PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation

Abstract

Background: The gut microbiota play important roles in host adaptation and evolution, but are understudied in natural population of wild mammals. To address host adaptive evolution and improve conservation efforts of threatened mammals from a metagenomic perspective, we established a high-quality gut microbiome catalog of the giant panda (pandaGUT) to resolve the microbiome diversity, functional, and resistome landscapes using approximately 7 Tbp of long- and short-read sequencing data from 439 stool samples.

Results: The pandaGUT catalog comprises 820 metagenome-assembled genomes, including 40 complete closed genomes, and 64.5% of which belong to species that have not been previously reported, greatly expanding the coverage of most prokaryotic lineages. The catalog contains 2.37 million unique genes, with 74.8% possessing complete open read frames, facilitating future mining of microbial functional potential. We identified three microbial enterotypes across wild and captive panda populations characterized by Clostridium, Pseudomonas, and Escherichia, respectively. We found that wild pandas exhibited host genetic-specific microbial structures and functions, suggesting host-gut microbiota phylosymbiosis, while the captive cohorts encoded more multi-drug resistance genes.

Conclusions: Our study provides largely untapped resources for biochemical and biotechnological applications as well as potential intervention avenues via the rational manipulation of microbial diversity and reducing antibiotic usage for future conservation management of wildlife. Video Abstract.

Keywords: Adaptive evolution; Conservation; Diversity; Giant panda; Gut microbiome; Wild mammal.

Fig. 1

Construction and assessment of the pandaGUT database quality and representation. A Geographic distribution of the 439 giant panda stool samples collected from across all three wild genetic populations and most captive populations, with individuals spanning different dietary regimes, ages, sexes, and geographic distributions. B Maximum-likelihood tree constructed using at least 25 concatenated protein sequences from 469 medium- and high-quality metagenome-assembled genomes (MAGs). Clades are colored according to taxonomic class. From the inner circle to the outer circle, genome characteristics are indicated successively including sample source, circular genomes, N50 of contigs, genome similarity, genome occurrence ratio, and genome completeness. The solid and empty circles correspond to samples from captive and wild giant pandas, respectively. The 40 circular MAGs are indicated by triangles. In the outer layers, genome information (contig N50, genome similarity, and completeness), the occurrence ratio, and average relative abundance in all samples for each MAG are presented, respectively. C Average nucleotide identity (ANI) heatmaps for the 40 circularized MAGs. Taxonomic information is shown on the left. The solid and empty circles correspond to samples of captive and wild giant pandas, respectively. Diamonds represent 17 new species based on a < 95% nucleotide identity threshold. D Rarefaction curves depicting the coverage ratios of sequencing reads in the indicated samples against all sequence reads in all investigated samples. The number of unique identified genes finally reached a saturated state with increasing sample numbers, suggesting complete coverage of the gene catalog. E Percentage of mapping rates (left) and annotation rates (right) of de novo gene predictions generated in this study (as indicated by *) and for previously published giant panda gut metagenome datasets in comparison against the reference catalog pandaGUT

Fig. 2

Host genetic-specific associated with the compositions and functions of wild giant panda gut microbiomes. A Alpha diversity of gut microbiomes among the three wild genetic populations (n = 45). The FDR-corrected Wilcoxon rank sum test was used to determine significance. **p < 0.01, *p < 0.05. The following group colors are the same as in A. B Principal coordinates analysis revealed two distinct clusters of microbial communities along the first principal component belonging to the QIN and QXL populations, respectively, with MIN communities exhibiting mixed compositions. The solid and empty ellipses were constructed based on multivariate normal distributions at 50% and 70% confidence levels, respectively. C Bar plot showing the assemblage patterns of giant panda gut microbiota at the genus level. Patterns were determined based on hierarchical clustering. D Characterization of host genetic-specific genes and KEGG pathways of gut microbiomes. Venn diagrams showing shared and unique genes in the gut microbiomes of each genetic population. The bar plot shows the gene counts at the second KEGG pathway level for each pairwise comparison. The inset bar graph shows the total numbers of microbial genes in each population annotated to KEGG orthologs. E Hierarchy of KEGG pathways showing the functional differentiation of gut microbiomes among the three genetic populations based on LEfSe analysis. Pathways with LDA > 2 and p < 0.05 are shown. The numbers refer to pathways designated in Fig. S8A

Fig. 3

Strain-level analysis of variation in gut microbial composition and functional profiles of QIN giant pandas between leaf and shoot eating seasons. A The variation in composition and abundance in the gut microbiota of giant pandas (n = 57) at the genus and species levels. Significantly differentially abundant taxa between seasons are indicated by asterisks. The size of the circle indicates the abundances and colors indicate the abundances of taxa that significantly differed between leaf- (green) and shoot-eating (orange) seasons. B The 15 most differentially abundant KEGG pathways and quantitative contributions to functional profiles that belonged to Clostridium (orange), Escherichia (blue), and other taxa (grey). The size of the circle indicates KEGG pathway relative abundances and the area of the sector indicates the relative contributions of each taxon. The color outlines indicate that the KEGG pathways significantly differed between the leaf- (green) and shoot-eating (orange) seasons. C Reconstruction of acyl chain-associated pathways that were differentially enriched between samples from different seasons. Histogram colors indicate genes that significantly differed in communities collected between leaf- (gray) and shoot-eating (black) seasons. FDR-corrected Wilcoxon rank sum tests were used to determine significance. **p < 0.01, *p < 0.05

Fig. 4

Characteristics of gut microbiome enterotypes and resistomes across wild and captive giant panda populations. A PCoA plot revealing three clusters in the gut microbiome, with each being significantly identifiable by variation in the relative abundances of Clostridium, Pseudomonas, and Escherichia. The lines connected to the center of each ellipse correspond to the affiliation. The inset mosaic plot shows the association between giant panda cohorts and enterotypes. The Pearson residuals were used to assess the individual contribution to the Pearson statistic. The blue, red, and grey colours correspond to positive, negative, and lack of associations, respectively. The area of each plot represents the sample size of each group. B The box plots show the relative abundances of major bacterial contributors of each enterotype. FDR-corrected Wilcoxon rank sum tests were used to determine statistical significance. ***p < 0.001, **p < 0.01, *p < 0.05. C Heatmap of antibiotic resistance genes (ARGs) identified among the three enterotypes. Information regarding resistance mechanisms and antibiotics is highlighted at the top. Color intensity indicates ARG abundances and darker colors indicate higher abundances. The sources and corresponding enterotypes of the sample in each line are marked in the tree (right)