Abstract
Forest musk deer (Moschus berezovskii) is a globally endangered species, and its conservation has long been a matter of concern. Wild populations are scarce, while artificially captive populations are also constrained by health issues such as digestive system diseases. To reveal the differences in gut microbiota between captive and wild forest musk deer from different geographical regions, fecal samples were collected from captive individuals in Nanyang, Henan (HN) and Gaoping, Shanxi (SX), as well as wild individuals in Baotianman, Henan (YS), with 5 samples per group. High-throughput 16S rRNA sequencing was employed to analyze the microbial community structure and function. The sequencing revealed Firmicutes, Bacteroidota, and Proteobacteria as the dominant phyla across all three groups, with Actinobacteriota exhibiting a significantly higher abundance in the YS wild group (11.13%) than in the HN (1.29%) captive and SX (6.12%) captive groups. There were no significant differences in α-diversity among the groups. However, β-diversity analysis (PCoA and NMDS) indicated a clear separation in microbial community structure between captive and wild groups, with some individuals in the SX captive group clustering with the wild group. LEfSe analysis identified 36 differential biomarkers: the YS wild group was enriched in genera including Bacillus, Arthrobacter, and Microbacterium, whereas the HN captive group was enriched in Bacteroides, Clostridium, and Eubacterium, while the SX captive group was enriched in Skermanella (genus) and Cytophagales (order). Functional prediction analysis revealed that the gut microbiota of the wild group was significantly enriched in the pathways of xenobiotics biodegradation and metabolism as well as lipid metabolism, whereas the captive groups showed higher activity in the translation and nucleotide metabolism pathways. This study reveals the impacts of rearing methods and geographical factors on the gut microbial community structure and function of forest musk deer. These findings can serve as a theoretical foundation for promoting healthy breeding of captive populations and as a reference for evaluating the health status of wild populations.
1 Introduction
The forest musk deer (Moschus berezovskii) is a small, solitary cervid species inhabiting mountain forests in East Asia. Due to the illegal poaching of musk glands in males, its population has been declining steadily, and it is currently listed as an endangered species by the International Union for Conservation of Nature (IUCN) (1). Despite the long-standing reliance on its musk resources in traditional medicine and the perfume industry, the focus of current conservation efforts has shifted to integrating habitat conservation with artificial breeding, and exploring sustainable, non-invasive musk collection techniques to achieve a balance between ecological protection and resource utilization (2). However, digestive tract diseases are prevalent in captive forest musk deer (3, 4), and the underlying gut microbial mechanisms remain unclear, hindering the improvement of population health management strategies. The gut ecosystem is characterized by dynamic and reciprocal interactions between the host and bacteria (5). The complex microbial communities colonizing host animals, particularly the gut microbiota, can be regarded as a “functional extension system” (6), which plays a vital role in mediating key physiological processes such as nutrient metabolism, immune regulation, and environmental adaptation (7, 8). In ruminants, the efficient digestion of high-fiber diets relies on the synergistic interaction between rumen and gut microorganisms, such as the secretion of cellulases and the production of short-chain fatty acids (SCFAs) mediated by specific bacterial groups (9, 10).
Studies have shown that dietary simplification and spatial confinement under captive conditions can lead to reduced diversity and functional dysfunction of the gut microbiota in endangered species such as giant pandas (Ailuropoda melanoleuca) and snub-nosed monkeys (Rhinopithecus brelichi) (11, 12). The structure and function of the gut microbiota in captive forest musk deer vary with seasonal changes (13, 14). The α-diversity of the gut microbiota in forest musk deer is higher in the cold season than in the warm season, suggesting that seasonal alternation can affect the diversity of musk deer gut microbiota. This change is beneficial to their environmental adaptation and food digestion and metabolism (13). Recent studies on forest musk deer gut microbiota have focused on comparing wild populations from different regions with local captive individuals, revealing that wild groups enrich Pseudomonadota and lignin-degrading genera, while captive groups show increased Bacillota and pathogenic bacteria (15). Differences in the plant composition of the feed is likely to influence the community structure of the forest musk deer’s gut microbiota through varying nutrient inputs and secondary metabolite intake (16). However, the gut microbial divergence among captive populations from different geographical locations, and whether specific captive management can drive microbiota convergence to wild counterparts, remain unaddressed.
