The space-time continuum in neurological disorders of the autophagosome-lysosome fusion machinery
Bibliographic record
Abstract
Autophagy is a highly conserved cellular pathway for the degradation and recycling of defective intracellular cargo and plays a vital role in the homeostasis of post-mitotic tissues, particularly the nervous system. Autophagosome-lysosome fusion represents the final critical step in macroautophagy with a tightly regulated process mediated by a complex molecular machinery of tethering vesicles for degradation. Since the first reports of human autophagy disorders, the scientific and clinical focus condensed on severe phenotypes with biallelic-truncating genotypes as monogenic models of near-complete autophagy perturbation. Recent reports suggest a much wider disease spectrum with defective autophagy, ranging from neurodevelopmental disorders to neurodegenerative phenotypes with later manifestation due to "milder" genotypes, including Alzheimer's disease (AD), Parkinson's disease (PD), and Amyotrophic Lateral Sclerosis-Frontotemporal Dementia (ALS-FTD). In addition, recent evidence identified molecular connections between physiological autophagy regulation during normal aging and pathophysiological hallmarks of aging-related disorders. These translational observations led to a more comprehensive understanding of autophagy at health and disease, in particular: 1) genetic location and allelism of pathogenic variants ("genomic space"); 2) protein-protein interaction in functional protein complexes ("proteomic space"); 3) metabolic autophagic flux with positive and negative regulators ("metabolomic space"); 4) age-related phenotypic progression over time. Here, we review the autophagosome-lysosome fusion machinery as a key structure both on the molecular level and with regards to the pathogenesis of the autophagy-related disease spectrum. We highlight the clinicopathological signature of disorders in the autophagosome-lysosome fusion machinery, in particular features warranting awareness from clinicians and geneticists to inform adequate diagnosis, surveillance, and patient guidance.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".