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Open Access

Peer-reviewed

Research Article

Impacts of host phylogeny, diet, and geography on the gut microbiome of rodents

  • Sufia Akter Neha ,

    Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft

    sneha@ttu.edu

    Affiliation Department of Biological Sciences, Texas Tech University, Lubbock, Texas, United States of America

    ⨯
  • John D. Hanson,

    Roles Data curation, Investigation, Methodology, Resources, Validation

    Affiliation Blackhawk Genomics, Lubbock, Texas, United States of America

    ⨯
  • Jeremy E. Wilkinson,

    Roles Data curation, Investigation, Methodology, Resources, Validation

    Affiliation PacBio, Menlo Park, California, United States of America

    ⨯
  • Robert D. Bradley,

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

    Affiliations Department of Biological Sciences, Texas Tech University, Lubbock, Texas, United States of America, Natural Science Research Laboratory, Museum of Texas Tech University, Lubbock, Texas, United States of America

    ⨯
  • Caleb D. Phillips

    Roles Conceptualization, Formal analysis, Methodology, Resources, Software, Supervision, Validation, Writing – review & editing

    Affiliations Department of Biological Sciences, Texas Tech University, Lubbock, Texas, United States of America, Natural Science Research Laboratory, Museum of Texas Tech University, Lubbock, Texas, United States of America

    ⨯

Abstract

Mammalian gut microbial communities are thought to play a variety of important roles in health and fitness, including digestion, metabolism, nutrition, immune response, behavior, and pathogen protection. Gut microbiota diversity among hosts is strongly shaped by diet as well as phylogenetic relationships among hosts. Although various host factors may influence microbial community structure, the relative contribution may vary depending on several variables, such as taxonomic scales of the species studied, dietary patterns, geographic location, and gut physiology. The present study focused on 12 species of rodents representing 3 rodent families and 3 dietary guilds (herbivores, granivores, and omnivores) to evaluate the influence of host phylogeny, dietary guild and geography on microbial diversity and community composition. Colon samples were examined from rodents that were collected from 7 different localities in Texas and Oklahoma which were characterized using 16S rRNA gene amplicon sequencing targeting the V1-V3 variable regions. The microbiota of colon samples was largely dominated by the family Porphyromonadaceae (Parabacteriodes, Coprobacter) and herbivorous hosts harbored richer gut microbial communities than granivores and omnivores. Differential abundance analysis showed significant trends in the abundance of several bacterial families when comparing herbivores and granivores to omnivores, however, there were no significant differences observed between herbivores and granivores. The gut microbiotas displayed patterns consistent with phylosymbiosis as host phylogeny explained more variation in gut microbiotas (34%) than host dietary guilds (10%), and geography (3%). Overall, results indicate that among this rodent assemblage, evolutionary relatedness is the major determinant of microbiome compositional variation, but diet and to a lesser extent geographic provenance are also influential.

Citation: Neha SA, Hanson JD, Wilkinson JE, Bradley RD, Phillips CD (2025) Impacts of host phylogeny, diet, and geography on the gut microbiome of rodents. PLoS ONE 20(1): e0316101. https://doi.org/10.1371/journal.pone.0316101

Editor: Muniyandi Nagarajan, Central University of Kerala, INDIA

Received: July 2, 2024; Accepted: December 4, 2024; Published: January 16, 2025

Copyright: © 2025 Neha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Raw sequence data has been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject ID PRJNA1107948.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Gut microbiomes, which are communities of microbes residing within hosts’ digestive systems, are important to several aspects of biology including digestion, metabolism, nutrition, immune response, behavior, and pathogen invasion [1–5]. Considering the importance of gut microbiota for host health and fitness, researchers have examined the factors that influence its diversity and composition. An emerging view from comparisons across species is that gut microbiome structure is shaped in large part by host phylogeny; such patterns have been observed in a wide variety of taxa such as mammals, birds, and invertebrates [6–9]. The effect of host phylogeny results in closely related hosts tending to have higher similarity in gut microbiota composition than distantly related hosts [10,11], a phenomenon referred to as phylosymbiosis [10,12,13]. Giant pandas are an extreme example of the apparent strength of host phylogeny; although they have altered their diet drastically in the past to herbivory, their gut microbiome more closely resembles that of their carnivore relatives [14].

