Dysbiosis and Accumulation of Antimicrobial Resistance in the Gut Bacterial Reservoir of Captive Sloth Bear (Melursus ursinus)

Dysbiosis and Accumulation of Antimicrobial Resistance in the Gut Bacterial Reservoir of Captive Sloth Bear (Melursus ursinus) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dysbiosis and Accumulation of Antimicrobial Resistance in the Gut Bacterial Reservoir of Captive Sloth Bear (Melursus ursinus) Vedam Pavankumar, Vanitha Kumar, Arun Sha, Sneha Narayan, S Ilayaraja, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7941374/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Captivity-associated conditions are major factors which modulate the gut microbiota in wild animals, as they receive several medications/ antibiotics, and are also exposed to various anthropogenic stressors throughout their captive lives. Our understanding of how these factors modify the gut microbiome, its resistome and associated mobile genetic elements (MGEs) is nascent at the very best. In this study, we analysed metagenomic data from 215 captive sloth bears in multiple facilities across India to describe their gut microbiota in association to age, gender, duration in captivity, health status and body condition, recent exposure to antibiotics/ other medications, and presence of chronic hepatic/ renal disorders and tuberculosis. Overall, we found low microbial diversity across all sampled locations, and this seems to decrease with age of animal and duration in captivity. Streptococcus , Sarcina , Escherichia , Clostridium and Klebsiella were the most abundant genera, richly populated with E. coli , E. faecium , K. variicola and S. alactolyticus . We observed a very diverse resistome, and almost all bears consistently showed high levels of antimicrobial resistance (AMR), even when not treated recently with antibiotics, indicating pleiotrophic effects in pathogenic bacteria adapting to captivity stress and horizontal gene transfer through MGEs. Abundances of Shigella , Escherichia , Salmonella , Citrobacter and Kluyvera positively correlated to ARG abundance and richness. We further observed that ARGs, which confer resistance to multiple drugs, were associated with plasmids and other MGEs making them highly virulent. In summary, our study highlights the importance of large scale metagenomic studies to understand the effects of captivity on the gut health of endangered animals, and to ultimately help improve conservation strategies. Biological sciences/Microbiology Biological sciences/Molecular biology Sloth bear gut microbiota antimicrobial resistance mobile genetic elements Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction In recent years, exponentially growing evidence highlight the influence, interplay and interactions of the animal gut microbiome with various aspects of life 1 , 2 . The gut microbiome, which is primarily responsible in aiding its host to digest and absorb nutrients from the diet, is now known to be involved in multiple important metabolic, immunological, and neuropsychological functions 3 , 4 . It also plays a key role in helping the host adapt to a rapidly changing environment 5 , 6 . Although it varies from animal to animal, the initial gut microbiota is considered to be inherited vertically from the mother during gestation and parturition, and during post-natal interactions like suckling, feeding and grooming 7 , 8 . This microbiome consistently remains diverse in adulthood, varying based on a plethora of factors such as diet, environment, weather, stress, hormones, etc., and the diversity generally declines only as the individual ages 2 , 9 . This consortium of organisms is also responsible for training the immune cells present in the gut for pathogen identification, besides helping in the maintenance and regeneration of gut tissues whenever necessary 10 . Recent studies also show that the gut microbiome plays a vital role in reproductive success in animals, an aspect which holds special importance for endangered species 2 , 11 . In addition to all these functions, the gut microbiome also serves as a very important reservoir, wherein probiotic, commensal and pathogenic organisms all reside, interact and influence each other. At this juncture, the use of antibiotics to treat infections gains prominence, as indiscriminate and improper exposures to broad-spectrum drugs are capable of erasing the beneficial bacterial strains while enriching pathogenic strains with a ntibiotic r esistant g enes (ARGs) 12 . Accumulation of these ARGs leads to a nti m icrobial r esistance (AMR), which is a growing global threat and is recognized for its ability to render standard treatments ineffective against infectious microorganisms 13 . Presently, AMR is of utmost concern globally and is declared as a top priority threat by the World Health Organisation 14 . Extensive research is being conducted on human subjects to investigate the status of AMR in various microbial niches 15 , 16 . Similar studies are also being carried out on animals in the food sector and its associated industries to understand their current AMR status 17 , 18 , 19 with outcomes aligned to anthropocentric needs. There are however very limited investigations on AMR in wild animals 20 , 21 . A few studies solely consider wildlife as a key reservoir or disseminator of AMR 22 , 23 , 24 , instead of the more likely opposing paradigm that human interactions and interventions inadvertently lead to the propagation of AMR in wildlife 25 , 26 , 27 . This aspect is particularly important in captive vulnerable and endangered animal populations, where there is a general increase in antibiotic usage to manage health conditions. In captive settings, close and at many times unhygienic quarters, and routine administration of antibiotics create a situation conducive to the development and spread of resistant bacteria. Animals in zoos live in an environment that is greatly different from their natural habitats. They are introduced to therapeutics as prophylactic treatments and routine veterinary care. Moreover feed, like poultry, milk and eggs, provided in zoos and captive facilities may be priorly treated with antibiotics to ensure their quality and safety, hereby unintentionally introducing excessive antimicrobials into the animal’s system. Our present study revolves around assessing the gut microbiome of captive sloth bears housed at various zoos and rescue centers across India and investigating their load of antimicrobial resistance genes and signatures. Our study species, the sloth bear ( Melursus ursinus ), belongs to Ursidae family and is endemic to the Indian subcontinent 28 . Sloth bear can be identified with its white ‘V’ shaped chest patch against an overall dense shaggy black coat. In India, sloth bear occupies diverse habitats from tropical forests to shrublands, and grasslands 29 , 30 . It is largely a solitary creature and wanders through forests in search of termites and fruits. It has an elongated muzzle and long curved front claws that help in extracting termites and ants from ant-hills and crevices. Sloth bear is classified as ‘Vulnerable’ in the IUCN Red List of Threatened Species 31 , and is listed in Schedule 1 of the Indian Wildlife Protection Act (1972). Contributors to population decline include habitat loss and fragmentation, poaching and trade in body parts, and conflict. Historically in India, sloth bear cubs were captured and subjected to harsh conditions as part of the "dancing bear" tradition, where they endured severe physical and psychological abuse 32 , 33 . Long-term collaborative efforts between the Government of India and NGOs like Wildlife SOS ensured that these animals were successfully rescued and rehabilitated, allowing them to be reintegrated into more humane environments that prioritize their welfare and conservation. Given the vulnerable status of sloth bears, understanding their gut microbial composition and the AMR burden they carry is essential for effective ex-situ conservation strategies. AMR poses an additional layer of risk to their health, potentially reducing the efficacy of future treatments of infections in both wild and captive populations. As the role of captive facilities in the conservation of endangered species increases, understanding how management practices contribute to AMR will be critical in ensuring the long-term survival of these animals. Materials and Methods Study area and sample collection For this study, we collected 231 sloth bear samples from 14 zoos and rescue centers across India (Fig. 1 ) between November 2021 to September 2022. A large proportion of these samples (n = 187) were from animals in Wildlife SOS rescue centers at Agra, Bannerghatta and Bhopal. Sloth bears housed at the Wildlife SOS centers have a history of extensive abuse where they were beaten, de-clawed, had their nasal septum pierced to string ropes through it and chained up constantly, and were starved and malnourished. These animals were rescued by the Wildlife SOS teams and government officials at different points and rehabilitated at the Agra-Bear Rescue Facility, Bengaluru-Bannerghatta Bear Rescue and Rehabilitation Centre and Bhopal-Van Vihar Bear Rescue Facility. In their new homes, these bears now have large open spaces to wander and explore and live the rest of their lives in peace. Veterinary facilities have been made readily available at all times and there are enrichment paraphernalia for the bears to engage in various activities. We collected fecal samples within 15 mins of defecation after moving the concerned animal to another enclosure with the help of animal keepers. Samples were collected with sterile spatula and forceps into fresh 50ml microfuge tubes. As a precautionary measure we used gloves and masks during sample collection. We sterilized the spatula and forceps after each collection with bleach and absolute alcohol to avoid any cross-contamination. Sample tubes were labelled with details such as sample ID, date of collection, location, age and gender of the animal, and stored in Zedblox Actipod™ at -10°C before transferring to -80°C in the laboratory for further analysis. In addition to samples, we also recorded the history, feeding and medication details, duration in captivity, and health status of each animal at every facility. DNA isolation and sequencing We isolated DNA from all samples using Nucleospin DNA Stool kit (Machery-Nagel, Germany) adhering to the company’s protocol and only increasing incubation time to get a better DNA yield. To minimize contamination, we aliquoted and carried out isolation in separate biosafety cabinets. Quality of isolated DNA samples were checked spectrophotometrically. DNA was stored at -20°C until further analysis. For each sample, 100ng of DNA was fragmented and tagged with adapters using bead-linked transposomes (BLT). Post tagmentation, cleanup was performed and the tagmented DNA was amplified through a 5-cycle PCR program to add index adapters. We constructed metagenomic sequencing libraries using Illumina DNA Prep Kit (Illumina Inc., USA), according to the manufacturer’s protocols. Final library quality, concentration and size were determined with Qubit 4 (ThermoFisher Scientific, USA) and TapeStation (Agilent Technologies, USA). We sequenced the libraries on NovaSeq 6000 (Illumina Inc., USA) with 2x150 chemistry. Two hundred and fifteen samples out of 231 passed QC and were used for subsequent analysis. Bioinformatics analysis The sequence data obtained was converted and demultiplexed from BCL to FASTQ format with bcl2fastq conversion software and further quality checked for Phred score, per base N content, sequence length distribution, over-represented sequences and adapter content using FastQC ver. 0.12.1 34 and MultiQC ver. 1.17 35 . We noticed contamination with Nextera transposase adapter, and therefore filtered the reads for adapter contamination using Trimmomatic ver. 0.39 36 with the following parameters: ILLUMINACLIP: NexteraPE-PE.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:10:20 MINLEN:75. Post trimming, reads were re-checked to ensure the removal of the adapter sequences. Following confirmation of adapter sequence removal, the cleaned reads were assembled into contigs using MEGAHIT ver. 1.2.9 37 with the option: –k-min 35 –k-max 141 –k-step 28. We then mapped the reads to the index built with assembled contigs to check for the overall assembly rate using Bowtie2 ver. 2.5.2 38 . The mapped SAM files from Bowtie2 were converted to BAM format using SAMtools ver. 1.18 39 , and were coordinate sorted for further analysis. Taxonomic Classification Trimmed reads were filtered using fastp ver. 0.22.0 40 , and cleaned reads were classified using PlusPFP database, which contains Standard (RefSeq archaea, bacteria, viral, plasmid, human, UniVec_Core) plus RefSeq protozoa, fungi & plant, as referenced by Kraken2 ver. 2.1.2 41 . Further, the tool Bracken (Bayesian Re-estimation of Abundance with KrakEN) was employed to re-assign unclassified reads to various Operational Taxonomic Units (OTUs) using the Kraken report and database to obtain the final estimated read counts. These finalized read counts from bracken output were used for abundance calculations. Kraken-biom software was used to build a BIOM table for abundance counts of all samples, with rows as taxa and columns as samples. The biom file was imported to RStudio ver 4.3.3 as a phyloseq object using the R package ‘phyloseq’ 42 , and pruned and filtered using the microbiomeutilities package 43 . We retained only those taxa with a minimum total of 100 mapped reads across the 215 samples, and these OTUs were updated to ‘besthit’ format. The phyloseq object was filtered only for bacterial taxa, and these taxa were agglomerated at the genus level (Rank 6). The final OTU table was saved as a CSV file with read counts across samples normalized for sequencing depth using the RPM (Reads per million bacterial reads) method. We investigated the presence of various OTUs at different taxonomic levels and their relative abundances were calculated as ‘fraction total reads’ classified to the OTU by total number of reads. Identification of antimicrobial resistance genes and mobile genetic elements We analysed the assembled contigs with Resistance Gene Identifier (RGI) ver. 6.0.3 which uses the C omprehensive A ntibiotic R esistance D atabase (CARD) as reference 44 . The RGI tool predicts the resistome from nucleotide sequences of contigs and reports a comprehensive list of identified AMR genes present in respective samples with varying identities. We filtered ARGs with more than 98% identity for further analysis. The RGI output for all samples was saved as BED files and was version-sorted using the sort command in Linux. The sorted BED file along with the sorted BAM files were used to calculate coverage of the contigs with ARGs using the BEDtools coverage command. ARG abundance was quantified based on FPKM 45 . $$\:\text{F}\text{P}\text{K}\text{M}\:=\:\frac{\text{R}\text{e}\text{a}\text{d}\text{s}\text{M}\text{a}\text{p}\text{p}\text{e}\text{d}}{\text{G}\text{e}\text{n}\text{e}\text{L}\text{e}\text{n}\text{g}\text{t}\text{h}\:\times\:\:\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\text{R}\text{e}\text{a}\text{d}\text{s}}{\text{1,000,000}}}$$ Wherein, ReadsMapped corresponds to number of reads mapped to ARGs fragments, GeneLength corresponds to length of ARG, and TotalReads corresponds to total number of bacterial reads per sample. We identified mobile genetic elements (MGEs) in the form of plasmids and pro-viral elements using geNomad 46 . Plasmid-annotated contigs were investigated for presence of ARGs identified previously with RGI-finder, to establish co-occurrence of ARGs with MGEs such as integrons and transposons using BacAnt ver. 3.4.0 47 . Visualizations and Statistical Analyses Results were visualized using RStudio ver. 4.3.3 and RStudio server ver. 4.4.1. 