The Influence of Innate Immunity, Adaptive Immunity and Diet on Intestinal Microbiota Following Trichuris muris Infection
Bridgious Walusimbi, Kelly S. Hayes, Melissa A. E. Lawson, Seona Thompson, Allison J. Bancroft, Alison M. Elliott, Richard K. Grencis
TL;DR
This study shows how diet and immune system interact to affect gut microbes and resistance to a parasitic worm infection in mice.
Contribution
The study reveals diet can influence parasite clearance through immune-independent, microbiota-mediated pathways.
Findings
High-fat diet reduced worm burden in mice lacking adaptive immunity.
Diet altered gut microbiota composition, enriching Bacteroides and Parabacteroides.
Immune status and diet jointly determine susceptibility to helminth infection.
Abstract
Trichuris trichiura infects nearly 500 million people worldwide, causing intestinal inflammation, malnutrition, and growth impairment, particularly in children from low‐resource settings. While host immunity is central to parasite clearance, diet and the gut microbiota may also modulate infection. Using the Trichuris muris model, we examined how immune competence and diet interact to influence worm burden, antibody responses, and gut microbiota composition. Wild‐type (WT), RAG1‐deficient (lacking adaptive immunity), and RAG1/γc‐deficient (lacking both adaptive and innate lymphoid immunity) mice were fed either a normal diet (ND) or high‐fat diet (HFD) and infected with a low dose of T. muris. WT mice on ND developed chronic infection with strong IgG2a/c responses, consistent with Th1‐biased immunity. In contrast, WT mice on HFD achieved near‐complete parasite clearance, accompanied by…
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FIGURE 10| Immune phenotype | Diet | Group (DIET_mousetype) | Number of mice |
|
|---|---|---|---|---|
| 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 |
| RAG | ND | RAG | 7 | Infected |
| RAG | HFD | RAG | 8 | Infected |
| WT | ND | WT_N | 5 | Naive |
| RAG‐KO | ND | RAG_N | 4 | Naive |
- —Wellcome Trust10.13039/100010269
- —Royal Society10.13039/501100000288
- —National Institute for Health and Care Research10.13039/501100000272
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Taxonomy
TopicsParasites and Host Interactions · Gut microbiota and health · Parasitic Infections and Diagnostics
Introduction
1
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, 3, 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, 9, 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, 14, 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 utilised three genetically distinct mouse models: wild‐type (WT), RAG1‐deficient (RAG1 ^−^/^−^; lacking T and B cells), and RAG1/γc‐deficient (RAG1 ^−^/^−^ 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.
Methods
2
Animal Experiments and Dietary Intervention
2.1
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 RAG1 ^−/−^ deficient (RAG1 KO) mice lacking T and B lymphocytes, and C57BL/6j RAG1 ^ −/− ^ /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 (normal chow or high fat) and water. Animals were maintained in individually ventilated cages with 65% humidity at 21°C°–23°C. 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. Mice were randomly assigned into cages and age/sex matched in experimental groups as far as possible. Weight and body condition were monitored throughout the experimental period and were comparable between groups within dietary regime.
At 5 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 25 embryonated T. muris eggs. Animals were monitored regularly and euthanized at week 17, corresponding to day 42 post‐infection when the infection had reached patency. At necropsy, caecal content, faecal pellets, and serum were collected and immediately stored at −80°C for subsequent experiments and downstream analyses as shown in Figure 1.
Flowchart of the research experiment drawn using icons from BioRender.
Worm Burden Quantification
2.2
Worm burdens were quantified blindly 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
2.3
Serum levels of T. muris–specific IgG1 and IgG2a/c were measured by enzyme‐linked immunosorbent assay as previously described [16]. Plates were coated with T. muris excretory/secretory antigen, and bound antibodies were detected using biotinylated anti‐mouse IgG1 or IgG2a/c followed by streptavidin–horseradish peroxidase and 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) substrate. Additional methodological details are provided in the Supporting Information.
Microbiome Sample Preparation and Sequencing
2.4
Microbial DNA was extracted from faecal samples using the QIAamp DNA Stool Mini Kit (Qiagen). The V3–V4 hypervariable region of the 16S rRNA gene was amplified using the Illumina 16S Metagenomic Sequencing Library Preparation protocol [17].
Sequencing was done 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 Transnetyx (UK; https://www.transnetyx.com) using Illumina short‐read technology. Additional methodological details are provided in the Supporting Information.
