Transcriptional regulation of the mouse <i>EphA4</i> , <i>Ephrin-B2</i> and <i>Ephrin-A3</i> genes by the circadian clock machinery
Bibliographic record
Abstract
Circadian rhythms originate from molecular feedback loops. In mammals, the transcription factors CLOCK and BMAL1 act on regulatory elements (i.e. E-boxes) to shape biological functions in a rhythmic manner. The EPHA4 receptor and its ligands Ephrins (EFN) are cell adhesion molecules regulating neurotransmission and neuronal morphology. Previous studies showed the presence of E-boxes in the genes of EphA4 and specific Ephrins, and that EphA4 knockout mice have an altered circadian rhythm of locomotor activity. We thus hypothesized that the core clock machinery regulates the gene expression of EphA4, EfnB2 and EfnA3. CLOCK and BMAL1 (or NPAS2 and BMAL2) were found to have transcriptional activity on distal and proximal regions of EphA4, EfnB2 and EfnA3 putative promoters. A constitutively active form of glycogen synthase kinase 3β (GSK3β; a negative regulator of CLOCK and BMAL1) blocked the transcriptional induction. Mutating the E-boxes of EphA4 distal promoter sequence reduced transcriptional induction. EPHA4 and EFNB2 protein levels did not show circadian variations in the mouse suprachiasmatic nucleus or prefrontal cortex. The findings uncover that core circadian transcription factors can regulate the gene expression of elements of the Eph/Ephrin system, which might contribute to circadian rhythmicity in biological processes in the brain or peripheral tissues.
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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".