ArticleFrontiers in microbiology2026
Whole-genome sequencing reveals lineage-associated drug resistance and enables machine learning-based prediction in
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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 Methods: A total of 160 clinical Results: 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 Conclusion: This study demonstrates the potential utility of integrating phenotypic testing, whole-genome sequencing, and interpretable machine learning approaches for characterizing and predicting drug-resistant
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