Islet amyloid disrupts MHC Class II antigen presentation and protects NOD mice from autoimmune diabetes
Bibliographic record
Abstract
Abstract Islet amyloid contributes to beta cell failure in type 2 diabetes through several mechanisms, one being the potent induction of local islet inflammation through activating inflammatory pathways in islet macrophages. We performed an unbiased phenotypic investigation of islet macrophages in the early stage of islet amyloid formation using single cell RNA sequencing of resident islet macrophages in mice with and without the amyloidogenic form of human islet amyloid polypeptide (hIAPP). This revealed that MHC Class II antigen presentation genes were strongly down-regulated in islet macrophages during islet amyloid formation. As islet amyloid has recently been reported in pancreases of people with type 1 diabetes, we sought to investigate the impact of islet amyloid in the NOD mouse model of type 1 diabetes. Both overexpression and physiological expression of hIAPP delayed diabetes in NOD mice relative to littermate controls, corresponding with decreased markers of antigen presentation and activation, as well as decreased immune cell infiltration in islets. Adoptive transfer studies showed that systemic autoimmune function remained intact and beta cells from hIAPP transgenic mice did not evade immune recognition by diabetogenic T cells, collectively indicating the protection from diabetes was mediated by localized disruption of antigen presentation in the pancreas. Consistent with this, incubation of dendritic cells with IAPP aggregates decreased MHC Class II surface expression and diminished antigen-specific T cell activation in vitro, through a phagocytosis-dependent mechanism. Collectively our data show that despite the well-established pro-inflammatory response of macrophages to IAPP aggregates, the uptake of IAPP aggregates during early amyloid formation also disrupts MHC Class II antigen presentation and slows beta cell autoimmunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".