Gut microbiota profiling of Javan pangolin (Manis javanica)

Abstract

Chitin digestion in pangolins and other anteaters is thought to be aided by commensal bacteria in the digestive tract, in addition to their chitinase. This study characterized the gut microbiota of captive Javan pangolins using amplicon sequencing. Fecal samples were collected from two individuals and were sampled twice over one week. The dominant bacterial phyla identified were Firmicutes (Bacillota), Bacteroidetes (Bacteroidota), Proteobacteria (Pseudomonadota), and Actinobacteria (Actinomycetota). The most prevalent genera included Clostridium, Bacteroides, Lactobacillus, Bifidobacterium, Streptococcus, and Sporosarcina. Alpha and beta diversity were relatively low between paired samples, but the short sampling interval limits conclusions about microbial stability. These findings provide insights into the Javan pangolin’s gut microbiota and support future research on microbial contributions to their digestion, health, and conservation.

Pangolins, often called ‘anteaters’ for their diet of ants and termites, have a unique stomach lined with keratinized epithelium to protect against abrasion from insect exoskeletons [20]. Pangolins face significant challenges when kept in captivity (ex situ) for conservation purposes due to their specialized diet. The disparity between captive environments and their natural habitats, combined with their specialized diet of ants and termites, exacerbates the risk of parasitic infections and gastrointestinal diseases. These health issues are the leading causes of mortality among captive pangolins [13, 30]. Therefore, addressing concerns related to proper nutrition and disease resistance is crucial for the successful rescue and rehabilitation of these animals.

Chitin, a complex carbohydrate polymer found in ants’ and termites’ exoskeletons, can only be digested by the enzyme chitinase, which most mammals do not produce. Therefore, chitin digestion in mammals is thought to be conducted by commensal bacteria in the digestive tract [15, 16]. Study reported that ant-eating mammals possess an acidic chitinase gene, although pangolins have a different functional paralog, with a different expression pattern in their digestive organs [1]. To date, research on the gut microbiota of pangolins is limited, indicating this area is still largely unexplored. Current research highlights the important role of gut microbiota in overall health [2, 9, 14]. Dysbiosis, which refers to imbalances in the gut microbiota, can occur due to a decrease in normal microbiota, an overgrowth of pathogenic bacteria, or alterations in microbial metabolic activity, all of which are linked to adverse health outcomes [14, 29]. Studying the pangolin gut microbiota offers insights into microbiome composition and health, particularly for this endangered species. This research aimed to identify the chitinolytic microbiota in pangolin fecal samples and its role in insect chitin metabolism.

Fecal samples were collected from two adult captive pangolins, labeled Pangolins 1 and 2, at the Laboratory of Wildlife Ex situ Conservation, Faculty of Forestry and Environment, IPB University. These pangolins had been kept in a cage for 10 years and received a formulated diet, including the whole “kroto” (larvae and pupae of Asian weaver ant), in addition to chicken meat, eggs, maggot flour, and vitamins C, B complex, as well as K. Their captivity was authorized by the Ministry of Environment and Forestry of Indonesia (Decree No. SK.570/Menhut-II/2013). We did not seek ethical approval as only fecal samples were taken, with no harm to the animals. Fecal sampling was repeated twice at different times (codes 1A, 1B, 2A, 2B) within a week. To reduce contamination, fresh samples were collected from the center of the fecal mass with sterile tools, placed in sealed plastic bags, wrapped in foil, and stored in a cold box at 2–10°C during transport, and refrigerated at 4°C until analysis, as described in our previous study [27].

DNA was extracted from 250 mg of each sample using the Qiagen DNeasy PowerSoil Kit (Qiagen Inc., Germantown, MD, USA). The V3–V4 regions of the 16S rRNA gene were amplified with primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′) using Phusion^® High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA). Amplicons with a 470 bp band on 2% agarose gel were pooled and purified using the Qiagen Gel Extraction Kit (Qiagen Inc., Hilden, Germany). They were then prepared for sequencing with the NEBNext^® UltraTM DNA Library Prep Kit for Illumina (New England Biolabs) and sequenced on the Illumina MiSeq platform, generating paired-end reads of 2 × 250 bp.

