Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis

Austin N. Bond, Marek Orzechowski, Shuting Zhang, Ishay Ben-Zion, Allison Lemmer, Nathaniel Garry, Katie Lee, Michael Chen, Kayla Delano, Emily Gath, A. Lorelei Golas, Raymond Nietupski, Michael Fitzgerald, Sabine Ehrt, Eric J. Rubin, Christopher M. Sassetti, Dirk Schnappinger, Noam Shoresh, Diana K. Hunt, James E. Gomez () and Deborah T. Hung ()
Additional contact information
Austin N. Bond: Broad Institute of MIT and Harvard; Cambridge
Marek Orzechowski: Broad Institute of MIT and Harvard; Cambridge
Shuting Zhang: Broad Institute of MIT and Harvard; Cambridge
Ishay Ben-Zion: Broad Institute of MIT and Harvard; Cambridge
Allison Lemmer: Broad Institute of MIT and Harvard; Cambridge
Nathaniel Garry: Broad Institute of MIT and Harvard; Cambridge
Katie Lee: Broad Institute of MIT and Harvard; Cambridge
Michael Chen: Broad Institute of MIT and Harvard; Cambridge
Kayla Delano: Broad Institute of MIT and Harvard; Cambridge
Emily Gath: Broad Institute of MIT and Harvard; Cambridge
A. Lorelei Golas: Broad Institute of MIT and Harvard; Cambridge
Raymond Nietupski: Broad Institute of MIT and Harvard; Cambridge
Michael Fitzgerald: Broad Institute of MIT and Harvard; Cambridge
Sabine Ehrt: New York
Eric J. Rubin: Harvard T. H. Chan School of Public Health
Christopher M. Sassetti: UMass Chan Medical School
Dirk Schnappinger: New York
Noam Shoresh: Broad Institute of MIT and Harvard; Cambridge
Diana K. Hunt: Broad Institute of MIT and Harvard; Cambridge
James E. Gomez: Broad Institute of MIT and Harvard; Cambridge
Deborah T. Hung: Broad Institute of MIT and Harvard; Cambridge

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract We previously reported an antibiotic discovery screening platform that identifies whole-cell active compounds with high sensitivity while simultaneously providing mechanistic insight, necessary for hit prioritization. Named PROSPECT, (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), this platform measures chemical-genetic interactions between small molecules and pooled Mycobacterium tuberculosis mutants, each depleted of a different essential protein. Here, we introduce Perturbagen CLass (PCL) analysis, a computational method that infers a compound’s mechanism-of-action (MOA) by comparing its chemical-genetic interaction profile to those of a curated reference set of 437 known molecules. In leave-one-out cross-validation, we correctly predict MOA with 70% sensitivity and 75% precision, and achieve comparable results (69% sensitivity, 87% precision) with a test set of 75 antitubercular compounds with known MOA previously reported by GlaxoSmithKline (GSK). From 98 additional GSK antitubercular compounds with unknown MOA, we predict 60 to act via a reference MOA and functionally validate 29 compounds predicted to target respiration. Finally, from a set of ~5,000 compounds from larger unbiased libraries, we identify a novel QcrB-targeting scaffold that initially lacked wild-type activity, experimentally confirming this prediction while chemically optimizing this scaffold. PCL analysis of PROSPECT data enables rapid MOA assignment and hit prioritization, streamlining antimicrobial discovery.

Date: 2025
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DOI: 10.1038/s41467-025-64662-x

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