Literature DB >> 33836000

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

Linnea Österberg1,2,3, Iván Domenzain3,4, Julia Münch1,2, Jens Nielsen3,4,5, Stefan Hohmann3, Marija Cvijovic1,2.   

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.

Entities:  

Year:  2021        PMID: 33836000     DOI: 10.1371/journal.pcbi.1008891

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  7 in total

1.  Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing.

Authors:  Barbara Schnitzer; Linnea Österberg; Iro Skopa; Marija Cvijovic
Journal:  PLoS Comput Biol       Date:  2022-07-07       Impact factor: 4.779

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

Authors:  Benjamín Sánchez; Mihail Anton; Iván Domenzain; Eduard J Kerkhoven; Aarón Millán-Oropeza; Céline Henry; Verena Siewers; John P Morrissey; Nikolaus Sonnenschein; Jens Nielsen
Journal:  Nat Commun       Date:  2022-06-30       Impact factor: 17.694

3.  Enhancing Microbiome Research through Genome-Scale Metabolic Modeling.

Authors:  Nana Y D Ankrah; David B Bernstein; Matthew Biggs; Maureen Carey; Melinda Engevik; Beatriz García-Jiménez; Meiyappan Lakshmanan; Alan R Pacheco; Snorre Sulheim; Gregory L Medlock
Journal:  mSystems       Date:  2021-12-14       Impact factor: 6.496

4.  Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

Authors:  Hongzhong Lu; Feiran Li; Le Yuan; Iván Domenzain; Rosemary Yu; Hao Wang; Gang Li; Yu Chen; Boyang Ji; Eduard J Kerkhoven; Jens Nielsen
Journal:  Mol Syst Biol       Date:  2021-10       Impact factor: 11.429

Review 5.  Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae.

Authors:  Sebastian Persson; Sviatlana Shashkova; Linnea Österberg; Marija Cvijovic
Journal:  FEMS Yeast Res       Date:  2022-03-11       Impact factor: 2.796

Review 6.  D-Xylose Sensing in Saccharomyces cerevisiae: Insights from D-Glucose Signaling and Native D-Xylose Utilizers.

Authors:  Daniel P Brink; Celina Borgström; Viktor C Persson; Karen Ofuji Osiro; Marie F Gorwa-Grauslund
Journal:  Int J Mol Sci       Date:  2021-11-17       Impact factor: 5.923

7.  The choice of the objective function in flux balance analysis is crucial for predicting replicative lifespans in yeast.

Authors:  Barbara Schnitzer; Linnea Österberg; Marija Cvijovic
Journal:  PLoS One       Date:  2022-10-13       Impact factor: 3.752

  7 in total

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