This study selected two representative captive populations of forest musk deer from Nanyang, Henan Province and Gaoping, Shanxi Province, as well as a wild population from the Baotianman National Nature Reserve in Henan Province, as the research subjects. Through a comparative design across multiple geographical populations and combined with 16S rRNA sequencing technology, the differences in community structure and functional adaptive differentiation of the gut microbiota between captive and wild forest musk deer were analyzed. This provides a scientific basis for the breeding management of captive forest musk deer.
2 Materials and methods
2.1 Sample collection
Sampling was conducted in April (spring), which is outside the peak musk secretion season of forest musk deer (17, 18). Fecal samples of captive forest musk deer were collected from two breeding farms: Nanyang City (HN, 33°21′N, 111°25′E, n = 5) in Henan Province and Gaoping City (SX, 35°45′N, 113°5′E, n = 5) in Shanxi Province, China. All forest musk deer were housed in semi-open enclosures with separate indoor pens and outdoor activity areas. The HN farm had brick-paved activity areas (15–20 m2) with a small vegetated section, whereas the SX farm had cement-paved activity areas (20–30 m2) with a larger soil area planted with trees, providing musk deer with greater access to natural soil and vegetation. The daily diet of captive forest musk deer consists of tender twigs, buds, and herbaceous plants from local vegetation, supplemented with a certain proportion of concentrated feed. All captive individuals (HN and SX groups) were clinically healthy, with no antibiotics or probiotics administered for at least 3 months prior to sampling. Enclosures were cleaned 1 day prior to sampling to ensure the collection of fresh morning feces. Fecal samples of wild forest musk deer were collected from the Baotianman National Nature Reserve in Henan Province (YS, 33°20′–33°36′N, 111°47′–112°04′E, n = 5). The survey was conducted using the transect method, with feces that appeared fresh in form and moist in surface collected along the survey route. Samples were collected at intervals ≥1,000 meters along transects, with distinct fecal morphology, size, and deposition sites to ensure independence, although molecular individual identification was not performed (the sampling sites were shown in Supplementary Figure S1).
The contamination-free central part of fresh feces was selected and placed into sterile sampling bags, immediately transported back to the laboratory within 4 h using a portable vehicle-mounted refrigerator at 4 °C, and then transferred to an ultra-low temperature refrigerator at −80 °C for long-term storage until use (detailed sample information can be found in Supplementary Table S1).
2.2 Genomic DNA extraction and PCR amplification
Genomic DNA was extracted from the samples using the MagPure Stool DNA KF Kit B (Magen, Guangzhou, China) in accordance with the manufacturer’s instructions. The DNA concentration and purity (A260/A280 ratio) were determined using a NanoDrop 2000 spectrophotometer (IMPLEN, Westlake Village, CA, United States). The integrity of the DNA was further assessed by 1% agarose gel electrophoresis.
Universal primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5’-GGACTACHVGGGTWTCTAAT-3′), which are equipped with sequencing adapters, were used to amplify the V3–V4 hypervariable region of the bacterial 16S rRNA gene (19, 20). PCR reactions were performed using the TransStart Fastpfu DNA Polymerase kit (TransGen, Beijing, China). Three technical replicates were prepared for each sample, with a reaction mixture volume of 20 μL. The PCR program was as follows: pre-denaturation at 95 °C for 3 min; 30 cycles of denaturation at 95 °C for 30 s, annealing at 56 °C for 30 s, and extension at 72 °C for 45 s; and a final extension at 72 °C for 10 min. After pooling the three PCR replicates from the same sample, the mixture was subjected to 2% agarose gel electrophoresis for detection. The target bands were excised and purified using the AxyPrep DNA Gel Extraction Kit (AXYGEN, Hangzhou, China), and then eluted with Tris–HCl buffer.
