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dc.contributor.authorBrown, Tyler S.
dc.contributor.authorNarechania, Apurva
dc.contributor.authorWalker, John R.
dc.contributor.authorPlanet, Paul J.
dc.contributor.authorBifani, Pablo J.
dc.contributor.authorKolokotronis, Sergios-Orestis
dc.contributor.authorKreiswirth, Barry N.
dc.contributor.authorMathema, Barun
dc.date.accessioned2022-08-23T18:59:55Z
dc.date.available2022-08-23T18:59:55Z
dc.date.issued2016-11-21
dc.identifier.citationBrown TS, Narechania A, Walker JR, Planet PJ, Bifani PJ, Kolokotronis SO, Kreiswirth BN, Mathema B. Genomic epidemiology of Lineage 4 Mycobacterium tuberculosis subpopulations in New York City and New Jersey, 1999-2009. BMC Genomics. 2016 Nov 21;17(1):947. doi: 10.1186/s12864-016-3298-6. PMID: 27871225; PMCID: PMC5117616.en_US
dc.identifier.eissn1471-2164
dc.identifier.doi10.1186/s12864-016-3298-6
dc.identifier.pmid27871225
dc.identifier.pii3298
dc.identifier.urihttp://hdl.handle.net/20.500.12648/7489
dc.description.abstractBackground: Whole genome sequencing (WGS) has rapidly become an important research tool in tuberculosis epidemiology and is likely to replace many existing methods in public health microbiology in the near future. WGS-based methods may be particularly useful in areas with less diverse Mycobacterium tuberculosis populations, such as New York City, where conventional genotyping is often uninformative and field epidemiology often difficult. This study applies four candidate strategies for WGS-based identification of emerging M. tuberculosis subpopulations, employing both phylogenomic and population genetics methods. Results: M. tuberculosis subpopulations in New York City and New Jersey can be distinguished via phylogenomic reconstruction, evidence of demographic expansion and subpopulation-specific signatures of selection, and by determination of subgroup-defining nucleotide substitutions. These methods identified known historical outbreak clusters and previously unidentified subpopulations within relatively monomorphic M. tuberculosis endemic clone groups. Neutrality statistics based on the site frequency spectrum were less useful for identifying M. tuberculosis subpopulations, likely due to the low levels of informative genetic variation in recently diverged isolate groups. In addition, we observed that isolates from New York City endemic clone groups have acquired multiple non-synonymous SNPs in virulence- and growth-associated pathways, and relatively few mutations in drug resistance-associated genes, suggesting that overall pathoadaptive fitness, rather than the acquisition of drug resistance mutations, has played a central role in the evolutionary history and epidemiology of M. tuberculosis subpopulations in New York City. Conclusions: Our results demonstrate that some but not all WGS-based methods are useful for detection of emerging M. tuberculosis clone groups, and support the use of phylogenomic reconstruction in routine tuberculosis laboratory surveillance, particularly in areas with relatively less diverse M. tuberculosis populations. Our study also supports the use of wider-reaching phylogenomic and population genomic methods in tuberculosis public health practice, which can support tuberculosis control activities by identifying genetic polymorphisms contributing to epidemiological success in local M. tuberculosis populations and possibly explain why certain isolate groups are apparently more successful in specific host populations.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.urlhttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-3298-6en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGeneticsen_US
dc.subjectBiotechnologyen_US
dc.subjectMycobacterium tuberculosisen_US
dc.subjectPhylogenomicsen_US
dc.subjectSurveillanceen_US
dc.subjectWhole genome sequencingen_US
dc.titleGenomic epidemiology of Lineage 4 Mycobacterium tuberculosis subpopulations in New York City and New Jersey, 1999–2009en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleBMC Genomicsen_US
dc.source.volume17
dc.source.issue1
dc.description.versionVoRen_US
refterms.dateFOA2022-08-23T18:59:55Z
dc.description.institutionSUNY Downstateen_US
dc.description.departmentEpidemiology and Biostatisticsen_US
dc.description.degreelevelN/Aen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International