Evidence map›Paper›PMID 22470121›Full record

ArticleAntimicrobial agents and chemotherapy2012

Effect of mutation and genetic background on drug resistance in Mycobacterium tuberculosis.

Lukas Fenner, Matthias Egger, Thomas Bodmer, Ekkehardt Altpeter, Marcel Zwahlen, Katia Jaton, Gaby E Pfyffer, Sonia Borrell, Olivier Dubuis, Thomas Bruderer and 9 more

Abstract read
In one paragraph

Article in Antimicrobial agents and chemotherapy, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
–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

60 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Phenotypic and genotypic characterization ofFrontiers in microbiology · 2024
    Article
  7. A descriptive study on isoniazid resistance-associated mutations, clustering and treatment outcomes of drug-resistant tuberculosis in a high burden country.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024
    Article
  8. Review
  9. Molecular Analysis of Anti-Tuberculosis Drug Resistance ofAntibiotics (Basel, Switzerland) · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Frontiers in cellular and infection microbiology · 2022
    Review
  14. The evolving biology ofFrontiers in cellular and infection microbiology · 2022
    Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

19 authors.

Lukas FennerInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. lfenner@ispm.unibe.ch
Matthias Egger
Thomas Bodmer
Ekkehardt Altpeter
Marcel Zwahlen
Katia Jaton
Gaby E Pfyffer
Sonia Borrell
Olivier Dubuis
Thomas Bruderer
Hans H Siegrist
Hansjakob Furrer
Alexandra Calmy
Jan Fehr
Jesica Mazza Stalder
Béatrice Ninet
Erik C Böttger
Sebastien Gagneux
Swiss HIV Cohort Study and the Swiss Molecular Epidemiology of Tuberculosis Study Group

Funding

Observational Antiretroviral Studies In Southern Africa (OASIS) CollaborationU01AI069924 · NIAID · UNIVERSITAT BERN · PI Cleophas Chimbetete, Mary-Ann Davies · 2006 to 2026
$55.9M
Mycobacterium tuberculosis antigen diversityR01AI090928 · NIAID · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI ERNST, JOEL D., GAGNEUX, SEBASTIEN · 2010 to 2014
$3.2M
NIAID NIH HHS R01 AI090928NIAID NIH HHS R01-AI090928-01NIAID NIH HHS U01 AI069924NIAID NIH HHS U01AI069924PHS HHS HHSN266200700022C
6 · The paper itself

Abstract

Bacterial factors may contribute to the global emergence and spread of drug-resistant tuberculosis (TB). Only a few studies have reported on the interactions between different bacterial factors. We studied drug-resistant Mycobacterium tuberculosis isolates from a nationwide study conducted from 2000 to 2008 in Switzerland. We determined quantitative drug resistance levels of first-line drugs by using Bactec MGIT-960 and drug resistance genotypes by sequencing the hot-spot regions of the relevant genes. We determined recent transmission by molecular methods and collected clinical data. Overall, we analyzed 158 isolates that were resistant to isoniazid, rifampin, or ethambutol, 48 (30.4%) of which were multidrug resistant. Among 154 isoniazid-resistant strains, katG mutations were associated with high-level and inhA promoter mutations with low-level drug resistance. Only katG(S315T) (65.6% of all isoniazid-resistant strains) and inhA promoter -15C/T (22.7%) were found in molecular clusters. M. tuberculosis lineage 2 (includes Beijing genotype) was associated with any drug resistance (adjusted odds ratio [OR], 3.0; 95% confidence interval [CI], 1.7 to 5.6; P < 0.0001). Lineage 1 was associated with inhA promoter -15C/T mutations (OR, 6.4; 95% CI, 2.0 to 20.7; P = 0.002). We found that the genetic strain background influences the level of isoniazid resistance conveyed by particular mutations (interaction tests of drug resistance mutations across all lineages; P < 0.0001). In conclusion, M. tuberculosis drug resistance mutations were associated with various levels of drug resistance and transmission, and M. tuberculosis lineages were associated with particular drug resistance-conferring mutations and phenotypic drug resistance. Our study also supports a role for epistatic interactions between different drug resistance mutations and strain genetic backgrounds in M. tuberculosis drug resistance.

Indexed as

Antitubercular AgentsDrug Resistance, Multiple, BacterialEthambutolGenotypeIsoniazidMicrobial Sensitivity TestsMutationMycobacterium tuberculosisRifampinTuberculosis, Multidrug-ResistantAntitubercular AgentsEthambutolIsoniazidRifampin

Identifiers

PMID22470121
PMCPMC3370767

What OpenQuestion holds

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Registered trials

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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.