Ultradian rhythms of AKT phosphorylation and gene expression emerge in the absence of the circadian clock components Per1 and Per2
Bibliographic record
Abstract
Rhythmicity of biological processes can be elicited either in response to environmental cycles or driven by endogenous oscillators. In mammals, the circadian clock drives about 24-hour rhythms of multitude metabolic and physiological processes in anticipation to environmental daily oscillations. Also at the intersection of environment and metabolism is the protein kinase-AKT. It conveys extracellular signals, primarily feeding-related signals, to regulate various key cellular functions. Previous studies in mice identified rhythmicity in AKT activation (pAKT) with elevated levels in the fed state. However, it is still unknown whether rhythmic AKT activation can be driven through intrinsic mechanisms. Here, we inspected temporal changes in pAKT levels both in cultured cells and animal models. In cultured cells, pAKT levels showed circadian oscillations similar to those observed in livers of wild-type mice under free-running conditions. Unexpectedly, in livers of Per1,2-/- but not of Bmal1-/- mice we detected ultradian (about 16 hours) oscillations of pAKT levels. Importantly, the liver transcriptome of Per1,2-/- mice also showed ultradian rhythms, corresponding to pAKT rhythmicity and consisting of AKT-related genes and regulators. Overall, our findings reveal ultradian rhythms in liver gene expression and AKT phosphorylation that emerge in the absence of environmental rhythms and Per1,2-/- genes.
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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".