Age-Related Changes in the Gut Microbiota Composition of Hog Deer (Axis porcinus)

Hui-juan Yan1, Wei Wang2,*, Jian-qiu Yu1, Jun Yi2, Li-li Niu1, Hong-wei Chen1, Yu Qu1, Yang Pu 1, Ang Chen1, Yan Zhong1, Wei-gang Chen1 and Xing-ming Yu1

1Chengdu Zoo, Chengdu, Sichuan, P.R. China

2Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, Sichuan, P.R. China

ABSTRACT

Comprehensive investigation of gut microbiota composition is important for understanding digestive physiology in mammals. The hog deer (Axis porcinus) is a small mammal at high risk of extinction in the wild. In the present study, we sequenced the V4 region of 16S rRNA gene and compared the gut microbiota composition among three ages of captive hog deer, including one infant (Z1, six months old), three young (Z2, ten months old) and 12 adult (Z3, 4-5 years old). A total of 26 phyla (15, 22 and 25 in Z1, Z2 and Z3) and 310 genera (153, 254 and 304 in Z1, Z2 and Z3) were identified. In Z1, Z2 and Z3, Firmicutes (63.65%, 62.07% and 61.96%) was the predominant phylum, followed by Bacteroidetes (29.08%, 29.62% and 30.02%) and Tenericutes (3.08%, 3.24% and 2.78%). The alpha diversity (Shannon Index and observed species) of Z2 was significantly higher than that of Z3 (P < 0.05). The Shannon Index of Z1 was higher than that of Z2. At the genus level, Christensenellaceae_R-7_group and Ruminiclostridium_5 had higher abundance in Z2 compared with Z3, whereas other genera, such as Fibrobacter, Lachnospiraceae_AC2044_group and Oscillibacter were enriched in Z3. In conclusion, our results reveal significant microbiota composition changes that occur with age in captive hog deer.

Article Information

Received 29 July 2019

Revised 12 May 2020

Accepted 20 January 2021

Available online 21 July 2023

(early access)

Published 19 July 2024

Authors’ Contribution

HY and WW performed the procedure, analysed the data, and wrote the manuscript. HY, WW and JY conceived and designed the experiments. HY, WW, LN, YQ and YP extracted data for the analysis. JD, AC, YZ, WC and XY contributed to the sample processing and sequencing.

Key words

Hog deer, Gut microbiota, Age-related change, Next-generation sequencing, 16S rRNA gene, Axis porcinus.

DOI: https://dx.doi.org/10.17582/journal.pjz/20190729100714

* Corresponding author: wws20062127@163.com

0030-9923/2024/0005-2175 $ 9.00/0

Copyright 2024 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Axis porcinus is a ruminant in the Hyelaphus genus (Cervidae family) and an endemic species of South and Southeast Asia. It is also known as the Indochinese hog deer in China, Cambodia, Thailand, Laos and Vietnam and as the Indian hog deer in Pakistan, Nepal, India, Bangladesh and Burma (Tanushree et al., 2000). Axis porcinus has nearly been extirpated in many countries including China; therefore, it has been on the Red List at the endangered level by the International Union for the Conservation of Nature (IUCN) since 2008 (Timmins et al., 2015). Now, in China, most Axis porcinus have been reared in Chengdu Zoo in Sichuan (total number 45) because finding wild Axis porcinus is extremely difficult. Captive rearing is increasingly important, as wild hog deer are approaching extinction in the wild. The zoo plays an important role in artificial feeding, enlarging the herd protecting the species Axis porcinus. Presently, the urgent and necessary goal is to maintain herd health with artificial feeding. Normal gut physiology and microbiota are important for animal health. Therefore, an investigation of the gut microbiome is important for hog deer conservation.

With the rapid development of next-generation sequencing technology, the gut microbiome has become a hot research topic worldwide. The gut microbiota has become increasingly important to understanding the immunological and physiological functions that maintain the health of both artificially fed and wild animals (Guan et al., 2017). Herbivores have higher gut microbial diversity than omnivores and carnivores (Price et al., 2012). These microbiota, with approximately 542 genera found throughout cattle gastrointestinal tracts (Mao et al., 2015), have multiple functions, such as digesting the cellulose of plant cells, eliminating the anti-nutrition factors in the diet and producing vitamins and microbial protein (Kohl et al., 2018; LeBlanc et al., 2013). Gut microbiota have been investigated in some species of ruminants, but such investigations are limited in many uncommon species, especially in endangered animals (Guan et al., 2017; Li et al., 2018). Investigation of the gut microbiota of Axis porcinus is important to understanding the gut physiology and digestion of the species, which will be beneficial for maintaining animal health and enlarging the herd population size. However, limited data exist on the gut microbiota of Axis porcinus. Previous studies on Axis porcinus focused on the mitochondrial genome for a genotyping (Hill et al., 2017; Wang et al., 2017) assessment of a hog deer population (Lwin et al., 2016) and for endoparasite assessment (Rana et al., 2015; Hussain et al., 2022). Although one study investigated the faecal microbiome of six Cervini species, which included Axis porcinus (Li et al., 2018), only 3 adult hog deer were used to collect the samples. Hence, a thorough investigation of the gut microbiota of Axis porcinus, with a determination of the differences between the microbiota community structure of this species and that of other species of Cervidae or ruminants, is necessary.