Diet is another strong predictor of gut microbiome diversity and composition. For example, Ley et al. 2008 [15] found that not only host phylogeny but also diet strongly explained microbial flora in 13 taxonomic orders of mammals. Furthermore, dietary preference has been predominantly linked to host gut morphology with complex gastrointestinal tracts in herbivorous mammals required to digest plant-derived materials compared to the simple gut systems in carnivores, thus allowing herbivores to harbor diverse bacterial communities [15]. Although both host phylogeny and diet may influence microbial community composition, they are not independent and their relative contribution may vary depending on a number of variables, such as taxonomic scale of the species studied, host habitat, dietary variability and gut morphology [9,16–18]. Recent studies showed that the microbiota of closely related species, such as mice, voles, and shrews, inhabiting similar habitats and eating similar foods, are more similar than that of the same species inhabiting different habitats and eating different foods [19]. In contrast, distantly related taxa with similar diets tend to have similar microbial communities [20]. For example, folivorous primates with overlapping diets in different geographic locations display similarities in their gut microbiota [21].

Gut microbiome composition in mammals may further be influenced by geography [17,22–24]. Geographic location has been shown to affect the composition of gut microbial communities of humans [25], rodents [22,26], bats [9], and insects [27]. For example, sampling locality accounted for 16% variation in the gut microbiota of black-tailed prairie dogs in Texas [22]. Dispersal abilities of microbes and direct contact between hosts may have a significant influence on microbiome variation [17,24]. In a homogenous environment with limited dispersal, we would anticipate hosts living nearby to have a more similar gut microbial structure compared to hosts living further away [3]. Geographic locations vary in environmental features such as topography, vegetation, and altitude which may also shape the gut microbiome composition. For an example of continuous geographic variation, it was found that the gut microbiota of wild mice changed significantly over an elevational gradient varying in temperature, air pressure, and oxygen concentration [26].

A more comprehensive understanding of gut microbial community structure in wild mammals requires the interaction of various factors that are critical to inform the symbiotic relationship between hosts and their microbes. There have been multiple studies investigating the mechanisms influencing gut microbial communities, but most of the existing research concentrated on a single host species at multiple geographic locations or several species at one location [7,22,28,29]. A few studies have investigated the combined effects of host phylogeny, diet, and geographical provenance on microbial communities in wild mammals [6,7,9], particularly rodent populations that are widely distributed [30,31].

Herein, we examined the microbiomes of 12 rodent species occurring in Texas and Oklahoma including Pinon deermouse (Peromyscus truei), brush deermouse (Peromyscus boylii), cotton deermouse (Peromyscus gossypinus), white-footed deermouse (Peromyscus leucopus), northern rock deermouse (Peromyscus nasutus) Mexican woodrat (Neotoma mexicana), white-toothed woodrat (Neotoma leucodon), eastern woodrat (Neotoma floridana), hispid pocket mouse (Chaetodipus hispidus), hispid cotton rat (Sigmodon hispidus), Attwater’s pocket gopher (Geomys attwateri), Baird’s pocket gopher (Geomys breviceps). These rodent species represent three families (Cricetidae, Geomyidae, and Heteromyidae) and three 3 dietary guilds (herbivores, granivores, and omnivores). The objectives of this study were to characterize the gut microbiota profiles among these rodents and examine the relative effects of host phylogeny, dietary guild and geographic location on gut microbial community composition. Overall, this study will enhance our understanding of the factors shaping gut microbial communities across diverse host species, which is crucial for improving conservation efforts and informing management practices for these species.

Methods

Rodents sampling

A total of 71 gut samples were collected from 12 species of rodents over a two-week field period in Texas and Oklahoma in 2016. Among them, 22 samples were obtained from Fayette County, 14 samples from Hopkins County, 5 samples from Delta County, 25 samples from Cimarron County, 5 samples from Cleveland County, 2 samples from Love County, and 1 sample from Blaine County (Fig 1 and Table 1). The geographic regions where the samples were collected vary in climate, rainfall, and vegetation. Texas counties like Fayette, Hopkins, and Delta have a humid subtropical climate with around 35–45 inches of annual rainfall, supporting a mix of grasslands, oak-hickory forests, and agricultural areas. In contrast, Oklahoma counties like Cimarron experience a semi-arid climate with less rainfall (around 17 inches) and are dominated by shortgrass prairies. The other Oklahoma counties (Cleveland, Love, and Blaine) have more rainfall and support a combination of grasslands, woodlands, and farmland. Sherman live traps baited with oats were placed in the trapline about 2–10 m apart each night. The following morning individuals were euthanized with isoflurane for 1–2 minutes. The animals were dissected, and colon samples were taken from each captured individual, which was stored in liquid nitrogen and then kept at -80°C for later analysis. Specimens were collected using the methods outlined in the American Society of Mammalogists guidelines [32]. The sampling protocol received approval from the Texas Tech University Institutional Animal Care and Use Committee (IACUC permit #: 14006–01) and Scientific Permit of Texas (#SPR-0393-593).

