Distinct features of multivesicular body‐lysosome fusion revealed by a new cell‐free content‐mixing assay
Bibliographic record
Abstract
When marked for degradation, surface receptor and transporter proteins are internalized and delivered to endosomes where they are packaged into intralumenal vesicles (ILVs). Many rounds of ILV formation create multivesicular bodies (MVBs) that fuse with lysosomes exposing ILVs to hydrolases for catabolism. Despite being critical for protein degradation, the molecular underpinnings of MVB-lysosome fusion remain unclear, although machinery underlying other lysosome fusion events is implicated. But how then is specificity conferred? And how is MVB maturation and fusion coordinated for efficient protein degradation? To address these questions, we developed a cell-free MVB-lysosome fusion assay using Saccharomyces cerevisiae as a model. After confirming that the Rab7 ortholog Ypt7 and the multisubunit tethering complex HOPS (homotypic fusion and vacuole protein sorting complex) are required, we found that the Qa-SNARE Pep12 distinguishes this event from homotypic lysosome fusion. Mutations that impair MVB maturation block fusion by preventing Ypt7 activation, confirming that a Rab-cascade mechanism harmonizes MVB maturation with lysosome fusion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".