Next Article in Journal
The Effect of the Type and Concentration of Garlic (Allium sativum) on Heinz Body Concentrations in Canine Erythrocytes—An In Vitro Study
Previous Article in Journal
A Pilot Study on the Effects of a 10-Session Underwater Treadmill Programme on Canine Joint Range of Motion
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 15
Issue 21
10.3390/ani15213187
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gut Microbiome of Two Rodent Species (Niviventer confucianus and Apodemus agrarius) from Two Regions Exhibit Different Structures and Assembly Mechanisms

by
Haotian Li
1,2,
Qian Gao
1,
Jiawen Han
1,
Qiuyue Song
1,
Fangheng Yan
1,
Yunzhao Xu
1,
Chuansheng Zhang
1,
Xin Wang
1 and
Yuchun Li
2,*
1
College of Agriculture and Biology, Liaocheng University, Liaocheng 252059, China
2
Marine College, Shandong University (Weihai), Weihai 264209, China
*
Author to whom correspondence should be addressed.
Animals 2025, 15(21), 3187; https://doi.org/10.3390/ani15213187
Submission received: 22 August 2025 / Revised: 16 October 2025 / Accepted: 30 October 2025 / Published: 1 November 2025
(This article belongs to the Section Mammals)
Browse Figures
Versions Notes

Simple Summary

The influence of different regions on the structure and assembly mechanisms of the animal gut microbiome has long been of interest to scientists. This study investigated the gut microbiome of Niviventer confucianus and Apodemus agrarius collected from Anhui and Hubei provinces. We aimed to characterize the bacterial co-occurrence networks and elucidate the assembly mechanisms of the gut microbiota in conspecific hosts across different geographical regions. We found that the geographic region shaped the gut microbiome of conspecific rodents by altering the alpha diversity, co-occurrence networks, and assembly processes. However, further analysis is needed to determine which factor of the two geographical regions is the main factor affecting the gut microbiota of the two rodent species.

Abstract

The structure and assembly mechanism of wild animal gut microbiota represent persistent research hotspots. Among, the impact of geographic factors on the bacterial co-occurrence network characteristics and assembly mechanism of the gut microbiome remains unclear. Therefore, this study analyzed the gut microbiome of Niviventer confucianus and Apodemus agrarius from Anhui and Hubei provinces. The same alpha diversity pattern was found in the gut microbiome of species from the same region. The gut microbiome of the two rodent species in Anhui region exhibited “small world” characteristics, such as nodes with more local connections to allow interaction information (such as metabolites) to rapidly spread throughout the entire microbial community. In addition, dispersal limitations and heterogeneous selection accounted for higher proportions of the gut microbiome in the rodents from the Anhui and Hubei regions, respectively. The higher proportion of heterogeneous selection may exacerbate selection pressure in the Hubei region. Multiple regression on distance matrices analysis revealed that geographic region exerted a limited but significant influence (0 < R2 < 0.2, * p < 0.05) on the gut microbiome, surpassing the effects of host phylogeny, gender, and weight. Nevertheless, the roles of regional factors—such as environmental microbes, pollutants, and diet—remain unexamined, and their potential as key drivers of microbiota variation in these rodents warrants further investigation.

1. Introduction

The gut microbiome has been proven to be a complex ecosystem containing diverse and complex microbial communities that energize host life activities [1], supplement the digestive capacity [2], and maintain gut homeostatic balance [3]. In addition, the gut microbiome has been found to play an important role in enhancing animal endocrine [4], perception [5], memory functions [6], pathogen defense [7], and toxin metabolism [8]. Increasing evidence suggests that various internal and external factors, such as host genotype, diet, social network, and habitat environment, affect the composition and function of the gut microbiota in host animals [9,10,11,12,13].
Studies have also shown that geographic region is one of the most important factors influencing the gut microbiome composition of the same species. For example, 16% of the variation in the gut microbiome of wild house mice in Western Europe can be attributed to the geographical region [14]. Wang found significant differences in the alpha diversity of the rodent gut microbiome from different geographical locations (diet and environments) but similar gut microbiome compositions in different rodents from the same location [15]. Moreover, geographic region (shared microbial pool) was significantly correlated with the alpha diversity of the gut microbiome in rodents, whereas host genotype had a minimal effect on the abundance of the microbiota [14,16]. However, in other rodent species, host genetic identity has been shown to be a better predictor of microbiome structure and diversity [17,18]. Previous studies have not investigated the co-occurrence network characteristics and assembly mechanisms of the gut microbiomes of the same species from different regions. In different geographic regions, how do the co-occurrence network characteristics and assembly mechanisms of the gut microbiome differ in the same rodent? An open question is whether the established convergence in alpha diversity of gut microbiome in sympatric rodents also applies to their co-occurrence network characteristics and assembly mechanisms. Based on the above studies, we hypothesized that while geographic regions drive divergence in co-occurrence network characteristics and assembly mechanisms of gut microbiome within a species, sympatric rodent species will display convergence in these characteristics.
Our field survey identified Apodemus agrarius and Niviventer confucianus as the dominant rodent species in Hubei and Anhui provinces. Building upon previous extensive studies on the gut microbiota of A. agrarius and other rodents across different geographical regions [15,16], we collected samples of A. agrarius and N. confucianus from two distinct habitats—the mountainous areas of Anhui and the farmland of Hubei. To address our proposed scientific question, we characterized the gut microbiome structure, co-occurrence network characteristics, and assembly mechanisms of the two rodent species from two regions using 16S rRNA sequencing, bacterial co-occurrence network, and null model analyses.

