Perturbed mitochondria–ER contacts in live neurons that model the amyloid pathology of Alzheimer's disease
Bibliographic record
Abstract
ABSTRACT The use of fixed fibroblasts from familial and sporadic Alzheimer's disease patients has previously indicated an upregulation of mitochondria–ER contacts (MERCs) as a hallmark of Alzheimer's disease. Despite its potential significance, the relevance of these results is limited because they were not extended to live neurons. Here we performed a dynamic in vivo analysis of MERCs in hippocampal neurons from McGill-R-Thy1-APP transgenic rats, a model of Alzheimer's disease-like amyloid pathology. Live FRET imaging of neurons from transgenic rats revealed perturbed ‘lipid-MERCs’ (gap width <10 nm), while ‘Ca2+-MERCs’ (10–20 nm gap width) were unchanged. In situ TEM showed no significant differences in the lipid-MERCs:total MERCs or lipid-MERCs:mitochondria ratios; however, the average length of lipid-MERCs was significantly decreased in neurons from transgenic rats as compared to controls. In accordance with FRET results, untargeted lipidomics showed significant decreases in levels of 12 lipids and bioenergetic analysis revealed respiratory dysfunction of mitochondria from transgenic rats. Thus, our results reveal changes in MERC structures coupled with impaired mitochondrial functions in Alzheimer's disease-related neurons. This article has an associated First Person interview with the first author of the paper.
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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 | 0.000 |
| 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".