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Gut microbiome reveals the trophic variation and significant adaption of three sympatric forest-dwelling ungulates on the eastern Qinghai-Xizang Plateau

BMC Microbiology volume 25, Article number: 128 (2025) Cite this article

Abstract

Background

The gut microbiome of herbivorous mammals regulates numerous physiological processes, including digestion and energy metabolism. The complex stomach architecture of ruminants, in conjunction with the metabolic capabilities of their microbiota, confers a considerable adaptive advantage to these animals. Nevertheless, a significant gap persists in comparative studies on the variations in the gut microbiome among sympatric ruminants and their potential adaptive implications. Accordingly, in this study, 16S rRNA gene sequencing and metagenomic approaches were used to analyse the composition and functional attributes of the gut microbiome of sympatric Moschus chrysogaster, Capricornis sumatraensis, and Cervus albirostris inhabiting the eastern periphery of the Qinghai-Xizang Plateau.

Results

The gut microbiome of C. albirostris exhibited a higher diversity than that of M. chrysogaster and C. sumatraensis, whereas those of M. chrysogaster and C. sumatraensis were similar. Although species-specific variations existed among the three mammalian microbiomes, the microbiomes of C. albirostris and C. sumatraensis were more similar, whereas that of M. chrysogaster was markedly distinct. Metagenomic analysis revealed a pattern of functional convergence in the gut microbiome of the three species, with the gut microbiome of C. albirostris exhibiting a pronounced emphasis on carbohydrate metabolism, significantly surpassing that of M. chrysogaster and C. sumatraensis. Compared to the other two species, the gut microbiome of C. sumatraensis presented significantly elevated levels of amino acids and energy metabolism, whereas that of M. chrysogaster presented an increased capacity for 3-hydroxyacyl- [acyl carrier protein]-dehydratase production.

Conclusion

These findings suggest that the gut microbiome of sympatric M. chrysogaster, C. sumatraensis, and C. albirostris tend to converge. Metabolic variations within their gut microbiome may result in differential food resource utilisation, potentially indicating significant nutritional and ecological trait characteristics for stable coexistence.

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Background

The gut microbiome is integral to a myriad of physiological processes within the host, including digestion [1], metabolism [2], immunity [3], behaviour, and development [4]. While mammalian genomes can only encode a limited number of digestive enzymes, the gut microbiome produces a wide variety of metabolic enzymes. These enzymes are crucial for digestion, especially in herbivorous animals, as they help break down plant cellulose [5]. Ruminants constitute a highly successful group of animals, comprising the largest group among all ungulates [6]. Notably, the gut microbiome plays an indispensable role in the unique digestive process of ruminants. Their gut microorganisms perform various metabolic functions such as cellulose degradation, hemicellulose dissolution, and starch hydrolysis, facilitating the breakdown of otherwise indigestible plant fibres within the host [7]. Faeces, distinct from intestinal contents, are typically used to represent the overall metabolic function and microbial composition of an animal’s intestine [8]. For example, variations in faecal microbiome can influence nutrient absorption and environmental adaptation [9]. In addition, faecal microbiota transplantation can alter the host’s gut microbiota composition and enhance immunity [10]. Additionally, it demonstrated that faecal microbial composition reflects the composition and function of gut bacteria [11]. These findings suggest that use of faecal samples to understand the succession and metabolic functions of ruminant microbial communities is both effective and convenient. Through the process of coevolution, species within the same ecological domain undergo niche differentiation, leading to the establishment of distinct ecological niches across various dimensions of resource utilisation, ultimately facilitating stable coexistence [12–13]. The rumen microbiome of ruminants is pivotal for the digestion of fibre-rich food, representing a crucial aspect of stable coexistence among cooccurring ruminants. However, few studies have been conducted on the correlation between the microbiomes of cooccurring ruminants and their coexistence.

The eastern region of the Qinghai-Xizang Plateau is influenced by the East Asian monsoon, resulting in lush vegetation and rich biodiversity [14]. Concurrently, the eastern region of the Qinghai-Xizang Plateau features a high-altitude environment, characterised by elevations exceeding 3,500 m, coupled with low temperatures and reduced oxygen levels [15]. Historically, human economic activities have had relatively minimal impacts on this area, allowing the natural ecosystem to remain predominantly intact. Consequently, this has fostered a diverse array of wild ungulate species, establishing an ideal environment for the survival and evolution of montane forest-dwelling ungulates. The family Moschidae includes Moschus chrysogaster, also referred to as the Alpine musk deer (AMD). They inhabit the high-altitude, extremely cold forest shrubbery zones and the relatively cold, arid mountain forests on the northeastern periphery of the plateau [16]. AMD primarily forage on Dicotyledon plants, with occasional consumption of Monocotyledon plants. It has exhibited minimal interest in plants from the Poaceae [17]. The family Bovidae comprises Capricornis sumatraensis, known as the mainland serow (MS), which primarily inhabits steep, stony forests, and shrubberies at elevations ranging from 1,000 to 4,500 m [18]. The family Cervidae includes Cervus albirostris, also recognised as the white-lipped deer (WLD), which inhabits shrublands and open grasslands at altitudes exceeding 3,500 m. This species is rare, endangered, and endemic to the Qinghai-Xizang Plateau of China [19]. They mainly consume Gramineae, Cyperaceae and some dicotyledonous plants [20]. Some studies on AMD and WLD have been conducted, but the research on their gut microbiome is still relatively deficient. Existing research has mainly focused on the effect of artificial captivity, seasonal variations on the composition and diversity of the gut microbiome of AMD and WLD [16, 21]. A notable gap in research on the gut microbiome of MS persists. In addition, studies comparing the gut microbiome across multiple animal species face several limitations. For example, the study have primarily analysed the influence of altitude on the host microbiota while neglecting the considerable distance between sampling locations, and the inevitable differences in the animals’ living environments [15]. Moreover, although the influence of the host on the microbiome has been examined, only a superficial comparison of the composition of interspecies gut microbiome has been undertaken. This comparison lacks a thorough investigation into the potential functions of these microbial communities [22]. All three species are sympatric ruminants and exhibit similar nutritional and ecological characteristics in this study. Owing to past unsustainable practices, both the WLD and the AMD have been classified as endangered species, while the MS is recognised as a vulnerable species. Therefore, the present study aims to elucidate the similarities and differences in the gut microbiome among these three ungulate species, as well as to investigate the implications of these variations for their digestive physiology and survival adaptations. To this end, we investigated the microbial characteristics and metabolic functions of the gut microbiome of these three ruminant species using 16 S rRNA amplicon and metagenomic sequencing methods. The results of this study provide foundational insights for a comprehensive understanding of the stable coexistence of forest-dwelling ungulates in the eastern Qinghai-Xizang Plateau, while also offering theoretical support for both in-situ and ex-situ conservation efforts for these endangered species.

