The Comprehensive Resistance Prediction for Tuberculosis International Consortium (CRyPTIC) research project has gathered the world's biggest dataset of clinical M. tuberculosis samples, which includes 15,211 samples from 27 countries across five continents.
The researchers created a unique dataset by quantifying how changes in the genetic code of M. tuberculosis reduce how well different drugs kill these bacteria that cause TB. They did this using two key advances: 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. These breakthroughs, when paired with current research, have the potential to transform how TB patients are treated in the future.
Except for SARS-CoV-2, tuberculosis kills more people each year than any other bacteria, virus, or parasite. Drug resistance has become a big concern in the last three decades, despite the fact that it is curable. The most realistic approach of bringing drug resistance testing to every patient who needs it is to test for mutations in the M. tuberculosis genome to decide which medications will provide a patient the highest chance of cure.
'This unique, large-scale, multinational collaboration has enabled us to generate arguably the most thorough map of the genetic changes responsible for treatment resistance in tuberculosis,' stated Dr. Derrick Crook, Professor of Microbiology at the University of Oxford.
The researchers reveal in a series of nine new preprint articles, each describing a distinct facet of how the CRyPTIC initiative has progressed the field:
1. How to interpret the latest drug resistance tests and
2. how a big citizen science project helped solve the problem
3. How a new method for detecting and reporting genetic alterations in the entire TB genome sequence improves the detection of medication resistance-causing genetic variations
4. How these data were utilised to look for alterations in the TB genome sequence that were previously unknown to induce medication resistance.
5. Individual mutations and combinations of mutations can be linked not simply to crude measures of'resistance' or'susceptibility,' but also to even modest changes in the way a medicine kills M. tuberculosis, lowering treatment effectiveness,
6. with special attention dedicated to two innovative tuberculosis treatments.
7. How can artificial intelligence predict medication resistance based on DNA sequence signatures?
8. How these findings influenced the World Health Organization's first list of antibiotic resistance mutations in the TB genome that was approved for global use.
These findings aim to improve tuberculosis control and facilitate the World Health Organization's end-TB goal by paving the road for universal drug susceptibility testing and better, faster, and more targeted treatment of drug-resistant tuberculosis by genetic resistance prediction (DST).
Professor Crook says, "Our ultimate goal is to produce a sufficiently precise genetic prediction of resistance to most anti-tuberculosis medications, such that whole genome sequencing can replace culture-based DST for tuberculosis." This will make rapid-turnaround near-patient assays possible, revolutionising MDR-TB diagnosis and management.'
The data, which are now openly available, can be used by the rest of the scientific community to better understand medication resistance in tuberculosis and how to combat it.
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