Tuberculosis (TB) kills almost 2 million people worldwide each year. It is a bacterial infection that mainly affects the lungs.
Although, the condition can be treatable with the right antibiotics. Drug resistance has emerged as a significant problem over the past three decades.
To determine which drugs give a patient the best chance of cure, clinicians need to test for the mutations in the M. tuberculosis genome. It is the most realistic way of getting drug resistance testing for every patient who needs it.
Now, a large-scale, international collaboration has enabled scientists to create possibly the most comprehensive map yet of the genetic changes responsible for drug resistance in tuberculosis.
The Comprehensive Resistance Prediction for Tuberculosis International Consortium (CRyPTIC) research project has collected the largest ever global dataset of clinical M. tuberculosis samples worldwide, consisting of 15,211 samples from 27 countries on five continents.
Scientists have generated a unique dataset that the team has used to quantify how changes in the genetic code of M. tuberculosis reduce how well different drugs kill these bacteria that cause TB.
Scientists at the University of Oxford used advanced genomic sequencing techniques: a new quantitative test for drug resistance and a new approach that identifies all the genetic changes in a sample of drug-resistant TB bacteria. Using these techniques, scientists were able to identify the genomic variation that gives people resistance to 13 of the most common tuberculosis (TB) drug treatments.
The outcomes help improve controls of TB and facilitate health organizations better and faster treatment of tuberculosis via genetic resistance prediction. It could also lead to universal drug susceptibility testing (DST).
Dr. Derrick Crook, Professor of Microbiology at the University of Oxford, said, “Our ultimate goal is to achieve a sufficiently accurate genetic prediction of resistance to most anti-tuberculosis drugs so that whole-genome sequencing can replace culture-based DST for TB. This will enable rapid-turnaround near-to-patient assays to revolutionize MDR-TB identification and management.”
Manuscripts:
- Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis. The CRyPTIC Consortium (2021) medRxiv preprint. DOI: 10.1101/2021.02.24.21252386
- BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates. Fowler PW et al. (2021) biorXiv preprint. DOI: 10.1101/2021.07.20.453060
- Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes. Hunt M et al. (2021). bioRxiv preprint. DOI: 10.1101/2021.09.15.460475
- Genome-wide association studies of global Mycobacterium tuberculosis resistance to thirteen antimicrobials in 10,228 genomes. The CRyPTIC Consortium (2021). bioRxiv preprint. DOI: 10.1101/2021.09.14.460272
- Quantitative measurement of antibiotic resistance in M. tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach. The CRyPTIC Consortium (2021). bioRxiv preprint. DOI: 10.1101/2021.09.14.460353
- Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach. Sonnenkalb L et al. (2021) biorXiv preprint. DOI: 10.1101/2021.03.19.436148
- A generalizable approach to drug susceptibility prediction for M. tuberculosis using machine learning and whole-genome sequencing. The CRyPTIC Consortium (2021). bioRxiv preprint. DOI: 10.1101/2021.09.14.458035
- A data compendium of Mycobacterium tuberculosis antibiotic resistance. The CRyPTIC Consortium (2021) bioRxiv preprint. DOI: 10.1101/2021.09.14.460274
- The 2021 WHO catalog of M. tuberculosis complex mutations associated with drug resistance: A new global standard for molecular diagnostics. Walker et al. (2021) Lancet preprint. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3923444 http://www.crypticproject.org/wp-content/uploads/2021/09/CRyPTIC9-WHO-preprint.pdf