Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

Yu Chen, Eunice van Pelt-KleinJan, Berdien van Olst, Sieze Douwenga, Sjef Boeren, Herwig Bachmann, Douwe Molenaar, Jens Nielsen, Bas Teusink

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose-limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model-based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient-rich environments and thus form targets of fitness improvement.

Original languageEnglish
Article numbere10093
Pages (from-to)1-13
Number of pages13
JournalMolecular Systems Biology
Volume17
Issue number4
DOIs
Publication statusPublished - 1 Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors. Published under the terms of the CC BY 4.0 license.

Copyright:
This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine

Funding

The project is organized by and executed under the auspices of TiFN, a public–private partnership on precompetitive research in food and nutrition. The authors have declared that no competing interests exist in the writing of this publication. Funding for this research was obtained from Friesland Campina, CSK Food Enrichment, the Netherlands Organisation for Scientific Research and the Top‐sector Agri&Food. Y.C. and J.N. acknowledge funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 686070. Y.C. and J.N. also acknowledge funding from the Novo Nordisk Foundation (grant no. NNF10CC1016517). E.v.P.‐K., B.v.O., S.B., H.B., D.M., and B.T. acknowledge funding from the Netherlands Organisation for Scientific Research (grant no. ALWTF.2015.4). S.D., H.B., D.M., and B.T. acknowledge funding from the Top‐sector Agri&Food (grant no. AF‐15503). The computations were partially enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N partially funded by the Swedish Research Council through grant agreement no. 2018‐05973. Y.C. and J.N. acknowledge funding from the European Union?s Horizon 2020 research and innovation program under Grant Agreement No. 686070. Y.C. and J.N. also acknowledge funding from the Novo Nordisk Foundation (grant no. NNF10CC1016517). E.v.P.-K., B.v.O., S.B., H.B., D.M., and B.T. acknowledge funding from the Netherlands Organisation for Scientific Research (grant no. ALWTF.2015.4). S.D., H.B., D.M., and B.T. acknowledge funding from the Top-sector Agri&Food (grant no. AF-15503). The computations were partially enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N partially funded by the Swedish Research Council through grant agreement no. 2018-05973.

FundersFunder number
Friesland Campina
Top-sector Agri&Food
Top‐sector Agri&FoodAF‐15503
Horizon 2020 Framework Programme686070
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Vetenskapsrådet2018‐05973
Centralsjukhuset Kristianstad
Novo Nordisk FondenNNF10CC1016517

    Keywords

    • Lactococcus lactis
    • ccpA
    • laboratory evolution
    • metabolic modeling
    • proteome constraint

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