The Endogenous Retinoic Acid Receptor Pathway Is Exploited by Mycobacterium tuberculosis during Infection, Both In Vitro and In Vivo.

Retinoic acid (RA) is a fundamental vitamin A metabolite involved in regulating immune responses through the nuclear RA receptor (RAR) and retinoid X receptor. While performing experiments using THP-1 cells as a model for Mycobacterium tuberculosis infection, we observed that serum-supplemented cultures displayed high levels of baseline RAR activation in the presence of live, but not heat-killed, bacteria, suggesting that M. tuberculosis robustly induces the endogenous RAR pathway. Using in vitro and in vivo models, we have further explored the role of endogenous RAR activity in M. tuberculosis infection through pharmacological inhibition of RARs. We found that M. tuberculosis induces classical RA response element genes such as CD38 and DHRS3 in both THP-1 cells and human primary CD14+ monocytes via a RAR-dependent pathway. M. tuberculosis-stimulated RAR activation was observed with conditioned media and required nonproteinaceous factor(s) present in FBS. Importantly, RAR blockade by (4-[(E)-2-[5,5-dimethyl-8-(2-phenylethynyl)-6H-naphthalen-2-yl]ethenyl]benzoic acid), a specific pan-RAR inverse agonist, in a low-dose murine model of tuberculosis significantly reduced SIGLEC-F+CD64+CD11c+high alveolar macrophages in the lungs, which correlated with 2× reduction in tissue mycobacterial burden. These results suggest that the endogenous RAR activation axis contributes to M. tuberculosis infection both in vitro and in vivo and reveal an opportunity for further investigation of new antituberculosis therapies.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.