48 . We plotted microbial abundance bar plots with dplyr, forcats and ggplot2 packages. Bracken reports at various taxonomic levels were used to identify top domains/ phyla/ genera/ species across all samples from all locations. We identified the top few OTUs from each location based on their respective fraction of total reads as a measure of their relative abundance. Similarly, abundance barplots of ARGs were visualized by plotting RPKM values of respective AMR gene families and drug classes from samples of a given location using ggplot2 package. From this point onwards data from only five captive locations - Nehru Zoological Park, Hyderabad (n = 10), Sri Chamarajendra Zoological Gardens, Mysuru (n = 11), Wildlife SOS, Bhopal (n = 15), Wildlife SOS, Bannerghatta (n = 59), and Wildlife SOS, Agra (n = 97) were considered for further analysis, as all other locations had very few individuals. Shannon indices were derived using estimate_richness function from phyloseq package 42 . Within this group of five locations, we again analysed alpha diversities for the three Wildlife SOS centers with additional covariables like deworming medications given in the three months prior to sample collection, history of hepatic or kidney disorders, body score index, and seropositivity for tuberculosis. One-way ANOVA, followed by a post hoc correction for multiple comparisons using Tukey’s HSD (honestly significant difference) test, was performed to check the significance of our results. ANOVA pairs with adjusted p value less than 0.05 were considered significant. We plotted Principal Coordinates Analysis (PCoA) plots for beta diversity among the locations and for each of the above variable using Bray-Curtis dissimilarity matrices with vegdist and pco functions of vegan and ecodist packages in RStudio. PERMANOVA ( Per mutational M ultivariate An alysis o f Va riance), a non-parametric multivariate statistical permutation test, was performed on these distance matrices using adonis function of vegan package with 999 permutations, and a p value < 0.05 was considered significant 49 . We developed a Generalized Linear Model (GLM) to assess the effects of different factors on the diversity profiles. We fit Shannon alpha diversity values to the model with a Gaussian (normal) distribution with an identity link function using glm function in RStudio. Additionally, in order to understand the effect of covariables at different locations, diversity profiles were fit as a dependent variable to a Generalized Linear Mixed Model (GLMM) with gamma distribution and log link functions in lme4 package in RStudio. Location of sample was taken as the fixed effect, and variables like health status, age and sex of the animal were taken as random effects and added as intercepts for a better fit, as these accounted for location level variations. The model was fitted using maximum likelihood estimation and was also checked for over-dispersion in the gamma model by calculating the ratio of residual deviance to degrees of freedom. Models were validated and selected based on a lower score from Akaike information criterion (AIC). In order to understand the relationship between gut microbiome diversity and antimicrobial resistance gene (ARG) presence and abundance, we conducted correlation tests between Shannon indices of microbial diversity with both, total number of ARGs and sum total ARG abundance per sample. Spearman’s correlation was chosen over Pearson’s as it is a non-parametric, rank-based test and does not assume a linear relationship or normally distributed data. Significance was assessed at p-value less than 0.05. We derived Spearman’s correlation coefficient (ρ, rho) which ranges from − 1 to 1, and indicates the strength and direction of the monotonic relationship between ranked diversity and AMR abundance. Further, a distance correlation test was performed to assess both linear and non-linear associations between variables, with significance evaluated at p < 0.05. We also assessed the correlation of individual bacterial genus abundance with ARG abundance and numbers in each sample, and significant genera (p < 0.05) were identified. Additionally, to understand the effect of prolonged time in captivity on gut microbial diversity, number of ARGs and total ARG abundance in a sample, we extended the Spearman rank correlation test between these variables for 65 individuals for which we had information on when they were captured from the wild, and their duration in captivity (in years). ARGs co-occuring with MGEs were visualized as heatmaps, generated using complexheatmap, pheatmap and Heatmap functions in RStudio, with the relative abundance of ARGs as the color scale. Cohort description Out of the 231 sloth bear samples collected, 16 samples failed at the NGS QC level, and hence, data from 215 sequenced samples were used for further analysis. Out of these, a total of 171 samples were from the three Wildlife SOS centers, viz. 59 from Wildlife SOS, Bannerghatta; 15 from Wildlife SOS, Bhopal; and 97 from Wildlife SOS, Agra. Amongst the remaining 44 samples, 11 and 10 samples were from Sri Chamarajendra Zoological Gardens, Mysuru and Nehru Zoological Park, Hyderabad respectively; 4 samples each were from Tiger and Lion Safari, Thevarakoppa and Van Vihar National Park, Bhopal; 3 samples each were from Etawah Safari Park, Etawah; Kakatiya Zoological Park, Warangal; and Bannerghatta Biological Park and Rescue Center, Bengaluru. The smallest groups were 2 samples each from Kanpur Zoological Park, Kanpur and Gorewada Rescue Center, Nagpur, and one sample each from Alipore Zoological Gardens, Kolkata and Arignar Anna Zoological Park, Chennai (Fig. 1 ). All animals were further classified based on gender, age, health status, antibiotics given in the three months prior to sample collection, and duration in captivity at each location. Animals were grouped as young (0–3 years), adult (3–15 years) and old (15 + years) based on available literature on sexual maturity and reproduction in this species. Animals were considered healthy unless they were off-feed for multiple days and lethargic, there were visible external wounds or there was any history of disease and prolonged treatment. In case of the three Wildlife SOS centers, we had additional information on deworming medications given in the three months prior to sample collection, history of hepatic or kidney disorders, body score index, and seropositivity for tuberculosis. Each animal at Wildlife SOS centers was scored based on the condition of its body and weight (1 = skinny; 2 = thin; 3 = ideal; 4 = overweight; 5 = obese). Results We sequenced 215 out of the 231 samples collected, and lost 16 samples during library preparation for sequencing. These 215 sloth bear samples yielded an average of ~ 17.5 million forward and reverse reads each, per sample. Nearly 92% of forward and reverse reads survived after trimming to remove Nextera transposase adapter contamination. On an average, we observed 85% alignment rate in the samples, and average N50 value was 2106 bp indicating a moderate to good assembly. We identified a total of 2274 unique OTUs and 374 unique ARGs across 14 locations and their relative abundances were calculated using RPM and RPKM methods, respectively for further analysis. Gut microbiota composition and diversity Presence of pathogenic or opportunistic bacteria More than 80% of all reads across all samples from 14 locations classified to bacterial domain. Few reads mapped to Eukaryota, and this is not surprising, given that captive sloth bears are mostly fed a plant–based diet. Interestingly, we were also able to detect the presence of other potentially infectious domains such as viruses and fungi (Fig. S1 ). Approximately ~ 10% of all the classified reads in samples from Tiger and Lion Safari, Thevarakoppa and Wildlife SOS, Bhopal mapped to various phage viruses of class Caudoviricetes. We did not see any significant presence of Archaea. The most abundant phyla across all locations were Bacillota, Bacteroidota, Pseudomonadota and Actinomycetota, and made up for more than 98% of all the bacterial phyla found in the gut microbiome of these captive sloth bears (Fig. S2 ). At the genus level, five opportunistically pathogenic genera - Streptococcus , Sarcina , Escherichia , Clostridium and Klebsiella , were the most abundant genera identified across all locations. These five genera combined, constituted nearly 75% of all the classified reads. The relative abundance of the top 22 genera across all 14 locations is depicted in Fig. 2 A. We further mapped abundance plots at the species level to understand the sub-groups within the most abundant genera. More than half of the top 22 species across 14 locations were from the 5 major genera mentioned above (Fig. 2 B). Many of the highly abundant Streptococcus species belong to Group D gamma-hemolytic arm of the same genus, and were previously described from the gut of equine and bovine species 50 , 51 . It was interesting to note that although the top species and their respective abundances varied, animals from all the three Wildlife SOS centers - Agra, Bhopal and Banerghatta, showed a very similar composition of major species (Fig. 2 B), possibly reflecting similar diet plans and medication schedules at all the Wildlife SOS centers. Lower gut microbiome diversity in captive sloth bear We calculated alpha and beta diversities at the genus level to identify between-sample and between-location differences in microbial diversities. The median alpha diversity, as indicated by Shannon Index, for all five locations was between 2.5 to 1.5 indicating low bacterial diversity in all samples (Fig. 2 C). We noticed a slightly higher median Shannon index value (> 2) for the three Wildlife SOS centers compared to the other two groups - Nehru Zoological Park (NZP), Hyderabad and Sri Chamarajendra Zoological Gardens (SCZG), Mysuru (Fig. 2 C). One-way ANOVA showed that none of the tested variables – location, age, sex, and health status of animal, influenced the microbial diversity significantly at all the five studied locations. We performed a Generalized Linear Model (GLM) to quantify differences caused by each factor, and subsequently we fit a Generalized Linear Mixed Model (GLMM) to understand the influence of multiple factors (location, age, sex and health status) on the observed alpha diversity of all samples. GLMM showed that the combined effect of these variables was not significant on the alpha diversity. In case of microbial diversity, GLM was chosen over GLMM for its lower AIC (586.5) and better fit. The three Wildlife SOS centers showed a significantly higher (t-value > 2) diversity than the intercept – NZP, Hyderabad group (Intercept = 0.434081, GLM WSOS Bhopal = 0.547170, GLM WSOS Agra = 0.591565, GLM WSOS Banerghatta = 0.610021). We also found that samples from young animals (0–3 years) showed significantly higher diversity (GLM Young = 0.489647), while the samples from old animals (15 + years) exhibited significantly lesser diversity (GLM Old = -0.159612). The summary of these observations from the developed GLM model is represented in Fig. S3 . Beta-diversity plots based on Bray-Curtis dissimilarity matrix did not show significant clustering of samples from any of the locations (Fig. S4 ). Scatter plot with 95% confidence ellipses showed high overlap and hence, we concluded that there is no significant difference in the gut microbial diversity of captive sloth bear between all five locations. The same was corroborated by PERMOVA test with 999 permutations, with a very poor R-squared value for location (R 2 = 0.08533) although the p-value was 0.001, indicating a significant variation between groups which cannot be explained by location alone. Antibiotic resistance gene composition and diversity Highly abundant multi-drug resistance genes A wide-array of ARGs were identified at each of the 14 locations. The total number of ARGs, unique ARGs (without duplicates), and unique to the location ARGs are given in Table 1 . Total number of ARGs detected was nearly proportional to sample size, and this can be understood intuitively as a higher detection of ARGs with higher sampling. While the total number of unique ARGs in a location remained consistently between 35–78 for all the groups with sample sizes < 10, it increased to 100–200 for the groups with bigger sample sizes. Moreover, we see that the total number of ARGs did not stabilise after a certain sample size, and the unique ARGs - without duplicates – also kept increasing. Further the total number of ‘unique to location’ ARGs increased proportionally with increase in sample size. Therefore, irrespective of location, we can conclude that approximately 40 ARGs could be identified from each sample of captive sloth bear, and bigger the group, higher were the chances of finding new and unique ARGs. Table 1 Description of antibiotic resistance genes (ARGs) identified in captive sloth bear samples Location Total no. of Samples Total number of ARGs Total no. unique ARGs, without duplicates Total no. of ARGs unique to each location AAZP 1 42 41 0 ZGA 1 51 50 0 GRC 2 49 35 1 KNZP 2 92 73 2 ELSP 3 87 53 0 BBP 3 131 81 0 KAZP 3 184 90 5 TLST 4 166 76 1 VVNP 4 115 78 4 NZP 10 479 111 9 SCZG 11 760 151 8 WSOS-BH 15 847 135 22 WSOS-BNP 59 3158 214 48 WSOS-A 97 3777 254 72 A total of 93 unique AMR gene families were identified across all the 14 locations. We have represented the top 21 AMR gene families from these locations in Fig. 3 A. The most abundant genes are the ones conferring efflux-pump-based resistance towards antibiotics, and they constitute nearly 50% of the resistance at all locations. Another note-worthy aspect is that the profiles of AMR gene families for the three Wildlife SOS locations - Agra, Bhopal and Bannerghatta, remained similar, just as in the case of microbial diversity. Relative abundance of the top 25 drug classes out of a total of 76, is shown in Fig. 3 B. We designated groups with 3 or more than 3 drug classes as “Multi-drug resistant” groups (MDR-1, MDR-2,….) (Table 2 ). We observed that the top drug classes are all MDR groups, with nearly 50% of the groups conferring resistance to a wide range of commercially available antibiotics. The three Wildlife SOS centers again showed very similar resistome profiles at the drug class level (Fig. 3 B). Table 2 Multi-drug resistance drug classes identified in captive sloth bear samples MDR-1 Fluoroquinolone; monobactam; carbapenem; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; rifamycin; phenicol antibiotic; penem; disinfecting agents and antiseptics. MDR-2 Macrolides; fluoroquinolones; cephalosporin; cephamycin; penam; tetracycline MDR-3 Fluoroquinolones; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; rifamycin; phenicol antibiotic; disinfecting agents and antiseptics MDR-4 Fluoroquinolones; cephalosporin; glycylcycline; penam; tetracycline; rifamycin; phenicol antibiotic; disinfecting agents and antiseptics MDR-5 Macrolides; fluoroquinolones; penam MDR-6 Macrolides; fluoroquinolones; penam; tetracycline MDR-7 Macrolides; lincosamide; streptogramin; streptogramin A; streptogramin B MDR-8 Fluoroquinolones; lincosamide; nucleoside antibiotic; phenicol antibiotic; disinfecting agents and antiseptics MDR-9 Macrolides; aminoglycoside antibiotic; cephalosporin; tetracycline; peptide antibiotic; rifamycin; disinfecting agents and antiseptics MDR-10 Monobactam; cephalosporin; penam; penem MDR-11 Fluoroquinolones; glycylcycline; tetracycline; diaminopyrimidine antibiotic; nitrofuran antibiotic MDR-12 Monobactam; cephalosporin; penam MDR-13 Macrolides; fluoroquinolones; aminoglycoside antibiotic; carbapenem; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; peptide antibiotic; aminocoumarin; rifamycin; phenicol antibiotic; penem; disinfecting agents and antiseptics MDR-14 Macrolides; fluoroquinolones; aminoglycoside antibiotic; carbapenem; cephalosporin; penam; peptide antibiotic; penem MDR-15 Fluoroquinolones; aminoglycoside antibiotic; phosphonic acid antibiotic MDR-16 Monobactam; carbapenem; cephalosporin; cephamycin; penam; penem MDR-17 Lincosamide antibiotic; streptogramin antibiotic; pleuromutilin antibiotic MDR-18 Macrolides; lincosamide antibiotic; streptogramin antibiotic MDR-19 Fluoroquinolones; cephalosporin; cephamycin; penam MDR-20 Carbapenem; cephalosporin; cephamycin; penam MDR-21 Carbapenem; cephalosporin; penam MDR-22 Cephalosporin; penam; penem Uniformly high ARG diversities We calculated Shannon index values to understand the ARG diversity in all samples. The alpha diversity plot for all five locations - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal; Wildlife SOS, Bannerghatta; and Wildlife SOS, Agra showed a very high median Shannon index value (> 3) indicating the presence of diverse and highly abundant ARGs in all samples (Fig. 3 C). One-way ANOVA highlighted that two groups – Wildlife SOS, Agra - SCZG, Mysuru; and Wildlife SOS Agra - Wildlife SOS Bannerghatta showed significant differences in their ARG diversities. Similar to bacterial diversity, we performed a Generalized Linear Model (GLM) to quantify differences between different groups caused by each factor (location, age, sex and health status). We further fitted a Generalized Linear Mixed Model (GLMM) on the Shannon index values based on ARG abundance RPKM matrix to understand the influence of multiple factors on alpha diversity of all samples. Only one location – Wildlife SOS, Agra had a significantly lower (t-value > 2) ARG diversity than the intercept – Nehru Zoological Park, Hyderabad (GLM WSOSAgra = -0.743811, Intercept = 3.535647). Interestingly we found no significant difference in ARG diversities in animals which were given or not given antibiotics in the past 3 months prior to sample collection (Fig. 3 D). In case of GLMM, the combined effects of age and sex of the animal, while taking health status