Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
Artikel i vetenskaplig tidskrift, 2022
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
Författare
Iván Domenzain Del Castillo Cerecer
Chalmers, Biologi och bioteknik, Systembiologi
Novo Nordisk Fonden
Benjamín José Sánchez
Danmarks Tekniske Universitet (DTU)
Petre Mihail Anton
Science for Life Laboratory (SciLifeLab)
Chalmers, Biologi och bioteknik, Systembiologi
Eduard Kerkhoven
Chalmers, Biologi och bioteknik, Systembiologi
Novo Nordisk Fonden
Aaron Millan-Oropeza
Université Paris-Saclay
Céline Henry
Université Paris-Saclay
Verena Siewers
Chalmers, Biologi och bioteknik, Systembiologi
Novo Nordisk Fonden
John P. Morrissey
APC Microbiome Ireland
N. Sonnenschein
Danmarks Tekniske Universitet (DTU)
Jens B Nielsen
Novo Nordisk Fonden
Chalmers, Biologi och bioteknik, Systembiologi
BioInnovation Institute
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 13 1 3766Ämneskategorier
Biokemi och molekylärbiologi
Bioinformatik (beräkningsbiologi)
DOI
10.1038/s41467-022-31421-1
PubMed
35773252