Protein kinase A and casein kinases mediate sequential phosphorylation events in the circadian negative feedback loop
Bibliographic record
Abstract
Regulation of circadian clock components by phosphorylation plays essential roles in clock functions and is conserved from fungi to mammals. In the Neurospora circadian negative feedback loop, FREQUENCY (FRQ) protein inhibits WHITE COLLAR (WC) complex activity by recruiting the casein kinases CKI and CKII to phosphorylate the WC proteins, resulting in the repression of frq transcription. On the other hand, CKI and CKII progressively phosphorylate FRQ to promote FRQ degradation, a process that is a major determinant of circadian period length. Here, by using whole-cell isotope labeling and quantitative mass spectrometry methods, we show that the WC-1 phosphorylation events critical for the negative feedback process occur sequentially-first by a priming kinase, then by the FRQ-recruited casein kinases. We further show that the cyclic AMP-dependent protein kinase A (PKA) is essential for clock function and inhibits WC activity by serving as a priming kinase for the casein kinases. In addition, PKA also regulates FRQ phosphorylation, but unlike CKI and CKII, PKA stabilizes FRQ, similar to the stabilization of human PERIOD2 (hPER2) due to the phosphorylation at the familial advanced sleep phase syndrome (FASPS) site. Thus, PKA is a key clock component that regulates several critical processes in the circadian negative feedback loop.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".