Recent insights into the networking of <i>CLN</i> genes and proteins in mammalian cells
Bibliographic record
Abstract
Ceroid lipofuscinosis neuronal (CLN) genes encode 13 proteins that localize throughout the endomembrane system to regulate a variety of cellular processes. In humans, mutations in CLN genes cause a devastating form of neurodegeneration called neuronal ceroid lipofuscinosis (NCL), commonly known as Batten disease. Each CLN gene is associated with a specific subtype of the disease that differ from each other in severity and age of onset. The NCLs affect all ages and ethnicities worldwide but primarily affect children. The pathology underlying the NCLs is poorly understood, which has prevented the development of a cure or effective therapy for most subtypes of the disease. A growing body of literature supports the networking of CLN genes and proteins within cells, which aligns with the broadly similar cellular and clinical manifestations among the different subtypes of NCL. Here, all relevant literature is reviewed to provide a comprehensive overview of our current understanding of how CLN genes and proteins are networked in mammalian cells with an aim toward revealing new molecular targets for therapy development. Intriguingly, CLN gene and protein networking extends beyond the NCLs as recent work has linked several CLN genes and proteins to other forms of neurodegeneration such as Alzheimer's disease and Parkinson's disease. Thus, a deeper understanding of the pathways and cellular processes impacted by mutations in CLN genes will not only strengthen our knowledge of the pathological mechanisms underlying the NCLs but may also provide new insight into related forms of neurodegeneration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".