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Record W4416372978 · doi:10.1016/j.xgen.2025.101069

Genetic architecture of the murine red blood cell proteome reveals central role of hemoglobin beta cysteine 93 in maintaining redox balance

2025· article· en· W4416372978 on OpenAlexaff
Gregory R. Keele, Monika Dzieciątkowska, Ariel Hay, Matthew Vincent, Callan O'Connor, Daniel Stephenson, Julie A. Reisz, Travis Nemkov, Kirk C. Hansen, Grier P. Page, James C. Zimring, Gary A. Churchill, Angelo D’Alessandro

Bibliographic record

VenueCell Genomics · 2025
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsCanadian Blood Services
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsGlutathioneRed blood cellProteomeQuantitative trait locusGenetic architectureReactive oxygen speciesHemoglobinProteasomeFunction (biology)Cysteine

Abstract

fetched live from OpenAlex

Red blood cells (RBCs) transport oxygen but accumulate oxidative damage over time, reducing function in vivo and during storage, critical for transfusions. To explore the genetics of RBC resilience, we profiled proteins, metabolites, and lipids from fresh and stored RBCs from 350 genetically diverse mice. Our analysis identified over 6,000 quantitative trait loci (QTLs). Compared to other tissues, the prevalence of trans genetic effects over cis ones reflects the absence of de novo protein synthesis in anucleated RBCs. QTL hotspots at Hbb, Hba, Mon1a, and (storage-specific) Steap3 linked ferroptosis to hemolysis. Proteasome QTLs clustered at multiple loci, underscoring the importance of degrading oxidized proteins. Post-translational modification (PTM) QTLs mapped predominantly to hemoglobins, including cysteine residues. The loss of reactive C93 in humanized mice (hemoglobulin beta [HBB] C93A) disrupted redox balance, glutathione pools, glutathionylation, and redox PTMs. These findings highlight genetic regulation of RBC oxidation, with implications for transfusion biology and oxidative-stress-dependent hemolytic disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.199
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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