Differential effects of PKA-controlled CaMKK2 variants on neuronal differentiation
Bibliographic record
Abstract
Regulation between protein kinases is critical for the establishment of signaling pathways/networks to 'orchestrate' cellular processes. Besides posttranslational phosphorylation, alternative pre-mRNA splicing is another way to control kinase properties, but splicing regulation between two kinases and the effect of resulting variants on cells has barely been explored. Here we examined the effect of the protein kinase A (PKA) pathway on the alternative splicing and variant properties of the Ca²⁺/calmodulin-dependent protein kinase kinase 2 (CaMKK2) gene in B35 neuroblastoma cells. Inclusion of the exon 16 of CaMKK2 was significantly reduced by H89, a PKA selective inhibitor. Consistently, overexpressed PKA strongly promoted the exon inclusion in a CaMKK2 sequence-dependent way in splicing reporter assays. In vitro, purified CaMKKβ1 variant proteins were found to be kinase-active. In cells, they were differentially phosphorylated by PKA. In RNA interference assays, CaMKKβ1 was found to be essential for forskolin-induced neurite growth. Interestingly, overexpression of the variant without exon 16 (-E16) promoted neurite elongation while the other one (+E16) promoted neurite branching; in contrast, reduction of the latter one enhanced neurite elongation. Moreover, the variants are differentially expressed and the exon 16-containing transcripts highly enriched in the brain, particularly the cerebellum and hippocampus. Thus, PKA regulates the alternative splicing of CaMKK2 to produce variants that differentially modulate neuronal differentiation. Taken together with the many distinct variants of kinases, alternative splicing regulation likely adds another layer of modulation between protein kinases in cellular signaling networks.
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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 | 0.001 |
| 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".