Evidence map›Paper›PMID 37352142›Full record

ArticleMolecular biology and evolution2023

Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations.

Anna G Green, Roger Vargas, Maximillian G Marin, Luca Freschi, Jiaqi Xie, Maha R Farhat

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Multidrug-resistant tuberculosis.Nature reviews. Disease primers · 2024
    Review
  12. Review
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

6 authors.

Anna G GreenDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7548-3682
Roger VargasDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7116-5211
Maximillian G MarinDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-9108-3328
Luca FreschiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Jiaqi XieDepartment of Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Maha R FarhatDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-3871-5760

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
Orchestra: A high performance biomedical supercomputing collaborativeS10RR028832 · NCRR · HARVARD MEDICAL SCHOOL · PI DANUSER, GAUDENZ · 2010 to 2010
$3.7M
Integrating protein structure and genomic data to predict antibiotic resistance in Mycobacterium tuberculosisF32AI161793 · NIAID · HARVARD MEDICAL SCHOOL · PI GREEN, ANNA GUSTAFSON · 2021 to 2022
$139k
NCRR NIH HHS S10 RR028832NIAID NIH HHS F32 AI161793NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.

Indexed as

Mycobacterium tuberculosisAntitubercular AgentsMicrobial Sensitivity TestsMutationPhylogenyVirulenceAntitubercular Agentscomputational biologymicrobial evolutionmicrobial genomicsmicrobiologymycobacterium

Identifiers

PMID37352142
PMCPMC10292908

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.