Methods

Study site

The research area was located in Nim Township, Banbar County, northwest of Qamdo City in eastern Tibet (31°8′28″N, 94°18′39″). This region is distinguished by continuous towering mountains, with an average altitude exceeding 4,000 m, and has a temperate, semi-humid, plateau climate. The annual average temperature ranges from 7 °C to 8 °C. In July, the average temperature is between 13 °C and 15 °C, whereas in January, it ranges from − 15 °C to -5 °C. The temperature remains relatively low, with considerable daily fluctuations. Strong winds prevail in the winter and spring, and hailstorms occur more frequently in the summer and autumn. The average annual precipitation ranges from to 300–450 mm, occurring primarily from June to September. The vegetation displays distinct vertical zonation characterised by the presence of various forest and grassland types at different elevations, including valley forests, mixed coniferous and broad-leaved forests, pure coniferous forests, and alpine meadows.

Sample collection

In September 2023, faecal samples from three ruminant species were collected from a mixed coniferous and broadleaf forest, the most widely distributed area locally, situated at altitudes ranging from to 3600–4200 m. Near the study area, a forest musk deer breeding facility was present. We prioritized fresh faecal samples, using three-day observations of faecal consistency and field results. Faeces were collected from species-specific defecation sites, with exclusion of multi-species sites to ensure sample independence. The availability of abundant animal faeces supported this selection process. Samples were collected from the center of the faecal mound, which contains mucus, and transferred into 2 mL sterile centrifuge tubes. The date, coordinates, and elevation of each sampling location were recorded. Faecal samples were initially preserved at -20 ℃ after collection and subsequently transported to the laboratory on dry ice, where they were stored at -80 ℃ until DNA extraction. A total of 28 fresh faecal samples were analysed, and the species origins of the samples were determined using DNA sequencing, (12 samples from AMD, 8 from MS, and 8 from WLD).

DNA extraction and sequencing of 16S rRNA gene amplicons

DNA was extracted using a TGuide S96 Magnetic Soil/Stool DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.), according to the manufacturer’s instructions. The DNA concentration of the samples was measured using a Qubit dsDNA HS Assay Kit and Qubit 4.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, Oregon, USA).

The 338 F, 5′- ACTCCTACGGGAGGCAGCA-3′ and 806R, 5′ - GGACTACHVGGGTWTCTAAT-3′ universal primer set was used to amplify the V3–V4 region of the 16 S rRNA gene from the genomic DNA extracted from each sample. Both the forward and reverse 16 S primers were tailed with sample-specific Illumina index sequences for deep sequencing. PCR was performed in a total volume of 10 µL: DNA template, 5–50 ng; forward primer (10 µM), 0.30 µL; reverse primer (10 µM), 0.30 µL; KOD FX Neo Buffer, 5 µL; dNTPs, (2 mM each), 2 µL; KOD FX Neo, 0.20 µL; ddH2O, up to 10 µL. Vn F and Vn R were selected based on their amplification areas. Initial denaturation at 95 ℃ for 5 min was followed by 25 cycles of denaturation at 95 ℃ for 30 s, annealing at 50 ℃ for 30 s, and extension at 72 ℃ for 40 s, and a final step at 72 ℃ for 7 min. Total PCR amplicons were purified using Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA) and quantified using a Qubit dsDNA HS Assay Kit and Qubit 4.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, Oregon, USA). After the individual quantification steps, the amplicons were pooled in equal amounts. An Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA) was used to sequence the constructed library.

Bioinformatic analysis

Bioinformatics analysis was performed with the aid of the BMK-Cloud (Biomarker Technologies Co., Ltd., Beijing, China). Based on the quality of single nucleotides, raw data were filtered using Trimmomatic (version 0.33). Primer sequences were identified and removed using the Cutadapt software (version 1.9.1). The paired-end reads obtained in the previous steps were assembled using USEARCH (version 10.0), followed by chimera removal using UCHIME (version 8.1). The high-quality reads generated in the above steps were used for the subsequent analyses. Sequences with ≥ 97% similarity were clustered into the same operational taxonomic unit (OTU) using USEARCH (version10.0), and OTUs with re-abundance < 0.005% were filtered. Taxonomic annotation of the OTUs was performed using the naïve Bayes classifier in QIIME2 (version 2020.6.0) using the SILVA database (release 132), with a confidence threshold of 70%.

Meta genomic sequencing, assembly, and annotation

Fifteen samples (five AMD, five MS, and five WLD) were selected for metagenomic sequencing using microPITA to analyse the gut microbiome composition and functions [23]. Metagenomic sequencing was performed on an Illumina NovaSeq 6000 platform using a paired-end sequencing approach. To ensure the quality of subsequent analyses, Fastp (version 0.23.1) was used to perform quality control and filter the raw sequences, resulting in the generation of clean reads. Metagenomic assembly was subsequently conducted using MEGAHIT (version 2.2.4), excluding contig sequences shorter than 300 bp, and the resulting assembly was assessed using QUAST (version 2.3). MetaGeneMark software (http://exon.gatech.edu/meta_gmhmmp.cgi, version 3.26) was used for gene prediction and annotation. Based on the assembly results, MMseqs2 version 11-e1a1c (https://github.com/soedinglab/mmseqs2) was used to construct a non-redundant gene set by setting a 95% identity and 90% coverage of genes with longer sequences in the clustering. BLAST alignment of the protein sequences of non-redundant genes with those in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (with a diamond version0.9.29 alignment and screening threshold E-value of 1e-5) and eggNOG databases (with a diamond version0.9.29 alignment and screening threshold E-value of 1e-5) was performed. Additionally, HMMER software (version 3.0) was used to align the non-redundant protein sequences with the hidden Markov models of each family in the carbohydrate-active enzymes (CAZy) database. The alignment parameters were set to default, with a filtering threshold of “if alignment > 80 aa, use E-value < 1e-5; otherwise, use E-value < 1e-3; covered fraction of HMM > 0.3.”