Microbiome Bioinformatics Processing
2.5
Raw demultiplexed FASTQ files were processed in QIIME2 (v2023.2). Primers were trimmed using Cutadapt (v4.4), and reads were denoised, merged, and chimera‐checked with DADA2 to generate amplicon sequence variants (ASVs). Taxonomy was assigned using a Naive Bayes classifier trained on the Greengenes 13.8 database. Samples were rarefied to 20,000 sequences. Alpha diversity (Shannon index, observed richness) and beta diversity (Bray–Curtis dissimilarity) were computed and visualised using PCoA. Group differences in community composition were tested by PERMANOVA (999 permutations). Differential abundance analysis was performed in R using DESeq2 within the phyloseq framework, applying Wald tests and Benjamini–Hochberg FDR correction (q < 0.05). Significantly altered taxa (|log_2_FC| ≥ 1) were visualised using volcano and waterfall plots. Additional methodological details are provided in the Supporting Information.
Statistical Analysis
2.6
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 < 0.05 was considered statistically significant.
Results
3
We analysed faecal samples from a total of 49 mice, stratified by immune genotype (WT, RAG1‐deficient [RAG‐KO], and RAG1 ^ −/− ^ /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.
Host Immune Competence and Diet Influence Trichuris muris Worm Burden
3.1
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. This indicates that under normal dietary conditions and low dose infection, host immune status alone does not strongly influence the establishment of chronic infection.
Worm burden across mouse genotypes and dietary groups following T. muris infection. Boxplots show intestinal worm counts in infected wild‐type (WT), RAG‐deficient (RAG), and RAGγc‐deficient mice fed either a normal diet (ND) or a high‐fat diet (HFD). Consistent with impaired type‐2 immunity, RAG‐deficient mice on an ND exhibited the highest worm burdens, whereas WT mice on an HFD showed complete parasite clearance. Overall group differences were significant (Kruskal–Wallis test, p = 5.8 × 10−5). Post hoc Dunn tests (Benjamini–Hochberg correction) identified significant pairwise differences, including WT_H versus WT_N (p adj = 0.0060), WT_H versus RAG_N (p adj = 0.0013), WT_H versus RAGγc_N (p adj = 0.0051), WT_N versus RAG_H (p adj = 0.0307), WT_N versus RAGγc_H (p adj = 0.0435), RAG_H versus RAG_N (p adj = 0.0049), RAG_H versus RAGγc_N (p adj = 0.0262), RAG_N versus RAGγc_H (p adj = 0.0051), and RAGγc_H versus RAGγc_N (p adj = 0.0378). These comparisons clarify both diet‐specific effects within genotypes and genotype‐dependent effects within diets.
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. 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.
Post hoc Dunn tests (Benjamini–Hochberg correction) identified significant reductions in worm burden when comparing WT_H to its ND counterpart (WT_N) and to lymphocyte‐deficient groups (RAG_N and RAGγc_N), highlighting a strong diet effect within the WT background. Within the RAG and RAGγc genotypes, HFD also significantly reduced worm burden (e.g., RAG_H vs. RAG_N; RAGγc_H vs. RAGγc_N). Additionally, several significant genotype‐dependent differences emerged within diet strata (e.g., WT_N vs. RAG_H; WT_N vs. RAGγc_H).
Together, these analyses demonstrate that both immune competence and diet shape parasite clearance, with HFD exerting a broad suppressive effect on worm burden across genotypes, but complete parasite elimination occurring only in immune‐competent WT mice on HFD. Full pairwise Dunn test results are provided in Table S1.
Parasite‐Specific IgG1 and IgG2a/c Responses Show Diet‐Dependent Immune Polarisation
3.2
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 was associated with 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, characterised 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.
Parasite‐specific IgG1 and IgG2a/c antibody responses across immune genotypes and dietary exposures. (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. (B) Corresponding OD readings for parasite‐specific IgG2a/c across the same groups. WT_HFD mice exhibited the strongest IgG1 responses. WT_ND mice showed highest IgG2a/c antibody titres, while RAG1_KO and RAGγc _KO mice, which lack adaptive immunity, failed to mount detectable responses. Naïve mice served as uninfected controls.
As expected, RAG‐KO and RAG/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
3.3
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.
