Neuronal Differentiation Dictates Estrogen-Dependent Survival and ERK1/2 Kinetic by Means of Caveolin-1
Post-publication record
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Bibliographic record
Abstract
Estrogens promote a plethora of effects in the CNS that profoundly affect both its development and mature functions and are able to influence proliferation, differentiation, survival and neurotransmission. The biological effects of estrogens are cell-context specific and also depend on differentiation and/or proliferation status in a given cell type. Furthermore, estrogens activate ERK1/2 in a variety of cellular types. Here, we investigated whether ERK1/2 activation might be influenced by estrogens stimulation according to the differentiation status and the molecular mechanisms underling this phenomenon. ERK1/2 exert an opposing role on survival and death, as well as on proliferation and differentiation depending on different kinetics of phosphorylation. Hence we report that mesencephalic primary cultures and the immortalized cell line mes-c-myc A1 express estrogen receptor α and activate ERK1/2 upon E2 stimulation. Interestingly, following the arrest of proliferation and the onset of differentiation, we observe a change in the kinetic of ERKs phosphorylation induced by estrogens stimulation. Moreover, caveolin-1, a main constituent of caveolae, endogenously expressed and co-localized with ER-α on plasma membrane, is consistently up-regulated following differentiation and cell growth arrest. In addition, we demonstrate that siRNA-induced caveolin-1 down-regulation or disruption by means of ß-cyclodextrin treatment changes ERK1/2 phosphorylation in response to estrogens stimulation. Finally, caveolin-1 down-regulation abolishes estrogens-dependent survival of neurons. Thus, caveolin-1 appears to be an important player in mediating, at least, some of the non-genomic action of estrogens in neurons, in particular ERK1/2 kinetics of activation and survival.
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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".