Metagenomic analysis of human, animal, and environmental samples identifies potential emerging pathogens, profiles antibiotic resistance genes, and reveals horizontal gene transfer dynamics

Metagenomic analysis of human, animal, and environmental samples identifies potential emerging pathogens, profiles antibiotic resistance genes, and reveals horizontal gene transfer dynamics | 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 Metagenomic analysis of human, animal, and environmental samples identifies potential emerging pathogens, profiles antibiotic resistance genes, and reveals horizontal gene transfer dynamics Rajindra Napit, Anupama Gurung, Ajit Poudel, Ashok Chaudhary, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5133052/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Antimicrobial resistance (AMR) is a rapidly emerging global health crisis, projected to cause 10.2 million deaths annually by 2050. The unregulated and indiscriminate use of antibiotics is driving the swift emergence and spread of AMR, a problem worsened by the release of untreated wastewater from high-risk sources, such as hospitals, into rivers. Bacteria often acquire resistance through horizontal gene transfer, and specific environments, like the human gut or soil, can serve as hotspots for the emergence of novel antimicrobial resistance genes (ARGs) and multi-drug resistant (MDR) pathogens. Shotgun metagenomics can be used to profile the AMR of a given microbiome and help detect MDR bacteria that might otherwise go unnoticed. However, current AMR reporting is largely based on clinical cases, offering limited insights into specific pathogens and their associated AMR genes. Our study aims to advance the understanding of the natural distribution and dissemination of AMR. In particular, we focused on the presence of AMR mutations and gene transfer dynamics in human, animal, and environmental samples collected from a temporary settlement in Kathmandu, Nepal, using a One Health approach. Twenty-one samples were collected from a temporary settlement in Thapathali, Kathmandu, including fecal samples from birds (n = 3), humans (n = 14), and the environment (n = 4). Prevotella spp. was the dominant gut bacterium in human samples. A diverse range of phages and viruses were detected, including Stx-2 converting phages. In total, 72 virulence factors and 53 antimicrobial resistance gene (ARG) subtypes were identified, with poultry samples showing the highest number of ARG subtypes. Using a One Health-based metagenomics approach, we identified various pathogenic bacteria and virulence genes in both human and avian samples, underscoring the interconnectedness of antimicrobial resistance (AMR) across different domains. Heavy antibiotic use in poultry and clinical settings likely contributes to the spread of antimicrobial resistance genes (ARGs). Our analysis indicates frequent horizontal gene transfer, with gut microbiomes serving as key reservoirs for ARGs. Despite certain challenges, metagenomics shows significant potential for advancing our understanding of AMR dynamics. We emphasize the need for a One Health approach and robust global surveillance systems to enable the early detection and control of AMR, safeguarding public health. Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Microbiology Biological sciences/Molecular biology Earth and environmental sciences/Environmental sciences AMR ARG gut microbiome HGT One health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Antimicrobial resistance (AMR) is an escalating global health crisis 1 . The World Health Organization (WHO) has endorsed a global action plan for AMR surveillance and mitigation strategies 2 . As of 2022, drug-resistant infections are responsible for over 5 million deaths annually 3 . If the looming crisis is not averted, infections caused by multidrug-resistant (MDR) pathogens—or "superbugs"—could double, leading to 10.2 million deaths by 2050 4,5 . Unrestrained antibiotic use in agriculture and healthcare has driven the emergence of new bacterial populations carrying and transferring numerous antibiotic resistance genes (ARGs) 6,7 . In Nepal, one major source of AMR is untreated or minimally treated hospital wastewater, which is often released into rivers 8 . The majority of hospitals in Kathmandu discharge wastewater without any neutralization treatment, including the facility selected for this study 9 . Bacteria often acquire AMR through horizontal gene transfer (HGT), which allows them to obtain ARGs from related or distant species 5 . Mobile genetic elements (MGEs) within bacterial cells, such as plasmids, integrons, and transposons, are enhanced by recombination mechanisms like conjugation, transduction, and transformation. ARG reservoirs in microbial communities found in humans, animals, and the environment are crucial to the spread of AMR, making rapid characterization of these reservoirs essential 10 . Human and animal gut microbiota, densely populated microbial communities, have been shown to serve as ARG reservoirs 11,12 . The gut microbiota not only influences overall health by boosting the immune system and improving nutrient absorption but also represents a significant reservoir for ARGs due to the extensive use of antibiotics in humans and livestock 13,14 . These ARGs can evolve rapidly and be transmitted to pathogenic strains residing in the same environment 15 . Metagenomics has emerged as a vital tool for profiling the AMR capacity of gut microbiomes and identifying environmental niches that may serve as sources of AMR bacteria and resistance mechanisms 16,17 . Next-generation sequencing (NGS) data of short targeted biomarker reads enable the quantification of a wide range of transmissible resistance genes 18 . Since its first application in 2010, metagenomics has gained momentum as a cost-effective technique, allowing for the detection of microorganisms without presupposition—especially those difficult to identify with conventional diagnostic tools 18–20 . This approach is particularly valuable for early detection and surveillance of highly infectious zoonotic diseases 21 . Disease surveillance, including AMR monitoring, largely depends on reports from clinical and laboratory settings 22 . The COVID-19 pandemic has underscored the importance of broad yet precise surveillance for communicable diseases 23,24 . Detecting and monitoring virulence factors is critical for understanding the public health risks posed by potential infections. Virulence factors are often associated with a pathogen’s ability to adhere, colonize, invade, and sequester nutrients from its host, which increases its pathogenicity 18,25 . The clinical significance of a pathogenic bacterium can be predicted by identifying the AMR genes it carries, evaluating the associated virulence factors, and analyzing HGT mechanisms 26 . This retrospective study adopts a One Health approach to investigate the mechanisms of AMR, acknowledging the interconnectedness of human, animal, and environmental health. Archived samples were analyzed using shotgun metagenomics sequencing to explore the transfer of AMR genes across multiple bacterial species. A network analysis of the metagenomic data revealed critical insights into HGT of AMR genes. Our findings contribute to a deeper understanding of AMR within a One Health framework and support the development of effective strategies for AMR surveillance, prevention, and control. Material and methods Study site The sampling site was located in one of the major temporary settlements in Kathmandu, Thapathali ( Fig. 1 ). This settlement, home to an estimated 661 inhabitants, is situated along the banks of the Bagmati River, within a densely populated urban area in the Kathmandu Valley. Two large hospitals, Paropakar Maternity and Women’s Hospital and Norvic International Hospital, are located within 200 meters of the site, both discharging untreated wastewater directly into the nearby Bagmati River. Samples were collected only from households that reported human-animal contact. These included human fecal samples (Sample Id TH n = 14), bird samples [Sample Id TA, n = 3: Chicken (Gallus gallus domesticus) (n = 1) and common quails ( Coturnix coturnix ) (n = 2)], as well as soil (Sample Id ES, n = 1), drinking water (Sample Id EW, n = 1), and riverbed sediment (Sample Id ES, n = 1) samples from the vicinity. Ethics Declaration This study was conducted with Ethical approval granted by the Nepal Health Research Council (Reg. No. 792/2018). All research activities were conducted under strict guidelines and regulations of Nepal Health Research Council, with informed consent from participating residents/or their legal guardians. Sample Collection River water samples were collected in May 2019 using an electric auto-sampler (Biobot Analytics Inc., USA). A 500 mL grab sample and sediment samples were collected in zip lock bags using sterile plastic spatulas. Fecal samples from humans, chickens, and quails were collected in sterile plastic stool containers and then transferred into two vials: one containing 5 mL RNAlater (Thermo Fisher Scientific, USA) and the other containing glycerol buffer. The samples were homogenized uniformly, and 1 mL of the homogenized solution was transferred into five 2 mL cryovials for further processing. Additionally, 1 L of groundwater was collected in a sterile screw-capped bottle, and soil samples were collected in zip lock bags, avoiding surface debris. All samples were transported immediately to the laboratory in a cold chain box, maintaining a temperature of 2–8°C. DNA extraction DNA was extracted from fecal samples using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany), following the manufacturer’s instructions. For environmental samples, DNA extraction was performed using the PowerSoil DNA Isolation Kit (MO BIO Laboratories Inc., USA). DNA concentration was measured with a Qubit™ 3 Fluorometer (Invitrogen, USA), and the integrity and size of the extracted DNA were assessed via 0.8% agarose gel electrophoresis. 16S rRNA sequencing The 16S rRNA gene was amplified using archaeal and bacterial primers (515F and 806R), targeting the V3 and V4 regions 27 . The PCR products were purified with Ampure XP magnetic beads (Agencourt, USA), quantified using a Qubit™ 3 Fluorometer (Invitrogen, USA), indexed with the Nextera XT Indexing Kit v2, normalized to an even concentration of 4 pM, multiplexed, and sequenced using the Illumina MiSeq platform (Illumina, Inc., USA) with the Illumina sequencing kit V3.0 (2 x 300 bp) paired-end reads 28 . Metagenomic library preparation and sequencing For each sample, 1 ng of genomic DNA was used with the Illumina MiSeq Nextera XT DNA Library Preparation Kit (Illumina, Inc., USA) to construct paired-end libraries with a 500 bp insert for all 21 samples. DNA was cleaned using AMPure XP beads (Agencourt, USA), tagmented, and indexed with the Nextera XT Index Kit (Illumina, Inc., USA). The cleaned DNA was then quantified and assessed using a Qubit Fluorometer (Invitrogen, USA) and the Agilent Bioanalyzer DNA 1000 Kit (Agilent Technologies, UK). Finally, all samples were pooled at a concentration of 4 nM and paired-end [300 bp (2 x 151 bp)] sequencing was performed on the Illumina MiSeq platform (Illumina, Inc., USA). Data analysis16s rRNA bacterial taxonomic profiling Data were analyzed using the QIIME 2.0 pipeline. Raw sequences were de-multiplexed and quality-filtered with DADA2. The sequences were then clustered into Operational Taxonomic Units (OTUs) with 99% similarity using USEARCH and the open reference clustering protocol 29 . Taxonomy was assigned using the Silva_132_release database, and the resulting OTU table was rarefied to 21,383 reads per sample based on alpha rarefaction. Metagenomic taxonomic profiling Metagenomic data were processed using MetaPhlAn V 3.0 ( https://github.com/biobakery/MetaPhlAn ). Analyses were performed as per MetaPhlAn instructions using its pre-cured database 30 . Virulence factor (VF) and antimicrobial resistance gene (ARG) analysis AMR and virulence factor (VF) genes were profiled in the shotgun metagenomic data using the ShortBRED tool 31 ( https://github.com/biobakery/shortbred ) 32 . The Antibiotic Resistance Database (ARDB) and the Virulence Factors Database (VFDB) were used for profiling. Alpha diversity of ARG profiles was assessed using the Shannon diversity measure and statistically analyzed with ANOVA. Hierarchical clustering was performed using the Ward algorithm with the Bray-Curtis index as the distance measure. Ordination analysis was conducted using Non-metric Multidimensional Scaling (NMDS) with Bray-Curtis distances, and statistical significance was tested with Permutation MANOVA (PERMANOVA). These analyses, along with visualization of antimicrobial abundance data, were performed using the ResistoXplorer web platform 33 . Cross-Domain Dynamics of Horizontal Gene Transfer and Network Analysis of Antimicrobial Resistance Genes Horizontal gene transfer events were predicted using WAAFLE ( https://github.com/biobakery/waafle ) with the default taxonomy database, following the prescribed manual 34 . Network analysis of AMR relative abundance data obtained with ShortBRED was conducted using Gephi v0.92, with visualizations generated using default settings. Additionally, an integrative network analysis of ARG relative abundance and taxonomic profile data for different samples was performed on the ResistoXplorer web platform. This analysis was conducted with a sequence abundance cutoff of 10%, a correlation coefficient cutoff of 0.6, an adjusted p-value of 0.05, and 1,000 permutations. The resulting network highlights statistically significant associations between different ARGs and bacterial taxa. Results Out of the total samples, only 11 (comprising 8 human fecal samples and 3 bird fecal samples) provided sufficient 16S rRNA sequencing data. In contrast, the shotgun metagenomics method generated data for nearly all samples, with read counts ranging from 29,000 to 2.1 million per sample. However, one water sample (EW70) yielded fewer than 100 reads, resulting in it being classified as a sequencing failure. 