Gut microbiota composition across mouse genotypes. Stacked bar plots display the relative abundance of bacterial genera in faecal samples across the three mouse genotypes (WT, RAG, and RAGγc) and two dietary conditions (normal diet [ND] and high‐fat diet [HFD]). Taxonomic assignments are shown at the genus level, with species‐level annotations provided where available. Each bar represents an individual mouse, and colours correspond to distinct bacterial taxa, as detailed in the accompanying legend. Samples are ordered along the x‐axis according to genotype–diet group (e.g., WT_N = wild‐type on ND; RAG_H = RAG‐deficient on HFD; RAGγc_H = RAGγc‐deficient on HFD), with individual mouse IDs displayed below. Because genus‐level microbiota profiles encompass a large number of taxa, this figure is intended as a broad visual overview of community composition rather than a detailed comparative analysis. Several prominent genera, including Lactobacillus, Bacteroides, Blautia, Alistipes, and Escherichia–Shigella, are readily observed across groups. Clear shifts in dominant taxa are apparent between genotypes and diets, illustrating the combined influence of immune competence and dietary environment on gut microbial community structure.
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 stratified by diet alone is shown in Figure S1.
Gut Microbiota Composition Is Shaped by Diet and Immune Competence More Than Infection Status
3.4
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 visualised 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 RAG1/γc‐deficient (RAGγc_N vs. RAGγc_H) groups, further underscoring the impact of dietary exposure across different immune backgrounds (Figure 5A).
Principal coordinate analysis (PCoA) of gut microbiota profiles stratified by mouse genotype, diet, and infection status. (A) 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 = RAG1‐deficient mice on ND; RAG_H = RAG1‐deficient on HFD; RAGγc_N = RAG1/γc‐deficient mice on ND; RAGγc_H = RAG1/γc‐deficient mice on HFD. Colours represent mouse genotype categories, while point shapes denote diet type (ND = circles; HFD = squares). (B) PCoA 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. Colours represent combined mouse genotype–infection categories, while point shapes denote diet type (ND = circles; HFD = squares). PERMANOVA output: Mouse genotype and diet explained a significant proportion of microbiota variance (R 2 = 0.38595, p = 0.001), highlighting the dominant role of these factors in shaping gut microbial composition.
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 (R ^2^ = 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 localised 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, RAG1/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 (R ^2^ = 0.39, p = 0.001).
Because genotype and diet exerted such strong effects on both microbiota composition and worm burden, we interpret these associations as primarily reflective of the host immune and dietary environment rather than implying a causal microbiota‐driven effect on infection outcome.
Validation of 16S Data Using Shotgun Metagenomics
3.5
To extend these findings and achieve greater taxonomic resolution, we performed shotgun metagenomic sequencing on caecal samples. This enabled deeper characterisation 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 shown in Figure 6A, where samples are stratified by immune genotype (wild‐type, RAG1‐deficient, and RAG1/γc‐deficient mice), gut microbiota profiles clustered 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, RAG1‐deficient, which lack adaptive immunity, clustered separately, reflecting a unique microbial community shaped by their immunodeficiency. RAG1/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.
Principal coordinate analysis (PCoA) of gut microbiota profiles based on shotgun metagenomic sequencing. (A) Samples coloured 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. RAG mice (lacking adaptive immunity) cluster separately, consistent with a unique microbial community structure shaped by their immunodeficiency. RAG1/γc‐deficient (further impaired immune function) also form their own distinct cluster, showing that the degree of immune impairment strongly influences gut microbial composition. (B) Samples 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 infection status does not strongly separate samples 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. Naive mice (uninfected) on HFD sometimes overlap with their infected counterparts.
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, naive (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
3.6
To clarify 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)
3.6.1
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.
(A) 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 > 1, adjusted p < 0.05), blue points represent taxa significantly enriched in ND‐fed WT mice, and black points represent non‐significant taxa. (B) 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, Clostridium sensu stricto 1, and Tuzzerella, whereas ND‐fed WT mice had higher abundance of Akkermansia, Muribaculum, and Parasutterella. (C) 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. (D) 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. (E) Volcano plot displaying differentially abundant taxa in RAG1/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. (F) Corresponding waterfall plot for RAGγc mice. HFD‐fed RAGγc‐KO mice showed enrichment of Romboutsia, Acetatifactor, Lactococcus, and Tuzzerella, while ND‐fed RAGγc‐KO mice were enriched in Lachnospiraceae UCG‐001, Tyzzerella, Escherichia‐Shigella, and Gastranaerophilales. (G) Venn diagram summarising 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).
RAG‐Deficient Mice (RAG)
3.6.2
In 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.
RAG1/γc‐Deficient Mice (RAGγc‐KO)
3.6.3
Figure 7E displays the number and statistical significance of differentially abundant bacterial genera between RAG1/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
3.6.4
Figure 7G summarising 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 RAG/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 RAG/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.
