Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution

Alon Stern, Mariam Fokra, Boris Sarvin, Ahmad Abed Alrahem, Won Dong Lee, Elina Aizenshtein, Nikita Sarvin and Tomer Shlomi ()
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Alon Stern: Technion—Israel Institute of Technology
Mariam Fokra: Technion—Israel Institute of Technology
Boris Sarvin: Technion—Israel Institute of Technology
Ahmad Abed Alrahem: Technion—Israel Institute of Technology
Won Dong Lee: Technion—Israel Institute of Technology
Elina Aizenshtein: Technion—Israel Institute of Technology
Nikita Sarvin: Technion—Israel Institute of Technology
Tomer Shlomi: Technion—Israel Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

Date: 2023
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DOI: 10.1038/s41467-023-42824-z

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