2. Materials and Methods

2.1. Sample Collection and CYTB Gene Sequencing

The rodent feces used in this research were collected on 24 December 2019, in Anhui Province (Anhui Dabie Mountain Tourist Scenic Area, Lu’an City; average temperature in December, 3 °C; altitude, 234 m) and 18 January 2020, in Hubei Province (Jiufogang Village, Taohuashan Town, Shishou City; average temperature in January, 2 °C; altitude, 36 m). The distance between the two locations is approximately 500 km. We used Sherman live traps (7.62 × 8.89 × 22.86 cm; H. B. Sherman Traps, Inc., Tallahassee, FL, USA) to capture Niviventer confucianus and Apodemus agrarius, and the feces of each species was collected separately and used for sequencing. After collecting the feces samples, the rodents were immediately released into the wild. In total, 12 N. confucianus (ANC: 6 specimens of N. confucianus from Anhui, HNC: 6 specimens of N. confucianus from Hubei) and 10 A. agrarius (APA: 5 specimens of A. agrarius from Anhui, HPA: 5 specimens of A. agrarius from Hubei) were sampled. Detailed information on the rodent feces is shown in Table S1. Fecal samples were stored in sterile tubes and maintained at −20 °C during transfer. Before sequencing, all samples were stored in a −80 °C freezer at Liaocheng University.
Total DNA was extracted from the feces using the QIAamp DNA Stool Mini Kit (QIAGEN, Hamburg, Germany), following the manufacturer’s protocol. We amplified and sequenced the complete mitochondrial protein-coding gene cytochrome b (CYTB, 1140 bp) obtained from 22 individuals (12 from N. confucianus and 10 from A. agrarius) following the protocol described by Li et al. [19]. The sequences were manually edited in BioEdit v.7.2.5 [20] and aligned using MEGA 11 [21]. The CYTB gene sequences were subjected to BLAST+ v.2.17.0 analysis against the NCBI (National Center for Biotechnology Information) database, and species identification was determined using the percent identity threshold (PI) and query coverage (QC) methods. Phylogenetic status was determined using the maximum likelihood method (ML) based on the CYTB gene sequences in MEGA 11, with the GTR + G + I model. The bootstraps were obtained using a rapid bootstrapping algorithm with 1000 replicates.

2.2. 16S rRNA Gene Sequencing, Sequence Processing, and Bioinformatics Analysis

We used universal primers (338F, 5′-ACTCCTACGGGAGGCAGCA-3′; 806R, 5′- GGACTACHVGGGTWTCTAAT-3′) to amplify the V3–V4 hypervariable regions of bacterial 16S rRNA genes. The PCR amplification was performed in a 25 μL reaction mixture containing 5 μL reaction buffer (5×), 5 μL GC buffer (5×), 2 μL dNTP (2.5 mM), 1 μL forward primer (10 μM), 1 μL reverse primer (10 μM), 2 μL DNA template, 8.75 μL ddH2O, and 0.25 μL DNA Polymerase. The PCR conditions were as follows: initial denaturation at 98 °C for 2 min; 30 cycles of denaturation at 98 °C for 15 s, annealing at 65 °C for 30 s, and elongation at 72 °C for 30 s; and a final elongation at 72 °C for 5 min. DNA libraries were prepared from the PCR products using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s protocol. DNA libraries were sequenced on an Illumina NovaSeq PE250 platform (Illumina, USA).
Raw sequences were analyzed using EasyAmplicon software v.1.18.1 [22], with the Vsearch plugin used to merge paired-end reads of raw data, cut primers, and remove redundancies to generate a non-redundant sequence catalog. The unoise3 algorithm was used to denoise the sequences and cluster them into amplicon sequence variants (ASVs). The ASVs were annotated using the SILVA database v.138 with a threshold of 0.8–1 [23]. The Richness, Shannon, and Good’s coverage indices (alpha diversity) and Bray–Curtis distance (beta diversity) were calculated using the QIIME package v.191 [24]. The Tutools platform (http://cloudtutu.com.cn/) was used to generate rarefaction curves and species accumulation boxplots and perform principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and analysis of similarities (ANOSIM). STAMP software v.2.1.3 was used to detect bacteria with significant differences between regions [25]. A Spearman correlation coefficient (|r|) threshold > 0.7 and p < 0.001 were calculated after filtering operational taxonomic units with a mean relative abundance of less than 0.01% and accounting for less than 1/2 of the total sample size using R software v.4.5.1 (packages: WGCNA, psych, reshape2, and igraph), and network visualization was performed using Gephi v.0.10.1. The null model was also calculated and plotted using R software v.4.5.1 (packages: picante, parallel, ape, dplyr, and ggplot2).

2.3. Multiple Regression on (Dis)Similarity Matrices (MRMs)

The multiple regression on (dis)similarity matrices (MRMs) method was used to investigate the associations of host phylogeny (phylogenetic branch length), geographic location (latitude and longitude), gender (male and female were coded from 1 and 2, respectively), and weight with gut bacterial community α-diversity (i.e., Shannon index) and β-diversity distance (Bray–Curtis distances). Detailed information on phylogenetic branch length (total branch length), geographic location, gender, temperature, altitude, and weight for the two rodent species is shown in Table S1. The correlation coefficient (R2) and p-value were determined employing the MuMIn and ecodist packages in R software, with datasets were sampled 1000 times.