Many factors, including age and diet, can influence the gut microbial community structure and the dominant bacteria. The ruminal microbial community of goats from 7 days to 2 years old underwent significant changes in response to the shift in age, and a clear age-related pattern was observed in the diversity of the bacterial community with some bacteria (Wang et al., 2013). In cows, microbial change with age was also observed (Dill-McFarland et al., 2017). The same pattern of GI tract microbiota change with age is expected in Axis porcinus, and no microbial data have been collected in other growth stages, such as in the young or the sub-adults. Previous reports confirmed that faeces were used as representations of gastrointestinal tract samples (Tannock et al., 2000; Yan et al., 2018). Furthermore, faecal sampling is a beneficial method for use on endangered species because it is non-invasive (Hu et al., 2017). Therefore, in this study, fresh faecal samples were collected from 1 infant, 3 sub-adults and 12 adults of Axis porcinus and evaluated in this study. Their faecal microbiomes were characterized using techniques for sequencing the 16S rRNA gene with the V4 region. Our findings provide new knowledge about this endangered deer’s gastrointestinal microbiota structure and composition over age, which will benefit diet formulation for artificial feeding and further assist in health management or species conservation.

Materials and methods

Sample collection

From the herd of Axis porcinus captive in Chengdu Zoo, Sichuan province, China, 1 infant, 3 sub-adults and 12 adults were selected. We defined the growth stage as follows: infant, 3-8 months; sub-adult, 8-12 months; and adult, 4-5 years. All animals lived in the same enclosure. The total mixed rations were formulated by the zoo and provided to the animals. The infant animals ingested a different diet consisting of rye, alfalfa pellet, apple, carrot, sophorae leaves, and complete concentrate. The sub-adult and adult Axis porcinus both consumed a diet consisting of alfalfa bale, alfalfa pellet, apple, carrot, asparagus lettuce, green grass, and complete concentrate. The ingredients and nutritional composition of the diets are presented in Supplementary Table I. A total of 45 faecal samples were collected from the 1 infant (Z1), 3 sub-adult (Z2) and 12 adult (Z3) Axis porcinus. The collector monitored the defecation and animal ID of subjects between 9:00-10:00 am on August 22, 23 and 26, 2017. After observing an animal defecate, the collector immediately transferred the faeces (each faecal sample was larger than 4 g) to a clean sample container using a sterile spoon and then sealed the container in a plastic bag labelled with animal ID and sampling date. During this process, the part of the faeces that touched the ground was removed to guarantee the faeces had not touched anything with bacteria. The fresh faecal samples were stored in a foam cooler with dry ice, and every day after collection, the samples were transported back to the laboratory and stored at -80°C for sequencing. The sequencing information for the samples is provided in Supplementary Table II. All the selected animals lived in same environment and were provided diets by the zoo to meet their requirements. They were healthy and had received no any injection with antimicrobials or other treatments in the previous 6 months. The protocol of this study was approved by the Institution of Animal Care and Ethics Committee. The collection of the Axis porcinus faecal samples was approved by the Chengdu Zoo.

DNA extraction

According to the kit manual, the total genome DNA from the faecal samples (100 mg) was extracted using QIAamp® Stool Mini Kit (Qiagen, Germany). The concentration and purity of each DNA extract were determined using the Nanodrop One (Thermo Scientific, United States). Then, all DNA was sent to Novogene, Inc. (Chengdu, China) for PCR and sequencing.

16S rRNA gene PCR and Illumina HiSeq sequencing

The library preparation and Illumina HiSeq sequencing were performed at Novogene, Inc. The V4 hypervariable region of the 16S rRNA gene was amplified by 16S universal amplicon PCR primers: 515F 5’-GTGYCAGCMGCCGCGGTAA and 806R 5’-GGACTACNVGGGTWTCTAAT (Tamaki et al., 2011). A primer barcode was designed for incorporation into the differentiate samples. A final mixture volume of 50 μL was used for the polymerase chain reaction: 6 μL of template faecal DNA, 2 μL of forward and reverse primer (10 μM), 25 μL of 2 × Taq PCR Master Mix (0.1 U/μL), and 15 μL ddH2O. The PCR was carried out under the following conditions: 3 min at 95°C for initial denaturation, 30 cycles of steps 2 - 4 (95°C for 30 s, 55°C for 30 s and 72°C for 30 s) and the final extension at 72°C for 5 min. Each plate of PCR contained a positive and negative control for amplification confirmation. The positive control was mock community DNA that also was used in the sequencing. The negative control contains no template. The PCR products were measured in electrophoresis using 1.0% agarose gel and purified using a SanPrep DNA Gel Extraction Kit (Sangon Biotech, Shanghai, China). Then, the PCR products were purified with a Qiagen Gel Extraction Kit (Qiagen, Germany) for library preparation and sequencing. A TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) was used to build the sequencing libraries according to the manufacturer’s instructions. After being established by Qubit and qPCR, the library with achievement of quality parameters was sequenced on an Illumina HiSeq 2500 PE250 platform according to the manufacturer’s recommendations.

