Tissue-Specific Dissociation of Diurnal Transcriptome Rhythms During Sleep Restriction in Mice
Bibliographic record
Abstract
Study objectives: Shortened or mistimed sleep affects metabolic homeostasis, which may in part be mediated by dysregulation of endogenous circadian clocks. In this study, we assessed the contribution of sleep disruption to metabolic dysregulation by analysing diurnal transcriptome regulation in metabolic tissues of mice subjected to a sleep restriction (SR) paradigm. Methods: Male mice were subjected to 2 × 5 days of SR with enforced waking during the first 6 hours of the light phase. SR and control mice were sacrificed at different time points of the day and RNA preparations from the mediobasal hypothalamus (MBH), liver, and epididymal white adipose tissue (eWAT) were subjected to whole-genome microarray hybridization. Transcriptional rhythms were associated with changes in behavioral and physiological parameters such as sleep, body temperature, and food intake. Rhythm detection was performed with CircWave and transcription profiles were compared by 2-way analysis of variance and t-tests with Benjamini-Hochberg corrections. Results: Clock gene rhythms were blunted in all tissues, while transcriptome regulation was associated with either clock gene expression, sleep patterns, or food intake in a tissue-specific manner. Clock gene expression was associated with apoptosis pathways in the MBH and with tumor necrosis factor alpha signalling in liver. Food intake-associated genes included cilium movement genes in the MBH and lipid metabolism-associated transcripts in liver. Conclusions: In mice, repeated SR profoundly alters behavioral and molecular diurnal rhythms, disrupting essential signalling pathways in MBH, liver, and eWAT, which may underlie the metabolic and cognitive disturbances observed in sleep-restricted humans such as shift workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".