Statistical analysis

Standard R commands were used to identify differences in relative abundance across groups. Kruskal–Wallis tests were used to evaluate significant differences in the nonparametric profiles. Alpha diversity was calculated using QIIME2. Beta diversity was determined to evaluate the degree of similarity between the microbial communities from different samples using QIIME. Principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS) were used to analyse beta diversity. Furthermore, we employed linear discriminant analysis (LDA) effect size (LEfSe) to test for significant taxonomic differences among the groups. A logarithmic LDA score of 4.0 was set as the threshold for discriminative features. To explore the dissimilarities in the microbiomes among different factors, redundancy analysis (RDA) was performed in R using the ‘vegan’ package. The results were visualised using the R software ggplot2 package.

Results

Diversity of the gut microbiome

A total of 28 samples yielded 22,207,32 paired original reads, which were subjected to quality control and denoising to generate 1,574,264 effective sequences. The dataset comprised 690,242 records for AMD, 447,792 for MS, and 436,230 for WLD. The dilution curves suggested that all samples plateaued, indicating sufficient sequencing depth (Fig. 1) with coverage rates exceeding 99% for all three species. Following optimisation of the effective sequences, 27,154 OTUs were classified into species at a similarity level of 97%. The AMD, MS, and WLD groups contained 10,097 6,247, and 9,351OTUs, respectively. A total of 224 OTUs were shared among the three species, representing 2.22%, 3.10%, and 2.18% of the AMD, MS, and WLD groups respectively.

Fig. 1
figure 1

Dilution curves of the gut microbiome of the three species

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To evaluate the diversity of the gut microbiome, we utilised the ACE, Chao1, Shannon, and Simpson indices to assess the alpha diversity (Fig. 2). We observed no statistically significant differences (P > 0.05) in the alpha indices between the AMD and MS microbiomes. The alpha indices of the WLD exceeded those of the MS, with significant differences in the ACE, Chao1, and Shannon indices (P < 0.01), and a highly significant difference in the Simpson index (P < 0.05). The alpha indices of WLD were consistently greater than those of AMD, with all indices exhibiting highly significant differences (P < 0.01).

Fig. 2
figure 2

Comparison of partial alpha diversity indices among the three species: (A) ACE index, (B) Simpson index. *P < 0.05, **P < 0.001, ***P < 0.0001

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PCoA based on the Bray-Jaccard algorithm revealed distinct clustering of gut microbiome among the three species, indicating a closer resemblance and greater similarity in gut microbial composition between MS and WLD. The gut microbiome of AMD was distinct from those of MS and WLD, indicating a lower similarity between the gut microbiome of AMD and those of the other two species (Fig. 3A). NMDS analysis revealed that the gut microbiome of MS and WLD were more similar than those of AMD (Fig. 3B). NMDS analysis is considered reliable when the stress value is < 2, and our analysis yielded a stress value of 0.12, suggesting a high level of reliability. Similarly, we observed differences in the composition of the intestinal microbiota among the three species, with intergroup differences exceeding intragroup differences (R > 0, P < 0.01; Fig. 4).

Fig. 3
figure 3

Beta diversity analysis of the gut microbiome of the three species: (A) Principal coordinate analysis, (B) Nonmetric multidimensional scaling analysis

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Fig. 4
figure 4

Similarity analysis of the gut microbiome of the three species

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Composition and similarity of the gut microbiome

We identified a total of 27,154 OTUs across 28 samples representing three species, including 41 phyla, 95 classes, 259 orders, 547 families, 1,225 genera, and 1,553 species.

At the phylum level (Fig. 5A), the predominant phyla in the AMD, MS, and WLD groups were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Firmicutes accounted for 53.81%, 35.60%, and 34.08% of the gut microbiome of the three species, respectively, whereas Bacteroidetes account for 29.16%, 21.12%, and 31.38%, respectively. Proteobacteria comprised 11.44%, 26.30%, and 18.16% of the gut microbiome of the three species, with Actinobacteria accounting for 2.20%, 3.61%, and 7.09%, respectively. Collectively, these phyla demonstrated relative abundances exceeding 80% in the gut microbiome of all three species. The Firmicutes/Bacteroidetes (F/B) ratios in the AMD, MS, and WLD groups were 1.85, 1.69, and 1.09, respectively.

At the genus level (Fig. 5B), the predominant genera shared among the AMD, MS, and WLD groups were unclassified Lachnospiraceae, accounting for 9.15%, 5.81%, and 4.77%, respectively. In addition to the prevalent bacterial genera, AMD showed a significant prevalence of unclassified Muribaculaceae (11.38%), Monoglobus (6.74%), and unclassified UCG_010 (5.75%). The three other predominant bacterial genera in MS were Acinetobacter (6.06%), unclassified UCG_010 (4.74%) and UCG_005 (4.74%). The predominant bacterial genera observed in WLD were unclassified UCG_010 (4.03%), UCG_005 (4.73%), and Rikenellaceae_RC9_gut_group (3.97%).

Fig. 5
figure 5

Gut microbiome composition of three species: (A) phylum level, (B) genus level

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Differences in bacterial taxa

Using LEfSe analysis (LDA > 4.0), we examined the variations in bacterial communities among AMD, MS, and WLD (Fig. 6). Multiple biomarkers were detected in all the 28 samples. The relative abundances of Muribaculaceae, Clostridia_UCG_014, and Monoglobales were elevated in AMD. Additionally, the relative prevalence of biomarkers associated with Pseudomonadales, Burkholderiales, Acinetobacter, Moraxellaceae, and Acidovorax was higher in the group MS, whereas Bacteroidetes, Alphaproteobacteria, and Prevotellaceae were more abundant in the WLD group than in the other two groups.