16s rRNA bacterial and metagenomic taxonomic profiling We identified various bacterial genera (Supplementary Table 1) and phages (Table 1 ). Taxonomic classification of bacterial phyla revealed that Firmicutes and Bacteroidetes were dominant in human samples, Firmicutes and Proteobacteria were predominant in poultry samples, and Bacteroidetes and Proteobacteria were the most common in environmental samples (Supplementary Table 2). Bacteria profile in various samples Human samples primarily featured the genera Prevotella and Escherichia as the most prevalent bacteria. Other identified bacterial genera included Lachnospira, Roseburia, Eubacterium, Faecalibacterium, Bacteroides , and Butyrivibrio (Supplementary Table 1). Analysis of the 16S rRNA data revealed a diverse range of bacterial genera in human samples, with notable abundance in Agathobacter, Bacteroides, Prevotella, Escherichia, Clostridium, Streptococcus, Blautia, Lachnospira, Faecalibacterium, Dorea , and Roseburia . Additionally, over 50 other bacterial genera were detected (Supplementary Table 2). Both 16S and shotgun sequencing data indicated greater variation in bacterial populations within human samples compared to poultry and environmental samples. Poultry samples were primarily characterized by the dominance of genera such as Lawsonia, Escherichia, Gallibacterium, Helicobacter , and Chlamydia , among others (Supplementary Tables 1 and 2). Environmental samples were predominantly dominated by bacterial genera like Pseudomonas, Aeromonas, Acinetobacter , and Acrobacter (Fig. 2 ). We identified potential human pathogens, including E. coli, Campylobacter, Shigella , and Haemophilus , in human samples. In poultry samples, pathogens such as Chlamydia gallinacea, Gallibacterium anatis , and Helicobacter pullorum were detected. Additionally, among the poultry samples, we identified known probiotic organisms including Lactobacillus johnsonii, Lactobacillus agilis, Lactobacillus reuteri , and Lactobacillus salivarius. Taxonomic profile of phage Taxonomic analysis of phages revealed that the Siphoviridae family was dominant, followed by Podoviridae, both of which fall under the order Caudovirales. The majority of phages were those that infect the Enterobacteriaceae family, including Escherichia phage, Salmonella phage, and Klebsiella phage. Phage diversity varied among samples, with avian samples exhibiting the highest alpha diversity index. Escherichia phages, along with Stx-2 converting phages carrying shiga toxin genes, were abundant in both birds and humans compared to environmental samples, though the difference was not statistically significant. Environmental samples were characterized by the predominance of Planktothrix phage PaV LD, Lactococcus phage P087, and Achloplasma virus L2 (Table 1 and Fig. 3 ). Table 1 Taxonomic profile of viruses (phage) and their relative abundance of ≥ 10 in samples obtained through metagenomic sequencing. Phage name Family Order Sample Occurrence in sample (no.) Acholeplasma_virus_L2 Plasmpviridae Caudovirales Environment 1 Bacteroides_phage_B124_14 Siphoviridae Caudovirales Human 1 Bacteroides_phage_B40_8 Siphoviridae Caudovirales Human 1 Enterobacteria_phage_cdtI Siphoviridae Caudovirales Human 1 Enterobacteria_phage_P4 Caudovirales unclassified Caudovirales Human 1 Enterobacteria_phage_YYZ_2008 Siphoviridae Caudovirales Human 1 Escherichia_phage_KBNP21 Podoviridae Caudovirales Human 1 Escherichia_phage_TL_2011b Podoviridae Caudovirales Human 1 Escherichia_virus_K30 Podoviridae Caudovirales Human 1 Escherichia_virus_P1 Myoviridae Caudovirales Human & Poultry 2 Escherichia_virus_phiV10 Podoviridae Caudovirales Human 1 Escherichia_virus_wV8 Myoviridae Caudovirales Poultry 1 Klebsiella_phage_JD001 Myoviridae Caudovirales Human 1 Klebsiella_virus_KP32 Podoviridae Caudovirales Human 1 Lactococcus_phage_P087 Siphoviridae Caudovirales Human 1 Planktothrix_phage_PaV_LD Podoviridae Caudovirales Environment 1 Salmonella_phage_Vi_II_E1 Siphoviridae Caudovirales Environment 1 Salmonella_virus_Epsilon15 Podoviridae Caudovirales Human 1 Staphylococcus_phage_StB20 Siphoviridae Caudovirales Poultry 1 Streptococcus_virus_7201 Siphoviridae Caudovirales Human & Poultry 2 Streptococcus_virus_DT1 Siphoviridae Caudovirales Human 1 Streptococcus_virus_phiAbc2 Siphoviridae Caudovirales Human 1 Streptococcus_virus_Sfi21 Siphoviridae Caudovirales Human 1 Stx2_converting_phage_1717 Siphoviridae Caudovirales Human & Poultry 5 Virulence factors (VF) Profile A total of 72 virulence factor (VF) genes were detected by shotgun metagenomics, with a threshold of 99% identity and a relative abundance of ≥ 10. The detected genes included those coding for toxins, type I to VI secretion systems, regulatory proteins, adherence proteins, siderophores, and polysaccharides that compose capsules and exhibit anti-phagocytic properties. VF genes were predominantly found in human and poultry samples. The pilT gene of Pseudomonas aeruginosa was the only gene identified in the environmental sample (Supplementary Table 4). Most of the identified virulence genes were associated with Escherichia coli , Shigella dysenteriae , Yersinia pestis , and Salmonella enterica . A smaller proportion of VF genes were linked to Shigella flexneri , Pseudomonas aeruginosa , Legionella pneumophila , and Yersinia enterocolitica . Human samples contained genes associated with toxigenic E. coli and Shigella flexneri , i.e., sat1, ltb, lta, astA , and senB . These genes encode various toxins such as secreted autotransporter toxin of Enterobacteriaceae, enterotoxin of Enterotoxigenic E. coli (ETEC), heat-stable enterotoxin of Enteroaggregative E. coli , and enterotoxin 2 of Shigella flexneri (Table 2 ). Table 2 Detected toxin-coding genes associated with bacteria, as identified using the ShortBRED tool. VFDB database gene identifier Virulence genes Occurrence in sample Bacterial species source (according to VFDB) Description VFG0902 sat Human Enterobacteriaceae Aecreted auto transpoter toxin; diarrhea 35,36 VFG1827 sen B Human Shigella flexneri Shigella entertotoxin 2; shigellosis 35 VFG2038 & VFG2037 ltb and lta Human E. coli Enterotoxin of Enterotoxigenic Escherichia coli; diarrhoea VFG0863 ast A Human E. coli Enteroaggregative E. coli heat stable enterotoxin (EAST1); diarrhea 37 Antibiotic resistance determinants In total, 25 classes and 53 subtypes of antimicrobial resistance genes (ARGs) were identified across the 21 samples. Among these, the genes tetQ and ermF were found in 14 out of 21 samples, predominantly in human and avian samples. Additionally, tet(W) was detected in 13 out of 21 samples, while cfxA, tet(40) , and tet(0) were each present in 11 out of 21 samples. Avian samples exhibited the highest number of ARGs (n = 37), followed by human samples (n = 27), and environmental samples (n = 16). All human, avian, and environmental samples contained the integron integrase gene intl1 . These samples harbored genes conferring resistance to various antimicrobial agents, including: Fluoroquinolones: qnrB6, qnrS1 Sulfonamides: sul1, sul2, sul3 Macrolides: mphK, macB, macA_3, ereA2, ermCd, ermQ, ermG, ermF, ermGT, ermB Lincosamides: inuB Kausagamycin: ksgA Vancomycin: vanR Undecaprenyl pyrophosphate: bacA Trimethoprim: dfrA, dfrXV, dfrA14 Chloramphenicol: catA, catQ, floR Polymyxin: arnA Aminoglycosides: aacC3, aac(60)-Ie-aph(200)-Ia, aadA, aph6, ant(3"), ant(4), ant(6), aadB, aadE, aph(3')-IIIa, aph(3')-Ib Tetracycline: tetC, tet39, tetQ, tet32, tetW, tetM, tet40, tetA, tetL, tetB Beta-lactams: cepA, cfxA, blaTEM, blaVEB-1, blaCTX-M, blaEC Among the subtypes of ARGs, those conferring resistance to aminoglycosides, tetracycline, and beta-lactams were the most prevalent. Tetracycline resistance genes were the most dominant across all samples. Additionally, aminoglycoside resistance genes were commonly found in human and avian samples, while sulfonamide resistance genes were predominantly present in environmental samples (Table 3 and Fig. 4 ). The highest abundance of AMR genes was found in samples TA340, TA330, TH990, and TH920 (Fig. 4 a). The greatest diversity was observed in TA340 and TA330, as indicated by their notably high alpha diversity index (Fig. 4 b). The most varied ARGs were identified in common quail samples, whereas soil samples exhibited the fewest variations (Fig. 4 b). Principal Coordinates Analysis (PCoA) based on the relative abundances of ARGs showed a strong association between poultry and human samples, forming the fourth cluster (F-value = 2.187, R-squared = 0.38456, P-value < 0.003) (Fig. 4 c). Conversely, environmental samples' ARGs clustered independently without significant association with other samples, as indicated by the alpha-diversity-based cladogram (Fig. 4 d). Hierarchical clustering (dendrogram) revealed four main clusters of ARGs: two clusters comprised exclusively of human samples, one from environmental samples, and one containing ARGs common to both human and avian (poultry) samples. ARGs detected in human samples generally exhibited positive associations, except for TH990 and TH920. Table 3 Antimicrobial resistance genes found in human, poultry and environmental samples of this study. Antimicrobial resistance gene (ARG) Sample Antimicrobial resistance gene (ARG) Sample Human Poultry Environmental Human Poultry Environmental sul1 ˗ + + aph6-5 + ˗ + sul2 + ˗ + aph(3'')-Ib + + + sul3 ˗ + + aph(3')-IIIa ˗ + ˗ qnrS6 + ˗ ˗ aacC3 ˗ + ˗ qnrS1 + + ˗ aac(6')-Ie/aph(2'')-Ia ˗ + ˗ QRDR + + ˗ ant3 ˗ + ˗ macA_3 + + + ant6 ˗ + ˗ macB + + ˗ aadA ˗ + ˗ vanR ˗ ˗ + aadB ˗ + ˗ mphK + ˗ ˗ aadE + + ˗ inuB ˗ + ˗ tet(C) ˗ ˗ + ksgA + + ˗ tetA(39) ˗ + + ermB ˗ + ˗ tet(40) + + ˗ ermF + + ˗ tet(L) ˗ + ˗ ermGT + ˗ ˗ tet(B) ˗ + ˗ ermG ˗ + ˗ tetQ + + ˗ ereA2 ˗ ˗ + tetM_like + + ˗ dfrXV ˗ + + tet(32) + + ˗ dfrA + + ˗ tet(W) + + ˗ dfrA14 + ˗ ˗ tet(O) + ˗ ˗ floR ˗ ˗ + tet(M) + + + catA + + ˗ cepA + ˗ ˗ catQ ˗ ˗ + bla CTX−M−1 + ˗ ˗ arnA ˗ + ˗ cfxA + ˗ ˗ bacA + + + bla VEB−1 ˗ ˗ + bla TEM_137 ˗ + ˗ bla EC + + ˗ classD_beta lactamase ˗ + ˗ bla CTXM_83 + ˗ ˗ + Presence of ARG – Absence of ARG Cross-Domain Dynamics of Horizontal Gene Transfer Environmental bacteria exhibited evidence of horizontal gene transfer (HGT), particularly concerning genes involved in translational enzymes and RNA-directed DNA polymerase. Additionally, genes encoding integrase and transposase from IS element families (IS3/IS911 and IS1595), which may facilitate lysogenic transformation, were identified 38 , 39 . These genes also included those related to antimicrobial resistance, transcription regulation, DNA methyltransferase, and ATPases 40 . HGT occurrences were observed in poultry and human samples as well, especially involving genes responsible for replication, translation, and various pathway enzymes (Supplementary Table 3). In poultry samples, integrases and transposons from IS66 and IS21 families, known to promote the spread of antimicrobial resistance (AMR), were detected 41 . Furthermore, the Clindamycin resistance transfer factor BtgB, necessary for the conjugal transfer of clindamycin resistance genes in Bacteroides species 42 , was identified in the metagenomics data of poultry samples (Supplementary Table 3 and Table 5). Human samples revealed a diverse range of AMR-related genes involved in horizontal gene transfer within the gut microbiome. These genes include those encoding the ABC multidrug transport system, multidrug resistance protein (MATE family), VanY domain-containing protein, penicillin-binding protein (PBP) 1A, aminoglycoside phosphotransferase, tetR protein, and metallo-beta-lactamase domain protein (Table 4 ). Various integrase and transposase genes from IS families such as IS116/IS110/IS902, IS30, IS605, IS200, and IS4 were found to be involved in HGT events (Supplementary Table 3 and Table 6). These findings underscore the complex dynamics of horizontal gene transfer across different environments—environmental, avian, and human—highlighting the interconnectedness of antimicrobial resistance mechanisms. Table 4 Various antimicrobial resistance, virulence, transposase and integrase proteins involved in HGTs. Sample ID Sample HGT occurring between organisms Proteins that translated from genes transferred from HGT events TA330 Poultry Bacteroides_coprophilus Bacteroides_xylanisolvens IS66 family transposase TA340 Poultry Prevotella Bacteroides Clindamycin resistance transfer factor BtgB TH950 Human Eubacterium_eligens Clostridium_sp_L2_50 Transposase, IS200 