3. Results

3.1. Species Identification

In this study, the two rodent species were preliminarily identified based on their morphological characteristics. Then, we successfully amplified the complete 1140 bp mitochondrial CYTB sequences for 21 individuals except S4889. The BLAST search revealed rodent information using the CYTB sequence. All QC and PI values exceeded 99%, thus providing strong support for the reliability of the species identifications (Table S2). Although sequence data for S4489 were not obtained, the molecular results from all other specimens consistently supported the preliminary morphological identifications. Based on this congruence, individual S4889 was classified as A. agrarius. The CYTB phylogenetic tree displays the genetic relationships between 21 individuals in Supplementary Materials (Figure S1).

3.2. Data Statistics

After data quality control, 941,604 effective tags were obtained from 22 samples, for an average of 42,800. 18 effective tags per sample. The effective tags were classified into 1864 ASVs after the unoise3 algorithm analysis. The lower quartiles of the Good coverage index box plot (Figure 1A) were all above 90%, indicating that the bacterial communities in the samples effectively represented the bacteria in the guts of N. confucianus and A. agrarius. The species accumulation boxplot (Figure 1B) and 22 rarefaction curves (Figure 1C) were close to a plateau, indicating that the sample size and sequencing depth used in this study were reasonable for subsequent analyses.

3.3. Composition and Differences in the Gut Microbiome

At the phylum level (Figure 2A), Firmicutes (ANC, 29.50–69.80%; APA, 28.10–77.10%; HPA, 27.30–96.80%), Bacteroidetes (ANC, 26.70–49.5%; APA, 14.10–70.80%; HPA, 0.84–16.00%), and Proteobacteria (ANC, 2.52–15.10%; APA, 0.61–35.20%; HPA, 0.20–61.00%) were the main phyla in ANC, APA, and HPA, while Firmicutes (11.90–74.00%), Bacteroidetes (12.40% ± 59.80%), and Spirochaetae (0.039–39.20%) the main phyla in HNC. At the genus level (Figure 2B), Lachnospiraceae NK4A136 group (3.54–30.00%) was the most abundant in ANC, while Lactobacillus was the most abundant in APA (2.62–29.60%), HPA (12.80–86.90%), and HNC (2.41–66.9%).
At the genus level, differential bacteria in the same rodents from Anhui and Hubei provinces were identified by STAMP analysis (ANC vs. HNC; APA vs. HPA; p < 0.05). In Anhui Province, STAMP identified differences in the relative abundance of six genera (Ruminiclostridium 9, Ruminococcaceae NK4A214 group, Alloprevotella, Escherichia-Shigella, Pediococcus, and Proteiniphilum) and two genera (Pediococcus and Petrimonas) in ANC and APA, respectively (Figure 2C,D). However, in Hubei, only Hydrotalea was significantly enriched in N. confucianus (Figure 2C).

3.4. Alpha and Beta Analyses

The Richness and Shannon indices indicated that regional identity had a significant effect on the gut microbiome at the ASV level in both Anhui and Hubei Province (Richness, ANC vs. HNC, p ≤ 0.01, APA vs. HPA, p ≤ 0.01; Shannon, ANC vs. HNC, p ≤ 0.01, APA vs. HPA, p ≤ 0.01; Figure 3A,B). The same species exhibited different patterns in these two regions, with significantly greater alpha diversity observed for ANC and APA from Anhui Province than for HNC and HPA in Hubei Province.
At the ASV level, PCoA (Figure 3C) and non-metric multidimensional scaling (Figure 3D) analyses (Bray–Curtis distance) showed that the gut microbiome of the rodents were clustered by host species. These results indicated that the gut microbiome composition is highly similar between different individuals of the same rodent species. However, the ANOSIM analysis showed that the two rodent species in Anhui were significantly different from those in Hubei (Figure 3E, ANC vs. HNC, R = 0.469, p = 0.002; Figure 3F, APA vs. HPA, R = 0.276, p = 0.031).

3.5. MRMs Analysis

Compared to host factors (host phylogeny, weight, and gender), geographic location, temperature, and altitude exerted a greater influence on the gut microbiome (alpha and beta diversity) of the two rodent species (0 < R2 > 0.2, p < 0.05, Table 1). However, the correlation coefficient for geographical distance was low (0 < R2 < 0.2, p < 0.05). A substantial proportion of the variance remains unexplained, potentially due to unassessed variables such as diet and environmental pollutants.

3.6. Bacterial Co-Occurrence Network and Community Assemblages

Based on the ASV level, we constructed bacterial co-occurrence networks for N. confucianus and A. agrarius in the two regions (Table 2 and Figure 4). The positive proportion of bacterial co-occurrence networks in the two rodent species from the two regions was greater than the negative proportion. The microbial co-occurrence network in the species from Anhui surpassed that from Hubei in terms of nodes (139 APA vs. 14 HPA; 172 ANC vs. 55 HNC), edges (253 APA vs. 7 HPA; 197 ANC vs. 53 HNC), modularity (0.888 APA vs. 0.857 HPA; 0.960 ANC vs. 0.909 HNC), and average degree (3.640 APA vs. 1.000 HPA; 2.291 ANC vs. 1.927 HNC).
Using null models, we investigated the assembly processes of the gut microbiome of N. confucianus and A. agrarius in the two regions. Figure 5A showed that in the HNC, deterministic processes (|βNTI| > 2) generally dominated the assembly of bacterial communities, while in ANC, APA, and HPA, stochastic processes (|βNTI| < 2) dominated. The proportion of dispersal limitation was significantly higher in the gut microbiome of the two rodent species from Anhui region than from Hubei (Figure 5B). The proportion of heterogeneous selection was significantly higher in the gut microbiome of the two rodent species in Hubei region compared to those in Anhui region, especially HNC.