Fig. 6
figure 6

Linear discriminant analysis effect size analysis of the gut microbiome: (A) evolutionary bifurcation diagram, (B) LDA value (LDA > 4.0)

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Metabolism and function of the gut microbiome

To further investigate the functions of gut microbiome in the three ungulate species, we performed a metagenomic analysis. After quality control, 609,281,630 effective reads were obtained. Subsequent metagenomic assembly generated 6,697,354 contigs. Next, 8,640,588 genes were predicted, and a non-.

redundant gene set was constructed using a similarity threshold of 95% and a coverage threshold of 90%, resulting in 7,292,310 genes with an average length of 440.00 bp.

The KEGG database was used for the functional annotation of non-redundant genes, revealing that all genes were classified into four major categories: metabolism, and genetic information, environmental information, and cellular processing. Over half of the metabolic functions were primarily related to carbohydrate, nucleotide, amino acid, cofactors and vitamins, energy, and lipid metabolism. Genetic information processing encompassed essential functions such as replication, repair, and translation. In contrast, the remaining two functional categories did not significantly contribute to the overall outcome (Fig. 7). According to the annotation provided by the eggNOG database, the non-redundant gene functional annotation results predominantly encompassed processes such as replication, recombination and repair, translation, ribosome structure and biosynthesis, carbohydrate transport and metabolism, biosynthesis of cell walls/membranes, and amino acid transport and metabolism, all of which exhibit many known functions. The Kruskal–Wallis rank-sum test demonstrated significant variations in the functional profiles of the gut microbial communities among the three species. At level 2 of the KEGG pathway, carbonate metabolism in the gut microbiome was significantly higher in WLD than in MS or AMD. Compared to the WLD and AMD groups, the levels of amino acids and energy metabolism in the MS gut microbiome were significantly elevated (Fig. 8).

Fig. 7
figure 7

Kyoto encyclopedia of genes and genomes pathway functions of the gut microbiome of the three species

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Fig. 8
figure 8

Differential functional genes in the Kyoto Encyclopaedia of Genes and Genomes pathways (level 2) of the gut microbiome. *P < 0.05, **P < 0.001, ***P < 0.0001

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Non-redundant genes were classified based on the KEGG Orthology, which revealed significant variations in the predicted proteins/enzymes among the three species. Notably, these proteins included K02860 (16 S rRNA processing protein), K03306 (inorganic phosphate transporter), K02372 (3-hydroxyacyl-[acyl- carrier- protein] dehydratase), K09118 (uncharacterised protein) and K06885 (uncharacterised protein). The expression levels of K02860, K02372, K09118, and K06885 were significantly higher in AMD than those in WLD or MS. Furthermore, the expression level of K03306 was significantly higher in MS than that in AMD or WLD (Fig. 9). According to the CAZy database, the protein sequences of non-redundant genes were predominantly annotated as glycoside hydrolases at 41.90%, glycosyltransferases (GTs) at 36.90%, and noncatalytic carbohydrate-binding modules (CBMs) at 14.60%, whereas carbohydrate esterase (CE), polysaccharide lyase (PL), and auxiliary activities (AAs) constituted only small fractions of 4.90%, 1.00%, and 0.80%, respectively (Fig. 10A). To further explore variations in the predominant types of carbohydrate-degrading enzymes within the gut microbiome of each species, we conducted a Kruskal–Wallis rank- sum test. At present in Fig. 10B, GH levels were significantly higher in the gut microbiome of WLD than in MS and AMD. CBM levels in the gut microbiome of AMD were notably higher than those in WLD and MS. Conversely, CE levels were markedly higher in the gut microbiome of MS than in the other groups.

Fig. 9
figure 9

Kyoto Encyclopaedia of Genes and Genomes Orthology level ranks and tests of functional genes in the gut microbiome. *P < 0.05, **P < 0.001, ***P < 0.0001

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Fig. 10
figure 10

Proportion of carbohydrate-active enzymes and differentially expressed functional genes: (A) ratio of carbohydrate-active enzymes, (B) genes exhibiting differential expression. *P < 0.05, **P < 0.001, ***P < 0.0001

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Discussion

This study used 16S rRNA amplicon and metagenomic sequencing techniques to investigate the gut microbial composition and function of three coexisting ruminant ungulates from the same order: AMD from the family Moschidae, MS from the family Bovidae, and WLD from the family Cervidae. The findings suggest that although the metabolic and functional profiles of the gut microbiome of the three species are generally similar, the differential expression of functional genes indicates distinct strategies for energy acquisition.

Current research has predominantly focused on factors such as animal age [24], diet [25–26], sex [27], and immunity [28], which can affect the composition of microorganisms within the host. Some studies have also indicated that the diversity of microorganisms within a host is influenced by its body size [29]. A previous study analysed the gut microbiome of 189 individuals encompassing 71 vertebrate species, including mammals, birds, and reptiles by 16 S rDNA analysis. The results suggested that the diversity of the gut microbiome increases with weight gain, irrespective of age, sex, dietary habits, or gastrointestinal anatomy [30]. The present study revealed similar patterns, as the body sizes of WLD, MS, and AMD, belonging to the order Artiodactyla but from different genera, decreased sequentially. We also observed distinct differences in gut microbial diversity among the three species. Firstly, the alpha diversity index of the gut microbiome in WLD was significantly greater than that observed in MS and AMD. Secondly, although the diversity of the gut microbiome did not differ significantly between MS and AMD, the diversity of the gut microbiome in MS surpassed that in AMD. The diversity of the microbial community is crucial for the host. Changes in microbial diversity are associated with disease occurrence [31–32]. Conversely, distinct bacterial communities within the host microbial system can contribute unique sets of digestive enzymes that enhance food processing and digestion. Therefore, a higher microbial diversity generally correlates with increased metabolic capacity and stability [33]. The greater the diversity of a microbial community, the more intricate and resilient its composition. This leads to enhanced resistance to external interference, heightened adaptability, and a more favourable environment for host health [34]. Consequently, the WLD might possess more potent metabolic and environmental adaptability.

