Extracellular Vesicle Marker Changes Associated With Disease Activity in Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are autoimmune disorders of the CNS causing severe neurologic impairment. Evidence suggests that extracellular vesicles (EVs) may play a disease-specific role in the orchestration of the immune cell response of MS, NMOSD, and MOGAD. In addition, EVs are considered as a potential source of biomarkers that may allow us to establish molecular signatures for these diseases and perhaps as well to follow treatment effects and disease progression. The aim of this study was to analyze the composition of EVs in patients with relapsing-remitting MS (RRMS) (n = 52), NMOSD (n = 19), and MOGAD (n = 10) and healthy controls ([HCs], n = 15). METHODS: The concentrations of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were determined in plasma using single-molecule array (SIMOA). The size and concentration of tetraspanin-presenting EVs were evaluated in plasma samples with a single-particle interferometric resonance imaging sensor (SP-IRIS). Tetraspanin-independent analyses were performed by nanoparticle-tracking analysis (NTA) after EV isolation by size exclusion (SmartSEC) and cryo-electron microscopy observations. EV epitopes were analyzed by extended multiplex analysis using flow cytometry. RESULTS: The plasma concentration of NfL and GFAP was significantly higher in patients with RRMS than in HCs. For patients with NMOSD, only GFAP increased. The density of EVs assessed by NTA was lower in plasma of patients with RRMS than in HC plasma. In addition, the 3 disease groups presented increased mean EV sizes in comparison with HCs. Tetraspanin-based EV analyses by SP-IRIS allowed us to observe a modest difference in the level of CD81 in RRMS EVs. In patients with RRMS, but not in those with NMOSD and MOGAD, multiplex/flow cytometry analyses revealed changes in the EV levels of CD29, CD31, and CD69 associated with the time elapsed since the last relapse. The negative correlations established between the vesicular levels of CD31, CD40, CD44, CD49c, CD69, and NfL or GFAP z-scores suggest a negative relationship specifically in RRMS. DISCUSSION: We speculate that the higher release of EVs containing CD29, CD31, CD40, CD44, CD49c, and CD69 in plasma, at low levels of circulating NfL/GFAP, may be associated with reduced immune cell activity in RRMS. These EV markers may characterize patients with RRMS in disease stabilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".