Cabbia, A.

Hilbers, P.A.J.

van Riel, N.A.W.

English

Pure OAI Repository

 Genome-scale metabolic models for personalized nutritionand healthy agingCitation for published version (APA):Cabbia, A., Hilbers, P. A. J., & van Riel, N. A. W. (2018). Genome-scale metabolic models for personalizednutrition and healthy aging.Document license:GNU GPLDocument status and date:Published: 25/09/2018Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 08. Feb. 2024Genome-scale metabolic models for personalized nutrition and healthy agingAndrea Cabbia¹, prof dr. Peter A.J. Hilbers¹, prof. dr. ir.  Natal A.W. van Riel ¹ ²1) Eindhoven University of Technology 2) Academic Medical Center, University of Amsterdam/ Department of  Biomedical engineering / Computational Biology What are they?Computational reconstructions and simulations of large-scale metabolic networksAim: To understand how diet impacts the aging process, and to find dietary interventions to slow the pace of aging, with a focus on skeletal muscle.How: Patient-derived genome-scale models, are built from muscle gene expression data of young and older subjects, using the CORDA algorithm [1] and a human metabolic network reconstruction, Recon 2.2 [2]. Flux Balance Analysis (FBA) [3] is then used to simulate metabolic flexibility (RQ) and protein synthesis rate between individuals.● Metabolic flexibility is the ability to readily adapt to changes in fuel availability (e.g. between glucose and fatty acids)  [4] and is associated with metabolic health and longer lifespan in mammals [5]● RQ simulations are a tool to gain mechanistic understanding of the underlying causes of metabolic flexibility, and to study the link between metabolic health and agingMetabolic flexibility Conclusions and Future Work ● The model ensemble correctly simulates expected Respiratory Quotient values when metabolizing different carbon sources● Results show expected behavior, but also reveal substantial heterogeneity in substrate utilization patterns across patient-derived GSMMs● NEXT: simulate protein synthesis rate in response to different nutrient profiles, to gain mechanistic understanding of the role of nutrition in counteracting muscle loss (sarcopenia) during aging ReferencesResultsGenome-scale metabolic models (GSMM) [1] Schultz A. & Qutub A.A, PLoS Computational Biology (2016) DOI: 10.1371/journal.pcbi.1004808[2] Swainston N et al., Metabolomics(2016) DOI: 10.1007/s11306-016-1051-4 [3] Orth J.D et al. at ,Nature Biotechnology(2010)  DOI; 10.1038/nbt.1614[4] Goodpaster B.H &Sparks L.M, , Cell Metabolism (2017) DOI: 10.1016/j.cmet.2017.04.015[5] Riera C.E, & Dillin A.  Nature Cell Biology (2015). DOI:10.138/ncb3107Table 1: Summary of the 127 patient-derived metabolic models generated during this study Figure 1: RQ simulated in different individualized models. Each row corresponds to a different carbon source. The model ensemble predictions confirm theoretical RQ values.FedFastedA BC DFigure 2: (A) Reciprocal modulation of lipid (CPT1) and glucose (GLUT4) uptake fluxes. (B-D) Different patient-derived models show differential substrate utilization during the fasting-to-fed transition. 

Genome-scale metabolic models for personalized nutrition and healthy aging

https://pure.tue.nl/ws/files/109843274/POSTER_CAGE2018_final.pdf

Genome-scale metabolic models for personalized nutrition and healthy aging

Abstract

Similar works

Full text

Available Versions

Pure OAI Repository