Repression of GAD Autoantigen Expression in Pancreas β-Cells by Delivery of Antisense Plasmid/PEG-g-PLL Complex
Bibliographic record
Abstract
It was previously reported that silencing of the expression of glutamic acid decarboxylase (GAD) in transgenic nonobese diabetic (NOD) mice completely protected islet beta-cells against development of diabetes. This suggests that the repression of GAD autoantigen by somatic gene delivery can prevent autoimmune destruction of pancreatic beta-cells. To repress GAD expression in islet beta-cells, we delivered an antisense GAD mRNA expression plasmid (pRIP-AS-GAD) using poly(ethylene glycol)-grafted poly-L-lysine (PEG-g-PLL) as a gene carrier. In a gel retardation assay, the pRIP-AS-GAD/PEG-g-PLL complex was completely retarded above a weight ratio of 1:1.5 (plasmid: PEG-g-PLL). PEG-g-PLL protected the plasmid DNA from DNase I for more than 60 minutes. In a reporter gene transfection assay, PEG-g-PLL showed the highest transfection efficiency at a weight ratio of 1:3. We also transfected pRIP-AS-GAD/PEG-g-PLL complex into a GAD-producing mouse insulinoma (MIN6) cell line. The antisense mRNA was expressed specifically in beta-cells and expression was dependent on glucose level. The repression of GAD after transfection of pRIP-AS-GAD was confirmed by immunoblot assay. In addition, in vivo expression of antisense RNA in pancreas was confirmed by RT-PCR after intravenous injection of the complex into mice. Therefore, our study revealed that the pRIP-AS-GAD/PEG-g-PLL system is applicable for the repression of GAD autoantigen expression.
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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) | 0.000 | 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".