Eliza J. R. Peterson

Anti-TB drugs act non-uniformly on Mycobacterium tuberculosis (Mtb) because of its distinct physiological states across diverse infection niches shaped by host-derived nutrients and stresses. Agonists of the adenylyl cyclase Rv1625c are a novel drug class that selectively inhibits Mtb growth in macrophages and cholesterol-rich conditions by an unknown mechanism. Combining condition-resolved transcriptomics, genome-scale metabolic modeling, and genetic perturbation, we show these agonists cause a blockade in the electron transport chain, which likely activates Rv1625c. The elevated cAMP in turn drives global transcriptional and post-translational remodeling of central carbon and lipid metabolism. The resulting methylcitrate cycle reversal chokes cholesterol breakdown products from entering central metabolism, diverting carbon toward cell wall and virulence-lipid (phthiocerol dimycocerosate) synthesis and inhibiting growth. These drugs thus hijack an endogenous switch that reroutes carbon from biomass to virulence-lipid production. Hence, nutrients that restore carbon flux and relieve ETC blockade reduce activity, whereas Rv1625c overexpression and ETC inhibitors potentiate drug action even in refractory conditions.

Mycobacterium tuberculosis has a lipid-rich cell envelope that is remodeled throughout infection to enable adaptation within the host. Few transcriptional regulators have been characterized that coordinate synthesis of mycolic acids, the major cell wall lipids of mycobacteria. Here, we show that the mycolic acid desaturase regulator (MadR), a transcriptional repressor of the mycolate desaturase genes desA1 and desA2, controls mycolic acid desaturation and biosynthesis in response to cell envelope stress. A madR-null mutant of M. smegmatis exhibited traits of an impaired cell wall with an altered outer mycomembrane, accumulation of a desaturated α-mycolate, susceptibility to antimycobacterials, and cell surface disruption. Transcriptomic profiling showed that enriched lipid metabolism genes that were significantly down-regulated upon madR deletion included acyl-coenzyme A (aceyl-CoA) dehydrogenases, implicating it in the indirect control of β-oxidation pathways. Electromobility shift assays and binding affinities suggest a unique acyl-CoA pool–sensing mechanism, whereby MadR is able to bind a range of acyl-CoAs, including those with unsaturated as well as saturated acyl chains. MadR repression of desA1/desA2 is relieved upon binding of saturated acyl-CoAs of chain length C₁₆ to C₂₄, while no impact is observed upon binding of shorter chain and unsaturated acyl-CoAs. We propose this mechanism of regulation as distinct to other mycolic acid and fatty acid synthesis regulators and place MadR as the key regulatory checkpoint that coordinates mycolic acid remodeling during infection in response to host-derived cell surface perturbation. Significance Our studies show that the mycolic acid desaturase regulator (MadR) acts as a molecular switch, controlling the desaturation and biosynthesis of mycolic acids, key lipids of the cell envelopes of mycobacteria. MadR works by a distinct mechanism wherein it binds various acyl-coenzyme As (aceyl-CoAs), but only saturated acyl-CoAs relieve DNA binding and repression. This suggests a unique mechanism that involves sensing of acyl-CoA pools as a checkpoint for coordinating mycolic acid remodeling and biosynthesis in response to cell surface perturbation. Our findings further our understanding of how mycobacteria control cell wall composition in response to stress across various environments ranging from soil to an intracellular niche in infected macrophages, with implications for understanding strategies for pathogenesis in the tubercle bacillus.