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Fig 1. A map of ecoregions of Texas and Oklahoma (geospatial data were downloaded from US environmental protection agency [33]) showing the sampling locations of the twelve species of rodents.

The map was created with ArcGIS Pro (v 2.9.0).

https://doi.org/10.1371/journal.pone.0316101.g001

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Table 1. Sample information of rodent species.

Numbers inside parentheses are sample sizes obtained from each locality.

https://doi.org/10.1371/journal.pone.0316101.t001

DNA extraction and library preparation

DNA was extracted from the distal portion of the colon containing feces using Qiagen DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s guidelines. Samples were characterized by targeting V1-V3 variable regions of 16S rRNA gene as described by [34]. Briefly, the forward primer was designed with the following sequence (5′-3′): the Illumina i5 adapter (AATGATACGGCGACCACCGAGATCTACAC), an 8–10 bp barcode, a primer pad, and 28F (GAGTTTGATCNTGGCTCAG) and the reverse primer was designed with the following sequence (5′-3′): the Illumina i7 adapter (CAAGCAGAAGACGGCATACGAGAT), an 8–10 bp barcode, a primer pad, and 519R (GAGTTTGATCNTGGCTCAG). Amplifications were carried out using 1 μl of each 5μM forward and reverse primer along with 1.0 μl of template DNA. The thermal conditions were as follows: 95°C for 5 minutes, 35 cycles of 94°C for 30 seconds for initial denaturation, followed by annealing at 54°C for 40 seconds; extension at 72°C for 1 minute, and a final extension at 72°C for 10 minutes and a final hold at 4°C. The amplification products were visualized using eGels (Life Technologies, Grand Island, New York) to determine library quantity and AMPure XP beads were used for reaction cleanup. After pooling equimolar libraries, the libraries were quantified with the Qubit 2.0 Fluorometer (Thermo Scientific, USA). Libraries were sequenced for 2 × 300 bp reads with Illumina MiSeq protocol (Illumina Inc, San Diego, CA, USA) at RTLGenomics (Lubbock, TX, USA). Raw sequence data has been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject ID PRJNA1107948.

Bioinformatics

Read pairs were merged using PEAR [35], and sequences with more than one estimated error were eliminated using USEARCH fastq-filter function. The remaining sequences were clustered into zero-radius operational taxonomic units (ZOTUs) using the denoising algorithm UNOISE3 [36], and USEARCH was used to build the OTU table from the ZOTUs. ZOTUs detect and correct sequencing errors, generating sequence variants that are 100% identical. They offer higher resolution than OTUs by distinguishing sequences that differ by even a single nucleotide, enabling a more precise and accurate representation of microbial diversity. Representative ZOTU sequences were compared to the SILVA database (v. 123) [37] to assign taxonomy. SSU-ALIGN [38] was used to create the ZOTU alignment and a phylogenetic tree to summarize the evolutionary relationship among ZOTUs was estimated using FastTree2 [39].

Statistical analyses

The ZOTU, taxonomy tables and phylogeny were combined into a relational phyloseq object [40]. Analyses were conducted with the following packages in R [41]: phytools [42], ape [43], vegan [44], picante [45], scales [46], reshape2 [47], pairwiseAdonis [48], lme4 [49], and ggplot2 [50].

Sequencing effort was summarized using scaled ranked subsampling (SRS) to normalize the ZOTU counts to the size of the smallest sample in the dataset, employing SRS R package [51]. The rarefied dataset was further used for diversity analyses. Good’s coverage was also used to make sure that samples were well represented, and what proportion of total species present in the community were detected in the sample [52]. Alpha diversity was summarized as Hill0, which is number of observed species (i.e., richness); Hill1, which is the exponentiation of Shannon diversity, as well as Faith’s phylogenetic diversity [53] which summarizes alpha diversity proportional to branch length of the community phylogeny observed in each sample. The Shapiro-Wilk test was used to test for normality, and all diversity estimates appeared normally distributed (p > 0.05). Also, homogeneity of variance testing yielded non-significance of variance differences (host species: F = 1.555, p = 0.137; dietary guild: F = 1.834, p = 0.158; geographic location: F = 1.252, p = 0.616) further supporting the use of parametric statistics. Multiple regression was used to simultaneously evaluate the effects of host species, dietary guild, and geographic location on alpha diversity measures. This was followed by post-hoc pairwise testing (Tukey’s HSD) to determine which pairwise comparisons were significant. Phylogenetic non-independence was controlled by implementing the phy.anova function in the geiger package [54].

