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Machine Learning Pr...
Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts
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- Zelezniak, Aleksej, 1984 (författare)
- KTH,Francis Crick Inst, Mol Biol Metab Lab, London, England.;Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.;Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden.;KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden.
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- Vowinckel, Jakob (författare)
- Biognosys AG,University Of Cambridge,Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.;Biognosys AG, Schlieren, Switzerland.
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- Capuano, Floriana (författare)
- University Of Cambridge,Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.
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- Messner, Christoph B. (författare)
- The Francis Crick Institute,Francis Crick Inst, Mol Biol Metab Lab, London, England.
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- Demichev, Vadim (författare)
- The Francis Crick Institute,University Of Cambridge,Francis Crick Inst, Mol Biol Metab Lab, London, England.;Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.
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- Polowsky, Nicole (författare)
- University Of Cambridge,Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.
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- Mülleder, Michael (författare)
- University Of Cambridge,The Francis Crick Institute,Francis Crick Inst, Mol Biol Metab Lab, London, England.;Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.
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- Kamrad, Stephan (författare)
- University College London (UCL),The Francis Crick Institute,Francis Crick Inst, Mol Biol Metab Lab, London, England.;UCL, Dept Genet Evolut & Environm, London, England.
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- Klaus, Bernd (författare)
- European Molecular Biology Laboratory,EMBL, Ctr Stat Data Anal, Heidelberg, Germany.
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- Keller, M. A. (författare)
- Medizinische Universität Innsbruck,Medical University of Innsbruck,University Of Cambridge,Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.;Med Univ Innsbruck, Innsbruck, Austria.
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- Ralser, M. (författare)
- Charité Universitätsmedizin Berlin,Charité University Medicine Berlin,The Francis Crick Institute,University Of Cambridge,Francis Crick Inst, Mol Biol Metab Lab, London, England.;Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.;Charite Univ Med Berlin, Dept Biochem, Berlin, Germany.
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KTH Francis Crick Inst, Mol Biol Metab Lab, London, England;Univ Cambridge, Dept Biochem, Cambridge, England.;Univ Cambridge, Cambridge Syst Biol Ctr, Cambridge, England.;Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden.;KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden. (creator_code:org_t)
- Elsevier BV, 2018
- 2018
- Engelska.
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 7:3, s. 269-283
- Relaterad länk:
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https://research.cha... (primary) (free)
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http://www.cell.com/...
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Genetik (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Genetics (hsv//eng)
Nyckelord
- hierarchical regulation
- high-throughput proteomics
- metabolic control analysis
- metabolism
- enzyme abundance
- machine learning
- genotype-phenotype problem
- multi-omics
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Zelezniak, Aleks ...
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Vowinckel, Jakob
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Capuano, Florian ...
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Messner, Christo ...
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Demichev, Vadim
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Polowsky, Nicole
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Mülleder, Michae ...
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Kamrad, Stephan
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Klaus, Bernd
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Keller, M. A.
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Ralser, M.
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Cell Systems
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