QUAKING Regulates Microexon Alternative Splicing of the Rho GTPase Pathway and Controls Microglia Homeostasis
Bibliographic record
Abstract
The role of RNA binding proteins in regulating the phagocytic and cytokine-releasing functions of microglia is unknown. Here, we show that microglia deficient for the QUAKING (QKI) RNA binding protein have increased proinflammatory cytokine release and defects in processing phagocytosed cargo. Splicing analysis reveals a role for QKI in regulating microexon networks of the Rho GTPase pathway. We show an increase in RhoA activation and proinflammatory cytokines in QKI-deficient microglia that are repressed by treating with a Rock kinase inhibitor. During the cuprizone diet, mice with QKI-deficient microglia are inefficient at supporting central nervous system (CNS) remyelination and cause the recruited oligodendrocyte precursor cells to undergo apoptosis. Furthermore, the expression of QKI in microglia is downregulated in preactive, chronic active, and remyelinating white matter lesions of multiple sclerosis (MS) patients. Overall, our findings identify QKI as an alternative splicing regulator governing a network of Rho GTPase microexons with implications for CNS remyelination and MS patients.
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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".