ISGylation enhances dsRNA-induced interferon response and NFκB signaling in fallopian tube epithelial cells
Bibliographic record
Abstract
Heritable mutations in BRCA1 associate with increased risk of high-grade serous tubo-ovarian cancer. Nongenetic risk factors associated with this cancer, which arises from fallopian tube epithelial (FTE) cells, suggests a role for repetitive ovulation wherein FTE cells are exposed to inflammatory signaling molecules within follicular fluid. We previously reported increased NFκB and EGFR signaling in BRCA1-deficient primary FTE cells, with follicular fluid exposure further increasing abundance of interferon-stimulated gene (ISG) transcripts, including the ubiquitin-like protein ISG15 and other ISGylation pathway members. Both NFκB and type I interferon signaling are upregulated by stimulation of cGAS-STING or MDA5 and RIGI pattern recognition receptors. Since some pattern recognition receptors and their signal transduction pathway members are ISGylated, we tested the impact of ISG15 and ISGylation on interferon regulatory factor 3 (IRF3) and NFκB signaling through cGAS-STING or RIGI and MDA5 activation. Expression of ISG15 or UBA7, the E1-like ISG15-activating enzyme, in immortalized FTE cells was disrupted by CRISPR gene editing. Activation of IRF3 by RIGI or MDA5 but not cGAS-STING was attenuated by loss of either ISG15 or UBA7 and this was reflected by a similar effect on NFκB activation and downstream targets. Loss of ISGylation decreased levels of both MDA5 and RIGI, with knockdown of RIGI but not MDA5, decreasing IRF3 and NFκB activation in parental cells. These finding indicate that ISGylation enhances the ability of dsRNA to activate cytokine release and proinflammatory signaling. Further work to explore ISGylation as a target for prevention of high-grade serous tubo-ovarian cancer in BRCA1 mutation carriers is warranted.
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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 | 0.001 |
| 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".