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Defining the ATPome reveals cross-optimization of metabolic pathways.

ABSTRACT: Disrupted energy metabolism drives cell dysfunction and disease, but approaches to increase or preserve ATP are lacking. To generate a comprehensive metabolic map of genes and pathways that regulate cellular ATP-the ATPome-we conducted a genome-wide CRISPR interference/activation screen integrated with an ATP biosensor. We show that ATP level is modulated by distinct mechanisms that promote energy production or inhibit consumption. In our system HK2 is the greatest ATP consumer, indicating energy failure may not be a general deficiency in producing ATP, but rather failure to recoup the ATP cost of glycolysis and diversion of glucose metabolites to the pentose phosphate pathway. We identify systems-level reciprocal inhibition between the HIF1 pathway and mitochondria; glycolysis-promoting enzymes inhibit respiration even when there is no glycolytic ATP production, and vice versa. Consequently, suppressing alternative metabolism modes paradoxically increases energy levels under substrate restriction. This work reveals mechanisms of metabolic control, and identifies therapeutic targets to correct energy failure.

SUBMITTER: Bennett NK

PROVIDER: S-EPMC7455733 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Defining the ATPome reveals cross-optimization of metabolic pathways.

Bennett Neal K NK Nguyen Mai K MK Darch Maxwell A MA Nakaoka Hiroki J HJ Cousineau Derek D Ten Hoeve Johanna J Graeber Thomas G TG Schuelke Markus M Maltepe Emin E Kampmann Martin M Mendelsohn Bryce A BA Nakamura Jean L JL Nakamura Ken K

Nature communications 20200828 1

Disrupted energy metabolism drives cell dysfunction and disease, but approaches to increase or preserve ATP are lacking. To generate a comprehensive metabolic map of genes and pathways that regulate cellular ATP-the ATPome-we conducted a genome-wide CRISPR interference/activation screen integrated with an ATP biosensor. We show that ATP level is modulated by distinct mechanisms that promote energy production or inhibit consumption. In our system HK2 is the greatest ATP consumer, indicating energ ...[more]

PMID: 32859923

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Project description:BackgroundDuring the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework.ResultsIn this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results.ConclusionsThe proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Dataset Information

Defining the ATPome reveals cross-optimization of metabolic pathways.

Publications

Defining the ATPome reveals cross-optimization of metabolic pathways.

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