and location as random effects, yielded the best fit based on low REML criterion at convergence value (REML = 506.6). Hence, although none of these values are significant individually, in combination they have an influence on the alpha diversity of ARGs (Fig. S5 ). In order to understand whether the high ARG diversity was only due to a few samples or characteristic of the entire group, we plotted beta diversity as a principal coordinate analysis (PCoA) plot based on Bray-Curtis dissimilarity matrix (Fig. 4 A). Although the scatter plot did not show any significant clustering, it had interestingly taken a horse-shoe shape. Samples from all five locations were evenly dispersed and the 95% confidence ellipses were overlapping, indicating that the high abundance of ARGs is in fact a common feature across all locations and that all these locations are more or less uniformly diverse. PERMANOVA test, with 999 permutations, performed on these groups, resulted in a poor R 2 value for location (R 2 = 0.08749), implying that the ARGs were not clustering based on locations, and the differences between ARG diversities found across the locations is not significant. Since we had a significant sample size (n = 97) from the Wildlife SOS, Agra group, we analysed this group further to understand the underlying causes for the horse-shoe shape. While most of the ARGs exhibited multi-drug resistance and did not show any specific antibiotic targets, we did see a variation between the two arms of the horseshoe. The left arm had a higher abundance of ARGs with an efflux-pump-based resistance mechanism, while the right arm had a higher abundance of ARGs with resistance mechanisms involving target replacement or target alteration. (Fig. 4 B). Spearman’s correlation analysis revealed a significant negative correlation between Shannon indices for microbial diversity and total number of ARGs (ρ = -0.1781553, p-value = 0.01343) and total ARG abundance (ρ = -0.2337814, p-value = 0.0011), indicating that animals with lower gut microbial diversity were more likely to have higher richness and abundance of ARGs (Fig. 5 A). Distance correlation test, with 999 replicates, revealed a significant relationship between microbiome diversity and total number of ARGs (dCor = 0.29854, p < 0.01) and ARG abundance (dCor = 0.35743, p-value = 0.001), indicating a moderately dependent non-monotonic or non-linear relationship. We obtained 1942 OTUs at the genus level from a total of 2274 OTUs. Out of these, 1732 genera which correlated significantly (p-value < 0.05) with number of ARGs and sum total of ARG abundances in the sample along with their rho (ρ) values are depicted in Fig. 5 B as red for positive coefficients and blue for negative coefficients. Genera with the highest positive correlation with AMR richness were Shigella (ρ = 0.726282985), Escherichia (ρ = 0.702811531), Salmonella (ρ = 0.618653975), Citrobacter (ρ = 0.585561099) and Kluyvera (ρ = 0.53738361). Only 30 genera had positive correlation coefficients, while the remaining 1702 genera had negative correlation coefficients. Genera with the lowest coefficients were Caldalkalibacillus (ρ = -0.524756288), Terribacillus (ρ = -0.526572546), Calothrix (ρ = -0.537874559), Planktothrix (ρ = -0.54130103), and Leptospira (ρ = -0.545090414). In case of OTU abundance against sum total ARG abundance in a sample, 91 genera were not significantly associated while the remaining 1851 genera were significantly linked. Genera with highest positive correlation were Shigella (ρ = 0.923282859), Escherichia (ρ = 0.892355583), Salmonella (ρ = 0.78988408), Candidatus Moranella (ρ = 0.712798321) and Shimwellia (ρ = 0.690250813). Only 109 genera showed any positive correlation, and all the remaining 1742 genera had negative correlation. Genera with the lowest coefficient were Erysipelothrix (ρ = -0.531026461), Planktothrix (ρ = -0.53294066), Geobacillus (ρ = -0.534334346), Leptospira (ρ = -0.54505657) and Terribacillus (ρ = -0.554513965). We did not find any significant effect of the time spent in captivity on overall gut bacterial diversity (ρ = − 0.0448054, p = 0.725), ARG count (ρ = -0.0439508, p = 0.7281) and total ARG abundance (ρ = 0.0411219, p = 0.745). However, when we looked at each of the 1942 bacterial genera, three genera – Gayadomonas (ρ = − 0.2894, p = 0.019376), Saccharobesus (ρ = − 0.2551, p = 0.040279) and Lactiplantibacillus (ρ = − 0.3008, p = 0.015156) — were found to have statistically significant correlations with time in captivity, highlighting the possibility of key taxa being negatively influenced by prolonged periods in captive environments. Prevalence of mobile genetic elements (MGEs) co-occurring with antibiotic resistant genes (ARGs) ARGs co-occurring with various MGEs are listed in Figs. 6 and 7 . Our analysis revealed that 21 out of 22 multidrug resistant groups co-occur with various mobile genetic elements, further highlighting the fact that not only is there a high prevalence of multidrug resistant ARGs, but most of them also co-occur with plasmids or pro-viral elements, thereby facilitating their horizontal transfer across species, resulting in spread of AMR. Barring a few locations with low sample size, this trend was consistent for plasmids across all locations, and we found more plasmids associated with ARGs in the 5 locations with higher sample sizes - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal; Wildlife SOS, Bannerghatta; and Wildlife SOS, Agra (Fig. 6 ). The pro-viral element co-occurrence, on the other hand, was only noticed in the above 5 locations (Fig. 7 ), possibly implying that a larger sampling would be necessary for the detection of such co-occurence. Across all locations, we observed that the most abundant plasmid-associated ARGs are acrAB-tolC, marA and mutated E. coli SoxR/S (Fig. 6 ). These ARGs confer resistance against multiple drugs, with tetracycline resistance being the predominant drug class and primarily enriched in locations like Van Vihar National Park, Bhopal and Sri Chamarajendra Zoological Gardens, Mysuru, followed by Nehru Zoological Park, Hyderabad and the three Wildlife SOS centers. The above genes are mainly responsible for controlling the bacterium’s response to oxidative stress, and also confers antibiotic resistance through efflux pump and target alteration 52 . Animals in Nehru Zoological Park show the lowest gut bacterial diversity, and in parallel have a high abundance of plasmid-associated e rmB gene which confers resistance to macrolide, lincosamide, and streptogramin B (MLSB) antibiotics. We also observed a higher abundance of plasmids carrying carbapenem resistant bla OXA−232 in sloth bears at Sri Chamarajendra Zoological Gardens. Similar to plasmids, efflux-based E.coli genes ( emrE ) were mostly noted to be associated with pro-viral elements at the five locations with higher sample sizes (Fig. 7 ). In case of animals at the Wildlife SOS, Agra, we also observed higher abundance of two genes PmrF and ugd , associated with pro-viral elements, which provide resistance to the last resort drug class, polymyxin (Fig. 7 ). Further analysis within plasmid regions revealed that the most abundant ARGs also frequently co-occur with additional MGEs like integrons and transposons. Highly abundant ARGs like dfrA12 , aadA2 , ANT(3”)-lla , sul3 and cmlA1 that have been noted in plasmids (Fig. 6 ) at Van Vihar National Park are present here along with integron cassettes (Fig. 7 ), further underlining their virulence. These genes confer resistance to several drug classes such as aminoglycoside, tetracycline, sulfonamide, quinolone, chloramphenicol and diaminopyrimidine antibiotics. A set of diaminopyrimidine and aminoglycoside drug class resistant ARGs were found along with integrons in animals at Wildlife SOS, Bannerghatta. Transposons were observed to co-occur with ARGs in animals at 9 out of the 14 locations in our dataset, but individuals at three locations – Van Vihar National Park, Bhopal; Wildlife SOS, Bannerghatta and Wildlife SOS, Agra in particular, had multiple genes that confer multi-drug resistance via efflux pumps. The plasmid-associated e rmB gene, mentioned earlier in bears at Nehru Zoological Park, was seen to be flanked by transposons. We also found the common plasmid-associated efflux pump gene, acrAB-tolC , co-occurring with transposons in sloth bears at Van Vihar National Park (Fig. 7 ). These ARGs coincided with high levels of plasmid association at their respective locations, indicating location-specific dissemination of ARGs mediated by MGEs. Presence of fungal and viral genera We additionally report the presence of other groups of microorganisms in the gut of captive sloth bear. We identified fungal reads belonging to Nakaseomyces genera in samples from Gorewada Rescue Center, Nagpur; Alipore Zoological Gardens, Kolkata; and Wildlife SOS, Bhopal. We also detected Fusarium, Candida, Saccharomyces and Aspergillus genera at multiple locations albeit at low abundances. As mentioned earlier we identified tailed bacteriophages belonging to Caudoviricetes family in samples from Tiger and Lion Safari, Thevarakoppa and Wildlife SOS, Bhopal. Discussion Endangered and vulnerable species, and the microorganisms they harbour mainly in their gut, are subject to several environmental stressors like habitat loss, climate change and pollution, competition for resources and food, and hunting. Exponential growth and global movement of human populations, and forest fragmentation have brought wildlife in close proximity to humans and livestock, exposing it to environmental pollutants and antimicrobial drugs 24 , 26 . This situation is further aggravated in captivity where animals are treated for injuries and/ or disease, and are inadvertently fed antibiotic- and pesticide-treated food leading to dysbiosis of the gut microbiome and a buildup in antimicrobial resistance (AMR) 53 . To the best of our knowledge this is the first comprehensive metagenomic/ shotgun sequencing study on the gut microbiome of bears. Our study assessed captive sloth bears housed across an extensive, biodiverse landscape, wherein we derive crucial understandings on the possible effects of location, age of animal, and duration in captivity on gut microbial diversity and AMR status of the animal. Given that studies which focus on the extent and impact of AMR on wildlife health are negligible 54 , our research offers valuable insights for the management and conservation of endangered species. We observed that Bacillota, Bacteroidota, Pseudomonadota, and Actinomycetota were the most dominant bacterial phyla in all the sloth bear samples. This is similar to studies based on 16S rRNA sequencing in Andean bear 55 , black bear 56 , brown bear 57 , and polar bear 58 , 59 irrespective of whether the study animals were captive or wild. Strikingly we found Streptococcus , Sarcina , Escherichia , Clostridium and Klebsiella as the most abundant genera at all the sampled locations. These genera were richly populated by species like E. coli and E. faecium designated by WHO as critical priority pathogens capable of developing resistance to last resort antibiotics 60 , as well as K. variicola and S. alactolyticus which are flagged off as emerging pathogens and are reported to carry multiple MGE-associated ARGs 61 , 62 . Overall, we detected lower microbial diversity in all sloth bear samples from Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal, Bannerghatta and Agra, although the diversity was higher in the three Wildlife SOS centers than Hyderabad and Mysuru zoos, and it was higher in younger animals than older individuals. These findings are similar to those reported in several species of canids, primates and equids 63 , 64 , gaint panda 65 and brown kiwi 66 . Although we do not have samples from wild sloth bear for comparison, our data indicate that the microbial diversity gradually declines with duration in captivity and with increasing age of the animal. This is a very critical change in captive animals as higher gut bacterial diversity acts as an indicator of positive gut microbiota that would resist the growth of infectious agents and also acts as a barrier for AMR buildup 67 . Hence, low diversity combined with higher abundance of opportunistically pathogenic genera can potentially make these sloth bears vulnerable to invasive pathogens. The immune response in these animals would also be weaker compared to animals with healthier gut flora 10 . There are a rising number of studies globally on wildlife species as reservoirs, disseminators or sentinels of anthropogenic AMR contamination in the environment (reviewed extensively by Li et al. 26 ). These studies generally focus on migratory birds and wild mammals which move close to or scavenge in human habitation or agriculture land. However, studies on prevalence or buildup of AMR in endangered species in captivity which could affect their health and survival are limited 65 , 68 – 72 . We observed that the ARGs identified in captive sloth bears belong to various AMR gene families and antibiotic drug classes indicating diverse resistance to various antibiotics, as also shown by high alpha diversity in the samples. The four most abundant AMR gene families confer resistance to more than six broad-spectrum, last resort antibiotics like fluoroquinolones, macrolides, cephalosporins, tetracycline and carbapenems. These ARGs confer resistance mostly via efflux pump across antibiotic classes, largely through ABC (ATP-binding cassette antibiotic efflux pump), MFS (Major facilitator superfamily antibiotic efflux pump), and RND (Resistance-nodulation-cell division antibiotic efflux pump). Our study not only shows that ARG richness and diversity are very high in captive animals, but also that the total number of ARGs as well as ARGs unique to a location increase as we sample more individuals. Campbell et al. 69 similarly reported high ARG abundance and richness in captive apes dominated by β-lactam and tetracycline resistance genes. More importantly, we observed that almost all captive sloth bears consistently have high levels of AMR even when they have not been treated with antibiotics in the recent past (≤ 3 months). While we do not have much information on reversibility of antibiotic resistance 73 , our findings suggest that the gut bacteria of captive animals might be evolving in response to other stressors like environment and nutrition, and in the process developing resistance to various antibiotics as pleiotropic effects 74 . The other possible explanation is the horizontal transfer of antibiotic resistance mediated by mobile genetic elements like plasmids 75 , 76 . Interestingly, we saw the horseshoe or Guttman effect 77 in the PCoA plot of AMR diversities. Although the scatter plots did not show any effect of location on AMR diversity and abundance, samples seem to separate along two sides of the horseshoe based on mechanism of antibiotic resistance and the associated mobile genetic elements. Samples with efflux-pump-based multidrug resistance associated with plasmids clustered largely along the left, while samples with specific ARGs that confer resistance via target replacement/ alteration and associated with integrons and transposons were along the right arm. The gut bacterial diversity in captive sloth bear showed significant inverse correlation with both ARG abundance and richness. As mentioned earlier a rich microbial composition is crucial to prevent AMR buildup 78 , and this is true across diverse microbial niches 78 – 80 . Abundances of pathogenic genera like Shigella , Escherichia and Salmonella showed highly significant positive correlations with ARG abundance and richness. We also see significant positive correlations with lesser studied enterobacteria like Citrobacter and Kluyvera which are fast emerging globally as organisms resistant to broad spectrum, last resort antibiotics 81 – 83 . Our results indicate the possibility of a selectively thriving group of organisms or even the likely accumulation of resistant genes in microbiomes with reduced compositional diversity. This statement can be supported by the fact that the abundances of very few genera are elevated along with higher AMR abundance and richness while the abundances of approximately 1700 genera are reduced indicating lowered microbial diversity. At this stage it is very difficult to tease apart the effects of age of animal and duration in captivity on microbial and AMR abundances and richness, however we do observe that the abundances of a few genera reduced significantly with time spent in captivity. One genus, Lactiplantibacillus , in particular caught our attention and warrants further investigation. Several studies highlight the beneficial and protective roles of Lactiplantibacillus in mammalian gut by building resistance against pathogens, stimulating the host immune and neurological functions, and by generally improving