Investigating the gut bacteria structure and function of hibernating bats through 16S rRNA high-throughput sequencing and culturomics

Abstract

The gut microbiota of bats is vital for their roles in health and the ecosystem, yet studies on hibernating bats in southwest China, particularly in the unique karst landscape of Guizhou, are limited. We captured three hibernating bat species-Pipistrellus (PB), Rhinolophus (RB), and Myotis (MB)-in Liping County, collecting rectal samples for 16S rRNA amplicon sequencing. Data processing involved Trimmomatic, Flash, and Qiime2 for operational taxonomic unit (OTU) standardization and species annotation via the Greengenes database. Differential abundance was analyzed using LEfSe, and diversity metrics were assessed through alpha and beta diversity analyses. The RB group was predominantly composed of Proteobacteria (80.99%), while MB and PB exhibited diverse compositions with significant OTU richness (729 in MB). Notable genera included Hafnia and Yersinia in RB and Cosenzaea myxofaciens in MB. High proportions of unclassified taxa were observed, particularly in RB (83.81%). Functional predictions indicated metabolic pathways, with a significant representation of human diseases in PB. Culturomics revealed the successful cultivation of Huaxiibacter chinensis and Enterobacter chengduensis from bats for the first time and appears to have identified a new bacterium that is likely closely related to Clostridium paraputrificum.IMPORTANCEOur research reveals significant differences in the composition and diversity of the gut microbiota among three bat groups (PB, MB, and RB) from Guizhou. While Proteobacteria predominates in all groups, its abundance varies. Notably, the high richness of operational taxonomic units (OTUs) in the MB group suggests a more diverse microbial ecosystem, underscoring the complex interactions between species diversity, diet, gut microbiota, and overall ecological dynamics in bats. Furthermore, the substantial presence of unknown bacterial species in their intestines highlights the critical importance of cultivation-based approaches. The presence of specific taxa may have potential health implications for both bats and humans. These findings emphasize the need for further investigations into the functional roles of these microbiota and their contributions to host health. Future research should focus on longitudinal studies to elucidate these intricate interactions.

Keywords: 16S rRNA; culturomics; gut bacteria; hibernating bats; high-throughput sequencing.

Fig 1

Relative distribution of bacterial species richness. (A) Bar chart depicting the relative distribution at the phylum level (top 20 by relative abundance). (B) Bar chart showing the relative distribution at the genus level (top 20 by relative abundance). (C) Bar chart illustrating the relative distribution at the species level (top 20 by relative abundance). (D) Venn diagram of OTUs.

Fig 2

Clustering and LEfSe analysis. Hierarchical clustering heatmap displaying the absolute abundance of the top 20 taxa, with a clustering tree on the left illustrating the similarity in species abundance distributions across groups. The central heatmap represents the log10-transformed absolute abundance. (A) Clustering heatmap at the phylum level. (B) Clustering heatmap at the genus level. (C) Clustering heatmap at the species level. (D) LDA bar chart from LEfSe analysis at the genus and higher taxonomic levels, with each horizontal bar representing a species, where the length corresponds to the LDA score and longer bars indicate greater differences. (E) Cladogram from LEfSe analysis. The cladogram depicts different taxonomic levels from phylum to genus, with connecting lines representing hierarchical relationships. Each circular node represents a species; nodes colored yellow indicate no significant differences between groups, while non-yellow nodes represent characteristic microbes of the corresponding colored group (with significantly higher abundance). Color-coded sectors highlight the subordinate taxonomic ranges of characteristic microbes.

Fig 3

Alpha diversity analysis. (A) Results of Chao1 diversity analysis. (B) Results of Faith’s phylogenetic diversity (Faith-pd) analysis. (C) Results of observed feature diversity analysis. (D) Results of Shannon diversity analysis. (E) Results of Simpson diversity analysis. * and ** indicate P < 0.05 and P < 0.01, respectively. ◆ denotes outliers.

Fig 4

Beta diversity analysis. (A) NMDS analysis. (B) PCoA. (C) PCA. (D) Heatmap of beta diversity indices, with colors representing the dissimilarity coefficient between samples; smaller coefficients indicate greater similarity in species diversity. The numerical values in the heatmap correspond to Bray–Curtis distance, weighted UniFrac distance, and unweighted UniFrac distance, respectively.

Fig 5

Phylogenetic tree and intergroup abundance heatmap. On the left, the phylogenetic tree with branches colored by phylum; terminal branches represent individual OTUs, annotated with their respective genera. On the right, a heatmap of standardized abundance, where higher values indicate greater relative abundance.

Fig 6

Gene function prediction. (A) Gene functions at Level 1 in the KEGG database. (B) Gene functions at Level 2 in the KEGG database (top 20). (C) Gene functions at Level 3 in the KEGG database (top 20). (D) Pathway prediction results in the MetaCyc database (top 20).

Fig 7

Predictive functional PCA. (A) PCA of KEGG pathways at the L1 level. (B) PCA of KEGG pathways at the L2 level. (C) PCA of KEGG pathways at the L3 level. (D) PCA of MetaCyc pathways.

Fig 8

Results of culturomics. (A) Venn diagram of cultured bacterial species. (B) Bacterial species grown under different culturing conditions. (C) Number of species isolated from each sample.