Blood donor exposome and impact of common drugs on red blood cell metabolism
Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study
Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study
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Research Article Hematology Metabolism

Blood donor exposome and impact of common drugs on red blood cell metabolism

Abstract

Computational models based on recent maps of the RBC proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to RBC storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic, or environmental exposures (“exposome”) may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and posttransfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and over-the-counter drugs in blood units donated by 250 healthy volunteers in the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study. Based on high-throughput drug screenings of 1366 FDA-approved drugs, we report that approximately 65% of the tested drugs had an impact on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR), suggesting that these drugs have a direct, conserved, and substantial impact on erythrocyte metabolism. As a proof of principle, here we show that the antacid ranitidine — though rarely detected in the blood donor population — has a strong effect on RBC markers of storage quality in vitro. We thus show that supplementation of blood units stored in bags with ranitidine could — through mechanisms involving sphingosine 1–phosphate–dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin — improve erythrocyte metabolism and storage quality.

Authors

Travis Nemkov, Davide Stefanoni, Aarash Bordbar, Aaron Issaian, Bernhard O. Palsson, Larry J. Dumont, Ariel Hay, Anren Song, Yang Xia, Jasmina S. Redzic, Elan Z. Eisenmesser, James C. Zimring, Steve Kleinman, Kirk C. Hansen, Michael P. Busch, Angelo D’Alessandro, the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell–Omics (REDS-III RBC-Omics) Study

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Figure 5

High-throughput metabolomics screening of human RBCs.

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High-throughput metabolomics screening of human RBCs. (A) Prior to high-...
(A) Prior to high-throughput metabolomics screening, donor RBCs were incubated with 1366 FDA-approved drugs for 24 hours at 37°C. (B) Retention times for most commonly identified drugs in the REDS-III cohort were validated in the high-throughput screening (y scale is in AU, normalized across all drugs). Hydrochloroth., hydrochlorothiazide. (C) TSNE plot of 1507 metabolomics samples of erythrocytes exposed to small molecule compounds (1368 samples), treated with vehicle (121 samples, dark gray), or left untreated (18 samples, light gray). Vehicle-treated and untreated samples cluster together. Though there is not a clear clustering of small molecule compounds into specific groups, they generally cluster by drug perturbation z score. (D) Heat map of subset of the metabolomics data of erythrocytes exposed to small molecules. Biclustering was used to identify modules, which are sets of drugs and set of metabolites whose changes significantly correlate with each other. Modules are shown in dark black boxes. Twenty-five total modules were identified due to perturbations by 331 small molecules affecting 151 metabolites. Identified modules and associated small molecules and metabolites are provided in Supplemental Table 1. (E) Highlight of metabolites and drug in cluster 24 from the biclustering analysis. (F) Effects of small molecule drugs from the high-throughput screening on the metabolite S1P (one of the metabolites most significantly affected in cluster 24). Four compounds that decrease S1P the most and 3 drugs that increase S1P the most are shown. Melatonin is known to have an effect on the S1P signaling pathway. Carbazochrome is a hemostatic agent. PAS is an antibiotic for tuberculosis but is contraindicated in individuals with G6PDH deficiency, as it induced hemolysis. Methazolamide is a carbonic anhydrase inhibitor. Ranitidine is an antihistamine; histamine is known to affect S1P signaling.