Early growth response‐1‐mediated down‐regulation of drebrin correlates with loss of dendritic spines
Bibliographic record
Abstract
Post-synaptic dendritic spines are structurally composed of actin cytoskeleton, which undergoes dynamic morphological changes to accommodate incoming synaptic activity. Drebrin is an actin-binding protein highly expressed in dendritic spines that serves an important role in regulating spine morphology. Functionally, loss of drebrin directly correlates with deficits in learning and memory, as is the case observed in Alzheimer's disease. Despite these findings, the regulatory factor responsible for drebrin loss remains unclear. Here, we show that early growth response-1 (Egr-1), an inducible zinc finger transcription factor, down-regulates drebrin expression. Chromatin immunoprecipitation analyses identified Egr-1 binding sites upstream of the drebrin start site in neuronal cells. Over-expression of Egr-1 in vitro in primary hippocampal neurons or in vivo in homogenates prepared from the hippocampi of an inducible mouse model of Egr-1 show reduced drebrin mRNA and protein levels. Conversely, increased drebrin was detected in hippocampal samples isolated from Egr-1-deficient brain. These data demonstrate that Egr-1 interacts with the drebrin promoter and negatively regulates drebrin expression. Furthermore, immunocytochemical and Golgi staining analyses revealed reduced drebrin protein and dendritic spine density as well as reduced expression of synaptic markers in in vitro hippocampal neurons over-expressing Egr-1 and in vivo inducible mouse model of Egr-1. In contrast, increased drebrin expression correlated with increased dendritic spine density was detected in samples from Egr-1-deficient mice. These data provide evidence that Egr-1 is a novel regulator of drebrin expression, which is linked to changes in dendritic spine density.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".