Targeting SRSF3 restores immune mRNA translation in microglia/macrophages following cerebral ischemia
Bibliographic record
Abstract
We recently described a novel ribosome-based regulatory mechanism/checkpoint that controls innate immune gene translation and microglial activation in non-sterile inflammation orchestrated by RNA binding protein SRSF3. Here we describe a role of SRSF3 in the regulation of microglia/macrophage activation phenotypes after experimental stroke. Using a model-system for analysis of the dynamic translational state of microglial ribosomes we show that 24 h after stroke highly upregulated immune mRNAs are not translated resulting in a marked dissociation of mRNA and protein networks in activated microglia/macrophages. Next, microglial activation after stroke was characterized by a robust increase in pSRSF3/SRSF3 expression levels. Targeted knockdown of SRSF3 using intranasal delivery of siRNA 24 h after stroke caused a marked knockdown of endogenous protein. Further analyses revealed that treatment with SRSF3-siRNA alleviated translational arrest of selected genes and induced a transient but significant increase in innate immune signaling and IBA1+ immunoreactivity peaking 5 days after initial injury. Importantly, delayed SRSF3-mediated increase in immune signaling markedly reduced the size of ischemic lesion measured 7 days after stroke. Together, our findings suggest that targeting SRSF3 and immune mRNA translation may open new avenues for molecular/therapeutic reprogramming of innate immune response after ischemic injury.
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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) | 0.001 | 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) | 0.001 | 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".