Systematic evolution [35], dietary patterns [16, 36], and environmental factors collectively influence the structure of the microbial community within the host [10]. Although AMD and MS are closely related [37], our results revealed that the gut microbiome structure of WLD was more similar to that of MS, exhibited greater differences from that of AMD. Moreover, the impact of systematic evolution on the composition of the gut microbiome across hosts was not statistically significant. Maybe this lack of significance could be attributed to variations in the gut microbial composition potentially arising from dietary factors. WLD predominantly consume Poaceae, Cyperaceae, and certain Dicotyledon plants and are classified as herbivores grazers [20], MS predominantly forage on Rosaceae, Caprifoliaceae, and Poaceae families. Despite consuming a diverse array of vegetation, MS exhibit dietary selectivity, presenting as a mixed feeder [38]. AMD primarily forage on Dicotyledon plants, with occasional consumption of Monocotyledon plants. It has exhibited minimal interest in plants from the Poaceae [17] and is recognised as a browser. WLD and MS have a relatively high dietary fibre content, which may be the determining factor driving the relatively similar gut microbiome of these two species.

Our study systematically examined the gut microbiome composition of three species and yielded results consistent with those of previous studies, demonstrating that Firmicutes and Bacteroidetes are predominant in herbivores [39–40]. Firmicutes can degrade cellulose and Bacteroides can degrade starch, pectin, and xylan [41]. The F/B ratios for the three species were 1.85 for AMD, 1.69 for MS, and 1.09 for WLD. High F/B values in the microbiome play crucial roles in energy absorption and maintenance of the host energy balance [42]. Specifically, a reduction in dietary fibre content is linked to an increase in the F/B ratio [43]. Consequently, we infer that AMD demonstrates a superior capacity for energy absorption and storage. Grazers and mixed feeders possess a relatively greater rumen capacity, enabling prolonged food retention times in the digestive tract. In contrast, browsers have lower relative rumen weights and capacities, resulting in accelerated food passage and reduced retention times [44]. To fulfil their energy requirements, browsers must exhibit increased metabolic efficiency, and a high F/B ratio is indicative of the nutritional strategies employed by AMD.

At the taxonomic level, the three species collectively exhibited an unclassified Lachnospira as the predominant genus. Lachnospira, a member of Firmicutes, has a broad spectrum of metabolic capabilities, including cellulose degradation, sugar degradation, and protein hydrolysis. Furthermore, they are likely to participate in volatile fatty acid biosynthesis [45–46]. The relative abundances of Muribaculaceae, Clostridia_UCG_014, and Monoglobaceae were higher in AMD than in MS and WLD. Muribaculaceae can produce enzymes that degrade complex carbohydrates, thereby facilitating the breakdown of cellulose in their diets [47]. Clostridia_UCG_014 is implicated in starch degradation, and its abundance increases concomitantly with an increase in the proportion of concentrated feed [48]. The presence of Monoglobaceae in animal intestines is uncommon and its role in the gastrointestinal tract remains unclear [49]. Compared with AMD and WLD, the relative abundances of Pseudomonas, Burkholderiales, Acinetobacter, and Acidovorax were greater in the gut microbiome of MS. Pseudomonas plays a role in carbohydrate metabolism and is capable of denitrifying organic matter [50]. Burkholderiales, classified as Betaproteobacteria, have been demonstrated to degrade specific secondary metabolites in plants [51]. The other two types of bacteria may be related to diseases [52–53]. Compared with AMD and MS, the relative abundances of Bacteroidetes, Alphaproteobacteria, and Prevotellaceae were greater in the gut microbiome of WLD. Bacteroidetes primarily metabolise carbohydrates, such as starch, pectin, and xylan, as well as proteins, thereby facilitating the development of the host intestinal immune system [54]. Alphaproteobacteria function as methane-oxidising bacteria, indicating their ability to utilise methane as a carbon source [55]. Prevotellaceae can degrade hemicellulose and complex carbohydrates while also demonstrating the ability to synthesise short-chain fatty acids [56]. Therefore, the primary functions of the three phyla of beneficial bacteria, Firmicutes, Bacteroidetes, and Proteobacteria, mainly involve carbohydrate metabolism and other metabolic processes.

The composition of the gut microbiome directly affects the energy acquisition and fat storage in the host. This investigation revealed that each of the three species possesses a distinct metabolic advantage facilitated by its gut microbiome. AMD is characterised by relatively high levels of 3-hydroxy acyl- [acyl carrier protein]- dehydratase produced by the gut microbiome, which is primarily responsible for the synthesis of unsaturated fatty acids. These findings suggest that AMD has a greater capacity to synthesise unsaturated fatty acids than WLD or MS. Unsaturated fatty acids play a crucial role in promoting animal growth and improving the efficiency of food utilization. This phenomenon may be linked to the elevated F/B ratio observed in the intestinal microbiota of AMD. Additionally, specific bacterial genera within the Firmicutes phylum are capable of producing 3-hydroxyacyl-[acyl carrier protein] dehydratase [57]. A greater relative abundance of gut microbiome genera belonging to the Firmicutes phylum correlates with an enhanced capacity for synthesizing unsaturated fatty acids. Unsaturated fatty acids can be detrimental to microbes residing in the host body, particularly those involved in fibre digestion [58]. This may explain the lower gut microbial diversity observed in AMD than in WLD or MS. Nevertheless, the addition of high concentrations of unsaturated fatty acids can augment carbohydrate metabolism in animals, albeit at the expense of reduced fibre digestibility [59]. This also explains why AMD is a browser. The amino acid metabolism levels in MS were significantly higher than those in WLD and AMD. Microbially produced amino acids can be absorbed by the host and used to synthesise short-chain fatty acids, thereby providing energy for physiological activities [60]. Humblot et al. reported that animals that consumed high-fat diets presented a greater prevalence of amino acid metabolic functions in their gut microbiome, whereas animals that consumed low-fat diets presented a greater prevalence of carbohydrate metabolic functions in their gut microbiome [61]. MS, while exhibiting a gut microbiome similar to that of WLD, inhabits an ecological niche between the grazers and browsers. They exhibited a preference for foods with higher fat and lower fibre content, in contrast to the WLD, which possesses a gut microbiome with enhanced carbohydrate metabolism capabilities, enabling the consumption of a broader range of lower-quality foods with higher fibre content.

