Stimulation of Insulin Signaling and Inhibition of JNK-AP1 Activation Protect Cells from Amyloid-β-Induced Signaling Dysregulation and Inflammatory Response
Bibliographic record
Abstract
One of the hallmarks of Alzheimer's disease (AD) is the accumulation and deposition of amyloid-β (Aβ) peptides in the brain and cerebral vasculature. Aβ evokes neuroinflammation and has been implicated in insulin signaling disruption and JNK-AP1 activation, contributing to AD neuropathologies including oxidative injury and vascular insufficiencies. In this study we aim to better understand the protective mechanisms of insulin signaling and JNK-AP1 inhibition on the adverse effects of Aβ. Four-hour treatment of hCMEC/D3, the immortalized human brain endothelial cells (iHBEC), with Aβ1-42 resulted in significant c-Jun phosphorylation, oxidative stress, and cell toxicity. Concurrent treatment with Aβ1-42 and insulin or Aβ1-42 and JNK inhibitor SP600125 significantly improved cell viability. Cytokine array on conditioned media showed that insulin and SP600125 strongly reduced all Aβ1-42-induced cytokines. ELISA confirmed the protective effect of insulin and SP600125 on Aβ-induced expression of interleukin (IL)-8 and Growth related oncogene-α (Gro-α). qRT-PCR revealed that insulin and SP600125 protected iHBEC from Aβ1-42-induced inflammatory gene expression. Transcription factor profiling showed that treatment of iHBEC with Aβ1-42, insulin, or SP600125 alone or in combination resulted in profound changes in modulating the activities of multiple transcription factors and relevant pathways, some of which were validated by western blot. Insulin treatment and JNK inhibition in vitro synergistically reduced c-Jun phosphorylation and thus JNK-AP1 signaling activation. The study suggests that activation of insulin and blocking of JNK-AP1 signaling inhibits Aβ-induced dysregulation of insulin signaling and inflammatory response.
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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) | 0.002 | 0.001 |
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".