Identification of new inhibitors of essential functions in M. tuberculosis by high-throughput metabolic profiling

Project Summary/Abstract
While the emergence of multi-drug resistant tuberculosis raises an urgent need for antimicrobials with new
Modes of Action, their discovery remains a major challenge. Many of the techniques to unravel drug Modes
of Action rely on low-throughput, time-consuming and target-specific approaches that provide low-
dimensional views into the broader functional impact of potential drugs. Together with the Zampieri lab at
ETH Zurich, Switzerland, by leveraging CRISPR technology and non-targeted metabolomics, we
developed a combined computational/experimental strategy that is based on the comparison of genetic
and drug induced metabolic effects and allows to perform high-throughput de novo functional annotations
of large compound libraries. Unraveling the mechanistic basis of drug or gene perturbations of thousands
of metabolites provides rich multidimensional information complementary to classical phenotypic profiling
and can be used to investigate the effect and mode of action of any drug candidate.
The overall goal of the present proposal is to achieve functional annotation of 500 anti-TB compounds with
known potency but unknown MoA, which will pave the way to new unconventional strategies to eradicate
TB. We will build a compendium of metabolic responses of Mtb to essential gene knockdown, transcription
factor overexpression and a unique selection of libraries of Mtb growth inhibitors. We will use our custom-
developed computational tools to categorize drug action and gene knockdowns according to metabolic
profiles, make testable hypothesis about unconventional drug MoA and move prioritized compounds to
genetic and biochemical hit validation. An important collateral benefit of our proposed work will be
functional annotation of genes with yet unknown function in M. tuberculosis, and an information dense
database on gene-drug-metabolic interactions in M. tuberculosis.

Grant Number: 5R01AI173328-03
NIH Institute/Center: NIH
Principal Investigator: Michael Berney