Multi-omics Evidence for Inheritance of Energy Pathways in Red Blood Cells

Abstract

Each year over 90 million units of blood are transfused worldwide. Our dependence on this blood supply mandates optimized blood management and storage. During storage, red blood cells undergo degenerative processes resulting in altered metabolic characteristics which may make blood less viable for transfusion. However, not all stored blood spoils at the same rate, a difference that has been attributed to variable rates of energy usage and metabolism in red blood cells. Specific metabolite abundances are heritable traits; however, the link between heritability of energy metabolism and red blood cell storage profiles is unclear. Herein we performed a comprehensive metabolomics and proteomics study of red blood cells from 18 mono- and di-zygotic twin pairs to measure heritability and identify correlations with ATP and other molecular indices of energy metabolism. Without using affinity-based hemoglobin depletion, our work afforded the deepest multi-omic characterization of red blood cell membranes to date (1280 membrane proteins and 330 metabolites), with 119 membrane protein and 148 metabolite concentrations found to be over 30% heritable. We demonstrate a high degree of heritability in the concentration of energy metabolism metabolites, especially glycolytic metabolites. In addition to being heritable, proteins and metabolites involved in glycolysis and redox metabolism are highly correlated, suggesting that crucial energy metabolism pathways are inherited en bloc at distinct levels. We conclude that individuals can inherit a phenotype composed of higher or lower concentrations of these proteins together. This can result in vastly different red blood cells storage profiles which may need to be considered to develop precise and individualized storage options. Beyond guiding proper blood storage, this intimate link in heritability between energy and redox metabolism pathways may someday prove useful in determining the predisposition of an individual toward metabolic diseases.

Fig. 1.

Red blood cell proteins and metabolites show clusters of high correlation. Pearson correlation values were calculated between every combination of proteins and metabolites and plotted using Perseus. ™KEGG pathway enrichment of various clusters was measured using enrichr and the negative log of the p value for pathways of interest is reported in the bar charts to the right, where the color of the bars corresponds to the section of the dendrogram where the pathways are enriched. See supplemental information for complete KEGG enrichment results.

Fig. 2.

Relative abundance of glycolytic proteins and glycolytic metabolites is conserved among twin pairs. The average protein (A) and metabolite level (B) in three representative monozygotic twin pairs show variation in glycolytic activity within the population, indicating abundances of these proteins are influenced en bloc. All protein and metabolite levels were normalized to the percentage of maximum protein and metabolite level (see example calculation) using feature scaling. Pyruvate was excluded from the metabolites as it was found to not correlate with the other members. The difference between each protein (C) and metabolite (D) is reported in a histogram. Sequential proteins and metabolites in glycolysis were subtracted to give a scaled difference components of the pathway. The peak is centered at zero for proteins and metabolites indicating that individuals inherit high or low levels of glycolytic compounds together. Glycolytic metabolites appear to be more tightly conserved than protein.

Fig. 3.

(A) A total of 1,280 proteins and (B) 330 metabolites were detected in red blood cells. Of these, 119 and 148 were found to be over 30% heritable, respectively. To calculate heritability in proteins, measurements were required to be present in all three out of five dizygotic twin pairs and 10 out of 13 dizygotic twin pairs. C, Proteins and metabolites greater than 30% heritable from glycolysis and glutathione metabolism are reported.

Fig. 4.

A high number of positive correlations are observed between both proteins and metabolites in the glycolytic and glutathione metabolism pathways. A Pearson correlation greater than 0.5 or less than −0.5 is required to show a connection. Heritability of these pathways can be observed in the shade of the node outline as well as by the gradient outside the network. This figure was created using Cytoscape™ (58).

Fig. 5.

Poststorage ATP levels are determined by several key factors. A, Low ATP levels following 42 days of storage are correlated with high levels of band 3, BPGM, and carbonic anhydrase. Band 3 binds glycolytic proteins decreasing flux through glycolysis whereas BPGM shunts intermediates to the luebering-rapoport pathway away from the generation of ATP. Similarly, high levels of carbonic anhydrase produce acidic conditions and subsequently inhibit PFK. In support of this we observe negative correlations between carbonic anhydrase and pH level during storage. Low poststorage ATP is additionally correlated with low pH. Correlation values of greater than 0.3 were required for consideration. B, The opposite model leads to the generation of high ATP levels following 42 days of storage. The size of the protein in each case is representative of the concentration associated with each phenotype.