The Transcription Factor Nerve Growth Factor-Inducible Protein A Mediates Epigenetic Programming: Altering Epigenetic Marks by Immediate-Early Genes
Bibliographic record
Abstract
Maternal care alters epigenetic programming of glucocorticoid receptor (GR) gene expression in the hippocampus, and increased postnatal maternal licking/grooming (LG) behavior enhances nerve growth factor-inducible protein A (NGFI-A) transcription factor binding to the exon 1(7) GR promoter within the hippocampus of the offspring. We tested the hypothesis that NGFI-A binding to the exon 1(7) GR promoter sequence marks this sequence for histone acetylation and DNA demethylation and that such epigenetic alterations subsequently influence NGFI-A binding and GR transcription. We report that (1) NGFI-A binding to its consensus sequence is inhibited by DNA methylation, (2) NGFI-A induces the activity of exon 1(7) GR promoter in a transient reporter assay, (3) DNA methylation inhibits exon 1(7) GR promoter activity, and (4) whereas NGFI-A interaction with the methylated exon 1(7) GR promoter is reduced, NGFI-A overexpression induces histone acetylation, DNA demethylation, and activation of the exon 1(7) GR promoter in transient transfection assays. Site-directed mutagenesis assays demonstrate that NGFI-A binding to the exon 1(7) GR promoter is required for such epigenetic reprogramming. In vivo, enhanced maternal LG is associated with increased NGFI-A binding to the exon 1(7) GR promoter in the hippocampus of pups, and NGFI-A-bound exon 1(7) GR promoter is unmethylated compared with unbound exon 1(7) GR promoter. Knockdown experiments of NGFI-A in hippocampal primary cell culture show that NGFI-A is required for serotonin-induced DNA demethylation and increased exon 1(7) GR promoter expression. The data are consistent with the hypothesis that NGFI-A participates in epigenetic programming of GR expression.
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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".