Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Abstract

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.

Transcriptional regulator-induced phenotype screen reveals drug potentiators in Mycobacterium tuberculosis

“…RNA was isolated from these cultures and were prepared for sequencing as described previously 14 , 49 , 50 . Briefly, cell pellets in Trizol were transferred to a tube containing Lysing Matrix B (QBiogene) and vigorously shaken at maximum speed for 30 s in a FastPrep 120 homogenizer (QBiogene) three times, cooling on ice between shakes.…”

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

“…Further, directly relating in vitro antimicrobial activity to in vivo efficacy does not necessarily capture the full range of antimicrobial activity that occurs within granulomas and may partially account for any discrepancies between our simulation results and clinical observations. An additional limitation of our model is that it currently assumes there are no interactions occurring between antibiotics, and synergistic or antagonistic combinations may be relevant in determining regimen efficacy (Swaminathan et al, 2016;Ma et al, 2019). Going forward, we are currently introducing synergistic and antagonistic antibiotic interactions to improve the PD model and further refine our estimates and predictions of granuloma sterilization (Chandrasekaran et al, 2016;Cokol et al, 2017;Cokol et al, 2018).…”

Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality

“…Studies in bacteria treated with antimicrobials and in cancer cells treated with chemotherapeutics have demonstrated that all cells elicit protective transcriptional responses to better tolerate cytotoxic drug effects (Huang et al, 2020;Niepel et al, 2017). Secondary drugs targeting new vulnerabilities within active regulatory and metabolic networks of the tolerant state can potentiate the cytotoxic effect of the primary drug (Cokol et al, 2018;Ma et al, 2019;Peterson et al, 2016;Plaisier et al, 2016). Similarly, we posit that new vulnerabilities in the regulatory and metabolic networks of a virus-infected cell can be targeted with drugs that will have no cytotoxicity to uninfected cells.…”