4. Discussion

Different geographical regions can significantly alter alpha diversity, beta diversity, bacterial abundance, co-occurrence network characteristics, and assembly mechanisms of the gut microbiome in wild species. Many studies have indicated that habitat environmental factors are the main drivers of alpha and beta diversity in wild animals [1,3,15,26]. In our study, the gut microbiome of the two rodent species from the same region had the same alpha diversity pattern. Through alpha diversity differential analysis, PCoA, and ANOSIM, we found that the gut microbiome composition, alpha and beta diversity of the same species differed significantly across the different regions.
At the network level, the increased edge count and high proportion of positive connections in the Anhui region suggest more frequent and potentially cooperative microbial interactions within the gut communities of both rodent species. Such modular topological patterns have been shown to reflect functional units and potential ecological niches within microbial communities, supporting the interpretation that regional factors foster distinct inter-bacterial associations [27,28,29]. The increase in modularity may reflect a denser functional association and/or niche partitioning in the gut microbiome of the two rodent species in Anhui. The bacterial co-occurrence networks of the two rodent species in Anhui had a higher average degree (3.640 APA vs. 1.000 HPA; 2.291 ANC vs. 1.927 HNC); thus, these species exhibited “small world” characteristics. In “small world” systems, most nodes have more local connections, which allows interaction information (such as metabolites) to rapidly spread throughout the entire microbial community [30,31,32]. The null models revealed that the proportion of dispersal limitations in the gut microbiome of the two rodent species from Anhui region was significantly higher than that in the species from Hubei region. Previous studies have shown that dispersal limitations and deterministic processes are the primary factors promoting bacterial community diversity [33,34]. Furthermore, the gut microbiome of the two rodent species from Anhui was influenced by four assembly processes, whereas that of the rodents from Hubei was influenced by three assembly processes. This suggests that the gut microbiome of the two rodent species from Anhui was affected by more complex dynamics. Therefore, the interactions between the gut microbiome of the two rodent species from Anhui was more complex and may have more intricate dynamics relative to that in the rodents from Hubei [10,35].
However, the proportion of heterogeneous selection in the gut microbiome of the two rodent species from Hubei was significantly higher than that in the rodents from Anhui. This result indicates that selection pressure may have been exacerbated in the two rodent species from Hubei. Increased selection pressure reduces the role of random processes in the gut microbiome assembly. Our MRM analysis showed that geographic regions had limited explanatory power (R2 < 0.2), while host phylogeny, gender, and weight exhibited no significant influence. Moreover, the MRM analysis revealed that factors such as elevation and temperature exhibited consistent R2 values with geographic location. Therefore, multiple environmental factors—including elevation and temperature across different geographic regions—collectively influenced rodent gut microbiota diversity. This indicated that geographic regional factors should be considered as an integrated unit and not analyzed in isolation [36,37,38]. However, the R2 value for geographic location was low (0 < R2 < 0.2), indicating that over 80% of the variation was explained by other factors, such as differences in microbial pools, diet, and pollutant exposure. Winter food scarcity occurs in both study regions, with limited residual grains available from Hubei’s farmlands. Although dietary factors are likely influential, we cannot assess their role due to insufficient compositional data. Notably, the sampling sites in Hubei were closer to human settlements and likely experienced higher pollution levels than those in Anhui. Since pollutants can significantly reshape gut microbiota structure and assembly, we suggest that pesticide exposure may be a strong candidate driver of the observed differences.
This hypothesis was further supported by the results of the STAMP analysis. A greater number of beneficial bacteria was significantly enriched in the gut microbiomes of the two rodent species in Anhui, indicating that the gut microbiome composition of rodents in Anhui is more stable. For example, Pediococcus is an important genus of lactic acid bacteria that contains a wide range of probiotic bacteria [39,40]. Researchers have found that Pediococcus spp. can effectively improve the gut microbiome and inflammatory status of mice [41,42]. Of note, Petrimonas, Ruminiclostridium, Alloprevotella, and Proteiniphilum are associated with the generation of short-chain fatty acids [43,44,45,46], which can provide energy for animal life activities and maintain gut homeostasis [47,48].

5. Conclusions

We report that the gut microbiome of N. confucianus and A. agrarius from the same region had similar alpha diversity pattern, co-occurrence network characteristics, and assembly mechanisms. Different geographic regions can affect the gut microbiome composition and co-occurrence networks of the gut microbiome in the same rodent species. For example, the two rodent species from Anhui region exhibited “small world” characteristics and possessed more complex microbial community structures relative to those from Hubei. The assembly processes of the gut microbiome (null model analysis) can partially explain this result. Geographic region exerted a limited yet significant effect (0 < R2 < 0.2, p < 0.05) on the gut microbiome, surpassing the influence of host phylogeny, gender, and weight in the MRM analysis. However, the influence of regional factors including environmental microbes, pollutants, and food composition, remains to be evaluated. Thus, whether they represent the main drivers of gut microbiota variations in these rodents requires further study.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani15213187/s1, Table S1: Detailed information of specimen in this study; Table S2: Species identification information; Figure S1: Phylogenetic tree of two rodent species based on CYTB gene.

