Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis.

Ruth Großeholz,Brett Olivier,Nadine Veith,Madlen Strauss,Ching-Chiek Koh,Bernd Kreikemeyer,Tomas Fiedler,Olga T Schubert,Frank Bergmann,Ursula Kummer,Ben C Collins,Ruedi Aebersold

doi:10.1038/npjsba.2016.17

Abstract

Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.

Highlights

Genome-scale models link genomic information and experimentally observable biological phenotypes by allowing in silico experiments on a whole-cell scale.[1]
We observed a decrease in biomass from 1.98 to 1.51 gDW l − 1 upon the pH shift, which was associated with an increased glucose uptake rate (Figure 2b, Supplementary Tables S5,S6)
We observed increased uptake rates for L-arginine and L-serine during the pH shift (Figure 2c). Both amino acids are used for energy generation and the role of L-arginine in the adaptive response to decreasing extracellular pH values has already been discussed for other lactic acid bacteria.[28]

Summary

Introduction

Genome-scale models link genomic information and experimentally observable biological phenotypes by allowing in silico experiments on a whole-cell scale.[1]. We describe a method to integrate whole-cell proteomic data into genome-scale models, facilitating both mapping of detected proteins and concentration changes thereof to constrain the solution space.

Results

Conclusion