment st.rongly influences gut microbial composition. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eSamples stratified by diet and infection status. Dietary exposure remains a key driver of microbial community clustering, with minimal separation by infection status. This suggests that T. muris infection exerts modest influence on global microbiota structure relative to diet and immune status. Mice fed on HFD consistently cluster away from those on ND, across all immune backgrounds. The \u003c/em\u003e\u003cem\u003e\u003cstrong\u003einfection status does not strongly separate samples\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e within the same diet-immunity groups, mirroring our earlier findings from 16S rRNA analysis. Infection may induce more subtle, taxon-specific changes rather than global community shifts. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eNaive mice (uninfected) on HFD\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e sometimes overlap with their infected counterparts.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/eb2e3a8eacf1bdbd50954fa6.png"},{"id":92478961,"identity":"1727712c-08b9-43cc-b252-c484b1551cb4","added_by":"auto","created_at":"2025-09-30 07:35:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1632148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Volcano plot showing differentially abundant taxa in WT mice comparing HFD to ND. Red points indicate taxa significantly enriched in HFD-fed WT mice (log₂ fold change \u0026gt; 1, adjusted p \u0026lt; 0.05), blue points represent taxa significantly enriched in ND-fed WT mice, and black points represent non-significant taxa. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Corresponding waterfall plot for WT mice highlighting the key taxa that were significantly enriched in each diet group. WT mice on HFD exhibited increased abundance of Lactococcus\u003c/em\u003e, \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e, \u003cem\u003eand\u003c/em\u003e \u003cem\u003eTuzzerella, whereas ND-fed WT mice had higher abundance of Akkermansia\u003c/em\u003e, \u003cem\u003eMuribaculum\u003c/em\u003e, \u003cem\u003eand Parasutterella.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Volcano plot showing differentially abundant taxa in RAG-deficient (RAG-KO) mice comparing HFD to ND groups. Similar to panel A, red and blue points indicate taxa significantly enriched in HFD and ND groups, respectively.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Corresponding waterfall plot for RAG-KO mice. HFD-fed RAG-KO mice exhibited enrichment of Alloprevotella, Lactococcus, and Acetatifactor, while ND-fed RAG mice had increased abundance of Lachnospiraceae NK4A136 group, Eubacterium xylanophilum group, and Anaerotruncus. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(E)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Volcano plot displaying differentially abundant taxa in RAG2/γc-deficient mice comparing HFD to ND groups. Significant taxa enriched in HFD-fed RAGγc-KO mice are shown in red, and those enriched in ND-fed RAGγc-KO mice are shown in blue. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(F)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Corresponding waterfall plot for RAGγc\u003c/em\u003e \u003cem\u003emice. HFD-fed RAGγc-KO mice showed enrichment of Romboutsia, Acetatifactor\u003c/em\u003e, \u003cem\u003eLactococcus, and Tuzzerella, while ND-fed RAGγc-KO mice were enriched in Lachnospiraceae UCG-001, Tyzzerella\u003c/em\u003e, \u003cem\u003eEscherichia-Shigella, and Gastranaerophilales. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(G)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Venn diagram summarizing the overlap of differentially abundant taxa across the three genotypes (WT, RAG-KO, and RAGγc-KO). A total of eight microbial taxa were commonly affected by HFD across all mouse types, whereas each genotype also exhibited a substantial number of unique taxa responsive to dietary intervention (21 in WT, 22 in RAG-KO, and 32 in RAGγc-KO).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/d1f24dfb0efbfd30564f17e5.png"},{"id":92478964,"identity":"f4e13df7-a602-4dbd-b8d8-88271b0f85f6","added_by":"auto","created_at":"2025-09-30 07:35:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1423762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImpact of immune genotype and diet on gut microbiota composition in\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eT. muris\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e-infected mice.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n(A) Principal coordinate analysis (PCoA) plots based on Bray–Curtis dissimilarity showing gut microbial community structures across wild-type (WT) and RAG2/γc-deficient (RAGγc-KO) mice under ND and HFD conditions. Distinct clustering patterns were observed, with greater separation between genotypes under HFD exposure. These three plots compare microbial community structures between: \u0026nbsp;RAGγc _N_INF vs. WT_N_INF (ND) vs. WT_H_INF (HFD). PCoA plots B and C show clear separation between RAG2/γc-deficient and wild-type mice in \u0026nbsp;ND and HFD groups respectively, suggesting distinct microbial community structures driven by immune genotype. Separation is more pronounced on the HFD, indicating that diet amplifies microbiome differences between these immune backgrounds.\u003cbr\u003e\n(D) Differentially abundant taxa between RAGγc_N_INF and WT_N_INF mice fed a ND. The waterfall plot shows taxa enriched in RAGγc_N_INF (red) and WT_N_INF (blue), with log₂fold-change on the x-axis. WT_N_INF microbiomes are enriched in beneficial genera like Bacteroides, Turicibacter, and Romboutsia. RAGγc_N_INF microbiomes are enriched in taxa such as Faecalibaculum, Candidatus Arthromitus, Escherichia-Shigella, and Gastranaerophilales, which may reflect dysbiosis and immune dysfunction. (E) Differentially abundant taxa between RAGγc_H_INF and WT_H_INF mice fed a HFD. Taxa enriched in RAGγc_H_INF are shown in red, and those enriched in WT_H_INF are shown in blue. RAGγc_H_INF mice are enriched in Acetatifactor, Rikenella, and Candidatus Saccharimonas, indicating a distinct microbial profile under HFD. WT_H_INF mice maintain beneficial microbes like Blautia, Turicibacter, Dubosiella, and Helicobacter. RAGγc_N_INF- infected RAG2/γc-deficient mice on ND. RAGγc_H_INF-\u003c/em\u003e \u003cem\u003eRAG2/γc-deficient mice on HFD, WT_N_INF-infected WT mice on ND, WT_H_INF -infected Wild type mice on HFD diet\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/754b02d9ff37f536b73b8513.png"},{"id":92482726,"identity":"5ef7d163-6681-4bf1-b623-5e161b6bdaa0","added_by":"auto","created_at":"2025-09-30 07:59:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":847813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImpact of diet and \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eT. muris\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e infection on gut microbiota composition in wild-type mice.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n(A) Principal coordinate analysis (PCoA) plot showing microbial clustering of uninfected WT mice on HFD (WT_H_UNINF) versus ND (WT_N_UNINF).\u003cbr\u003e\n(B) PCoA plot comparing infected WT mice on HFD (WT_H_INF) versus ND (WT_N_INF).\u003cbr\u003e\n(C) PCoA plot showing microbial separation between infected and uninfected WT mice on a ND.\u003cbr\u003e\n(D) PCoA plot of infected and uninfected WT mice on HFD.\u003cbr\u003e\n(E) Differential abundance analysis comparing WT_N_INF and WT_N_UNINF mice. Taxa enriched in infected mice are shown in red, while those enriched in uninfected mice are shown in blue, with log₂ fold-change on the x-axis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/a64380477b767fb7d1002fe7.png"},{"id":92480790,"identity":"45cee287-70f8-4e00-a897-fb05a4252dbd","added_by":"auto","created_at":"2025-09-30 07:43:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":479243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDifferentially abundant microbial metabolic pathways in infected wild-type mice fed a HFD versus ND.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\nFunctional prediction analysis using PICRUSt2 identified pathways significantly enriched in the microbiomes of infected wild-type mice on HFD (WT_H_INF, red) and those on ND (WT_N_INF, blue). The x-axis represents log₂ fold change in pathway abundance. WT_H_INF mice exhibited enrichment of pathways related to amino acid degradation, aromatic compound degradation, and fatty acid metabolism. In contrast, WT_N_INF mice showed enrichment of carbohydrate and nucleotide degradation and biosynthesis pathways.