An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates.

Publication date: Oct 21, 2024

Tuberculosis (TB) remains one of the leading infectious disease killers in the world. The ongoing development of novel anti-TB medications has yielded potent compounds that often target single sites with well-defined mechanisms of action. However, despite the identification of resistance-associated mutations through target deconvolution studies, comparing these findings with the diverse Mycobacterium tuberculosis populations observed in clinical settings is often challenging. To address this gap, we constructed an open-access database encompassing genetic variations from > 50,000 clinical isolates, spanning the entirety of the M. tuberculosis protein-encoding genome. This resource offers a valuable tool for investigating the prevalence of target-based resistance mutations in any drug target within clinical contexts. To demonstrate the practical application of this dataset in drug discovery, we focused on drug targets currently undergoing phase II clinical trials. By juxtaposing genetic variations of these targets with resistance mutations derived from laboratory-adapted strains, we identified multiple positions across three targets harbouring resistance-associated mutations already present in clinical isolates. Furthermore, our analysis revealed a discernible correlation between genetic diversity within each protein and their predicted essentiality. This meta-analysis, openly accessible via a dedicated dashboard, enables comprehensive exploration of genetic diversity pertaining to any drug target or resistance determinant in M. tuberculosis.

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Concepts Keywords
Genetic Antitubercular Agents
Killers Antitubercular Agents
Mycobacterium Bacterial Proteins
Valuable Bacterial Proteins
Clinical isolate
Conservation
Databases, Genetic
Drug Resistance, Bacterial
Genetic diversity
Genetic Variation
Genome, Bacterial
Humans
Mutation
Mycobacterium tuberculosis
Mycobacterium tuberculosis
Non-synonymous changes
Tuberculosis

Semantics

Type Source Name
disease MESH Tuberculosis
pathway KEGG Tuberculosis
disease MESH infectious disease
pathway REACTOME Infectious disease
disease IDO protein
disease MESH infection
disease IDO pathogen
disease MESH COVID 19
drug DRUGBANK Spinosad
disease MESH drug toxicity
disease IDO bacteria
drug DRUGBANK Coenzyme M
drug DRUGBANK Streptomycin
drug DRUGBANK Linezolid
disease IDO drug susceptibility
disease MESH leprosy
drug DRUGBANK Ethylenediamine
drug DRUGBANK Tilmicosin
disease IDO country
drug DRUGBANK P54
disease IDO host
drug DRUGBANK Bedaquiline
drug DRUGBANK Clofazimine
drug DRUGBANK L-Alanine
drug DRUGBANK L-Cysteine
drug DRUGBANK L-Aspartic Acid
drug DRUGBANK Glutamic Acid
drug DRUGBANK L-Phenylalanine
drug DRUGBANK Glycine
drug DRUGBANK Histidine
drug DRUGBANK L-Isoleucine
drug DRUGBANK L-Lysine
drug DRUGBANK L-Leucine
drug DRUGBANK Methionine
drug DRUGBANK L-Asparagine
drug DRUGBANK Proline
drug DRUGBANK L-Glutamine
drug DRUGBANK L-Arginine
drug DRUGBANK Serine
drug DRUGBANK L-Threonine
drug DRUGBANK L-Valine
disease MESH tryptophan
drug DRUGBANK L-Tryptophan
drug DRUGBANK L-Tyrosine
drug DRUGBANK Etodolac
drug DRUGBANK Alpha-1-proteinase inhibitor
disease IDO cell
drug DRUGBANK Epetraborole
drug DRUGBANK Norvaline
drug DRUGBANK Adenosine
drug DRUGBANK Lansoprazole
disease IDO antibiotic resistance
drug DRUGBANK Methylergometrine
drug DRUGBANK D-Alanine
pathway REACTOME Reproduction

Original Article

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