Protection from diabetes development by single-chain antibody-mediated delivery of a NF-κB inhibitor specifically to β-cells in vivo
Bibliographic record
Abstract
Recently, we reported the generation of single-chain antibodies (SCAs) highly specific for rodent and human β-cells. Our current report describes the generation of a fusion protein of one of these SCAs (SCA B1) with a NF-κB essential modifier (NEMO)-binding domain (NBD) peptide, thereby creating a selective inhibitor of NF-κB activation in β-cells. The SCA B1-NBD fusion protein was cloned in the pIRES-EGFP, expressed in bacteria, and purified by metal affinity chromatography; the newly generated complex was then administered intravenously to rodents and evaluated for its ability to protect β-cells against cytokines in vitro and diabetogenic agents in vivo. First, it was shown clearly that our SCA B1-NBD fusion protein binds highly selective to CD rat β-cells in vivo. Second, we observed that SCA B1-mediated in vivo delivery of the NBD peptide completely blocked IL-1β + IFNγ- and TNFα + IFNγ-mediated induction of NF-κB as well as islet dysfunction in culture. Finally, repeated intravenous injection of SCA B1-NBD prior to multiple low-dose administration of streptozotocin in CD mice not only induced a striking resistance to diabetes development but also preserved β-cell mass. In conclusion, our data show for the first time that a SCA B1-NBD fusion peptide reliably protects β-cells against cytokines in vitro and allows protection from diabetes development in CD mice in vivo.
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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".