Gut Microbiota Profiles Cluster by Host Immune Background and Diet
3.7
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 RAG1/γc‐deficient (RAGγc‐KO) mice following T. muris infection under both ND and HFD conditions.
We observed distinct clustering by both genotype and diet among T. muris–infected mice (Figure 8A). RAG1/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.
Impact of immune genotype and diet on gut microbiota composition in T. muris‐infected mice. (A) Principal coordinate analysis (PCoA) plots based on Bray–Curtis dissimilarity showing gut microbial community structures across wild‐type (WT) and RAG1/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: RAGγc_N_INF versus WT_N_INF (ND) versus WT_H_INF (HFD). PCoA plots B and C show clear separation between RAG1/γc‐deficient and wild‐type mice in 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. (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 log2 fold‐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 RAG1/γc‐deficient mice on ND. RAGγc_H_INF‐RAG1/γc‐deficient mice on HFD, WT_N_INF‐infected WT mice on ND, WT_H_INF‐infected Wild type mice on HFD diet.
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 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.
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
3.8
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 contributions 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.
Impact of diet and T. muris infection on gut microbiota composition in wild‐type mice. (A) Principal coordinate analysis (PCoA) plot showing microbial clustering of uninfected WT mice on HFD (WT_H_UNINF) versus ND (WT_N_UNINF). (B) PCoA plot comparing infected WT mice on HFD (WT_H_INF) versus ND (WT_N_INF). (C) PCoA plot showing microbial separation between infected and uninfected WT mice on ND. (D) PCoA plot of infected and uninfected WT mice on HFD. (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.
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.
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 harboured 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 remodelling, potentially through immune‐mediated or niche‐altering mechanisms. When interpreted alongside diversity and clustering analyses (Figure 9A–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. Given the magnitude of the diet‐associated shifts observed in overall community structure, any infection‐related effects appeared comparatively small and did not meet our detection thresholds 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 (Figures S2–S4). 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
3.9
Functional pathway analysis using PICRUSt2 (Figure 10)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.
Differentially abundant microbial metabolic pathways in infected wild‐type mice fed a HFD versus ND. Functional 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.
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
4
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.
RAG‐KO mice, which lack adaptive immunity, and RAGγc‐KO mice which lack adaptive and innate lymphoid cells unsurprisingly harboured 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 [18, 19]. 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 different diets and immunocompetence models. Given the established role of the microbiome in shaping immunity [20] and helminth susceptibility [15, 21, 22], as well as the effect of HFD on the gut microbiome [23], 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 characterised 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 [24, 25, 26], with immune deficiency often predisposing to dysbiotic shifts [27, 28] and HFD promoting inflammation‐associated taxa [23]. Thus, we refrain from inferring a causal role for microbiota differences in shaping infection phenotypes. Instead, we interpret our findings as identifying correlative microbial signatures that are associated with diet‐ and immunity‐driven changes in host susceptibility. Definitive mechanistic work, such as microbiota transfer, gnotobiotic colonisation, or immune–specific interventions, will be required to establish whether individual microbial taxa actively contribute to parasite resistance.
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 [23]. 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 [29, 30], promoting epithelial barrier integrity [31], and shaping anti‐inflammatory responses [30, 32, 33]. 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 [29, 30]. Similarly, Lactococcus lactis has been linked to enhanced mucosal IgA production and modulation of CD4^+^ T cell responses [30, 31], while Parabacteroides spp. have demonstrated the capacity to reduce systemic inflammation and support gut homeostasis in metabolic contexts [33, 34].
These microbial changes may provide a mechanistic link between diet, immune system, and resistance to T. muris infection. By promoting a mucosal environment that favours 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 [32, 35], 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 earlier research [36, 37]. This limited community‐wide restructuring could reflect the immunomodulatory strategies of chronic T. muris infection, which often stabilise 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 RAG/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 [38, 39, 40, 41], bile acid metabolism [42, 43], and inflammation [38], 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 [44, 45, 46, 47, 48, 49], 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 colonisation 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 utilisation and anti‐inflammatory metabolite production [50, 51, 52, 53]. Butanoate, for instance, is known to support epithelial barrier integrity and modulate immune responses, potentially reinforcing Th2 polarisation [54, 55, 56]. 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 [57]. 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.
