{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T22:46:17Z","timestamp":1787870777095,"version":"build-2784847793"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"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. Microbiol."],"abstract":"<jats:p><jats:italic>Mycobacterium tuberculosis<\/jats:italic> (Mtb) is among the most successful bacterial pathogens, with multidrug-resistant strains posing significant challenges to global tuberculosis control. Traditional single-genome analyses, while essential for identifying strain-specific mutations, are limited in capturing the full spectrum of genetic diversity related to virulence, drug susceptibility, and transmission dynamics. Pangenomics examines the complete gene repertoire across all sequenced representatives of a species and addresses these limitations by enabling comprehensive, species-wide assessments of genetic variation. In this review, we summarize current knowledge of the Mtb pangenome, focusing on structural organization, methodological frameworks, and clinical applications. The Mtb pangenome exhibits a highly conserved genetic structure, with core genome estimates ranging from 1,166 to 3,767 genes, depending on the analytical thresholds and methodological approaches. Significant controversy regarding its classification as open or closed arises primarily from differences in computational pipelines (Roary, BPGA, Panaroo), core genome inclusion criteria (95%\u2013100% presence), and dataset composition rather than fundamental biological disagreement. Despite these methodological challenges, pangenomic applications have demonstrated transformative potential in molecular epidemiology, drug resistance prediction, and virulence profiling. This perspective underscores a shift toward diversity-inclusive approaches, with integration of machine learning and standardization of analytical protocols identified as key priorities for future tuberculosis research and therapeutic innovation.<\/jats:p>","DOI":"10.3389\/fmicb.2025.1695567","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T05:23:49Z","timestamp":1760937829000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Beyond H37Rv: Mycobacterium tuberculosis pangenome structure and applications"],"prefix":"10.3389","volume":"16","author":[{"given":"Andrea Monserrat","family":"Negrete-Paz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"V\u00e1zquez-Marrufo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ma. 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