family TH950 Human Roseburia_intestinalis Roseburia_inulinivorans Transposase IS116/IS110/IS902 family TH910 Human Faecalibacterium_prausnitzii Subdoligranulum_sp_4_3_54A2FAA ABC-type multidrug transport system TH950 Human Oribacterium_sp_oral_taxon_078 Roseburia_intestinalis Antitoxin; DUF2185 domain-containing protein TH1010 Human Roseburia_inulinivorans Eubacterium_rectale Multidrug resistance protein, MATE family TH1010 Human Bacteroides Prevotella_stercorea Transposase, IS116/IS110/IS902 family TH1020 Human Megamonas_rupellensis Megamonas_funiformis IS605 family transposase TH1120 Human Ruminococcus_bromii Butyrivibrio_crossotus Penicillin-binding protein 1A TH1120 Human Butyrivibrio_crossotus Prevotella_copri Aminoglycoside phosphotransferase TH1130 Human Anaerostipes_hadrus Clostridiales ABC-type multidrug transport system protein TH1130 Human Ruminococcus Clostridiales VanY domain-containing protein, ABC-type multidrug transport system protein TH1110 Human Veillonella_sp_HPA0037 Megasphaera_elsdenii Transposase IS200-family protein; TetR protein ES060 Soil Pseudoxanthomonas_sp_GW2 Alcanivorax_pacificus Copper resistance protein B ESD060 Sediment Thauera_sp_27 Dechloromonas_aromatica Integrase, & Transposase IS3/IS911 ESD060 Sediment Cupriavidus_sp_HMR_1 Gammaproteobacteria DDE_Tnp_IS1595 domain-containing protein TH910 Human Ruminococcus_sp_5_1_39BFAA Eubacterium_eligens Multidrug resistance MATE family protein TH950 Human Roseburia_intestinalis Eubacterium_ramulus Transposase IS4 family TH950 Human Roseburia_inulinivorans Eubacterium_rectale Transposase IS116/IS110/IS902 family TH980 Human Catenibacterium_mitsuokai Clostridiales Transposase, IS605 family TH1030 Human Clostridium_sp_ATCC_BAA_442 Ruminococcus_sp_JC304 Metallo-beta-lactamase domain protein Network Analysis of Antimicrobial Resistance Genes The network analysis of antimicrobial resistance genes (ARGs) across environmental, avian, and human samples underscore the complex dynamics of the One Health interface. In environmental samples (ES050, ES060, and ESD060), significant associations were observed with genes such as aminoglycoside phosphotransferase, aminoglycoside nucleotidyltransferase, sul, dfrA, acr , and ABC efflux pumps. Poultry samples (TA340, TA330, and TA320) showed a notable association with tetracycline resistance genes, while human samples exhibited strong connections with bacA, erm, acr, tet , class A beta-lactamase genes, and Tet efflux (Figs. 5 and Fig. 6 ). Further analysis using the Maximal Information Coefficient (MIC) correlation coefficient assessed the relationships between bacterial genera and ARGs at the class level. The network analysis focused on robust connections with significant p-values (adjusted p < 0.05). This analysis revealed strong co-abundance signals among various bacteria, with Eubacterium identified as a central node, exhibiting robust links to other taxa such as Faecalibacterium , Roseburia , and Collinsella . Notable co-abundant pairs included Faecalibacterium and Lachnospira , as well as Faecalibacterium and Bacteroides . Concerning ARG data, genes like qnr, ermD, arnA , and aac showed substantial associations with different bacterial genera. Efflux pumps were particularly prevalent and strongly linked with Escherichia , emerging as a central hub for numerous ARGs and efflux pumps (Fig. 6 ). These findings emphasize the intricate interplay of antimicrobial resistance across environmental, animal, and human domains, highlighting the importance of a One Health approach to understand and address the spread of resistance genes. Discussion Antimicrobial resistance (AMR) has emerged as a major public health concern of the 21st century 43 . The rise of new pathogens, whether bacteria or viruses, underscores the need for comprehensive surveillance and understanding 44 . Continuous misuse of antibiotics can lead to multidrug-resistant bacteria, or "superbugs," and AMR often develops through mechanisms similar to those of new bacterial emergence 38 , 39 , 45 . Recent advancements have highlighted the utility of metagenomics in epidemiological and environmental studies 40 – 42 . In this study, we employed a metagenomic approach to investigate AMR presence and transfer dynamics from a One Health perspective. Shotgun sequencing revealed Prevotella as the most prevalent genus in human samples (Supplementary Table 1 and Table 2 ). Prevotella spp. are known for producing short-chain fatty acids (SCFAs) and are common in individuals with high-carbohydrate diets 46 , aligning with the carbohydrate-rich Nepalese diet. Prevotella spp. were also found to be prevalent in another study conducted in a country where rice is a staple food ( 10.3904/kjim.2019.373 ). Pathogenic bacteria such as Shigella, Campylobacter, Haemophilus , and E. coli were detected across various samples 47 . Virulence genes associated with disease causation were identified, including the Shiga toxin gene in E. coli , which is linked to bloody diarrhea and hemolytic uremic syndrome (HUS) 48 . Specific virulence genes like senB for Shigella flexneri, csgG and rpoS for Salmonella enterica var. typhimurium, and fliR for Yersinia enterocolitica suggest the presence of these pathogens (Supplementary Table 4). E. coli virulence genes such as astA, ltb , and lta indicate enterotoxigenic (ETEC) and enteroaggregative (EAEC) strains, which can often evade conventional diagnostics 49 (Fig. 2 ). Chlamydia gallinacea , which causes slow growth in chickens and mild symptoms in humans 50 , 51 , was found in two poultry samples from backyard farms. Helicobacter pullorum , linked to enteritis in poultry and zoonotic colitis in humans, as well as Gallibacterium anatis , which impacts egg production in hens, were also identified 52 , 53 and have not been previously documented in poultry in Nepal (Fig. 2 ). Phages detected in this study belonged to Siphoviridae, Podoviridae, and Myoviridae. Phages infecting enterobacteria like E. coli , Klebsiella, Shigella , and gut bacteria such as Bacteroides, Prevotella, Roseburia , and Lachnospira were the most prevalent (Fig. 3 ). The Stx-2 converting bacteriophage, crucial for inducing Shiga toxin-producing E. coli (STEC), was identified in seven samples (human and poultry) 54 , 55 . The pervasiveness of E. coli in every sample of this study suggests a significant probability for STEC production 49 . Phage and bacterial diversity were similar across most samples, with human samples exhibiting the highest diversity (Table 1 and Supplementary Tables 1 and 2). Our study identified numerous antimicrobial resistance gene (ARG) subtypes (n = 53), including some previously unreported in Nepal, such as inuB, catQ, ksgA, floR , and blaEC (Table 3 ) 9 , 54 – 57 . Our study also uncovered bla CTX−M and bla TEM genes, which have been previously found in hospital and environmental samples of Nepal 57 – 61 along with qnrS, sul1 , and tetB genes that were earlier detected in animal and environmental samples. Additionally, we detected ermB genes that had previously been identified in samples from school children of 9 , 56 , 62 . In regards to poultry samples, heavy antibiotic use in poultry likely contributes to the high number of ARG subtypes found in poultry samples, with 27 subtypes found in human samples, likely due to over-prescription and easy access to antibiotics 63 , 64 . Network analysis revealed strong associations between certain bacteria and ARGs ( qnr, ermD, arnA , and aac ) (Fig. 6 ). The presence of nearby hospitals discharging untreated waste into rivers may contribute to ARG originating from hospitals. Many detected ARGs encode proteins that aid bacterial replication and function, with some ( tetR, vanY, PBP 1A, BtgB ) associated with the gut microbiome. Integrases and transposases facilitating AMR transfer were detected in poultry and human samples 65 – 71 . The enteric bacteria in human gut microbiomes may act as ARG reservoirs 72 , contributing to the spread of ARGs in enteric pathogens. Studies suggest that gut microbiomes act as reservoirs for AMR, which is crucial for understanding the emergence of new ARGs 10 , 66 , 73 , 74 . Despite the challenges in metagenomic data analysis, metagenomics offers promise for improving our understanding of AMR dynamics and with the employment of clinical metagenomics, it can help in guiding targeted interventions 20 , 75 . Global surveillance systems are essential for early detection and control of AMR-related infections, given the increasing threats from population density, antimicrobial use, and environmental changes 21 , 76 – 78 . Conclusion Our study highlights the interconnectedness of antimicrobial resistance across different domains. The presence of ARGs in environmental, animal, and human samples underscores the need for a One Health approach. Heavy antibiotic use in poultry and clinical settings likely contributes to ARG dissemination, emphasizing the need for responsible antibiotic stewardship. Our network analysis identified strong associations between bacteria and ARGs, indicating potential horizontal gene transfer and spread of resistance. The gut microbiome, particularly in humans and animals, emerges as a significant reservoir for ARGs. Integrated surveillance efforts are essential for monitoring and mitigating AMR emergence and spread. Despite challenges, clinical metagenomics holds promise for enhancing our understanding of AMR dynamics and guiding effective interventions. A One Health approach and robust global surveillance systems are crucial for addressing the complex issue of antimicrobial resistance and protecting public health. We emphasize the need for a One Health approach and global surveillance systems for early detection and control of AMR-related infections. This holistic strategy could be an essential tool to combat the escalating issue of antimicrobial resistance and protect public health. Declarations Author Contribution R.N. and D.K. conceptualized and designed the study. A.G., A.P. and A.C. helped in field data acquisition. A.N.S., S.R., S.M.P and JJ were involved in conducting laboratory analysis. P.M helped in Bioinformation analysis. M.P. and M.G. assisted in data analysis and edited the manuscript. R.N., D.K. and R.M.R. were involved in data analysis and manuscript preparation. Manuscript was partially polished using AI tool. Acknowledgement We are grateful to the Nepal Health Research Council (NHRC), Government of Nepal, for granting permission to conduct this research. Our sincere thanks go to the field team of CMDN, led by Mr. Bishwo P. Shrestha, for their tireless efforts in collecting samples for this study. We also extend our gratitude to the Massachusetts Institute of Technology (MIT) for providing the BioBot Automatic Sampler, which was instrumental in our sampling activities. Data Availability All the data generated in this study has been included in manuscript and supplementary files. The sequencing data (raw fastq files) have been submitted to NCBI SRA database and can be found under BioProject accession number PRJNA881338. References Aarestrup, F. M. The livestock reservoir for antimicrobial resistance: a personal view on changing patterns of risks, effects of interventions and the way forward. Philos. Trans. R. Soc. London. Ser. B, Biol. 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Infect. Dis. 23 , S47–S52 (2017). Carroll, S. P. et al. Applying evolutionary biology to address global challenges. Science 346 , 1245993 (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx SupplementaryTabletosubmitforpaper.docx Supplementary table caption Supplementary Table 1: List of Bacterial species found in different samples via NGS sequencing Supplementary Table 2: List of Bacteria found in different samples via 16s rRNA sequencing Supplementary Table 3: Proteins transferred in HGT between two clades of bacteria Supplementary Table 4: Virulence factors detected from Metagenomic sequencing data obtained from ShortBRED with their function and source (sample type). Supplementary Table 5: Samples and its detail that was collected from Thapathali temporary settlement for this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5133052","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":362894297,"identity":"4bf376fa-826b-44a8-b711-3478aa5fb9fd","order_by":0,"name":"Rajindra Napit","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Rajindra","middleName":"","lastName":"Napit","suffix":""},{"id":362894298,"identity":"97fe0f7e-1052-45de-8ea2-ae0f749fe49a","order_by":1,"name":"Anupama Gurung","email":"","orcid":"","institution":"Victorian Clinical Genetics Services (VCGS)","correspondingAuthor":false,"prefix":"","firstName":"Anupama","middleName":"","lastName":"Gurung","suffix":""},{"id":362894299,"identity":"6fa45ffa-1082-4c2f-8661-8b0b69df0196","order_by":2,"name":"Ajit Poudel","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Ajit","middleName":"","lastName":"Poudel","suffix":""},{"id":362894300,"identity":"5fc41769-ab42-40c9-a131-8694e4669adc","order_by":3,"name":"Ashok Chaudhary","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"","lastName":"Chaudhary","suffix":""},{"id":362894301,"identity":"2b455791-164c-4701-b3f1-22e0fdb71a33","order_by":4,"name":"Prajwol Manadhar","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Prajwol","middleName":"","lastName":"Manadhar","suffix":""},{"id":362894302,"identity":"219af773-d87b-48f2-b898-28716255da00","order_by":5,"name":"Ajay Narayan Sharma","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"Narayan","lastName":"Sharma","suffix":""},{"id":362894303,"identity":"c6281053-5206-46f1-8156-6cdd005d8b22","order_by":6,"name":"Samita Raut","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Samita","middleName":"","lastName":"Raut","suffix":""},{"id":362894304,"identity":"373b5202-b8a6-4aa4-a905-ee9a298d899a","order_by":7,"name":"Saman Man Pradhan","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Saman","middleName":"Man","lastName":"Pradhan","suffix":""},{"id":362894305,"identity":"2a405af4-6024-4bff-b317-c429687c7260","order_by":8,"name":"Jyotsna Joshi","email":"","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":false,"prefix":"","firstName":"Jyotsna","middleName":"","lastName":"Joshi","suffix":""},{"id":362894306,"identity":"6edf21e4-532b-4687-91a7-ca1d08a965ba","order_by":9,"name":"Mathilde Poyet","email":"","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Poyet","suffix":""},{"id":362894307,"identity":"c541f64c-f8da-479e-b458-16c72318a72d","order_by":10,"name":"Mathieu Groussin","email":"","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mathieu","middleName":"","lastName":"Groussin","suffix":""},{"id":362894308,"identity":"72ae5d43-3441-4d2a-a9e0-5734b19fe491","order_by":11,"name":"Rajesh M. 