Blood donor exposome and impact of common drugs on red blood cell metabolism
Bibliographic record
Abstract
Abstract Computational models based on recent maps of the red blood cell proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to red blood cell 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 post-transfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and off-the-counter drugs in 250 units donated by healthy volunteers from the REDS-III RBC Omics study. Based on high-throughput drug screenings of 1,366 FDA-approved drugs, we report a significant impact of ∼65% of the tested drugs 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 significant impact on erythrocyte metabolism. We then focused on ranitidine – a common antiacid – as a representative drug with the potential to improve human erythrocyte storage quality and post-transfusion performances in mice. By combining tracing experiments with 1,2,3- 13 C 3 -glucose, proteome integral solubility alteration assays, genetic ablation of S1P synthesis capacity, in silico docking and 1D NMR, we show that ranitidine triggers metabolic mechanisms involving sphingosine 1-phosphate (S1P)-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin. Graphical Abstract RBC exposome from the REDS III study revealed that blood from a subset of donors contains traces of the most common drugs in the United States. RBCs can uptake these drugs, in some cases can metabolize them to their bioactive metabolites and in others the drug can directly impact RBC metabolism during storage. Key points Blood donor exposomes include metabolites of environmental exposure, traces of common prescription or off-the-counter drugs; 65% of 1366 FDA- approved drug significantly affect RBC metabolism. Ranitidine significantly impacts glycolysis and S1P metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".