Lysosome remodelling and adaptation during phagocyte activation
Bibliographic record
Abstract
Lysosomes are acidic and hydrolytic organelles responsible for receiving and digesting cargo acquired during endocytosis, phagocytosis, and autophagy. For macrophages and dendritic cells, the lysosome is kingpin, playing a direct role in microbe killing and antigen processing for presentation. Strikingly, the historic view that lysosomes are homogeneous and static organelles is being replaced with a more elegant paradigm, in which lysosomes are heterogeneous, dynamic, and respond to cellular needs. For example, lysosomes are signalling platforms that integrate stress detection and molecular decision hubs such as the mTOR complex 1 and AMPK to modulate cellular activity. These signals can even adjust lysosome activity by modulating transcription factors such as transcription factor EB (TFEB) and TFE3 that govern lysosome gene expression. Here, we review lysosome remodelling and adaptation during macrophage and dendritic cell stimulation. First, we assess the functional outcomes and regulatory mechanisms driving the dramatic restructuring of lysosomes from globular organelles into a tubular network during phagocyte activation. Second, we discuss lysosome adaptation and scaling in macrophages driven by TFEB and TFE3 stimulation in response to phagocytosis and microbe challenges. Collectively, we are beginning to appreciate that lysosomes are dynamic and adapt to serve phagocyte differentiation in response to microbes and immune stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".