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

• Published in: bioRxiv
• Main Authors: Selva Rupa Christinal Immanuel, Arrieta-Ortiz, Mario L, Ruiz, Rene A, Pan, Min, Adrian Lopez Garcia De Lomana, Peterson, Eliza J R, Baliga, Nitin S
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 31.01.2021
• Subjects: Adaptation; Genomes; Immune system; Immunosuppressive agents; Metabolic networks; Metabolism; Mycobacterium tuberculosis; Phenotypes; Prediction models; Tuberculosis
• DOI: 10.1101/2021.01.29.428876

Abstract 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. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/baliga-lab/PRIME