Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Pearcy, Nicole; Hu, Yue; Baker, Michelle; Maciel-Guerra, Alexandre; Xue, Ning; Wang, Wei; Kaler, Jasmeet; Peng, Zixin; Li, Fengqin; Dottorini, Tania

Abstract

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

Citation

Pearcy, N., Hu, Y., Baker, M., Maciel-Guerra, A., Xue, N., Wang, W., Kaler, J., Peng, Z., Li, F., & Dottorini, T. (2021). Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms. mSystems, 6(4), Article e00913-20. https://doi.org/10.1128/mSystems.00913-20

• Journal Article Type: Article
• Acceptance Date: Jun 24, 2021
• Online Publication Date: Aug 3, 2021
• Publication Date: Jul 1, 2021
• Deposit Date: Aug 26, 2021
• Publicly Available Date: Aug 26, 2021
• Journal: mSystems
• Electronic ISSN: 2379-5077
• Publisher: American Society for Microbiology
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 4
• Article Number: e00913-20
• DOI: https://doi.org/10.1128/mSystems.00913-20
• Keywords: Computer Science Applications; Genetics; Molecular Biology; Modelling and Simulation; Ecology, Evolution, Behavior and Systematics; Biochemistry; Physiology; Microbiology
• Public URL: https://nottingham-repository.worktribe.com/output/6093734
• Publisher URL: https://journals.asm.org/doi/10.1128/mSystems.00913-20