Bamboo-eating giant panda (Ailuropoda melanoleuca) is an enigmatic species, which possesses a carnivore-like short and simple gastrointestinal tract (GIT). Despite the remarkable studies on giant panda, its diet adaptability status continues to be a matter of debate. To resolve this puzzle, we investigated the functional potential of the giant panda gut microbiome using shotgun metagenomic sequencing of fecal samples. We also compared our data with similar data from other animal species representing herbivores, carnivores, and omnivores from current and earlier studies. We found that the giant panda hosts a bear-like gut microbiota distinct from those of herbivores indicated by the metabolic potential of the microbiome in the gut of giant pandas and other mammals. Furthermore, the relative abundance of genes involved in cellulose- and hemicellulose-digestion, and enrichment of enzymes associated with pathways of amino acid degradation and biosynthetic reactions in giant pandas echoed a carnivore-like microbiome. Most significantly, the enzyme assay of the giant panda's feces indicated the lowest cellulase and xylanase activity among major herbivores, shown by an in-vitro experimental assay of enzyme activity for cellulose and hemicellulose-degradation. All of our results consistently indicate that the giant panda is not specialized to digest cellulose and hemicellulose from its bamboo diet, making the giant panda a good mammalian model to study the unusual link between the gut microbiome and diet. The increased food intake of the giant pandas might be a strategy to compensate for the gut microbiome functions, highlighting a strong need of conservation of the native bamboo forest both in high- and low-altitude ranges to meet the great demand of bamboo diet of giant pandas.

To obtain full details of gut microbiota, including bacteria, fungi, bacteriophages, and helminths, in giant pandas (GPs), we created a comprehensive microbial genome database and used metagenomic sequences to align against the database. We delineated a detailed and different gut microbiota structures of GPs. A total of 680 species of bacteria, 198 fungi, 185 bacteriophages, and 45 helminths were found. Compared with 16S rRNA sequencing, the dominant bacterium phyla not only included Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria but also Cyanobacteria and other eight phyla. Aside from Ascomycota, Basidiomycota, and Glomeromycota, Mucoromycota, and Microsporidia were the dominant fungi phyla. The bacteriophages were predominantly dsDNA Myoviridae, Siphoviridae, Podoviridae, ssDNA Inoviridae, and Microviridae. For helminths, phylum Nematoda was the dominant. In addition to previously described parasites, another 44 species of helminths were found in GPs. Also, differences in abundance of microbiota were found between the captive, semiwild, and wild GPs. A total of 1,739 genes encoding cellulase, β-glucosidase, and cellulose β-1,4-cellobiosidase were responsible for the metabolism of cellulose, and 128,707 putative glycoside hydrolase genes were found in bacteria/fungi. Taken together, the results indicated not only bacteria but also fungi, bacteriophages, and helminths were diverse in gut of giant pandas, which provided basis for the further identification of role of gut microbiota. Besides, metagenomics revealed that the bacteria/fungi in gut of GPs harbor the ability of cellulose and hemicellulose degradation.

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While the health effects of the colonization of the reproductive tracts of mammals by bacterial communities are widely known, there is a dearth of knowledge specifically in relation to giant panda microbiomes. In order to investigate the vaginal and uterine bacterial diversity of healthy giant pandas, we used high-throughput sequence analysis of portions of the 16S rRNA gene, based on samples taken from the vaginas (GPV group) and uteri (GPU group) of these animals. Results showed that the four most abundant phyla, which contained in excess of 98% of the total sequences, were Proteobacteria (59.2% for GPV and 51.4% for GPU), Firmicutes (34.4% for GPV and 23.3% for GPU), Actinobacteria (5.2% for GPV and 14.0% for GPU) and Bacteroidetes (0.3% for GPV and 10.3% for GPU). At the genus level, Escherichia was most abundant (11.0%) in the GPV, followed by Leuconostoc (8.7%), Pseudomonas (8.0%), Acinetobacter (7.3%), Streptococcus (6.3%) and Lactococcus (6.0%). In relation to the uterine samples, Janthinobacterium had the highest prevalence rate (20.2%), followed by Corynebacterium (13.2%), Streptococcus (19.6%), Psychrobacter (9.3%), Escherichia (7.5%) and Bacteroides (6.2%). Moreover, both Chao1 and abundance-based coverage estimator (ACE) species richness indices, which were operating at the same sequencing depth for each sample, demonstrated that GPV had more species richness than GPU, while Simpson and Shannon indices of diversity indicated that GPV had the higher bacterial diversity. These findings contribute to our understanding of the potential influence abnormal reproductive tract microbial communities have on negative pregnancy outcomes in giant pandas.