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A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis.

ABSTRACT: In the last 40 years, only a single new antituberculosis drug was FDA approved. New tools that improve the drug development process will be essential to accelerate the development of next-generation antituberculosis drugs. The drug development process seems to be hampered by the inefficient transition of initially promising hits to candidate compounds that are effective in vivo. In this study, we introduce an inexpensive, rapid, and BSL-2 compatible infection model using macrophage-passaged Mycobacterium tuberculosis (Mtb) that forms densely packed Mtb/macrophage aggregate structures suitable for drug efficacy testing. Susceptibility to antituberculosis drugs determined with this Mtb/macrophage aggregate model differed from commonly used in vitro broth-grown single-cell Mtb cultures. Importantly, altered drug susceptibility correlated well with the reported ability of the respective drugs to generate high tissue and cerebrospinal fluid concentrations relative to their serum concentrations, which seems to be the best predictors of in vivo efficacy. Production of these Mtb/macrophage aggregates could be easily scaled up to support throughput efforts. Overall, its simplicity and scalability should make this Mtb/macrophage aggregate model a valuable addition to the currently available Mtb drug discovery tools.

SUBMITTER: Schaaf K

PROVIDER: S-EPMC4991579 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis.

Schaaf Kaitlyn K Hayley Virginia V Speer Alexander A Wolschendorf Frank F Niederweis Michael M Kutsch Olaf O Sun Jim J

Assay and drug development technologies 20160621 6

In the last 40 years, only a single new antituberculosis drug was FDA approved. New tools that improve the drug development process will be essential to accelerate the development of next-generation antituberculosis drugs. The drug development process seems to be hampered by the inefficient transition of initially promising hits to candidate compounds that are effective in vivo. In this study, we introduce an inexpensive, rapid, and BSL-2 compatible infection model using macrophage-passaged Myco ...[more]

PMID: 27327048

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Project description:The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

Project description:BackgroundDepletion of T cells following infection by Mycobacterium tuberculosis (Mtb) impairs disease resolution, and interferes with clinical test performance that relies on cell-mediated immunity. A number of mechanisms contribute to this T cell suppression, such as activation-induced death and trafficking of T cells out of the peripheral circulation and into the diseased lungs. The extent to which Mtb infection of human macrophages affects T cell viability however, is not well characterised.Methodology/principal findingsWe found that lymphopenia (<1.5 × 10(9) cells/l) was prevalent among culture-positive tuberculosis patients, and lymphocyte counts significantly improved post-therapy. We previously reported that Mtb-infected human macrophages resulted in death of infected and uninfected bystander macrophages. In the current study, we sought to examine the influence of infected human alveolar macrophages on T cells. We infected primary human alveolar macrophages (the primary host cell for Mtb) or PMA-differentiated THP-1 cells with Mtb H37Ra, then prepared cell-free supernatants. The supernatants of Mtb-infected macrophages caused dose-dependent, caspase-dependent, T cell apoptosis. This toxic effect of infected macrophage secreted factors did not require TNF-α or Fas. The supernatant cytotoxic signal(s) were heat-labile and greater than 50 kDa in molecular size. Although ESAT-6 was toxic to T cells, other Mtb-secreted factors tested did not influence T cell viability; nor did macrophage-free Mtb bacilli or broth from Mtb cultures. Furthermore, supernatants from Mycobacterium bovis Bacille de Calmette et Guerin (BCG)- infected macrophages also elicited T cell death suggesting that ESAT-6 itself, although cytotoxic, was not the principal mediator of T cell death in our system.ConclusionsMtb-Infected macrophages secrete heat-labile factors that are toxic to T cells, and may contribute to the immunosuppression seen in tuberculosis as well as interfere with microbial eradication in the granuloma.

Dataset Information

A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis.

Publications

A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis.

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