The Itinerary of Autophagosomes: From Peripheral Formation to Kiss‐and‐Run Fusion with Lysosomes
Bibliographic record
Abstract
Macroautophagy, a constitutive process in higher eukaryotic cells, mediates degradation of many long-lived proteins and organelles. The actual events occurring during the process in the dynamic system of a living cell have never been thoroughly investigated. We aimed to develop a live-cell assay in which to follow the complete itinerary of an autophagosome. Our experiments show that autophagosomes are formed randomly in peripheral regions of the cell. They then move bidirectionally along microtubules, accumulating at the microtubule-organizing centre, in a similar way to lysosomes. Their centripetal movement is dependent on the motor protein dynein and is important for their fusion with lysosomes. Initially, autophagosomes dock on to lysosomes, independent of lysosomal acidification. Two kinds of fusion then occur: complete fusions, creating a hybrid organelle, or more often kiss-and-run fusions, i.e. transfer of some content while still maintaining two separate vesicles. Surprisingly, the autophagolysosomal compartment seems to be more long lived than expected. Our study documents many aspects of autophagosome behaviour, adding to our understanding of the mechanism and control of autophagy. Indeed, although the formation of autophagosomes is completely different from any other vesicular structures, their later itinerary appears to be very similar to those of other trafficking pathways.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".