The Interplay Between Cholesterol and Amyloid-β on HT22 Cell Viability, Morphology, and Receptor Tyrosine Kinase Signaling
Bibliographic record
Abstract
BACKGROUND: There is a lack of understanding in the molecular and cellular mechanisms of Alzheimer's disease that has hindered progress on therapeutic development. The focus has been on targeting toxic amyloid-β (Aβ) pathology, but these therapeutics have generally failed in clinical trials. Aβ is an aggregation-prone protein that has been shown to disrupt cell membrane structure in molecular biophysics studies and interfere with membrane receptor signaling in cell and animal studies. Whether the lipid membrane or specific receptors are the primary target of attack has not been determined. OBJECTIVE: This work elucidates some of the interplay between membrane cholesterol and Aβ42 on HT22 neuronal cell viability, morphology, and platelet-derived growth factor (PDGF) signaling pathways. METHODS: The effects of cholesterol depletion by methyl-β-cyclodextrin followed by treatment with Aβ and/or PDGF-AA were assessed by MTT cell viability assays, western blot, optical and AFM microscopy. RESULTS: Cell viability studies show that cholesterol depletion was mildly protective against Aβ toxicity. Together cholesterol reduction and Aβ42 treatment compounded the disruption of the PDGFα receptor activation. Phase contrast optical microscopy and live cell atomic force microscopy imaging revealed that cytotoxic levels of Aβ42 caused morphological changes including cell membrane damage, cytoskeletal disruption, and impaired cell adhesion; cell damage was ameliorated by cellular cholesterol depletion. CONCLUSIONS: Cholesterol depletion impacted the effects of Aβ42 on HT22 cell viability, morphology, and receptor tyrosine kinase signaling.
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 | 0.001 | 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".