Loss of Ca2+/Calmodulin Dependent Protein Kinase Kinase 2 Leads to Aberrant Transferrin Phosphorylation and Trafficking: A Potential Biomarker for Alzheimer's Disease
Bibliographic record
Abstract
Ca2+/calmodulin-dependent protein kinase kinase 2 (CaMKK2) is a serine/threonine kinase that is activated following an increase in the intracellular Ca2+ concentration and activates multiple signaling cascades that control physiologically important neuronal processes. CaMKK2 has been implicated in schizophrenia, bipolar disease, neurodegeneration, and cancer. Using isolectric focusing (IEF) and mass spectrometry based proteomic analysis, it was found that knockdown (KD) of CaMKK2 in cultured adult primary dorsal root ganglion (DRG) neurons resulted in the reduction of transferrin (TF) phosphorylation at multiple functionally relevant residues which corresponded to loss of an acidic fraction (pH3-4) of TF. In vitro studies using CRISPR/Cas9 based CaMKK2 knockout (KO) HEK293 and HepG2 cells lines validated previous findings and revealed that loss of CaMKK2 interfered with TF trafficking and turnover. TF is an iron transporter glycoprotein. Abnormal accumulation of iron and/or deregulated Ca2+ homeostasis leads to neurodegeneration in Alzheimer’s disease (AD). Therefore, it was hypothesized that aberrant CaMKK2 in AD may lead to aberrant phosphorylated transferrin (P-TF: pH3-4 fraction) which may serve as a hallmark biomarker for AD. A significant reduction of P-TF in the brain and serum of CaMKK2 KO mice and a triple-transgenic mouse model of AD (3xTg-AD) supported this hypothesis. In addition, analysis of early (65 years) postmortem human AD cerebrospinal fluid (CSF) and serum samples revealed that aberrant P-TF (pH3-4 fraction) profile was associated with both early and late-stage AD compared to age-matched controls. This indicates P-TF (pH3-4 fraction) profile may be useful as a minimally invasive biomarker for AD. In addition, this study provides a link between aberrant CaMKK2 with TF trafficking and turnover which provides a novel insight into the neurodegeneration process.
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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 | 0.001 | 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".