{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T06:06:39Z","timestamp":1784700399839,"version":"3.55.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B03032-2"],"award-info":[{"award-number":["2022B03032-2"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Drug-resistant tuberculosis (TB) remains a major obstacle to global TB control, particularly in high-burden settings where timely detection of resistance is limited. This study aimed to integrate whole-genome sequencing and machine learning approaches to characterize the genomic architecture of drug resistance and develop predictive models for phenotypic resistance in clinical\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      isolates from Kashi, Xinjiang, China.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 160 clinical\n                      <jats:italic>M. tuberculosis<\/jats:italic>\n                      isolates were collected from culture-confirmed pulmonary tuberculosis patients at First People\u2019s Hospital of Kashi over a five-year period (January 2020\u2013December 2024). Whole-genome sequencing was performed, and sequencing reads were mapped to the H37Rv reference genome for high-confidence single nucleotide polymorphism (SNP) and insertion\/deletion (INDEL) calling. Resistance-associated mutations were identified using curated resistance mutation catalogues, and comparative genomic analyses were conducted to assess mutation burden across phenotypic resistance groups. Phylogenetic reconstruction based on genome-wide SNPs was performed to evaluate lineage distribution and clustering of resistant strains. In addition, random forest machine learning models were trained using WGS-derived coding and promoter mutations to predict phenotypic resistance to first-line anti-tuberculosis drugs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Phenotypic drug susceptibility testing classified 63 isolates (39.4%) as drug-sensitive, 67 (41.9%) as single-drug resistant (SDR), and 28 (17.5%) as multidrug-resistant (MDR), with a small number of extensively drug-resistant isolates. Resistance was dominated by first-line drugs, particularly isoniazid, streptomycin, and rifampicin. Phylogenetic analysis revealed non-random distribution of resistant isolates, with enrichment of MDR strains within Lineage 2 (Beijing lineage). Genomic profiling demonstrated a progressive increase in resistance-associated mutational burden from drug-sensitive to SDR and MDR isolates, characterized by accumulation of canonical mutations in\n                      <jats:italic>rpo<\/jats:italic>\n                      B,\n                      <jats:italic>kat<\/jats:italic>\n                      G,\n                      <jats:italic>emb<\/jats:italic>\n                      B,\n                      <jats:italic>gyr<\/jats:italic>\n                      A,\n                      <jats:italic>pnc<\/jats:italic>\n                      A, and\n                      <jats:italic>rrs<\/jats:italic>\n                      . Machine learning\u2013based prediction achieved strong performance for rifampicin, isoniazid, and streptomycin resistance, with key resistance-conferring mutations and promoter variants emerging as dominant predictive features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study demonstrates the potential utility of integrating phenotypic testing, whole-genome sequencing, and interpretable machine learning approaches for characterizing and predicting drug-resistant\n                      <jats:italic>M. tuberculosis<\/jats:italic>\n                      . The observed burden of resistance-associated mutations and lineage-associated phylogenetic clustering of resistant isolates support the value of WGS-based surveillance and predictive modelling for tuberculosis control strategies.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1828991","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:53:22Z","timestamp":1784699602000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Whole-genome sequencing reveals lineage-associated drug resistance and enables machine learning\u2013based prediction in Mycobacterium tuberculosis clinical isolates"],"prefix":"10.3389","volume":"17","author":[{"given":"Zelin","family":"Hao","sequence":"first","affiliation":[{"name":"College of Life Science and Technology, Xinjiang University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases, First People\u2032s Hospital of Kashi","place":["Kashi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Peng","sequence":"additional","affiliation":[{"name":"The Sixth People's Hospital of Xinjiang Uygur Autonomous Region","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Life Science and Technology, Xinjiang University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3389\/fmicb.2019.00309","article-title":"Whole genome sequencing of Mycobacterium tuberculosis clinical isolates from India reveals genetic heterogeneity and region-specific variations that might affect drug susceptibility","volume":"10","author":"Advani","year":"2019","journal-title":"Front. 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