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/7b8e088734cae182789d08af.png"},{"id":92483104,"identity":"c971e3c8-a4cd-48ef-89ae-d17af92b1788","added_by":"auto","created_at":"2025-09-30 08:08:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17568860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/b43b3c77-90e5-41e9-8802-f0a287471dbc.pdf"},{"id":92480785,"identity":"6c40b79a-8b7b-40d6-a403-831c8b1d00d5","added_by":"auto","created_at":"2025-09-30 07:43:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1061504,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7713991/v1/fc565c505418bd707f2f88c6.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe influence of innate immunity, adaptive immunity and diet on intestinal microbiota following \u003cem\u003eTrichuris muris \u003c/em\u003einfection\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParasitic helminths such as \u003cem\u003eTrichuris trichiura\u003c/em\u003e remain a major public health concern in low- and middle-income countries (LMICs), where they affect over 500\u0026nbsp;million individuals and contribute substantially to the global burden of neglected tropical diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Chronic infection, particularly in paediatric populations, is associated with impaired linear growth, micronutrient deficiencies, anaemia, and persistent intestinal inflammation [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These parasitic diseases now increasingly intersect with emergent health challenges in LMICs, where rapid urbanisation and globalisation have driven widespread shifts in dietary patterns, most notably, increased consumption of ultra-processed, high-fat foods [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This transition has coincided with a growing burden of metabolic dysfunction, creating a syndemic landscape in which helminth infections and diet-induced inflammation coexist within the same host [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the immunological mechanisms of helminth clearance, especially the requirement for CD4⁺ Th2-polarised responses, are well characterised [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], far less is known about how dietary exposures and immune competence jointly modulate host susceptibility, anti-helminth immunity, and gut microbial ecology. Diets rich in saturated fats have been shown to impair intestinal barrier function and drive low-grade systemic inflammation, potentially attenuating effective immune responses to helminth infection and altering microbial composition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, genetic ablation of adaptive or innate immune pathways, including T, B, and innate lymphoid cells (ILCs), can lead to marked dysbiosis [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the extent to which these immunological deficits interact with dietary factors to shape helminth\u0026ndash;microbiota dynamics remains poorly defined.\u003c/p\u003e\u003cp\u003eTo begin to address this gap in knowledge, we used a tractable murine model of \u003cem\u003eTrichuris muris\u003c/em\u003e infection, a close immunological and ecological analogue of human \u003cem\u003eT. trichiura\u003c/em\u003e, to delineate the individual and combined contributions of host immunity and diet in shaping gut\u0026ndash;parasite\u0026ndash;microbiota interactions. We utilized three genetically distinct mouse models: wild-type (WT), \u003cem\u003eRAG2\u003c/em\u003e-deficient (\u003cem\u003eRAG2\u003c/em\u003e⁻/⁻; lacking T and B cells), and \u003cem\u003eRAG2/γc\u003c/em\u003e-deficient (\u003cem\u003eRAG2\u003c/em\u003e⁻/⁻\u003cem\u003eγc\u003c/em\u003e⁻/⁻; lacking T, B, and ILCs) mice, and exposed them to either a normal diet (ND) or high fat diet (HFD) prior to infection. Parasite burden, intestinal microbial composition, and antigen-specific IgG1 and IgG2a/c titres were assessed following infection.\u003c/p\u003e\u003cp\u003eOur findings show that both immune competence and diet are important determinants of helminth expulsion and gut microbial structure. Notably, mice deficient in adaptive or innate lymphoid immunity exhibited marked alterations in microbial community assembly, which were further modulated by dietary fat intake. These results highlight the interactive and often synergistic effects of diet and immune status on host\u0026ndash;microbe\u0026ndash;parasite interactions, with implications for understanding helminth-associated pathophysiology in the context of ongoing nutritional and epidemiological transitions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimal Experiments and Dietary Intervention\u003c/h2\u003e\u003cp\u003eThree genetically distinct mouse models were used in this study to investigate the effects of innate immunity, adaptive immunity and diet on gut microbiota and clearance of \u003cem\u003eTrichuris muris\u003c/em\u003e infection. These included WT C57BL/6j immunocompetent mice, C57BL/6j \u003cem\u003eRAG2\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e deficient (RAG2 KO) mice lacking T and B lymphocytes, and C57BL/6j \u003cem\u003eRAG2\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/γc\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e deficient (RAGγc) mice deficient in T cells, B cells, and innate lymphoid cells (ILCs). All mice were bred and housed under specific pathogen-free (SPF) conditions at University of Manchester, with \u003cem\u003ead libitum\u003c/em\u003e access to food and water. Experiments were performed under the regulations of the Home Office Scientific Procedures Act (1986), (Licence P043A082) and were subject to local ethical review by the University of Manchester Animal Welfare and Ethical Review Body (AWERB) and followed ARRIVE 2.0 guidelines. Male and female mice were used.\u003c/p\u003e\u003cp\u003eAt five weeks of age, mice were randomly assigned to receive either a standard chow diet (ND) or a high fat diet (HFD) (DIO Rodent Purified Diet containing 60% energy from fat, blue, irradiated; Research Diets, Inc.). Diets were maintained throughout the experiment. At week 12, all mice were orally infected with approximately 25 embryonated \u003cem\u003eT. muris\u003c/em\u003e eggs. Animals were monitored regularly and euthanized at week 17, corresponding to day 35 post-infection when the infection has reached patency. At necropsy, caecal content, faecal pellets, and serum were collected and immediately stored at -80\u003csup\u003eo\u003c/sup\u003e C for subsequent experiments and downstream analyses as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWorm Burden Quantification\u003c/h3\u003e\n\u003cp\u003eWorm burdens were quantified by carefully dissecting the caeca, opening them longitudinally in phosphate-buffered saline (PBS), and counting \u003cem\u003eT. muris\u003c/em\u003e worms under a dissecting microscope.\u003c/p\u003e\n\u003ch3\u003eParasite-specific Antibody ELISA\u003c/h3\u003e\n\u003cp\u003eSystemic humoral responses to \u003cem\u003eT. muris\u003c/em\u003e infection were assessed by quantifying parasite-specific IgG1 and IgG2a/c antibodies in mouse serum using enzyme-linked immunosorbent assay (ELISA). High-binding 96-well plates (NUNC MaxiSorp) were coated overnight at 4\u0026deg;C with \u003cem\u003eT. muris\u003c/em\u003e excretory/secretory (E/S) antigen at a concentration of 5 \u0026micro;g/mL in 0.05 M carbonate-bicarbonate buffer (pH 9.6). Plates were washed five times with phosphate-buffered saline containing 0.05% Tween-20 (PBS-T) and blocked with 3% bovine serum albumin (BSA) in PBS for 1 hour at room temperature.\u003c/p\u003e\u003cp\u003eSerum samples were initially diluted 1:20 in PBS-T, followed by serial two-fold dilutions. Fifty microliters of diluted serum were added per well and incubated for 1 hour at room temperature. After washing, 50 \u0026micro;L of biotinylated rat anti-mouse IgG1 (Bio-Rad MCA336B, 1:2000) or IgG2a/c (BD 553388, 1:1000) diluted in PBS-T were added to the appropriate wells and incubated for 1 hour. Plates were washed again and incubated with 75 \u0026micro;L of streptavidin\u0026ndash;horseradish peroxidase (HRP) (Roche, 1:1000 dilution) for 1 hour at room temperature.\u003c/p\u003e\u003cp\u003eColour development was achieved using ABTS substrate, freshly prepared by mixing 1 mL ABTS stock (0.5 g ABTS in 50 mL citrate buffer) with 9 mL citrate buffer and 1 \u0026micro;L of 30% hydrogen peroxide. One hundred microliters of substrate were added to each well, and absorbance was read at 405 nm (reference 490 nm) using a VersaMax microplate reader (Molecular Devices, UK). Antibody responses were plotted as optical density (OD) values across serial serum dilutions.\u003c/p\u003e\n\u003ch3\u003eMicrobiome Sample Preparation and Sequencing\u003c/h3\u003e\n\u003cp\u003eMicrobial DNA was extracted from faecal samples using the QIAamp DNA Stool Mini Kit (Qiagen) according to the manufacturer\u0026rsquo;s instructions. The V3\u0026ndash;V4 hypervariable region of the 16S rRNA gene was amplified using the Illumina 16S Metagenomic Sequencing Library Preparation protocol (Part # 15044223 Rev. B).