bioRxivMicrobiologyPreprint
Bioenergetic reprogramming of macrophages reduces drug tolerance in Mycobacterium tuberculosis
Vikas YadaviD, Sarthak SahooiD, Nitish MalhotraiD, Richa MishraiD, Raju S Rajmani, Siva Kumar Shanmugam, Radha Krishan ShandiliD, Shridhar NarayananiD, Vivek V. ThackeriD, Mohit Kumar JollyiD, Aswin Sai Narain SeshasayeeiD, Amit SinghiD
Abstract
Eradication of Mycobacterium tuberculosis (Mtb) requires strategies targeting bacteria inside the host. Mtb exhibits heterogeneity in redox metabolism inside macrophages to evade killing by anti-TB drugs. If and how macrophage physiology correlates with bacterial redox heterogeneity and drug tolerance remains unclear. Using a fluorescent reporter of mycobacterial redox potential, flow sorting, and RNA sequencing of infected macrophages, we characterized transcriptional and metabolic responses of macrophages harboring redox-diverse Mtb populations. We found that macrophages with suppressed glycolysis and elevated oxidative phosphorylation (OXPHOS) correlated with Mtb populations exhibiting reductive stress and drug tolerance. Conversely, macrophages with elevated glycolysis and suppressed OXPHOS displayed higher mitochondrial reactive oxygen species through reverse electron transport, resulting in oxidative stress in Mtb and enhancing drug efficacy. Computational and genetic approaches identified Nrf2 as a key regulator of macrophage bioenergetics driving redox heterogeneity and drug tolerance in Mtb. Redirecting macrophage metabolism from OXPHOS to glycolysis using an FDA- approved antiemetic drug, meclizine, subverted redox heterogeneity and diminished drug tolerance in macrophages and mice. The pharmacological profile of meclizine (Cmax and AUClast) indicated no adverse interactions with first-line anti-TB drugs in mice. Our data demonstrate the feasibility of reprogramming macrophage metabolism to reduce drug tolerance in Mtb infection.
- organism: human
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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