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Karmacharya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDCCwzwMDIwNNkBWAmla0kjRcgCs5TAJWviO8x58+HPHeXnd9twDDB/31BLWInmYL9mY98xtw21n3iUwznh2nLAWg8M8ZtKMbbcTzG7kGDDzHDhGlBbznz/bzpGmxYyBt+0ATEsNcX6R5m1LBvrljcHBGQcOENbCd/7swY8/2+zkzY7nGD74cKCOsBYUALTiMIlagIBUW0bBKBgFo2AkAAAKMkBMQhoanAAAAABJRU5ErkJggg==","orcid":"","institution":"Center for Molecular Dynamics - Nepal","correspondingAuthor":true,"prefix":"","firstName":"Dibesh","middleName":"B.","lastName":"Karmacharya","suffix":""}],"badges":[],"createdAt":"2024-09-22 15:11:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5133052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5133052/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90777-8","type":"published","date":"2025-04-09T16:05:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70205125,"identity":"c51947e9-c421-4432-bde5-37ad11075798","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57108,"visible":true,"origin":"","legend":"\u003cp\u003eThe bioinformatics data analysis workflow used in this study included determining bacterial taxonomic profiles, identifying virulence factors and antimicrobial resistance genes (ARGs), predicting horizontal gene transfer (HGT) events, and conducting AMR-associated network analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/b802433dca0c35733d9dcb25.png"},{"id":70206046,"identity":"73b070bc-92fb-4071-8452-c5d996a7bc01","added_by":"auto","created_at":"2024-11-29 13:42:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial phyla detected in various samples were analyzed based on (A) 16S rRNA sequencing data obtained from QIIME 2 and (B) shotgun sequencing data obtained from MetaPhlAn v2.0.\u003c/strong\u003e The plots were generated using ggplot2 in R Studio 2022.07.1 Build 554.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/d5d8b5a3c213465f101354ed.png"},{"id":70205127,"identity":"75b37c4c-c801-4bec-a5d9-d2557e6ff7c4","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of virus familiesin the samples obtained from metagenomic sequencing data: A) Distribution of virus(phage) families detected; B) Various phages detected in different samples.\u003c/strong\u003eThe bar plots were generated using ggplot2 in R Studio 2022.07.1 Build 554 with R version 4.2.0.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/0b7c5b9baca389f2dcdaaa51.png"},{"id":70205128,"identity":"5cf6451e-ab82-4db4-84b1-14a2c4e0217a","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of ARG profiles from shotgun sequencing data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea) \u003cstrong\u003eHeatmap:\u003c/strong\u003e Cluster analysis of ARG profiles from various samples and their sources, classified at the class level. Clustering distance was calculated using the Bray-Curtis Index, and theWard clustering algorithm was applied.\u003c/p\u003e\n\u003cp\u003eb) \u003cstrong\u003eAlpha Diversity Analysis:\u003c/strong\u003e Diversity of ARG profiles at the class level, measured using the Shannon index. ANOVA testing indicated significant differences in alpha diversity between samples (P-value = 0.019166).\u003c/p\u003e\n\u003cp\u003ec) \u003cstrong\u003eOrdination Analysis:\u003c/strong\u003eAnalysis of significant similarities between different sample types and sources, with F-value = 2.187, R-squared = 0.38456, and P-value\u0026lt; 0.003. Greenshades indicate samples with associations within a P-value of \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003ed) \u003cstrong\u003eHierarchical Clustering (Dendrogram):\u003c/strong\u003eClustering of samples based on the alpha diversity of ARG profiles.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/cf866c93e263c7ca9b6d32c0.png"},{"id":70206036,"identity":"f5437327-92bf-48b9-80eb-1aefa524a51b","added_by":"auto","created_at":"2024-11-29 13:42:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161056,"visible":true,"origin":"","legend":"\u003cp\u003eAssociative network analysis of antimicrobial resistance genes (ARGs) across diverse samples was performed using abundance data and associated metadata, analyzed with Gephi V0.92. In the visual representation, the size of each circle indicates both the quantity of ARGs observed in the sample and the intensity of associations—larger circles represent a greater number of ARGs and stronger connections with other ARGs and samples. The thickness of the lines connecting the circles reflects the strength of these associations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/ce34433c9d31e968fc12ae9a.png"},{"id":70205132,"identity":"d7336ee5-2bc8-4ba3-a168-8bbf0e9bb01c","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":277337,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrative Maximal Information Coefficient (MIC) analysis of antimicrobial resistance genes (ARGs) and bacterial hosts across various samples was performed using the ResistoXplorer web platform. In the network visualization, nodes are color-codedand shaped to denote their type or profile: Resistome data is represented by yellow squares, while Microbiome data is shown as purple circles. The size of each node reflects its centrality within the network, measured by either degree or betweenness. The color and width of the edges between nodes represent the strength of correlation, with MIC values ranging from 0 to 1. Node size is adjusted based on degree in scenario A and by betweenness in scenario B.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/9987fb380e03f65aaa60a981.png"},{"id":80559630,"identity":"5707bd0e-fb81-4638-91c1-c9e04d1cf9bc","added_by":"auto","created_at":"2025-04-14 16:18:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2955794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/74c04dff-a665-409c-85ca-b92589a8296a.pdf"},{"id":70205130,"identity":"81b72914-d9c7-45e5-afc3-1df7a9281f2c","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":301497,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/27eec2a5a85e4c1b49e29f84.docx"},{"id":70205126,"identity":"71df7b4b-baf5-4d3e-b354-9b2ebc1b88f5","added_by":"auto","created_at":"2024-11-29 13:34:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":70323,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table caption\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1: List of Bacterial species found in different samples via NGS sequencing\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2: List of Bacteria found in different samples via 16s rRNA sequencing\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3: Proteins transferred in HGT between two clades of bacteria\u003c/p\u003e\n\u003cp\u003eSupplementary Table 4: Virulence factors detected from Metagenomic sequencing data obtained from ShortBRED with their function and source (sample type).\u003c/p\u003e\n\u003cp\u003eSupplementary Table 5: Samples and its detail that was collected from Thapathali temporary settlement for this study.\u003c/p\u003e","description":"","filename":"SupplementaryTabletosubmitforpaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-5133052/v1/571c269ad73dce9efebe9dc8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic analysis of human, animal, and environmental samples identifies potential emerging pathogens, profiles antibiotic resistance genes, and reveals horizontal gene transfer dynamics","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eAntimicrobial resistance (AMR) is an escalating global health crisis\u0026nbsp;\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e The World Health Organization (WHO) has endorsed a global action plan for AMR surveillance and mitigation strategies\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e. As of 2022, drug-resistant infections are responsible for over 5 million deaths annually\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. If the looming crisis is not averted, infections caused by multidrug-resistant (MDR) pathogens—or \"superbugs\"—could double, leading to 10.2 million deaths by 2050\u0026nbsp;\u003csup\u003e4,5\u003c/sup\u003e.\u0026nbsp;Unrestrained antibiotic use in agriculture and healthcare has driven the emergence of new bacterial populations carrying and transferring numerous antibiotic resistance genes (ARGs)\u0026nbsp;\u003csup\u003e6,7\u003c/sup\u003e. In Nepal, one major source of AMR is untreated or minimally treated hospital wastewater, which is often released into rivers\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e. The majority of hospitals in Kathmandu discharge wastewater without any neutralization treatment, including the facility selected for this study\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBacteria often acquire AMR through horizontal gene transfer (HGT), which allows them to obtain ARGs from related or distant species\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e. Mobile genetic elements (MGEs) within bacterial cells, such as plasmids, integrons, and transposons, are enhanced by recombination mechanisms like conjugation, transduction, and transformation. ARG reservoirs in microbial communities found in humans, animals, and the environment are crucial to the spread of AMR, making rapid characterization of these reservoirs essential\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e. Human and animal gut microbiota, densely populated microbial communities, have been shown to serve as ARG reservoirs\u0026nbsp;\u003csup\u003e11,12\u003c/sup\u003e. The gut microbiota not only influences overall health by boosting the immune system and improving nutrient absorption but also represents a significant reservoir for ARGs due to the extensive use of antibiotics in humans and livestock\u0026nbsp;\u003csup\u003e13,14\u003c/sup\u003e. These ARGs can evolve rapidly and be transmitted to pathogenic strains residing in the same environment\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomics\u003c/strong\u003e has emerged as a vital tool for profiling the AMR capacity of gut microbiomes and identifying environmental niches that may serve as sources of AMR bacteria and resistance mechanisms\u0026nbsp;\u003csup\u003e16,17\u003c/sup\u003e. Next-generation sequencing (NGS) data of short targeted biomarker reads enable the quantification of a wide range of transmissible resistance genes\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e. Since its first application in 2010, metagenomics has gained momentum as a cost-effective technique, allowing for the detection of microorganisms without presupposition—especially those difficult to identify with conventional diagnostic tools\u0026nbsp;\u003csup\u003e18–20\u003c/sup\u003e. This approach is particularly valuable for early detection and surveillance of highly infectious zoonotic diseases\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDisease surveillance, including AMR monitoring, largely depends on reports from clinical and laboratory settings\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. The COVID-19 pandemic has underscored the importance of broad yet precise surveillance for communicable diseases\u0026nbsp;\u003csup\u003e23,24\u003c/sup\u003e. Detecting and monitoring virulence factors is critical for understanding the public health risks posed by potential infections. Virulence factors are often associated with a pathogen’s ability to adhere, colonize, invade, and sequester nutrients from its host, which increases its pathogenicity\u0026nbsp;\u003csup\u003e18,25\u003c/sup\u003e. The clinical significance of a pathogenic bacterium can be predicted by identifying the AMR genes it carries, evaluating the associated virulence factors, and analyzing HGT mechanisms\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis retrospective study adopts a \u003cstrong\u003eOne Health\u003c/strong\u003e approach to investigate the mechanisms of AMR, acknowledging the interconnectedness of human, animal, and environmental health. Archived samples were analyzed using shotgun metagenomics sequencing to explore the transfer of AMR genes across multiple bacterial species. A network analysis of the metagenomic data revealed critical insights into HGT of AMR genes. Our findings contribute to a deeper understanding of AMR within a One Health framework and support the development of effective strategies for AMR surveillance, prevention, and control.