Loss of MicroRNA-7 Regulation Leads to α-Synuclein Accumulation and Dopaminergic Neuronal Loss In Vivo
Bibliographic record
Abstract
Abnormal alpha-synuclein (α-synuclein) expression and aggregation is a key characteristic of Parkinson's disease (PD). However, the exact mechanism(s) linking α-synuclein to the other central feature of PD, dopaminergic neuron loss, remains unclear. Therefore, improved cell and in vivo models are needed to investigate the role of α-synuclein in dopaminergic neuron loss. MicroRNA-7 (miR-7) regulates α-synuclein expression by binding to the 3' UTR of the Synuclein Alpha Non A4 Component of Amyloid Precursor (SNCA) gene and inhibiting its translation. We show that miR-7 is decreased in the substantia nigra of patients with PD and, therefore, may play an essential role in the regulation of α-synuclein expression. Furthermore, we have found that lentiviral-mediated expression of miR-7 complementary binding sites to stably induce a loss of miR-7 function results in an increase in α-synuclein expression in vitro and in vivo. We have also shown that depletion of miR-7 using a miR-decoy produces a loss of nigral dopaminergic neurons accompanied by a reduction of striatal dopamine content. These data suggest that miR-7 has an important role in the regulation of α-synuclein and dopamine physiology and may provide a new paradigm to study the pathology of PD.
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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) | 0.001 | 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".