The scaffolding protein AKAP12 regulates mRNA localization and translation
Bibliographic record
Abstract
Regulation of subcellular messenger (m)RNA localization is a fundamental biological mechanism, which adds a spatial dimension to the diverse layers of post-transcriptional control of gene expression. The cellular compartment in which mRNAs are located may define distinct aspects of the encoded proteins, ranging from production rate and complex formation to localized activity. Despite the detailed roles of localized mRNAs that have emerged over the past decades, the identity of factors anchoring mRNAs to subcellular domains remains ill-defined. Here, we used an unbiased method to profile the RNA-bound proteome in migrating endothelial cells (ECs) and discovered that the plasma membrane (PM)–associated scaffolding protein A-kinase anchor protein (AKAP)12 interacts with various mRNAs, including transcripts encoding kinases with Actin remodeling activity. In particular, AKAP12 targets a transcript coding for the kinase Abelson Tyrosine-Protein Kinase 2 (ABL2), which we found to be necessary for adequate filopodia formation and angiogenic sprouting. Moreover, we demonstrate that AKAP12 is necessary for anchoring ABL2 mRNA to the PM and show that in the absence of AKAP12, the translation efficiency of ABL2 mRNA is reduced. Altogether, our work identified a unique post-transcriptional function for AKAP12 and sheds light into mechanisms of spatial control of gene expression.
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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 | 0.001 |
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".