\u003c/p\u003e\u003cp\u003eIn brief, the first PCR used universal primers targeting the V3\u0026ndash;V4 region (forward primer: 341F 5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;; reverse primer: 806R 5\u0026rsquo;-GACTACHVGGGTATCTAATCC-3\u0026rsquo;) with Illumina overhang adapters. PCR cycling conditions were as follows: initial denaturation at 95\u0026deg;C for 3 minutes, followed by 25 cycles of denaturation at 95\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 30 seconds, extension at 72\u0026deg;C for 30 seconds, and a final extension at 72\u0026deg;C for 5 minutes.\u003c/p\u003e\u003cp\u003ePCR amplicons were cleaned using AMPure XP magnetic beads (Beckman Coulter). Index PCR was performed using Nextera XT dual indices under the following conditions: initial denaturation at 95\u0026deg;C for 3 minutes, 8 cycles of denaturation at 95\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 30 seconds, extension at 72\u0026deg;C for 30 seconds, and a final extension at 72\u0026deg;C for 5 minutes. Libraries were purified again with AMPure XP beads, quantified using Qubit dsDNA HS Assay (Thermo Fisher Scientific), and normalized to a final concentration of 4 nM. Libraries were pooled, denatured, and sequenced on an Illumina MiSeq platform using a 2 \u0026times; 300 bp paired-end MiSeq Reagent Kit v3 (600-cycle) at the Bioinformatics Core Facility, University of Manchester.\u003c/p\u003e\u003cp\u003eCaecal contents were subjected to shotgun metagenomic sequencing. DNA was extracted using standardized protocols, and libraries were prepared and sequenced by Transnetyx (UK; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.transnetyx.com\u003c/span\u003e\u003cspan address=\"https://www.transnetyx.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Illumina short-read technology. Quality control, host DNA removal, and taxonomic profiling were performed using standard bioinformatic pipelines.\u003c/p\u003e\n\u003ch3\u003eMicrobiome Bioinformatics Processing\u003c/h3\u003e\n\u003cp\u003eRaw demultiplexed FASTQ files were processed using QIIME2 (version 2023.2). Primers were removed using Cutadapt (version 4.4) with default parameters. Reads were denoised, quality filtered, merged, and checked for chimeras using DADA2 (implemented within QIIME2) to generate amplicon sequence variants (ASVs) with high-resolution taxonomic specificity. ASVs were classified against the Greengenes 13.8 reference database using a Naive Bayes classifier.\u003c/p\u003e\u003cp\u003eTo standardize sampling depth, rarefaction was performed to 20,000 sequences per sample. Microbial alpha diversity was assessed using the Shannon diversity index and observed richness.Beta diversity was calculated using Bray\u0026ndash;Curtis dissimilarity matrices to assess differences in overall microbial community composition between groups. These differences were visualized using Principal Coordinate Analysis (PCoA) plots, which display the major axes of variation in microbial profiles across samples. Statistical significance of group-level differences in beta diversity was tested using permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. To identify microbial taxa differentially abundant across experimental groups, we performed differential abundance (DA) testing using the DESeq2 method implemented within the phyloseq and DESeq2 packages in R. A Wald test was used to estimate log₂ fold changes in microbial abundance between groups. Resulting p-values were corrected for multiple testing using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR) procedure. Taxa with an FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.To visualize differentially abundant taxa, we generated volcano plots (log₂ fold change vs. \u0026ndash;log₁₀ FDR-adjusted p-value) and waterfall plots. In the latter, we ranked significantly differentially abundant taxa by descending absolute log₂ fold change, using a cutoff of |log₂ fold change| \u0026ge; 1 to highlight the most strongly altered microbes.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R (version 4.1.3). Group comparisons for worm burden and alpha diversity indices were conducted using one-way ANOVA or Kruskal-Wallis tests, depending on data distribution. Two-way ANOVA was used to compare parasite-specific antibody responses across serum dilution series. For all tests, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe analyzed fecal samples from a total of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e49 mice\u003c/strong\u003e, stratified by immune genotype (WT, RAG2-deficient [RAG-KO], and \u003cem\u003eRAG2\u003csup\u003e-/-\u003c/sup\u003e/\u0026gamma;c\u003csup\u003e-/-\u003c/sup\u003e\u003c/em\u003e deficient [RAG\u0026gamma;c]), dietary exposure (normal chow [ND]vs. high-fat diet [HFD]), and \u003cem\u003eT. muris\u003c/em\u003e infection status (infected vs. na\u0026iuml;ve), as shown in table 1. Among infected groups, mice included \u003cstrong\u003eWT_N (n = 7), WT_H (n = 4), RAG_N (n = 7), RAG_H (n = 7),\u0026nbsp;\u003c/strong\u003e RAG\u0026gamma;c\u003cstrong\u003e\u0026nbsp;_N (n = 7),\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRAG\u0026gamma;c\u003cstrong\u003e\u0026nbsp;_H (n = 8)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e In addition, \u003cstrong\u003euninfected (naive) control groups\u0026nbsp;\u003c/strong\u003ecomprised \u003cstrong\u003eWT_N (n = 5)\u003c/strong\u003e and \u003cstrong\u003eRAG_N (n = 4)\u003c/strong\u003e mice maintained on a ND.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Mouse group characteristics by genetic background, diet, and infection status.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImmune Phenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGroup (DIET_mousetype)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of \u0026nbsp;Mice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eT. muris\u003c/em\u003e Infection Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWT_N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWT_H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG-KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAG_N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG-KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAG_H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG\u0026gamma;c-KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG\u0026gamma;c_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG\u0026gamma;c-KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG\u0026gamma;c_H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWT_N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNaive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAG-KO\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAG_N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNaive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWT\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026ndash;intact immunity, \u003cstrong\u003eRAG-KO\u003c/strong\u003e \u0026ndash; RAG knockout mice (no adaptive immunity), RAG\u0026gamma;c\u003cstrong\u003e-KO\u003c/strong\u003e \u0026ndash; RAG2/\u0026gamma;c-deficient mice (deficient in innate and adaptive immunity). HFD- High Fat Diet. ND \u0026ndash; Normal diet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHost Immune Competence and Diet Influence \u003cem\u003eTrichuris muris\u003c/em\u003e Worm Burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate how host immune competence and dietary environment influence parasite clearance, we assessed the impact of host immune genotype and diet on \u003cem\u003eT. muris\u003c/em\u003e worm burden across different mouse models (Figure 2). Under a normal diet (ND), all three genotypes \u0026ndash; wild-type (WT_N), RAG-KO (RAG_N), and RAG\u0026gamma;c-KO (RAG\u0026gamma;c\u003cstrong\u003e_N\u003c/strong\u003e) \u0026ndash; harboured \u0026nbsp;low worm burdens, with no significant differences between groups despite slight variation in mean worm counts (WT_N: 14.6 worms, 95% CI 11.2\u0026ndash;18.0; RAG_N: 21.0 worms, 95% CI 19.6\u0026ndash;22.4; RAG\u0026gamma;c\u003cstrong\u003e_N\u003c/strong\u003e: 14.7 worms, 95% CI 11.6\u0026ndash;17.8). This indicates that under normal dietary conditions, and low dose infection, host immune status alone does not strongly influence the establishment of chronic infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, worm burdens were markedly reduced under HFD. WT mice on HFD (WT_H) showed complete parasite clearance across all individuals while RAG-KO and\u0026nbsp;RAG\u0026gamma;c\u003cstrong\u003e_N\u003c/strong\u003e KO mice exhibited substantial but incomplete clearance of worms (RAG_H: 2.1 worms, 95% CI 0.9\u0026ndash;3.4; RAG\u0026gamma;c_H: 4.5 worms, 95% CI 1.0\u0026ndash;8.0) A Kruskal-Wallis test confirmed significant differences in worm burdens across groups (p = 5.8 \u0026times; 10⁻⁵). These results demonstrate that the interaction of host immune competence and diet is essential for parasite clearance, with HFD exposure strongly reducing worm burden across genotypes and immunity, but complete parasite clearance occurring only in immune-competent mice.