Bioinformatics

After the raw sequences were obtained, the barcodes and original primers from the HiSeq platform were removed. The sequence assembly and quality filtering were conducted using FLASH (Version 1.2.7) (Magoč et al., 2011) and QIIME (Version 1.7.0) (Bokulich et al., 2013). To eliminate the chimeric sequences, the Gold database was used for comparison with the filtered tags using the UCHIME algorithm (Edgar et al., 2011). Then, effective tags were assigned into the operational taxonomic units (OTU) with a similarity greater than or equal to 97%, and species annotation based on RDP classifier algorithm (Edgar et al., 2011) was performed using the Greengene Database (DeSantis et al., 2006). The datasets generated and analysed during the current study are available in the NCBI BioProject database with the BioProject ID PRJNA533948.

The QIIME was performed to calculate alpha diversities (Shannon Index and observed species) and beta diversity (unweighted and weighted UniFrac distance). The figure of the UPFMA (unweighted pair-group method with arithmetic mean) tree was built in QIIME based on the UniFrac distance. The phylogenetic tree for the genera was generated by using GraPhlAn software (http://segatalab.cibio.unitn.it/tools/graphlan/). The analysis of similarities (ANOSIM) was used to test statistically whether a significant difference existed between the two treatments. The heatmap were made in R using the top 35 genera across all samples. The PCA, PCoA and figures were displayed using R software. The T-test for identification of the bacteria between the sub-adult and adult used non-parametric tests that were archived in R. The R packages used in our analysis contained ggplot2 v.2.2.1, ade4 v.‎1.7-13, WGCNA v.1.64, stats v. 3.6.0, vegan v.2.5-2 and agricolae v.1.2-8.

Results

Overview of the sequencing data

A total of 2,758,076 high-quality reads with an average length of 371 ± 80 bp were obtained after the quality control. These sequences from 45 faeces samples

 

 

of Axis porcinus were classified into 2215 OTUs, with 97% similarity. The rarefaction curves (Fig. 1A), which are plots of the number of species as a function of the number of samples, flattened gradually, with more species yielding more plateaus. This result indicated that the sequencing depth was sufficient and rational. At the same time, the rank abundance curves that displayed relative species abundance in the faecal samples are presented in Figure 1B. The rank abundance curves visually illustrate both richness and evenness of species.

The core microbiome of Axis porcinus

The top 10 species of each sample or growth stage were selected, and the relative abundance of these were used to generate a stacked histogram at the phylum level (Fig. 2A). In Z1, Z2 and Z3, 15, 22 and 25 phyla were identified. At the phylum level of Z1, Z2 and Z3, Firmicutes (63.65%, 62.07% and 61.96%) was the predominant phylum, followed by Bacteroidetes (29.08%, 29.62% and 30.02%) and Tenericutes (3.08%, 3.24% and 2.78%). Proteobacteria (1.24%, 1.87% and 1.88%) and Spirochaetes (1.63%, 0.98% and 1.06%) were next in Z1, Z2 and Z3.

The genus levels of the gut microbiome of Axis porcinus are shown in Figure 2B. In Z1, Z2 and Z3, a total of 153, 254 and 304 genera were identified. At the genus level, the dominant bacteria of Z1, Z2 and Z3 were Ruminococcaceae_UCG-005 (24.63%, 23.26% and 22.59%), Ruminococcaceae_UCG-010 (14.58%, 16.87% and 18.52%), Bacteroides (6.13%, 7.63% and 7.74%), Rikenellaceae_RC9_gut_group (4.97%, 4.71% and 4.41%), Alistipes (4.02%, 2.75% and 2.90%), Christensenellaceae_R-7_group (3.58%, 4.22% and 3.74%) and [Eubacterium]_coprostanoligenes_group (2.44%, 2.89% and 3.04%). Nine other genera with a relative abundance higher than 1% were identified: Prevotella_1 (2.82%, 1.06% and 1.02%), Ruminococcaceae_UCG-013 (2.20%, 2.24% and 2.00%), Prevotellaceae_UCG-003 (1.51%, 2.10% and 1.77%), Phocaeicola (0.86%, 1.52% and 1.46%), Prevotellaceae_UCG-004 (1.00%, 1.62% and 1.43%), Alloprevotella (0.67%, 1.25% and 1.31%), Ruminococcaceae_UCG-014 (1.23%, 1.35% and 1.28%), Ruminococcus_1 (0.89%, 1.15% and 1.25%) and Treponema_2 (1.62%, 0.96% and 1.04%).

Visual representation of the microbial composition

To better understand the microbial community composition, a heatmap for clustering with relative abundance of genera is shown in Figure 3A. Based on the clustered heatmap, the microbes from Z1 were grouped together, while those microbes from Z2 and Z3, which were more similar, were grouped together. The species that accounted for different proportions were also marked by different colours and positions of crowding in the heatmap. A significant difference in the microbial composition could be observed. Regarding the weighted UniFrac and unweighted UniFrac distance matrix, we made the unweighted pair-group method with arithmetic mean (UPGMA) clustering analysis to study the similarity between samples in Figure 3B. The dendrograms of UPGMA was similar to the result in the clustered heatmap.

