Regulation of kynurenine metabolism by blood donor genetics and biology impacts red cell hemolysis in vitro and in vivo
Bibliographic record
Abstract
ABSTRACT: In the field of transfusion medicine, the clinical relevance of the metabolic markers of the red blood cell (RBC) storage lesion is incompletely understood. Here, we performed metabolomics of RBC units from 643 donors enrolled in the Recipient Epidemiology and Donor Evaluation Study, REDS RBC Omics. These units were tested on storage days 10, 23, and 42 for a total of 1929 samples and also characterized for end-of-storage hemolytic propensity after oxidative and osmotic insults. Our results indicate that the metabolic markers of the storage lesion poorly correlated with hemolytic propensity. In contrast, kynurenine was not affected by storage duration and was identified as the top predictor of osmotic fragility. RBC kynurenine levels were affected by donor age and body mass index and were reproducible within the same donor across multiple donations from 2 to 12 months apart. To delve into the genetic underpinnings of kynurenine levels in stored RBCs, we thus tested kynurenine levels in stored RBCs on day 42 from 13 091 donors from the REDS RBC Omics study, a population that was also genotyped for 879 000 single nucleotide polymorphisms. Through a metabolite quantitative trait loci analysis, we identified polymorphisms in SLC7A5, ATXN2, and a series of rate-limiting enzymes (eg, kynurenine monooxygenase, indoleamine 2,3-dioxygenase, and tryptophan dioxygenase) in the kynurenine pathway as critical factors affecting RBC kynurenine levels. By interrogating a donor-recipient linkage vein-to-vein database, we then report that SLC7A5 polymorphisms are also associated with changes in hemoglobin and bilirubin levels, suggestive of in vivo hemolysis in 4470 individuals who were critically ill and receiving single-unit transfusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".