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eParasite-specific IgG1 and IgG2a/c responses show diet-dependent immune polarization\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eGiven the differences in worm burden, we next assessed parasite-specific IgG1 and IgG2a/c responses as proxy serological markers of Type 2 and Type 1 immunity respectively (Figure 3). This allowed us to determine whether impaired parasite expulsion associatedwith altered antibody production across genotypes and dietary exposures. As expected, WT mice on a normal diet (WT_ND) with low-dose chronic infection developed a mixed response, characterized by detectable IgG1 and a predominant IgG2a/c signal, consistent with Type 1-skewed immunity in the context of persistent infection. In contrast, WT mice on a high-fat diet (WT_H) that expelled their parasites mounted a strong IgG1 response with only weak IgG2a/c production, reflecting a shift toward Type 2 immunity associated with effective clearance.\u003c/p\u003e\n\u003cp\u003eAs expected, \u003cstrong\u003eRAG-KO\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eand RAG2/\u0026gamma;c-deficient mice failed to generate detectable parasite-specific antibodies, confirming their lack of adaptive immunity. Naive mice showed no measurable parasite specific antibody responses. These results demonstrate that dietary environment modulates the balance of Type 1 versus Type 2 immunity in WT mice, in line with the patterns reported by Fujinka et al [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelative abundance of the gut microbiota across mouse genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures 2 and 3 establish key host phenotypes in response to \u003cem\u003eTrichuris muris\u003c/em\u003e infection, namely parasite burden and systemic humoral immunity across mouse genotypes and dietary exposures. While these outcomes highlight immunological differences in worm clearance and antibody production, they do not reveal how such host-intrinsic and diet influence the gut microbiota itself, which may be both a modulator and a target of helminth-driven immunomodulation. To address this, we next present the overall taxonomic composition of the gut microbiota across individual mice (Figure 4). This stacked taxonomy bar plot provides a detailed summary of the microbial community structure at the genus level, enabling visual comparison of dominant and subdominant taxa across genotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWT mice fed on a ND, relative to those on HFD, displayed higher relative abundances of taxa such as\u003cem\u003e\u0026nbsp;Muribaculaceae, Dubosiella and Lactobacillus\u003c/em\u003e. Immune-deficient genotypes (RAG\u0026gamma;c-KO and RAG-KO) on ND compared to their counterparts on HFD, exhibited increased representation of genera such as \u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e, suggestive of dysbiosis. Dietary exposure further modulated microbial profiles: HFD was generally associated with expansion of \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, and \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eBacteriodes\u0026nbsp;\u003c/em\u003eacross genotypes. Notably, several taxa such as \u003cem\u003eFaecalibaculum\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, and \u003cem\u003eRomboutsia\u003c/em\u003e varied in abundance in a genotype-specific manner, highlighting the interactive effects of host immunity and diet in shaping gut microbial communities.\u0026nbsp;\u003cstrong\u003eGut microbiota composition stratifed by diet alone is shown in supplementary figure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut microbiota composition is shaped by diet and immune competence more than infection status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaving established distinct taxonomic signatures across mouse genotypes and diets (Figure. 4), we next sought to understand how these compositional differences relate to broader microbial community structure. To this end, we profiled overall microbiota composition using 16S rRNA gene sequencing and visualized community-level differences using principal coordinate analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarity. This approach enabled us to assess how gut microbial diversity clusters across immune genotypes, diets, and infection states.\u003c/p\u003e\n\u003cp\u003eWT mice on a ND (WT_N) formed a tightly clustered group, indicating a stable and consistent microbial community under immunocompetent conditions and conventional feeding. In contrast, WT mice on a HFD (WT_H) clustered separately, demonstrating that diet alone induces substantial shifts in microbiota composition. Similar separation was observed between RAG knockout (RAG_N vs. RAG_H) and RAG2/\u0026gamma;c-deficient (RAG\u0026gamma;c_N vs.\u0026nbsp;RAG\u0026gamma;c_H) groups, further underscoring the impact of dietary exposure across different immune backgrounds (Figure 5A).\u003c/p\u003e\n\u003cp\u003eWhen stratified by infection status (Figure 5B), microbial profiles remained predominantly clustered by genotype and diet, with minimal separation between infected and uninfected mice within each dietary group. This suggests that while \u003cem\u003eT. muris\u003c/em\u003e infection may influence the microbiome at a more specific taxonomic or functional level, its impact on overall microbial community structure is modest compared to the strong effects of immune status and dietary composition.\u003c/p\u003e\n\u003cp\u003ePERMANOVA analysis confirmed that the combination of diet and immune competence (mouse_Diet) significantly explained variation in gut microbiota profiles (R\u0026sup2; = 38.6%, p = 0.001). These results collectively indicate that immune competence and dietary exposure are the dominant factors shaping gut microbial communities in this model, while \u003cem\u003eT. muris\u003c/em\u003e infection exerts subtler or more localized effects on the microbiome.\u003c/p\u003e\n\u003cp\u003eGut microbiota profiles, assessed by principal coordinate analysis (PCoA) using Bray-Curtis dissimilarity, revealed that microbial community structure clustered distinctly by mouse genotype and dietary exposure. WT, RAG2/\u0026gamma;c-deficient, and RAG-deficient mice each formed separate clusters, with diet further stratifying microbiota profiles within each genotype. PERMANOVA analysis confirmed significant associations between microbiota composition, immune genotype, and diet (R\u0026sup2; = 0.39, p = 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of 16S data using shotgun metagenomics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo extend these findings and achieve greater taxonomic resolution, we performed shotgun metagenomic sequencing on caecal samples. This enabled deeper characterization of microbial shifts observed in 16S analysis and confirmed the dominant roles of host genotype and diet in shaping the microbiome. In the principal coordinate analysis (PCoA) plot presented in Figure 6A, where samples are stratified by mouse\u0026nbsp;genotype RAG2/\u0026gamma;c-deficient , Wild type and RAG2-deficient, gut microbiota profiles clustered distinctly according to immune competence. Wild-type (WT) mice formed a relatively tight cluster, indicating a stable and consistent gut microbiota structure under intact immune conditions. In contrast, RAG2-deficient, which lack adaptive immunity, clustered separately, reflecting a unique microbial community shaped by their immunodeficiency. RAG2/\u0026gamma;c-deficient mice, with further impaired immune function, also formed their own distinct cluster, demonstrating that increasing degrees of immune impairment strongly influence gut microbiota composition. These observations confirm that host immune architecture is a major determinant of microbiome structure and a contribution of both adaptive and innate lymphoid cells, even when using high-resolution shotgun metagenomic profiling.\u003c/p\u003e\n\u003cp\u003eIn Figure 6B, where samples are stratified by both dietary exposure and infection status, diet emerged as the dominant factor shaping microbial community structure. Mice fed HFD consistently clustered away from those on ND across all immune genotypes, indicating that dietary composition has a strong and consistent impact on the gut microbiota. Infection status did not significantly separate samples within the same diet-genotype groups, supporting earlier findings from 16S rRNA analysis that \u003cem\u003eT. muris\u003c/em\u003e infection induces more subtle, taxon-specific microbiome changes rather than large-scale community shifts. Interestingly, na\u0026iuml;ve (uninfected) mice on HFD often overlapped with their infected counterparts, further suggesting that diet exerts a stronger influence on microbiome structure than infection in this experimental context.