Paired-end reads were demultiplexed, trimmed, and merged using FLASH v1.2.7 (http://ccb.jhu.edu/software/FLASH/) [19] to create ‘raw tags’. Quality filtering was conducted with QIIME2 (v2024.5, https://view.qiime2.org) [4], yielding high-quality clean tags [7]. Chimera sequences were removed using the UCHIME algorithm [10] with the SILVA database (http://www.arb-silva.de/) [23, 24]. Effective tags were clustered into operational taxonomic units (OTUs) at 97% similarity using Uparse (v7.0.1090, http://drive5.com/uparse) [11]. Each OTU was identified, and representative sequences were annotated using QIIME2 and applied to the small subunit rRNA database from SILVA for species annotation at each taxonomic rank (threshold=0.81). Multiple sequence alignment was conducted with MUSCLE v3.8.31 (http://www.drive5.com/muscle) [12] for phylogenetic analysis. The OTU abundance was normalized for alpha (Chao1, Shannon, Simpson) and beta diversity (Bray–Curtis, UniFrac, PCoA) analyses. Statistical tests included Analysis of Similarities and Permutational Multivariate ANOVA. Data visualization was performed using R v4.4.1 and QIIME2.

Quality screening with FastQC (QIIME2: https://view.qiime2.org) confirmed that all reads were free of primer and adapter sequences, making them suitable for analysis. Pangolin 1A had the highest number of reads at 183,394, while Pangolin 2A had the lowest at 17,165. In addition, the number of read counts for Pangolin 1B and 2B were 172,714 and 177,648, respectively. To standardize read counts, the data were rarefied to a consistent sequencing depth before conducting analyses, ensuring unbiased comparisons. The OTUs were identified and classified at 97% similarity to analyze the microbial community composition using effective tags from all samples. Basic information, including effective and low-frequency tags, was collected during this process. No significant differences were found in the relative abundance of dominant phyla between pangolin samples 1 and 2. The dominant phyla identified were Firmicutes (Bacillota), Bacteroidetes (Bacteroidota), Proteobacteria (Pseudomonadota), and Actinobacteria (Actinomycetota) (Fig. 1A), whereas the dominant genera varied between samples A and B. In samples 1B, 2A, and 2B, Streptococcus was dominant, while Sporosarcina was predominant in sample 1A (Fig. 1B). Other significant genera included Lactobacillus, Clostridium, Bacteroidota/Bacteroides, Enterococcus, and Bifidobacterium, with Escherichia-Shigella dominating in sample 2B.

Fig. 1.

The distribution histogram of the relative abundance of taxa in phyla level (A) and genus level (B) of pangolin.

The alpha diversity analysis of a single sample indicates the richness and diversity of microbial communities. The Chao1, Shannon, and Simpson diversity indices indicated no significant differences in bacterial diversity between the A and B pangolin samples (Chao1’s P=0.62527; Shannon’s P=0.73918; Simpson’s P=0.69018). Venn diagrams (Fig. 2) revealed that Pangolin 2A had 165 unique bacterial taxa, while Pangolin 1A had 88 taxa, with both sharing 417 taxa, indicating a significant overlap. Additionally, Pangolin 1B and Pangolin 2B shared a core microbiome of 515 taxa, reflecting a high similarity. Beta diversity measures differences in microbial community composition between sample pairs using dissimilarity indices like Bray-Curtis or UniFrac (see Fig. 3). Pairwise comparisons, including intra-individual (1A–1B and 2A–2B) and inter-individual (1A–2A, 1A–2B, 1B–2B, and 1B–2A) analyses, were performed to evaluate microbial similarities and unique taxa contributing to individual-specific variation. In Pangolin 1, samples 1A and 1B (red and green) show significant microbiota variation within the same individual. For Pangolin 2, samples 2A and 2B (blue and cyan) are also distant, with sample 2B indicating a microbial shift in line with signs of diarrhea, suggesting possible dysbiosis. Although both pangolins showed intra-individual variation between samples A and B, the distinct microbial shift observed in sample 2B corresponded with the recorded signs of diarrhea; however, this association remains preliminary and requires further verification through clinical and functional analyses.