2.3 Library construction and sequencing
The sequencing library was constructed using the VAHTS Universal DNA Library Prep kit for Illumina V3 (Vazyme, Nanjing, China). The workflow included DNA fragmentation, end repair, adapter ligation, as well as library enrichment and size selection. The constructed library was quantified using a Qubit® 3.0 Fluorometer, and the fragment size distribution was detected by an Agilent 2,100 Bioanalyzer. The quality-checked libraries were subjected to paired-end (PE) 250 bp sequencing on the Illumina NovaSeq 6,000 platform at Shanghai Yuanxin Biopharmaceutical Technology Co., Ltd. (Shanghai, China).
2.4 Bioinformatics analysis
2.4.1 Data quality control
Raw Reads data were subjected to quality control as follows: data were demultiplexed based on sample-specific barcodes and primer sequences; FLASH software (v1.2.11) was used to assemble paired-end reads; Trimmomatic software (v0.39) was employed to filter low-quality sequences (quality score Q < 20, sequence length <50 bp) (21). Finally, Usearch software (v7.0.1090) was used to remove chimeric sequences by combining denoising (denovo) and reference-based approaches against the gold database, yielding high-quality clean tags for each sample (22, 23).
2.4.2 OTU clustering and taxonomic analysis
All high-quality clean tags were clustered into Operational Taxonomic Units (OTUs) at a 97% similarity threshold using Usearch software (v7.0.1090). OTU-based approach was chosen to ensure methodological consistency and comparability with previous studies on forest musk deer gut microbiota that used the same methodology (13, 14). One representative sequence was selected from each OTU. The RDP Classifier bayesian algorithm (v2.2, confidence threshold = 0.7) was used for taxonomic annotation of OTU representative sequences (24). The species composition of each sample was analyzed at six taxonomic levels: phylum, class, order, family, genus, and species.
2.4.3 Diversity analysis
α-diversity indices, including Observed species, Chao1, ACE, Shannon, and Simpson indices, were calculated using mothur software (v1.41.0) (25). The Kruskal-Wallis test (implemented in R software, v3.4.4) was used to compare differences among three groups, with a significance level set at p < 0.05. The adequacy of sequencing depth and sample size was assessed by plotting rarefaction curves and species accumulation curves. β-diversity distance matrices were calculated using the Bray-Curtis and Unweighted UniFrac distance algorithms. Principal Coordinates Analysis (PCoA) and Non-metric Multidimensional Scaling (NMDS) were performed using the “vegan” and “ggplot2” packages in R software (v3.4.4), and Adonis tests were conducted to evaluate the statistical significance of differences between groups.
2.4.4 Differential species analysis
Linear discriminant analysis (LDA) was performed using LEfSe (v1.0) to identify biomarkers with significantly different abundance among three groups (26). The Kruskal-Wallis test was used to screen differentially abundant taxa between groups (p < 0.05). The LDA analysis was used to calculate the effect size of differences. Taxa with LDA score > 2 were considered differentially abundant. To identify taxa with larger effect sizes, a more stringent threshold of LDA > 3 was applied, and these were defined as major differential taxa (27, 28).
2.4.5 Functional prediction analysis
The PICRUSt2 software (v2.5.2) was used to predict the metagenomic functional potential of the gut microbial community based on the OTU abundance table (29). The predicted gene families were mapped to the KEGG database to obtain pathway abundance information at different levels (Level 1, 2, and 3). The STAMP software (v2.1.3) was used to perform non-parametric tests (Kruskal-Wallis test) on the abundance of pathways at Level 2 between groups, and Dunn’s post hoc tests were used for pairwise comparisons. A p-value < 0.05 was considered statistically significant.