The relationships of each genus

To investigate the systematic phylogenetic relationship of the species at the genus level, a combination of genus relative abundance and relations among the top 100 genera is represented. The inside components of the phylogenetic tree comprise the representative sequences of the genera, and the colour of the branches represent the corresponding phylum (Fig. 4). The outside cycle indicates the relative abundance of genera in each group, and the colour of the abundance is represented by the different groups. The core genera of Ruminococcaceae_UCG-005, Ruminococcaceae_UCG-010, Ruminococcaceae_UCG-013 and Ruminococcaceae_UCG-014 were in same phylum (Firmicutes), but they are evolutionarily distantly related. The genera of Bacteroides, Alloprevotella, Prevotellaceae_UCG-004, Prevotella_1, Phocaeicola, Prevotellaceae_UCG-003, Alloprevotella, Rikenellaceae_RC9_gut_group and Alistipes belong to the phylum of Bacteroidetes and show a close genetic distance. Additionally, these genera under Bacteroidetes have a close distance with Treponema_2 (phylum Spirochaetes) rather than with the genera under Firmicutes. The [Eubacterium]_coprostanoligenes_group and Ruminococcus_1 belonging to Firmicutes shows similar evolutionary relationships.

 

 

Alpha and beta diversities of Axis porcinus in different growth stages

Alpha diversities between the different growth stages are reported in Figure 5. The Shannon Index and observed species of Z2 were significantly higher than for Z3 (Wilcoxon test: p value= 0.028, p value = 0.025).

 

The Beta-diversity using principle coordinates analysis (PCoA), unweighted UniFrac and principal component analysis (PCA) plot among Z1, Z2 and Z3 are shown in Figure 6. ANOSIM was used to test the differences (Fig. 6B) and showed no difference between Z2 and Z3. From the plot, we can observe that Z1 is separate from Z2 and Z3, in spite of having only 1 sample in Z1. Neither the PCA nor the PCoA plot showed a different cluster pattern between Z2 and Z3, but large variation occurred within groups in the ANOSIM results for these age groups. The PCA plot clustered with groups (Fig. 6D), indicating that Z2 and Z3 exhibited some difference, but this difference was not significant. The results indicate the individual variation observed within the treatment was large compared to that observed between sub-adult and adult Axis porcinus.

Bacterial taxa differentially represented in sub-adult and adult periods

Based on the results of the alpha and beta diversity, we confirmed that the microbial community structure changed at the different growth stages. Therefore, determining which bacteria caused this change in structure alteration was necessary. To find the different species between Z2 and Z3, a T-test was performed. Figure 7 uses a bar plot to show the species with significantly different relative abundances in Z2 and Z3 at the levels of phylum and genus. At the phylum level, the abundance of Fibrobacteres was higher in Z3 than in Z2. At the genus level, while Christensenellaceae_R-7_group and Ruminiclostridium_5 had higher abundance in Z2 compared with Z3, other genera such as Fibrobacter, Lachnospiraceae_AC2044_group and Oscillibacter were enriched in Z3.

Discussion

A strong association exists between the gut microbiota and health or disease in domestic animals. The community structure of gut microbiota in these animals has already been reported, but data for these endangered animals are limited. The measurement of the gut microbiome of Axis porcinus contributes to our understanding of the species’ gut physiology and will be helpful for diet formulation, which will have a positive impact on Axis porcinus health and conservation. Since Axis porcinus is nearly extinct, maintaining their health and enlarging the population is important. In consideration of the few remaining populations of Axis porcinus, the first step is to investigate the hog deer gastrointestinal microbiota according to its growth stage. We characterized the composition and structure of the faecal microflora of Axis porcinus in 3 growth stages (infant, sub-adult and adult); further, we provided the core gut microbiota in the different stages and demonstrated the dissimilarities of this microbiome with those of other cervids.

According to next-generation sequencing analysis, the predominant phyla detected in the faecal samples were Firmicutes (63.65%, 62.07% and 61.96% in Z1, Z2 and Z3) and Bacteroidetes (29.08%, 29.62% and 30.02% in Z1, Z2 and Z3). Small differences were observed in the predominant bacteria among the age groups in this study. These differences may be attributed to the single sample obtained from an infant and the small difference that existed between the sub-adult and adult groups. The sub-adult period of Axis porcinus had a similar gut physiological environment to that of the adult due to the age, diet and habitation. In this study, the sub-adult and adult lived in the same pen and consumed the same diet. Since sexual maturity in Axis porcinus occurs at 8-12 months of age, the lack of alteration in the gut microbiome composition after maturity is reasonable (Odamaki et al., 2016). In terms of the core phyla, Li et al. (2018) found that Firmicutes (49.61%) was the predominant phylum followed by Bacteroidetes (37.74%) in six adult Cervini species (A. porcinus, C. elaphus, C. nippon, R. unicolor, D. dama, and E. davidianus) and one Muntiacini species (E. cephalophus). The relative abundance of Firmicutes in a wild and captive sika deer group was 77.62% and 50.71%, respectively, and

 

