Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

TitleQuantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.
Publication TypeJournal Article
Year of Publication2017
AuthorsYurkovich JT, Zielinski DC, Yang L, Paglia G, Rolfsson O, Sigurjónsson OE, Broddrick JT, Bordbar A, Wichuk K, Brynjólfsson S, Palsson S, Gudmundsson S, Palsson BO
JournalJ Biol Chem
PubMed Date10/2017

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g., basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependency of the human red blood cell (RBC) metabolic network between 4C and 37C through absolutely quantified exo- and endo-metabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89+\-1.03 for 48 individual metabolite concentrations, within the expected range of 2-3 for biological processes. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73+\-0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4C and 37C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

Alternate JournalJ. Biol. Chem.
PubMed ID29030425
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