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDifferential impact of high-fat diet on gut microbiota across immune genotypes\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe then sought to disentangle the independent effect of diet by comparing microbiota composition between high-fat and ND conditions within each genotype, in the absence of infection. This clarified how diet alone alters microbial ecology across different immune backgrounds. We performed differential abundance analysis to compare the gut microbiota profiles of mice fed on a HFD versus a ND within each immune genotype. This stratified approach allowed us to isolate the specific influence of diet within distinct immune backgrounds.\u003c/p\u003e\n\u003ch4\u003eWild-Type Mice (WT)\u003c/h4\u003e\n\u003cp\u003eIn WT mice, the volcano plot (Figure 7A) revealed a substantial number of significantly differentially abundant taxa between the HFD (10) and ND (11) groups. Key taxa such as \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eMuribaculum\u003c/em\u003e, and \u003cem\u003eParasutterella\u0026nbsp;\u003c/em\u003ewere enriched in the ND group, while \u003cem\u003eLactococcus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e were significantly enriched in the HFD group (Figure 7B), indicating that HFD reshaped the gut microbial landscape in immunocompetent hosts.\u003c/p\u003e\n\u003ch4\u003eRAG-Deficient Mice\u0026nbsp;(RAG)\u003c/h4\u003e\n\u003cp\u003eIn severely immune-compromised RAG-deficient mice dietary impact was also evident as seen in Figure 7C where HFD-fed mice had 14 enriched taxa compared to 8 taxa that were more enriched in the ND-fed mice. HFD-fed\u0026nbsp;\u003cstrong\u003eRAG-KO\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e mice exhibited enrichment of taxa such as \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eEubacterium coprostanoligenes\u003c/em\u003e group, while \u003cem\u003eLachnospiraceae NK4A136\u003c/em\u003e and \u003cem\u003eEubacterium xylanophilum\u003c/em\u003e were more abundant in the ND group (Figure 7D). The pronounced microbial shifts in this group suggest that diet can still modulate microbial communities even when key immune components are absent.\u003c/p\u003e\n\u003ch4\u003eRAG2/\u0026gamma;c-deficient\u0026nbsp;\u0026nbsp;Mice\u0026nbsp;(RAG\u0026gamma;c-KO)\u003c/h4\u003e\n\u003cp\u003eThe volcano plot Figure 7E displays the number and statistical significance of differentially abundant bacterial genera between RAG2/\u0026gamma;c-deficient\u003cem\u003e\u0026nbsp;\u003c/em\u003emice fed a HFD (21) versus a ND (11). Genera with significant differences in abundance are highlighted based on adjusted p-value and fold change. The accompanying waterfall plot Figure 7F illustrates the direction and magnitude of change for selected genera, with enrichment of \u003cstrong\u003e\u003cem\u003eRomboutsia\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eAcetatifactor\u003c/em\u003e\u003c/strong\u003e, \u003cem\u003eBacteriodes\u003c/em\u003e, and \u003cstrong\u003e\u003cem\u003eLactococcus\u003c/em\u003e\u003c/strong\u003e in HFD-fed mice, and higher levels of \u003cstrong\u003e\u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eClostridia vadinBB60\u003c/em\u003e\u003c/strong\u003e, and\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eParasutterella\u003c/em\u003e\u003c/strong\u003e in mice on a ND.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eShared and Unique Microbial Signatures Across Genotypes\u003c/h3\u003e\n\u003cp\u003eThe Venn diagram (Figure 7G) summarizing differentially abundant taxa across genotypes revealed that only eight microbial taxa were consistently affected by diet across all mouse types. In contrast, a substantial number of taxa were unique to each genotype, with 21 taxa specific to WT mice, 32 unique to RAG2/\u0026gamma;c-deficient mice, and 22 unique to RAG-KO mice. These findings indicate that while some dietary effects on the microbiome are conserved across immune backgrounds, the majority of microbial responses to diet are strongly genotype-specific. In WT mice, HFD feeding was associated with an enrichment of \u003cem\u003eLactococcus\u003c/em\u003e and \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e, while \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eMuribaculum\u003c/em\u003e, and \u003cem\u003eParasutterella\u003c/em\u003e were more abundant in those on a ND. In RAG2/\u0026gamma;c-deficient mice, \u003cem\u003eRomboutsia\u003c/em\u003e, \u003cem\u003eAcetatifactor\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e were enriched under HFD conditions, whereas \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eClostridia vadinBB60\u003c/em\u003e dominated in the ND group. Similarly, in \u003cstrong\u003eRAG-KO\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003emice, \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, and \u003cem\u003eEubacterium coprostanoligenes group\u003c/em\u003e were enriched under HFD, while \u003cem\u003eLachnospiraceae NK4A136\u003c/em\u003e and \u003cem\u003eEubacterium xylanophilum\u003c/em\u003e were more abundant in mice fed a ND. These results underscore the complex and immune-genotype-specific nature of dietary impacts on gut microbiome composition.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eGut microbiota profiles cluster by host immune background and diet\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eHaving defined the baseline effects of diet, we next examined how infection interacts with diet and immune status to modulate microbial community structure. This analysis enabled us to determine whether infection amplifies or overrides diet-induced differences.We compared microbiota profiles between WT and RAG2/\u0026gamma;c-deficient (RAG\u0026gamma;c-KO) mice following \u003cem\u003eT. muris\u003c/em\u003e infection under both ND and HFD conditions.\u003c/p\u003e\n\u003cp\u003ePrincipal coordinate analysis (PCoA) of gut microbiota composition revealed distinct clustering by both genotype and diet among \u003cem\u003eT. muris\u003c/em\u003e\u0026ndash;infected mice (Figure 8A). RAG2/\u0026gamma;c-deficient and wild-type mice formed clearly separated clusters, and within each genotype, HFD and ND induced marked shifts in microbial community structure. These findings demonstrate that both immune competence and dietary exposure independently contribute to shaping the gut microbiota, even in the context of helminth infection.\u003c/p\u003e\n\u003cp\u003eTo unravel the specific contribution of host immune status from dietary influence, we next focused on \u003cem\u003eT. muris\u003c/em\u003e\u0026ndash;infected mice maintained on a ND (Figure 8B). Comparing RAG\u0026gamma;c-deficient and wild-type mice under this controlled dietary condition revealed strong genotype-dependent clustering. This indicates that the absence of adaptive immune cells together with innate lymphoid cells is sufficient to drive significant alterations in microbial community structure. This focused comparison isolates the immunological effect observed in Figure 8A and establishes immune competence as a primary determinant of microbiota composition.\u003c/p\u003e\n\u003cp\u003eFinally, to test whether this immune-driven separation persists under HFD diet, we compared RAG\u0026gamma;c-deficient and wild-type mice fed a HFD (Figure 8C). Again, PCoA revealed robust genotype-dependent separation, mirroring the pattern observed under ND. This confirms that loss of the common \u0026gamma; chain\u0026mdash;and the consequent absence of adaptive and innate lymphoid cells\u0026mdash;consistently reshapes the gut microbiota across dietary contexts. Taken together, these three analyses establish that while both diet and immune status shape the gut microbiome, immune competence exerts a dominant and reproducible influence, regardless of dietary environment.\u003c/p\u003e\n\u003cp\u003eTo understand the taxonomic drivers of these observed compositional shifts, we performed differential abundance analysis within each dietary context. Under ND, WT mice were enriched for taxa typically associated with gut health, including \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, and \u003cem\u003eRomboutsia\u003c/em\u003e (Figure 8D). In contrast, RAG\u0026gamma;c-deficient mice exhibited higher abundances of potentially dysbiotic genera such as \u003cem\u003eFaecalibaculum\u003c/em\u003e, \u003cem\u003eCandidatus Arthromitus\u003c/em\u003e, \u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e, and unclassified members of the \u003cem\u003eGastranaerophilales\u003c/em\u003e order,suggesting that the absence of adaptive and innate lymphoid compartments disrupts microbial homeostasis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder HFD conditions, genotype-specific microbial signatures remained evident (Figure 8E). RAG\u0026gamma;c-deficient mice were enriched in taxa such as \u003cem\u003eAcetatifactor\u003c/em\u003e, \u003cem\u003eRikenella\u003c/em\u003e, and \u003cem\u003eCandidatus Saccharimonas\u003c/em\u003e, whereas WT mice maintained a microbial community dominated by \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003eDubosiella\u003c/em\u003e, and \u003cem\u003eHelicobacter\u003c/em\u003e. These shifts indicate that HFD exacerbates genotype-driven microbial divergence, potentially amplifying maladaptive microbiota configurations in immunodeficient hosts.