 

in Bacteroidetes was 18.29% and 32.00%, respectively (Guan et al., 2017). The phylum of Firmicutes might be highly related to deer ingestion or fermentation of a high-fibre diet (Guan et al., 2017; Costa et al., 2012). Bacteroidetes in the faecal microbiota played an important role in the degradation of high fat and protein from intestinal secretions (Flint et al., 2015). Bacterial phyla with low abundance, including Tenericutes, Proteobacteria, Spirochaetes, Fibrobacteres, Verrucomicrobia and Cyanobacteria, in this study were also observed in other ruminants (Jami et al., 2013; Pitta et al., 2010). Tenericutes accounted for approximately 3% of the microbiota detected in this study, but only 1.3% was observed in wild sika deer, with Tenericutes ranking ahead of Proteobacteria (0.540%) (Guan et al., 2017). The function of Tenericutes in the gut remains unclear but is speculated to be related to artificial rearing. Proteobacteria could help degrade lignin in its main food source (Fang et al., 2012). These core phyla were highly related to the diet. Based on our data, even though the relative abundance of the core bacteria is different from other species of deer, the dominant gut bacterial species of Axis porcinus and other Cervidae species were similar.

Regarding the genera represented in the faeces, the Ruminococcaceae_UCG-005 and Ruminococcaceae_UCG-010 were 2 main genera found in this study and belonged to the phylum Firmicutes, which is consistent with previous research on the golden takin (Li et al., 2017), sika deer (Guan et al., 2017) and wild forest musk deer (Li et al., 2017). Ruminococcaceae made contributions to fibre digestion (Marteau et al., 2004; Fernando et al., 2010), which meant these Axis porcinus could adapt to a wild environment even though they were presently feeding in a zoo. Additionally, alfalfa provided a higher fibre concentration in their diet. Bacteroides, Rikenellaceae_RC9_gut_group and Alistipes belonged to the phylum Bacteroidetes and were also some of the dominant bacteria. The similar results could also be found in sika deer and musk deer (Guan et al., 2017; Li et al., 2017). Bacteroides not only boosts host immunity but also sustains gut bacterial ecological balance, which could reflect adaptive changes of microbiota under long-term feeding in the zoo. At the same time, some members of Bacteroides are opportunistic pathogens that could cause endogenous infections when the normal microbiome stability is disordered. Previous reports found that some Bacteroides species increased while lactic acid bacteria species were reduced in animals with gastrointestinal diseases (Marteau et al., 2004). The abundance of Prevotella_1 decreased with increasing age according to our data. Prevotella is the most abundant genus in other ruminant animals, such as dairy cattle, and shows an increased abundance when the cattle are provided a diet with a high ration of grain (Fernando et al., 2010); thus, the reduction of Prevotella_1 was understandable because a high ratio of grain was provided in the diet supplied to the infant Axis porcinus, and a higher percent of fibre was present in the diet fed to the adult ruminants. Christensenellaceae_R-7_group was also found in lambs (Huang et al., 2017). Other dominant genera in our result could also be found in other ruminant (Martínez et al., 2010; Cockburn et al., 2016).

The alpha diversities showed significant differences among the different ages. The community structure of the gut microbiota changes during the ageing process, and clear differences in the microbial composition were observed among the infants, adults and the elderly (Conlon et al., 2014). Preserving adequate microbial richness and diversity is vital for providing gut microbiota with functional redundancy, adaptability and thus systematic robustness against environmental challenges (Odamaki et al., 2016). A decreased alpha diversity from sub-adults to adults in this study might be caused by the intake of nutrients. The well-distributed diet of concentrate and forage was consumed for sub-adult Axis porcinus, whereas the high-fibre diet might be easily accessible to adult animals. From the PCoA plot, little difference existed between the sub-adult and adult. The distance between the infant Axis porcinus and the other age groups could be attributed to the single sample used. Moreover, the PCA analysis also showed the distinct cluster of Z1 separate from the other groups (Z2 and Z3), which is related to the change in core microbiota shown in both the heatmap and the phylogenetic trees. The microbiome of this single infant could be representative for the age, indicating the impact of age on the gut microbiota; however, it could be an error. Either way, this difference provides a direction for future research. Overall, distinct microbiome alpha diversity in the faecal microbiota of Axis porcinus at different ages is confirmed, and no difference was observed in the beta diversity between the sub-adults and adults. Wang et al. (2016) observed that the beta diversity of goat rumen microbiota showed no clear clustering patterns based on age groups, and alpha diversity did show an age-dependent pattern. Dill-McFarland et al. (2017) found the Shannon Index of faecal microbiota in a 2-year-old cow showed little decrease compared with that present when the cow was 1 year old. Possibly, the abundance or diversity of faecal bacteria decreased to a level from birth to adult due to the greater maturity of stomach function, which results in lower nutrition entry to the large intestine (Dill-McFarland et al., 2017). Further studies about gut microbiota of Axis porcinus need to investigate more age stages and collect more infant samples for analysis.