One limitation of our study is that worm burden was quantified at a single terminal time point. Therefore, while HFD‐fed mice exhibited markedly reduced parasite loads across all genotypes, we cannot fully distinguish whether this reflects enhanced clearance or impaired early establishment of T. muris. Much as previous work from our group suggests that HFD can augment type‐2 immune pathways that promote accelerated expulsion [16], longitudinal sampling would be necessary to resolve these kinetics. Future experiments incorporating multiple stages of infection will be important for dissecting these temporal dynamics.
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
5
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.
Author Contributions
B.W. performed microbiome and immunological data analysis, and wrote the manuscript. B.W., K.S.H., M.A.E.L., S.T., and A.J.B. performed mouse experiments, sample processing, and laboratory assays. A.M.E. and R.K.G. provided overall supervision, conceptual guidance, and critical manuscript revision. All authors contributed to data interpretation, reviewed the manuscript, and approved the final version.
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.
Supporting information
Figure S1: Gut microbiota composition across diet types. Stacked bar plots representing the relative abundance of bacterial genera across individual faecal samples. Taxa are shown at the genus level, with taxonomy annotated down to species where available. Each bar represents one sample, and colours correspond to distinct bacterial taxa, as indicated in the legend on the right. Figure S2: Comparison of alpha diversity in wild‐type WT mice fed either a ND or a HFD. (A) Shannon diversity index of faecal microbiota based on 16S rRNA gene sequencing, showing significantly reduced microbial diversity in WT mice fed a HFD (WT_H) compared to those on a ND (WT_N) (p = 0.0027, Wilcoxon test). (B) Shannon diversity index of caecal microbiota based on shotgun metagenomic sequencing. A trend toward reduced diversity in WT_HFD compared to WT_Normal was observed (p = 0.06, Wilcoxon test). These results suggest a consistent diet‐associated reduction in microbial diversity across different gut compartments and sequencing platforms. Figure S3: Gut microbial alpha diversity (Shannon index) in naive and infected mice across immune genotypes and dietary groups. (A–C) Comparisons of Shannon diversity in naive (uninfected) mice. (A) Alpha diversity by immune genotype (WT, RAG‐KO, RAGγc‐KO), regardless of diet. (B) Diversity stratified by both immune genotype and diet (ND vs. HFD). (C) Focused comparison within RAG‐KO naive mice, showing reduced diversity in mice on a HFD (RAG_H) versus ND (RAG_N). (D–F) Corresponding comparisons now including infected mice, to assess how Trichuris muris infection modifies gut microbial diversity across the same strata. (D) Diversity by genotype (infected vs. naive mice combined), (E) Stratified by both genotype and diet, (F) Focused comparison within RAG‐KO mice on HFD versus ND under infection. Figure S4: Microbial diversity (Shannon index) across different mouse genotypes under varying dietary and infection conditions. (A) Naive RAGγc‐KO, RAG‐KO, and WT mice on a ND, profiled using 16S rRNA sequencing of faecal samples. (B) Infected RAGγc‐KO, RAG‐KO, and WT mice on a ND, also assessed by 16S rRNA sequencing of faecal samples. (C) Infected RAGγc‐KO, RAG‐KO, and WT mice on a ND, profiled via shotgun metagenomics of faecal samples. (D) Naive RAGγc‐KO, RAG‐KO, and WT mice on a HFD, analysed using 16S rRNA sequencing of faecal samples. (E) Infected RAGγc‐KO, RAG‐KO, and WT mice on a HFD, with microbial diversity assessed from faecal samples using 16S rRNA sequencing. (F) Infected RAGγc‐KO, RAG‐KO, and WT mice on a HFD, profiled by shotgun metagenomics of caecal content. This comparative design highlights genotype‐specific shifts in microbial diversity across dietary and infectious states, using both amplicon‐ and shotgun‐based profiling approaches. Table S1: Pairwise post hoc comparisons of intestinal worm burdens across genotype–diet groups.Dunn's post hoc tests were performed following a significant Kruskal–Wallis test (p = 5.8 × 10^−5^) to assess pairwise differences in worm counts between wild‐type (WT), RAG‐deficient (RAG), and RAGγc‐deficient (RAGγc) mice fed either a normal diet (ND) or high‐fat diet (HFD). The table reports the test statistic, unadjusted p‐values, Benjamini–Hochberg–adjusted p‐values (p.adj), and significance calls. Significant comparisons (p.adj < 0.05) highlight both genotype‐dependent effects (e.g., impaired parasite control in RAG mice) and diet‐specific effects within genotypes (e.g., improved clearance in WT mice on HFD).
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