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 language | English |
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Article number | e10093 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Molecular Systems Biology |
Volume | 17 |
Issue number | 4 |
DOIs | |
Publication status | Published - 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.
Funders | Funder number |
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Friesland Campina | |
Top-sector Agri&Food | |
Top‐sector Agri&Food | AF‐15503 |
Horizon 2020 Framework Programme | 686070 |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | |
Vetenskapsrådet | 2018‐05973 |
Centralsjukhuset Kristianstad | |
Novo Nordisk Fonden | NNF10CC1016517 |
Keywords
- Lactococcus lactis
- ccpA
- laboratory evolution
- metabolic modeling
- proteome constraint