3 Results
3.1 OTU clustering and microbial community composition analysis
Fecal samples from 15 forest musk deer were subjected to sequencing, and a total of 1,422,996 high-quality sequences were obtained after quality control (detailed sequencing data are provided in Supplementary Table S2). Both the rarefaction curves and species accumulation curves tended to plateau, indicating that the sequencing depth was sufficient to cover the majority of microbial species in the samples (Supplementary Figure S2).
At a 97% similarity threshold, all sequences were clustered into 2,582 Operational Taxonomic Units (OTUs). The Venn diagram analysis (Figure 1A) showed that the three groups (HN, SX, YS) shared 1,409 OTUs, accounting for 54.6% of the total OTUs, which constituted the core gut microbial community of forest musk deer. The HN, SX, and YS groups each had 172, 138, and 158 unique OTUs, respectively. All OTUs were annotated to 26 phyla, 53 classes, 138 orders, 275 families, and 646 genera. A bar chart was constructed by selecting the top 30 dominant genera with the highest relative abundance at the genus level across all samples (Figure 1B). Dominant genera such as Lachnospiraceae_unclassified, Acinetobacter, Pedobacter, Christensenellaceae_R-7_group, and Bacteroides formed the stable foundation of the forest musk deer gut microbiota.
Figure 1
At the phylum level (Figure 1C), Firmicutes, Bacteroidota, and Proteobacteria were the absolute dominant phyla shared by all three groups. Notably, the average abundance of Actinobacteriota in the YS wild group (11.13%) was significantly higher than that in the two captive groups (HN: 1.29%; SX: 6.12%). The HN captive group exhibited a community structure dominated by Firmicutes (59.01%), which was significantly higher than that in the SX captive group (35.61%) and YS wild group (37.45%). The SX captive group showed a more similar ratio of Firmicutes and Proteobacteria to the YS wild group.
At the genus level (Figure 1D), the dominant genera differed among the groups. In the HN captive group, the dominant genera were UCG-005 (13.38%), Acinetobacter (7.12%), Lachnospiraceae_unclassified (6.57%), Bacteroides (6.40%), and UCG-010 norank (5.53%). In the SX captive group, the genus Escherichia-Shigella had the highest abundance (11.05%), followed by Pedobacter (5.51%), UCG-005 (5.01%), Massilia (4.28%), and Lachnospiraceae_unclassified (4.03%). The dominant genera in the YS wild group included UCG-005 (7.33%), Pedobacter (6.11%), Escherichia-Shigella (4.85%), Bacillus (4.26%), and Massilia (3.65%).
3.2 Microbial diversity analysis
α-diversity analysis (Figure 2) showed that the Coverage index of all groups was above 99%, indicating that the sequencing depth was sufficient to reflect the true structure of the microbial community (α-diversity statistics in Supplementary Table S3). The SX captive group had slightly higher values of the ACE and Chao1 indices, suggesting that the microbial richness of the SX captive group was numerically higher. The HN captive group had a slightly higher Shannon index and the lowest Simpson index, indicating that its microbial community had higher diversity and more evenness. However, there were no significant differences in the ACE, Chao1, Shannon, and Simpson indices of the gut microbiota among the three groups (p > 0.05). This suggests that the captive versus wild environment, as well as the feeding conditions of different geographically captive populations, did not significantly affect the overall species richness and diversity level of the forest musk deer’s gut microbiota.
