The SCN Clock Governs Circadian Transcription Rhythms in Murine Epididymal White Adipose Tissue
Bibliographic record
Abstract
The circadian master pacemaker in the suprachiasmatic nucleus (SCN) orchestrates peripheral clocks in various organs and synchronizes them with external time, including those in adipose tissue, which displays circadian oscillations in various metabolic and endocrine outputs. Because our knowledge about the instructive role of the SCN clock on peripheral tissue function is based mainly on SCN lesion studies, we here used an alternative strategy employing the Cre/ loxP system to functionally delete the SCN clock in mice. We performed whole-genome microarray hybridizations of murine epididymal white adipose tissue (eWAT) RNA preparations to characterize the role of the SCN clock in eWAT circadian transcriptome regulation. Most of the rhythmic transcripts in control animals were not rhythmic in SCN mutants, but a significant number of transcripts were rhythmic only in mutant eWAT. Core clock genes were rhythmic in both groups, but as was reported before for other tissues, rhythms were dampened and phase advanced in mutant animals. In SCN-mutant mice, eWAT lost the rhythm of metabolic pathway-related transcripts, while transcripts gaining rhythms in SCN-mutant mice were associated with various immune functions. These data reveal a complex interaction of SCN-derived and local circadian signals in the regulation of adipose transcriptome programs.
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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".