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O'Keeffe, S.

Publications and source records attributed to O'Keeffe, S..

2 recordsLinked to original sources

Ensemble quantitation of absolute metabolite concentrations in T cells reveals conserved features of immunometabolism

T cell diversity and differentiation define individual adaptive immunity. Metabolism fuels adaptive immunity by driving proliferation, differentiation, and activation; however, how individual and lineage differences shape T cell metabolism remains unknown. Here we develop an ensemble method for absolute metabolome quantitation and quantify 84 metabolites in human primary T cells. Liquid chromatography-mass spectrometry of metabolites co-extracted from T cells and 13C-labeled reference cells reveals absolute concentrations en masse. Across subtypes and individuals, T cell metabolomes resemble one another. T cells possess thermodynamically forward-driven glycolysis, high adenylate energy charge, large amino acid pools, and favorable redox ratios for anabolism and antioxidant defense. Across metabolism, metabolite concentrations exceed their associated Michaelis constants and inhibitor constants two thirds and half of the time, respectively. The conserved features of T cell metabolomes underlie a design principle: metabolite levels prime T cells for increased energy demand, oxidative stress, and rapid regulation that accompany immune response.

biochemistry↗

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns

All life forms operate metabolism in constant flux. Metabolic fluxes offer a direct readout of cellular state, detailing the rates and driving forces of metabolic pathways. However, indirect, iterative solvers for mapping isotope patterns from tracing experiments onto metabolic fluxes leave much of cellular state uncharted. Here, we streamline metabolic flux quantitation by innovating a machine learning framework, ML-Flux, that deciphers complex isotope labeling patterns. We train neural networks using isotope pattern-flux pairs across central carbon metabolism from 26 key 13C-glucose, 2H-glucose, and 13C-glutamine tracers. ML-Flux takes variable-size isotope labeling patterns as input, imputes missing isotope patterns, and outputs mass-balanced metabolic fluxes. Computation of fluxes using ML-Flux is more accurate and faster than that of leading metabolic flux analysis software employing a least-squares method. Our biochemical networks and machine learning models constitute a curated and growing online knowledgebase of metabolic flux and free energy to democratize quantitative metabolic profiling.

systems biology↗