Evidence map›Paper›PMID 40576365›Full record

ArticleThe Journal of infectious diseases2025

Molecular Epidemiology of Mycobacterium tuberculosis Across 3 Distinct Geographic Sites in South Africa.

Theresa S Ryckman, Lincoln Hopkins, Linrui Tang, Patrick Biché, Mbali Mohlamonyane, Matshidiso Morolo, Bareng Aletta Sanny Nonyane, Khatija Ahmed, Neil Martinson, Colleen F Hanrahan and 3 more

Abstract read
In one paragraph

Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Theresa S RyckmanDivision of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine.ORCID 0000-0003-3214-3228
Lincoln HopkinsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.ORCID 0000-0002-3874-3585
Linrui TangDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.ORCID 0000-0003-0144-0019
Patrick BichéCenter for TB Research, Johns Hopkins University.
Mbali MohlamonyanePerinatal HIV Research Unit, University of the Witwatersrand, Johannesburg.
Matshidiso MoroloSetshaba Research Center, Soshanguve, South Africa.
Bareng Aletta Sanny NonyaneCenter for TB Research, Johns Hopkins University.
Khatija AhmedSetshaba Research Center, Soshanguve, South Africa.
Neil MartinsonCenter for TB Research, Johns Hopkins University.
Colleen F HanrahanCenter for TB Research, Johns Hopkins University.
Shaheed V OmarNational Institute for Communicable Diseases, Johannesburg, South Africa.
Barun MathemaDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York.
David W DowdyCenter for TB Research, Johns Hopkins University.ORCID 0000-0003-0481-7475

Funding

Innovative Contact Tracing Strategies for Detecting TB in Mobile Rural and Urban South African PopulationsR01AI147681 · NIAID · JOHNS HOPKINS UNIVERSITY · PI DOWDY, DAVID WESLEY · 2019 to 2023
$3.2M
Integrating genomic and spatial insights on Mtb transmission into policy-relevant modelsK01AI182503 · NIAID · JOHNS HOPKINS UNIVERSITY · PI RYCKMAN, THERESA · 2024 to 2025
$249k
NIAID NIH HHS K01 AI182503NIAID NIH HHS R01 AI147681US National Institutes of Health, National Institute of Allergy and Infectious Diseases K01AI182503US National Institutes of Health, National Institute of Allergy and Infectious Diseases R01AI147681
6 · The paper itself

Abstract

backgroundWhole genome sequence data can generate insights about Mycobacterium tuberculosis (Mtb) transmission. We used whole genome sequencing and linked epidemiology data from a recent randomized trial to characterize Mtb relatedness across 3 geographically distinct South African sites.

methodsWe sequenced culture isolates from participants with culture-positive tuberculosis in the Kharituwe study, which evaluated household contact investigation strategies in 1 urban and 2 rural sites. We adapted a previous bioinformatic pipeline to clean, extract, and filter Mtb reads; perform reference alignment; calculate single-nucleotide polymorphism (SNP) distances between isolates; and group isolates into clusters linked by recent transmission based on 3 SNP-based cutoffs. Sequence data were linked to individual data on demographics and risk factors. We analyzed clustering across and within study sites and used log-binomial regression to assess characteristics associated with clustering.

resultsAt a cutoff of 12 SNPs, 213 of 714 sequenced isolates passing quality control filters were clustered. While only 3 of 45 pairs included participants from different sites, the majority of clusters with ≥4 participants included representation from at least 2 sites. Expanding to a 20-SNP cutoff revealed a large cluster containing 10% of isolates, with urban/rural representation mirroring that of all the isolates (61% urban, 39% rural). Participants from the urban site, TB household contacts, and participants reporting a history of incarceration were more likely to be in a cluster.

conclusionsObserved clustering and strain diversity across sites indicate the presence of multiple ongoing and geographically dispersed outbreaks in this setting.

Indexed as

Mycobacterium tuberculosisTuberculosisAdolescentAdultCluster AnalysisFemaleGenome, BacterialHumansMaleMiddle AgedMolecular EpidemiologyPolymorphism, Single NucleotideRisk FactorsRural PopulationSouth AfricaWhole Genome SequencinggenomicsSouth Africaspatialtransmissiontuberculosis

Identifiers

PMID40576365
PMCPMC12526890

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.