A Novel Synthetic GMCSF and MCP3 Fusion Cytokine Induces IL10-Producing B Cells with Immune Modulatory Properties on Antigen Presentation and Th17 Development
Bibliographic record
Abstract
Abstract Abstract 587 Therapeutic strategies based on the modulation of immune system can be exploited to treat a variety of pathologies including autoimmune diseases and cancer. We previously described the immune suppressive and receptor-specific apoptotic effect of a fusion cytokine GMME1, generated by the linkage of granulocyte macrophage-colony stimulating factor (GM-CSF) at the N-terminal of MCP1 (6-76). To better understand the immunobiology of N-terminal-modified CC chemokine, we described the novel function of GMCSF-MCP3 (5-76) (GMME3) in its induction of IL10+, regulatory-competent B cells. Ex vivo cultured B lymphocyte with GMME3 leads to a significant increase in S and G2/M phases, IL10 production and the expression of CD1.d and CD5. Treatment of experimental autoimmune encephalomyelitis (EAE) mice with GMME3-activated wild type B cells (BGMME3), but not IL10 null B cells, significantly inhibits the disease progression and immune infiltrates into the CNS. We demonstrated that the mechanism by which BGMME3 exerts their effect is through IL10-mediated inhibition of class II antigen presentation of macrophages; moreover, BGMME3 inhibits the reactivation of EAE-derived Th17 cells by augmenting Th1 pathway. Hence, N-terminal-GMCSF-fused CC chemokine, GMME3 modulates the immunological phenotype of B cells in a manner permissive for personalized cell therapy of autoimmune ailments. Disclosures: No relevant conflicts of interest to declare.
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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".