The enzymatic breakdown of plant cell walls, which are primarily composed of polysaccharides such as cellulose and starch, plays a crucial role in the physiological processes and energy metabolism of herbivores. Herbivores mammals derive this energy from microorganisms residing in their gastrointestinal tract. The host metabolises dietary carbohydrates into soluble oligosaccharides and fermentable monosaccharides, which are utilised to generate energy through the cooperative action of microbial carbohydrate-degrading enzymes. A comparison with the CAZy database revealed that the predominant carbohydrate-degrading enzymes produced by the gut microbiome of the three species were GH (41.90%), GT (36.90%), and CBM (14.60%). Compared to those produced by MS and AMD, the levels of GH enzymes produced by the gut microbiome of WLD were significantly higher, indicating a more robust capacity for carbohydrate metabolism. On one hand, the analysis of 16S rRNA gene results indicates that the gut microbiome of WLD exhibits greater diversity, encompassing a wider variety of species than those found in the other two species. This phenomenon may be attributed to the comparatively elevated abundances of Bacteroides and Prevotella within the gut microbiome of the WLD, both of which are adept at producing a larger quantity of carbohydrate-degrading enzymes. On the other hand, its larger body size and digestive tract, when compared to those of AMD and MS, provide an optimal habitat for the gut microbiome, enabling ingested food to remain in the body for an extended period. These factors create conducive conditions for cellulose-decomposing bacteria.

In summary, 16S rRNA gene amplicons and metagenomic sequencing analyses revealed that the composition and functions of the gut microbiome of the three species within the same order were similar. However, each species possesses a distinct gut microbiome and functions, demonstrating diverse nutritional and ecological adaptations. In conclusion, this study revealed the microbial composition and metabolic functions of sympatric forest-dwelling ruminant herbivores, inhabiting the eastern Tibetan Plateau, including AMD, MS, and WLD. These findings provide essential insights into the adaptation of these species to plateau forest environments and shed light on their interspecific coexistence, thereby contributing to the formulation of effective conservation strategies. Further studies using multi-omics joint analyses are essential to comprehensively characterize the role of the gut microbiome in the coexistence of the three ungulate species.

Data availability

The raw amplicon and metagenome sequences were deposited under the NCBI BioProjects: PRJNA1146191 and PRJNA1146968.

Abbreviations

AMD:

Alpine musk deer

MS:

Mainland serow

WLD:

White-lipped deer

OTU:

Operational taxonomic unit

KEGG:

Kyoto Encyclopaedia of Genes and Genomes

CAZy:

Carbohydrate-active enzymes

PCoA:

Principal coordinate analysis

LDA:

Linear discriminant analysis

LEfSe:

Linear discriminant analysis effect size

NMDS:

Nonmetric multidimensional scaling

GHs:

Glycoside hydrolases

GTs:

Glycosyltransferases

CBMs:

Noncatalytic carbohydrate-binding modules

CE:

Carbohydrate esterase

PL:

Polysaccharide lyase

AAs:

Auxiliary activities

References

  1. Kaoutari A, El, Armougom F, Gordon JI, Raoult D, Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiome. Nat Rev Microbiol. 2013;11:497–504.

    Article  PubMed  Google Scholar 

  2. Shiffman ME, Soo RM, Dennis PG, Morrison M, Tyson GW, Hugenholtz P. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for Eucalyptus digestion. PeerJ. 2017;2017:1–32.

    Google Scholar 

  3. Yoo JY, Groer M, Dutra SVO, Sarkar A, McSkimming DI. Gut microbiome and immune system interactions. Microorganisms. 2020;8:1–22.

    Article  Google Scholar 

  4. Colston TJ. Gut microbiome transmission in lizards. Mol Ecol. 2017;26:972–4.

    Article  PubMed  Google Scholar 

  5. Gong G, Zhou S, Luo R, Gesang Z, Suolang S. Metagenomic insights into the diversity of carbohydrate-degrading enzymes in the yak fecal microbial community. BMC Microbiol. 2020;20:1–15.

    Article  Google Scholar 

  6. DeMiguel D, Azanza B, Morales J. Key innovations in ruminant evolution: a paleontological perspective. Integr Zool. 2014;9:412–33.

    Article  PubMed  Google Scholar 

  7. O’Hara E, Neves ALA, Song Y, Guan LL. The role of the gut microbiome in cattle production and health: driver or passenger? Annu Rev Anim Biosci. 2020;8:199–220.

    Article  PubMed  Google Scholar 

  8. Zierer J, Jackson MA, Kastenmüller G, Mangino M, Long T, Telenti A, et al. The fecal metabolome as a functional readout of the gut microbiome. Nat Genet. 2018;50:790–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Li Y, Li X, Wu Y, Zhang W. Effects of fecal microbiota transplantation from yaks on weaning diarrhea, fecal microbiota composition, microbial network structure and functional pathways in Chinese holstein calves. Front Microbiol. 2022;13.

  10. Jiang Y, Han X, Li M, Feng N, Yang P, Zhao H, et al. Changes in the gut microbiome of forest musk deer (Moschus berezovskii) during ex situ conservation. Front Microbiol. 2022;13:1–16.

    Article  Google Scholar 

  11. YIN X jiao JIS, kun DUANC, hui TIANP, zhi JUS, si YANH, et al. The succession of fecal bacterial community and its correlation with the changes of serum immune indicators in lambs from birth to 4 months. J Integr Agric. 2023;22:537–50.

    Article  Google Scholar 

  12. Letten AD, Ke PJ, Fukami T. Linking modern coexistence theory and contemporary niche theory. Ecol Monogr. 2017;87:161–77.

    Article  Google Scholar 

  13. Carvalho JC, Cardoso P. Decomposing the causes for Niche differentiation between species using Hypervolumes. Front Ecol Evol. 2020:1–7.

    Google Scholar 

  14. Bai-ping Z, Xiao-dong C, Bao-lin L. Yong-Hui Y. Biodiversity and conservation in the Tibetan Plateau. J Geogr Sci. 2002;12:135–43.

    Article  Google Scholar 

  15. Ma Y, Ma S, Chang L, Wang H, Ga Q, Ma L, et al. Gut microbiome adaptation to high altitude in indigenous animals. Biochem Biophys Res Commun. 2019;516:120–6.

