Cellular Mechanisms of CCL22-Mediated Attenuation of Autoimmune Diabetes
Bibliographic record
Abstract
Autoimmune destruction of insulin-producing β cells in type 1 diabetes and islet transplantation involves a variety of immune pathways but is primarily mediated by self-reactive T cells. Chemokines can modulate local immune responses in inflammation and tumors by recruiting immune cells. We have reported that expression of the chemokine CCL22 in pancreatic β cells in the NOD mouse prevents autoimmune attack by recruiting T regulatory cells (Tregs), protecting mice from diabetes. In this study we show that invariant NKT cells are also recruited to CCL22-expressing islet transplants and are required for CCL22-mediated protection from autoimmunity. Moreover, CCL22 induces an influx of plasmacytoid dendritic cells, which correlates with higher levels of IDO in CCL22-expressing islet grafts. In addition to its chemotactic properties, we found that CCL22 activates Tregs and promotes their ability to induce expression of IDO by dendritic cells. Islet CCL22 expression thus produces a tolerogenic milieu through the interplay of Tregs, invariant NKT cells, and plasmacytoid dendritic cells, which results in suppression of effector T cell responses and protection of β cells. The immunomodulatory properties of CCL22 could be harnessed for prevention of graft rejection and type 1 diabetes as well as other autoimmune disorders.
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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".