Variation in community composition was summarized using unweighted and weighted UniFrac phylogenetically-informed distances. Unweighted UniFrac distance [55] is based on presence/absence of species, whereas weighted UniFrac accounts for relative abundance of taxa. Unweighted UniFrac may be optimal in cases when compared communities broadly differ in occurrence of species, and weighted UniFrac will be more discriminating in cases where compared communities share many species but differ in proportions. To determine predictors of community composition, distance-based multivariate analysis of variance (function dbrda) was used, and predictors in this model included individual-level pairwise genetic distances of host species in addition to host species, dietary guild and locality. Post-hoc pairwise testing was used to determine which pairwise comparisons were different. To visualize the grouping across samples, redundancy analysis (RDA) was used. Because significant multivariate analysis of variance results can arrive from group differences in location or spread in multivariate space, Euclidean distances from group centroids resulting from the RDA were also computed and differences in dispersion were assessed with ANOVA. The relative abundance of bacterial taxa was visualized using bar plots.

Linear Discriminant Analysis Effect Size (LEfSe) was employed to identify differentially abundant families between dietary guilds. This analysis utilized the microbiomeMarker package [56] and included the Kruskal-Wallis sum-rank test to determine significant differential abundance, set at a significance level of p = 0.05. Subsequently, the analysis employed Linear Discriminant Analysis (LDA) to estimate effect sizes, represented as log(10) values [57]. The results were visualized to highlight taxa demonstrating an LDA of at least 2 for effect size.

Phylogenetic relationships among hosts were determined based on the mitochondrial cytochrome-b gene (1143 bp). In total, 127 sequences were acquired from NCBI GenBank (S1 Table). Sylvilagus holzneri was used as the outgroup for the phylogeny. The resulting sequences were aligned using MUSCLE [58] for downstream analyses. The RAxML [59] was employed to eliminate similar sequences from the dataset, resulting in a final set of 111 sequences for phylogenetic analyses. jModelTest-2.1.10 [60] evaluated 88 maximum likelihood models and the corrected Akaike information criterion [61] was used to determine the best-fit model for the cytochrome-b dataset. HKY+I [62] was chosen as the best-fit model (-lnL = 12291.279095) and the following parameters for likelihood analysis were used: base frequencies (A = 0.30253, C = 0.284131, G = 0.128856, and T = 0.284479). Bootstrap support (BS) values with 1,000 iterations were used [63], and BS ≥ 65 determined moderate to strong nodal support. Bayesian inference was performed (BI) in MrBayes [64] to generate the posterior probability values (PPV). In Bayesian analysis, we used GTR + I + Γ model and estimated parameters based on the following: 4 Markov chains with two independent runs, 10 million generations, and 1,000 generation sample frequency. Based on likelihood scores, 10 percent of the first 1,000,000 trees were removed, and 50% majority rule consensus tree was constructed. The PPV≥ 0.95 indicated strong nodal support [65]. Host dietary information was obtained from published articles (S2 Table).

We illustrated the phylosymbiosis findings by creating host species phylogeny and contrasted it with a dendrogram of gut microbiota similarity using hierarchical clustering resulting from Unweighted UniFrac distances. The correlation between host evolutionary lineage and the microbial community was evaluated using partial mantel test.

Results

Rodents gut microbiota profile

There were 1,461,683 raw sequences from 71 gut samples, averaging 20,587 reads/sample (range = 9,034–60,726; SD = 9,378). Upon quality filtering and denoising, 1,257,245 non-chimeric sequences yielded an average of 17,707 reads/sample (range = 7,689–52,041; SD = 8,005). In total, 15,736 ZOTUs were detected across all samples. Rarefaction curves of Hill0 and Hill1 indicated sequencing effort was sufficient to adequately characterize bacterial communities and reached a plateau at the sequence depth of 6,000 (S1 Fig). Good’s coverage values averaged 95.91 ± 6.44 across all samples which also supported that sample coverage was sufficient to compare microbial communities.

