Harnessing Kupffer Cell Metabolic Rewiring: Rapamycin–Gliadin Nanoparticle as a Pivotal Strategy for Immune Tolerance in Celiac Disease
Bibliographic record
Abstract
Celiac disease (CeD), triggered by gliadin exposure, necessitates therapeutic strategies that establish an antigen-specific immune tolerance. This study explores the therapeutic efficacy and mechanism of rapamycin–gliadin composite nanoparticles (PLN-GR) for CeD treatment. In vivo analyses demonstrated the efficient uptake of PLN-GR by antigen-presenting cells (APCs), particularly Kupffer cells and splenic dendritic cells (DCs), driving their tolerogenic phenotypic transformation. In a murine CeD model, PLN-GR administration significantly enhanced gluten tolerance and mitigated intestinal inflammation, as indicated by reduced paw edema and improved histopathological parameters. Mechanistically, PLN-GR induced macrophage metabolic reprogramming from glycolysis to oxidative phosphorylation, concomitant with elevated serum itaconate levels. This metabolic shift potentiated interorgan immunoregulatory crosstalk, expanding PD-L1 + tolerogenic splenic DCs while suppressing pathogenic Th1 cell populations. Bone marrow-derived macrophages (BMDMs) from Acod1 –/– mice (deficient in itaconate synthesis) failed to induce DC tolerance upon PLN-GR treatment. However, supplementation with the itaconate derivative 4-octyl itaconate (4-OI) restored PD-L1 expression in DC2.4 cells in vitro, revealing that itaconate induces and stabilizes the tolerant DC phenotype. These findings underscore PLN-GR as a novel nanotherapeutic platform for CeD, achieving gliadin-specific tolerance through hepatic–splenic immunometabolic reprogramming and itaconate-dependent PD-L1 regulation, thereby offering a translatable strategy for autoimmune disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".