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDiet and infection independently and jointly shape microbial diversity in wild-type mice\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo dissect the effects of infection and diet on microbial diversity, we performed principal coordinate analysis (PCoA) of gut microbiota composition across WT mice under varying dietary and infection conditions. We included four distinct PCoA comparisons, each capturing a specific intersection of diet (ND vs. HFD) and infection status (\u003cem\u003eT. muris\u003c/em\u003e\u0026ndash;infected vs. uninfected), allowing us to systematically assess the contribution of each factor to microbial community structure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder uninfected conditions, comparison of WT mice on ND versus HFD revealed clear separation in microbiota profiles\u0026nbsp;(Figure 9A). Axis 1 and axis 2 accounted for 28.0% and 16.9% of the variance, respectively, indicating that dietary composition alone is sufficient to drive substantial alterations in the gut microbiota, even in the absence of infection.\u003c/p\u003e\n\u003cp\u003eIn the presence of infection, microbial divergence between dietary groups became more pronounced. Infected WT mice fed a HFD clustered distinctly from those on a ND\u0026nbsp;(Figure 9B), with axis 1 and axis 2 explaining 37.6% and 22.6% of the variance, respectively. These results suggest that infection interacts with dietary background to induce greater microbiome restructuring.\u003c/p\u003e\n\u003cp\u003eWe next examined the effect of infection within each dietary context. Among mice maintained on a ND, infected and uninfected WT mice showed moderate separation in community composition (Figure 9C; axis 1: 24.8%, axis 2: 16.5%), indicating that infection alters the microbiota, but within a relatively stable nutritional environment. In contrast, the HFD amplified these infection-associated shifts. WT mice on HFD showed a trend toward separation between infected and uninfected\u0026nbsp;groups (Figure 9D), with axis 1 and axis 2 capturing 36.5% and 20.9% of the variance, respectively,consistent with a combined effect of diet and infection, although the clustering was less distinct than under ND conditions..\u003c/p\u003e\n\u003cp\u003eTo identify specific microbial taxa altered by \u003cem\u003eT. muris\u003c/em\u003e infection, we performed differential abundance analysis comparing infected (WT_N_INF) and uninfected (WT_N_UNINF) wild-type mice maintained on a ND (Figure 9E). The analysis revealed significant compositional shifts, with infection associated with both enrichment and depletion of distinct bacterial genera.\u003c/p\u003e\n\u003cp\u003eInfected mice exhibited increased relative abundances of genera such as \u003cem\u003eMarvinbryantia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, suggesting a restructuring of the gut ecosystem in response to helminth exposure. Conversely, uninfected mice harbored higher levels of taxa including \u003cem\u003eGastranaerophilales, Bilophila, Anaerotruncus, Ruminococcus, Desulfovibrio, Intestinimonas,\u003c/em\u003e and \u003cem\u003eAlloprevotella.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings indicate that even in the absence of dietary perturbation, helminth infection drives targeted microbial remodeling, potentially through immune-mediated or niche-altering mechanisms. When interpreted alongside diversity and clustering analyses (Figure 9 A\u0026ndash;D), these taxon-level changes provide insight into how infection selectively modulates gut microbial communities beyond the overall shifts in composition.\u003c/p\u003e\n\u003cp\u003eIn contrast to the infection-driven microbial shifts observed under normal dietary conditions, differential abundance analysis revealed \u003cstrong\u003eno significantly altered genera\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ebetween infected and uninfected WT mice fed a HFD.\u0026nbsp;\u0026nbsp;Our interpretation is that the high-fat diet produces such a dominant shift in overall community structure that any infection-related changes are comparatively subtle and fall below detection in differential abundance testing. We observed consistent reductions in microbial alpha diversity associated with HFD, immune deficiency,\u0026nbsp;and \u003cem\u003eT. muris\u003c/em\u003e infection. These effects were evident across gut compartments and sequencing platforms, including 16S rRNA and shotgun metagenomics (Supplementary Figures. 2\u0026ndash;4). Genotype-, diet-, and infection-specific comparisons further revealed that immune-compromised mice, particularly under dietary and infectious stress, exhibited the greatest losses in alpha microbial diversity.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDiet and infection influence microbial functional potential\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFinally, to explore the functional implications of the observed compositional changes, we predicted microbial metabolic pathways in infected wild-type mice (Figure 10). Functional pathway analysis using PICRUSt2 provided insight into how dietary environment modulates microbiome function during infection. Infected WT mice on a HFD showed significant enrichment of amino acid degradation pathways, butanoate fermentation, fatty acid \u0026beta;-oxidation, and aromatic compound degradation that coincided with their enhanced resistance to \u003cem\u003eT. muris\u003c/em\u003e infection. Conversely, infected WT mice on a ND, which were more susceptible to \u003cem\u003eT. muris\u003c/em\u003e infection, exhibited higher abundance of pathways involved in carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism. These shifts suggest that HFD reprograms the microbiome\u0026apos;s metabolic capacity in infected hosts, potentially impacting host energy balance and immune responses. Functional pathway predictions using PICRUSt2 revealed distinct microbial metabolic signatures driven by diet and infection. Infected WT mice on HFD exhibited enrichment of pathways related to amino acid degradation, butanoate fermentation, fatty acid \u0026beta;-oxidation, and aromatic compound degradation. Conversely, infected WT mice on a ND had greater abundance of pathways involved in carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that HFD exposure alters the metabolic capacity of the gut microbiome, potentially impacting host energy balance, immune responses, and helminth-host interactions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study demonstrates that host immune competence and diet interact to shape the outcomes of \u003cem\u003eTrichuris muris\u003c/em\u003e infection and gut microbiota composition. Across mouse models of varying immune capability, we observed that adaptive immunity plays a critical role in parasite expulsion and both innate and adaptive immunity play key roles in the structuring of gut microbial communities, with the diet further modulating these effects.\u003c/p\u003e\n\u003cp\u003eTo evaluate how host immune competence and diet influence \u003cem\u003eT. muris\u003c/em\u003e expulsion, we compared worm burdens and parasite-specific antibody responses across genotypes and dietary conditions. RAG-KO mice, which lack adaptive immunity, and\u0026nbsp;RAG\u0026gamma;c -KO mice which lack adaptive and innate lymphoid cells\u0026nbsp;unsurprisingly harbored persistent full worm burdens from a low dose infection, consistent with the essential role of immunity in parasite control [8]. WT mice on a normal diet also failed to expel worms from a low dose infection as reflected by worm recovery at day 35 and establishing chronic infection as previously shown [8]. \u0026nbsp; WT mice on a ND (WT_N) showed robust parasite-specific antibody responses, particularly IgG2a/c consistent with a Th1-skewed profile. In the context of \u003cem\u003eT. muris\u003c/em\u003e, such IgG2a/c-dominated responses are characteristic of chronic infection\u0026nbsp;[17, 18]. The expulsion of worms in WT mice on a HFD (WT_HFD) was accompanied by elevated IgG1 titres and reduced IgG2a/c levels, suggesting a diet-induced shift toward a Th2-skewed immune response. Our findings are consistent with recent work by Funjika et al.