Characterization of the gut microbiota in the golden takin (Budorcas taxicolor bedfordi)
- Jun Chen†1,
- Huanxin Zhang†2,
- Xiaoyang Wu†1,
- Shuai Shang2,
- Jiakuo Yan1,
- Yao Chen1,
- Honghai Zhang1Email author and
- Xuexi Tang2Email author
© The Author(s) 2017
Received: 5 January 2017
Accepted: 21 March 2017
Published: 17 April 2017
The gut microbiota of mammals is a complex ecosystem, which is essential for maintaining gut homeostasis and the host’s health. The high throughput sequencing allowed us to gain a deeper insight into the bacterial structure and diversity. In order to improve the health status of the endangered golden takins, we first characterized the fecal microbiota of healthy golden takins using high throughput sequencing of the 16S rRNA genes V3–V4 hypervariable regions. Our results showed that, Firstly, the gut microbiota community comprised 21 phyla, 40 classes, 62 orders, 96 families, and 216 genera. Firmicutes (67.59%) was the most abundant phylum, followed by Bacteroidetes (23.57%) and Proteobacteria (2.37%). Secondly, the golden takin maintained higher richness in spring than in the winter while community diversity and evenness was not significantly different. Thirdly, four female golden takins demonstrated highly similar microbiota and the five golden takin males had relatively highly similar microbiota. All of our results might indicate that the fecal microbiota of golden takins were influenced by the season and the animal’s sex. The findings provided theoretical basis regarding the gut microbiota of golden takins and may offer new insights to protect this endangered species.
KeywordsGut microbiota 16SrRNA Illumina sequencing Seasonal changes Diet quality Golden takin (B. t. bedfordi)
The takin (Budorcas taxicolor) is a large Himalayan ungulate in the family Bovidae, which is classified into four subspecies based on distinct differences in physical characteristics and geographic location, including the golden takin (B. t.bedfordi), the Mishmi takin (B. t.taxicolor), the Sichuan takin (B. t. Tibetana), and the Bhutan takin (B. t.whitei). The golden takin and the Sichuan takin are endemic subspecies of China. Furthermore, relative to the other subspecies, the golden takin is listed as vulnerable by the International Union for Conservation of Nature (IUCN 2008) and is under state protection (category I) in China. The golden takin is a type of social bovid herbivore. As a ruminant, this animal primarily forages on the browse of trees and shrubs (Schaller et al. 1986). In the spring and summer, golden takins undertake seasonal migration to the higher elevations of the uppermost tree line. During the autumn and winter, they move to lower elevations with smaller groups (Ming et al. 2003).
The gut microbiota offers substantial benefits to the host, such as aiding in digestion, promoting immune system development and competing for niches with pathogens (Cadwell 2014). To date, there have been many studies investigating the gut microbiota in herbivores (Bian et al. 2013; Costa et al. 2012; Liu et al. 2014). For instance, Firmicutes and Bacteroidetes, represented by the Ruminococcaceae, Lachnospiraceae, Rikenellaceae and Prevotellaceae families (Bian et al. 2013), dominate the microbiota in the healthy rhinoceros. Costa et al. (2012) described the predominance of Firmicutes (68%) in the feces of healthy horses, followed by Bacteroidetes (14%) and Proteobacteria (10%). The core microbiota of donkeys is dominated by Firmicutes (64% males and 64% females) and Bacteroidetes (23% males and 21% females), followed by Verrucomicrobia, Euryarchaeota, Spirochaetes and Proteobacteria (Liu et al. 2014). Considerable evidence supports the complexity of the gut microbiota in many mammals and the necessity of gut bacterial communities to maintain gut homeostasis. However, surprisingly, little research was on the gut microbiota of the golden takin. Therefore, in present research, we characterized the fecal microbiota of nine golden takins by performing high-throughput sequencing. On this basis, we also compared gut microbiota variation from winter to spring, which is the season of animals likely to get sick. Our result indicated that season was a factor that might impact the composition of the gut microbiota.
