Advances in understanding the function of alpha-synuclein: implications for Parkinson’s disease
Bibliographic record
Abstract
The critical role of alpha-synuclein in Parkinson's disease represents a pivotal discovery. Some progress has been made over recent years in identifying disease-modifying therapies for Parkinson's disease that target alpha-synuclein. However, these treatments have not yet shown clear efficacy in slowing the progression of this disease. Several explanations exist for this issue. The pathogenesis of Parkinson's disease is complex and not yet fully clarified and the heterogeneity of the disease, with diverse genetic susceptibility and risk factors and different clinical courses, adds further complexity. Thus, a deep understanding of alpha-synuclein physiological and pathophysiological functions is crucial. In this review, we first describe the cellular and animal models developed over recent years to study the physiological and pathological roles of this protein, including transgenic techniques, use of viral vectors and intracerebral injections of alpha-synuclein fibrils. We then provide evidence that these tools are crucial for modelling Parkinson's disease pathogenesis, causing protein misfolding and aggregation, synaptic dysfunction, brain plasticity impairment and cell-to-cell spreading of alpha-synuclein species. In particular, we focus on the possibility of dissecting the pre- and postsynaptic effects of alpha-synuclein in both physiological and pathological conditions. Finally, we show how vulnerability of specific neuronal cell types may facilitate systemic dysfunctions leading to multiple network alterations. These functional alterations underlie diverse motor and non-motor manifestations of Parkinson's disease that occur before overt neurodegeneration. However, we now understand that therapeutic targeting of alpha-synuclein in Parkinson's disease patients requires caution, since this protein exerts important physiological synaptic functions. Moreover, the interactions of alpha-synuclein with other molecules may induce synergistic detrimental effects. Thus, targeting only alpha-synuclein might not be enough. Combined therapies should be considered in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".