Whole-Genome Sequencing Applications in Tuberculosis Drug Resistance

Whole-genome sequencing has transformed the detection and characterisation of drug resistance in Mycobacterium tuberculosis by enabling comprehensive analysis of genetic variation across the bacterial genome. This approach identifies both known resistance-conferring mutations and novel variants, supporting tailored therapeutic regimens and guiding public health responses. In clinical settings, WGS can predict resistance profiles within days of culture positivity, reducing delays of phenotypic testing. Large-scale sequencing efforts have produced mutation catalogues that underpin diagnostic standards, while bioinformatics pipelines facilitate routine data processing even in resource-limited environments. Beyond diagnostics, WGS informs transmission dynamics, phylogenetic lineage assignment and the emergence of resistance clones. Recent advances include rapid de Bruijn graph algorithms, expert-curated knowledgebases for variant interpretation and machine-learning models for robust phenotype prediction, all contributing to global efforts against multidrug-resistant tuberculosis.

Research from Nature Portfolio

De Bruijn graph-based algorithms for rapid resistance detection demonstrated species identification and resistance prediction directly from raw reads in under three minutes, using clinician-friendly reports on standard laptops. High sensitivity and specificity were achieved for key first- and second-line drugs, with detection of minor allele populations linked to extreme resistance and compatibility with emerging nanopore platforms for near-patient testing.

An integrated variant pipeline combined thousands of Mycobacterium tuberculosis sequences with phenotypic data to assign confidence-graded resistance mutations. Rigorous quality control, lineage assignment and aggregation of globally sourced isolates established a reference framework for harmonised interpretation, aiding assay development and cross-laboratory consistency.

Whole-Genome Sequencing Applications in Tuberculosis Drug Resistance publication trend

The graph below shows the total number of articles in whole-genome sequencing applications in tuberculosis drug resistance across all publications each year (not limited to Nature Index journals).

Technical terms

Whole-genome sequencing: The process of determining the complete DNA sequence of an organism’s genome at a single time.

Drug susceptibility testing (DST): Laboratory procedures that assess the sensitivity of bacterial isolates to antimicrobial agents.

Minimum inhibitory concentration (MIC): The lowest concentration of an antibiotic that prevents visible growth of a microorganism in vitro.

Mutation catalogue: A curated list of genetic variants known to confer drug resistance or susceptibility.

Genotypic prediction: Inference of drug resistance phenotypes based on identified genetic mutations.

Pan-susceptible: Referring to isolates predicted to be sensitive to all tested antibiotics.

Multidrug-resistant: Designation for strains resistant to at least isoniazid and rifampicin, the two most potent first-line drugs.

References

1. Role of the first WHO mutation catalogue in the diagnosis of antibiotic resistance in Mycobacterium tuberculosis in the Valencia Region, Spain: a retrospective genomic analysis. The Lancet Microbe (2023).
2. Afro-TB dataset as a large scale genomic data of Mycobacterium tuberuclosis in Africa. Scientific Data (2023).
3. Rapid, comprehensive, and affordable mycobacterial diagnosis with whole-genome sequencing: a prospective study. The Lancet Respiratory Medicine (2015).
4. MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates. PeerJ (2018).
5. The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis. The Lancet Microbe (2022).
6. Integrating standardized whole genome sequence analysis with a global Mycobacterium tuberculosis antibiotic resistance knowledgebase. Scientific Reports (2018).