Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks.

AMR Antimicrobial resistance Cytoscape Enrichment analysis Gene interaction network M. tuberculosis

Journal

Advances in protein chemistry and structural biology
ISSN: 1876-1631
Titre abrégé: Adv Protein Chem Struct Biol
Pays: Netherlands
ID NLM: 101497281

Informations de publication

Date de publication:
2023
Historique:
entrez: 1 3 2023
pubmed: 2 3 2023
medline: 4 3 2023
Statut: ppublish

Résumé

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Identifiants

pubmed: 36858742
pii: S1876-1623(22)00106-7
doi: 10.1016/bs.apcsb.2022.11.017
pii:
doi:

Substances chimiques

Anti-Bacterial Agents 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

53-74

Informations de copyright

Copyright © 2022. Published by Elsevier Inc.

Auteurs

Keerthana G (K)

Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, India; Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India; Faculty of Allied Health Sciences, Meenakshi Academy of Higher Education and Research (Deemed to be University), Chennai, India; Department of Biomedical Sciences, College of Health and Sciences, QU Health, Qatar University, Doha, Qatar.

Karthick Vasudevan (K)

Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, India. Electronic address: karthick.1087@gmail.com.

Hrituraj Dey (H)

Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, India.

Tasmia Kausar (T)

Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, India.

S Udhaya Kumar (S)

Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

D Thirumal Kumar (D)

Faculty of Allied Health Sciences, Meenakshi Academy of Higher Education and Research (Deemed to be University), Chennai, India.

Hatem Zayed (H)

Department of Biomedical Sciences, College of Health and Sciences, QU Health, Qatar University, Doha, Qatar.

C George Priya Doss (C)

Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India. Electronic address: georgepriyadoss@vit.ac.in.

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Classifications MeSH