Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

被引:64
作者
Domenzain, Ivan [1 ,2 ]
Sanchez, Benjamin [3 ,4 ]
Anton, Mihail [5 ]
Kerkhoven, Eduard J. [2 ]
Millan-Oropeza, Aaron [6 ]
Henry, Celine [6 ]
Siewers, Verena [2 ]
Morrissey, John P. [7 ,8 ]
Sonnenschein, Nikolaus [3 ]
Nielsen, Jens [1 ,2 ,9 ]
机构
[1] Chalmers Univ Technol, Dept Biol & Biol Engn, SE-41296 Gothenburg, Sweden
[2] Chalmers Univ Technol, Novo Nordisk Fdn Ctr Biosustainabil, SE-41296 Gothenburg, Sweden
[3] Tech Univ Denmark, Dept Biotechnol & Biomed, DK-2800 Lyngby, Denmark
[4] Tech Univ Denmark, Novo Nordisk Fdn Ctr Biosustainabil, DK-2800 Lyngby, Denmark
[5] Chalmers Univ Technol, Dept Biol & Biol Engn, Natl Bioinformat Infrastruc Sweden Sci Life Lab, Kemivagen 10, SE-41258 Gothenburg, Sweden
[6] Univ Paris Saclay, MICALIS Inst, INRAE, Plateforme Analyse Proteom Paris Sud Ouest PAPPSO, F-78350 Jouy En Josas, France
[7] Univ Coll Cork, Sch Microbiol, Environm Res Inst, Cork T12 K8AF, Ireland
[8] Univ Coll Cork, APC Microbiome Ireland, Cork T12 K8AF, Ireland
[9] BioInnovat Inst, Ole Maaloes Vej 3, DK-2200 Copenhagen, Denmark
基金
欧盟地平线“2020”;
关键词
ESCHERICHIA-COLI; ACID PRODUCTION; BALANCE; RESOURCE; SEQUENCE; PROTEIN; YEAST;
D O I
10.1038/s41467-022-31421-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:13
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共 77 条
  • [31] Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica
    Kerkhoven, Eduard J.
    Pomraning, Kyle R.
    Baker, Scott E.
    Nielsen, Jens
    [J]. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 2016, 2
  • [32] Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion
    King, Zachary A.
    O'Brien, Edward J.
    Feist, Adam M.
    Palsson, Bernhard O.
    [J]. METABOLIC ENGINEERING, 2017, 39 : 220 - 227
  • [33] Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast
    Lahtvee, Petri-Jaan
    Sanchez, Benjamin J.
    Smialowska, Agata
    Kasvandik, Sergo
    Elsemman, Ibrahim E.
    Gatto, Francesco
    Nielsen, Jens
    [J]. CELL SYSTEMS, 2017, 4 (05) : 495 - +
  • [34] X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification
    Langella, Olivier
    Valot, Benoit
    Balliau, Thierry
    Blein-Nicolas, Melisande
    Bonhonarne, Ludovic
    Zivy, Michel
    [J]. JOURNAL OF PROTEOME RESEARCH, 2017, 16 (02) : 494 - 503
  • [35] In silico method for modelling metabolism and gene product expression at genome scale
    Lerman, Joshua A.
    Hyduke, Daniel R.
    Latif, Haythem
    Portnoy, Vasiliy A.
    Lewis, Nathan E.
    Orth, Jeffrey D.
    Schrimpe-Rutledge, Alexandra C.
    Smith, Richard D.
    Adkins, Joshua N.
    Zengler, Karsten
    Palsson, Bernhard O.
    [J]. NATURE COMMUNICATIONS, 2012, 3
  • [36] Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
    Li, Gang
    Hu, Yating
    Zrimec, Jan
    Luo, Hao
    Wang, Hao
    Zelezniak, Aleksej
    Ji, Boyang
    Nielsen, Jens
    [J]. NATURE COMMUNICATIONS, 2021, 12 (01)
  • [37] Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae
    Li, Mingji
    Borodina, Irina
    [J]. FEMS YEAST RESEARCH, 2015, 15 (01)
  • [38] A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
    Lu, Hongzhong
    Li, Feiran
    Sanchez, Benjamin J.
    Zhu, Zhengming
    Li, Gang
    Domenzain, Ivan
    Marcisauskas, Simonas
    Anton, Petre Mihail
    Lappa, Dimitra
    Lieven, Christian
    Beber, Moritz Emanuel
    Sonnenschein, Nikolaus
    Kerkhoven, Eduard J.
    Nielsen, Jens
    [J]. NATURE COMMUNICATIONS, 2019, 10 (1)
  • [39] The effects of alternate optimal solutions in constraint-based genome-scale metabolic models
    Mahadevan, R
    Schilling, CH
    [J]. METABOLIC ENGINEERING, 2003, 5 (04) : 264 - 276
  • [40] Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model
    Marcisauskas, Simonas
    Ji, Boyang
    Nielsen, Jens
    [J]. BMC BIOINFORMATICS, 2019, 20 (01)