Identification of Thyroid Hormone Receptor Binding Sites and Target Genes Using ChIP-on-Chip in Developing Mouse Cerebellum
Bibliographic record
Abstract
Thyroid hormone (TH) is critical to normal brain development, but the mechanisms operating in this process are poorly understood. We used chromatin immunoprecipitation to enrich regions of DNA bound to thyroid receptor beta (TRbeta) of mouse cerebellum sampled on post natal day 15. Enriched target was hybridized to promoter microarrays (ChIP-on-chip) spanning -8 kb to +2 kb of the transcription start site (TSS) of 5000 genes. We identified 91 genes with TR binding sites. Roughly half of the sites were located in introns, while 30% were located within 1 kb upstream (5') of the TSS. Of these genes, 83 with known function included genes involved in apoptosis, neurodevelopment, metabolism and signal transduction. Two genes, MBP and CD44, are known to contain TREs, providing validation of the system. This is the first report of TR binding for 81 of these genes. ChIP-on-chip results were confirmed for 10 of the 13 binding fragments using ChIP-PCR. The expression of 4 novel TH target genes was found to be correlated with TH levels in hyper/hypothyroid animals providing further support for TR binding. A TRbeta binding site upstream of the coding region of myelin associated glycoprotein was demonstrated to be TH-responsive using a luciferase expression system. Motif searches did not identify any classic binding elements, indicating that not all TR binding sites conform to variations of the classic form. These findings provide mechanistic insight into impaired neurodevelopment resulting from TH deficiency and a rich bioinformatics resource for developing a better understanding of TR binding.
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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 | 0.001 | 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".