Pharmacological inhibition of Polo-like kinase 2 modulates Alzheimer's disease pathology in a sex-dependent manner
Bibliographic record
Abstract
ncreasing evidence suggest that phosphorylation plays an important role in the aggregation and toxicity of amyloid beta (Aβ) resulting from the amyloid precursor protein (APP) cleavage, and Tau, the major neuropathological hallmarks of Alzheimer´s disease (AD). Our laboratory has reported an accumulation of Polo-like kinase 2 (PLK2) in the brains of AD patients. Thus, the aberrant accumulation and activity of PLK2 may contribute to AD. Our goal is focused on examining the effect of PLK2 pharmacological inhibition on APP and Tau accumulation and toxicity in cells and transgenic mouse models of AD. HEK293T cells were used to examine the effect of PLK2 and its inhibition on APP and Tau protein levels by immunoblotting. In vivo, behavioral analysis incorporated evaluation of different learning and memory tasks. Biochemical and histological analysis of AD neuropathology (APP, Tau, their phosphorylated forms, and synaptic dysfunction) were performed using immunoblotting and immunohistochemistry. We observed that PLK2 overexpression decreases APP and Tau levels in a PLK2-concentration dependent manner, counteracted by PLK2 pharmacological inhibition in cells. In vivo, our results showed cognitive decline and AD hallmarks in symptomatic mice, as well as a decrease in some pathological aspects upon PLK2 inhibition in a sex-dependent manner, both at the behavioral and molecular levels. Overall, this project will shed light onto novel mechanisms by which phosphorylation regulates Tau and APP aggregation and toxicity, providing a novel therapeutic target for AD and related dementia.
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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) | 0.002 | 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".