Reprogramming of B Cells into Regulatory Cells with Engineered Fusokines
Bibliographic record
Abstract
B cells play a pivotal role in host adaptive immunity against pathogenic microorganisms, but may also maladaptively contribute to the pathogenesis of autoimmune diseases. In contrast, distinct B cell subsets have the capacity to regulate host immune response, and suppress inflammation. B regulatory cells are a rare population of endogenous Blymphocytes defined in part by production of the anti-inflammatory cytokine IL-10. Although "natural" B regulatory cells exist in vivo, the low frequency of B regulatory cells may be a limiting factor on their impact in autoimmune ailments. In answer to this unmet need, we have developed a novel strategy for alternate lymphoid activation: fusokines. These wholly engineered chimeric leukines fuse two functionally unrelated cytokines for the purpose of alternate immune modulation. The GM-CSF- and IL-15-derived fusokine: GIFT15, possesses entirely novel and unheralded immune modulating properties mediated through the IL15 receptor which reprograms naive B cells into B regulatory cells (Bregs). In this article, we review the current approaches to generate Bregs in vitro, and highlight gain-of-function mechanisms by which GIFT15- induced Bregs abrogate pathogenic autoimmunity in mice. We also demonstrate that the human equivalent of inducible Bregs may also serve as a new potent therapeutic tool for treatment of autoimmune disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".