the intestinal flora 84 , 85 . Reduction of this genus in the gut flora of captive sloth bear over time does not bode well for the host, and the animal requires systematic interventions to improve its gut health. Multi-drug resistance mediated through efflux pump genes was found to be largely associated with a high abundance of plasmids at most of the captive locations, more so at the five locations - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal, Bannerghatta and Agra – with higher numbers of sloth bears. A large number of plasmids were seen to be associated with E. coli mutations leading to resistance to several broad-spectrum antibiotics like tetracycline, ciprofloxacin, ceftazidime, carbapenems and other β-lactams, etc. thereby compromising the clinical utility of widely used drugs against Gram-negative bacteria. In their review, Castañeda-Barba et al. 86 describe plasmids as a cornerstone in the dissemination of AMR between bacterial hosts in different environments. The emergence, transfer and persistence of plasmid-mediated resistance, even in the absence of selection pressures like active antibiotic treatment, are extremely complex phenomena which occur simultaneously at different biological levels. Although difficult to eliminate, careful and conscientious administration of antibiotics to animals will definitely restrict selection of plasmid-associated AMR. It is also important that these animals are housed in hygienic conditions with good sanitation and nutrition. Buildup of plasmid-mediated resistance in pathogenic bacteria can be diluted and even prevented by maintaining high microbial diversity in the gut 87 . We saw a similar trend even in locations with fewer numbers of animals. For example, Van Vihar National Park in Bhopal had four sloth bears at the time of sample collection, all of which have been in captivity for 14 + years. We observed a dominance of Sarcina species in the gut microflora of these individuals, and also higher abundance of plasmid- and MGE-associated ARGs primarily against tetracycine ( acrAB-tolC, marA, soxR/S ). marA gene encodes a global transcription activator, and its upregulation leads to increased expression of the a crAB-tolC efflux pump system 52 . This combination is a well characterized mechanism contributing to broad-spectrum resistance, indicating that efflux-mediated resistance mechanism seems to be a staple in these environments. The dual role of ARGs further highlights an evolutionary advantage to their retention by the bacterium, wherein environmental stressors can enhance resistant phenotypes, adding more weight to their pleiotropic effects 74 . Presence of diverse resistance genes in this single site could either mean intense and persistent antibiotic selection pressure or a highly permissive environment for microbial gene exchange. The latter is more likely as the average gut bacterial diversity is low in this location, combined with the presence of virulent MGEs. While the abundances of non-plasmid mobile genetic elements like pro-viral elements, integrons and transposons associated with ARGs were much lower than plasmid abundance in captive sloth bear gut, these too play important roles in horizontal gene transfer in bacterial communities and in developing antimicrobial resistance. Macrolides are broad spectrum antibiotics effective against a wide variety of Gram-positive bacteria, and we see efflux pump-based macrolide resistance genes associated to pro-viral elements in sloth bear. Horizontal transfer and spread of such ARGs can have serious consequences on managing the heath of captive animals. Conclusions Overall, our study indicates a lowered gut microbial diversity and highly elevated AMR diversity and richness in captive sloth bears across multiple facilities in India. Many of the dominant bacterial taxa are flagged as top priority pathogens and are capable of developing resistance to last resort antibiotics. We show that the microbial diversity gradually declines with duration in captivity and with increasing age of the animal. Further studies are required to assess whether this decline can be arrested and bacterial diversity can be improved by regular use of different types of pre- and probiotics. One of the main limitations of our study is the lack of samples from wild animals, not exposed to antibiotics and other anthropogenic stressors, which would help us understand their innate bacterial composition and resistance profiles in a given region. We describe the dynamics of bacterial genera abundance and diversity in association with ARG abundance and show that a few pathogenic genera dominant with increase in ARG abundance. One worrying finding is that the highly abundant AMR gene families confer resistance to multiple antibiotics, and not necessarily due to recent exposure to medications. Here it is important to identify and understand stressors in captivity which reduce the animal’s immunity and give certain bacteria a survival edge over others. For the first time we describe a comprehensive analysis of mobile genetic elements in the gut bacteria of bears, and highlight how ARGs, which confer resistance to multiple drugs, are associated with plasmids and other MGEs making them highly virulent. Such in-depth metagenomic studies on gut bacterial dynamics are critical for managing captive animals, and can guide dietary planning, probiotic supplementation and ideal ex-situ conditions to improve gut health and overall wellbeing of animals in alien environments. Declarations Ethical Statement Permissions to collect samples for this study were granted by Chief Wildlife Wardens of West Bengal (No.1703/WL/4R-31/2021, dated 01/09/2021), Maharashtra (No: Desk-22(8)/WL/Research/CR-37(21–22)/1344/21–21, Nagpur, dated 07/09/2021), Telangana (No.26803/2012/WL-2, dated 14/09/2021), Tamil Nadu (No.4822/2021/WL1, dated 28/01/2022), Madhya Pradesh (No./M.H.-II/ Research/2824, Bhopal, dated 13/04/2022), Karnataka (No.PCCF(WL)/E2/CR-37/2021-22, dated 17/05/2022), and Uttar Pradesh (No. 23-2-12(G), Lucknow, dated 20/05/2022). Funding This study is part of the project “SBI Foundation Centre of Excellence for Genome-guided Pandemic Prevention” (GAP570) funded by SBI Foundation, India. Author Contribution V.P.P., V.S.K., A.B.S. and P.A.R. conceived and planned the study, and designed experiments. S.N., N.S. and G.K. collected samples and metadata, and isolated DNA. S.N. prepared samples for sequencing. V.P.P. and V.S.K. performed bioinformatics analyses. A.A.S., S.I., M.V.B., M.K.P. and M.A.H. provided logistic support and resources. A.B.S. and P.A.R. arranged for funds and managed the project. V.P.P., V.S.K., S.N., A.B.S. and P.A.R. wrote the manuscript. All authors reviewed and edited the manuscript. Acknowledgement We thank the Chief Wildlife Wardens of Uttar Pradesh, West Bengal, Madhya Pradesh, Maharashtra, Telangana, Tamil Nadu and Karnataka for permitting us to collect fecal samples of sloth bear in captivity. Support extended by the Wildlife SOS management is acknowledged. We sincerely thank logistic support and guidance extended by our CCMB colleagues Dr. Karthik Bharadwaj and Dr. Divya Tej Sowpati. Tulasi Nagabandi and Bishwajeet Singha in the NGS facility at CSIR-CCMB helped us generate the metagenome data. All bioinformatics analyses were carried out on the Ramanujan-HPC at CSIR-CCMB. We are grateful for the support extended by staff of zoos and rescue centers. 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Supplementary Files FigureS1.docx Figure S1 Gut microbial community profile at the domain level in sloth bear samples from 14 captive locations in India FigureS2.docx Figure S2 Gut bacterial community profile at the phylum level in sloth bear samples from 14 captive locations in India FigureS3.docx Figure S3 Generalised linear model (GLM) outputs showing the influence of location and age of the animal on gut microbial diversities of captive sloth bears FigureS4.docx Figure S4 Beta diversity plot based on Bray-Curtis dissimilarity matrix for gut microbial diversity in captive sloth bear FigureS5.docx Figure S5 GLM and GLMM (Generalised linear mixed model) analyses on effects of different variables on AMR diversities in captive sloth bear Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":126935,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of zoos and rescue centers in India where captive sloth bear samples were collected. 1 – Zoological Garden Alipore, Kolkata (ZGA); 2 – Wildlife SOS, Agra (WSOS-A); 3 – Etawah Safari Park (ESP); 4 – Kanpur Zoological Park (KNZP); 5 – Van Vihar National Park, Bhopal (VVNP); 6 – Wildlife SOS, Bhopal (WSOS-BH); 7 – Gorewada Rescue Center, Nagpur (GRC); 8 – Kakatiya Zoological Park, Warangal (KAZP); 9 – Nehru Zoological Park, Hyderabad (NZP); 10 – Tiger and Lion Safari, Thevarakoppa (TLST); 11 – Sri Chamarajendra Zoological Garden, Mysuru (SCZG); 12 – Bannerghatta Biological Park (BBP); 13 – Wildlife SOS, Bannerghatta National Park (WSOS-BNP); 14 – Arignar Anna Zoological Park, Chennai (AAZP). 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samples.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/7ec3c2f97df3d4393737d00e.png"},{"id":94582264,"identity":"1e6debf8-787f-41cf-9470-caf113a3d85e","added_by":"auto","created_at":"2025-10-28 18:13:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":547564,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map depicting the co-occurrence of plasmids with ARGs grouped by antibiotic drug classes, AMR gene families and resistance mechanisms in captive sloth bear samples.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/fa6a802a8067b08b6112307b.png"},{"id":94581734,"identity":"28b73bdc-0880-4dde-a468-6040ce7e326c","added_by":"auto","created_at":"2025-10-28 18:12:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":469103,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map depicting the co-occurrence of non-plasmid mobile genetic elements (pro-viral elements/ phages, integrons and transposons) with ARGs grouped by antibiotic drug classes, AMR gene families and resistance mechanisms in captive sloth bear samples.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/c171914051439f94cf985940.png"},{"id":96452963,"identity":"73a2deb5-03b1-4d2f-a59d-9a40c9ee19a7","added_by":"auto","created_at":"2025-11-21 09:56:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3535204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/be0172ad-fd3d-487b-93f0-8bcced9ceb80.pdf"},{"id":94582610,"identity":"8d9eec4c-b891-4c7a-b05a-ef4f2ed2f140","added_by":"auto","created_at":"2025-10-28 18:13:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":98054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1 \u003c/strong\u003eGut microbial community profile at the domain level in sloth bear samples from 14 captive locations in India\u003c/p\u003e","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/f08c9dd4a6cdb7fd25e05db0.docx"},{"id":94582112,"identity":"29b71fe3-63a2-4e00-968d-c9693167a3ad","added_by":"auto","created_at":"2025-10-28 18:12:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":130848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e Gut bacterial community profile at the phylum level in sloth bear samples from 14 captive locations in India\u003c/p\u003e","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/dba1827efdf8f843185a0ca8.docx"},{"id":94581660,"identity":"b1d80879-c54c-4b7c-a176-5a7d563613c8","added_by":"auto","created_at":"2025-10-28 18:12:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":49990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e Generalised linear model (GLM) outputs showing the influence of location and age of the animal on gut microbial diversities of captive sloth bears\u003c/p\u003e","description":"","filename":"FigureS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/2aae48cd50727eadb545650f.docx"},{"id":94580991,"identity":"4857949e-842b-4b55-a56b-eba28b1bb673","added_by":"auto","created_at":"2025-10-28 18:12:18","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":204913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e Beta diversity plot based on Bray-Curtis dissimilarity matrix for gut microbial diversity in captive sloth bear\u003c/p\u003e","description":"","filename":"FigureS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/bcfeae9b6115b8d5df12c4d5.docx"},{"id":94581319,"identity":"fa54891d-45a1-48c2-bf3d-c7870cbb05d6","added_by":"auto","created_at":"2025-10-28 18:12:27","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":43373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e GLM and GLMM (Generalised linear mixed model) analyses on effects of different variables on AMR diversities in captive sloth bear\u003c/p\u003e","description":"","filename":"FigureS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7941374/v1/5acb834bbaec576245846409.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysbiosis and Accumulation of Antimicrobial Resistance in the Gut Bacterial Reservoir of Captive Sloth Bear (Melursus ursinus)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, exponentially growing evidence highlight the influence, interplay and interactions of the animal gut microbiome with various aspects of life\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The gut microbiome, which is primarily responsible in aiding its host to digest and absorb nutrients from the diet, is now known to be involved in multiple important metabolic, immunological, and neuropsychological functions\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It also plays a key role in helping the host adapt to a rapidly changing environment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although it varies from animal to animal, the initial gut microbiota is considered to be inherited vertically from the mother during gestation and parturition, and during post-natal interactions like suckling, feeding and grooming\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This microbiome consistently remains diverse in adulthood, varying based on a plethora of factors such as diet, environment, weather, stress, hormones, etc., and the diversity generally declines only as the individual ages\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This consortium of organisms is also responsible for training the immune cells present in the gut for pathogen identification, besides helping in the maintenance and regeneration of gut tissues whenever necessary\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent studies also show that the gut microbiome plays a vital role in reproductive success in animals, an aspect which holds special importance for endangered species\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to all these functions, the gut microbiome also serves as a very important reservoir, wherein probiotic, commensal and pathogenic organisms all reside, interact and influence each other. At this juncture, the use of antibiotics to treat infections gains prominence, as indiscriminate and improper exposures to broad-spectrum drugs are capable of erasing the beneficial bacterial strains while enriching pathogenic strains with \u003cb\u003ea\u003c/b\u003entibiotic \u003cb\u003er\u003c/b\u003eesistant \u003cb\u003eg\u003c/b\u003eenes (ARGs)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Accumulation of these ARGs leads to \u003cb\u003ea\u003c/b\u003enti\u003cb\u003em\u003c/b\u003eicrobial \u003cb\u003er\u003c/b\u003eesistance (AMR), which is a growing global threat and is recognized for its ability to render standard treatments ineffective against infectious microorganisms\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Presently, AMR is of utmost concern globally and is declared as a top priority threat by the World Health Organisation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Extensive research is being conducted on human subjects to investigate the status of AMR in various microbial niches\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Similar studies are also being carried out on animals in the food sector and its associated industries to understand their current AMR status\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e with outcomes aligned to anthropocentric needs. There are however very limited investigations on AMR in wild animals\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A few studies solely consider wildlife as a key reservoir or disseminator of AMR\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, instead of the more likely opposing paradigm that human interactions and interventions inadvertently lead to the propagation of AMR in wildlife\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis aspect is particularly important in captive vulnerable and endangered animal populations, where there is a general increase in antibiotic usage to manage health conditions. In captive settings, close and at many times unhygienic quarters, and routine administration of antibiotics create a situation conducive to the development and spread of resistant bacteria. Animals in zoos live in an environment that is greatly different from their natural habitats. They are introduced to therapeutics as prophylactic treatments and routine veterinary care. Moreover feed, like poultry, milk and eggs, provided in zoos and captive facilities may be priorly treated with antibiotics to ensure their quality and safety, hereby unintentionally introducing excessive antimicrobials into the animal\u0026rsquo;s system.