| Title: | Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast |
| Author(s): | Goncalves E; Raguz Nakic Z; Zampieri M; Wagih O; Ochoa D; Sauer U; Beltrao P; Saez-Rodriguez J; |
| Address: | "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine (JRC-COMBINE), Aachen" |
| DOI: | 10.1371/journal.pcbi.1005297 |
| ISSN/ISBN: | 1553-7358 (Electronic) 1553-734X (Print) 1553-734X (Linking) |
| 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 in silico 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. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico 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" |
| Keywords: | "Databases, Genetic Gene Expression Regulation, Fungal/drug effects/*genetics Metabolomics/*methods Pheromones/pharmacology Proteins/genetics/metabolism Saccharomyces cerevisiae/*genetics/*metabolism Sodium Chloride/pharmacology Systems Biology Transcripti;" |
| Notes: | "MedlineGoncalves, Emanuel Raguz Nakic, Zrinka Zampieri, Mattia Wagih, Omar Ochoa, David Sauer, Uwe Beltrao, Pedro Saez-Rodriguez, Julio eng 2017/01/11 PLoS Comput Biol. 2017 Jan 10; 13(1):e1005297. doi: 10.1371/journal.pcbi.1005297. eCollection 2017 Jan" |