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe sampling site was located in one of the major temporary settlements in Kathmandu, Thapathali (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e This settlement, home to an estimated 661 inhabitants, is situated along the banks of the Bagmati River, within a densely populated urban area in the Kathmandu Valley. Two large hospitals, Paropakar Maternity and Women\u0026rsquo;s Hospital and Norvic International Hospital, are located within 200 meters of the site, both discharging untreated wastewater directly into the nearby Bagmati River. Samples were collected only from households that reported human-animal contact. These included human fecal samples (Sample Id TH n\u0026thinsp;=\u0026thinsp;14), bird samples [Sample Id TA, n\u0026thinsp;=\u0026thinsp;3: Chicken (Gallus gallus domesticus) (n\u0026thinsp;=\u0026thinsp;1) and common quails (\u003cem\u003eCoturnix coturnix\u003c/em\u003e) (n\u0026thinsp;=\u0026thinsp;2)], as well as soil (Sample Id ES, n\u0026thinsp;=\u0026thinsp;1), drinking water (Sample Id EW, n\u0026thinsp;=\u0026thinsp;1), and riverbed sediment (Sample Id ES, n\u0026thinsp;=\u0026thinsp;1) samples from the vicinity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics Declaration\u003c/h3\u003e\n\u003cp\u003eThis study was conducted with Ethical approval granted by the Nepal Health Research Council (Reg. No. 792/2018). All research activities were conducted under strict guidelines and regulations of Nepal Health Research Council, with informed consent from participating residents/or their legal guardians.\u003c/p\u003e\n\u003ch3\u003eSample Collection\u003c/h3\u003e\n\u003cp\u003eRiver water samples were collected in May 2019 using an electric auto-sampler (Biobot Analytics Inc., USA). A 500 mL grab sample and sediment samples were collected in zip lock bags using sterile plastic spatulas. Fecal samples from humans, chickens, and quails were collected in sterile plastic stool containers and then transferred into two vials: one containing 5 mL RNAlater (Thermo Fisher Scientific, USA) and the other containing glycerol buffer. The samples were homogenized uniformly, and 1 mL of the homogenized solution was transferred into five 2 mL cryovials for further processing. Additionally, 1 L of groundwater was collected in a sterile screw-capped bottle, and soil samples were collected in zip lock bags, avoiding surface debris. All samples were transported immediately to the laboratory in a cold chain box, maintaining a temperature of 2\u0026ndash;8\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eDNA extraction\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from fecal samples using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany), following the manufacturer\u0026rsquo;s instructions. For environmental samples, DNA extraction was performed using the PowerSoil DNA Isolation Kit (MO BIO Laboratories Inc., USA). DNA concentration was measured with a Qubit\u0026trade; 3 Fluorometer (Invitrogen, USA), and the integrity and size of the extracted DNA were assessed via 0.8% agarose gel electrophoresis.\u003c/p\u003e\n\u003ch3\u003e16S rRNA sequencing\u003c/h3\u003e\n\u003cp\u003eThe 16S rRNA gene was amplified using archaeal and bacterial primers (515F and 806R), targeting the V3 and V4 regions \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The PCR products were purified with Ampure XP magnetic beads (Agencourt, USA), quantified using a Qubit\u0026trade; 3 Fluorometer (Invitrogen, USA), indexed with the Nextera XT Indexing Kit v2, normalized to an even concentration of 4 pM, multiplexed, and sequenced using the Illumina MiSeq platform (Illumina, Inc., USA) with the Illumina sequencing kit V3.0 (2 x 300 bp) paired-end reads \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic library preparation and sequencing\u003c/h2\u003e \u003cp\u003eFor each sample, 1 ng of genomic DNA was used with the Illumina MiSeq Nextera XT DNA Library Preparation Kit (Illumina, Inc., USA) to construct paired-end libraries with a 500 bp insert for all 21 samples. DNA was cleaned using AMPure XP beads (Agencourt, USA), tagmented, and indexed with the Nextera XT Index Kit (Illumina, Inc., USA). The cleaned DNA was then quantified and assessed using a Qubit Fluorometer (Invitrogen, USA) and the Agilent Bioanalyzer DNA 1000 Kit (Agilent Technologies, UK). Finally, all samples were pooled at a concentration of 4 nM and paired-end [300 bp (2 x 151 bp)] sequencing was performed on the Illumina MiSeq platform (Illumina, Inc., USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData analysis16s rRNA bacterial taxonomic profiling\u003c/h3\u003e\n\u003cp\u003eData were analyzed using the QIIME 2.0 pipeline. Raw sequences were de-multiplexed and quality-filtered with DADA2. The sequences were then clustered into Operational Taxonomic Units (OTUs) with 99% similarity using USEARCH and the open reference clustering protocol \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Taxonomy was assigned using the Silva_132_release database, and the resulting OTU table was rarefied to 21,383 reads per sample based on alpha rarefaction.\u003c/p\u003e\n\u003ch3\u003eMetagenomic taxonomic profiling\u003c/h3\u003e\n\u003cp\u003eMetagenomic data were processed using MetaPhlAn V 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biobakery/MetaPhlAn\u003c/span\u003e\u003cspan address=\"https://github.com/biobakery/MetaPhlAn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Analyses were performed as per MetaPhlAn instructions using its pre-cured database \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVirulence factor (VF) and antimicrobial resistance gene (ARG) analysis\u003c/h2\u003e \u003cp\u003eAMR and virulence factor (VF) genes were profiled in the shotgun metagenomic data using the ShortBRED tool \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biobakery/shortbred\u003c/span\u003e\u003cspan address=\"https://github.com/biobakery/shortbred\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The Antibiotic Resistance Database (ARDB) and the Virulence Factors Database (VFDB) were used for profiling. Alpha diversity of ARG profiles was assessed using the Shannon diversity measure and statistically analyzed with ANOVA. Hierarchical clustering was performed using the Ward algorithm with the Bray-Curtis index as the distance measure. Ordination analysis was conducted using Non-metric Multidimensional Scaling (NMDS) with Bray-Curtis distances, and statistical significance was tested with Permutation MANOVA (PERMANOVA). These analyses, along with visualization of antimicrobial abundance data, were performed using the ResistoXplorer web platform \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCross-Domain Dynamics of Horizontal Gene Transfer and Network Analysis of Antimicrobial Resistance Genes\u003c/h2\u003e \u003cp\u003eHorizontal gene transfer events were predicted using WAAFLE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biobakery/waafle\u003c/span\u003e\u003cspan address=\"https://github.com/biobakery/waafle\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the default taxonomy database, following the prescribed manual \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Network analysis of AMR relative abundance data obtained with ShortBRED was conducted using Gephi v0.92, with visualizations generated using default settings. Additionally, an integrative network analysis of ARG relative abundance and taxonomic profile data for different samples was performed on the ResistoXplorer web platform. This analysis was conducted with a sequence abundance cutoff of 10%, a correlation coefficient cutoff of 0.6, an adjusted p-value of 0.05, and 1,000 permutations. The resulting network highlights statistically significant associations between different ARGs and bacterial taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOut of the total samples, only 11 (comprising 8 human fecal samples and 3 bird fecal samples) provided sufficient 16S rRNA sequencing data. In contrast, the shotgun metagenomics method generated data for nearly all samples, with read counts ranging from 29,000 to 2.1\u0026nbsp;million per sample. However, one water sample (EW70) yielded fewer than 100 reads, resulting in it being classified as a sequencing failure.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e16s rRNA bacterial and metagenomic taxonomic profiling\u003c/h2\u003e \u003cp\u003eWe identified various bacterial genera (Supplementary Table\u0026nbsp;1) and phages (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Taxonomic classification of bacterial phyla revealed that Firmicutes and Bacteroidetes were dominant in human samples, Firmicutes and Proteobacteria were predominant in poultry samples, and Bacteroidetes and Proteobacteria were the most common in environmental samples (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBacteria profile in various samples\u003c/h2\u003e \u003cp\u003eHuman samples primarily featured the genera \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e as the most prevalent bacteria. Other identified bacterial genera included \u003cem\u003eLachnospira, Roseburia, Eubacterium, Faecalibacterium, Bacteroides\u003c/em\u003e, and \u003cem\u003eButyrivibrio\u003c/em\u003e (Supplementary Table\u0026nbsp;1). Analysis of the 16S rRNA data revealed a diverse range of bacterial genera in human samples, with notable abundance \u003cem\u003ein Agathobacter, Bacteroides, Prevotella, Escherichia, Clostridium, Streptococcus, Blautia, Lachnospira, Faecalibacterium, Dorea\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e. Additionally, over 50 other bacterial genera were detected (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eBoth 16S and shotgun sequencing data indicated greater variation in bacterial populations within human samples compared to poultry and environmental samples. Poultry samples were primarily characterized by the dominance of genera such as \u003cem\u003eLawsonia, Escherichia, Gallibacterium, Helicobacter\u003c/em\u003e, and \u003cem\u003eChlamydia\u003c/em\u003e, among others (Supplementary Tables\u0026nbsp;1 and 2). Environmental samples were predominantly dominated by bacterial genera like \u003cem\u003ePseudomonas, Aeromonas, Acinetobacter\u003c/em\u003e, and \u003cem\u003eAcrobacter\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified potential human pathogens, including \u003cem\u003eE. coli, Campylobacter, Shigella\u003c/em\u003e, and \u003cem\u003eHaemophilus\u003c/em\u003e, in human samples. In poultry samples, pathogens such as \u003cem\u003eChlamydia gallinacea, Gallibacterium anatis\u003c/em\u003e, and \u003cem\u003eHelicobacter pullorum\u003c/em\u003e were detected. Additionally, among the poultry samples, we identified known probiotic organisms including \u003cem\u003eLactobacillus johnsonii, Lactobacillus agilis, Lactobacillus reuteri\u003c/em\u003e, and \u003cem\u003eLactobacillus salivarius.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic profile of phage\u003c/h2\u003e \u003cp\u003eTaxonomic analysis of phages revealed that the Siphoviridae family was dominant, followed by Podoviridae, both of which fall under the order Caudovirales. The majority of phages were those that infect the Enterobacteriaceae family, including Escherichia phage, Salmonella phage, and Klebsiella phage. Phage diversity varied among samples, with avian samples exhibiting the highest alpha diversity index. Escherichia phages, along with Stx-2 converting phages carrying shiga toxin genes, were abundant in both birds and humans compared to environmental samples, though the difference was not statistically significant. Environmental samples were characterized by the predominance of Planktothrix phage PaV LD, Lactococcus phage P087, and Achloplasma virus L2 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eTaxonomic profile of viruses (phage) and their relative abundance of \u0026ge;\u0026thinsp;10 in samples obtained through metagenomic sequencing.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003ePhage name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOccurrence in sample (no.