Evidence map›Paper›PMID 31345251›Full record

ReviewGenome medicine2019

Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges.

Keira A Cohen, Abigail L Manson, Christopher A Desjardins, Thomas Abeel, Ashlee M Earl

Open access · goldAbstract readReview
In one paragraph

Review in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
98citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 1% of its field
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

98 citing papers in PubMed, 1 synthesis or guideline pooled it, 159 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. A Comprehensive Review of Genetic Mechanisms ofInfection and drug resistance · 2026
    Review
  8. Article
  9. Protein Architecture and Composition in Mycobacterium tuberculosis.Advances in experimental medicine and biology · 2026
    Review
  10. Review
  11. Genomic characterization of drug-resistantFrontiers in tuberculosis · 2026
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

38 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 2 countries.

Keira A CohenDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MA, 21205, USA. kcohen8@jhmi.edu.
Abigail L MansonBroad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, 02142, USA.
Christopher A DesjardinsBroad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, 02142, USA.
Thomas AbeelBroad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, 02142, USA.
Ashlee M EarlBroad Institute of Harvard and Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA, 02142, USA. aearl@broadinstitute.org.ORCID 0000-0001-7857-9145
Broad Institute · USJohns Hopkins University · US

Funding

Viral Genomics: evolution, spread, and host interactionsU19AI110818 · NIAID · BROAD INSTITUTE, INC. · PI BIRREN, BRUCE W., NEAFSEY, DANIEL E · 2014 to 2024
$66.5M
Improved Tools for Nontuberculous Mycobacteria Lung DiseaseK08HL139994 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI COHEN, KEIRA ALEXIS · 2018 to 2022
$794k
NHLBI NIH HHS K08 HL139994NIAID NIH HHS U19 AI110818
6 · The paper itself

Abstract

Tuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.

Indexed as

Drug Resistance, BacterialGenome, BacterialGenomicsAntitubercular AgentsCoinfectionComputational BiologyEvolution, MolecularGenes, BacterialGenome-Wide Association StudyMicrobial Sensitivity TestsMutationMycobacterium tuberculosisTranslational Research, BiomedicalTuberculosisAntitubercular Agents

Identifiers

PMID31345251
PMCPMC6657377
OpenAlexW2963643844

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.