The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: A new global standard for molecular diagnostics
WHO preprint 21 September 2021
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CRyPTIC: cataloguing drug resistance mutations in M. tuberculosis
Profiling M. tuberculosis strains from 27 countries to reveal causes of drug resistance
In collaboration with colleagues across the world including EMBL’s European Bioinformatics Institute (EMBL-EBI), the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) project has generated the world’s largest data resource for the study of drug resistance in TB.
CRyPTIC aims to reveal the genetic causes of drug resistant TB to enable the development of DNA-based diagnostics. Using a new experimental assay, the consortium has semi-quantitatively measured the level of drug resistance and scanned the genome of over 15,000 Mycobacterium tuberculosis (M. tuberculosis) strains from across the globe.
Together these form a unique, rich dataset, which can be used to probe the genetic causes of drug resistance in TB. The end goal is to apply these insights to DNA-based diagnostics, predicting the drug resistance of a patient TB strain to different drugs allowing for personalised treatment.
These data are hosted at EMBL-EBI where they will be made freely available to the scientific community.
Improving TB treatment
“TB currently causes over a million deaths per year all across the globe, and appropriate therapy is dependent on identifying which drugs will and won’t work for each patient’s particular infection,” said Zamin Iqbal, Group Leader at EMBL-EBI. “Determining the genetic basis of drug resistant TB enables rapid DNA-based diagnostics for identifying resistance to drugs, and informing therapy choice. By using quantitative measurements, rather than binary yes or no to resistance, we have been able to discern the fine-scale impact of specific mutations on resistance. In the process, this has generated a huge resource for science which we expect to be used for years to come.”
This work is detailed in nine new studies, covering a range of topics including:
- new approaches to detecting genetic mutations in the M. tuberculosis genome
- discovering the genetic causes of drug resistance and the precise impact of each mutation
- how artificial intelligence can predict drug resistance from DNA signatures
- how these data were used to build the new WHO TB resistance catalogue
You can find out more about this work in the preprints listed below.
Find out more
Learn more in this press release published on the Oxford University website.
Funding
The CRyPTIC project is funded by the MRC Newton Fund, Wellcome, and the Bill and Melinda Gates Foundation.
This post was originally published on EMBL-EBI News
Source article(s)
Genome-wide association studies of global Mycobacterium tuberculosis resistance to thirteen antimicrobials in 10,228 genomes
BioRxiv 16 September 2021
10.1101/2021.09.14.460272
A generalisable approach to drug susceptibility prediction for M. Tuberculosis using machine learning and whole-genome sequencing
BioRxiv 16 September 2021
10.1101/2021.09.14.458035
Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach
BioRxiv 16 September 2021
10.1101/2021.09.14.460353
Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis
MedRxiv 16 September 2021
10.1101/2021.02.24.21252386
A data compendium of Mycobacterium tuberculosis antibiotic resistance
BioRxiv 16 September 2021
10.1101/2021.09.14.460274
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
BioRxiv 13 September 2021
10.1101/2021.07.20.453060
Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes
BioRxiv 15 September 2021
10.1101/2021.09.15.460475
Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach
BioRxiv 13 September 2021
10.1101/2021.03.19.436148