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcholeplasma_virus_L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasmpviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironment\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\u003eBacteroides_phage_B124_14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eBacteroides_phage_B40_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEnterobacteria_phage_cdtI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEnterobacteria_phage_P4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaudovirales unclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEnterobacteria_phage_YYZ_2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEscherichia_phage_KBNP21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEscherichia_phage_TL_2011b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEscherichia_virus_K30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEscherichia_virus_P1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMyoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman \u0026amp; Poultry\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\u003eEscherichia_virus_phiV10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eEscherichia_virus_wV8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMyoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoultry\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\u003eKlebsiella_phage_JD001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMyoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eKlebsiella_virus_KP32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eLactococcus_phage_P087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003ePlanktothrix_phage_PaV_LD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironment\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\u003eSalmonella_phage_Vi_II_E1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironment\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\u003eSalmonella_virus_Epsilon15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePodoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eStaphylococcus_phage_StB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoultry\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\u003eStreptococcus_virus_7201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman \u0026amp; Poultry\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\u003eStreptococcus_virus_DT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eStreptococcus_virus_phiAbc2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eStreptococcus_virus_Sfi21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\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\u003eStx2_converting_phage_1717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiphoviridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaudovirales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman \u0026amp; Poultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\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 \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVirulence factors (VF) Profile\u003c/h2\u003e \u003cp\u003eA total of 72 virulence factor (VF) genes were detected by shotgun metagenomics, with a threshold of 99% identity and a relative abundance of \u0026ge;\u0026thinsp;10. The detected genes included those coding for toxins, type I to VI secretion systems, regulatory proteins, adherence proteins, siderophores, and polysaccharides that compose capsules and exhibit anti-phagocytic properties. VF genes were predominantly found in human and poultry samples. The pilT gene of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e was the only gene identified in the environmental sample (Supplementary Table\u0026nbsp;4). Most of the identified virulence genes were associated with \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eShigella dysenteriae\u003c/em\u003e, \u003cem\u003eYersinia pestis\u003c/em\u003e, and \u003cem\u003eSalmonella enterica\u003c/em\u003e. A smaller proportion of VF genes were linked to \u003cem\u003eShigella flexneri\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eLegionella pneumophila\u003c/em\u003e, and \u003cem\u003eYersinia enterocolitica\u003c/em\u003e. Human samples contained genes associated with toxigenic \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eShigella flexneri\u003c/em\u003e, i.e., \u003cem\u003esat1, ltb, lta, astA\u003c/em\u003e, and \u003cem\u003esenB\u003c/em\u003e. These genes encode various toxins such as secreted autotransporter toxin of Enterobacteriaceae, enterotoxin of Enterotoxigenic \u003cem\u003eE. coli\u003c/em\u003e (ETEC), heat-stable enterotoxin of Enteroaggregative \u003cem\u003eE. coli\u003c/em\u003e, and enterotoxin 2 of \u003cem\u003eShigella flexneri\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDetected toxin-coding genes associated with bacteria, as identified using the ShortBRED tool.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFDB database\u003c/p\u003e \u003cp\u003egene identifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirulence genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOccurrence in sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBacterial species source (according\u003c/p\u003e \u003cp\u003eto VFDB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFG0902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esat\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacteriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAecreted auto transpoter toxin; diarrhea \u003csup\u003e35,36\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFG1827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003esen\u003c/em\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eShigella flexneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e entertotoxin 2; shigellosis \u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFG2038 \u0026amp;\u003c/p\u003e \u003cp\u003eVFG2037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eltb\u003c/em\u003e and \u003cem\u003elta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnterotoxin of Enterotoxigenic Escherichia coli; diarrhoea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFG0863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003east\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnteroaggregative E. coli heat stable enterotoxin (EAST1); diarrhea\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e\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 \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAntibiotic resistance determinants\u003c/h2\u003e \u003cp\u003eIn total, 25 classes and 53 subtypes of antimicrobial resistance genes (ARGs) were identified across the 21 samples. Among these, the genes \u003cem\u003etetQ\u003c/em\u003e and \u003cem\u003eermF\u003c/em\u003e were found in 14 out of 21 samples, predominantly in human and avian samples. Additionally, \u003cem\u003etet(W)\u003c/em\u003e was detected in 13 out of 21 samples, while \u003cem\u003ecfxA, tet(40)\u003c/em\u003e, and \u003cem\u003etet(0)\u003c/em\u003e were each present in 11 out of 21 samples. Avian samples exhibited the highest number of ARGs (n\u0026thinsp;=\u0026thinsp;37), followed by human samples (n\u0026thinsp;=\u0026thinsp;27), and environmental samples (n\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e \u003cp\u003eAll human, avian, and environmental samples contained the integron integrase gene \u003cem\u003eintl1\u003c/em\u003e. These samples harbored genes conferring resistance to various antimicrobial agents, including:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFluoroquinolones: \u003cem\u003eqnrB6, qnrS1\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSulfonamides: \u003cem\u003esul1, sul2, sul3\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMacrolides: \u003cem\u003emphK, macB, macA_3, ereA2, ermCd, ermQ, ermG, ermF, ermGT, ermB\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLincosamides: \u003cem\u003einuB\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKausagamycin: \u003cem\u003eksgA\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVancomycin: \u003cem\u003evanR\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUndecaprenyl pyrophosphate: \u003cem\u003ebacA\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTrimethoprim: \u003cem\u003edfrA, dfrXV, dfrA14\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChloramphenicol: \u003cem\u003ecatA, catQ, floR\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePolymyxin: \u003cem\u003earnA\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAminoglycosides: \u003cem\u003eaacC3, aac(60)-Ie-aph(200)-Ia, aadA, aph6, ant(3\"), ant(4), ant(6), aadB, aadE, aph(3')-IIIa, aph(3')-Ib\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTetracycline: \u003cem\u003etetC, tet39, tetQ, tet32, tetW, tetM, tet40, tetA, tetL, tetB\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBeta-lactams: \u003cem\u003ecepA, cfxA, blaTEM, blaVEB-1, blaCTX-M, blaEC\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAmong the subtypes of ARGs, those conferring resistance to aminoglycosides, tetracycline, and beta-lactams were the most prevalent. Tetracycline resistance genes were the most dominant across all samples. Additionally, aminoglycoside resistance genes were commonly found in human and avian samples, while sulfonamide resistance genes were predominantly present in environmental samples (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest abundance of AMR genes was found in samples TA340, TA330, TH990, and TH920 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The greatest diversity was observed in TA340 and TA330, as indicated by their notably high alpha diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The most varied ARGs were identified in common quail samples, whereas soil samples exhibited the fewest variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal Coordinates Analysis (PCoA) based on the relative abundances of ARGs showed a strong association between poultry and human samples, forming the fourth cluster (F-value\u0026thinsp;=\u0026thinsp;2.187, R-squared\u0026thinsp;=\u0026thinsp;0.38456, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Conversely, environmental samples' ARGs clustered independently without significant association with other samples, as indicated by the alpha-diversity-based cladogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Hierarchical clustering (dendrogram) revealed four main clusters of ARGs: two clusters comprised exclusively of human samples, one from environmental samples, and one containing ARGs common to both human and avian (poultry) samples. ARGs detected in human samples generally exhibited positive associations, except for TH990 and TH920.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntimicrobial resistance genes found in human, poultry and environmental samples of this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntimicrobial resistance gene (ARG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntimicrobial resistance gene (ARG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHuman\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePoultry\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHuman\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePoultry\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esul1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaph6-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esul2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaph(3'')-Ib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esul3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaph(3')-IIIa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqnrS6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaacC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqnrS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaac(6')-Ie/aph(2'')-Ia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQRDR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eant3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emacA_3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eant6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emacB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaadA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003evanR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaadB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emphK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eaadE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003einuB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003etet(C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eksgA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003etetA(39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eermB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003etet(40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eermF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003etet(L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eermGT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003cp\u003e\u003cb\u003etet(O)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efloR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003etet(M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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\u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecatQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ebla\u003c/b\u003e\u003csub\u003e\u003cb\u003eCTX\u0026minus;M\u0026minus;1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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\u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebacA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ebla\u003c/b\u003e\u003csub\u003e\u003cb\u003eVEB\u0026minus;1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM_137\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ebla\u003c/b\u003e\u003csub\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eclassD_beta