Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis

There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and ``MLSynergy,'' algorithms to perform rapid drug response assays and predict response of Mycobacterium tuberculosis (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alternative to time- and resourceintensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict synergistic and antagonistic multidrug combinations using transcriptomes of Mtb treated with single drugs. Together, DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations.

Automated summary

The study presents a method named DRonA and MLSynergy, which uses transcriptome signature to detect drug responses of Mycobacterium tuberculosis and predict their cell killing effects, providing a new framework for rapid monitoring of drug effects and discovery of effective drug combinations.