Figure 2
β-diversity analysis was performed using the Unweighted UniFrac distance and Bray-Curtis distance to reveal the structural differences in the gut microbiota of the three groups of forest musk deer (Figure 3). Unweighted UniFrac distance analysis (Figures 3A,B) revealed that the first and second principal components of PCoA explained 31.69 and 11.12% of the total microbial community variation, respectively. The microbial community structure of wild forest musk deer (YS) was most distinctly separated from the HN captive group, while the separation from the SX captive group was relatively weaker. Captive groups HN and SX also exhibited certain population specificity. NMDS analysis (stress = 0.0549) further verified this separation pattern, indicating that the ordination results were reliable. Adonis (PERMANOVA) test demonstrated that the structural differences in the gut microbiota among the three groups of forest musk deer were statistically significant (R2 = 0.22, p = 0.039). Bray-Curtis distance analysis (Figures 3C,D) showed that the first principal component of PCoA explained 44.65% of the microbial community variation. The between-group separation pattern was consistent with that observed in the Unweighted UniFrac analysis, but the degree of separation was slightly weaker. NMDS analysis (stress = 0.0837) also supported these results.
Figure 3
Combining the results of α and β diversity analyses, it was found that the overall diversity level of the gut microbiota of captive and wild forest musk deer was similar, but their species composition and structure differed significantly. The microbial community structure of captive forest musk deer from different geographical populations also exhibited geographical differentiation, which was mainly reflected at the level of species presence/absence, while the contribution of differences in microbial abundance was relatively small. The above results provide important insights for elucidating the shaping mechanism of the captive environment on the gut microbiota of forest musk deer and optimizing captive breeding management strategies.
The clustering heatmap generated based on the Top 50 most abundant genera further verified the above results (Figure 4). The three groups of forest musk deer samples clustered in a pattern of within-group aggregation and between-group separation, which was consistent with the results of the β-diversity analysis. Samples from the HN captive group independently clustered into a distinct branch, while those from the YS and SX groups clustered together into a larger branch. In the HN captive group, the abundances of genera such as Parabacteroides, Campylobacter, Bacteroides, and UCG-005 were significantly increased. In the YS wild group, the genera with relatively high abundances included Novosphingobium, Sphingomonas, Pedobacter, and Devosia. Although many genera in the SX captive group had similar abundances to those in the YS wild group, the genus Escherichia-Shigella exhibited a higher abundance in the SX captive group.
Figure 4
3.3 Differential biomarker species analysis
LEfSe analysis was employed to screen for microbial taxa with significant inter-group differences (LDA score > 2.0). The cladogram (Figure 5A) showed that differential taxa formed distinct evolutionary branches according to the groups: the Actinobacteriota taxa enriched in the YS wild group clustered on one side of the evolutionary tree, while the Bacteroidota and Firmicutes taxa enriched in the HN captive group clustered on the other side. Only a small number of taxa were enriched in the SX captive group. The LDA value bar chart further identified major differential taxa with more prominent effect sizes (LDA score >3.0, Figure 5B), and a total of 36 biomarkers significantly enriched across different groups were identified. The YS wild group was enriched with 18 biomarkers, with Actinobacteriota and its subordinate taxa as the core, including genera such as Bacillus, Arthrobacter, Williamsia, Microbacterium, and Psychrobacillus, as well as family/order-level taxa such as Cellulomonadaceae, Nocardioidaceae, Propionibacteriales, and Micrococcaceae. The HN captive group was enriched with 16 biomarkers, dominated by taxa related to Bacteroidota and Firmicutes, including genera such as Bacteroides, Clostridia, Lachnospiraceae_UCG-010, and Eubacterium, as well as family/order-level taxa such as Anaerovoracaceae, Peptostreptococcales-Tissierellales, Campylobacterales, and Clostridia vadinBB60 group. The SX captive group was only enriched with 2 taxa, namely Skermanella (genus) and Cytophagales (order), with the fewest characteristic taxa among the three groups.