Identifying the bacteria that are differentially represented among the growth stages is important for a better understanding of gut microbiome function with age, which could provide clues about why the composition of the gut microbiota changes with age even for humans. The T-test was performed to find differences in the species between the sub-adult and adult groups since only 1 infant was included in the youngest group. At the phylum level, only 1 phylum (Fibrobacteres) with higher abundance in the adults was found compared with those phyla observed in the other two groups. The phylum Fibrobacteres was associated with the concentration of fibre in the diet in previous research and was higher in deer (Guan et al., 2017). Our results confirm that the adult Axis porcinus has a strong ability to digest fibre in feed compared to the sub-adult, which is rational for ruminants. At the genus level, Ruminiclostridium_5 and Christensenellaceae_R-7_group were lower in adult animals. These 2 genera were positively related with fasting insulin and a decrease of blood glucose (Zhang et al., 2017). Regarding glucose metabolism, the young animals are prone to low blood sugar; thus, these 2 genera are enriched in the sub-adult Axis porcinus. Other species, including Fibrobacter, Oscillibacter and Lachnospiraceae_AC2044_group, had higher abundance in the adults. Based on taxonomy, we knew that Fibrobacter belonged to Fibrobacteres, with four other genera are annotated as Firmicutes. Thus, the Fibrobacter genus caused the change in the relative abundance of the phylum Fibrobacteres. Oscillibacter and Lachnospiraceae_AC2044_group were highly related with volatile fatty acid digestion and fermentation in ruminants (Kumar et al., 2012; Lemaire et al., 2018). These species play a role in fibre digestion and absorption with increasing age.

This study on Axis porcinus characterized the fundamental gut microbial composition and structure in the infant, sub-adult and adult groups using next-generation sequencing technology and illustrated the significant change that occurred in gut microbiota over the different ages. This understanding is important for Axis porcinus gut physiology, health, feeding and for better protection. In Axis porcinus, the main phylum was Firmicutes and Bacteroidetes, and 2 main genera (Ruminococcaceae_UCG-005 and Ruminococcaceae_UCG-010) were found. Significant microbial abundance changes with age were found, and the bacteria included the following: Christensenellaceae_R-7_group, Lachnospiraceae_AC2044_group, Ruminiclostridium_5, Fibrobacter and Oscillibacter. These microbes are related to diet digestion and nutrient metabolism. Our data could provide insights for animal feeding and/or diet formulation for these captive-reared deer.

A limitation of this study might be the single sample obtained from an infant Axis porcinus. However, the gut microbiota of the infant in our data could still show the core bacteria. Considering that age, sex, diet and environmental factors affect gut microbiota (Kozik, 2017; Bergmann, 2017), more detailed studies on Axis porcinus should be conducted, including an investigation of diet nutrition levels and source. If possible, the rumen microbial composition that plays an important role in digesting fibre should be determined because Axis porcinus is a ruminant. Moreover, since Axis porcinus is an endangered animal, investigation of its microbiota when the captive animals are free and in a simulated wild environment is important for better conservation and protection of the species. Of course, these kinds of studies should be performed when enough animals are present. Furthermore, the metabolic pathway of these different bacterial species through the different ages of the deer should be conducted at the metagenomic level to discover the deeper mechanism.

Conclusion

Using next-generation sequencing technology, we first investigated the core phyla and genera of the gut microbiome of Axis porcinus in different growth stages (young, sub-adult and adult). Significant microbial composition changes were observed with age. Additionally, 5 significant genera were determined to change as Axis porcinus ages. Faecal sample collection can provide a non-invasive method to research the gut microbiome of endangered animals and produce enough information to understand gut physiology.

Acknowledgements

This work was financially supported by The Chengdu Giant Panda Breeding Research Foundation Project (CPF2017-07).

Ethics statement

In the present study, the fresh faecal samples were collected by the keepers while they were cleaning and feeding. The study design and all experimental methods were approved by the Animal Care and Use Committee of the Chengdu Zoo.

Supplementary material

There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20190729100714

Statement of conflict of interest

The authors have declared no conflict of interests.

References

Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D., Gordon, J.I., Knight, R., Mills, D.A. and Caporaso, J.G., 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Meth., 10: 57. https://doi.org/10.1038/nmeth.2276

Bergmann, G.T., 2017. Microbial community composition along the digestive tract in forage-and grain-fed bison. BMC Vet. Res.., 13: 253. https://doi.org/10.1186/s12917-017-1161-x

Costa, M.C., Arroyo, L.G., Emma, A.V., Stämpfli, H.R., Kim, P.T., Amy, S. and Weese, J.S., 2012. Comparison of the fecal microbiota of healthy horses and horses with colitis by high throughput sequencing of the V3-V5 region of the 16S rRNA gene. PLoS One, 7: e41484. https://doi.org/10.1371/journal.pone.0041484

Cockburn, D.W. and Koropatkin, N.M., 2016. Polysaccharide degradation by the intestinal microbiota and its influence on human health and disease. J. mol. Biol., 428: 3230-3252. https://doi.org/10.1016/j.jmb.2016.06.021

Conlon, M.A. and Bird, A.R., 2014. The impact of diet and lifestyle on gut microbiota and human health. Nutrients, 7: 17-44. https://doi.org/10.3390/nu7010017