\u003c/p\u003e\u003cp\u003eOur present study revolves around assessing the gut microbiome of captive sloth bears housed at various zoos and rescue centers across India and investigating their load of antimicrobial resistance genes and signatures. Our study species, the sloth bear (\u003cem\u003eMelursus ursinus\u003c/em\u003e), belongs to Ursidae family and is endemic to the Indian subcontinent\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Sloth bear can be identified with its white \u0026lsquo;V\u0026rsquo; shaped chest patch against an overall dense shaggy black coat. In India, sloth bear occupies diverse habitats from tropical forests to shrublands, and grasslands\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. It is largely a solitary creature and wanders through forests in search of termites and fruits. It has an elongated muzzle and long curved front claws that help in extracting termites and ants from ant-hills and crevices. Sloth bear is classified as \u0026lsquo;Vulnerable\u0026rsquo; in the IUCN Red List of Threatened Species\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and is listed in Schedule 1 of the Indian Wildlife Protection Act (1972). Contributors to population decline include habitat loss and fragmentation, poaching and trade in body parts, and conflict. Historically in India, sloth bear cubs were captured and subjected to harsh conditions as part of the \"dancing bear\" tradition, where they endured severe physical and psychological abuse\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Long-term collaborative efforts between the Government of India and NGOs like Wildlife SOS ensured that these animals were successfully rescued and rehabilitated, allowing them to be reintegrated into more humane environments that prioritize their welfare and conservation.\u003c/p\u003e\u003cp\u003eGiven the vulnerable status of sloth bears, understanding their gut microbial composition and the AMR burden they carry is essential for effective \u003cem\u003eex-situ\u003c/em\u003e conservation strategies. AMR poses an additional layer of risk to their health, potentially reducing the efficacy of future treatments of infections in both wild and captive populations. As the role of captive facilities in the conservation of endangered species increases, understanding how management practices contribute to AMR will be critical in ensuring the long-term survival of these animals.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area and sample collection\u003c/h2\u003e\u003cp\u003eFor this study, we collected 231 sloth bear samples from 14 zoos and rescue centers across India (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between November 2021 to September 2022. A large proportion of these samples (n\u0026thinsp;=\u0026thinsp;187) were from animals in Wildlife SOS rescue centers at Agra, Bannerghatta and Bhopal. Sloth bears housed at the Wildlife SOS centers have a history of extensive abuse where they were beaten, de-clawed, had their nasal septum pierced to string ropes through it and chained up constantly, and were starved and malnourished. These animals were rescued by the Wildlife SOS teams and government officials at different points and rehabilitated at the Agra-Bear Rescue Facility, Bengaluru-Bannerghatta Bear Rescue and Rehabilitation Centre and Bhopal-Van Vihar Bear Rescue Facility. In their new homes, these bears now have large open spaces to wander and explore and live the rest of their lives in peace. Veterinary facilities have been made readily available at all times and there are enrichment paraphernalia for the bears to engage in various activities.\u003c/p\u003e\u003cp\u003eWe collected fecal samples within 15 mins of defecation after moving the concerned animal to another enclosure with the help of animal keepers. Samples were collected with sterile spatula and forceps into fresh 50ml microfuge tubes. As a precautionary measure we used gloves and masks during sample collection. We sterilized the spatula and forceps after each collection with bleach and absolute alcohol to avoid any cross-contamination. Sample tubes were labelled with details such as sample ID, date of collection, location, age and gender of the animal, and stored in Zedblox Actipod\u0026trade; at -10\u0026deg;C before transferring to -80\u0026deg;C in the laboratory for further analysis. In addition to samples, we also recorded the history, feeding and medication details, duration in captivity, and health status of each animal at every facility.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA isolation and sequencing\u003c/h3\u003e\n\u003cp\u003eWe isolated DNA from all samples using Nucleospin DNA Stool kit (Machery-Nagel, Germany) adhering to the company\u0026rsquo;s protocol and only increasing incubation time to get a better DNA yield. To minimize contamination, we aliquoted and carried out isolation in separate biosafety cabinets. Quality of isolated DNA samples were checked spectrophotometrically. DNA was stored at -20\u0026deg;C until further analysis.\u003c/p\u003e\u003cp\u003eFor each sample, 100ng of DNA was fragmented and tagged with adapters using bead-linked transposomes (BLT). Post tagmentation, cleanup was performed and the tagmented DNA was amplified through a 5-cycle PCR program to add index adapters. We constructed metagenomic sequencing libraries using Illumina DNA Prep Kit (Illumina Inc., USA), according to the manufacturer\u0026rsquo;s protocols. Final library quality, concentration and size were determined with Qubit 4 (ThermoFisher Scientific, USA) and TapeStation (Agilent Technologies, USA). We sequenced the libraries on NovaSeq 6000 (Illumina Inc., USA) with 2x150 chemistry. Two hundred and fifteen samples out of 231 passed QC and were used for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformatics analysis\u003c/h3\u003e\n\u003cp\u003eThe sequence data obtained was converted and demultiplexed from BCL to FASTQ format with bcl2fastq conversion software and further quality checked for Phred score, per base N content, sequence length distribution, over-represented sequences and adapter content using FastQC ver. 0.12.1\u003csup\u003e34\u003c/sup\u003e and MultiQC ver. 1.17\u003csup\u003e35\u003c/sup\u003e. We noticed contamination with Nextera transposase adapter, and therefore filtered the reads for adapter contamination using Trimmomatic ver. 0.39\u003csup\u003e36\u003c/sup\u003e with the following parameters: ILLUMINACLIP: NexteraPE-PE.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:10:20 MINLEN:75. Post trimming, reads were re-checked to ensure the removal of the adapter sequences. Following confirmation of adapter sequence removal, the cleaned reads were assembled into contigs using MEGAHIT ver. 1.2.9\u003csup\u003e37\u003c/sup\u003e with the option: \u0026ndash;k-min 35 \u0026ndash;k-max 141 \u0026ndash;k-step 28. We then mapped the reads to the index built with assembled contigs to check for the overall assembly rate using Bowtie2 ver. 2.5.2\u003csup\u003e38\u003c/sup\u003e. The mapped SAM files from Bowtie2 were converted to BAM format using SAMtools ver. 1.18\u003csup\u003e39\u003c/sup\u003e, and were coordinate sorted for further analysis.\u003c/p\u003e\n\u003ch3\u003eTaxonomic Classification\u003c/h3\u003e\n\u003cp\u003eTrimmed reads were filtered using fastp ver. 0.22.0\u003csup\u003e40\u003c/sup\u003e, and cleaned reads were classified using PlusPFP database, which contains Standard (RefSeq archaea, bacteria, viral, plasmid, human, UniVec_Core) plus RefSeq protozoa, fungi \u0026amp; plant, as referenced by Kraken2 ver. 2.1.2\u003csup\u003e41\u003c/sup\u003e. Further, the tool Bracken (Bayesian Re-estimation of Abundance with KrakEN) was employed to re-assign unclassified reads to various Operational Taxonomic Units (OTUs) using the Kraken report and database to obtain the final estimated read counts. These finalized read counts from bracken output were used for abundance calculations. Kraken-biom software was used to build a BIOM table for abundance counts of all samples, with rows as taxa and columns as samples. The biom file was imported to RStudio ver 4.3.3 as a phyloseq object using the R package \u0026lsquo;phyloseq\u0026rsquo;\u003csup\u003e42\u003c/sup\u003e, and pruned and filtered using the microbiomeutilities package\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. We retained only those taxa with a minimum total of 100 mapped reads across the 215 samples, and these OTUs were updated to \u0026lsquo;besthit\u0026rsquo; format. The phyloseq object was filtered only for bacterial taxa, and these taxa were agglomerated at the genus level (Rank 6). The final OTU table was saved as a CSV file with read counts across samples normalized for sequencing depth using the RPM (Reads per million bacterial reads) method. We investigated the presence of various OTUs at different taxonomic levels and their relative abundances were calculated as \u0026lsquo;fraction total reads\u0026rsquo; classified to the OTU by total number of reads.\u003c/p\u003e\n\u003ch3\u003eIdentification of antimicrobial resistance genes and mobile genetic elements\u003c/h3\u003e\n\u003cp\u003eWe analysed the assembled contigs with Resistance Gene Identifier (RGI) ver. 6.0.3 which uses the \u003cb\u003eC\u003c/b\u003eomprehensive \u003cb\u003eA\u003c/b\u003entibiotic \u003cb\u003eR\u003c/b\u003eesistance \u003cb\u003eD\u003c/b\u003eatabase (CARD) as reference\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The RGI tool predicts the resistome from nucleotide sequences of contigs and reports a comprehensive list of identified AMR genes present in respective samples with varying identities. We filtered ARGs with more than 98% identity for further analysis. The RGI output for all samples was saved as BED files and was version-sorted using the sort command in Linux. The sorted BED file along with the sorted BAM files were used to calculate coverage of the contigs with ARGs using the BEDtools coverage command. ARG abundance was quantified based on FPKM\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\text{P}\\text{K}\\text{M}\\:=\\:\\frac{\\text{R}\\text{e}\\text{a}\\text{d}\\text{s}\\text{M}\\text{a}\\text{p}\\text{p}\\text{e}\\text{d}}{\\text{G}\\text{e}\\text{n}\\text{e}\\text{L}\\text{e}\\text{n}\\text{g}\\text{t}\\text{h}\\:\\times\\:\\:\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\text{R}\\text{e}\\text{a}\\text{d}\\text{s}}{\\text{1,000,000}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWherein, ReadsMapped corresponds to number of reads mapped to ARGs fragments, GeneLength corresponds to length of ARG, and TotalReads corresponds to total number of bacterial reads per sample.\u003c/p\u003e\u003cp\u003eWe identified mobile genetic elements (MGEs) in the form of plasmids and pro-viral elements using geNomad\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Plasmid-annotated contigs were investigated for presence of ARGs identified previously with RGI-finder, to establish co-occurrence of ARGs with MGEs such as integrons and transposons using BacAnt ver. 3.4.0\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eVisualizations and Statistical Analyses\u003c/h2\u003e\u003cp\u003eResults were visualized using RStudio ver. 4.3.3 and RStudio server ver. 4.4.1.\u003csup\u003e48\u003c/sup\u003e. We plotted microbial abundance bar plots with dplyr, forcats and ggplot2 packages. Bracken reports at various taxonomic levels were used to identify top domains/ phyla/ genera/ species across all samples from all locations. We identified the top few OTUs from each location based on their respective fraction of total reads as a measure of their relative abundance. Similarly, abundance barplots of ARGs were visualized by plotting RPKM values of respective AMR gene families and drug classes from samples of a given location using ggplot2 package.\u003c/p\u003e\u003cp\u003eFrom this point onwards data from only five captive locations - Nehru Zoological Park, Hyderabad (n\u0026thinsp;=\u0026thinsp;10), Sri Chamarajendra Zoological Gardens, Mysuru (n\u0026thinsp;=\u0026thinsp;11), Wildlife SOS, Bhopal (n\u0026thinsp;=\u0026thinsp;15), Wildlife SOS, Bannerghatta (n\u0026thinsp;=\u0026thinsp;59), and Wildlife SOS, Agra (n\u0026thinsp;=\u0026thinsp;97) were considered for further analysis, as all other locations had very few individuals. Shannon indices were derived using estimate_richness function from phyloseq package\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Within this group of five locations, we again analysed alpha diversities for the three Wildlife SOS centers with additional covariables like deworming medications given in the three months prior to sample collection, history of hepatic or kidney disorders, body score index, and seropositivity for tuberculosis. One-way ANOVA, followed by a post hoc correction for multiple comparisons using Tukey\u0026rsquo;s HSD (honestly significant difference) test, was performed to check the significance of our results. ANOVA pairs with adjusted p value less than 0.05 were considered significant.\u003c/p\u003e\u003cp\u003eWe plotted Principal Coordinates Analysis (PCoA) plots for beta diversity among the locations and for each of the above variable using Bray-Curtis dissimilarity matrices with vegdist and pco functions of vegan and ecodist packages in RStudio. PERMANOVA (\u003cb\u003ePer\u003c/b\u003emutational \u003cb\u003eM\u003c/b\u003eultivariate \u003cb\u003eAn\u003c/b\u003ealysis \u003cb\u003eo\u003c/b\u003ef \u003cb\u003eVa\u003c/b\u003eriance), a non-parametric multivariate statistical permutation test, was performed on these distance matrices using adonis function of vegan package with 999 permutations, and a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe developed a Generalized Linear Model (GLM) to assess the effects of different factors on the diversity profiles. We fit Shannon alpha diversity values to the model with a Gaussian (normal) distribution with an identity link function using glm function in RStudio. Additionally, in order to understand the effect of covariables at different locations, diversity profiles were fit as a dependent variable to a Generalized Linear Mixed Model (GLMM) with gamma distribution and log link functions in lme4 package in RStudio. Location of sample was taken as the fixed effect, and variables like health status, age and sex of the animal were taken as random effects and added as intercepts for a better fit, as these accounted for location level variations. The model was fitted using maximum likelihood estimation and was also checked for over-dispersion in the gamma model by calculating the ratio of residual deviance to degrees of freedom. Models were validated and selected based on a lower score from Akaike information criterion (AIC).\u003c/p\u003e\u003cp\u003eIn order to understand the relationship between gut microbiome diversity and antimicrobial resistance gene (ARG) presence and abundance, we conducted correlation tests between Shannon indices of microbial diversity with both, total number of ARGs and sum total ARG abundance per sample. Spearman\u0026rsquo;s correlation was chosen over Pearson\u0026rsquo;s as it is a non-parametric, rank-based test and does not assume a linear relationship or normally distributed data. Significance was assessed at p-value less than 0.05. We derived Spearman\u0026rsquo;s correlation coefficient (ρ, rho) which ranges from \u0026minus;\u0026thinsp;1 to 1, and indicates the strength and direction of the monotonic relationship between ranked diversity and AMR abundance. Further, a distance correlation test was performed to assess both linear and non-linear associations between variables, with significance evaluated at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We also assessed the correlation of individual bacterial genus abundance with ARG abundance and numbers in each sample, and significant genera (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified. Additionally, to understand the effect of prolonged time in captivity on gut microbial diversity, number of ARGs and total ARG abundance in a sample, we extended the Spearman rank correlation test between these variables for 65 individuals for which we had information on when they were captured from the wild, and their duration in captivity (in years).