Materials and methods
Information on the characteristics of the golden takins used in this study
Jinan wild animal park
Jinan wild animal park
Jinan wild animal park
Jinan wild animal park
Jinan animal park
Jinan animal park
Jinan animal park
Jinan animal park
Jinan animal park
DNA extraction, 16S rRNA gene PCR and sequencing
Total genomic DNA was isolated from fecal samples using a QIAamp DNA Stool Mini Kit (Qiagen, Germany) according to the manufacturer’s recommended protocol. We used a UV–Vis spectrophotometer (NanoDrop 2000c, USA) to determine DNA quantification and quality. DNA concentrations were measured to ensure final concentrations greater than 20 ng/μl for PCR amplification of the V3–V4 hypervariable regions of the 16SrRNA gene using the primers 341F (CCTAYGGGRBGCASCAG) and 806R (GGACTACNNGGGTATCTAAT), as previously described (Hugerth et al. 2014). Amplification was performed in a 30-μl volume, and the amplified fragment size was approximately 410 bp. Specifically, PCR reactions contained 15 μl of Phusion Master Mix (2er), 1.5 µl of each primer, 10 µl of microbial genomic DNA, and 2 µl ddH2O to constitute a final volume of 30 µl. The following PCR protocol was used: 1 min at 98 °C for initial denaturation, 35 cycles of 10 s at 98 °C for denaturation, 30 s at 55 °C for annealing, and 30 s at 72 °C for elongation, and a final extension at 72 °C for 5 min. PCR products were mixed with same volume of 1× loading buffer (containing SYBR green) and separated by electrophoresis on a 2% agarose gel. Samples containing a bright band between 400 and 450 bp were chosen and mixed in equidensity ratios. Then, this mixture of PCR products was purified with a Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were generated using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following the manufacturer’s recommendations, and index codes were added. Library quality was assessed with a Qubit@ 2.0 Fluorometer (Thermo Scientific) and an Agilent Bioanalyzer 2100 system. Finally, the DNA library was sequenced on an IlluminaHiSeq 2500 platform, and 250-bp paired-end reads were generated.
Paired-end reads were assigned to samples based on their unique barcodes and truncated by cutting off the barcodes and primer sequences. Paired-end reads were then merged using FLASH (V1.2.7) (Magoč and Salzberg 2011). The raw tags were subjected to specific filtering conditions to obtain high-quality clean tags using the QIIME (V1.7.0) (Caporaso et al. 2010) quality control process. The tags were compared with the reference database (Gold database) using the UCHIME algorithm (UCHIME Algorithm) (Edgar et al. 2011) to detect chimeric sequences, which were then removed. Finally, effective tags were obtained. Sequence analysis was performed using Uparse software (Uparse v7.0.1001) (Edgar 2013). Sequences demonstrating greater than or equal to 97% similarity were assigned to the same operational taxonomic unit (OTU). For each representative sequence, the GreenGene Database (Desantis et al. 2006) was used and the RDP classifier algorithm (Version 2.2) (Wang et al. 2007) was employed to annotate taxonomic information. To study the phylogenetic relationships between different OTUs and identify dominant species in different samples (groups), multiple sequence alignment was carried out using MUSCLE software (Version 3.8.31) (Edgar 2004). Ultimately, we obtained 54,748 sequences for diversity analysis. Alpha diversity was evaluated by calculating six indices, specifically the Observed-species, Chao1, Shannon, Simpson, ACE and Good-coverage indices. All indices were calculated with QIIME (V1.7.0) and displayed with R software (Version 2.15.3). Beta diversity based on both Weighted and Unweighted Unifrac distances was calculated using QIIME software (Version 1.7.0). Cluster analysis was preceded by principal component analysis (PCA) using the FactoMineR package and ggplot2 package in R software (Version 2.15.3). The data set supporting the results of this article is available in the Sequencing Read Archive (SRA) database, accession numbers SRP090712.
Alpha-diversity of the gut microbiota in the nine golden takins in this study
At the family level, we detected 96 families, and the abundance of unclassified bacteria in the samples was 17.04%. The distribution proportion was greater than 0.5% for 10 families (Fig. 3b). Among these 10 families, Ruminococcaceae was predominant, with an abundance of 49.61%, followed by Rikenellaceae (10.93%), Lachnospiraceae (6.41%), Christensenellaceae (3.68%), Clostridiales_vadinBB60_group (3.15%), Prevotellaceae (2.92%), Bacteroidaceae (2.64%), Bacteroidales_RF16_group (2.32%), Verrucomicrobiaceae (0.79%), and Bacillaceae (0.50%). The most abundant families were Ruminococcaceae, Rikenellaceae and Lachnospiraceae, which comprised approximately 66.95% of total sequences.
