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. 2024 Dec 30;15(1):57.
doi: 10.3390/ani15010057.

Comparative Metagenomic Analysis of the Gut Microbiota of Captive Pangolins: A Case Study of Two Species

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Comparative Metagenomic Analysis of the Gut Microbiota of Captive Pangolins: A Case Study of Two Species

Zhengyu Dai et al. Animals (Basel). .

Abstract

Pangolins, one of the most trafficked mammals, face significant health challenges in captivity, including digestive disorders and immune dysfunctions. These issues are closely linked to alterations in their gut microbiota, which play vital roles in the host metabolism, immunity, and overall health. This study investigated the differences in the gut microbiota composition and function between two pangolin species, Chinese pangolins (Manis pentadactyla) and Malayan pangolins (Manis javanica), under identical captive conditions to better understand their ecological adaptability and health implications. Using metagenomic sequencing, fecal samples from eight adult captive pangolins were analyzed, including four male Malayan pangolins and three male and one female Chinese pangolins. Comparative analyses of the alpha and beta diversities, microbial community structure, and functional profiles were performed. Both species harbored gut microbiota dominated by Firmicutes, Bacteroidetes, and Proteobacteria. However, the Chinese pangolins exhibited higher microbial diversity (Shannon index, p = 0.042; Simpson index, p = 0.037) and lower relative abundance of Proteobacteria compared with the Malayan pangolins. A functional analysis revealed significant differences in the metabolic pathways, where the Chinese pangolins demonstrated a higher potential for fiber degradation, whereas the Malayan pangolins exhibited elevated levels of antibiotic resistance genes and pathogenic taxa, such as Escherichia coli. These findings suggest that captivity duration and environmental stress likely contribute to the observed differences, with the Malayan pangolins experiencing greater dysbiosis due to longer captivity periods. This study provides valuable insights into the role of gut microbiota in pangolin health and offers a foundation for improving conservation strategies and captive care protocols.

Keywords: Manis javanica; Manis pentadactyla; gut microbiota; metabolic pathways; metagenomics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure A1
Figure A1
The PCoA (95% confidence interval) and PERMANOVA results at different classification levels between the MP and MJ groups.
Figure A2
Figure A2
The NMDS (95% confidence interval) and Anosim results at different classification levels between the MP and MJ groups.
Figure A3
Figure A3
The PCoA (95% confidence interval) and PERMANOVA results at the genus level between the MP and MJ groups (without MP19).
Figure A4
Figure A4
NMDS of KEGG level 2 in two groups of pangolins. Each point represents a sample, with the distance between points indicating the degree of difference between samples. Samples from the same group are represented by the same color; stress is the stress coefficient, indicating the accuracy of the analysis. When the stress is less than 0.2, an NMDS is considered reliable.
Figure A5
Figure A5
Bacteria involved in chitin degradation in the guts of pangolins.
Figure 1
Figure 1
Comparison between groups of Shannon and Simpson diversity indices across two species of pangolin utilizing boxplot plots. The figures show the distribution of diversity indices in each group and whether there were significant differences in the diversity indices between the groups. The horizontal axis represents the different groups (MJ—Manis pentadactyla; MP—Manis javanica). The vertical axis represents the index values. “*” represents a statistically significant difference between the two species of pangolins.
Figure 2
Figure 2
Differential of gut microbiota species compositions in the two pangolin species by LEfSe analysis. The figure shows the scores of the differential species, with different colors representing different groups. The red bars indicate the species with a relatively higher abundance in the Malayan pangolins, while the green bars indicate the species with a relatively higher abundance in the Chinese pangolins.
Figure 3
Figure 3
KEGG database gene annotation count chart. The numbers on the bars represent the non-redundant gene numbers annotated. Different colors represent the KEGG level 1 functional categories, with annotations detailed in the corresponding legend.
Figure 4
Figure 4
Heat map of the gut microbial functional composition in the eight pangolin individuals. The horizontal axis represents the sample information; the vertical axis represents the functional annotation information. The clustering tree on the left represents the functional clustering; the clustering branches above represent the samples from the different groups, with green representing the Chinese pangolins and purple representing the Malayan pangolins. Orange indicates a higher functional relative abundance, and blue indicates a lower functional relative abundance.
Figure 5
Figure 5
The statistical chart of the gene numbers annotated by the CAZy database. The numbers on the bars represent the non-redundant gene numbers annotated; the coordinate axis marks the CAZy class functional categories, mainly covering six major functions: glycoside hydrolases (GHs), glycosyl transferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), auxiliary activities (AAs), and carbohydrate-binding modules (CBMs).
Figure 6
Figure 6
(A) Top 10 ARO annotations from the CARD database. Different colors in the pie chart represent the proportion of genes annotated to the respective ARO, with the number of genes annotated to the ARO in the legend at the left top. (B) The functional abundance heat map of resistance mechanisms in the two pangolin species. The heat map indicates the relative abundance of bacterial strains or genes with specific resistance mechanisms within the microbial community. Orange indicates a higher functional relative abundance, and blue indicates a lower functional relative abundance.

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