Mechanisms regulating the sorting of soluble lysosomal proteins
Bibliographic record
Abstract
Lysosomes are key regulators of many fundamental cellular processes such as metabolism, autophagy, immune response, cell signalling and plasma membrane repair. These highly dynamic organelles are composed of various membrane and soluble proteins, which are essential for their proper functioning. The soluble proteins include numerous proteases, glycosidases and other hydrolases, along with activators, required for catabolism. The correct sorting of soluble lysosomal proteins is crucial to ensure the proper functioning of lysosomes and is achieved through the coordinated effort of many sorting receptors, resident ER and Golgi proteins, and several cytosolic components. Mutations in a number of proteins involved in sorting soluble proteins to lysosomes result in human disease. These can range from rare diseases such as lysosome storage disorders, to more prevalent ones, such as Alzheimer's disease, Parkinson's disease and others, including rare neurodegenerative diseases that affect children. In this review, we discuss the mechanisms that regulate the sorting of soluble proteins to lysosomes and highlight the effects of mutations in this pathway that cause human disease. More precisely, we will review the route taken by soluble lysosomal proteins from their translation into the ER, their maturation along the Golgi apparatus, and sorting at the trans-Golgi network. We will also highlight the effects of mutations in this pathway that cause human disease.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".