# A systems analysis of drug tolerance in Mycobacterium tuberculosis

> **NIH NIH R01** · INSTITUTE FOR SYSTEMS BIOLOGY · 2020 · $848,588

## Abstract

PROJECT SUMMARY
This project will address the critical need for new and effective antitubercular drugs. Our primary
objective is to elucidate the mechanisms by which Mycobacterium tuberculosis tolerates
antitubercular drug treatment. Our motivating hypothesis is that M. tuberculosis tolerates drug
induced stress by differentially regulating detoxification enzymes, efflux pumps, metabolic
activity, pellicle-forming factors, and cell wall remodeling systems. Further, we postulate that a
secondary drug targeting one or few regulators of these tolerance strategies will potentiate the
primary drug-treatment, and potentially reduce the emergence of resistance. We propose a
systems biology approach to generate a network perspective of drug-induced tolerance
mechanisms and how they are coordinated by one or few regulators that could be targeted for
overcoming drug-specific tolerance using combinatorial treatment regimens. Hence, the
innovation of our proposed research emerges from integrating network characterization of drug-
specific tolerance mechanisms into the rational discovery of novel drug combinations. In Aim 1,
we will transcriptionally profile M. tuberculosis following treatment with ten selected drugs
(primary drugs). Using techniques developed in our laboratory, differentially expressed genes
will be mapped onto a systems-scale gene regulatory network model of M. tuberculosis to infer
drug-specific tolerance sub-networks and elucidate key regulators. We will also identify
tolerance sub-networks by generating genome-wide fitness profiles in the presence of the
selected primary drugs. Drug-associated fitness defects will reveal genes that are important for
dealing with drug-induced stress and are hypothesized to cluster together in drug-specific
tolerance sub-networks. In Aim 2, we will transcriptionally profile ~250 secondary drugs and
perform combination high-throughput screens of all primary and secondary drug combinations.
Data from these studies will be used to iteratively refine the model and develop a machine
learning algorithm to identify gene- and network-level features that are predictive of synergistic
drug interactions. Finally, mechanism of synergistic drug combinations will be characterized by
selectively perturbing the predicted regulators of the tolerance sub-networks. This project will
propel the development of systems biology tools to accurately predict novel synergistic drug
combinations, thereby guiding experimental assessment and accelerating the delivery of new
treatments to patients with tuberculosis infection.

## Key facts

- **NIH application ID:** 9827520
- **Project number:** 5R01AI128215-04
- **Recipient organization:** INSTITUTE FOR SYSTEMS BIOLOGY
- **Principal Investigator:** Nitin S Baliga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $848,588
- **Award type:** 5
- **Project period:** 2016-12-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9827520

## Citation

> US National Institutes of Health, RePORTER application 9827520, A systems analysis of drug tolerance in Mycobacterium tuberculosis (5R01AI128215-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9827520. Licensed CC0.

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