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      Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift During C. elegans Aging

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          Abstract

          In this contribution, we describe a multi-omics systems biology study of the metabolic changes that occur during aging in Caenorhabditis elegans. Sampling several time points from young adulthood until early old age, our study covers the full duration of aging and include transcriptomics, and targeted MS-based metabolomics. In order to focus on the metabolic changes due to age we used two strains that are metabolically close to wild-type, yet are conditionally non-reproductive. Using these data in combination with a whole-genome model of the metabolism of C. elegans and mathematical modeling, we predicted metabolic fluxes during early aging. We find that standard Flux Balance Analysis does not accurately predict in vivo measured fluxes nor age-related changes associated with the Citric Acid cycle. We present a novel Flux Balance Analysis method where we combined biomass production and targeted metabolomics information to generate an objective function that is more suitable for aging studies. We validated this approach with a detailed case study of the age-associated changes in the Citric Acid cycle. Our approach provides a comprehensive time-resolved multi-omics and modeling resource for studying the metabolic changes during normal aging in C. elegans.

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          COBRApy: COnstraints-Based Reconstruction and Analysis for Python

          Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/
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            Genetic pathways that regulate ageing in model organisms.

            Searches for genes involved in the ageing process have been made in genetically tractable model organisms such as yeast, the nematode Caenorhabditis elegans, Drosophila melanogaster fruitflies and mice. These genetic studies have established that ageing is indeed regulated by specific genes, and have allowed an analysis of the pathways involved, linking physiology, signal transduction and gene regulation. Intriguing similarities in the phenotypes of many of these mutants indicate that the mutations may also perturb regulatory systems that control ageing in higher organisms.
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              The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR.

              Metabolism and ageing are intimately linked. Compared with ad libitum feeding, dietary restriction consistently extends lifespan and delays age-related diseases in evolutionarily diverse organisms. Similar conditions of nutrient limitation and genetic or pharmacological perturbations of nutrient or energy metabolism also have longevity benefits. Recently, several metabolites have been identified that modulate ageing; however, the molecular mechanisms underlying this are largely undefined. Here we show that α-ketoglutarate (α-KG), a tricarboxylic acid cycle intermediate, extends the lifespan of adult Caenorhabditis elegans. ATP synthase subunit β is identified as a novel binding protein of α-KG using a small-molecule target identification strategy termed drug affinity responsive target stability (DARTS). The ATP synthase, also known as complex V of the mitochondrial electron transport chain, is the main cellular energy-generating machinery and is highly conserved throughout evolution. Although complete loss of mitochondrial function is detrimental, partial suppression of the electron transport chain has been shown to extend C. elegans lifespan. We show that α-KG inhibits ATP synthase and, similar to ATP synthase knockdown, inhibition by α-KG leads to reduced ATP content, decreased oxygen consumption, and increased autophagy in both C. elegans and mammalian cells. We provide evidence that the lifespan increase by α-KG requires ATP synthase subunit β and is dependent on target of rapamycin (TOR) downstream. Endogenous α-KG levels are increased on starvation and α-KG does not extend the lifespan of dietary-restricted animals, indicating that α-KG is a key metabolite that mediates longevity by dietary restriction. Our analyses uncover new molecular links between a common metabolite, a universal cellular energy generator and dietary restriction in the regulation of organismal lifespan, thus suggesting new strategies for the prevention and treatment of ageing and age-related diseases.
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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                06 February 2019
                2019
                : 6
                : 2
                Affiliations
                [1] 1Department of Epigenetics, Babraham Institute , Cambridge, United Kingdom
                [2] 2Department of Computational Biology, University of Lausanne , Lausanne, Switzerland
                Author notes

                Edited by: Arthur S. Edison, University of Georgia, United States

                Reviewed by: Timothy Garrett, University of Florida, United States; Benedicte Elena-Herrmann, INSERM U1209 Institut Pour l'Avancée des Biosciences (IAB), France; Hunter N. B. Moseley, University of Kentucky, United States

                *Correspondence: Olivia Casanueva olivia.casanueva@ 123456babraham.ac.uk

                This article was submitted to Metabolomics, a section of the journal Frontiers in Molecular Biosciences

                Article
                10.3389/fmolb.2019.00002
                6372924
                30788345
                4b8dc4e8-93b1-451a-a824-f4eb6b2e42cc
                Copyright © 2019 Hastings, Mains, Virk, Rodriguez, Murdoch, Pearce, Bergmann, Le Novère and Casanueva.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 August 2018
                : 17 January 2019
                Page count
                Figures: 8, Tables: 0, Equations: 7, References: 58, Pages: 18, Words: 12944
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council 10.13039/501100000268
                Categories
                Molecular Biosciences
                Original Research

                metabolomics,flux balance analysis,aging,systems biology,multi-omics,whole genome model,c. elegans

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