Regulation of interferon regulatory factor 1 by protein inhibitor of activated STAT 3
Bibliographic record
Abstract
Oncolytic viruses exploit tumor-specific cellular changes for selective replication, inducing cancer cell death. Our previous research showed that Ras/mitogen-activated protein kinase kinase (MEK) downregulation of interferon regulatory factor 1 (IRF1) is a major mechanism underlying viral oncolysis. Protein inhibitor of activated STAT 3 (PIAS3) is known as the E3 ligase of IRF1 sumoylation. The objective of this study was to identify if, and how, PIAS3 modulates cellular sensitivity to oncolytic viruses via regulation of IRF1. By conducting co-immunoprecipitation, I found that IRF1 has no direct interaction with PIAS3 found within HT1080 cells. However, CRISPR knockdown of PIAS3 increases IRF1 expression as well as transcription of IRF1-responsive anti-viral genes. Furthermore, PIAS3 knockdown HT1080 cells were equally sensitive to viral infection as their parent HT1080 cells. Together, these results demonstrate that PIAS3 does not directly interact with IRF1 but regulates expression and transcriptional activity of IRF1. Moreover, as CRISPR knockdown of PIAS3 did not change cellular sensitivity to viral infection, IRF1 modulation via PIAS3 does not play very critical roles in host innate antiviral responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".