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Systematic analysis of transcriptional and post-transcriptional regulation of metabolism in yeast

View ORCID ProfileEmanuel Gonçalves, Zrinka Raguz, Mattia Zampieri, View ORCID ProfileOmar Wagih, View ORCID ProfileDavid Ochoa, Uwe Sauer, View ORCID ProfilePedro Beltrao, View ORCID ProfileJulio Saez-Rodriguez
doi: https://doi.org/10.1101/057398
Emanuel Gonçalves
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBLEBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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  • ORCID record for Emanuel Gonçalves
Zrinka Raguz
3Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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Mattia Zampieri
3Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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Omar Wagih
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBLEBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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David Ochoa
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBLEBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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Uwe Sauer
3Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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Pedro Beltrao
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBLEBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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  • For correspondence: pbeltrao@ebi.ac.uk saezrodriguez@combine.rwthaachen.de
Julio Saez-Rodriguez
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBLEBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
2RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine (JRCCOMBINE), Wendlingweg 2 D52074 Aachen
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  • For correspondence: pbeltrao@ebi.ac.uk saezrodriguez@combine.rwthaachen.de
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Abstract

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism, conveying a total of 143 unique conditions. Our approach accurately estimated the change in activity of transcription factors, kinases and phosphatases and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

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Posted June 23, 2016.
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Systematic analysis of transcriptional and post-transcriptional regulation of metabolism in yeast
Emanuel Gonçalves, Zrinka Raguz, Mattia Zampieri, Omar Wagih, David Ochoa, Uwe Sauer, Pedro Beltrao, Julio Saez-Rodriguez
bioRxiv 057398; doi: https://doi.org/10.1101/057398
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Systematic analysis of transcriptional and post-transcriptional regulation of metabolism in yeast
Emanuel Gonçalves, Zrinka Raguz, Mattia Zampieri, Omar Wagih, David Ochoa, Uwe Sauer, Pedro Beltrao, Julio Saez-Rodriguez
bioRxiv 057398; doi: https://doi.org/10.1101/057398

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