<i>SNCA</i> triplication disrupts proteostasis and extracellular architecture prior to neurodegeneration in human midbrain organoids
Bibliographic record
Abstract
Abstract Synucleinopathies, including Parkinson’s disease, are characterized by α-synuclein (SNCA) aggregation and progressive neurodegeneration, yet the early molecular events linking SNCA gene dosage to disrupted proteostasis remain poorly understood. Here, we used human midbrain organoids derived from induced pluripotent stem cells (iPSC) carrying an SNCA triplication ( SNCA Trip) and the isogenic corrected line ( SNCA Isog) to dissect early pathogenic mechanisms in a 3D human model of synucleinopathy. We combined immunohistochemistry, immunoblotting, tandem mass tag proteomics, bulk RNA sequencing, and ribosome profiling to systematically characterize molecular alterations in SNCA Trip organoids at day 50 (D50) and day 100 (D100) of maturation. SNCA Trip organoids exhibited increased α-synuclein accumulation, neuromelanin deposition, and activation of mTORC1 (p-rpS6), ERK1/2, AKT and p-eIF2α signalling pathways by D100. Proteomic and transcriptomic analyses revealed upregulation of cytoskeletal, synaptic, and axonal development pathways, alongside significant downregulation of extracellular matrix (ECM) components and upregulation of perineuronal net (PNN) genes. Ribosome profiling showed minimal global translational changes but uncovered selective translational buffering of neuronal and ECM-associated transcripts. Confocal imaging confirmed progressive disorganization of pericellular and interstitial ECM structures around neurons in SNCA Trip organoids. Our findings demonstrate that SNCA triplication induces early proteostatic disruption and extracellular matrix remodelling prior to neurodegeneration and suggest that altered gene expression and ECM homeostasis may contribute to disease initiation and progression. Targeting these early aberrant mechanisms may offer new therapeutic opportunities for synucleinopathies, such as Parkinson’s Disease.
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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 | 0.000 |
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".