Abstract

In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.

Highlights

  • Chromobacterium violaceum is abundantly present in the soil and water as microbiota in tropical and subtropical regions around the world

  • The reconstruction was transformed into a functional model with 1255 reactions and 971 metabolites and 858 genes representing C. violaceum metabolism

  • Data mining through PubMed search engine resulted in 750 research articles related to C. violaceum (Fig A in S1 File). 472 papers provided evidence for gene protein reaction relationships and helped in model curation

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Summary

Introduction

Chromobacterium violaceum is abundantly present in the soil and water as microbiota in tropical and subtropical regions around the world. Our previous study integrating genomics, limited phenomics and metabolomics data [11], unraveled disruption of critical redox homeostasis by specific metabolite supplementation causing death of streptomycin resistant (StrpR) and chloramphenicol resistant (ChlR) populations of C. violaceum It highlighted the utility of a core model of metabolism integrated in the context of systems-level data, to generate hypothesis and predict emergent properties of antibiotic resistance. This study represents C. violaceum in silico and correlates it’s metabolic features to antibiotic resistance and predicts related metabolite vulnerabilities. Such approaches could lead to scalable pipelines using OMICS derived constraints-based flux balance models for clinical isolates

Results
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