Neuron-derived extracellular vesicles extracted from plasma show altered size and miRNA cargo as a function of antidepressant drug response
Bibliographic record
Abstract
Abstract Previous work has demonstrated that microRNAs (miRNAs) change as a function of antidepressant treatment (ADT) response. However, it is unclear how representative these peripherally detected miRNA changes are to those occurring in the brain. This study aimed to use peripherally extracted neuron-derived extracellular vesicles (NDEVs) to circumvent these limitations and investigate neuronal miRNA changes associated with antidepressant response. Samples were collected at two time points (baseline and after 8 weeks of follow-up) from depressed patients who responded (N=20) and did not respond (N=20) to escitalopram treatment, as well as controls (N=20). Total extracellular vesicles (EVs) were extracted from plasma, and then further enriched for NDEVs by immunoprecipitation with L1CAM. EV size was measured using tunable resistive pulse sensing, and NDEV miRNA cargo was extracted and sequenced. Subsequently, studies in cell lines and postmortem tissue were conducted. Characterization of NDEVs revealed they were smaller than other EVs isolated from plasma (p<0.0001), had brain-specific neuronal markers, and contained miRNAs enriched for brain functions (p<0.0001) Furthermore, NDEVs from depressed patients were smaller than controls (p<0.05), and NDEV size increased with ADT response (p<0.01). Finally, changes in NDEV cargo, specifically changes in miR-21-5p, miR-30d-5p and miR-486-5p together (p<0.01), were associated with ADT response. Targets of these three miRNAs were altered in brain tissue from depressed individuals (p<0.05). Together, this study indicates that changes in peripherally isolated NDEVs can act as both a clinically accessible and informative biomarker of ADT response specifically through size and cargo.
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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 | 0.000 |
| 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".