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      A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

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          Abstract

          The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells.

          Author summary

          Elucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer, and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of signaling pathways, like Snf1, TORC1, and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect steady-state metabolism. The model has been tested under respiration and fermentation, revealing novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan.

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          Most cited references125

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          UniProt: a worldwide hub of protein knowledge

          (2018)
          Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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            Double-slit photoelectron interference in strong-field ionization of the neon dimer

            Wave-particle duality is an inherent peculiarity of the quantum world. The double-slit experiment has been frequently used for understanding different aspects of this fundamental concept. The occurrence of interference rests on the lack of which-way information and on the absence of decoherence mechanisms, which could scramble the wave fronts. Here, we report on the observation of two-center interference in the molecular-frame photoelectron momentum distribution upon ionization of the neon dimer by a strong laser field. Postselection of ions, which are measured in coincidence with electrons, allows choosing the symmetry of the residual ion, leading to observation of both, gerade and ungerade, types of interference.
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              Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease

              Krabbe disease (KD) is a neurodegenerative disorder caused by the lack of β- galactosylceramidase enzymatic activity and by widespread accumulation of the cytotoxic galactosyl-sphingosine in neuronal, myelinating and endothelial cells. Despite the wide use of Twitcher mice as experimental model for KD, the ultrastructure of this model is partial and mainly addressing peripheral nerves. More details are requested to elucidate the basis of the motor defects, which are the first to appear during KD onset. Here we use transmission electron microscopy (TEM) to focus on the alterations produced by KD in the lower motor system at postnatal day 15 (P15), a nearly asymptomatic stage, and in the juvenile P30 mouse. We find mild effects on motorneuron soma, severe ones on sciatic nerves and very severe effects on nerve terminals and neuromuscular junctions at P30, with peripheral damage being already detectable at P15. Finally, we find that the gastrocnemius muscle undergoes atrophy and structural changes that are independent of denervation at P15. Our data further characterize the ultrastructural analysis of the KD mouse model, and support recent theories of a dying-back mechanism for neuronal degeneration, which is independent of demyelination.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: MethodologyRole: Visualization
                Role: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                9 April 2021
                April 2021
                : 17
                : 4
                Affiliations
                [1 ] Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
                [2 ] Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
                [3 ] Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
                [4 ] Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
                [5 ] BioInnovation Institute, Copenhagen, Denmark
                King’s College London, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-20-01648
                10.1371/journal.pcbi.1008891
                8059808
                33836000
                7d3236c4-c855-45cb-8e7b-2b4d8e69446d
                © 2021 Österberg et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 1, Pages: 27
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: VR2016-03744
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001729, Stiftelsen för Strategisk Forskning;
                Award ID: FFL15-0238
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 720824
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100009708, Novo Nordisk Fonden;
                Award ID: NNF10CC1016517
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse;
                Award Recipient :
                This work was supported by the Swedish Research Council (VR2016-03744) to SH, Swedish Foundation for Strategic Research (Grant Nr.FFL15-0238) to MC and European Union’s Horizon 2020 research and innovation program, project CHASSY (grant agreement 720824) to ID. Part of this work was funded by the Novo Nordisk Foundation (grant no. NNF10CC1016517) and the Knut and Alice Wallenberg Foundation to JN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Biology and Life Sciences
                Biochemistry
                Proteins
                Enzymes
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzyme Chemistry
                Enzyme Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzyme Chemistry
                Enzyme Regulation
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Cell Signaling
                Glucose Signaling
                Computer and Information Sciences
                Network Analysis
                Metabolic Networks
                Biology and Life Sciences
                Cell Biology
                Cell Physiology
                Cell Metabolism
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-04-21
                All relevant data are within the manuscript and its Supporting Information files. The model, code, and datasets used for this study can be found in the GitHub repository YeastHybridModelingFramework https://github.com/cvijoviclab/YeastHybridModelingFramework.

                Quantitative & Systems biology

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