5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018)

Exploring the Metabolic Role of Multidrug-Resistance Efflux Pumps in Salmonella Typhimurium

Authors

Sean G. Mack - Presenter, University of Maryland
Daniel J. Dwyer, University of Maryland
Xuan Wang-Kan, University of Birmingham
Laura J. V. Piddock, University of Birmingham
The surge in antibiotic resistance poses a serious threat to human health. In Gram-negative pathogens, diverse resistance-nodulation-division (RND) efflux pumps provide broad-spectrum resistance and therefore greatly contribute to antibiotic treatment failure [1]. While the structure and protective roles of these pumps has been established [2], their homeostatic contributions are poorly understood. Recent work has suggested a broader metabolic role for the AcrB efflux pump in Salmonella enterica Typhimurium, with AcrB being the most common member of the RND family. The loss of AcrB efflux, but not expression, led to a loss in virulence and the downregulation of various pathogenicity factors [3]. Given that AcrB is highly conserved across all Enterobacteriaceae [2], further exploration of its potential metabolic role is clearly needed.

To address this gap in understanding, we have developed a computational framework to predict and compare the metabolic behaviors of wild-type and the loss-of-function AcrB mutant of S. Typhimurium. Specifically, we analyzed RNAseq data from S. Typhimurium WT and mutant strains grown in minimal media for differentially expressed genes. Using a modified version of the metabolic transformation algorithm (MTA) [5], transcriptomic data were integrated with a genome-scale metabolic model for S. Typhimurium [6] to generate metabolic flux predictions for each strain. From these flux profiles, we identified genetic perturbations that would shift WT metabolism closer to mutant metabolism (and vice versa). Our findings suggest the loss in AcrB efflux leads to increased fatty acid biosynthesis which may contribute to the overall loss in virulence. To validate these predictions, we are currently testing our top targets via metabolomics and infection assays.

  1. Blair, J. M. A. et al. Nature Reviews Microbiology (2015).
  2. Du, D. et al. Biological Chemistry (2015).
  3. Wang-Kan, X. et al. mBio (2017).
  4. Yizhak, K. et al. Nature Communications (2013).
  5. Thiele, I. et al. BMC Systems Biology (2011).