Figure 5
3.4 Intestinal microbial functional prediction analysis
Functional prediction based on the KEGG database (Figure 6A) showed that the gene functions of the forest musk deer gut microbiota were mainly enriched in seven major categories, among which the metabolism related pathways dominated. The pathways of amino acid metabolism (10.23%) and carbohydrate metabolism (10.09%) had the highest abundance, indicating that the gut microbiota of forest musk deer play important roles in amino acid and carbohydrate metabolism. Notably, the predicted abundance of the membrane transport pathway under the category of environmental information processing reached 11.90%, suggesting that the gut microbiota of forest musk deer may exhibit strong substance transport capacity when adapting to different environmental stresses. These analyses provide functional-level insights into understanding the digestive adaptation mechanism of forest musk deer to high-fiber diets. Further analysis revealed that a total of 11 Level 2 pathways showed significant differences among the three groups (p < 0.05) (Figure 6B). Among them, the predicted abundance of the pathways xenobiotics biodegradation and metabolism, lipid metabolism in the YS wild group was significantly higher than that in the two captive groups (p < 0.001). Conversely, the two captive groups (HN and SX) showed significantly higher abundance of the pathways translation and nucleotide metabolism compared with the wild group (p < 0.01). Between the two captive groups, only the pathway xenobiotics biodegradation and metabolism was significantly higher in the SX group than in the HN group (p < 0.05). These results suggest that the gut microbiota of wild forest musk deer may have greater functional potential in xenobiotic detoxification and lipid metabolism, while the microbiota of captive forest musk deer exhibit higher activities of protein synthesis and nucleotide metabolism, indicating significant differences in functional adaptability of the microbiota between the two living environments.
Figure 6
4 Discussion
This study systematically compared the gut microbial communities of captive forest musk deer from Henan and Shanxi Provinces with that of wild forest musk deer from Baotianman, Henan Province. The results indicate that a combination of factors—including housing conditions, dietary composition, and geographic location—is associated with the differentiation of forest musk deer gut microbiota. These factors likely interact to shape the microbial characteristics of captive populations.
In terms of community structure, the three groups of forest musk deer all had Firmicutes, Bacteroidota, and Proteobacteria as their dominant microbial phyla, which is consistent with the characteristics of the gut microbiota of most herbivorous mammals (30–32). However, the abundance of Actinobacteriota in the intestinal tract of wild forest musk deer (YS group) was significantly higher than that in the two captive groups. Actinobacteriota can secrete enzyme systems such as cellulases and ligninases to efficiently degrade the diverse high-fiber plants ingested by wild forest musk deer. Meanwhile, the bioactive substances it produces (e.g., antibiotic precursors) can enhance the host’s resistance to the complex wild environment (15). Previous studies have confirmed that genes related to Actinobacteriota are significantly enriched in the metagenome of wild forest musk deer (15, 33), which is consistent with the results of this study and further corroborates the importance of Actinobacteriota for wild forest musk deer to utilize natural food resources. Captive forest musk deer, on the other hand, showed a high abundance of Firmicutes, especially the HN group, where the abundance of Firmicutes reached 59.01%, a phenomenon that is common in captive herbivorous animals (34, 35). The increased ratio of Firmicutes to Bacteroidota is usually associated with increased intake of easily fermentable carbohydrates in the diet. It can produce short-chain fatty acids through the rapid fermentation of non-structural carbohydrates, thereby meeting the host’s energy requirements (36, 37). The abundance of Firmicutes in SX captive group was closer to that of the wild group, which likely reflects the differences in breeding conditions between the two farms. Notably, the abundance of Escherichia-Shigella in the SX captive group (11.05%) was significantly higher than in the other two groups. While this genus contributes to maintaining gut microbial balance, its overgrowth under captive conditions may be associated with intestinal dysbiosis and an increased risk of digestive disorders (38). Together, these differences likely reflect variations in the dietary composition, housing conditions, and management practices between the two captive populations (39–41).