At the genus level, the percentage of unclassified bacteria averaged 42.52% (ranging from 36.7 to 50.27%), and the remainder were classified into 216 genera. The distribution proportion was greater than 0.5% in 10 genera (Fig. 3c). Among these 10 genera, Ruminococcaceae_UCG-010 was predominant, with an abundance of 21.44%, followed by Ruminococcaceae_UCG-005 (13.93%), Rikenellaceae_RC9_gut_group (7.30%), Christensenellaceae_R-7_group (3.56%), Alistipes (2.84%), Bacteroides (2.64%), [Eubacterium]_coprostanoligenes_group (2.36%), Ruminococcaceae_UCG-013 (2.11%), Akkermansia (0.79%), and Bacillus (0.50%). Of the 10 genera, Ruminococcaceae_UCG-010, Ruminococcaceae_UCG-005, Christensenellaceae_R-7_group, Ruminococcaceae_UCG-013, Bacillus, and [Eubacterium]_coprostanoligenes_group belong to Firmicutes. Rikenellaceae_RC9_gut_group, Alistipes and Bacteroides belong to Bacteroidetes, and Akkermansia belongs to Verrucomicrobia.
Analysis of species abundance
Seasonal (winter and spring) comparisons for species abundance at the phylum, class, order and family levels
Avg (A) (%)
Avg (B) (%)
Seasonal (winter and spring) comparisons for species abundance at the genus level
Avg (A) (%)
Avg (B) (%)
In present study, we first performed high-throughput sequencing to characterize the microbial community in the golden takin. Similar to previous researches (Costa et al. 2012; Oikonomou et al. 2013), the golden takin gut microbiota was also dominated by Firmicutes (67.59%) and Bacteroidetes (23.57%), followed by Proteobacteria (2.37%). Firstly, Firmicutes was an advantaged group of bacteria in the gastrointestinal tract and fecal flora of various animals (Ley et al. 2008). The advantage exhibited by Firmicutes might be related to the anatomical physiology and diet of specific species that feed primarily upon insoluble fiber and in which the cecum and colon were the major sites of fermentation (Costa et al. 2012). Within Firmicutes, Clostridiales (65.42%) dominated in the feces of golden takins. According to previous studies, Clostridiales accounted for 24–59% of human fecal samples and was also the dominant order in the canine gut microbiota; thus, this family was an important index of intestinal bacterial ecosystem function and metabolic differences (Wang et al. 2005). Additionally, Ruminococcaceae dominated in the nine golden takins, and Lachnospiraceae was present at elevated levels; these bacteria were regarded as fiber-degraders in the herbivore rumen and hindgut (Bian et al. 2013; Jami and Mizrahi 2012). Trace amounts of another major fiber-degrader in the rumen, Fibrobacter, were detected in the feces of the nine golden takins. Secondly, Bacteroidetes were the second-most predominant components of the gut flora in the nine golden takins, which were consistent with previous researches (Bian et al. 2013; Costa et al. 2012; Oikonomou et al. 2013). Conversely, Bacteroidetes constitutes the dominant group in the rumens of other ruminants (Bhatt et al. 2013; Jami and Mizrahi 2012). Bacteroidetes degraded high-molecular-weight organic matter, such as protein and carbohydrates, and complemented the host genome to degrade resistant foods such as plant cell wall compounds (including cellulose, pectin, and xylan) (Thoetkiattikul et al. 2013). Bacteroides also degraded host sources of carbohydrates, primarily derived from gastrointestinal tract secretions, such as mucins and chondroitin sulfate polysaccharides (Salyers et al. 1977). Additionally, Bacteroidetes produces butyrate, which was the product of colonic fermentation. Bacteroidetes possessed antitumor properties, which played important roles in maintaining intestinal health (Kim and Milner 2007), and participated in toxic or mutagenic bile acid metabolism and transformation (Smith et al. 2005). In our study, the families Rikenellaceae, Prevotellaceae and Bacteroidaceae dominated within this phylum. Thus, Bacteroidetes played an equally important role as Firmicutes in the digestive system of herbivorous animals. Thirdly, Proteobacteria constituted a large group of bacteria comprising various bacterial taxa that are capable of catabolizing a wide range of feedstuff components (Evans et al. 2010), which advantages these bacteria in the panda (Wei et al. 2012). These differences may be caused by different community structures in the host diet and bowel. In our study, at the genus level, the highest species distribution proportions were observed for Ruminococcaceae_UCG-010 and Ruminococcaceae_UCG-00, which belonged to Ruminococcaceae and were involved in fiber digestion. Fourthly, unclassified bacteria in the nine samples accounted for 18.82–22.48% of sequences. According to previous studies, many animal gut microbiomes contained certain proportions of unclassified bacteria (Bian et al. 2013; Costa et al. 2012; Wu et al. 2016). The reason of this might due to the limited database of 16S RNA gene sequences and little research on classification of fecal microbes. Unclassified bacteria and their functions in different hosts required further research.
