Microglia degrade Alzheimer’s amyloid-beta deposits extracellularly via digestive exophagy
Bibliographic record
Abstract
How microglia digest Alzheimer’s fibrillar amyloid-beta (Aβ) plaques that are too large to be phagocytosed is not well understood. Here, we show that primary microglial cells create acidic extracellular compartments, lysosomal synapses, on model plaques and digest them with exocytosed lysosomal enzymes. This mechanism, called digestive exophagy, is confirmed by electron microscopy in 5xFAD mouse brains, which shows that a lysosomal enzyme, acid phosphatase, is secreted toward the plaques in structures resembling lysosomal synapses. Signaling studies demonstrate that the PI3K-AKT pathway modulates the formation of lysosomal synapses, as inhibition of PI3K1β or AKT1/2 reduces both lysosome exocytosis and actin polymerization, both required for the formation of the compartments. Finally, we show that small fibrils of Aβ previously internalized and trafficked to lysosomes are exocytosed toward large Aβ aggregates by microglia. Thus, the release of lysosomal contents during digestive exophagy may also contribute to the spread and growth of fibrillar Aβ. • Microglia use digestive exophagy to digest Aβ plaques that cannot be phagocytosed • Microglia form extracellular acidic compartments -lysosomal synapses- on Aβ plaques • Lysosomal synapses are acidic and receive lysosomal enzymes via lysosomal exocytosis • Microglia also secrete indigestible intralysosomal Aβ fibrils into lysosomal synapses Jacquet et al. shows that microglia use digestive exophagy to engage large Aβ deposits that cannot be phagocytosed, forming acidic extracellular compartments on the aggregates into which lysosomal enzymes are exocytosed. The PI3K-AKT pathway modulates this process. Microglia also exocytose intralysosomal undigested fibrillar Aβ toward extracellular aggregates, potentially contributing to plaque growth.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".