The gut microbial communities of captive forest musk deer from different geographic origins exhibit significant differentiation. In the SX captive group, the gut microbiota of some individuals clustered with those of the wild group, whereas the HN captive group showed a significant difference from the wild group. It has been reported for Francois’ langurs and Asian black bears that geographic isolation significantly affects the gut microbial community structure by altering vegetation composition and climatic conditions (42, 43). Geographically, Gaoping in Shanxi Province (located on the Loess Plateau, with a temperate continental monsoon climate) and Nanyang in Henan Province (situated in the Nanyang Basin, with a climate transitional from subtropical to warm temperate) differ essentially in climate and native vegetation types. The forage in Gaoping is dominated by the stems and leaves of drought-resistant plants with high lignin content and complex structure (e.g., Robinia pseudoacacia leaves, Ziziphus jujuba leaves, Alfalfa and Artemisia species), which are closer to the food resources naturally available to forest musk deer. In contrast, the forage in Nanyang is centered on tender, juicy plants rich in water-soluble fiber, pectin, and starch (e.g., Mulberry leaves, Broussonetia papyrifera leaves, Kudzu leaves and White clover), along with a high proportion of concentrate feed, forming a high-energy and easily fermentable dietary system. In this study, the geographical distance between the SX captive group and the YS wild group (approximately 400 km) was greater than that between the HN captive group and the YS wild group (approximately 200 km), yet the microbiota similarity was higher. This indicates that the impact of geographic factors on gut microbiota is exerted through specific environmental variables rather than a simple distance effect. This discovery provides a new approach for the geographic adaptation management of captive forest musk deer that simulating the vegetation composition and environmental conditions of wild habitats, we can promote the convergence of captive gut microbiota toward the wild type, thereby reducing the risk of diseases (44, 45).
In addition to diet, housing conditions may further amplify the microbiota differences: the SX group had a larger activity area with natural soil and vegetation, whereas the HN group was housed in smaller, brick-paved enclosures. Such environmental differences can alter exposure to soil microbes and activity levels, which also influence gut microbiota composition (30). The presence of soil-associated bacteria, particularly Skermanella, enriched in the SX captive group warrants explanation. A study on captive red-crowned cranes found that Skermanella and Deinococcus was enriched after the cranes were placed outdoors and fed fresh fruits and vegetables (46). Nevertheless, other management practices, such as enclosure cleaning frequency and human contact intensity, may also influence gut microbiota by altering environmental microbial exposure or inducing stress responses, though these factors were not quantitatively assessed in the present study. Previous studies have confirmed that season, age, gender, and mating status significantly influence the gut microbial composition of forest musk deer (47–49). In our study, all samples were collected in April (spring), which partially controls for seasonal variation. However, differences in age and gender composition among groups, as well as unknown mating status, may still act as potential confounding factors influencing gut microbial variation.
KEGG notation revealed that the gut microbiota of forest musk deer are predominantly enriched in major functional categories including metabolism and environmental information processing. Key pathways identified were amino acid metabolism (10.23%), carbohydrate metabolism (10.09%), and membrane transport (11.90%), suggesting a potential role of gut microbiota in nutrient metabolism and absorption (14, 15). However, the differences in microbial community structure directly lead to adaptive differentiation at the functional level (50). The YS wild group exhibited significantly higher abundance in xenobiotics biodegradation and metabolism pathways compared to the captive group, which is closely related to the secondary metabolites such as phenols and terpenoids in the plants they consume (51). The gut microbiota detoxify or transform these substances through specific enzyme systems, reducing the host’s risk of poisoning (52, 53). The higher predicted abundance of lipid metabolism pathways in the wild YS group could be related to their natural foraging behavior and possibly to musk secretion activity. Musk secretion is a lipid-rich process requiring substantial energy and precursor metabolites. Recent studies (54, 55) have shown that gut microbiota-derived short-chain fatty acids and lipid metabolites may influence musk biosynthesis.
