Hunting down the mutations behind drug resistance

July 05, 2013 − by Suzanne Elvidge − in Healthcare big data analysis − No Comments

Drug resistance is a growing issue, and for life-threatening and infectious diseases like tuberculosis (TB), it’s a grave concern worldwide. Researchers are finding mechanisms of action of drug resistance through mutations in genes, and data mining and analysis could have an important role in this type of research.

Pyrazinamide is one of the major drugs used to treat infection with Mycobacterium tuberculosis, the bacterium that causes TB. It kills dormant bacteria and is effective in both drug-susceptible and multi-drug-resistant TB (MDR-TB). However, resistance to pyrazinamide, a prodrug, is frequent, and researchers at Johns Hopkins Bloomberg School of Public Health (USA) and Huashan Hospital, Fudan University (China) have looked at the mechanisms behind this resistance. The research was published in Emerging Microbes & Infections.

Resistance to pyrazinamide is most commonly linked to mutations in the pncA gene, which converts pyrazinamide to its active form, pyrazinoic acid. Mutations in the rpsA gene are also involved in resistance, but bacteria that exhibit neither of these mutations can also be resistant. To find out more, the researchers took 174 resistant forms of Mycobacterium tuberculosis and found that five did not have mutations in either pncA or rpsA. After genome sequencing, the researchers found mutations in the gene panD in these strains, and in drug-resistant clinical isolates.

“There is significant recent interest in understanding pyrazinamide, since it is the only TB drug that cannot be replaced without compromising the efficacy of the therapy. It’s indispensible,” said Ying Zhang, MD, PhD, senior author of the study and professor in the Bloomberg School’s W. Harry Feinstone Department of Molecular Microbiology and Immunology.

While more study is needed, Zhang and his colleagues believe panD could be a potential target for new antibiotic therapies.

According to Zhang, the process of identifying the correct resistance mutations “was quite tedious and took about two years to complete.” GenoKey uses combinatorial analysis to look at combinations and permutations of different mutations and link these in with clinical data to find their importance, in a process that can drastically reduce the time taken. In a study of people with bipolar disorder, GenoKey’s technology was able to find four clusters of around 45 combinations of three SNP genotypes and link this genetic data with clinical symptoms. GenoKey’s researchers are currently looking at SNPs and clinical data from patients with neuroblastoma, and are eager to try the technology in more diseases.





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