Molecular networking in the neuronal ceroid lipofuscinoses: insights from mammalian models and the social amoeba Dictyostelium discoideum
Bibliographic record
Abstract
The neuronal ceroid lipofuscinoses (NCLs), commonly known as Batten disease, belong to a family of neurological disorders that cause blindness, seizures, loss of motor function and cognitive ability, and premature death. There are 13 different subtypes of NCL that are associated with mutations in 13 genetically distinct genes (CLN1-CLN8, CLN10-CLN14). Similar clinical and pathological profiles of the different NCL subtypes suggest that common disease mechanisms may be involved. As a result, there have been many efforts to determine how NCL proteins are connected at the cellular level. A main driving force for NCL research has been the utilization of mammalian and non-mammalian cellular models to study the mechanisms underlying the disease. One non-mammalian model that has provided significant insight into NCL protein function is the social amoeba Dictyostelium discoideum. Accumulated data from Dictyostelium and mammalian cells show that NCL proteins display similar localizations, have common binding partners, and regulate the expression and activities of one another. In addition, genetic models of NCL display similar phenotypes. This review integrates findings from Dictyostelium and mammalian models of NCL to highlight our understanding of the molecular networking of NCL proteins. The goal here is to help set the stage for future work to reveal the cellular mechanisms underlying the NCLs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".