Regulation of STING expression via the RIG-I dependent RNA sensing pathway
Bibliographic record
Abstract
T cytoplasmic pattern recognition receptor RIG-I is essential for recognizing RNA viruses with a 5’ triphosphate (ppp) signature. Upon viral RNA recognition, RIG-I recruits adaptor protein MAVS to trigger the activation of IRF3 and NFkB transcription factors through TBK1-IKK complexes, leading to the production of type I IFNs (α, β), pro-inflammatory cytokines, and antiviral factors. STING has been identified as an RIG-I signaling cofactor and a critical adaptor protein in a recently identified cGAS-mediated cytosolic DNA sensing pathway. In a recent functional study aiming to gain system-wide insight into downstream effector function of RIG-I, we identified STING among a plethora of differentially expressed genes induced by the RIG-I agonist 5’ppp RNA; in the present study, we further detail the mechanism of STING regulation. Our data shows that Sendai virus (SeV) infection induces STING expression at both the mRNA and protein levels in various cell types including A549, Huh7, PC3, and U87. Furthermore, by employing multiple RIG-I deficient or pathway-impaired cell lines, STING induction is shown to be dependent on functional RIG-I signaling. STING induction by the RIG-I agonist 5’ppp RNA was recognized as a delayed event resulting from an autocrine/paracrine mechanism. Indeed, co-treatment with TNFα and IFNα has a synergistic effect on the regulation of STING expression. Following SeV infection or TNFα-IFNα combined treatment, STING induction is partially decreased by siRelA or siIRF3; and is strongly diminished under combined siRelA and siIRF3 condition. Taken together, these observations demonstrate that STING expression is regulated via RIG-I signaling.
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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".