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

The success of Mycobacterium tuberculosis (Mtb) is largely due to its ability to withstand multiple stresses encountered in the host. Here, we present a data-driven model that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. The model captures the genome-wide distribution of cis -acting gene regulatory elements and the conditional influences of transcription factors at those elements to elicit environment-specific responses. Analysis of transcriptional responses that may be essential for Mtb to survive acidic stress within the maturing macrophage, identified regulatory control by the MtrAB two-component signal system. Using genome-wide transcriptomics as well as imaging studies, we have characterized the MtrAB circuit by tunable CRISPRi knockdown in both Mtb and the non-pathogenic organism, M. smegmatis (Msm). These experiments validated the essentiality of MtrA in Mtb, but not Msm. We identified that MtrA regulates multiple enzymes that cleave cell wall peptidoglycan and is required for efficient cell division. Moreover, our results suggest that peptidoglycan cleavage, regulated by MtrA, is necessary for Mtb to survive intracellular stress. Further, we present MtrA as an attractive drug target, as even weak repression of mtrA results in loss of Mtb viability and completely clears the bacteria with low-dose isoniazid or rifampicin treatment.

The treatment of tuberculosis (TB), which kills 1.8 million each year, remains difficult, especially with the emergence of multidrug resistant strains of Mycobacterium tuberculosis (Mtb). While there is an urgent need for new drug regimens to treat TB, the process of drug evaluation is slow and inefficient owing to the slow growth rate of the pathogen, the complexity of performing bacteriologic assays in a high-containment facility, and the context-dependent variability in drug sensitivity of the pathogen. Here, we report the development of “DRonA” and “MLSynergy”, algorithms to perform rapid drug response assays and predict response of Mtb to novel drug combinations. Using a novel transcriptome signature for cell viability, DRonA accurately detects bacterial killing by diverse mechanisms in broth culture, macrophage infection and patient sputum, providing an efficient, and more sensitive alternative to time- and resource-intensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict novel synergistic and antagonistic multi-drug combinations using transcriptomes of Mtb treated with single drugs. Together DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations.

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states, underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME ( P henotype of R egulatory influences I ntegrated with M etabolism and E nvironment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

Mycobacterium tuberculosis (MTB) generates phenotypic diversity to persist and survive the harsh conditions encountered during infection. MTB avoids immune effectors and antibacterial killing by entering into distinct physiological states. The surviving cells, persisters, are a major barrier to the timely and relapse-free treatment of tuberculosis (TB). We present for the first time, PerSort, a method to isolate and characterize persisters in the absence of antibiotic or other pressure. We demonstrate the value of PerSort to isolate translationally dormant cells that preexisted in small numbers within Mycobacterium species cultures growing under optimal conditions but that dramatically increased in proportion under stress conditions. The translationally dormant subpopulation exhibited multidrug tolerance and regrowth properties consistent with those of persister cells. Furthermore, PerSort enabled single-cell transcriptional profiling that provided evidence that the translationally dormant persisters were generated through a variety of mechanisms, including vapC30, mazF, and relA/spoT overexpression. Finally, we demonstrate that notwithstanding the varied mechanisms by which the persister cells were generated, they converge on a similar low-oxygen metabolic state that was reversed through activation of respiration to rapidly eliminate persisters fostered under host-relevant stress conditions. We conclude that PerSort provides a new tool to study MTB persisters, enabling targeted strategies to improve and shorten the treatment of TB. IMPORTANCEMycobacterium tuberculosis (MTB) persists and survives antibiotic treatments by generating phenotypically heterogeneous drug-tolerant subpopulations. The surviving cells, persisters, are a major barrier to the relapse-free treatment of tuberculosis (TB), which is already killing >1.8 million people every year and becoming deadlier with the emergence of multidrug-resistant strains. This study describes PerSort, a cell sorting method to isolate and characterize, without antibiotic treatment, translationally dormant persisters that preexist in small numbers within Mycobacterium cultures. Characterization of this subpopulation has discovered multiple mechanisms by which mycobacterial persisters emerge and unveiled the physiological basis for their dormant and multidrug-tolerant physiological state. This analysis has discovered that activating oxygen respiratory physiology using l-cysteine eliminates preexisting persister subpopulations, potentiating rapid antibiotic killing of mycobacteria under host-relevant stress. PerSort serves as a new tool to study MTB persisters for enabling targeted strategies to improve and shorten the treatment of TB.