Evidence map›Paper›PMID 30337649›Full record

ArticleScientific reports2018

Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis.

Stephanie Portelli, Jody E Phelan, David B Ascher, Taane G Clark, Nicholas Furnham

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
5.3field-weighted citation impact, top 3% 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

45 citing papers in PubMed, 76 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Computational and structural biotechnology journal · 2025
    Article
  6. Article
  7. Article
  8. International journal of microbiology · 2024
    Article
  9. AI-Driven Enhancements in Drug Screening and Optimization.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  16. Frontiers in microbiology · 2023
    Article
  17. Article
  18. Article
  19. The Structural Basis ofMolecules (Basel, Switzerland) · 2022
    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

5 authors at 2 institutions in 2 countries.

Stephanie PortelliDepartment of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Victoria, 3051, Australia.
Jody E PhelanDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.ORCID http://orcid.org/0000-0001-8323-7019
David B AscherDepartment of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Victoria, 3051, Australia.
Taane G ClarkDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.ORCID http://orcid.org/0000-0001-8985-9265
Nicholas FurnhamDepartment of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Nick.Furnham@lshtm.ac.uk.
University of London · GBThe University of Melbourne · AU

Funding

Medical Research Council MC_PC_15103Medical Research Council MR/K000551/1Medical Research Council MR/K020420/1Medical Research Council MR/M01360X/1Medical Research Council MR/M026221/1Medical Research Council MR/N010469/1
6 · The paper itself

Abstract

Genomic studies of Mycobacterium tuberculosis bacteria have revealed loci associated with resistance to anti-tuberculosis drugs. However, the molecular consequences of polymorphism within these candidate loci remain poorly understood. To address this, we have used computational tools to quantify the effects of point mutations conferring resistance to three major anti-tuberculosis drugs, isoniazid (n = 189), rifampicin (n = 201) and D-cycloserine (n = 48), within their primary targets, katG, rpoB, and alr. Notably, mild biophysical effects brought about by high incidence mutations were considered more tolerable, while different structural effects brought about by haplotype combinations reflected differences in their functional importance. Additionally, highly destabilising mutations such as alr Y388, highlighted a functional importance of the wildtype residue. Our qualitative analysis enabled us to relate resistance mutations onto a theoretical landscape linking enthalpic changes with phenotype. Such insights will aid the development of new resistance-resistant drugs and, via an integration into predictive tools, in pathogen surveillance.

Indexed as

Models, GeneticPoint MutationAntitubercular AgentsDrug Resistance, BacterialMycobacterium tuberculosisAntitubercular Agents

Identifiers

PMID30337649
PMCPMC6193939
OpenAlexW2896220734

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.