Identification of activity-induced Egr3-dependent genes reveals genes associated with DNA damage response and schizophrenia
Bibliographic record
Abstract
Bioinformatics and network studies have identified the immediate early gene transcription factor early growth response 3 (EGR3) as a master regulator of genes differentially expressed in the brains of patients with neuropsychiatric illnesses ranging from schizophrenia and bipolar disorder to Alzheimer's disease. However, few studies have identified and validated Egr3-dependent genes in the mammalian brain. We have previously shown that Egr3 is required for stress-responsive behavior, memory, and hippocampal long-term depression in mice. To identify Egr3-dependent genes that may regulate these processes, we conducted an expression microarray on hippocampi from wildtype (WT) and Egr3-/- mice following electroconvulsive seizure (ECS), a stimulus that induces maximal expression of immediate early genes including Egr3. We identified 69 genes that were differentially expressed between WT and Egr3-/- mice one hour following ECS. Bioinformatic analyses showed that many of these are altered in, or associated with, schizophrenia, including Mef2c and Calb2. Enrichr pathway analysis revealed the GADD45 (growth arrest and DNA-damage-inducible) family (Gadd45b, Gadd45g) as a leading group of differentially expressed genes. Together with differentially expressed genes in the AP-1 transcription factor family genes (Fos, Fosb), and the centromere organization protein Cenpa, these results revealed that Egr3 is required for activity-dependent expression of genes involved in the DNA damage response. Our findings show that EGR3 is critical for the expression of genes that are mis-expressed in schizophrenia and reveal a novel requirement for EGR3 in the expression of genes involved in activity-induced DNA damage response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".