Dissemin is shutting down on January 1st, 2025

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American Chemical Society, ACS Chemical Biology, 10(8), p. 2224-2234, 2013

DOI: 10.1021/cb4004817

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Identification of Novel Inhibitors of Nonreplicating Mycobacterium tuberculosis Using a Carbon Starvation Model

This paper is available in a repository.
This paper is available in a repository.

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Abstract

During Mycobacterium tuberculosis infection, a population of bacteria is thought to exist in a non-replicating state, refractory to antibiotics, which may contribute to the need for prolonged antibiotic therapy. The identification of inhibitors of the non-replicating state provides tools that can be used to probe this hypothesis and the physiology of this state. The development of such inhibitors also has the potential to shorten the duration of antibiotic therapy required. Here we describe the development of a novel non-replicating assay amenable to high-throughput chemical screening coupled with secondary assays that use carbon starvation as the in vitro model. Together these assays identify compounds with activity against replicating and non-replicating M. tuberculosis as well as compounds that inhibit the transition from non-replicating to replicating stages of growth. Using these assays we successfully screened over 300,000 compounds and identified 786 inhibitors of non-replicating M. tuberculosis. In order to understand the relationship among different non-replicating models, we teste 52 of these molecules in a hypoxia model and four different chemical scaffolds in a stochastic persist model and a streptomycin dependent model. We found that compounds display varying levels of activity in different models for the non-replicating state, suggesting important differences in bacterial physiology between models. Therefore, chemical tools identified in this assay may be useful for determining the relevance of different non-replicating in vitro models to in vivo M. tuberculosis infection. Given our current limited understanding, molecules that are active across multiple models may represent more promising candidates for further development.