Lysosome enlargement during inhibition of the lipid kinase PIKfyve proceeds through lysosome coalescence
Bibliographic record
Abstract
Lysosomes receive and degrade cargo from endocytosis, phagocytosis and autophagy. They also play an important role in sensing and instructing cells on their metabolic state. The lipid kinase PIKfyve generates phosphatidylinositol-3,5-bisphosphate to modulate lysosome function. PIKfyve inhibition leads to impaired degradative capacity, ion dysregulation, abated autophagic flux and a massive enlargement of lysosomes. Collectively, this leads to various physiological defects, including embryonic lethality, neurodegeneration and overt inflammation. The reasons for such drastic lysosome enlargement remain unclear. Here, we examined whether biosynthesis and/or fusion-fission dynamics contribute to swelling. First, we show that PIKfyve inhibition activates TFEB, TFE3 and MITF, enhancing lysosome gene expression. However, this did not augment lysosomal protein levels during acute PIKfyve inhibition, and deletion of TFEB and/or related proteins did not impair lysosome swelling. Instead, PIKfyve inhibition led to fewer but enlarged lysosomes, suggesting that an imbalance favouring lysosome fusion over fission causes lysosome enlargement. Indeed, conditions that abated fusion curtailed lysosome swelling in PIKfyve-inhibited cells.
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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 | 0.001 |
| 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".