Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME

• Published in: NPJ systems biology and applications Vol. 7; no. 1; p. 43
• Main Authors: Immanuel, Selva Rupa Christinal, Arrieta-Ortiz, Mario L., Ruiz, Rene A., Pan, Min, Lopez Garcia de Lomana, Adrian, Peterson, Eliza J. R., Baliga, Nitin S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.12.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/326/22; 631/553/2711; Adaptation; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Genomes; Humans; Immune system; Immunosuppressive agents; Life Sciences; Metabolic networks; Metabolic Networks and Pathways - genetics; Metabolism; Mycobacterium tuberculosis - genetics; Mycobacterium tuberculosis - metabolism; Phenotype; Phenotypes; Prediction models; Systems Biology; Tuberculosis; Tuberculosis - drug therapy; Tuberculosis - genetics; Tuberculosis - microbiology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-021-00205-6

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.