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

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.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

“…Models of gene regulation networks have been expanded to include the networks of small non-coding RNAs (sRNAs) and discover new sRNA targets and interactions between sRNAs and TFs that were then experimentally validated ( Modi et al., 2011 ). Other models integrate gene regulation networks with metabolic networks to show how information flows between the different types of networks ( Chandrasekaran and Price, 2010 ; Covert et al., 2001 ; Immanuel et al., 2021 ). Gene regulatory network models integrated with metabolism are immensely useful in interpreting the causal, mechanistic, and physiological drivers of pathogen response to host-relevant stresses and drug treatment.…”

“…Gene regulatory network models integrated with metabolism are immensely useful in interpreting the causal, mechanistic, and physiological drivers of pathogen response to host-relevant stresses and drug treatment. In so doing, the models are useful to uncover mechanisms of drug action at a systems level and also discover novel vulnerabilities that can be exploited as new drug targets, or means to identify synergistic drug combinations ( Immanuel et al., 2021 ; Peterson et al., 2016 , 2020 ).…”