Sleep‐wake variation in body temperature regulates tau phosphorylation, secretion and splicing
Bibliographic record
Abstract
BACKGROUND: Aggregates of hyperphosphorylated tau protein are a hallmark of Alzheimer's disease (AD) and other tauopathies. Sleep disturbances-common in AD-have been associated with altered tau phosphorylation and secretion in both animal models and humans, and insufficient sleep is recognized as a potential risk factor for AD. Moreover, sleep deprivation affects mRNA splicing, polyadenylation, and miRNA-mediated degradation. However, how tau phosphorylation, secretion, and splicing are physiologically regulated across the sleep-wake cycle remains unknown. METHOD: Here, we combine in vitro, in vivo and clinical methods to investigate whether tau phosphorylation, secretion and splicing are governed by circadian rhythms linked to sleep and body temperature (BT). To elucidate underlying mechanisms, we exposed neuronal cells to the physiological temperatures observed under each condition. RESULT: We found that tau phosphorylation undergoes sleep-driven circadian variations, as it is hyperphosphorylated during sleep, when BT is lower. Similar changes in tau phosphorylation were reproduced in neuronal cells exposed to temperatures recorded during the sleep-wake cycle. In addition, we reveal a novel pathway by which BT modulates tau secretion, with higher temperature increasing extracellular and circulating tau levels. Tau splicing also varied with temperature, as lower temperatures favored exon 10 exclusion. Finally, in humans, the increase in CSF tau levels observed post-wakefulness correlated with BT increase during wakefulness. CONCLUSION: Overall, these findings demonstrate that tau phosphorylation, secretion, and splicing follow a circadian pattern primarily regulated by BT and sleep. Given the prevalence of sleep disruptions and thermoregulatory deficits in AD, this study offers new insight into how tau pathology may develop and spread.
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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 | 0.000 |
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".