    Article  PubMed  CAS  Google Scholar 

  16. Jiang F, Song P, Liu D, Zhang J, Qin W, Wang H, et al. Marked variations in gut microbial diversity, functions, and disease risk between wild and captive alpine musk deer. Appl Microbiol Biotechnol. 2023;107:5517–29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Syed Z, Ilyas O. Habitat preference and feeding ecology of alpine musk deer (Moschus chrysogaster) in Kedarnath Wildlife Sanctuary, Uttarakhand, India. Anim Prod Sci. 2016;56:978–87.

    Article  Google Scholar 

  18. Thuc PD, Baxter G, Smith C, Hieu DN. Population status of the southwest China serow capricornis milneedwardsii: a case study in Cat Ba Archipelago, Vietnam. Pac Conserv Biol. 2014;20:385–91.

    Article  Google Scholar 

  19. KAJI K, OHTAISHI N. Distribution and status of White-lipped deer Cervus albirostris in the Qinghai‐Xizang (Tibet) Plateau, China. Mamm Rev. 1989;19:35–44.

    Article  Google Scholar 

  20. Takatsuki S. A note on fecal and Rumen contents White-tipped deer on Eastern Qinghai-Tibet of Plateau. 1988;13:133–7.

  21. Li B, Gao H, Song P, Liang C, Jiang F, Xu B et al. Captivity shifts gut microbiome communities in White-Lipped deer (Cervus albirostris). Animals. 2022;12.

  22. Sun G, Xia T, Wei Q, Dong Y, Zhao C, Yang X, et al. Analysis of gut microbiome in three species belonging to different genera (Hemitragus, Pseudois, and Ovis) from the subfamily Caprinae in the absence of environmental variance. Ecol Evol. 2021;11:12129–40.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lan Y, Wang D, He J, Yang H, Hou Y, Di W et al. The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function. Ren Fail. 2023;45.

  24. Luo T, Li Y, Zhang W, Liu J, Shi H. Rumen and fecal microbiota profiles associated with immunity of young and adult goats. Front Immunol. 2022;13:1–14.

    Google Scholar 

  25. Shah T, Ding L, Ud Din A, Hassan FU, Ahmad AA, Wei H, et al. Differential effects of Natural Grazing and Feedlot Feeding on Yak Fecal Microbiota. Front Vet Sci. 2022;9:1–10.

    Article  CAS  Google Scholar 

  26. Yan L, Tang L, Zhou Z, Wei LU, Wang B, Sun Z, et al. Metagenomics reveals contrasting energy utilization efficiencies of captive and wild camels (Camelus ferus). Integr Zool. 2022;17:333–45.

    Article  PubMed  CAS  Google Scholar 

  27. Elbir H, Alhumam NA. Sex differences in fecal microbiome composition and function of dromedary camels in Saudi Arabia. Animals. 2022;12.

  28. Gomez DE, Arroyo LG, Costa MC, Viel L, Weese JS. Characterization of the fecal bacterial microbiota of healthy and diarrheic dairy calves. J Vet Intern Med. 2017;31:928–39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Aspen T, Reese RRD. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance; 2018.

  30. Godon JJ, Arulazhagan P, Steyer JP, Hamelin J. Vertebrate bacterial gut diversity: size also matters. BMC Ecol. 2016;16:1–9.

    Article  Google Scholar 

  31. Manichanh C, Rigottier-Gois L, Bonnaud E, Gloux K, Pelletier E, Frangeul L, et al. Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach. Gut. 2006;55:205–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Ademe M. Benefits of fecal microbiota transplantation: a comprehensive review. J Infect Dev Ctries. 2020;14:1074–80.

    Article  PubMed  CAS  Google Scholar 

  33. Fan Q, Cui X, Wang Z, Chang S, Wanapat M, Yan T, et al. Rumen Microbiota of Tibetan Sheep (Ovis aries) adaptation to extremely cold season on the Qinghai-Tibetan Plateau. Front Vet Sci. 2021;8:1–13.

    Article  Google Scholar 

  34. Jiang F, Gao H, Qin W, Song P, Wang H, Zhang J, et al. Marked Seasonal Variation in structure and function of gut microbiome in Forest and Alpine musk deer. Front Microbiol. 2021;12:1–13.

    Article  Google Scholar 

  35. Zhang XY, Khakisahneh S, Liu W, Zhang X, Zhai W, Cheng J et al. Phylogenetic signal in gut microbial community rather than in rodent metabolic traits. Natl Sci Rev. 2023;10.

  36. Gong R, Song S, Ai Y, Wang S, Dong X, Ren Z et al. Exploring the growing forest musk deer (Moschus berezovskii) dietary protein requirement based on gut microbiome. Front Microbiol. 2023;14.

  37. Guha S, Goyal SP, Kashyap VK. Molecular phylogeny of musk deer: a genomic view with mitochondrial 16S rRNA and cytochrome b gene. Mol Phylogenet Evol. 2007;42:585–97.

    Article  PubMed  CAS  Google Scholar 

  38. Takada H, Minami M. Food habits of the Japanese serow (Capricornis crispus) in an alpine habitat on Mount Asama, central Japan. Mammalia. 2019;83:455–60.

    Article  Google Scholar 

  39. Liu H, Han X, Zhao N, Hu L, Wang X, Luo C, et al. The gut microbiome determines the high-altitude adaptability of tibetan wild asses (Equus kiang) in Qinghai-Tibet Plateau. Front Microbiol. 2022;13:1–18.

    Google Scholar 

  40. Wang X, Wu X, Shang Y, Gao Y, Li Y, Wei Q et al. High-altitude drives the convergent evolution of Alpha Diversity and Indicator Microbiota in the gut microbiomes of ungulates. Front Microbiol. 2022;13 July.

  41. Li Y, Hu X, Yang S, Zhou J, Zhang T, Qi L, et al. Comparative analysis of the gut microbiome composition between captive and wild forest musk deer. Front Microbiol. 2017;8:1–10.

    Google Scholar 

  42. Magne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S, Navarrete P et al. The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients? Nutrients. 2020;12.

