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

Autor: Immanuel SRC; Institute for Systems Biology, Seattle, WA, USA., Arrieta-Ortiz ML; Institute for Systems Biology, Seattle, WA, USA., Ruiz RA; Institute for Systems Biology, Seattle, WA, USA., Pan M; Institute for Systems Biology, Seattle, WA, USA., Lopez Garcia de Lomana A; Institute for Systems Biology, Seattle, WA, USA.; Center for Systems Biology, University of Iceland, Reykjavik, Iceland., Peterson EJR; Institute for Systems Biology, Seattle, WA, USA. eliza.peterson@isbscience.org., Baliga NS; Institute for Systems Biology, Seattle, WA, USA. nitin.baliga@isbscience.org.; Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA. nitin.baliga@isbscience.org.; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA. nitin.baliga@isbscience.org.; Lawrence Berkeley National Lab, Berkeley, CA, USA. nitin.baliga@isbscience.org.
Jazyk: angličtina
Zdroj: NPJ systems biology and applications [NPJ Syst Biol Appl] 2021 Dec 06; Vol. 7 (1), pp. 43. Date of Electronic Publication: 2021 Dec 06.
DOI: 10.1038/s41540-021-00205-6
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
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