lactamase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ebla\u003c/b\u003e\u003csub\u003e\u003cb\u003eCTXM_83\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csub\u003e+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e˗\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\u003e \u003cb\u003e+ Presence of ARG\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003cp\u003e\u0026ndash; Absence of ARG\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCross-Domain Dynamics of Horizontal Gene Transfer\u003c/h2\u003e \u003cp\u003eEnvironmental bacteria exhibited evidence of horizontal gene transfer (HGT), particularly concerning genes involved in translational enzymes and RNA-directed DNA polymerase. Additionally, genes encoding integrase and transposase from IS element families (IS3/IS911 and IS1595), which may facilitate lysogenic transformation, were identified \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These genes also included those related to antimicrobial resistance, transcription regulation, DNA methyltransferase, and ATPases \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. HGT occurrences were observed in poultry and human samples as well, especially involving genes responsible for replication, translation, and various pathway enzymes (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eIn poultry samples, integrases and transposons from IS66 and IS21 families, known to promote the spread of antimicrobial resistance (AMR), were detected \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Furthermore, the Clindamycin resistance transfer factor BtgB, necessary for the conjugal transfer of clindamycin resistance genes in \u003cem\u003eBacteroides\u003c/em\u003e species \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, was identified in the metagenomics data of poultry samples (Supplementary Table\u0026nbsp;3 and Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eHuman samples revealed a diverse range of AMR-related genes involved in horizontal gene transfer within the gut microbiome. These genes include those encoding the ABC multidrug transport system, multidrug resistance protein (MATE family), VanY domain-containing protein, penicillin-binding protein (PBP) 1A, aminoglycoside phosphotransferase, tetR protein, and metallo-beta-lactamase domain protein (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Various integrase and transposase genes from IS families such as IS116/IS110/IS902, IS30, IS605, IS200, and IS4 were found to be involved in HGT events (Supplementary Table\u0026nbsp;3 and Table\u0026nbsp;6). These findings underscore the complex dynamics of horizontal gene transfer across different environments\u0026mdash;environmental, avian, and human\u0026mdash;highlighting the interconnectedness of antimicrobial resistance mechanisms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVarious antimicrobial resistance, virulence, transposase and integrase proteins involved in HGTs.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHGT occurring between organisms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProteins that translated from genes transferred from HGT events\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoultry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacteroides_coprophilus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBacteroides_xylanisolvens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIS66 family transposase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoultry\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClindamycin resistance transfer factor BtgB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEubacterium_eligens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClostridium_sp_L2_50\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase, IS200 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_intestinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_inulinivorans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase IS116/IS110/IS902 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium_prausnitzii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSubdoligranulum_sp_4_3_54A2FAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABC-type multidrug transport system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOribacterium_sp_oral_taxon_078\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_intestinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAntitoxin; DUF2185 domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_inulinivorans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEubacterium_rectale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultidrug resistance protein, MATE family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePrevotella_stercorea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase, IS116/IS110/IS902 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMegamonas_rupellensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMegamonas_funiformis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIS605 family transposase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus_bromii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eButyrivibrio_crossotus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePenicillin-binding protein 1A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eButyrivibrio_crossotus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePrevotella_copri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAminoglycoside phosphotransferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnaerostipes_hadrus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClostridiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABC-type multidrug transport system protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClostridiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVanY domain-containing protein, ABC-type multidrug transport system protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVeillonella_sp_HPA0037\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMegasphaera_elsdenii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase IS200-family protein; TetR protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eES060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSoil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePseudoxanthomonas_sp_GW2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAlcanivorax_pacificus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCopper resistance protein B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESD060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSediment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eThauera_sp_27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDechloromonas_aromatica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntegrase, \u0026amp; Transposase IS3/IS911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESD060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSediment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCupriavidus_sp_HMR_1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGammaproteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDDE_Tnp_IS1595 domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus_sp_5_1_39BFAA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEubacterium_eligens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultidrug resistance MATE family protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_intestinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEubacterium_ramulus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase IS4 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRoseburia_inulinivorans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEubacterium_rectale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase IS116/IS110/IS902 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCatenibacterium_mitsuokai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eClostridiales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransposase, IS605 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH1030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eClostridium_sp_ATCC_BAA_442\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus_sp_JC304\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMetallo-beta-lactamase domain protein\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 \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis of Antimicrobial Resistance Genes\u003c/h2\u003e \u003cp\u003eThe network analysis of antimicrobial resistance genes (ARGs) across environmental, avian, and human samples underscore the complex dynamics of the One Health interface. In environmental samples (ES050, ES060, and ESD060), significant associations were observed with genes such as aminoglycoside phosphotransferase, aminoglycoside nucleotidyltransferase, \u003cem\u003esul, dfrA, acr\u003c/em\u003e, and ABC efflux pumps. Poultry samples (TA340, TA330, and TA320) showed a notable association with tetracycline resistance genes, while human samples exhibited strong connections with \u003cem\u003ebacA, erm, acr, tet\u003c/em\u003e, class A beta-lactamase genes, and Tet efflux (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis using the Maximal Information Coefficient (MIC) correlation coefficient assessed the relationships between bacterial genera and ARGs at the class level. The network analysis focused on robust connections with significant p-values (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This analysis revealed strong co-abundance signals among various bacteria, with \u003cem\u003eEubacterium\u003c/em\u003e identified as a central node, exhibiting robust links to other taxa such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, and \u003cem\u003eCollinsella\u003c/em\u003e. Notable co-abundant pairs included \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eLachnospira\u003c/em\u003e, as well as \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e. Concerning ARG data, genes like \u003cem\u003eqnr, ermD, arnA\u003c/em\u003e, and \u003cem\u003eaac\u003c/em\u003e showed substantial associations with different bacterial genera. Efflux pumps were particularly prevalent and strongly linked with \u003cem\u003eEscherichia\u003c/em\u003e, emerging as a central hub for numerous ARGs and efflux pumps (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings emphasize the intricate interplay of antimicrobial resistance across environmental, animal, and human domains, highlighting the importance of a One Health approach to understand and address the spread of resistance genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAntimicrobial resistance (AMR) has emerged as a major public health concern of the 21st century \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The rise of new pathogens, whether bacteria or viruses, underscores the need for comprehensive surveillance and understanding \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Continuous misuse of antibiotics can lead to multidrug-resistant bacteria, or \"superbugs,\" and AMR often develops through mechanisms similar to those of new bacterial emergence \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Recent advancements have highlighted the utility of metagenomics in epidemiological and environmental studies \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In this study, we employed a metagenomic approach to investigate AMR presence and transfer dynamics from a One Health perspective.