  43. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al. Impact of diet in shaping gut microbiome revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A. 2010;107:14691–6.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hofmann RR. Evolutionary steps of ecophysiological adaptation and diversification of ruminants: a comparative view of their digestive system. J Ethol. 1995;13:69–75.

    Google Scholar 

  45. Biddle A, Stewart L, Blanchard J, Leschine S. Untangling the genetic basis of fibrolytic specialization by lachnospiraceae and ruminococcaceae in diverse gut communities. Diversity. 2013;5:627–40.

    Article  Google Scholar 

  46. Blasco L, Kahala M, Tampio E, Vainio M, Ervasti S, Rasi S. Effect of inoculum pretreatment on the composition of microbial communities in anaerobic digesters producing volatile fatty acids. Microorganisms. 2020;8:1–21.

    Article  Google Scholar 

  47. Lagkouvardos I, Lesker TR, Hitch TCA, Gálvez EJC, Smit N, Neuhaus K, et al. Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome. 2019;7:1–15.

    Article  Google Scholar 

  48. Yi S, Dai D, Wu H, Chai S, Liu S, Meng Q, et al. Dietary concentrate-to-forage ratio affects Rumen bacterial community composition and metabolome of yaks. Front Nutr. 2022;9:1–15.

    Article  Google Scholar 

  49. Mtshali K, Khumalo ZTH, Kwenda S, Arshad I, Thekisoe OMM. Exploration and comparison of bacterial communities present in bovine faeces, milk and blood using 16S rRNA metagenomic sequencing. PLoS ONE. 2022;17(8):1–29.

    Google Scholar 

  50. Finlayson-Trick ECL, Getz LJ, Slaine PD, Thornbury M, Lamoureux E, Cook J, et al. Taxonomic differences of gut microbiomes drive cellulolytic enzymatic potential within hind-gut fermenting mammals. PLoS ONE. 2017;12:1–22.

    Article  Google Scholar 

  51. Perumbakkam RMR, Delorme MJM. Discovery of novel microorganisms involved in ergot alkaloid detoxification: an approach. 2007:395–8.

  52. Sladecek V, Senk D, Stolar P, Bzdil J, Holy O. Predominance of Acinetobacter pseudolwoffii among Acinetobacter species in domestic animals in the Czech Republic. Vet Med (Praha). 2023;68:419–27.

    Article  PubMed  CAS  Google Scholar 

  53. Ai B, Mei Y, Liang D, Wang T, Cai H, Yu D. Uncovering the special microbiota associated with occurrence and progression of gastric cancer by using RNA-sequencing. Sci Rep. 2023;13.

  54. Spence C, Wells WG, Smith CJ. Characterization of the primary starch utilization operon in the obligate anaerobe Bacteroides fragilis: regulation by carbon source and oxygen. J Bacteriol. 2006;188:4663–72.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Mitsumori M, Ajisaka N, Tajima K, Kajikawa H, Kurihara M. Detection of Proteobacteria from the rumen by PCR using methanotroph-specific primers. Lett Appl Microbiol. 2002;35:251–5.

    Article  PubMed  CAS  Google Scholar 

  56. Baothman OA, Zamzami MA, Taher I, Abubaker J, Abu-Farha M. The role of gut microbiome in the development of obesity and diabetes. Lipids Health Dis. 2016;15:1–8.

    Article  Google Scholar 

  57. Polansky O, Sekelova Z, Faldynova M, Sebkova A, Sisak F, Rychlik I, et al. Important metabolic pathways and biological processes expressed by Chicken Cecal Microbiota. Appl Environ Microbiol. 2016;82:1569–76.

    Article  PubMed Central  CAS  Google Scholar 

  58. Jalč D, Potkański A, Szumacher-Strabel M, Kowalczyk J, Cieślak A. The effect of a high forage diet and different oil blends on rumen fermentation in vitro. J Anim Feed Sci. 2006;15:1:141–4.

    Article  Google Scholar 

  59. Sun X, Wang Q, Yang Z, Xie T, Wang Z, Li S et al. Altering methane emission, fatty acid composition, and microbial profile during in vitro ruminant fermentation by manipulating dietary fatty acid ratios. Fermentation. 2022;8.

  60. Lin R, Liu W, Piao M, Zhu H. A review of the relationship between the gut microbiome and amino acid metabolism. Amino Acids. 2017;49:2083–90.

    Article  PubMed  CAS  Google Scholar 

  61. Humblot C, Seyoum Y, Turpin W, Mrabt R, List EO, Berryman DE, et al. Long Term Weight Cycling affects fecal microbiota of mice. Mol Nutr Food Res. 2022;66:1–8.

    Article  Google Scholar 

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Acknowledgements

We thank the assistance of Luopei Suolang, a Tibetan individual, in our sample collection. We would like to thank Editage (www.editage.cn) for English language editing.

Funding

This work was supported by the Zhangzhou Pientzehuang Pharmaceutical Co., Ltd. supporting the protection of musk deer resources in Tibet. (Grant No. YC-20018.), and Supported by Science and Technology Planning Project of Fujian Province. (Grant No. 2023I0046).

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Authors and Affiliations

  1. School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, 100083, China

    Haonan Zhang, Yichen Wang, Zhengwei Luo, Baofeng Zhang, Xianna Lan & Defu Hu

  2. Zhangzhou Pientzehuang Pharmaceutical Co., Ltd, Zhangzhou, 363000, China

    Liancheng Xu, Xuxin Li & Zhixin Huang

  3. Banbar County Xiangrui Poverty Alleviation and Development Investment Co., Ltd, Banbar, 855500, China

    Jin Bai

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Contributions

H.Z. and D.H. conceived and designed the project. H.Z. participated in the bioinformatic analyses and manuscript preparation. Y.W. and Z.L. contributed to statistical analyses and carried out sample collection. B.Z. and X.L. contributed to bioinformatic analyses and sample processing. L.X., X.L., Z.H. and J.B. carried out sample processing. D.H. collaborated in the design, coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

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Zhang, H., Wang, Y., Luo, Z. et al. Gut microbiome reveals the trophic variation and significant adaption of three sympatric forest-dwelling ungulates on the eastern Qinghai-Xizang Plateau. BMC Microbiol 25, 128 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03812-z

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