PINK1-dependent NFKB signaling contributes to amyloid pathology in Alzheimer disease
Bibliographic record
Abstract
Mitochondrial dysfunction plays a preponderant role in the development of Alzheimer disease (AD). We have demonstrated that activation of PINK1 (PTEN induced kinase 1)-dependent mitophagy ameliorates amyloid pathology, attenuates mitochondrial and synaptic dysfunction, and improves cognitive function. However, the underlying mechanisms remain largely unknown. Using a newly generated PINK1-AD transgenic mouse model and AD neuronal cell lines, we provide substantial evidence supporting the contribution of PINK1-mediated mitochondrial ROS (reactive oxygen species) and NFKB/NF-κB (nuclear factor kappa B) signaling to altering APP (amyloid beta precursor protein) processing and Aβ metabolism. Enhancing neuronal PINK1 is sufficient to suppress Aβ-induced activation of NFKB signal transduction in PINK1-overexpressed Aβ-AD mice and Aβ-producing neurons. Blocking PINK1-mediated NFKB activation inhibits activities of BACE1 (beta-secretase 1) and γ-secretase, which are key enzymes for cleavage of APP processing to produce Aβ. Conversely, loss or knockdown of PINK1 produces excessive ROS, along with increased phosphorylated NFKB1/p50 and RELA/p65 subunits, APP-related BACE1 and γ-secretase, and Aβ accumulation. Importantly, these detrimental effects were robustly blocked by the addition of scavenging PINK1 Aβ-induced mitochondrial ROS, leading to the suppression of NFKB activation, restoration of normal APP processing, and limitation of Aβ accumulation. Thus, our findings highlight a novel mechanism underlying PINK1-mediated modulation of Aβ metabolism via a ROS-NFKB-APP processing nexus. Activation of PINK1 signaling could be a potential therapeutic avenue for the early stages of AD by combining improving mitochondrial quality control with limiting amyloid pathology in AD.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".