Dill-McFarland, K.A., Breaker, J.D. and Suen, G., 2017. Microbial succession in the gastrointestinal tract of dairy cows from 2 weeks to first lactation. Scient. Rep., 7: 40864. https://doi.org/10.1038/srep40864

DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L. and Keller, K., Huber, T., Dalevi, D., Hu, P. and Andersen, G.L., 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. environ. Microbiol., 72: 5069-5072. https://doi.org/10.1128/AEM.03006-05

Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C. and Knight, R., 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27: 2194-2200. https://doi.org/10.1093/bioinformatics/btr381

Flint, H.J., Duncan, S.H., Scott, K.P. and Louis, P., 2015. Links between diet, gut microbiota composition and gut metabolism. Proc. Nutr. Soc., 74: 13-22. https://doi.org/10.1017/S0029665114001463

Fang, W., Fang, Z., Peng, Z., Fei, C. and Xiao, Y., 2012. Evidence for lignin oxidation by the giant panda fecal microbiome. PLoS One, 7: e50312. https://doi.org/10.1371/journal.pone.0050312

Fernando, S.C., Purvis, H.T., Najar, F.Z., Sukharnikov, L.O., Krehbiel, C.R., Nagaraja, T.G., Roe, B.A. and Desilva, U., 2010. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. environ. Microbiol., 76: 7482-7490. https://doi.org/10.1128/AEM.00388-10

Guan, Y., Yang, H., Han, S., Feng, L., Wang, T. and Ge, J., 2017. Comparison of the gut microbiota composition between wild and captive sika deer (Cervus nippon hortulorum) from feces by high-throughput sequencing. AMB Express, 7: 212. https://doi.org/10.1186/s13568-017-0517-8

Hill, E., Linacre, A.M., Toop, S., Murphy, N.P. and Strugnell, J.M., 2017. The complete mitochondrial genome of Axis porcinus (Mammalia: Cervidae) from Victoria, Australia, using MiSeq sequencing. Mitochondr. DNA Part B, 2: 453-454. https://doi.org/10.1080/23802359.2017.1357451

Hu, X., Gang, L., Shafer, A., Wei, Y., Zhou, J., Lin, S., Wu, H., Mi, Z., Hu, D. and Liu, S., 2017. Comparative analysis of the gut microbial communities in forest and alpine musk deer using high-throughput sequencing. Front. Microbiol., 8: 572. https://doi.org/10.3389/fmicb.2017.00572

Huang, C., Chen, J., Wang, J., Zhou, H., Lu, Y., Lou, L., Zheng, J., Tian, L., Wang, X., Cao, Z. and Zeng, Y., 2017. Dysbiosis of intestinal microbiota and decreased antimicrobial peptide level in Paneth cells during hypertriglyceridemia-related acute necrotizing pancreatitis in rats. Front. Microbiol., 8: 776. https://doi.org/10.3389/fmicb.2017.00776

Hussain, Z., Ali, Z. and Ahmad, R., 2022. Causes of morbidity and mortality in wild animals and birds at captive breeding facilities of Punjab, Pakistan. Pakistan J. Zool., 54: 2337-2351. https://dx.doi.org/10.17582/journal.pjz/20210805120803

Jami, E., Israel, A., Kotser, A. and Mizrahi, I., 2013. Exploring the bovine rumen bacterial community from birth to adulthood. The ISME J., 7: 1069. https://doi.org/10.1038/ismej.2013.2

Kohl, K.D., Varner, J., Wilkening, J.L. and Dearing, M.D., 2018. Gut microbial communities of American pikas (Ochotona princeps): Evidence for phylosymbiosis and adaptations to novel diets. J. Anim. Ecol., 87: 323-330. https://doi.org/10.1111/1365-2656.12692

Kumar, S., Rhee, M.S. and Lee, D.W., 2012. Genome sequence of Oscillibacter ruminantium strain GH1, isolated from rumen of Korean Native cattle. J. Bact., 194: 6362. https://doi.org/10.1128/JB.01677-12

Kozik, A.J., 2017. Sex, age, and TNF influence the gut microbiota in a mouse model of TNBS colitis. The FASEB J., 31: 657.12-.12.

LeBlanc, J.G., Milani, C., De, G.G.S., Sesma, F., Van, S.D. and Ventura, M., 2013. Bacteria as vitamin suppliers to their host: A gut microbiota perspective. Curr. Opin. Biotechnol., 24: 160-168. https://doi.org/10.1016/j.copbio.2012.08.005

Li, J., Zhan, S., Liu, X., Lin, Q., Jiang, J. and Li, X., 2018. Divergence of fecal microbiota and their associations with host phylogeny in Cervinae. Front. Microbiol., 9: 1823. https://doi.org/10.3389/fmicb.2018.01823

Lwin, N., Linkie, M., Harihar, A., Aung, S.S., Lin, A.K. and Momberg, F., 2016. Initiating conservation of a newly discovered population of the endangered hog deer Axis porcinus in Myanmar. Oryx, 52: 1-8. https://doi.org/10.1017/S0030605316000727

Li, Y., Hu, X., Yang, S., Zhou, J., Zhang, T., Qi, L., Sun, X., Fan, M., Xu, S., Cha, M., Zhang, M., Lin, S., Liu, S. and Hu, D., 2017. Comparative analysis of the gut microbiota composition between captive and wild forest musk deer. Front. Microbiol., 8: 1705. https://doi.org/10.3389/fmicb.2017.01705