Microbiome composition

A total of 120 families, 281 genera, and 553 species of bacteria were observed study wide. Approximately 80% of the gut microbiota was represented by the top ten families including Porphyromonadaceae (40.0%), Marinilabiliaceae (6.7%), Lachnospiraceae (6.1%), Lactobacillaceae (5.0%), Clostridiaceae (4.7%), Eubacteriaceae (4.3%), Ruminococcaceae (3.9%), Eggerthellaceae (3.4%), Flammeovirgaceae (3.1%) and Unclassified_Clostridiales (3.0%). The most abundant genera were Parabacteroides (22.1%), Lactobacillus (5.0%), Coprobacter (4.8%), Barnesiella (4.3%), and Eubacterium (4.2%). Distributions of bacteria across host species, and dietary guilds are provided in Fig 2.

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Fig 2.

Stacked bar plots showing the relative abundance of top 20 bacterial families and genera grouped by host species (A and C) and dietary guild (B and D).

https://doi.org/10.1371/journal.pone.0316101.g002

Host phylogeny and testing for phylosymbiosis

A total of 127 complete cytochrome-b sequences (1,143 bp) were obtained from GenBank, representing the 12 host species. The phylogenetic analysis of host sepcies produced similar tree topologies when using Maximum Likelihood (ML) and Bayesian Inference (BI), but only the BI topology is presented here (Fig 3). Based on BI analysis, two major clades were identified as supported. G. attwateri and G. breviceps constituted a strongly supported clade (PPV > 95%, nucleotide distance of 0.13; see S3 Table), and this clade was identified as the sister group to C. hispidus. S. hispidus formed a sister clade to another large and well-supported monophyletic clade containing P. leucopus, P. gossypinus, P. nasutus, P. boylii, P. truei, N. leucodon, N. floridana, and N. mexicana. However, the nodal support for P. nasutus, P. boylii, P. truei was moderately supported in ML analysis (BS = 65) but strongly supported in BI analysis (PPV > 95%) (Fig 3). The dendrogram depicting gut microbiota dissimilarity was obtained from hierarchical clustering based on unweighted UniFrac distances by averaging the OTU counts for each host species (Fig 3). The explanatory power of host phylogeny was assessed with a partial mantel test through which a significant effect of host phylogeny on microbiome was observed (r = 0.32, p < 0.001).

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Fig 3. Relationship between host phylogeny and gut microbiota similarity.

(A) Bayesian phylogeny of 12 host species based on mitochondrial cytochrome-b. Node posterior probability values (PPV ≥ 0.95) are indicated by asterisks, and likelihood bootstrap support values occur after the slashes (BS ≥ 0.65). (B) Dendrogram showing similarities in gut microbiota using hierarchical clustering based on unweighted UniFrac distances. Lines connect species positions in both trees.

https://doi.org/10.1371/journal.pone.0316101.g003

Microbial alpha diversity

The alpha diversity of microbial communities was 739.85±31.29 for Hill0, 5.23±0.11 for Hill1, and 15.75±0.49 for Faith’s phylogenetic diversity (S4 Table). Hill0 was not significantly explained by host species, dietary guild, or locality in the multiple regression model while controlling for phylogenetic non-independence (host species: F = 1.554, P = 0.137; dietary guild: F = 2.166, P = 0.123; locality: F = 1.274, P = 0.273). However, Hill1 was found to be significantly explained by host species and dietary guild but not by locality (host species: F = 3.681, P < 0.001; dietary guild: F = 11.53, P < 0.001; locality: F = 1.826, P = 0.089; Fig 4A–4C). Post-hoc pairwise testing of Hill1 revealed that N. floridana and S. hispidus had significantly higher Hill1 than P. leucopus and P. gossypinus (BH adjusted p-values <0.05; Tables 2 and S5), respectively, and herbivores had significantly higher Hill1 compared to omnivores (BH adjusted p-values <0.05, S5 Table). Faith’s phylogenetic diversity was not explained by host species, dietary guild, or locality (host species: F = 1.322, P = 0.235; dietary guild: F = 0.446, P = 0.652; locality: F = 1.285, P = 0.283).

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Fig 4.

Boxplots illustrating Hill1 distributions grouped by (A) host species; and (B) dietary guilds. The boxes of the boxplots are defined by 1st and 3rd quartiles, the horizontal lines within boxes are medians, and whiskers calculated as 1.5 times the interquartile range. Benjamini-Hochberg adjusted p-values given by Tukey’s HSD tests to control for multiple comparisons.

https://doi.org/10.1371/journal.pone.0316101.g004

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Table 2. Microbial alpha diversity summary statistics grouped by host species and dietary guild.

