Protective role of calreticulin in HFE hemochromatosis
Bibliographic record
Abstract
Background/Aims: HFE mutations are associated with over 80% of cases of Hereditary Hemochromatosis (HH), an iron‐overload disease in which the liver is the most frequently affected organ. Research on HFE has traditionally focused on its interaction with the transferrin receptor. Recent studies suggested a more complex function for this non‐classical MHC‐I protein. The aim of this study was to examine how HFE and its two most common mutations affect the expression of selected genes in a hepatocyte‐like cell line. Methods: Gene expression was analyzed in HepG2 cells over‐expressing wild‐type and mutant HFE. The effect of HFE in iron import and oxidative stress levels was assessed. Unfolded protein response (UPR)‐activated gene expression was analyzed in peripheral blood mononuclear cells from HH patients. Results: C282Y‐HFE down‐regulated hepcidin and enhanced calreticulin mRNA expression. Calreticulin levels correlated with intracellular iron increase and were associated with protection from oxidative stress. In C282Y +/+ patients calreticulin mRNA levels correlated with the expression of the UPR marker BiP and showed a negative association with the number of HH clinical manifestations. Conclusions: The data show that expression of C282Y‐HFE triggers a stress‐protective response and suggest a role for calreticulin as a modifier of the clinical expression of HH.
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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".