The Lysosome Signaling Platform: Adapting With the Times
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lysosomes are the terminal degradative compartment of autophagy, endocytosis and phagocytosis. What once was viewed as a simple acidic organelle in charge of macromolecular digestion has emerged as a dynamic organelle capable of integrating cellular signals and producing signal outputs. In this review, we focus on the concept that the lysosome surface serves as a platform to assemble major signaling hubs like mTORC1, AMPK, GSK3 and the inflammasome. These molecular assemblies integrate and facilitate cross-talk between signals such as amino acid and energy levels, membrane damage and infection, and ultimately enable responses such as autophagy, cell growth, membrane repair and microbe clearance. In particular, we review how molecular machinery like the vacuolar-ATPase proton pump, sestrins, the GATOR complexes, and the Ragulator, modulate mTORC1, AMPK, GSK3 and inflammation. We then elaborate how these signals control autophagy initiation and resolution, TFEB-mediated lysosome adaptation, lysosome remodeling, antigen presentation, inflammation, membrane damage repair and clearance. Overall, by being at the cross-roads for several membrane pathways, lysosomes have emerged as the ideal surveillance compartment to sense, integrate and elicit cellular behavior and adaptation in response to changing environmental and cellular conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it