P4–282: Effects of indirubin, a GSK3/CDK5 inhibitor, on the Aβ and tau pathways of Alzheimer's Disease
Bibliographic record
Abstract
Extracellular deposition of Aβ peptide in plaques and intracellular accumulations of tau in tangles are histopathological features of Alzheimer's Disease (AD). Whereas Aβ is generated by the action of two endoproteases denoted BACE and γ–secretase and tau hyperphosphorylation is attributed to neuronal kinases including Cdk5 and GSK–3β, events that might link these two biochemical pathways are disputed. Here we investigate properties of indirubin, an anti–leukemia compound in Chinese traditional medicine and a potent GSK3/CDK5 inhibitor. Used at micromolar concentrations, indirubin not only affects phosphorylation of a 4R tau transgene, but also affects amyloidogenic processing of human βAPP holoprotein to Aβ. An inactive sister compound had no effect upon Aβ levels. The inhibitory effect was seen both in HEK293 cells and in differentiated cerebellar neurons from TgAPP (TgCRND8) mice. While processing via α–secretase was apparently unaltered, and γ–site cleavage was only mildly affected, (i) differential effects upon APP holoprotein and C100 substrates, and (ii) alterations in the ratio of C99 and C83 APP C–terminal stubs implied an effect upon APP cleavage at the β–site. As yet, we have been unable to demonstrate an impact of indirubin upon phosphorylation of APP at Thr668. While it remains to be established that indirubin and related compounds affect the enzymatic activity of BACE, our data demonstrate that a single compound can address two pathological hallmarks of AD. Our findings are of potential importance with regards to therapeutic approaches, and to deciphering interconnections between the Aβ and tau cascades of pathogenesis.
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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) | 0.005 | 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".