Mean values are provided with their corresponding standard deviations (±) for each metric.

https://doi.org/10.1371/journal.pone.0316101.t002

Microbial beta diversity

To investigate determinants of bacterial community composition, we used dbRDA incorporating host phylogenetic distance among host species in combination with dietary guild and locality as predictors. Based on dbRDA, variance in unweighted UniFrac was significantly explained by host phylogeny (F = 2.82, R2 = 0.34, P < 0.001), host species (F = 4.24, R2 = 0.20, P < 0.001), dietary guild (F = 3.27, R2 = 0.10, P < 0.001), and geography (F = 2.452, R2 = 0.03, P < 0.001). Post-hoc pairwise testing among species, dietary guild, and locality revealed that most pairwise comparisons were significant (p < 0.001, S6 Table). The ordination illustrates how axes 1 and 2 tend to separate samples consistent with significant predictor variables (Fig 5A–5C). Significant differences in dispersion of samples from the group centroids in multivariate space was also observed for host species (F = 4.22, R2 = 0.27, p < 0.001, Fig 5D), dietary guild (F = 54.82, R2 = 0.22, p < 0.001, Fig 5E) and locality (F = 20.62, R2 = 0.08, p < 0.001, Fig 5F). Pairwise comparisons of host species showed significant dispersion for C. hispidus and G. attwateri relative to P. gossypinus and P. leucopus (Fig 5D). All pairwise comparisons between dietary guilds revealed significant differences (Fig 5E). For locality, Blain and Love counties exhibited significantly different dispersion compared to Hopkins, Fayette, and Cimarron counties (Fig 5F). However, no comparisons of compositional variance in weighted UniFrac were significant for any predictor variable (P > 0.05).

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Fig 5.

Beta diversity compositional variation summarized using dbrda with unweighted UniFrac distance among (A) host species; (B) dietary guilds and (C) locality. Corresponding PERMANOVA results are provided. Each data point corresponds to an individual sample, and ellipses indicate group 95% confidence distributions. Group dispersion based on unweighted UniFrac distance among (D) host species; (E) dietary guild and (F) locality. The boxes of the boxplots are defined by 1st and 3rd quartiles, the horizontal lines within boxes are medians, and whiskers calculated as 1.5 times the interquartile range. Test values are Benjamini-Hochberg adjusted p-values given by Tukey’s HSD tests to control for multiple comparisons.

https://doi.org/10.1371/journal.pone.0316101.g005

Differential abundance

The relative abundance of bacterial taxa was examined at the family level to identify any differentially abundant taxa between dietary groups. LEfSe analysis revealed that Lactobacillaceae was notably more abundant in omnivorous groups (71.6%) (Fig 6A), whereas Porphyromonadaceae predominated in granivores and herbivores (Fig 6A and 6B), representing 59.4% and 48.7%, respectively. However, no significant differences in bacterial abundance were observed between herbivore and granivore groups. Further LEfSe analysis with an LDA score > 2 highlighted Marinilabiliaceae, Erysipelotrichaceae, and Ruminococcaceae as the taxa contributing most to dissimilarity in herbivores, whereas Eubacteriaceae, Carnobacteriaceae, and Oscillatoriales were identified as the main contributors to dissimilarity in omnivores. Granivores exhibited a higher proportion of Lachnospiraceae, Micrococcaceae, and Helicobacteraceae.

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Fig 6.

Log10 abundance differences for significant bacterial families between (A) herbivores and omnivores, and (B) granivores and omnivores.

https://doi.org/10.1371/journal.pone.0316101.g006

Discussion

This study contributes to our understanding of how host phylogeny, host species, dietary guild, and geography affects gut microbial diversity and composition in rodents from a portion of the south-central US. The key findings of this study include: (i) several bacterial families showed significant differences in abundance when comparing herbivore and granivore to omnivore dietary guilds, while no significant differences were observed between herbivores and granivores; (ii) gut microbial alpha and beta diversity were jointly shaped by host phylogeny, host species, and dietary guild, but geographic provenance, at least in the context of the current study design, had a relatively weak influence; (iii) while controlling for influences of available explanatory variables host phylogenetic relatedness was the strongest predictor, explaining about 34% of overall variation in the unweighted UniFrac metric.

