Biology subjects

Ruiz, R. A.

Publications and source records attributed to Ruiz, R. A..

3 recordsLinked to original sources

bioRxiv2021

MtrA regulation of essential peptidoglycan cleavage in Mycobacterium tuberculosis during infection

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.

systems biology↗

bioRxiv2021

Transcriptome signature of cell viability predicts drug response and drug interaction for Tuberculosis

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.

microbiology↗

bioRxiv2021

Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks of Mycobacterium tuberculosis

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.

systems biology↗