\u003c/p\u003e\u003cp\u003eARGs co-occuring with MGEs were visualized as heatmaps, generated using complexheatmap, pheatmap and Heatmap functions in RStudio, with the relative abundance of ARGs as the color scale.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCohort description\u003c/h3\u003e\n\u003cp\u003eOut of the 231 sloth bear samples collected, 16 samples failed at the NGS QC level, and hence, data from 215 sequenced samples were used for further analysis. Out of these, a total of 171 samples were from the three Wildlife SOS centers, viz. 59 from Wildlife SOS, Bannerghatta; 15 from Wildlife SOS, Bhopal; and 97 from Wildlife SOS, Agra. Amongst the remaining 44 samples, 11 and 10 samples were from Sri Chamarajendra Zoological Gardens, Mysuru and Nehru Zoological Park, Hyderabad respectively; 4 samples each were from Tiger and Lion Safari, Thevarakoppa and Van Vihar National Park, Bhopal; 3 samples each were from Etawah Safari Park, Etawah; Kakatiya Zoological Park, Warangal; and Bannerghatta Biological Park and Rescue Center, Bengaluru. The smallest groups were 2 samples each from Kanpur Zoological Park, Kanpur and Gorewada Rescue Center, Nagpur, and one sample each from Alipore Zoological Gardens, Kolkata and Arignar Anna Zoological Park, Chennai (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All animals were further classified based on gender, age, health status, antibiotics given in the three months prior to sample collection, and duration in captivity at each location. Animals were grouped as young (0\u0026ndash;3 years), adult (3\u0026ndash;15 years) and old (15\u0026thinsp;+\u0026thinsp;years) based on available literature on sexual maturity and reproduction in this species. Animals were considered healthy unless they were off-feed for multiple days and lethargic, there were visible external wounds or there was any history of disease and prolonged treatment. In case of the three Wildlife SOS centers, we had additional information on deworming medications given in the three months prior to sample collection, history of hepatic or kidney disorders, body score index, and seropositivity for tuberculosis. Each animal at Wildlife SOS centers was scored based on the condition of its body and weight (1\u0026thinsp;=\u0026thinsp;skinny; 2\u0026thinsp;=\u0026thinsp;thin; 3\u0026thinsp;=\u0026thinsp;ideal; 4\u0026thinsp;=\u0026thinsp;overweight; 5\u0026thinsp;=\u0026thinsp;obese).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe sequenced 215 out of the 231 samples collected, and lost 16 samples during library preparation for sequencing. These 215 sloth bear samples yielded an average of ~\u0026thinsp;17.5\u0026nbsp;million forward and reverse reads each, per sample. Nearly 92% of forward and reverse reads survived after trimming to remove Nextera transposase adapter contamination. On an average, we observed 85% alignment rate in the samples, and average N50 value was 2106 bp indicating a moderate to good assembly. We identified a total of 2274 unique OTUs and 374 unique ARGs across 14 locations and their relative abundances were calculated using RPM and RPKM methods, respectively for further analysis.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGut microbiota composition and diversity\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003ePresence of pathogenic or opportunistic bacteria\u003c/h2\u003e\u003cp\u003eMore than 80% of all reads across all samples from 14 locations classified to bacterial domain. Few reads mapped to Eukaryota, and this is not surprising, given that captive sloth bears are mostly fed a plant\u0026ndash;based diet. Interestingly, we were also able to detect the presence of other potentially infectious domains such as viruses and fungi (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Approximately\u0026thinsp;~\u0026thinsp;10% of all the classified reads in samples from Tiger and Lion Safari, Thevarakoppa and Wildlife SOS, Bhopal mapped to various phage viruses of class Caudoviricetes. We did not see any significant presence of Archaea. The most abundant phyla across all locations were Bacillota, Bacteroidota, Pseudomonadota and Actinomycetota, and made up for more than 98% of all the bacterial phyla found in the gut microbiome of these captive sloth bears (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). At the genus level, five opportunistically pathogenic genera - \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSarcina\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e, were the most abundant genera identified across all locations. These five genera combined, constituted nearly 75% of all the classified reads. The relative abundance of the top 22 genera across all 14 locations is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. We further mapped abundance plots at the species level to understand the sub-groups within the most abundant genera. More than half of the top 22 species across 14 locations were from the 5 major genera mentioned above (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Many of the highly abundant \u003cem\u003eStreptococcus\u003c/em\u003e species belong to Group D gamma-hemolytic arm of the same genus, and were previously described from the gut of equine and bovine species\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. It was interesting to note that although the top species and their respective abundances varied, animals from all the three Wildlife SOS centers - Agra, Bhopal and Banerghatta, showed a very similar composition of major species (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), possibly reflecting similar diet plans and medication schedules at all the Wildlife SOS centers.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLower gut microbiome diversity in captive sloth bear\u003c/h2\u003e\u003cp\u003eWe calculated alpha and beta diversities at the genus level to identify between-sample and between-location differences in microbial diversities. The median alpha diversity, as indicated by Shannon Index, for all five locations was between 2.5 to 1.5 indicating low bacterial diversity in all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We noticed a slightly higher median Shannon index value (\u0026gt;\u0026thinsp;2) for the three Wildlife SOS centers compared to the other two groups - Nehru Zoological Park (NZP), Hyderabad and Sri Chamarajendra Zoological Gardens (SCZG), Mysuru (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). One-way ANOVA showed that none of the tested variables \u0026ndash; location, age, sex, and health status of animal, influenced the microbial diversity significantly at all the five studied locations.\u003c/p\u003e\u003cp\u003eWe performed a Generalized Linear Model (GLM) to quantify differences caused by each factor, and subsequently we fit a Generalized Linear Mixed Model (GLMM) to understand the influence of multiple factors (location, age, sex and health status) on the observed alpha diversity of all samples. GLMM showed that the combined effect of these variables was not significant on the alpha diversity. In case of microbial diversity, GLM was chosen over GLMM for its lower AIC (586.5) and better fit. The three Wildlife SOS centers showed a significantly higher (t-value\u0026thinsp;\u0026gt;\u0026thinsp;2) diversity than the intercept \u0026ndash; NZP, Hyderabad group (Intercept\u0026thinsp;=\u0026thinsp;0.434081, GLM\u003csub\u003eWSOS Bhopal\u003c/sub\u003e = 0.547170, GLM\u003csub\u003eWSOS Agra\u003c/sub\u003e = 0.591565, GLM\u003csub\u003eWSOS Banerghatta\u003c/sub\u003e = 0.610021). We also found that samples from young animals (0\u0026ndash;3 years) showed significantly higher diversity (GLM\u003csub\u003eYoung\u003c/sub\u003e = 0.489647), while the samples from old animals (15\u0026thinsp;+\u0026thinsp;years) exhibited significantly lesser diversity (GLM\u003csub\u003eOld\u003c/sub\u003e = -0.159612). The summary of these observations from the developed GLM model is represented in Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBeta-diversity plots based on Bray-Curtis dissimilarity matrix did not show significant clustering of samples from any of the locations (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Scatter plot with 95% confidence ellipses showed high overlap and hence, we concluded that there is no significant difference in the gut microbial diversity of captive sloth bear between all five locations. The same was corroborated by PERMOVA test with 999 permutations, with a very poor R-squared value for location (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08533) although the p-value was 0.001, indicating a significant variation between groups which cannot be explained by location alone.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAntibiotic resistance gene composition and diversity\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003eHighly abundant multi-drug resistance genes\u003c/h2\u003e\u003cp\u003eA wide-array of ARGs were identified at each of the 14 locations. The total number of ARGs, unique ARGs (without duplicates), and unique to the location ARGs are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Total number of ARGs detected was nearly proportional to sample size, and this can be understood intuitively as a higher detection of ARGs with higher sampling. While the total number of unique ARGs in a location remained consistently between 35\u0026ndash;78 for all the groups with sample sizes\u0026thinsp;\u0026lt;\u0026thinsp;10, it increased to 100\u0026ndash;200 for the groups with bigger sample sizes. Moreover, we see that the total number of ARGs did not stabilise after a certain sample size, and the unique ARGs - without duplicates \u0026ndash; also kept increasing. Further the total number of \u0026lsquo;unique to location\u0026rsquo; ARGs increased proportionally with increase in sample size. Therefore, irrespective of location, we can conclude that approximately 40 ARGs could be identified from each sample of captive sloth bear, and bigger the group, higher were the chances of finding new and unique ARGs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of antibiotic resistance genes (ARGs) identified in captive sloth bear samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal no. of Samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of ARGs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal no. unique ARGs, without duplicates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal no. of ARGs unique to each location\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAAZP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGRC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKNZP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eELSP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBBP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKAZP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLST\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVVNP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNZP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSCZG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWSOS-BH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWSOS-BNP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWSOS-A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of 93 unique AMR gene families were identified across all the 14 locations. We have represented the top 21 AMR gene families from these locations in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The most abundant genes are the ones conferring efflux-pump-based resistance towards antibiotics, and they constitute nearly 50% of the resistance at all locations. Another note-worthy aspect is that the profiles of AMR gene families for the three Wildlife SOS locations - Agra, Bhopal and Bannerghatta, remained similar, just as in the case of microbial diversity. Relative abundance of the top 25 drug classes out of a total of 76, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. We designated groups with 3 or more than 3 drug classes as \u0026ldquo;Multi-drug resistant\u0026rdquo; groups (MDR-1, MDR-2,\u0026hellip;.) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We observed that the top drug classes are all MDR groups, with nearly 50% of the groups conferring resistance to a wide range of commercially available antibiotics. The three Wildlife SOS centers again showed very similar resistome profiles at the drug class level (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-drug resistance drug classes identified in captive sloth bear samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolone; monobactam; carbapenem; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; rifamycin; phenicol antibiotic; penem; disinfecting agents and antiseptics.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; fluoroquinolones; cephalosporin; cephamycin; penam; tetracycline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; rifamycin; phenicol antibiotic; disinfecting agents and antiseptics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; cephalosporin; glycylcycline; penam; tetracycline; rifamycin; phenicol antibiotic; disinfecting agents and antiseptics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; fluoroquinolones; penam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; fluoroquinolones; penam; tetracycline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; lincosamide; streptogramin; streptogramin A; streptogramin B\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; lincosamide; nucleoside antibiotic; phenicol antibiotic; disinfecting agents and antiseptics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; aminoglycoside antibiotic; cephalosporin; tetracycline; peptide antibiotic; rifamycin; disinfecting agents and antiseptics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonobactam; cephalosporin; penam; penem\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; glycylcycline; tetracycline; diaminopyrimidine antibiotic; nitrofuran antibiotic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonobactam; cephalosporin; penam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; fluoroquinolones; aminoglycoside antibiotic; carbapenem; cephalosporin; glycylcycline; cephamycin; penam; tetracycline; peptide antibiotic; aminocoumarin; rifamycin; phenicol antibiotic; penem; disinfecting agents and antiseptics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; fluoroquinolones; aminoglycoside antibiotic; carbapenem; cephalosporin; penam; peptide antibiotic; penem\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; aminoglycoside antibiotic; phosphonic acid antibiotic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonobactam; carbapenem; cephalosporin; cephamycin; penam; penem\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLincosamide antibiotic; streptogramin antibiotic; pleuromutilin antibiotic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMacrolides; lincosamide antibiotic; streptogramin antibiotic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluoroquinolones; cephalosporin; cephamycin; penam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbapenem; cephalosporin; cephamycin; penam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbapenem; cephalosporin; penam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDR-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCephalosporin; penam; penem\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eUniformly high ARG diversities\u003c/h2\u003e\u003cp\u003eWe calculated Shannon index values to understand the ARG diversity in all samples. The alpha diversity plot for all five locations - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal; Wildlife SOS, Bannerghatta; and Wildlife SOS, Agra showed a very high median Shannon index value (\u0026gt;\u0026thinsp;3) indicating the presence of diverse and highly abundant ARGs in all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). One-way ANOVA highlighted that two groups \u0026ndash; Wildlife SOS, Agra - SCZG, Mysuru; and Wildlife SOS Agra - Wildlife SOS Bannerghatta showed significant differences in their ARG diversities.