The PCA and heatmap analyses indicated that the separation was observed between the microbiota of group A and group B. However, within groups, there was a high percent identity, suggesting the fecal microbiota of golden takins may be influenced by different seasons. Moreover, rarefaction curves revealed lower phylogenetic diversity and numbers of observed species (OTUs) in the winter microbiota than in the spring microbiota. At the same time, the spring microbiota exhibited higher numbers of OTUs with significant differences (Fig. 2). Sun et al. (2016) also found that the dietary shift from winter to spring affected the Tibetan macaque gut microbial composition and diversity. For our research, the diets of the nine golden takins in winter and spring were roughly the same. The significant differences (P < 0.05) among species showed that the relative abundances of Firmicutes and Bacteroidetes in spring were slightly decreased compared with those in winter, Acidobacteria differed significantly between the two seasons and was significantly higher in the spring at the phylum level. According to previous studies, Acidobacteria was present in many wetland soils and contained a number of genes encoding cellulases and hemicellulases. Thus, these bacteria might play an important roles in plant residue degradation (Kanokratana et al. 2011; Pankratov et al. 2011). At the class level, Coriobacteriia and Deltaproteobacteria were significantly more abundant in the spring than in the winter. Coriobacteriia belonged to Actinobacteria, and Deltaproteobacteria belonged to Proteobacteria. Actinobacteria was an important secondary metabolite (enzyme and antibiotics) producer that also played this role in animal intestines, and the secondary metabolites of Actinobacteria are potent antibiotics (Bull et al. 2005; Cundliffe 2006). We hypothesized that the rise in Coriobacteriia facilitated better resistance to harmful microorganisms external to the intestinal environment, and the rise in Deltaproteobacteria was attributable to decreased food quality. Instestingly, 18 genera were significantly different (P < 0.05) between spring and winter, and 13 of these 18 genera belonged to Firmicutes; among these 13 genera, two genera exhibited higher abundances in the winter than in the spring. In contrast, the other 11 genera were less abundant in the winter than in the spring. Thus, Firmicutes might play different roles in different seasons (Carey et al. 2012; Ley et al. 2008; Taglialatela et al. 2009). The weather in spring was highly variable, which was suitable for the proliferation of a variety of pathogenic microorganisms; thus, food might contain greater numbers of pathogenic microorganisms. We suspected that the seasonal differences might be due to the feeding quality, and the intestinal flora of the golden takin might change in the spring to enhance resistance and facilitate the ability to withstand changes in the outside world.
Previous researches also showed that the fecal microbiota of many mammals might be influenced by sex (Bian et al. 2013; Liu et al. 2014; Wu et al. 2016). In present study, the four female golden takins demonstrated highly similar microbiota and the five golden takin males had relatively highly similar microbiota, although J6 and J7 were relatively far from the other three samples, which might indicate that the fecal microbiota of golden takins might also be influenced by the animal’s sex. Sex-related differences might be induced by sex hormones and their effects on gene expression as well as the immune system but also might be attributable to innate physiological differences between males and females (Mcclelland and Smith 2011; Zhao et al. 2013). However, the physiological mechanisms underlying sexual influence were uncertain.
In conclusion, we describe the predominant fecal bacterial populations in golden takin and offered the taxonomic baseline for further research investigating the intestinal ecosystems in golden takins. We also found that the composition of gut microbiota was influenced by the season which might help to strengthen the management of feed quality in the spring to prevent disease in these animals. These observations also increased our understanding of the bacterial ecosystems in this vulnerable animal.
polymerase chain reaction
principal component analysis
operational taxonomic unit
Conceived and designed the experiments: HHZ, JC. Performed the experiments: JC, XYW. Analyzed the date: JC, HXZ. Contributed materials: JC, JKY, SS, YC. Wrote the paper: JC. Modify the manuscript: XXT. All authors read and approved the final manuscript.
We thank Ying Gao (Jinan Wild Animal Park), Xuchang Xie (Jinan Animal Park) for their assistance in the fecal samples collection.
The authors declare that they have no competing interests.
Availability of date and materials
The raw data obtained had been deposited in the NCBI Sequence Read Archive (SRA) with the Bio Project ID- PRJNA344965, Accession: SRP090712, ID: 090712.
Ethical approval and consent to participate
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
This work was supported in part by grants from the Special Fund for Forest Scientific Research in the Public Welfare (No. 201404420), the National Natural Science Fund of China (Nos. 31372220, 31672313), Science and technology research plan of Shandong Province (No. 2013GSF11707).
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