Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.

Treatments for tuberculosis remain lengthy, motivating a search for new drugs with novel mechanisms of action. However, it remains challenging to determine the direct targets of a drug and which disrupted cellular processes lead to bacterial killing. We developed a computational tool, DECIPHAER (decoding cross-modal information of pharmacologies via autoencoders), to select the important correlated transcriptional and morphological responses of Mycobacterium tuberculosis to treatment. By finding a reduced feature space, DECIPHAER highlighted essential features of cellular damage. DECIPHAER provides cell-death-relevant insight into uni-modal datasets, enabling interrogation of drug treatment responses for which transcriptional data are unavailable. Using morphological data alone with DECIPHAER, we discovered that respiration inhibition by the polypharmacological drugs SQ109 and BM212 can influence cell death more than their effects on the cell wall. This study demonstrates that DECIPHAER can extract the critical shared information from multi-modal measurements to identify cell-death-relevant mechanisms of TB drugs. A record of this paper's transparent peer review process is included in the supplemental information.