\u003c/p\u003e \u003cp\u003eShotgun sequencing revealed \u003cem\u003ePrevotella\u003c/em\u003e as the most prevalent genus in human samples (Supplementary Table\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003ePrevotella\u003c/em\u003e spp. are known for producing short-chain fatty acids (SCFAs) and are common in individuals with high-carbohydrate diets \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, aligning with the carbohydrate-rich Nepalese diet. Prevotella spp. were also found to be prevalent in another study conducted in a country where rice is a staple food (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3904/kjim.2019.373\u003c/span\u003e\u003cspan address=\"10.3904/kjim.2019.373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePathogenic bacteria such as \u003cem\u003eShigella, Campylobacter, Haemophilus\u003c/em\u003e, and \u003cem\u003eE. coli\u003c/em\u003e were detected across various samples \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Virulence genes associated with disease causation were identified, including the Shiga toxin gene in \u003cem\u003eE. coli\u003c/em\u003e, which is linked to bloody diarrhea and hemolytic uremic syndrome (HUS) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Specific virulence genes like \u003cem\u003esenB\u003c/em\u003e for Shigella flexneri, \u003cem\u003ecsgG\u003c/em\u003e and \u003cem\u003erpoS\u003c/em\u003e for Salmonella enterica var. typhimurium, and \u003cem\u003efliR\u003c/em\u003e for Yersinia enterocolitica suggest the presence of these pathogens (Supplementary Table\u0026nbsp;4). \u003cem\u003eE. coli\u003c/em\u003e virulence genes such as \u003cem\u003eastA, ltb\u003c/em\u003e, and \u003cem\u003elta\u003c/em\u003e indicate enterotoxigenic (ETEC) and enteroaggregative (EAEC) strains, which can often evade conventional diagnostics \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eChlamydia gallinacea\u003c/em\u003e, which causes slow growth in chickens and mild symptoms in humans \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, was found in two poultry samples from backyard farms. \u003cem\u003eHelicobacter pullorum\u003c/em\u003e, linked to enteritis in poultry and zoonotic colitis in humans, as well as \u003cem\u003eGallibacterium anatis\u003c/em\u003e, which impacts egg production in hens, were also identified \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and have not been previously documented in poultry in Nepal (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Phages detected in this study belonged to Siphoviridae, Podoviridae, and Myoviridae. Phages infecting enterobacteria like \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eKlebsiella, Shigella\u003c/em\u003e, and gut bacteria such as \u003cem\u003eBacteroides, Prevotella, Roseburia\u003c/em\u003e, and \u003cem\u003eLachnospira\u003c/em\u003e were the most prevalent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Stx-2 converting bacteriophage, crucial for inducing Shiga toxin-producing \u003cem\u003eE. coli\u003c/em\u003e (STEC), was identified in seven samples (human and poultry) \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The pervasiveness of \u003cem\u003eE. coli\u003c/em\u003e in every sample of this study suggests a significant probability for STEC production \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Phage and bacterial diversity were similar across most samples, with human samples exhibiting the highest diversity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Tables\u0026nbsp;1 and 2).\u003c/p\u003e \u003cp\u003eOur study identified numerous antimicrobial resistance gene (ARG) subtypes (n\u0026thinsp;=\u0026thinsp;53), including some previously unreported in Nepal, such as \u003cem\u003einuB, catQ, ksgA, floR\u003c/em\u003e, and \u003cem\u003eblaEC\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Our study also uncovered \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX\u0026minus;M\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eTEM\u003c/em\u003e\u003c/sub\u003e genes, which have been previously found in hospital and environmental samples of Nepal \u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59 CR60\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e along with \u003cem\u003eqnrS, sul1\u003c/em\u003e, and \u003cem\u003etetB\u003c/em\u003e genes that were earlier detected in animal and environmental samples. Additionally, we detected \u003cem\u003eermB\u003c/em\u003e genes that had previously been identified in samples from school children of \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In regards to poultry samples, heavy antibiotic use in poultry likely contributes to the high number of ARG subtypes found in poultry samples, with 27 subtypes found in human samples, likely due to over-prescription and easy access to antibiotics \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNetwork analysis revealed strong associations between certain bacteria and ARGs (\u003cem\u003eqnr, ermD, arnA\u003c/em\u003e, and \u003cem\u003eaac\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The presence of nearby hospitals discharging untreated waste into rivers may contribute to ARG originating from hospitals. Many detected ARGs encode proteins that aid bacterial replication and function, with some (\u003cem\u003etetR, vanY, PBP 1A, BtgB\u003c/em\u003e) associated with the gut microbiome. Integrases and transposases facilitating AMR transfer were detected in poultry and human samples \u003csup\u003e\u003cspan additionalcitationids=\"CR66 CR67 CR68 CR69 CR70\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The enteric bacteria in human gut microbiomes may act as ARG reservoirs \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, contributing to the spread of ARGs in enteric pathogens. Studies suggest that gut microbiomes act as reservoirs for AMR, which is crucial for understanding the emergence of new ARGs \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Despite the challenges in metagenomic data analysis, metagenomics offers promise for improving our understanding of AMR dynamics and with the employment of clinical metagenomics, it can help in guiding targeted interventions \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Global surveillance systems are essential for early detection and control of AMR-related infections, given the increasing threats from population density, antimicrobial use, and environmental changes \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights the interconnectedness of antimicrobial resistance across different domains. The presence of ARGs in environmental, animal, and human samples underscores the need for a One Health approach. Heavy antibiotic use in poultry and clinical settings likely contributes to ARG dissemination, emphasizing the need for responsible antibiotic stewardship. Our network analysis identified strong associations between bacteria and ARGs, indicating potential horizontal gene transfer and spread of resistance. The gut microbiome, particularly in humans and animals, emerges as a significant reservoir for ARGs. Integrated surveillance efforts are essential for monitoring and mitigating AMR emergence and spread. Despite challenges, clinical metagenomics holds promise for enhancing our understanding of AMR dynamics and guiding effective interventions. A One Health approach and robust global surveillance systems are crucial for addressing the complex issue of antimicrobial resistance and protecting public health. We emphasize the need for a One Health approach and global surveillance systems for early detection and control of AMR-related infections. This holistic strategy could be an essential tool to combat the escalating issue of antimicrobial resistance and protect public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.N. and D.K. conceptualized and designed the study. A.G., A.P. and A.C. helped in field data acquisition. A.N.S., S.R., S.M.P and JJ were involved in conducting laboratory analysis. P.M helped in Bioinformation analysis. M.P. and M.G. assisted in data analysis and edited the manuscript. R.N., D.K. and R.M.R. were involved in data analysis and manuscript preparation. Manuscript was partially polished using AI tool.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eWe are grateful to the Nepal Health Research Council (NHRC), Government of Nepal, for granting permission to conduct this research. Our sincere thanks go to the field team of CMDN, led by Mr. Bishwo P. Shrestha, for their tireless efforts in collecting samples for this study. We also extend our gratitude to the Massachusetts Institute of Technology (MIT) for providing the BioBot Automatic Sampler, which was instrumental in our sampling activities.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data generated in this study has been included in manuscript and supplementary files. The sequencing data (raw fastq files) have been submitted to NCBI SRA database and can be found under BioProject accession number PRJNA881338.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAarestrup, F. M. The livestock reservoir for antimicrobial resistance: a personal view on changing patterns of risks, effects of interventions and the way forward. \u003cem\u003ePhilos. Trans. R. Soc. London. Ser. B, Biol. Sci.\u003c/em\u003e \u003cstrong\u003e370\u003c/strong\u003e, 20140085 (2015).\u003c/li\u003e\n\u003cli\u003eWHO. 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Dis.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, S47\u0026ndash;S52 (2017).\u003c/li\u003e\n\u003cli\u003eCarroll, S. P. \u003cem\u003eet al.\u003c/em\u003e Applying evolutionary biology to address global challenges. \u003cem\u003eScience\u003c/em\u003e\u003cstrong\u003e346\u003c/strong\u003e, 1245993 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AMR, ARG, gut microbiome, HGT, One health","lastPublishedDoi":"10.21203/rs.3.rs-5133052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5133052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial resistance (AMR) is a rapidly emerging global health crisis, projected to cause 10.2\u0026nbsp;million deaths annually by 2050. The unregulated and indiscriminate use of antibiotics is driving the swift emergence and spread of AMR, a problem worsened by the release of untreated wastewater from high-risk sources, such as hospitals, into rivers. Bacteria often acquire resistance through horizontal gene transfer, and specific environments, like the human gut or soil, can serve as hotspots for the emergence of novel antimicrobial resistance genes (ARGs) and multi-drug resistant (MDR) pathogens. Shotgun metagenomics can be used to profile the AMR of a given microbiome and help detect MDR bacteria that might otherwise go unnoticed. However, current AMR reporting is largely based on clinical cases, offering limited insights into specific pathogens and their associated AMR genes. Our study aims to advance the understanding of the natural distribution and dissemination of AMR. In particular, we focused on the presence of AMR mutations and gene transfer dynamics in human, animal, and environmental samples collected from a temporary settlement in Kathmandu, Nepal, using a One Health approach.\u003c/p\u003e \u003cp\u003eTwenty-one samples were collected from a temporary settlement in Thapathali, Kathmandu, including fecal samples from birds (n\u0026thinsp;=\u0026thinsp;3), humans (n\u0026thinsp;=\u0026thinsp;14), and the environment (n\u0026thinsp;=\u0026thinsp;4). \u003cem\u003ePrevotella\u003c/em\u003e spp. was the dominant gut bacterium in human samples. A diverse range of phages and viruses were detected, including Stx-2 converting phages. In total, 72 virulence factors and 53 antimicrobial resistance gene (ARG) subtypes were identified, with poultry samples showing the highest number of ARG subtypes.\u003c/p\u003e \u003cp\u003eUsing a One Health-based metagenomics approach, we identified various pathogenic bacteria and virulence genes in both human and avian samples, underscoring the interconnectedness of antimicrobial resistance (AMR) across different domains. Heavy antibiotic use in poultry and clinical settings likely contributes to the spread of antimicrobial resistance genes (ARGs). Our analysis indicates frequent horizontal gene transfer, with gut microbiomes serving as key reservoirs for ARGs. Despite certain challenges, metagenomics shows significant potential for advancing our understanding of AMR dynamics. We emphasize the need for a One Health approach and robust global surveillance systems to enable the early detection and control of AMR, safeguarding public health.\u003c/p\u003e","manuscriptTitle":"Metagenomic analysis of human, animal, and environmental samples identifies potential emerging pathogens, profiles antibiotic resistance genes, and reveals horizontal gene transfer dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 13:34:26","doi":"10.21203/rs.3.rs-5133052/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-07T04:00:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T23:27:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T10:24:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-03T17:34:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66926195955160955612329884409420717123","date":"2024-09-30T08:59:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4578937795659004294755040598072829156","date":"2024-09-27T22:47:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325842012373614100796625785735150565522","date":"2024-09-27T16:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-27T11:50:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-27T11:40:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-25T08:56:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-23T07:31:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-22T15:09:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"656daf6a-172b-4a9f-b612-2c75d6026d76","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38598955,"name":"Biological sciences/Biological techniques"},{"id":38598956,"name":"Biological sciences/Biotechnology"},{"id":38598957,"name":"Biological sciences/Genetics"},{"id":38598958,"name":"Biological sciences/Microbiology"},{"id":38598959,"name":"Biological sciences/Molecular biology"},{"id":38598960,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-04-14T16:18:15+00:00","versionOfRecord":{"articleIdentity":"rs-5133052","link":"https://doi.org/10.1038/s41598-025-90777-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-09 16:05:49","publishedOnDateReadable":"April 9th, 2025"},"versionCreatedAt":"2024-11-29 13:34:26","video":"","vorDoi":"10.1038/s41598-025-90777-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90777-8","workflowStages":[]},"version":"v1","identity":"rs-5133052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5133052","identity":"rs-5133052","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}