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1.
J Infect Dis ; 221(6): 989-999, 2020 03 02.
Article in English | MEDLINE | ID: mdl-31665359

ABSTRACT

Novel antimicrobials for treatment of Mycobacterium tuberculosis are needed. We hypothesized that nicotinamide (NAM) and nicotinic acid (NA) modulate macrophage function to restrict M. tuberculosis replication in addition to their direct antimicrobial properties. Both compounds had modest activity in 7H9 broth, but only NAM inhibited replication in macrophages. Surprisingly, in macrophages NAM and the related compound pyrazinamide restricted growth of bacille Calmette-Guérin but not wild-type Mycobacterium bovis, which both lack a functional nicotinamidase/pyrazinamidase (PncA) rendering each strain resistant to these drugs in broth culture. Interestingly, NAM was not active in macrophages infected with a virulent M. tuberculosis mutant encoding a deletion in pncA. We conclude that the differential activity of NAM and nicotinic acid on infected macrophages suggests host-specific NAM targets rather than PncA-dependent direct antimicrobial properties. These activities are sufficient to restrict attenuated BCG, but not virulent wild-type M. bovis or M. tuberculosis.


Subject(s)
Macrophages/microbiology , Mycobacterium bovis/drug effects , Mycobacterium tuberculosis/drug effects , Niacinamide/pharmacology , Vitamin B Complex/pharmacology , Animals , CHO Cells , Cricetinae , Cricetulus , Cytokines , Gene Expression Regulation/drug effects , Humans , Interleukin-1beta/genetics , Interleukin-1beta/metabolism , Interleukin-6/genetics , Interleukin-6/metabolism , Macrophages/drug effects , Microbial Sensitivity Tests , Niacin/pharmacology , Niacinamide/administration & dosage , Tumor Necrosis Factor-alpha/genetics , Tumor Necrosis Factor-alpha/metabolism , U937 Cells
2.
mBio ; 10(6)2019 11 12.
Article in English | MEDLINE | ID: mdl-31719182

ABSTRACT

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.


Subject(s)
Antitubercular Agents/pharmacology , Drug Resistance, Bacterial/drug effects , Gene Expression Regulation, Bacterial/drug effects , Mycobacterium tuberculosis/drug effects , Mycobacterium tuberculosis/genetics , Transcriptome , Tuberculosis/microbiology , Antitubercular Agents/therapeutic use , Biomarkers , Drug Synergism , Drug Therapy, Combination , Gene Expression Profiling , Humans , Treatment Outcome , Tuberculosis/drug therapy
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