Erythrocyte-derived extracellular vesicles transcytose across the blood-brain barrier to induce Parkinson’s disease-like neurodegeneration
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative illness characterized by motor and non-motor features. Hallmarks of the disease include an extensive loss of dopaminergic neurons in the substantia nigra pars compacta, evidence of neuroinflammation, and the accumulation of misfolded proteins leading to the formation of Lewy bodies. While PD etiology is complex and identifying a single disease trigger has been a challenge, accumulating evidence indicates that non-neuronal and peripheral factors may likely contribute to disease onset and progression. The brain is shielded from peripheral factors by the blood-brain barrier (BBB), which tightly controls the entry of systemic molecules and cells from the blood to the brain. The BBB integrates molecular signals originating from the luminal (blood) and abluminal (brain) sides of the endothelial wall, regulating these exchanges. Of particular interest are erythrocytes, which are not only the most abundant cell type in the blood, but they also secrete extracellular vesicles (EVs) that display disease-specific signatures over the course of PD. Erythrocyte-derived EVs (EEVs) could provide a route by which pathological molecular signals travel from the periphery to the central nervous system. The primary objective of this study was to evaluate, in a human-based platform, mechanisms of EEV transport from the blood to the brain under physiological conditions. The secondary objective was to determine the ability of EEVs, generated by erythrocytes of healthy donors or patients, to induce PD-like features. We leveraged two in vitro models of the BBB, the transwell chambers and a microfluidic BBB chip generated using human induced pluripotent stem cells. Our findings suggest that EEVs transcytose from the vascular to the brain compartment of the human BBB model via a caveolin-dependant mechanism. Furthermore, EEVs derived from individuals with PD altered BBB integrity compared to healthy EEV controls, and clinical severity aggravated the loss of barrier integrity and increased EEV extravasation into the brain compartment. PD-derived EEVs reduced ZO-1 and Claudin 5 tight junction levels in BMEC-like cells and induced the selective atrophy of dopaminergic neurons. In contrast, non-dopaminergic neurons were not affected by treatment with PD EEVs. In summary, our data suggest that EEV interactions at the human BBB can be studied using a highly translational human-based brain chip model, and EEV toxicity at the neurovascular unit is exacerbated by disease severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".