Since we lack individual-level musk secretion data for the wild samples, we cannot exclude the possibility that some samples were collected from male deer during the pre-musk secretion period. In contrast, captive forest musk deer showed higher abundance of pathways related to translation and nucleotide metabolism, which may contribute to efficient protein synthesis and cell proliferation under stable feeding conditions (15, 56). The abundance of xenobiotics biodegradation pathway in the SX captive group was significantly higher than that in the HN captive group, which may reflect dietary and environmental differences between the two captive populations. These observations suggest that optimizing diet composition and semi-natural housing conditions may help modulate the gut microbiota function of captive forest musk deer (32, 44, 57). Combined with the α-diversity results, although there was no significant difference among the three groups, the β-diversity and functional pathway differences were significant. This indicates that the microbial community of structural differentiation precedes diversity changes, which provides an important warning for captive species conservation: even if microbial diversity does not decline, the cryptic differentiation of structure and function may still threaten host health, thus, strengthening the monitoring of microbial community structure is necessary.
Nevertheless, this study has several limitations. The small sample size may limit the generalizability of our conclusions, and the lack of molecular individual identification for wild samples and incomplete information on mating status and musk secretion status represent additional constraints. Furthermore, our study design does not allow fully disentangling the relative contributions of diet, housing conditions, and geographic factors, and validated gut health indices (e.g., GMHI/MDI) were not applied due to the absence of species-specific healthy reference standards for forest musk deer. Future studies with larger sample sizes and integrated multidimensional ecological data are needed to further validate and expand our findings.
5 Conclusion
The gut microbiota of wild forest musk deer is characterized by high abundance of the phylum Actinobacteriota and enrichment of functions related to xenobiotic degradation and lipid metabolism, whereas captive forest musk deer exhibit high abundance of the phylum Firmicutes and active translation and nucleotide metabolism. A combination of factors—including dietary composition, housing conditions, and geographic location—likely influences the microbial community structure of captive populations. A stable core microbiota exists in the forest musk deer’s gut, which provides the basis for maintaining basic intestinal functions.
Statements
Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: NCBI, accession number PRJNA1468187.
Ethics statement
The animal study was approved by Henan University of Urban Construction Animal Care and Use Committee. The study was conducted in accordance with the local legislation and institutional requirements.
Author contributions
ML: Writing – review & editing, Methodology, Visualization, Formal analysis, Conceptualization, Writing – original draft. LZ: Formal analysis, Writing – review & editing, Validation, Data curation. KD: Visualization, Writing – original draft, Data curation, Software, Investigation. YW: Methodology, Validation, Investigation, Writing – review & editing. SY: Supervision, Writing – review & editing, Funding acquisition, Resources. SL: Writing – review & editing, Supervision, Project administration. ZX: Funding acquisition, Supervision, Project administration, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Project of Research on the Habitat of Forest Musk Deer in the Baotianman National Nature Reserve (Grant Nos.: K-H2022137, K-X2022201).
Acknowledgments
We gratefully acknowledge the Neixiang Management Bureau of Baotianman National Nature Reserve for its invaluable support in field investigation and wild sample collection. The authors also wish to thank the breeding farms in Nanyang, Henan and Gaoping, Shanxi for their full assistance with captive sample collection and on-site work coordination.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2026.1824527/full#supplementary-material
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Summary
Keywords
16S rRNA sequencing, captive, forest musk deer, gut microbiota, wild
Citation
Lu M, Zhu L, Dai K, Wang Y, Yao S, Li S and Xie Z (2026) Differential analysis of gut microbiota between captive and wild forest musk deer (Moschus berezovskii) based on 16S rRNA sequencing. Front. Vet. Sci. 13:1824527. doi: 10.3389/fvets.2026.1824527
Received
06 March 2026
Revised
06 May 2026
Accepted
13 May 2026
Published
04 June 2026
Volume
13 - 2026
Edited by
Fabrice Martin-Laurent, Institut National de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), France
Reviewed by
Jielong Zhou, Southwest Forestry University, China
Zhongxian Xu, China West Normal University, China
Updates
Copyright
© 2026 Lu, Zhu, Dai, Wang, Yao, Li and Xie.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhaohui Xie, xiezhaohui@huuc.edu.cn; Shaobin Li, shaobinli@yangtzeu.edu.cn
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