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

Abstract

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 dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q₁₀) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q₁₀ coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. 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 4 and 37 °C. 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.

Keywords: computational biology; erythrocyte; metabolism; metabolomics; systems biology.

Figure 1.

Data generation and analysis workflow. A, human red blood cells were collected; stored in SAGM medium at 4, 13, 22, and 37 °C; and metabolically profiled across multiple time points. B, PCA of the eight extracellular biomarkers (same loading coefficients applied to data at each temperature). Overlaying these plots on the same axes shows that the shape of the three-phase metabolic decay is conserved but accelerated with increasing temperature as evidenced by the location of the day 7 time point. See supplemental Fig. S1 for a more detailed PCA plot at each temperature. The numbers in parentheses represent the amount of variance explained by each component. Black arrows and roman numerals label the three metabolic shifts that occur over the storage period. C, the first principal component was plotted against the time vector at each temperature to determine the relative storage time at each temperature. Linear regression was used to estimate the rate of change, showing strong correlation between PC1 and time at each temperature. D, these rates of change were then used to estimate the change in metabolic rate for every 10 °C (Q₁₀) from an Arrhenius-type log₂(rate) versus temperature plot.

Figure 2.

Distribution of Q₁₀ coefficients for metabolites and reactions. Q₁₀ coefficients for metabolites were calculated based on the observed change in metabolite concentration across temperature. The vertical dashed lines at Q₁₀ = 2 and Q₁₀ = 3 represent the typical estimated range of Q₁₀ coefficients for biological processes. STD, standard deviation.

Figure 3.

Metabolic map of glycolysis. Day 0 here is taken to be 1 day after the beginning of the storage period. Q₁₀ coefficients are provided for those metabolites and reactions that could be calculated. Error bars represent the range of measured data for each time point (line indicates mean). All abbreviations are from the BiGG database. G6P, glucose 6-phosphate; F6P, fructose 6-phosphate; FDP, fructose 1,6-bisphosphate; DHAP, dihydroxyacetone phosphate; G3P, glyceraldehyde 3-phosphate; 1,3-DPG, 3-phospho-d-glyceroyl phosphate; 3PG, 3-phospho-d-glycerate; 2PG, 2-phospho-d-glycerate; PEP, phosphoenolpyruvate; PYR, pyruvate; LAC, l-lactate; HEX1, hexokinase; PGI, glucose-6-phosphate isomerase; PFK, phosphofructokinase; FBA, fructose-bisphosphate aldolase; GAPD, glyceraldehyde-3-phosphate dehydrogenase; DPGM, diphosphoglyceromutase; PGK, pyruvate kinase; DPGase, diphosphoglycerate phosphatase; PGM, phosphoglycerate mutase; ENO, enolase; LDH, lactate dehydrogenase.