Proteomic analysis reveals dysregulation of peripheral blood neutrophils in patients with Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a complex auto-inflammatory disease affecting the brain and spinal cord, which results in axonal de-myelination and symptoms including fatigue, pain, and difficulties with vision and mobility. The involvement of the immune system in the pathology of MS is well established, particularly the adaptive T cell response, and there has been a particular focus on the IL-17-producing subset of Th17 cells and their role in driving disease. However, the importance of innate immune cells has not been so well characterized. Here we focussed on neutrophils, which are innate immune cells and rapid responders to inflammation, and which have recently been linked to other chronic autoimmune conditions. Multiple strands of evidence in patients with MS and in mice with the experimental autoimmune encephalomyelitis MS model suggest neutrophils may play a role in driving MS inflammation. Here, we performed proteomic analysis on neutrophils from patients with MS and healthy donors, revealing striking differences. In particular, granule proteins were significantly more abundant in the MS neutrophils compared to the healthy controls, with a particular overabundance of proteins in primary and secondary granules. In addition, members of the MAVS signalling pathway were differently regulated compared to healthy donor cells. Finally, we find that MS neutrophils do not suppress T cell activation equivalently to healthy neutrophils, and in particular are unable to suppress expression of CD161 on the T cells, indicative of a suppression of Th17 differentiation. We propose that neutrophil dysregulation in MS may contribute to dysfunctional T cell responses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".