The most abundant bacterial families observed in this study were Porphyromonadaceae Marinilabiliaceae, Lachnospiraceae, and Lactobacillaceae. These families are abundant bacterial taxa found in other herbivorous mammals including ruminants [66,67], leaf-eating primates [68], and rodents [31,69], which may be viewed as microbiome convergence among distantly related groups. Parabacteroides, Lactobacillus, and Coprobacter observed in this study are also considered dominant gut microbiota found in fiber-rich diets and provide protection to the host from toxic plant metabolites and contribute to immune system functions and anti-inflammatory responses [70–74].

Results indicated that gut microbial alpha diversity varied with host taxonomy and dietary guild but not with host geographic location. Whereas neither Hill0 (OTU richness) nor phylogenetic diversity were explained by host species and diet, significant differences in Hill1 were observed among host species and dietary guilds indicated that these groups differ in the compositional evenness of their microbiomes. When comparing Hill1 among host species, significance was detected in two species S. hispidus and N. floridana both having higher diversity to two other species P. gossypinus and P. leucopus. Both Peromyscus species are omnivores, and S. hispidus and N. floridana are herbivores. Thus, the observed species effects may actually be related to their diet. Among host dietary guilds, diversity was higher in herbivorous rodents. This is likely because herbivores consume higher amounts of fiber-rich plant materials, harboring more fiber-digesting bacterial taxa including Parabacteroides and Cellulosilyticum. This result is congruent with other studies that support herbivorous mammals showing higher microbial diversity compared to mammals with other foraging strategies [15,75].

LEfSe analysis revealed that Families Porphyromonadaceae, Marinilabiliaceae and Lachnospiraceae were significantly more abundant in herbivores and granivores as compared to omnivores. Functionally, such families are composed of bacteria capable of breaking down cellulose and other plant polysaccharides into absorbable molecules including short-chain fatty acids thus providing host energy [76,77]. Granivores had moderate levels of microbial diversity, containing cellulolytic and fibrolytic bacteria, which are predicted to function in the breakdown of simple sugars and complex carbohydrates [75]. In fact, granivores depend upon the gut microbiota to degrade complex fibers in order to increase their digestion rate through a combination of rapid transit times and a high activity level of digestive enzymes [78]. Lactobacillaceae and Eubacteriaceae were significantly more abundant in omnivores, groups which can promote the breakdown of complex carbohydrates including cellulose and hemicellulose and are also prevalent in a protein-based diet.

Gut microbial compositional variation assessed from the perspective of unweighted, but not weighted UniFrac resulted in significant variable effects. The contrast in test results between unweighted and weighted metrics indicates that much of the effect of assessed factors are on selection for presence/absence of taxa across bacterial phylogeny, but microbiome relative abundance distributions substantially vary. However, sampling for each individual occurred at a single time point, so how bacterial lineages change in abundance over time and in response to seasonality was untested. Longitudinal studies on changing proportions of influential species could test this hypothesis. For unweighted UniFrac, significant variables by decreasing effect size were host phylogeny, host species, dietary guild, and geographic provenance. Host phylogeny explaining the most compositional variation supports an important role of co-evolutionary mechanisms. Phylosymbiosis was supported from multivariate analyses jointly considering other predictor variables, as well as in phylogenetic congruence testing. The evidence for such host phylogenetic effects on microbiomes is accumulating. For example, similar patterns have been observed for 11 species of herbivorous mammals in Masai Mara [7], 12 species of Madagascar lemurs [79], and 7 species of deer mice in the wild and captive environment [80]. The underlying mechanistic explanation for this phenomenon is probably a multifaceted combination of phenotypic traits and ecological factors. Closely related host species on average have more similar gut morphology, physiology, cell structure, and immune function [12,81]. Closely related species are also more likely to share similar ecological, social, and behavioral characteristics which can influence microbe dispersal and colonization [82,83].

In addition to host phylogeny, host species also explained additional variation. In fact, almost all pairwise comparisons between species were significantly different. Consistent with explanations for host phylogeny, species effects are consistent with on-going co-evolutionary processes within host species, species-specific ecological, behavioral or dietary effects, or some combination of these. Finally, whereas dietary guild explained aforementioned differentially abundant taxa as well as alpha and beta diversity effects, comparison of beta diversity from the perspective of intra-group variation revealed that sampled omnivores have a more variable gut microbiome. A Previous study [7] suggested this may be due to their increased dietary variability.

