{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T20:11:53Z","timestamp":1789330313764,"version":"build-2803163510"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiomes"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The human gut microbiome harbors millions of bacterial species, including opportunistic pathogens, and this microbial community is exposed to antimicrobial agents present in food, the external environment, or drugs. Thus, it increases the risk of commensals being enriched with resistant genes, which may get even transmitted to opportunistic pathogens often with the help of mobile genetic elements. There is limited information about the current burden of resistant genes in the healthy gut microbiome of the Indian population, the latter is not only the largest in the world but is also periodically monitored for the prevalence of antibiotic resistance in clinical samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed publicly available fecal whole-metagenome shotgun sequencing data from 141 samples from three healthy Indian cohorts for antimicrobial-resistance burden, and their likely transmission modes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The overall resistance profile showed a higher number of resistance genes against tetracycline, glycopeptide, and aminoglycoside. Out of a total of 188 antimicrobial resistance genes identified in all cohorts, moderately to highly prevalent ones could potentially target seven of the \u2018reserve\u2019 group antibiotics (colistin, fosfomycin, Polymyxin). We also observed that geographical location affected the prevalence\/abundance of some of the resistance genes. The higher abundance of several tetracycline and vancomycin resistance genes in tribal cohorts compared to the other two urban locations was intriguing. Species\n                      <jats:italic>E. coli<\/jats:italic>\n                      had the highest number of resistant genes, and given its relatively modest abundance in gut microbiomes can pose a risk of becoming a hub for the horizontal transfer of resistance genes to others. Lastly, a subset of the resistance genes showed association with several types of mobile genetic elements, which potentially could facilitate their transmission within the gut community.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This is a first systematic report on AMR genes in healthy gut microbiome samples from multiple locations of India. While trends for several of the prevalent AMR genes showed similarity with global data, but a few population specific trends need further attention by policy-makers. The association of AMR genes with mobile elements may pose a risk for transmission to other gut bacteria.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frmbi.2024.1432646","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T05:35:05Z","timestamp":1721280905000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Antimicrobial resistance burden, and mechanisms of its emergence in gut microbiomes of Indian population"],"prefix":"10.3389","volume":"3","author":[{"given":"Nisha","family":"Chandel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremiah Paul","family":"Gorremuchu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Thakur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"D690","DOI":"10.1093\/nar\/gkac920","article-title":"CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database","volume":"51","author":"Alcock","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"D517","DOI":"10.1093\/nar\/gkz935","article-title":"CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database","volume":"48","author":"Alcock","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B3","article-title":"ICMR annual report 2021","volume-title":"Antimicrobial Resistance Research and Surveillance Network","year":"2021"},{"key":"B4","unstructured":"Andrews\n              S.\n            \n          \n          Babraham Bioinformatics-FastQC a Quality Control Tool for High Throughput Sequence Data\n          \n          2010"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1007\/s00248-018-1228-7","article-title":"Molecular insights into antimicrobial resistance traits of commensal human gut microbiota","volume":"77","author":"Bag","year":"2019","journal-title":"Microb. 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