Author Contributions

Conceptualization, H.L. and Y.L.; methodology, H.L.; software, Q.G.; validation, H.L. and Y.L.; formal analysis, H.L.; investigation, H.L., J.H., Q.S., F.Y., Y.X., C.Z. and X.W.; resources, H.L.; data curation, H.L.; writing—original draft preparation, H.L., Q.G., J.H., Q.S., F.Y., Y.X., C.Z. and X.W.; writing—review and editing, H.L.; visualization, H.L.; supervision, Y.L.; project administration, H.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 32270480 and the Natural Science Foundation of Shandong Province, China, grant number ZR2023QC255.

Institutional Review Board Statement

The sampling and experiment procedures complied with internationally recognized standards and were approved by the Special Committee on Scientific Research Ethic of Liaocheng University (Licence No. 2019100402).

Informed Consent Statement

Not applicable.

Data Availability Statement

The 16S rRNA gene sequencing data are available at the Genome Sequence Archive in BIG Data Center (Accession number PRJCA043574) and CYTB sequences are available at the ScienceDB (https://doi.org/10.57760/sciencedb.29591).

Acknowledgments

We thank all the team members for participating in the field collection.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Wang, X.; Gao, X.; Chen, Y.; Wu, X.; Shang, Y.; Zhang, Z.; Zhou, S.; Zhang, H. Comparative analysis of the gut microbiome of ungulate species from Qinghai-Xizang plateau. Ecol. Evol. 2024, 14, e70251. [Google Scholar] [CrossRef]
  2. Li, Y.; Zhang, T.; Shi, M.; Zhang, B.; Hu, X.; Xu, S.; Ding, J.; Liu, S.; Hu, D.; Rubenstein, D. Characterization of intestinal microbiota and fecal cortisol, T3, and IgA in forest musk deer (Moschus berezovskii) from birth to weaning. Integr. Zool. 2021, 16, 300–312. [Google Scholar] [CrossRef]
  3. Wang, X.; Shang, Y.; Xing, Y.; Chen, Y.; Wu, X.; Zhang, H. Captive environments reshape the compositions of carbohydrate active enzymes and virulence factors in wolf gut microbiome. BMC Microbiol. 2025, 25, 142. [Google Scholar] [CrossRef]
  4. Qi, X.; Yun, C.; Pang, Y.; Qiao, J. The impact of the gut microbiota on the reproductive and metabolic endocrine system. Gut Microbes 2021, 13, 1894070. [Google Scholar] [CrossRef] [PubMed]
  5. Zhao, X.; Xue, C.; Wang, Y.; Liu, X.; Li, R.; Yi, X. The potential of olfaction loss to induce cognitive impairment and anxiety behavior in mice via the microbiota-gut-brain axis. Front. Microbiol. 2025, 16, 1595742. [Google Scholar] [CrossRef] [PubMed]
  6. Zhao, X.; Guo, J.; Wang, Y.; Yi, X. High-tannin food enhances spatial memory and scatter-hoarding in rodents via the microbiota-gut-brain axis. Microbiome 2024, 12, 140. [Google Scholar] [CrossRef] [PubMed]
  7. Moran, N.A.; Ochman, H.; Hammer, T.J. Evolutionary and ecological consequences of gut microbial communities. Annu. Rev. Ecol. Evol. Syst. 2019, 50, 451–475. [Google Scholar] [CrossRef]
  8. Alvarenga, L.; Kemp, J.A.; Baptista, B.G.; Ribeiro, M.; Lima, L.S.; Mafra, D. Production of Toxins by the Gut Microbiota: The Role of Dietary Protein. Curr. Nutr. Rep. 2024, 13, 340–350. [Google Scholar] [CrossRef]
  9. Nouri, Z.; Zhang, X.; Khakisahneh, S.; Degen, A.; Wang, D. The microbiota-gut-kidney axis mediates host osmoregulation in a small desert mammal. NPJ Biofilms Microbiomes 2022, 8, 16. [Google Scholar] [CrossRef]
  10. Raulo, A.; Allen, B.E.; Troitsky, T.; Husby, A.; Firth, J.A.; Coulson, T.; Knowles, S.C.L. Social networks strongly predict the gut microbiota of wild mice. ISME J. 2021, 15, 2601–2613. [Google Scholar] [CrossRef]
  11. Rojas, A.; Ramírez-Barahona, S.; Holekamp, E.; Theis, R. Host phylogeny and host ecology structure the mammalian gut microbiota at different taxonomic scales. Anim. Microbiome 2021, 3, 33. [Google Scholar] [CrossRef]
  12. Shang, Y.; Wu, X.; Wang, X.; Chen, Y.; Dong, Y.; Wei, Q.; Pang, B.; Wang, Q.; Liu, G.; Dou, H.; et al. Microcystin exposure alters gut microbiota composition in fish: An in-Situ analysis of post-bloom effects in Hulun Lake, China. Environ. Pollut. 2024, 363 Pt 2, 125174. [Google Scholar] [CrossRef]
  13. Wang, X.; Wu, X.; Shang, Y.; Mei, X.; Zhou, S.; Wei, Q.; Sun, G.; Dong, Y.; Zhang, H. Convergent evolution of the gut microbiome in marine carnivores. Ecol. Evol. 2022, 12, e9373. [Google Scholar] [CrossRef] [PubMed]
  14. Linnenbrink, M.; Wang, J.; Hardouin, E.A.; Künzel, S.; Metzler, D.; Baines, J.F. The role of biogeography in shaping diversity of the intestinal microbiota in house mice. Mol. Ecol. 2013, 22, 1904–1916. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, Z.; Zhang, C.; Li, G.; Yi, X. The influence of species identity and geographic locations on gut microbiota of small rodents. Front. Microbiol. 2022, 13, 983660. [Google Scholar] [CrossRef] [PubMed]