Dietary guild classifications, such as granivores, herbivores, and omnivores, encompass broader feeding strategies and ecological roles. Moreover, as the only representative of the Heteromyidae family in our dataset, C. hispidus may exhibit unique microbial dynamics that result from its specific evolutionary history; its microbiome may reflect ecological and environmental influences that lead to differences with herbivore and omnivore categories. While the aggregate data reveals significant differences, the individual species comparisons may lack the statistical power needed to detect differences due to shared microbiome traits among others (Fig 5D and 5E). The two omnivore species that differ from C. hispidus may have specific adaptations or dietary influences that significantly alter their microbiome composition, while other omnivores might share characteristics that dampen their variability. Moreover, the dietary guild is inherently tied to phylogeny, where closely related species often share similar dietary preferences. This relationship poses a challenge when attempting to disentangle the effects of diet from those of evolutionary history. As a result, it is difficult to determine whether the observed patterns in gut microbiota are driven primarily by dietary habits or by phylogenetic relatedness. Future studies could benefit from using phylogenetic comparative methods or including species with more divergent diets within similar phylogenetic clades to better isolate these variables.

The relatively weak influence of geography may be explained by a combination of uneven host species representation across localities and/or the spatial scale and environmental differences among localities. Locality could influence resident host microbiomes through local chance dispersal from the environment into the host’s microbiome, as well as selectively by differences in local food resource availability. Similar findings have been reported in wild mice, where gut microbial composition was influenced by geographic location, even when sampling sites were up to 100 kilometers apart [84], or when comparing mouse populations from different countries [85]. In our study, distinct ecoregions—including prairies and high plains—were defined based on their geographic locations, each characterized by varying topography and vegetation. Previous research has shown that the diversity of food sources and their proportions in diets can substantially affect regional gut microbiome differences [86,87]. The limited sample sizes from certain localities may disproportionately skew our findings, emphasizing the necessity for a more balanced sampling strategy to draw robust conclusions about locality effects on microbial communities. However, a comprehensive analysis of the diets of species across these geographical locations is beyond the scope of this study. Future research employing metabarcoding and metagenomics sequencing techniques will provide insights into specific dietary components and their correlations with gut microbiome diversity.

In conclusion, the findings from this study of 12 species of wild rodents support that host microbiomes are simultaneously shaped by many variables. Although studying assemblages of species in the wild is difficult due to uneven sampling designs, researchers’ inability to control variables, and researcher ignorance to important variables, the ability to frame comparisons in a phylogenetic context can reveal the importance of evolutionary relatedness of hosts. Although there are multiple unaccounted inputs that may explain the phylogenetic, species-specific, and dietary guild effects, their significance seems to clearly document that gut microbiome structure has considerable deterministic input as opposed to random assembly from the local pool of encountered microbes. Overall, the results of this study provide insight into the microbial community structure and how they co-vary with host phylogeny, dietary guild, and geography.

Supporting information

S1 Fig.

Rarefaction curves of Hill0 (A) and Hill1 (B) estimates for host species with subsampling between 500 and 6,000 reads at a step size of 500.

https://doi.org/10.1371/journal.pone.0316101.s001

(TIF)

S1 Table. Cytochrome-b sequences of twelve species of hosts including one outgroup species obtained from GenBank.

GenBank accession numbers and museum voucher numbers used in this study are listed.

https://doi.org/10.1371/journal.pone.0316101.s002

(PDF)

S2 Table. Dietary profiles for the twelve rodent species included in this study.

https://doi.org/10.1371/journal.pone.0316101.s003

(PDF)

S3 Table. Pairwise genetic distances among host species.

https://doi.org/10.1371/journal.pone.0316101.s004

(PDF)

S4 Table. Summary of 16S rRNA amplicon sequencing results for twelve host species.

https://doi.org/10.1371/journal.pone.0316101.s005

(PDF)

S5 Table. Pairwise test results of alpha diversity (Hill1) among host species and dietary guilds.

Shown are difference in means, upper and lower boundaries and Benjamini-Hochberg adjusted p-values.

https://doi.org/10.1371/journal.pone.0316101.s006

(PDF)

S6 Table. Pairwise test results of the effect of host species, diet and locality on the microbiota composition based on unweighted UniFrac distances.

https://doi.org/10.1371/journal.pone.0316101.s007

(PDF)

Acknowledgments

The authors would like to express their gratitude to the Bradley lab group for their support in specimen collection and sample preparation, and to the Natural Science Research Laboratory for providing tissue loans. We also extend our thanks to the Texas Tech High Performance Computing Center for their access and support with bioinformatics analyses. Thanks to RTL Genomics (Lubbock, TX, USA) for generating the Illumina data.

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