Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis
Low genetic diversity of Mycobacterium tuberculosis suggests mainly human factors for variations in infection outcome. Published in PNAS, the ETH groups of Zampieri, Sauer, and Stelling with the Gagneux lab (Swiss TPH) developed an approach to systematically predict metabolic functionality of rare genetic variants and identify strain-specific metabolic vulnerabilities.
Because genetic diversity in Mycobacterium tuberculosis (MTB) is less pronounced than in other pathogens, the variable outcome of infection has been mainly attributed to host and environmental factors. The research teams at ETH and Swiss TPH revealed widely different metabolic phenotypes among different clinical MTB strains, suggesting that strain diversity may play an important role during infection. To unravel the genetic basis for metabolic diversity, the authors developed an approach that integrates metabolomic and genomic data for 18 MTB clinical strains. It allowed to investigate metabolic effects of rare genetic variants and predict mutations that associate with strain-specific metabolic vulnerabilities and inherent baseline susceptibility to antibiotics. This model-based approach is broadly applicable also when genome-wide association studies fail due to limited sample size, opening new possibilities for identifying more selective treatment strategies.
Link to the paper in external page PNAS.