Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC

Ramy K. Aziz, Valerie L. Khaw, Jonathan M. Monk, Elizabeth Brunk, Robert Lewis, Suh I. Loh, Arti Mishra, Amrita A. Nagle, Chitkala Satyanarayana, Saravanakumar Dhakshinamoorthy, Michele Luche, Douglas B. Kitchen, Kathleen A. Andrews, Bernhard O. Palsson, Pep Charusanti*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100-200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.

Original languageEnglish
Article number00958
JournalFrontiers in Microbiology
Volume6
Issue numberSEP
DOIs
Publication statusPublished - 2015

Bibliographical note

Publisher Copyright:
© 2015 Aziz, Khaw, Monk, Brunk, Lewis, Loh, Mishra, Nagle, Satyanarayana, Dhakshinamoorthy, Luche, Kitchen, Andrews, Palsson and Charusanti.

Other keywords

  • Antibiotic development
  • Bacterial metabolism
  • Drug discovery
  • Metabolic reconstruction
  • Model-based drug target discovery
  • Pathway gap filling
  • Synthetic lethality
  • Systems biology

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