  16. Wu, Y.; Zhou, T.; Gu, C.; Yin, B.; Yang, S.; Zhang, Y.; Wu, Y.; Wei, W. Geographical distribution and species variation of gut microbiota in small rodents from the agro-pastoral transition ecotone in northern China. Ecol. Evol. 2024, 14, e11084. [Google Scholar] [CrossRef]
  17. Knowles, S.C.L.; Eccles, R.M.; Baltrūnaitė, L. Species identity dominates over environment in shaping the microbiota of small mammals. Ecol. Lett. 2019, 22, 826–837. [Google Scholar] [CrossRef]
  18. Weinstein, S.B.; Martínez-Mota, R.; Stapleton, T.E.; Klure, D.M.; Greenhalgh, R.; Orr, T.J.; Dale, C.; Kohl, K.D.; Dearing, M.D. Microbiome stability and structure is governed by host phylogeny over diet and geography in woodrats (Neotoma spp.). Proc. Natl. Acad. Sci. USA 2021, 118, e2108787118. [Google Scholar] [CrossRef]
  19. Li, H.; Kong, L.; Wang, K.; Zhang, S.; Motokawa, M.; Wu, Y.; Wang, W.; Li, Y. Molecular phylogeographic analyses and species delimitations reveal that Leopoldamys edwardsi (Rodentia: Muridae) is a species complex. Integr. Zool. 2019, 14, 494–505. [Google Scholar] [CrossRef]
  20. Hall, T.A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 1999, 41, 95–98. [Google Scholar]
  21. Tamura, K.; Stecher, G.; Kumar, S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol. Biol. Evol. 2021, 38, 3022–3027. [Google Scholar] [CrossRef] [PubMed]
  22. Liu, Y.; Chen, L.; Ma, T.; Li, X.; Zheng, M.; Zhou, X.; Chen, L.; Qiao, X.; Xi, J.; Lu, H.; et al. EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research. iMeta 2023, 2, e83. [Google Scholar] [CrossRef] [PubMed]
  23. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef]
  24. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef]
  25. Parks, D.H.; Tyson, G.W.; Hugenholtz, P.; Beiko, R.G. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 2014, 30, 3123–3124. [Google Scholar] [CrossRef]
  26. Wang, X.; Wu, X.; Shang, Y.; Gao, Y.; Li, Y.; Wei, Q.; Dong, Y.; Mei, X.; Zhou, S.; Sun, G.; et al. High-altitude drives the convergent evolution of alpha diversity and indicator microbiota in the gut microbiomes of ungulates. Front. Microbiol. 2022, 13, 953234. [Google Scholar] [CrossRef]
  27. Chaffron, S.; Rehrauer, H.; Pernthaler, J.; von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 2010, 20, 947–959. [Google Scholar] [CrossRef]
  28. Li, X.; Wang, H.; Zang, Y.; Xue, S.; Xin, J.; Liu, L.; Tang, X.; Chen, J. Exploring the structure and assembly of seagrass microbial communities in rhizosphere and phyllosphere. Appl. Environ. Microbiol. 2025, 91, e0243724. [Google Scholar] [CrossRef]
  29. Wang, Y.; Zhou, P.; Zhou, W.; Huang, S.; Peng, C.; Li, D.; Li, G. Network Analysis Indicates Microbial Assemblage Differences in Life Stages of Cladophora. Appl. Environ. Microbiol. 2023, 89, e0211222. [Google Scholar] [CrossRef]
  30. Ma, Z.S.; Shi, P. Critical complex network structures in animal gastrointestinal tract microbiomes. Anim. Microbiome 2024, 6, 23. [Google Scholar] [CrossRef] [PubMed]
  31. Wang, X.; Wang, H.; Liang, Y.; McMinn, A.; Wang, M. Community organization and network complexity and stability: Contrasting strategies of prokaryotic versus eukaryotic microbiomes in the Bohai Sea and Yellow Sea. Msphere 2024, 9, e0039524. [Google Scholar] [CrossRef]
  32. Watts, D.J.; Strogatz, S.H. Collective dynamics of ‘small-world’ networks. Nature 1998, 393, 440–442. [Google Scholar] [CrossRef]
  33. Li, G.; Li, J.; Kohl, K.D.; Yin, B.; Wei, W.; Wan, X.; Zhu, B.; Zhang, Z. Dietary shifts influenced by livestock grazing shape the gut microbiota composition and co-occurrence networks in a local rodent species. J. Anim. Ecol. 2019, 88, 302–314. [Google Scholar] [CrossRef] [PubMed]
  34. Moeller, A.H.; Suzuki, T.A.; Lin, D.; Lacey, E.A.; Wasser, S.K.; Nachman, M.W. Dispersal limitation promotes the diversification of the mammalian gut microbiota. Proc. Natl. Acad. Sci. USA 2017, 114, 13768–13773. [Google Scholar] [CrossRef]
  35. Zeng, J.; Lin, Y.; Zhao, D.; Huang, R.; Xu, H.; Jiao, C. Seasonality overwhelms aquacultural activity in determining the composition and assembly of the bacterial community in Lake Taihu, China. Sci. Total Environ. 2019, 683, 427–435. [Google Scholar] [CrossRef]
  36. Couch, E.; Stagaman, K.; Spaan, S.; Combrink, J.; Sharpton, J.; Beechler, R.; Jolles, E. Diet and gut microbiome enterotype are associated at the population level in African buffalo. Nat. Commun. 2021, 12, 2267. [Google Scholar] [CrossRef]
  37. Fu, H.; Zhang, L.; Fan, C.; Liu, C.; Li, W.; Cheng, Q.; Zhao, X.; Jia, S.; Zhang, Y. Environment and host species identity shape gut microbiota diversity in sympatric herbivorous mammals. Microb. Biotechnol. 2021, 14, 1300–1315. [Google Scholar] [CrossRef]