\u0026nbsp;[16], who showed that provision of a high-fat diet enabled WT mice to expel worms, supporting the idea that dietary modulation can unlock protective immune responses and overcome the chronicity otherwise observed in WT animals under normal diet. \u0026nbsp;Their study showed that dietary fat primes CD4⁺\u0026nbsp;T cells to upregulate the IL-33 receptor ST2, leading to amplified Th2 cytokine production upon IL-33 stimulation. Together, these data support the idea that dietary lipids can reshape immune responses toward a Th2-dominant profile, enhancing protection against \u003cem\u003eT. muris\u003c/em\u003e infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur observations further suggested that the gut microbiota might contribute to or be modulated by the observed variation in parasite clearance across the dfferent diets and immunocompetence models. Given the established role of the microbiome in shaping immunity [19] and helminth susceptibility [15, 20, 21], as well as the effect of HFD on the gut microbiome [22], we hypothesized that differences in microbial composition could \u0026nbsp;impact the divergent \u0026nbsp;resistance phenotypes observed here.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings show that both host immune competence and dietary exposure are important in shaping the gut microbiota, exceeding the impact of \u003cem\u003eT. muris\u003c/em\u003e infection on microbial community structure. When compared to\u0026nbsp;RAG\u0026gamma;c-KO mice on ND, WT mice on\u0026nbsp;a ND showed consistent immunocompetent-associated microbiota characterized by high relative abundances of genera such as \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, while immune-deficient mice showed signatures of dysbiosis, including enrichment of \u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e and \u003cem\u003eCandidatus Saccharimonas\u003c/em\u003e. \u0026nbsp;HFD further disrupted microbial composition across all genotypes, increasing taxa such as \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eAlistipes\u003c/em\u003e. These observations align with previous work showing that host genotype and diet independently and interactively shape gut microbiota composition [23-25], with immune deficiency often predisposing to dysbiotic shifts [26, 27] and HFD promoting inflammation-associated taxa [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn support of our findings, recent work by Le and colleagues demonstrated that dietary cholesterol directly modulates the gut microbiome, selectively enriching microbial taxa such as \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e in both mice and humans [22]. These cholesterol-interacting microbes also exhibited enriched bile acid\u0026ndash;like and sulfotransferase-like gene functions, underpinning their metabolic adaptation to lipid-rich environments. Our observation of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e enrichment in HFD-fed mice therefore likely shows similar microbial sensing and metabolism of dietary lipids, which may contribute to a gut milieu that modulates host immunity and helminth susceptibility through microbiota\u0026ndash;lipid\u0026ndash;host interactions.\u003c/p\u003e\n\u003cp\u003eThe enhanced resistance to \u003cem\u003eT. muris\u003c/em\u003e observed in HFD\u0026ndash;fed mice may be partly driven by diet-induced shifts in gut microbial composition. Several taxa enriched in these mice including \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e have established roles in regulating mucosal immunity [28, 29], promoting epithelial barrier integrity [30], and shaping anti-inflammatory responses [29, 31, 32]. For instance, \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e spp. are prominent butyrate producers, known to reinforce barrier function and promote regulatory T cell differentiation via SCFA-mediated pathways\u0026nbsp;[28, 29]. Similarly, \u003cem\u003eLactococcus lactis\u003c/em\u003e has been linked to enhanced mucosal IgA production and modulation of CD4⁺\u0026nbsp;T cell responses\u0026nbsp;[29, 30], while \u003cem\u003eParabacteroides\u003c/em\u003e spp. have demonstrated the capacity to reduce systemic inflammation and support gut homeostasis in metabolic contexts\u0026nbsp;[32, 33].\u003c/p\u003e\n\u003cp\u003eThese microbial changes may provide a mechanistic link between diet, immune system, and resistance to \u003cem\u003eT. muris\u003c/em\u003e infection. By promoting a mucosal environment that favors type 2 immunity while maintaining epithelial integrity and suppressing low-grade inflammation, the HFD-associated microbiota may inadvertently enhance the host\u0026rsquo;s capacity to expel helminths. The enrichment of \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e, typically associated with metabolic inflammation [31, 34], may also contribute to controlled immune priming without overt pathology. Thus, rather than uniformly impairing immunity, HFD may alter microbial communities in a way that enhances specific protective responses, thereby highlighting the nuanced role of the microbiome in diet\u0026ndash;immune\u0026ndash;parasite interactions.\u003c/p\u003e\n\u003cp\u003eNotably, \u003cem\u003eT. muris\u003c/em\u003e infection did not produce global shifts in microbiota structure, suggesting that\u0026nbsp;\u003cem\u003eT. muris\u003c/em\u003e infection\u0026nbsp;effects may be taxon-specific as reported in ealier research [35, 36]. This limited community-wide restructuring could reflect the immunomodulatory strategies of chronic \u003cem\u003eT. muris\u003c/em\u003e infection, which often stabilize host\u0026ndash;microbe interactions rather than provoke widespread dysbiosis.\u0026nbsp;Alternatively, infection-induced changes may be more pronounced at the functional gene or metabolite level features not captured by 16S-based taxonomy. Future metagenomic or metabolomic profiling could help resolve whether helminth\u0026ndash;microbiome interactions unfold through targeted alterations in metabolic pathways or microbe\u0026ndash;immune links.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmune-deficient RAG knockout (RAG) and RAG2/\u0026gamma;c-deficient \u0026nbsp;mice, as expected, did not expel their worms irrespective of dietary exposure, though HFD further influenced microbial shifts in these groups. As such, even in the absence of adaptive and innate immunity, the microbiome remained responsive to dietary changes, reinforcing the potent influence of diet in shaping microbial ecosystems independent of immune surveillance.\u003c/p\u003e\n\u003cp\u003eGiven the pronounced resistance to \u003cem\u003eT. muris\u003c/em\u003e infection observed in HFD\u0026ndash;fed wild-type mice, we examined how infection status influenced gut microbial composition within this group compared to their ND counterparts. In WT mice on a ND, several taxa were differentially abundant between infected and uninfected mice. Specifically, microbes such as \u003cem\u003eMarvinbryantia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eBilophila\u003c/em\u003e, and \u003cem\u003eRomboutsia\u003c/em\u003e \u0026nbsp;that have been previously associated with mucosal immune responses [37-40], bile acid metabolism[41, 42], and inflammation [37], \u0026nbsp;were more abundant in infected mice,\u0026nbsp;suggesting that they either respond to or contribute to the immune responses during \u003cem\u003eT. muris\u003c/em\u003e infection. Conversely, \u003cem\u003eBilophila\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, and \u003cem\u003eDesulfovibrio\u003c/em\u003e were enriched in uninfected mice, and have been implicated in maintaining mucosal barrier integrity and modulating host immunity\u0026nbsp;[43-48], suggesting a potential protective or immunoregulatory role in the absence of infection. Their depletion in infected mice may reflect a shift toward a more inflammation-prone or permissive microbial state that facilitates colonization by \u003cem\u003eT. muris\u003c/em\u003e infection. When we conducted the same analysis in WT mice fed a HFD, no differentially abundant taxa were detected between infected and uninfected groups. This lack of microbial changes may reflect a pre-existing HFD-driven microbial structure that resists further reshaping upon infection. This may underpin the enhanced resistance to infection observed in this group, potentially through pre-activation of the innate immunity or altered metabolite production.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing infected WT mice on a HFD versus those on a ND was critical to uncover how diet shapes microbial functional capacity during infection.\u0026nbsp;The enrichment of amino acid degradation, butanoate fermentation, fatty acid \u0026beta;-oxidation, and aromatic compound degradation pathways in infected mice fed on HFD suggests a microbiome adapted for energy-dense substrate utilization and anti-inflammatory metabolite production [49-52]. Butanoate, for instance, is known to support epithelial barrier integrity and modulate immune responses, potentially reinforcing Th2 polarization [53-55]. These functional changes align with the enrichment of butyrate-producing taxa such as \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e, as well as \u003cem\u003eParabacteroides\u003c/em\u003e and \u003cem\u003eLactococcus\u003c/em\u003e, which have established roles in epithelial protection and immune modulation.