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.
Highlights
Mycobacterium tuberculosis (Mtb) kills more people than any other microbe, and it has far resisted every attempt to bring the pandemic under control
We have demonstrated that by incorporating how transcription factor (TF) act contextually in combinatorial schemes to regulate gene expression, PRIME outperformed PROM, IDREAM and IDREAM hybrid in accurately predicting how transcriptional regulation redirects metabolic flux to manifest in environment-specific phenotypes of Mtb
The shortcoming of PROM can be attributed to its reliance on P–D interactions for regulatory network, which are plagued with false positive interactions and false negative interactions because of lack of appropriate context
Summary
Mycobacterium tuberculosis (Mtb) kills more people than any other microbe, and it has far resisted every attempt to bring the pandemic under control. BeReTa20 does take into account weighted, combinatorial influences of TFs, but the analysis is restricted to genes encoding reactions of specific pathways of interest to an industrial application None of these algorithms were designed to predict systems level phenotypic consequences (e.g., fitness and growth rate) of perturbations to the transcriptional network. The accuracy of PRIME in predicting quantitative phenotypic effects of TF perturbations is demonstrated by high correlation between predicted and experimentally validated consequences of knocking out all metabolism-associated TFs (one-at-a-time) on isoniazid (INH) treatment-specific fitness of Mtb strains Through this analysis, we have discovered new vulnerabilities in Mtb that can potentiate recapitulated 2410 of the 4546 TF–gene interactions from a P–D network of Mtb, which was derived through both physical binding (from ChIP-seq experiments) and functional evidence (from transcriptional profiling)[21,25]. For a TF that regulates multiple genes encoding enzymes or enzyme
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