Natural Killer Cells From Children With Type 1 Diabetes Have Defects in NKG2D-Dependent Function and Signaling
Bibliographic record
Abstract
OBJECTIVE: Natural killer (NK) cells from NOD mice have numeric and functional abnormalities, and restoration of NK cell function prevents autoimmune diabetes in NOD mice. However, little is known about the number and function of NK cells in humans affected by type 1 diabetes. Therefore, we evaluated the phenotype and function of NK cells in a large cohort of type 1 diabetic children. RESEARCH DESIGN AND METHODS: Peripheral blood mononuclear blood cells were obtained from subjects whose duration of disease was between 6 months and 2 years. NK cells were characterized by flow cytometry, enzyme-linked immunosorbent spot assays, and cytotoxicity assays. Signaling through the activating NK cell receptor, NKG2D, was assessed by immunoblotting and reverse-phase phosphoprotein lysate microarray. RESULTS: NK cells from type 1 diabetic subjects were present at reduced cell numbers compared with age-matched, nondiabetic control subjects and had diminished responses to the cytokines interleukin (IL)-2 and IL-15. Analysis before and after IL-2 stimulation revealed that unlike NK cells from nondiabetic control subjects, NK cells from type 1 diabetic subjects failed to downregulate the NKG2D ligands, major histocompatibility complex class I-related chains A and B, upon activation. Moreover, type 1 diabetic NK cells also exhibited decreased NKG2D-dependent cytotoxicity and interferon-γ secretion. Finally, type 1 diabetic NK cells showed clear defects in NKG2D-mediated activation of the phosphoinositide 3-kinase-AKT pathway. CONCLUSIONS: These results are the first to demonstrate that type 1 diabetic subjects have aberrant signaling through the NKG2D receptor and suggest that NK cell dysfunction contributes to the autoimmune pathogenesis of type 1 diabetes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".