  38. Shin, J.H.; Sim, M.; Lee, J.Y.; Shin, D.M. Lifestyle and geographic insights into the distinct gut microbiota in elderly women from two different geographic locations. J. Physiol. Anthropol. 2016, 35, 31. [Google Scholar] [CrossRef] [PubMed]
  39. Jiang, S.; Cai, L.; Lv, L.; Li, L. Pediococcus pentosaceus, a future additive or probiotic candidate. Microb. Cell Fact. 2021, 20, 45. [Google Scholar] [CrossRef]
  40. Porto, M.C.; Kuniyoshi, T.M.; Azevedo, P.O.; Vitolo, M.; Oliveira, R.P. Pediococcus spp.: An important genus of lactic acid bacteria and pediocin producers. Biotechnol. Adv. 2017, 35, 361–374. [Google Scholar] [CrossRef] [PubMed]
  41. Dong, F.; Xiao, F.; Li, X.; Li, Y.; Wang, X.; Yu, G.; Zhang, T.; Wang, Y. Pediococcus pentosaceus CECT 8330 protects DSS-induced colitis and regulates the intestinal microbiota and immune responses in mice. J. Transl. Med. 2022, 20, 33. [Google Scholar] [CrossRef]
  42. Jiang, S.; Xia, J.; Lv, L.; Wang, Q.; Li, Y.; Xu, Q.; Bian, X.; Ye, J.; Yang, L.; Jiang, H.; et al. Effects of Pediococcus pentosaceus LI05 on immunity and metabolism in germ-free rats. Food Funct. 2021, 12, 5077–5086. [Google Scholar] [CrossRef]
  43. Dou, L.; Liu, C.; Chen, X.; Yang, Z.; Hu, G.; Zhang, M.; Sun, L.; Su, L.; Zhao, L.; Jin, Y. Supplemental Clostridium butyricum modulates skeletal muscle development and meat quality by shaping the gut microbiota of lambs. Meat Sci. 2023, 204, 109235. [Google Scholar] [CrossRef]
  44. Han, B.; Shi, L.; Bao, M.Y.; Yu, F.L.; Zhang, Y.; Lu, X.Y.; Wang, Y.; Li, D.X.; Lin, J.C.; Jia, W.; et al. Dietary ellagic acid therapy for CNS autoimmunity: Targeting on Alloprevotella rava and propionate metabolism. Microbiome 2024, 12, 114. [Google Scholar] [CrossRef]
  45. Plaza-Díaz, J.; Manzano, M.; Ruiz-Ojeda, F.J.; Giron, M.D.; Salto, R.; López-Pedrosa, J.M.; Santos-Fandila, A.; Garcia-Corcoles, M.T.; Rueda, R.; Gil, Á. Intake of slow-digesting carbohydrates is related to changes in the microbiome and its functional pathways in growing rats with obesity induced by diet. Front. Nutr. 2022, 9, 992682. [Google Scholar] [CrossRef]
  46. Wang, X.; Zhang, Y.; Li, Y.; Luo, Y.-L.; Pan, Y.-R.; Liu, J.; Butler, D. Alkaline environments benefit microbial K-strategists to efficiently utilize protein substrate and promote valorization of protein waste into short-chain fatty acids. Chem. Eng. J. 2021, 404, 127147. [Google Scholar] [CrossRef]
  47. Wang, X.; Shang, Y.; Wei, Q.; Wu, X.; Dou, H.; Zhang, H.; Zhou, S.; Sha, W.; Sun, G.; Ma, S.; et al. Comparative analyses of the gut microbiome of two fox species, the red fox (Vulpes vulpes) and corsac fox (Vulpes corsac), that occupy different ecological niches. Microb. Ecol. 2022, 83, 753–765. [Google Scholar] [CrossRef]
  48. Xu, B.; Song, P.; Jiang, F.; Cai, Z.; Gu, H.; Gao, H.; Li, B.; Liang, C.; Qin, W.; Zhang, J.; et al. Large-scale metagenomic assembly provide new insights into the genetic evolution of gut microbiomes in plateau ungulates. NPJ Biofilms Microbiomes 2024, 10, 120. [Google Scholar] [CrossRef]
Animals 15 03187 g001
Figure 1. Boxplot of Goods coverages index between four groups (A), species accumulation boxplot (B), and rarefaction curves (C). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province. The species accumulation boxplot and multiple rarefaction curves reached a plateau, implying that the sequenced samples were adequate for subsequent analysis.
Figure 1. Boxplot of Goods coverages index between four groups (A), species accumulation boxplot (B), and rarefaction curves (C). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province. The species accumulation boxplot and multiple rarefaction curves reached a plateau, implying that the sequenced samples were adequate for subsequent analysis.
Animals 15 03187 g001
Animals 15 03187 g002
Figure 2. Gut microbiome composition of the two rodent species from two regions. Cumulative relative abundance plot of the top 10 bacteria at the phylum (A) and genus (B) levels. At the genus level, STAMP analysis between same species from two regions ((C), ANC vs. HNC; (D), APA vs. HPA). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Figure 2. Gut microbiome composition of the two rodent species from two regions. Cumulative relative abundance plot of the top 10 bacteria at the phylum (A) and genus (B) levels. At the genus level, STAMP analysis between same species from two regions ((C), ANC vs. HNC; (D), APA vs. HPA). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Animals 15 03187 g002
Animals 15 03187 g003