\u003c/p\u003e\u003cp\u003eSimilar to bacterial diversity, we performed a Generalized Linear Model (GLM) to quantify differences between different groups caused by each factor (location, age, sex and health status). We further fitted a Generalized Linear Mixed Model (GLMM) on the Shannon index values based on ARG abundance RPKM matrix to understand the influence of multiple factors on alpha diversity of all samples. Only one location \u0026ndash; Wildlife SOS, Agra had a significantly lower (t-value\u0026thinsp;\u0026gt;\u0026thinsp;2) ARG diversity than the intercept \u0026ndash; Nehru Zoological Park, Hyderabad (GLM\u003csub\u003eWSOSAgra\u003c/sub\u003e = -0.743811, Intercept\u0026thinsp;=\u0026thinsp;3.535647). Interestingly we found no significant difference in ARG diversities in animals which were given or not given antibiotics in the past 3 months prior to sample collection (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In case of GLMM, the combined effects of age and sex of the animal, while taking health status and location as random effects, yielded the best fit based on low REML criterion at convergence value (REML\u0026thinsp;=\u0026thinsp;506.6). Hence, although none of these values are significant individually, in combination they have an influence on the alpha diversity of ARGs (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn order to understand whether the high ARG diversity was only due to a few samples or characteristic of the entire group, we plotted beta diversity as a principal coordinate analysis (PCoA) plot based on Bray-Curtis dissimilarity matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Although the scatter plot did not show any significant clustering, it had interestingly taken a horse-shoe shape. Samples from all five locations were evenly dispersed and the 95% confidence ellipses were overlapping, indicating that the high abundance of ARGs is in fact a common feature across all locations and that all these locations are more or less uniformly diverse. PERMANOVA test, with 999 permutations, performed on these groups, resulted in a poor R\u003csup\u003e2\u003c/sup\u003e value for location (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08749), implying that the ARGs were not clustering based on locations, and the differences between ARG diversities found across the locations is not significant. Since we had a significant sample size (n\u0026thinsp;=\u0026thinsp;97) from the Wildlife SOS, Agra group, we analysed this group further to understand the underlying causes for the horse-shoe shape. While most of the ARGs exhibited multi-drug resistance and did not show any specific antibiotic targets, we did see a variation between the two arms of the horseshoe. The left arm had a higher abundance of ARGs with an efflux-pump-based resistance mechanism, while the right arm had a higher abundance of ARGs with resistance mechanisms involving target replacement or target alteration. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed a significant negative correlation between Shannon indices for microbial diversity and total number of ARGs (ρ = -0.1781553, p-value\u0026thinsp;=\u0026thinsp;0.01343) and total ARG abundance (ρ = -0.2337814, p-value\u0026thinsp;=\u0026thinsp;0.0011), indicating that animals with lower gut microbial diversity were more likely to have higher richness and abundance of ARGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Distance correlation test, with 999 replicates, revealed a significant relationship between microbiome diversity and total number of ARGs (dCor\u0026thinsp;=\u0026thinsp;0.29854, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and ARG abundance (dCor\u0026thinsp;=\u0026thinsp;0.35743, p-value\u0026thinsp;=\u0026thinsp;0.001), indicating a moderately dependent non-monotonic or non-linear relationship.\u003c/p\u003e\u003cp\u003eWe obtained 1942 OTUs at the genus level from a total of 2274 OTUs. Out of these, 1732 genera which correlated significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with number of ARGs and sum total of ARG abundances in the sample along with their rho (ρ) values are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eB as red for positive coefficients and blue for negative coefficients. Genera with the highest positive correlation with AMR richness were \u003cem\u003eShigella\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.726282985), \u003cem\u003eEscherichia\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.702811531), \u003cem\u003eSalmonella\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.618653975), \u003cem\u003eCitrobacter\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.585561099) and \u003cem\u003eKluyvera\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.53738361). Only 30 genera had positive correlation coefficients, while the remaining 1702 genera had negative correlation coefficients. Genera with the lowest coefficients were \u003cem\u003eCaldalkalibacillus\u003c/em\u003e (ρ = -0.524756288), \u003cem\u003eTerribacillus\u003c/em\u003e (ρ = -0.526572546), \u003cem\u003eCalothrix\u003c/em\u003e (ρ = -0.537874559), \u003cem\u003ePlanktothrix\u003c/em\u003e (ρ = -0.54130103), and \u003cem\u003eLeptospira\u003c/em\u003e (ρ = -0.545090414). In case of OTU abundance against sum total ARG abundance in a sample, 91 genera were not significantly associated while the remaining 1851 genera were significantly linked. Genera with highest positive correlation were \u003cem\u003eShigella\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.923282859), \u003cem\u003eEscherichia\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.892355583), \u003cem\u003eSalmonella\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.78988408), \u003cem\u003eCandidatus Moranella\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.712798321) and \u003cem\u003eShimwellia\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.690250813). Only 109 genera showed any positive correlation, and all the remaining 1742 genera had negative correlation. Genera with the lowest coefficient were \u003cem\u003eErysipelothrix\u003c/em\u003e (ρ = -0.531026461), \u003cem\u003ePlanktothrix\u003c/em\u003e (ρ = -0.53294066), \u003cem\u003eGeobacillus\u003c/em\u003e (ρ = -0.534334346), \u003cem\u003eLeptospira\u003c/em\u003e (ρ = -0.54505657) and\u003c/p\u003e\u003cp\u003e\u003cem\u003eTerribacillus\u003c/em\u003e (ρ = -0.554513965).\u003c/p\u003e\u003cp\u003eWe did not find any significant effect of the time spent in captivity on overall gut bacterial diversity (ρ = \u0026minus;\u0026thinsp;0.0448054, p\u0026thinsp;=\u0026thinsp;0.725), ARG count (ρ = -0.0439508, p\u0026thinsp;=\u0026thinsp;0.7281) and total ARG abundance (ρ\u0026thinsp;=\u0026thinsp;0.0411219, p\u0026thinsp;=\u0026thinsp;0.745). However, when we looked at each of the 1942 bacterial genera, three genera \u0026ndash; \u003cem\u003eGayadomonas\u003c/em\u003e (ρ = \u0026minus;\u0026thinsp;0.2894, p\u0026thinsp;=\u0026thinsp;0.019376), \u003cem\u003eSaccharobesus\u003c/em\u003e (ρ = \u0026minus;\u0026thinsp;0.2551, p\u0026thinsp;=\u0026thinsp;0.040279) and \u003cem\u003eLactiplantibacillus\u003c/em\u003e (ρ = \u0026minus;\u0026thinsp;0.3008, p\u0026thinsp;=\u0026thinsp;0.015156) \u0026mdash; were found to have statistically significant correlations with time in captivity, highlighting the possibility of key taxa being negatively influenced by prolonged periods in captive environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of mobile genetic elements (MGEs) co-occurring with antibiotic resistant genes (ARGs)\u003c/h2\u003e\u003cp\u003eARGs co-occurring with various MGEs are listed in Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Our analysis revealed that 21 out of 22 multidrug resistant groups co-occur with various mobile genetic elements, further highlighting the fact that not only is there a high prevalence of multidrug resistant ARGs, but most of them also co-occur with plasmids or pro-viral elements, thereby facilitating their horizontal transfer across species, resulting in spread of AMR. Barring a few locations with low sample size, this trend was consistent for plasmids across all locations, and we found more plasmids associated with ARGs in the 5 locations with higher sample sizes - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal; Wildlife SOS, Bannerghatta; and Wildlife SOS, Agra (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The pro-viral element co-occurrence, on the other hand, was only noticed in the above 5 locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e), possibly implying that a larger sampling would be necessary for the detection of such co-occurence.\u003c/p\u003e\u003cp\u003eAcross all locations, we observed that the most abundant plasmid-associated ARGs are \u003cem\u003eacrAB-tolC, marA and\u003c/em\u003e mutated \u003cem\u003eE. coli SoxR/S\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These ARGs confer resistance against multiple drugs, with tetracycline resistance being the predominant drug class and primarily enriched in locations like Van Vihar National Park, Bhopal and Sri Chamarajendra Zoological Gardens, Mysuru, followed by Nehru Zoological Park, Hyderabad and the three Wildlife SOS centers. The above genes are mainly responsible for controlling the bacterium\u0026rsquo;s response to oxidative stress, and also confers antibiotic resistance through efflux pump and target alteration\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Animals in Nehru Zoological Park show the lowest gut bacterial diversity, and in parallel have a high abundance of plasmid-associated e\u003cem\u003ermB\u003c/em\u003e gene which confers resistance to macrolide, lincosamide, and streptogramin B (MLSB) antibiotics. We also observed a higher abundance of plasmids carrying carbapenem resistant \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eOXA\u0026minus;232\u003c/em\u003e\u003c/sub\u003e in sloth bears at Sri Chamarajendra Zoological Gardens. Similar to plasmids, efflux-based \u003cem\u003eE.coli\u003c/em\u003e genes (\u003cem\u003eemrE\u003c/em\u003e) were mostly noted to be associated with pro-viral elements at the five locations with higher sample sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In case of animals at the Wildlife SOS, Agra, we also observed higher abundance of two genes \u003cem\u003ePmrF\u003c/em\u003e and \u003cem\u003eugd\u003c/em\u003e, associated with pro-viral elements, which provide resistance to the last resort drug class, polymyxin (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther analysis within plasmid regions revealed that the most abundant ARGs also frequently co-occur with additional MGEs like integrons and transposons. Highly abundant ARGs like \u003cem\u003edfrA12\u003c/em\u003e, \u003cem\u003eaadA2\u003c/em\u003e, \u003cem\u003eANT(3\u0026rdquo;)-lla\u003c/em\u003e, \u003cem\u003esul3\u003c/em\u003e and \u003cem\u003ecmlA1\u003c/em\u003e that have been noted in plasmids (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003e) at Van Vihar National Park are present here along with integron cassettes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e), further underlining their virulence. These genes confer resistance to several drug classes such as aminoglycoside, tetracycline, sulfonamide, quinolone, chloramphenicol and diaminopyrimidine antibiotics. A set of diaminopyrimidine and aminoglycoside drug class resistant ARGs were found along with integrons in animals at Wildlife SOS, Bannerghatta. Transposons were observed to co-occur with ARGs in animals at 9 out of the 14 locations in our dataset, but individuals at three locations \u0026ndash; Van Vihar National Park, Bhopal; Wildlife SOS, Bannerghatta and Wildlife SOS, Agra in particular, had multiple genes that confer multi-drug resistance via efflux pumps. The plasmid-associated e\u003cem\u003ermB\u003c/em\u003e gene, mentioned earlier in bears at Nehru Zoological Park, was seen to be flanked by transposons. We also found the common plasmid-associated efflux pump gene, \u003cem\u003eacrAB-tolC\u003c/em\u003e, co-occurring with transposons in sloth bears at Van Vihar National Park (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These ARGs coincided with high levels of plasmid association at their respective locations, indicating location-specific dissemination of ARGs mediated by MGEs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePresence of fungal and viral genera\u003c/h2\u003e\u003cp\u003eWe additionally report the presence of other groups of microorganisms in the gut of captive sloth bear. We identified fungal reads belonging to Nakaseomyces genera in samples from Gorewada Rescue Center, Nagpur; Alipore Zoological Gardens, Kolkata; and Wildlife SOS, Bhopal. We also detected Fusarium, Candida, Saccharomyces and Aspergillus genera at multiple locations albeit at low abundances. As mentioned earlier we identified tailed bacteriophages belonging to Caudoviricetes family in samples from Tiger and Lion Safari, Thevarakoppa and Wildlife SOS, Bhopal.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndangered and vulnerable species, and the microorganisms they harbour mainly in their gut, are subject to several environmental stressors like habitat loss, climate change and pollution, competition for resources and food, and hunting. Exponential growth and global movement of human populations, and forest fragmentation have brought wildlife in close proximity to humans and livestock, exposing it to environmental pollutants and antimicrobial drugs\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This situation is further aggravated in captivity where animals are treated for injuries and/ or disease, and are inadvertently fed antibiotic- and pesticide-treated food leading to dysbiosis of the gut microbiome and a buildup in antimicrobial resistance (AMR)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. To the best of our knowledge this is the first comprehensive metagenomic/ shotgun sequencing study on the gut microbiome of bears. Our study assessed captive sloth bears housed across an extensive, biodiverse landscape, wherein we derive crucial understandings on the possible effects of location, age of animal, and duration in captivity on gut microbial diversity and AMR status of the animal. Given that studies which focus on the extent and impact of AMR on wildlife health are negligible\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, our research offers valuable insights for the management and conservation of endangered species.\u003c/p\u003e\u003cp\u003eWe observed that Bacillota, Bacteroidota, Pseudomonadota, and Actinomycetota were the most dominant bacterial phyla in all the sloth bear samples. This is similar to studies based on 16S rRNA sequencing in Andean bear\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, black bear\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, brown bear\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and polar bear\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e irrespective of whether the study animals were captive or wild. Strikingly we found \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSarcina\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e as the most abundant genera at all the sampled locations. These genera were richly populated by species like \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eE. faecium\u003c/em\u003e designated by WHO as critical priority pathogens capable of developing resistance to last resort antibiotics\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, as well as \u003cem\u003eK. variicola\u003c/em\u003e and \u003cem\u003eS. alactolyticus\u003c/em\u003e which are flagged off as emerging pathogens and are reported to carry multiple MGE-associated ARGs\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Overall, we detected lower microbial diversity in all sloth bear samples from Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal, Bannerghatta and Agra, although the diversity was higher in the three Wildlife SOS centers than Hyderabad and Mysuru zoos, and it was higher in younger animals than older individuals. These findings are similar to those reported in several species of canids, primates and equids\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, gaint panda\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and brown kiwi\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Although we do not have samples from wild sloth bear for comparison, our data indicate that the microbial diversity gradually declines with duration in captivity and with increasing age of the animal. This is a very critical change in captive animals as higher gut bacterial diversity acts as an indicator of positive gut microbiota that would resist the growth of infectious agents and also acts as a barrier for AMR buildup\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Hence, low diversity combined with higher abundance of opportunistically pathogenic genera can potentially make these sloth bears vulnerable to invasive pathogens. The immune response in these animals would also be weaker compared to animals with healthier gut flora\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are a rising number of studies globally on wildlife species as reservoirs, disseminators or sentinels of anthropogenic AMR contamination in the environment (reviewed extensively by Li et al.