Mesenchymal Stromal Cells Ameliorate Experimental Autoimmune Encephalomyelitis by Inhibiting CD4 Th17 T Cells in a CC Chemokine Ligand 2-Dependent Manner
Bibliographic record
Abstract
The administration of ex vivo culture-expanded mesenchymal stromal cells (MSCs) has been shown to reverse symptomatic neuroinflammation observed in experimental autoimmune encephalomyelitis (EAE). The mechanism by which this therapeutic effect occurs remains unknown. In an effort to decipher MSC mode of action, we found that MSC conditioned medium inhibits EAE-derived CD4 T cell activation by suppressing STAT3 phosphorylation via MSC-derived CCL2. Further analysis demonstrates that the effect is dependent on MSC-driven matrix metalloproteinase proteolytic processing of CCL2 to an antagonistic derivative. We also show that antagonistic CCL2 suppresses phosphorylation of AKT and leads to a reciprocal increased phosphorylation of ERK associated with an up-regulation of B7.H1 in CD4 T cells derived from EAE mice. CD4 T cell infiltration of the spinal cord of MSC-treated group was robustly decreased along with reduced plasma levels of IL-17 and TNF-alpha levels and in vitro from restimulated splenocytes. The key role of MSC-derived CCL2 was confirmed by the observed loss of function of CCL2(-/-) MSCs in EAE mice. In summary, this is the first report of MSCs modulating EAE biology via the paracrine conversion of CCL2 from agonist to antagonist of CD4 Th17 cell function.
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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) | 0.001 | 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".