Figure 3. Alpha and beta analyses. Wilcoxon test of Richness (A) and Shannon (B) indices between same species from two regions (**, p < 0.01). Principal coordinates analysis (C) and Non-metric multidimensional scaling (D) of gut microbiome composition. Each point represents a sequencing sample. Analysis of similarities analysis between same species from two regions ((E), ANC vs. HNC; (F), APA vs. HPA). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Figure 3. Alpha and beta analyses. Wilcoxon test of Richness (A) and Shannon (B) indices between same species from two regions (**, p < 0.01). Principal coordinates analysis (C) and Non-metric multidimensional scaling (D) of gut microbiome composition. Each point represents a sequencing sample. Analysis of similarities analysis between same species from two regions ((E), ANC vs. HNC; (F), APA vs. HPA). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Animals 15 03187 g003
Animals 15 03187 g004
Figure 4. Bacterial co-occurrence network in the APA (A), HPA (B), ANC (C), and HNC (D). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province. Nodes of different colors represent different phyla, and node size indicates the abundance of OTUs.
Figure 4. Bacterial co-occurrence network in the APA (A), HPA (B), ANC (C), and HNC (D). ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province. Nodes of different colors represent different phyla, and node size indicates the abundance of OTUs.
Animals 15 03187 g004
Animals 15 03187 g005
Figure 5. Evaluating the mechanisms of gut microbiome assembly in the same species from two regions using a null model analysis. (A). Contribution of deterministic processes (|βNTI| ≥ 2) and stochastic processes (|βNTI| < 2) to gut microbiome assembly of the two rodent species from two regions. (B). Proportion of gut microbiome assembly mechanisms in the two rodent species from two regions. ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Figure 5. Evaluating the mechanisms of gut microbiome assembly in the same species from two regions using a null model analysis. (A). Contribution of deterministic processes (|βNTI| ≥ 2) and stochastic processes (|βNTI| < 2) to gut microbiome assembly of the two rodent species from two regions. (B). Proportion of gut microbiome assembly mechanisms in the two rodent species from two regions. ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Animals 15 03187 g005
Table 1. MRM results of two rodent species from two different geographical locations.
Table 1. MRM results of two rodent species from two different geographical locations.
FactorsANC/HNCAPA/HPA
Alpha DiversityBeta DiversityAlpha DiversityBeta Diversity
R2pR2pR2pR2p
Weight0.0560.0540.0200.2650.0080.6940.0020.835
Gender0.0110.3430.0220.2660.0040.6150.0030.702
Geographic location0.1960.0090.1660.00020.1540.0110.0640.021
Altitude0.1960.0100.1660.0010.1540.0120.0640.019
Temperature0.1960.0110.1660.00090.1540.0110.0640.017
Phylogenetic branch length0.0290.0990.0070.5340.0050.6340.0430.254
Table 2. Properties of bacterial co-occurrence network.
Table 2. Properties of bacterial co-occurrence network.
PropertiesAPAHPAANCHNC
Nodes1391417255
Edges253719753
Modularity0.8880.8570.9600.909
Network density0.0260.0770.0130.036
Positive proportions90.909%85.714%97.462%100%
Negative proportions9.091%14.285%2.538%0
Average Degree3.6401.0002.2911.927
Note: ANC: N. confucianus in Anhui Province; HNC: N. confucianus in Hubei Province; APA: A. agrarius in Anhui Province; HPA: A. agrarius in Hubei Province.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, H.; Gao, Q.; Han, J.; Song, Q.; Yan, F.; Xu, Y.; Zhang, C.; Wang, X.; Li, Y. Gut Microbiome of Two Rodent Species (Niviventer confucianus and Apodemus agrarius) from Two Regions Exhibit Different Structures and Assembly Mechanisms. Animals 2025, 15, 3187. https://doi.org/10.3390/ani15213187

AMA Style

Li H, Gao Q, Han J, Song Q, Yan F, Xu Y, Zhang C, Wang X, Li Y. Gut Microbiome of Two Rodent Species (Niviventer confucianus and Apodemus agrarius) from Two Regions Exhibit Different Structures and Assembly Mechanisms. Animals. 2025; 15(21):3187. https://doi.org/10.3390/ani15213187

Chicago/Turabian Style

Li, Haotian, Qian Gao, Jiawen Han, Qiuyue Song, Fangheng Yan, Yunzhao Xu, Chuansheng Zhang, Xin Wang, and Yuchun Li. 2025. "Gut Microbiome of Two Rodent Species (Niviventer confucianus and Apodemus agrarius) from Two Regions Exhibit Different Structures and Assembly Mechanisms" Animals 15, no. 21: 3187. https://doi.org/10.3390/ani15213187

APA Style

Li, H., Gao, Q., Han, J., Song, Q., Yan, F., Xu, Y., Zhang, C., Wang, X., & Li, Y. (2025). Gut Microbiome of Two Rodent Species (Niviventer confucianus and Apodemus agrarius) from Two Regions Exhibit Different Structures and Assembly Mechanisms. Animals, 15(21), 3187. https://doi.org/10.3390/ani15213187

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop