Myelin Oligodendrocyte Glycoprotein (MOG)35–55 Mannan Conjugate Induces Human T-Cell Tolerance and Can Be Used as a Personalized Therapy for Multiple Sclerosis
Bibliographic record
Abstract
We have previously performed preclinical studies with the oxidized mannan-conjugated peptide MOG35-55 (OM-MOG35-55) in vivo (EAE mouse model) and in vitro (human peripheral blood) and demonstrated that OM-MOG35-55 suppresses antigen-specific T cell responses associated with autoimmune demyelination. Based on these results, we developed different types of dendritic cells (DCs) from the peripheral blood monocytes of patients with multiple sclerosis (MS) or healthy controls presenting OM-MOG35-55 or MOG-35-55 to autologous T cells to investigate the tolerogenic potential of OM-MOG35-55 for its possible use in MS therapy. To this end, monocytes were differentiated into different DC types in the presence of IL-4+GM-CSF ± dexamethasone (DEXA) ± vitamin D3 (VITD3). At the end of their differentiation, the DCs were loaded with peptides and co-cultured with T cells +IL-2 for 4 antigen presentation cycles. The phenotypes of the DC and T cell populations were analyzed using flow cytometry and the secreted cytokines using flow cytometry or ELISA. On day 8, the monocytes had converted into DCs expressing the typical markers of mature or immature phenotypes. Co-culture of T cells with all DC types for 4 antigen presentation cycles resulted in an increase in memory CD4+ T cells compared to memory CD8+ T cells and a suppressive shift in secreted cytokines, mainly due to increased TGF-β1 levels. The best tolerogenic effect was obtained when patient CD4+ T cells were co-cultured with VITD3-DCs presenting OM-MOG35-55, resulting in the highest levels of CD4+PD-1+ T cells and CD4+CD25+Foxp3+ Τ cells. In conclusion, the tolerance induction protocols presented in this work demonstrate that OM-MOG35-55 could form the basis for the development of personalized therapeutic vaccines or immunomodulatory treatments for MS.
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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".