Peptide-based sequestration of the adaptor protein Nck1 in pancreatic β cells enhances insulin biogenesis and protects against diabetogenic stresses
Bibliographic record
Abstract
One feature of diabetes is the failure of pancreatic β cells to produce insulin, but the molecular mechanisms leading to this failure remain unclear. Increasing evidence supports a role for protein kinase R–like endoplasmic reticulum kinase (PERK) in the development and function of healthy pancreatic β cells. Previously, our group identified the adaptor protein Nck1 as a negative regulator of PERK. Indeed, we demonstrated that Nck1, by directly binding PERK autophosphorylated on Tyr 561 , limits PERK activation and signaling. Accordingly, we found that stable depletion of Nck1 in β cells promotes PERK activation and signaling, increases insulin biosynthesis, and improves cell viability in response to diabetes-related stresses. Herein, we explored the therapeutic potential of abrogating the interaction between Nck and PERK to improve β-cell function and survival. To do so, we designed and used a peptide containing the minimal PERK sequence involved in binding Nck1 conjugated to the cell-permeable protein transduction domain from the HIV protein TAT. In the current study, we confirm that the synthetic TAT-Tyr(P) 561 phosphopeptide specifically binds the SH2 domain of Nck and prevents Nck interaction with PERK, thereby promoting basal PERK activation. Moreover, we report that treatment of β cells with TAT-Tyr(P) 561 inhibits glucolipotoxicity-induced apoptosis, whereas it enhances insulin production and secretion. Taken together, our results support the potential of sequestering Nck using a synthetic peptide to enhance basal PERK activation and create more robust β cells.
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) | 0.000 | 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".