In vivo induction of membrane damage by β-amyloid peptide oligomers
Bibliographic record
Abstract
Exposure to the β-amyloid peptide (Aβ) is toxic to neurons and other cell types, but the mechanism(s) involved are still unresolved. Synthetic Aβ oligomers can induce ion-permeable pores in synthetic membranes, but whether this ability to damage membranes plays a role in the ability of Aβ oligomers to induce tau hyperphosphorylation, or other disease-relevant pathological changes, is unclear. To examine the cellular responses to Aβ exposure independent of possible receptor interactions, we have developed an in vivo C. elegans model that allows us to visualize these cellular responses in living animals. We find that feeding C. elegans E. coli expressing human Aβ induces a membrane repair response similar to that induced by exposure to the CRY5B, a known pore-forming toxin produced by B. thuringensis. This repair response does not occur when C. elegans is exposed to an Aβ Gly37Leu variant, which we have previously shown to be incapable of inducing tau phosphorylation in hippocampal neurons. The repair response is also blocked by loss of calpain function, and is altered by loss-of-function mutations in the C. elegans orthologs of BIN1 and PICALM, well-established risk genes for late onset Alzheimer's disease. To investigate the role of membrane repair on tau phosphorylation directly, we exposed hippocampal neurons to streptolysin O (SLO), a pore-forming toxin that induces a well-characterized membrane repair response. We find that SLO induces tau hyperphosphorylation, which is blocked by calpain inhibition. Finally, we use a novel biarsenical dye-tagging approach to show that the Gly37Leu substitution interferes with Aβ multimerization and thus the formation of potentially pore-forming oligomers. We propose that Aβ-induced tau hyperphosphorylation may be a downstream consequence of induction of a membrane repair 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".