\u0026nbsp;In contrast, infected WT mice on a ND exhibited greater enrichment of carbohydrate degradation, nucleotide biosynthesis, and glycan metabolism pathways, indicative of a microbiome oriented toward rapid bacterial growth and mucosal carbohydrate processing [56]. Such a profile may be less conducive to the immunoregulatory or protective effects observed under HFD, potentially contributing to the increased susceptibility observed in these animals.\u003c/p\u003e\n\u003cp\u003eTo advance our understanding of host resistance to helminth infections, future studies should prioritize mechanistic approaches of microbiota\u0026ndash;immunity interactions. Identifying causal microbial taxa and their metabolites, and elucidating how these shape immune responses to intestinal parasites, will be critical to move beyond correlative associations. Gnotobiotic mouse models would offer a tractable system to validate the candidate microbes, and enable direct testing of their roles in immune modulation and parasite clearance. Complementary studies in helminth-endemic human populations are essential to determine whether similar diet\u0026ndash;microbiota\u0026ndash;immunity relationships are conserved in natural settings, and to identify population-specific microbial signatures or dietary patterns associated with susceptibility or resilience. Ultimately, these insights may inform the development of microbiome-targeted or dietary interventions, such as prebiotics, probiotics, or precision microbiota engineering to bolster host resistance against intestinal helminths.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings reveal a complex interplay between host immunity, diet, and the gut microbiota in determining resistance to \u003cem\u003eTrichuris muris\u003c/em\u003e infection. While adaptive immunity is essential for parasite expulsion, a HFD may enhance protective responses by shifting the microbiota toward taxa and functional pathways associated with epithelial integrity and immune modulation. The enrichment of butanoate fermentation and fatty acid β-oxidation pathways, alongside increases in butyrate-producing and immunoregulatory taxa, suggests that microbial metabolism may actively reinforce type 2 immunity and barrier function in the gut. Even in the absence of adaptive immunity, diet retained a strong influence over microbial community structure and function, underpinning the importance of diet in shaping the gut microbiome profiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll mouse experiments complied with the United Kingdom Animals (Scientific Procedures) Act 1986 and were conducted under Home Office project licence P043A3082, following approval by the institutional Animal Welfare and Ethical Review Body.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Assoc. Prof. Gyaviira Nkurunungi and Mr Alfred Ssekagiri for their expert comments on the manuscript.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Uganda Virus Research Institute High-Performance Computing (UVRI-HPC) facility for providing the computational resources used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBW was partially supported by\u0026nbsp;GCRF collaborative Grant (R120442) from the Royal Society awarded to Professors Richard Grencis and Alison Elliott; he is also partially funded by the National Institute for Health Research (NIHR) under its\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eGlobal Health Research Group on Vaccines for Vulnerable People in Africa (VAnguard)\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Grant Reference Number: NIHR134531), using UK aid from the UK Government to support global health research. This project has also been supported by Wellcome Trust Investigator Award Z10661/Z/18 /Z awarded to Professor Richard Grencis and the Wellcome Centre for Cell Matrix Research Grant 088785/Z/09/Z.The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Government. BW is based at the MRC/UVRI and LSHTM Uganda Research Unit which is jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders were not involved in the conceptualization of the study, writing of the paper and the decision to submit it for publication\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBehniafar H, Sepidarkish M, Tadi MJ, Valizadeh S, Gholamrezaei M, Hamidi F, et al. 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Gut Microbes. 2012;3(4):289-306; doi: 10.4161/gmic.19897.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Manchester","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"microbiota, Trichuris muris, Diet, innate immunity, adaptive immunity","lastPublishedDoi":"10.21203/rs.3.rs-7713991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7713991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003e\u003cem\u003eTrichuris trichiura\u003c/em\u003e (whipworm) affects nearly 500\u0026nbsp;million people globally, causing chronic intestinal inflammation and contributing to malnutrition, growth stunting, and impaired cognitive development especially in children living in low-resource settings. While host immune responses are central to parasite clearance, growing evidence suggests that diet and the gut microbiota may modulate both infection susceptibility and treatment outcomes. However, the mechanisms by which diet influences helminth expulsion, particularly in the context of immune deficiency, remain poorly defined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe used a \u003cem\u003eTrichuris muris\u003c/em\u003e infection model to investigate how host immune competence and diet influence worm burden, parasite-specific humoral responses and the gut microbiome. Wild-type (WT), RAG2-deficient (lacking adaptive immunity), and RAG2/γc-deficient (lacking both adaptive and innate lymphoid immunity) mice received either a standard normal diet (ND) or high-fat diet (HFD) and infected with a low dose of \u003cem\u003eT. muris\u003c/em\u003e. Worm burdens, parasite specific serum IgG1 and IgG2a/c responses were measured together with profiling of the intestinal microbiota using 16S rRNA gene sequencing and shotgun metagenomics\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eInfection of WT mice on a ND with a low dose infection resulted in a chronic infection. Antibody analysis showed a strong parasite-specific IgG2a/c response, consistent with Th1-biased immunity during chronic infection. Notably, WT mice fed a high fat diet (HFD) achieved near-complete parasite clearance, accompanied by elevated IgG1 and reduced IgG2a/c titres, suggesting a diet-induced skewing toward a protective Th2-type response. RAG2-deficient mice and RAG2/γc-deficient mice on a normal diet (ND) also maintained a low dose chronic infection, aligned with the role of immune responses in clearance: no parasite-specific antibodies were detected in either strain as expected. However, RAG2-deficient mice and RAG2/γc-deficient mice fed a HFD exhibited reduced parasite numbers but not complete worm loss as seen in WT mice suggesting a second mechanism of effect. Microbiota composition clustered primarily by genotype and diet, with infection status exerting a more subtle influence. HFD-fed mice exhibited enrichment of several taxa with known roles in mucosal immunity and metabolic regulation, including \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDiet and host immune competence jointly influence susceptibility to helminth infection and the composition and function of the gut microbiota. A HFD shapes the gut toward a state of resilience, characterized by enhanced Th2-biased immune responses and enrichment of epithelial barrier-supportive microbial taxa. This milieu promotes resistance to \u003cem\u003eT. muris\u003c/em\u003e infection, even in partially immunocompromised hosts. These findings underscore the importance of diet in modulating host\u0026ndash;microbiota\u0026ndash;parasite interactions and highlight the potential for diet or microbiome-based strategies to enhance resistance against intestinal helminths,pending studies that directly test microbiome causality.\u003c/p\u003e","manuscriptTitle":"The influence of innate immunity, adaptive immunity and diet on intestinal microbiota following Trichuris muris infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:35:45","doi":"10.21203/rs.3.rs-7713991/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d2fa643-375c-48bd-9622-22416f4d9cce","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55493509,"name":"Immunology"},{"id":55493510,"name":"Bioinformatics"},{"id":55493511,"name":"Parasitology"},{"id":55493512,"name":"Taxonomy"}],"tags":[],"updatedAt":"2025-09-30T07:35:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:35:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7713991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7713991","identity":"rs-7713991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}