\u003csup\u003e26\u003c/sup\u003e). These studies generally focus on migratory birds and wild mammals which move close to or scavenge in human habitation or agriculture land. However, studies on prevalence or buildup of AMR in endangered species in captivity which could affect their health and survival are limited\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan additionalcitationids=\"CR69 CR70 CR71\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. We observed that the ARGs identified in captive sloth bears belong to various AMR gene families and antibiotic drug classes indicating diverse resistance to various antibiotics, as also shown by high alpha diversity in the samples. The four most abundant AMR gene families confer resistance to more than six broad-spectrum, last resort antibiotics like fluoroquinolones, macrolides, cephalosporins, tetracycline and carbapenems. These ARGs confer resistance mostly via efflux pump across antibiotic classes, largely through ABC (ATP-binding cassette antibiotic efflux pump), MFS (Major facilitator superfamily antibiotic efflux pump), and RND (Resistance-nodulation-cell division antibiotic efflux pump).\u003c/p\u003e\u003cp\u003eOur study not only shows that ARG richness and diversity are very high in captive animals, but also that the total number of ARGs as well as ARGs unique to a location increase as we sample more individuals. Campbell et al.\u003csup\u003e69\u003c/sup\u003e similarly reported high ARG abundance and richness in captive apes dominated by β-lactam and tetracycline resistance genes. More importantly, we observed that almost all captive sloth bears consistently have high levels of AMR even when they have not been treated with antibiotics in the recent past (\u0026le;\u0026thinsp;3 months). While we do not have much information on reversibility of antibiotic resistance\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, our findings suggest that the gut bacteria of captive animals might be evolving in response to other stressors like environment and nutrition, and in the process developing resistance to various antibiotics as pleiotropic effects\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The other possible explanation is the horizontal transfer of antibiotic resistance mediated by mobile genetic elements like plasmids\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Interestingly, we saw the horseshoe or Guttman effect\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e in the PCoA plot of AMR diversities. Although the scatter plots did not show any effect of location on AMR diversity and abundance, samples seem to separate along two sides of the horseshoe based on mechanism of antibiotic resistance and the associated mobile genetic elements. Samples with efflux-pump-based multidrug resistance associated with plasmids clustered largely along the left, while samples with specific ARGs that confer resistance via target replacement/ alteration and associated with integrons and transposons were along the right arm.\u003c/p\u003e\u003cp\u003eThe gut bacterial diversity in captive sloth bear showed significant inverse correlation with both ARG abundance and richness. As mentioned earlier a rich microbial composition is crucial to prevent AMR buildup\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and this is true across diverse microbial niches\u003csup\u003e\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Abundances of pathogenic genera like \u003cem\u003eShigella\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e showed highly significant positive correlations with ARG abundance and richness. We also see significant positive correlations with lesser studied enterobacteria like \u003cem\u003eCitrobacter\u003c/em\u003e and \u003cem\u003eKluyvera\u003c/em\u003e which are fast emerging globally as organisms resistant to broad spectrum, last resort antibiotics\u003csup\u003e\u003cspan additionalcitationids=\"CR82\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Our results indicate the possibility of a selectively thriving group of organisms or even the likely accumulation of resistant genes in microbiomes with reduced compositional diversity. This statement can be supported by the fact that the abundances of very few genera are elevated along with higher AMR abundance and richness while the abundances of approximately 1700 genera are reduced indicating lowered microbial diversity. At this stage it is very difficult to tease apart the effects of age of animal and duration in captivity on microbial and AMR abundances and richness, however we do observe that the abundances of a few genera reduced significantly with time spent in captivity. One genus, \u003cem\u003eLactiplantibacillus\u003c/em\u003e, in particular caught our attention and warrants further investigation. Several studies highlight the beneficial and protective roles of \u003cem\u003eLactiplantibacillus\u003c/em\u003e in mammalian gut by building resistance against pathogens, stimulating the host immune and neurological functions, and by generally improving the intestinal flora\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Reduction of this genus in the gut flora of captive sloth bear over time does not bode well for the host, and the animal requires systematic interventions to improve its gut health.\u003c/p\u003e\u003cp\u003eMulti-drug resistance mediated through efflux pump genes was found to be largely associated with a high abundance of plasmids at most of the captive locations, more so at the five locations - Nehru Zoological Park, Hyderabad; Sri Chamarajendra Zoological Gardens, Mysuru; Wildlife SOS, Bhopal, Bannerghatta and Agra \u0026ndash; with higher numbers of sloth bears. A large number of plasmids were seen to be associated with \u003cem\u003eE. coli\u003c/em\u003e mutations leading to resistance to several broad-spectrum antibiotics like tetracycline, ciprofloxacin, ceftazidime, carbapenems and other β-lactams, etc. thereby compromising the clinical utility of widely used drugs against Gram-negative bacteria. In their review, Casta\u0026ntilde;eda-Barba et al.\u003csup\u003e86\u003c/sup\u003e describe plasmids as a cornerstone in the dissemination of AMR between bacterial hosts in different environments. The emergence, transfer and persistence of plasmid-mediated resistance, even in the absence of selection pressures like active antibiotic treatment, are extremely complex phenomena which occur simultaneously at different biological levels. Although difficult to eliminate, careful and conscientious administration of antibiotics to animals will definitely restrict selection of plasmid-associated AMR. It is also important that these animals are housed in hygienic conditions with good sanitation and nutrition. Buildup of plasmid-mediated resistance in pathogenic bacteria can be diluted and even prevented by maintaining high microbial diversity in the gut\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe saw a similar trend even in locations with fewer numbers of animals. For example, Van Vihar National Park in Bhopal had four sloth bears at the time of sample collection, all of which have been in captivity for 14\u0026thinsp;+\u0026thinsp;years. We observed a dominance of \u003cem\u003eSarcina\u003c/em\u003e species in the gut microflora of these individuals, and also higher abundance of plasmid- and MGE-associated ARGs primarily against tetracycine (\u003cem\u003eacrAB-tolC, marA, soxR/S\u003c/em\u003e). \u003cem\u003emarA\u003c/em\u003e gene encodes a global transcription activator, and its upregulation leads to increased expression of the a\u003cem\u003ecrAB-tolC\u003c/em\u003e efflux pump system\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. This combination is a well characterized mechanism contributing to broad-spectrum resistance, indicating that efflux-mediated resistance mechanism seems to be a staple in these environments. The dual role of ARGs further highlights an evolutionary advantage to their retention by the bacterium, wherein environmental stressors can enhance resistant phenotypes, adding more weight to their pleiotropic effects\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Presence of diverse resistance genes in this single site could either mean intense and persistent antibiotic selection pressure or a highly permissive environment for microbial gene exchange. The latter is more likely as the average gut bacterial diversity is low in this location, combined with the presence of virulent MGEs.\u003c/p\u003e\u003cp\u003eWhile the abundances of non-plasmid mobile genetic elements like pro-viral elements, integrons and transposons associated with ARGs were much lower than plasmid abundance in captive sloth bear gut, these too play important roles in horizontal gene transfer in bacterial communities and in developing antimicrobial resistance. Macrolides are broad spectrum antibiotics effective against a wide variety of Gram-positive bacteria, and we see efflux pump-based macrolide resistance genes associated to pro-viral elements in sloth bear. Horizontal transfer and spread of such ARGs can have serious consequences on managing the heath of captive animals.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our study indicates a lowered gut microbial diversity and highly elevated AMR diversity and richness in captive sloth bears across multiple facilities in India. Many of the dominant bacterial taxa are flagged as top priority pathogens and are capable of developing resistance to last resort antibiotics. We show that the microbial diversity gradually declines with duration in captivity and with increasing age of the animal. Further studies are required to assess whether this decline can be arrested and bacterial diversity can be improved by regular use of different types of pre- and probiotics. One of the main limitations of our study is the lack of samples from wild animals, not exposed to antibiotics and other anthropogenic stressors, which would help us understand their innate bacterial composition and resistance profiles in a given region. We describe the dynamics of bacterial genera abundance and diversity in association with ARG abundance and show that a few pathogenic genera dominant with increase in ARG abundance. One worrying finding is that the highly abundant AMR gene families confer resistance to multiple antibiotics, and not necessarily due to recent exposure to medications. Here it is important to identify and understand stressors in captivity which reduce the animal\u0026rsquo;s immunity and give certain bacteria a survival edge over others. For the first time we describe a comprehensive analysis of mobile genetic elements in the gut bacteria of bears, and highlight how ARGs, which confer resistance to multiple drugs, are associated with plasmids and other MGEs making them highly virulent. Such in-depth metagenomic studies on gut bacterial dynamics are critical for managing captive animals, and can guide dietary planning, probiotic supplementation and ideal \u003cem\u003eex-situ\u003c/em\u003e conditions to improve gut health and overall wellbeing of animals in alien environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Statement\u003c/h2\u003e\u003cp\u003ePermissions to collect samples for this study were granted by Chief Wildlife Wardens of West Bengal (No.1703/WL/4R-31/2021, dated 01/09/2021), Maharashtra (No: Desk-22(8)/WL/Research/CR-37(21\u0026ndash;22)/1344/21\u0026ndash;21, Nagpur, dated 07/09/2021), Telangana (No.26803/2012/WL-2, dated 14/09/2021), Tamil Nadu (No.4822/2021/WL1, dated 28/01/2022), Madhya Pradesh (No./M.H.-II/ Research/2824, Bhopal, dated 13/04/2022), Karnataka (No.PCCF(WL)/E2/CR-37/2021-22, dated 17/05/2022), and Uttar Pradesh (No. 23-2-12(G), Lucknow, dated 20/05/2022).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e This study is part of the project \u0026ldquo;SBI Foundation Centre of Excellence for Genome-guided Pandemic Prevention\u0026rdquo; (GAP570) funded by SBI Foundation, India.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eV.P.P., V.S.K., A.B.S. and P.A.R. conceived and planned the study, and designed experiments. S.N., N.S. and G.K. collected samples and metadata, and isolated DNA. S.N. prepared samples for sequencing. V.P.P. and V.S.K. performed bioinformatics analyses. A.A.S., S.I., M.V.B., M.K.P. and M.A.H. provided logistic support and resources. A.B.S. and P.A.R. arranged for funds and managed the project. V.P.P., V.S.K., S.N., A.B.S. and P.A.R. wrote the manuscript. All authors reviewed and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Chief Wildlife Wardens of Uttar Pradesh, West Bengal, Madhya Pradesh, Maharashtra, Telangana, Tamil Nadu and Karnataka for permitting us to collect fecal samples of sloth bear in captivity. Support extended by the Wildlife SOS management is acknowledged. We sincerely thank logistic support and guidance extended by our CCMB colleagues Dr. Karthik Bharadwaj and Dr. Divya Tej Sowpati. Tulasi Nagabandi and Bishwajeet Singha in the NGS facility at CSIR-CCMB helped us generate the metagenome data. All bioinformatics analyses were carried out on the Ramanujan-HPC at CSIR-CCMB. We are grateful for the support extended by staff of zoos and rescue centers.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data that support the findings of this study have been deposited in the National Center for Biotechnology Information with the primary accession code PRJNA1338688\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSommer, F. and B\u0026auml;ckhed, F. 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(2021) Contagious antibiotic resistance: Plasmid transfer among bacterial residents of the zebrafish gut. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, 87(9), e02735-20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/AEM.02735-20\u003c/span\u003e\u003cspan address=\"10.1128/AEM.02735-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sloth bear, gut microbiota, antimicrobial resistance, mobile genetic elements","lastPublishedDoi":"10.21203/rs.3.rs-7941374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7941374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCaptivity-associated conditions are major factors which modulate the gut microbiota in wild animals, as they receive several medications/ antibiotics, and are also exposed to various anthropogenic stressors throughout their captive lives. Our understanding of how these factors modify the gut microbiome, its resistome and associated mobile genetic elements (MGEs) is nascent at the very best. In this study, we analysed metagenomic data from 215 captive sloth bears in multiple facilities across India to describe their gut microbiota in association to age, gender, duration in captivity, health status and body condition, recent exposure to antibiotics/ other medications, and presence of chronic hepatic/ renal disorders and tuberculosis. Overall, we found low microbial diversity across all sampled locations, and this seems to decrease with age of animal and duration in captivity. \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSarcina\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e were the most abundant genera, richly populated with \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eE. faecium\u003c/em\u003e, \u003cem\u003eK. variicola\u003c/em\u003e and \u003cem\u003eS. alactolyticus\u003c/em\u003e. We observed a very diverse resistome, and almost all bears consistently showed high levels of antimicrobial resistance (AMR), even when not treated recently with antibiotics, indicating pleiotrophic effects in pathogenic bacteria adapting to captivity stress and horizontal gene transfer through MGEs. Abundances of \u003cem\u003eShigella\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eCitrobacter\u003c/em\u003e and \u003cem\u003eKluyvera\u003c/em\u003e positively correlated to ARG abundance and richness. We further observed that ARGs, which confer resistance to multiple drugs, were associated with plasmids and other MGEs making them highly virulent. In summary, our study highlights the importance of large scale metagenomic studies to understand the effects of captivity on the gut health of endangered animals, and to ultimately help improve conservation strategies.\u003c/p\u003e","manuscriptTitle":"Dysbiosis and Accumulation of Antimicrobial Resistance in the Gut Bacterial Reservoir of Captive Sloth Bear (Melursus ursinus)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:07:30","doi":"10.21203/rs.3.rs-7941374/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6dcd656-09f5-4c31-819a-1f38091b63c6","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56979760,"name":"Biological sciences/Microbiology"},{"id":56979761,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-02-08T15:08:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:07:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7941374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7941374","identity":"rs-7941374","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}