Fig. 2.

Venn diagram of pangolins. The number in each independent or overlapped area represents the number of unique or common features in each corresponding collection.

Fig. 3.

PCoA plots based on Bray-Curtis and UniFrac distance metric for beta diversity between different groups labeled as Pangolin.1A, Pangolin.1B, Pangolin.2A, and Pangolin.2B.

The predominant bacterial phyla found in the gut microbiota of two Javan pangolins in the present study are in accordance with those reported on Chinese and Javan pangolins, despite the difference in their composition [9, 31]. These findings suggest that such communities may contribute to nutrient processing and support host health [9, 14, 18]. However, the variability observed among dominant genera across different samples in this study may reflect influences from diet, environmental exposure, health status, or individual-specific factors [26].

Bacillota (Firmicutes), predominant in several samples, are involved in fiber and complex carbohydrate degradation, including cellulose and chitin, and they help produce short-chain fatty acid (SCFA) that support energy metabolism and gut health [15, 25, 31]. Moreover, some taxa identified in the present study are recognized in other systems to contain chitin-degrading species (e.g., Clostridium, Bacteroidota/Bacteroides) [3, 17, 28], despite the direct evidence could not be provided in the present study.

The pangolins’ gut microbiota identified in the present study shares similarities with that of the short-beaked echidna (Tachyglossus aculeatus, Myrmecophagidae) [5], whose gut ecosystems are dominated by Firmicutes, Bacteroidota, Actinobacteria, and Proteobacteria. The present study identified genera, such as Clostridium, Bacteroides, Lactobacillus, and Bifidobacterium, that are highly likely associated with chitin degradation and the fermentation of insect-derived nutrients into SCFAs [9]. A study by Perry et al. [21] demonstrated that captivity can influence microbial diversity in echidnas, often leading to increased abundance of fermentative bacteria such as Lactobacillus and Enterococcus. Although our study did not include wild pangolin samples, a similar trend might be plausible in M. javanica; however, this interpretation remains preliminary and requires confirmation through direct comparisons with wild individuals. Both animals exhibit microbial taxa that work synergistically: primary degraders such as Clostridium and Bacteroides break down complex substrates like chitin, while secondary fermenters such as Lactobacillus and Bifidobacterium turn oligosaccharides into energy-rich SCFAs. These bacterial taxa, although common across various mammalian species, may exhibit functional convergence in insectivorous mammals, where cooperative fermentation of insect-derived chitin and other complex carbohydrates supports host nutrition. This metabolic interaction likely reflects dietary adaptation rather than taxonomic specificity.

The microbial profile in sample 2B, which came from a pangolin exhibiting diarrhea, showed a marked shift in community composition, with Escherichia-Shigella becoming dominant, indicating a possible dysbiosis state. This finding highlights the potential impact of health status on gut microbiota composition and warrants further investigation with larger sample sizes.

The Venn diagram analysis showed a shared core microbiome among the samples, suggesting a microbial community that may be essential for pangolins’ gut homeostasis. At the same time, the presence of unique taxa in individual samples reflects variability likely due to individual-specific or environmental factors, such as stress, dietary intake, or health condition [6, 8, 22]. Low alpha and beta diversity between paired samples from the same individual indicates a relatively stable microbial community over short intervals [14]. However, we acknowledge the limitation that such short intervals may not fully capture temporal dynamics. A study on echidnas during gestation found that gut microbiota remained stable across reproductive stages [5]. This observation aligns with the relatively low intra-individual variation in the present study, suggesting that gut microbial communities in insectivorous mammals may exhibit resilience and structural stability, despite the physiological or environmental changes.

This study is limited by its small sample size and the insufficient functional metagenomic data, yet it offers initial insights into pangolin gut microbiota. It also emphasizes the need for further research with larger datasets and functional analyses to understand microbial roles related to diet and host health, particularly chitin metabolism. Nonetheless, given the conservation status of pangolins, enhancing our understanding of their gut microbiota could improve health monitoring and dietary management in captive breeding efforts.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare related to this publication.