Feedback loop between hepatocyte nuclear factor 1α and endoplasmic reticulum stress mitigates liver injury by downregulating hepatocyte apoptosis
Bibliographic record
Abstract
Abstract Hepatocyte nuclear factor alpha (HNF1α), endoplasmic reticulum (ER) stress, and hepatocyte apoptosis contribute to severe acute exacerbation (SAE) of liver injury. Here, we explore HNF1α–ER stress-hepatocyte apoptosis interaction in liver injury. LO2, HepG2 and SK-Hep1 cells were treated with thapsigargin (TG) or tunicamycin (TM) to induce ER stress. Carbon tetrachloride (CCl4) was used to induce acute liver injury in mice. Low-dose lipopolysaccharide (LPS) exacerbated liver injury in CCl4-induced mice. Significant apoptosis, HNF1α upregulation, and nuclear factor kappa B (NF-κB) activation were observed in human-derived hepatocytes during ER stress. Knockdown of Rela, NF-κB p65, inhibited the HNF1α upregulation. Following CCl4 treatment ER stress, apoptosis, HNF1α expression and RelA phosphorylation were significantly increased in mice. HNF1α knockdown reduced activating transcription factor 4 (ATF4) expression, and aggravated ER stress as well as hepatocyte apoptosis in vivo and in vitro. The double fluorescent reporter gene assay confirmed that HNF1α regulated the transcription of ATF4 promoter. LPS aggravated CCl4-induced liver injury and reduced HNF1α, and ATF4 expression. Therefore, in combination, HNF1α and ER stress could be mutually regulated forming a feedback loop, which helps in protecting the injured liver by down-regulating hepatocyte apoptosis. Low-dose LPS aggravates hepatocyte apoptosis and promotes the SAE of liver injury by interfering with the feedback regulation of HNF1α and ER stress in acute liver injury.
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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".