mTOR induces lysosome expansion by selective translation of lysosomal transcripts during phagocyte activation
Bibliographic record
Abstract
Abstract The molecular mechanisms that govern and adapt organelle number, size, morphology and activities to suit the needs of many cell types and the conditions that a cell may encounter remain poorly defined. Lysosomes are organelles that degrade cargo from a variety of routes including endocytosis, phagocytosis and autophagy. Lysosomes have emerged as a signalling platform that senses and couples stress signals such as nutrient deprivation to regulatory kinase hubs like mTOR and AMPK to modulate metabolic activity. For phagocytes and antigen-presenting cells like macrophages and dendritic cells, lysosomes are a kingpin organelle since they are essential to kill and process pathogens, and present antigens. During phagocyte activation, lysosomes undergo a striking reorganization, changing from dozens of globular structures to a tubular network, in a process that requires the phosphatidylinositol-3-kinase-Akt-mTOR signalling pathway. Ultimately, lysosome tubulation is thought to promote pinocytic retention and antigen presentation. We show that lysosome tubulation is accompanied by a rapid boost in lysosome volume and holding capacity during phagocyte activation with lipopolysaccharides. Unexpectedly, lysosome expansion was paralleled with the induction of lysosomal proteins, which was independent of TFEB and TFE3, transcription factors known to scale up lysosome biogenesis. Instead, we demonstrate a hitherto unappreciated mechanism of lysosome expansion via mTOR-dependent increase in translation of mRNAs encoding key lysosomal proteins including LAMP1 and V-ATPase subunits. Collectively, we identified a mechanism of rapid organelle expansion and remodelling driven by selective enhancement of protein synthesis.
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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 | 0.000 |
| 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".