Lemaire, M., Dou, S., Cahu, A., Formal, M., Normand, L.L., Romé, V., Nogret, I., Ferret-Bernard, S., Rhimi, M., Cuinet, I., Canlet, C., Tremblay-Franco, M., Ruyet, P.L., Baudry, C., Gérard, P., Huërou-Luron, I.L. and Blat, S., 2018. Addition of dairy lipids and probiotic Lactobacillus fermentum in infant formula programs gut microbiota and entero-insular axis in adult minipigs. Scient. Rep., 8: 11656. https://doi.org/10.1038/s41598-018-29971-w

Mao, S., Zhang, M., Liu, J. and Zhu, W., 2015. Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: membership and potential function. Scient. Rep., 5: 16116. https://doi.org/10.1038/srep16116

Magoč, T. and Salzberg, S.L., 2011. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics, 27: 2957-2963. https://doi.org/10.1093/bioinformatics/btr507

Marteau, P., Lepage, P., Mangin, I., Suau, A., Dore, J., Pochart, P. and Seksik, P., 2004. Gut flora and inflammatory bowel disease. Aliment. Pharmacol. Therap., 20: 18-23. https://doi.org/10.1111/j.1365-2036.2004.02062.x

Martínez, I., Kim, J., Duffy, P.R., Schlegel, V.L. and Walter, J., 2010. Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLoS One, 5: e15046. https://doi.org/10.1371/journal.pone.0015046

Odamaki, T., Kato, K., Sugahara, H., Hashikura, N., Takahashi, S., Xiao, J., Abe, F. and Osawa, R., 2016. Age-related changes in gut microbiota composition from newborn to centenarian: A cross-sectional study. BMC Microbiol., 16: 90. https://doi.org/10.1186/s12866-016-0708-5

Price, S.A., Hopkins, S.S., Smith, K.K. and Roth, V.L., 2012. Tempo of trophic evolution and its impact on mammalian diversification. Proc. natl. Acad. Sci., 109: 7008-7012. https://doi.org/10.1073/pnas.1117133109

Pitta, D.W., Pinchak, W.E., Dowd, S.E., Osterstock, J., Gontcharova, V., Youn, E., Dorton, K., Yoon, I., Min, B.R., Fulford, J.D., Wickersham, T.A. and Malinowski, D.P., 2010. Rumen bacterial diversity dynamics associated with changing from bermudagrass hay to grazed winter wheat diets. Microb. Ecol., 59: 511-522. https://doi.org/10.1007/s00248-009-9609-6

Rana, M.A., Jabeen, F., Shabnam, M., Ahmad, I. and Hassan, M., 2015. Comparative study of endo-parasites in captive hog deer (Axis porcinus). Int. J. Biosci., 6: 162-170. https://doi.org/10.12692/ijb/6.1.162-170

Tanushree, B. and Mathur, V., 2000. A review of the present conservation scenario of hog deer (Axis porcinus) in its native range. Indian Forester, 126: 1068-1084.

Timmins, A., Kumar, S., Islam, A. and Baral, S., 2015. Axis porcinus, hog deer. IUCN Red List of Threatened Species.

Tannock, G., Munro, K., Harmsen, H., Welling, G., Smart, J. and Gopal, P., 2000. Analysis of the fecal microflora of human subjects consuming a probiotic product containing Lactobacillus rhamnosus DR20. Appl. environ. Microbiol., 66: 2578-2588. https://doi.org/10.1128/AEM.66.6.2578-2588.2000

Tamaki, H., Wright, C.L., Li, X., Lin, Q., Hwang, C., Wang, S., Thimmapuram, J., Kamagata, Y. and Liu, W., 2011. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS One, 6: e25263. https://doi.org/10.1371/journal.pone.0025263

Wang, L., Xu, Q., Kong, F., Yang, Y., Wu, D., Mishra, S. and Li, Y., 2016. Exploring the goat rumen microbiome from seven days to two years. PLoS One, 11: e0154354. https://doi.org/10.1371/journal.pone.0154354

Wang, W., Yan, H., Yu, J., Yi, J., Qu, Y., Fu, M., Chen, A., Tang, H. and Niu, L., 2017. Discovery of genome-wideSNPs by RAD-seqand the genetic diversity of captive hog deer (Axis porcinus). PLoS One, 12: e0174299. https://doi.org/10.1371/journal.pone.0174299

Yan, W., Zheng, J., Wen, C., Ji, C., Zhang, D., Chen, Y., Sun, C. and Yang, N., 2018. Efficacy of fecal sampling as a gut proxy in the study of chicken gut microbiota. Front. Microbiol., 10: 2126. https://doi.org/10.3389/fmicb.2019.02126

Zhang, Q., Xiao, X., Li, M., Yu, M., Ping, F., Zheng, J., Wang, T. and Wang, X., 2017. Vildagliptin increases butyrate-producing bacteria in the gut of diabetic rats. PLoS One, 12: e0184735. https://doi.org/10.1371/journal.pone.0184735