Abstract 532: The RNA-binding Protein Quaking Critically Regulates Vascular Smooth Muscle Cell Phenotype
Bibliographic record
Abstract
In response to vascular injury, smooth muscle cells (VSMC) adopt a proliferative, synthetic hypocontractile phenotype. This phenotype switch is deemed instrumental in vascular remodeling in both health and disease. Here, we detail a decisive role for the RNA-binding protein Quaking (QKI) in regulating VSMC plasticity. We identified that the RNA-binding protein Quaking (QKI) is highly expressed by neointimal VSMCs of human coronary restenotic lesions, but not in healthy vessels. In a mouse model of vascular injury, we observed reduced neointima hyperplasia in Qk v mice, which have decreased QKI expression. Concordantly, abrogation of QKI attenuated fibroproliferative properties of VSMCs, while potently inducing contractile apparatus protein expression, rendering non-contractile VSMCs with the capacity to contract. We identified that QKI localizes to the spliceosome in proliferative VSMCs, where it interacts with and impacts myocardin (pre)-mRNA metabolism by mediating myocardin exon 2a exclusion. As such, in vitro and in vivo experiments indicate that the modulation of QKI expression directly influences the myocardin_v3 / myocardin_v1 mRNA balance, which could play a role in shifting the Myocardin-induced transcriptional coactivation profile following arterial damage. We propose that QKI is a central regulator of VSMC phenotypic plasticity and that intervention in QKI activity can ameliorate pathogenic, fibroproliferative responses to vascular 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) | 0.000 | 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) | 0.003 | 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".