Ferroptosis regulates hemolysis in stored murine and human red blood cells
Bibliographic record
Abstract
Red blood cell (RBC) metabolism regulates hemolysis during aging in vivo and in the blood bank. Here, we leveraged a diversity outbred mouse population to map the genetic drivers of fresh/stored RBC metabolism and extravascular hemolysis upon storage and transfusion in 350 mice. We identify the ferrireductase Steap3 as a critical regulator of a ferroptosis-like process of lipid peroxidation. Steap3 polymorphisms were associated with RBC iron content, in vitro hemolysis, and in vivo extravascular hemolysis both in mice and 13,091 blood donors from the Recipient Epidemiology and Donor evaluation Study. Using metabolite Quantitative Trait Loci analyses, we identified a network of gene products (FADS1/2, EPHX2 and LPCAT3) - enriched in donors of African descent - associated with oxylipin metabolism in stored human RBCs and related to Steap3 or its transcriptional regulator, the tumor protein TP53. Genetic variants were associated with lower in vivo hemolysis in thousands of single-unit transfusion recipients. Highlights: Steap3 regulates lipid peroxidation and extravascular hemolysis in 350 diversity outbred miceSteap3 SNPs are linked to RBC iron, hemolysis, vesiculation in 13,091 blood donorsmQTL analyses of oxylipins identified ferroptosis-related gene products FADS1/2, EPHX2, LPCAT3Ferroptosis markers are linked to hemoglobin increments in transfusion recipients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".