STAT1 and STAT2 Null Cells Are Resistant to RNA-Induced Apoptosis Due to Deficiency in Constitutive and Inducible Apoptosis-Regulating Genes
Bibliographic record
Abstract
Although much progress has been made in identifying the signaling pathways that mediate viral RNA-induced apoptosis and activation of interferon-stimulated genes, the role that bacterial RNA plays in regulating these responses has remained undetermined. Herein, we identified bacterial RNA as a novel inducer of the apoptotic cell death. Unlike the parental cells, STAT1 and STAT2 mutants display apoptotic defects which were reversed by restoring the expression of wild type proteins. While STAT1 mutants lacking tyrosine-701 or a functional SH2 domain were effective as the wild-type protein in restoring the apoptotic response, the mutant carrying a point mutation at serine-727 of STAT1 was resistant to bacterial RNA-induced apoptosis. We also determined that the lack of apoptosis in the STAT1 and STAT2 mutants was correlated with the constitutive and inducible activation of apoptosis regulating proteins. Furthermore, we show that bacterial RNA induces transcriptional activation of STAT1, STAT2, IRF1, and ISGF3, which was impaired in STAT1 or STAT2 mutants. These observations suggested that the participation of STATs in regulating the apoptotic response is independent of their downstream functions as cytokine-induced transcriptional activators. In addition to bacterial immunity, the results presented here may also have implications in cellular pathophysiology and RNA-based therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".