Role for Tissue-Dependent Methylation Differences in the Expression of FOXE1 in Nontumoral Thyroid Glands
Bibliographic record
Abstract
BACKGROUND: Discordance of monozygotic twins for thyroid dysgenesis suggests that epigenetic mechanisms may underlie defects in thyroid gland development. This prompted us to evaluate whether differentially methylated regions (DMRs) can be found between human thyroids (either eutopic or ectopic) and matched leukocytes. METHODS: To compare the genome-wide methylation profile of thyroids and leukocytes, immunoprecipitated methylated DNA was interrogated on human promoter plus CpG island tiling arrays. In addition, the methylation profile of the human FOXE1, PAX8, and NKX2.1 promoter was examined using bisulfite sequencing. Finally, the functional impact of CpG methylation of the promoter on FOXE1 expression was assessed with luciferase assays. RESULTS: Genome-wide methylation profiling and bisulfite sequencing of CpG islands of PAX8 and NKX2.1 promoters revealed no DMR between thyroid and leukocytes. However, bisulfite sequencing revealed that the methylation level of two consecutive CpG dinucleotides (CpG14 and CpG15, which were not covered by the genome-wide array) in one CpG island of the FOXE1 promoter (-1600 to -1140 from the transcription start site) is significantly higher in leukocytes than in eutopic or ectopic thyroid tissues, suggesting that methylation of this region may decrease FOXE1 gene expression. Indeed, luciferase activities were decreased when FOXE1 promoter constructs were methylated in vitro. Moreover, derepression of luciferase activity was observed when the methylation of CpG14 and CpG15 was prevented by mutations. CONCLUSION: We report a tissue-dependent DMR in the FOXE1 promoter. This DMR contains two consecutive CpG dinucleotides, which are epigenetic modifiers of FOXE1 expression in nontumoral 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".