γ-Aminobutyric Acid Type A (GABAA) Receptor Activation Modulates Tau Phosphorylation
Bibliographic record
Abstract
Abnormal phosphorylation and aggregation of the microtubule-associated protein Tau are hallmarks of various neurodegenerative diseases, such as Alzheimer disease. Molecular mechanisms that regulate Tau phosphorylation are complex and currently incompletely understood. We have developed a novel live cell reporter system based on protein-fragment complementation assay to study dynamic changes in Tau phosphorylation status. In this assay, fusion proteins of Tau and Pin1 (peptidyl-prolyl cis-trans-isomerase 1) carrying complementary fragments of a luciferase protein serve as a sensor of altered protein-protein interaction between Tau and Pin1, a critical regulator of Tau dephosphorylation at several disease-associated proline-directed phosphorylation sites. Using this system, we identified several structurally distinct GABA(A) receptor modulators as novel regulators of Tau phosphorylation in a chemical library screen. GABA(A) receptor activation promoted specific phosphorylation of Tau at the AT8 epitope (Ser-199/Ser-202/Thr-205) in cultures of mature cortical neurons. Increased Tau phosphorylation by GABA(A) receptor activity was associated with reduced Tau binding to protein phosphatase 2A and was dependent on Cdk5 but not GSK3β kinase activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".