Oestrogen Receptors and Signalling Pathways: Implications for Neuroprotective Effects of Sex Steroids in Parkinson’s Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is an age-related neurodegenerative disorder with a higher incidence in the male population. In the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD, 17β-oestradiol but not androgens were shown to protect dopamine (DA) neurones. We report that oestrogen receptors (ER)α and β distinctly contribute to neuroprotection against MPTP toxicity, as revealed by examining the membrane DA transporter (DAT), the vesicular monoamine transporter 2 (VMAT2) and tyrosine hyroxylase in ER wild-type (WT) and knockout (ERKO) C57Bl/6 male mice. Intact ERKOβ mice had lower levels of striatal DAT and VMAT2, whereas ERKOα mice were the most sensitive to MPTP toxicity compared to WT and ERKOβ mice and had the highest levels of plasma androgens. In both ERKO mice groups, treatment with 17β-oestradiol did not provide neuroprotection against MPTP, despite elevated plasma 17β-oestradiol levels. Next, the recently described membrane G protein-coupled oestrogen receptor (GPER1) was examined in female Macaca fascicularis monkeys and mice. GPER1 levels were increased in the caudate nucleus and the putamen of MPTP-monkeys and in the male mouse striatum lesioned with methamphetamine or MPTP. Moreover, neuroprotective mechanisms in response to oestrogens transmit via Akt/glycogen synthase kinase-3 (GSK3) signalling. The intact and lesioned striata of 17β-oestradiol treated monkeys, similar to that of mice, had increased levels of pAkt (Ser 473)/βIII-tubulin, pGSK3 (Ser 9)/βIII-tubulin and Akt/βIII-tubulin. Hence, ERα, ERβ and GPER1 activation by oestrogens is imperative in the modulation of ER signalling and serves as a basis for evaluating nigrostriatal neuroprotection.
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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 | 0.001 | 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".