Effect of iron on cholesterol 7α-hydroxylase expression in alcohol-induced hepatic steatosis in mice
Bibliographic record
Abstract
Both iron and lipids are involved in the progression of alcoholic fatty liver disease (AFLD), but the interaction between iron and lipids in AFLD is unclear. Here, we tested the hypothesis that iron regulates the expression of genes involved in lipid metabolism through iron regulatory proteins (IRPs), which interact with the iron-responsive elements (IREs) in the untranslated regions (UTRs) of genes, resulting in lipid accumulation. Using "RNA structure software", we predicted the mRNA secondary structures of more than 100 genes involved in lipid metabolism to investigate whether the IRE structure exists in novel mRNAs. Cholesterol 7α-hydroxylase ( Cyp7a1 ) has an IRE-like stem-loop, a noncanonical IRE structure, in its 3′-UTR. Cyp7a1 expression can be regulated by in vivo and in vitro iron treatment. In addition, the noncanonical IRE motif can efficiently bind both to IRP1 and IRP2. The results indicate that hepatic iron overloading in AFLD mice decreased Cyp7a1 expression and resulted in cholesterol accumulation, providing a new mechanism of iron-regulated gene transcription and translation through the interaction between iron and a noncanonical IRE structure in Cyp7a1 mRNA. This finding has significant implications in studying a proposed mechanism for the regulation of cholesterol homeostasis by an Fe/IRP/noncanonical IRE axis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".