Intracellular expression of IFN-λ4 leads to ER stress, enhanced IRF1 signaling and decreased proliferation in hepatic cells that might protect HCV+ patients from liver cirrhosis
Bibliographic record
Abstract
Abstract Several genetic variants in the human IFNL3/IFNL4 locus have been associated with reduced hepatic fibrosis despite poor clearance of HCV infection. We expanded this analysis to cirrhosis, a more advanced stage of fibrosis. In 2931 individuals with chronic HCV, the IFNL4 genotype that generates IFN-λ4 was associated with protection from cirrhosis (OR=0.65, p=0.012, adjusted for age and sex). The IFNL4 genotype affects the production of IFN-λ4 and, additionally, may affect expression levels of IFN-λ3, making it difficult to delineate the individual contribution of these IFNs. To address this, we established HepG2-based cell models engineered to inducibly express either IFN-λ3 or IFN-λ4. Using RNA-seq based expression profile generated in these hepatic cell lines, we explored the global transcriptome of liver tumors (n=373) from The Cancer Genome Atlas to identify transcription factor networks affected by these IFNs in the liver. Several networks, including IRF1, were upregulated by IFN-λ4 more strongly than by IFN-λ3. Intracellular expression of IFN-λ4 but not of IFN-λ3 also led to potent IRF1-dependent antiproliferative effects. Live cell imaging revealed that IFN-λ4 was poorly secreted, mainly accumulated in lysosomes, and caused apoptosis, suggesting increased ER-stress via the misfolded protein response. Knockdown of DNA damage-inducible transcript 3 (DDIT3), an ER-stress response effector, significantly attenuated the antiproliferative effects of IFN-λ4. This novel interplay of enhanced IRF1 signaling coupled with intracellular accumulation and induction of ER stress by IFN-λ4 may have complex consequences on liver homeostasis during chronic HCV infection but also mediate anti-cirrhotic phenotypes.
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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) | 0.002 | 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".