Using the social amoeba Dictyostelium to study the functions of proteins linked to neuronal ceroid lipofuscinosis
Bibliographic record
Abstract
Neuronal ceroid lipofuscinosis (NCL), also known as Batten disease, is a debilitating neurological disorder that affects both children and adults. Thirteen genetically distinct genes have been identified that when mutated, result in abnormal lysosomal function and an excessive accumulation of ceroid lipofuscin in neurons, as well as other cell types outside of the central nervous system. The NCL family of proteins is comprised of lysosomal enzymes (PPT1/CLN1, TPP1/CLN2, CTSD/CLN10, CTSF/CLN13), proteins that peripherally associate with membranes (DNAJC5/CLN4, KCTD7/CLN14), a soluble lysosomal protein (CLN5), a protein present in the secretory pathway (PGRN/CLN11), and several proteins that display different subcellular localizations (CLN3, CLN6, MFSD8/CLN7, CLN8, ATP13A2/CLN12). Unfortunately, the precise functions of many of the NCL proteins are still unclear, which has made targeted therapy development challenging. The social amoeba Dictyostelium discoideum has emerged as an excellent model system for studying the normal functions of proteins linked to human neurological disorders. Intriguingly, the genome of this eukaryotic soil microbe encodes homologs of 11 of the 13 known genes linked to NCL. The genetic tractability of the organism, combined with its unique life cycle, makes Dictyostelium an attractive model system for studying the functions of NCL proteins. Moreover, the ability of human NCL proteins to rescue gene-deficiency phenotypes in Dictyostelium suggests that the biological pathways regulating NCL protein function are likely conserved from Dictyostelium to human. In this review, I will discuss each of the NCL homologs in Dictyostelium in turn and describe how future studies can exploit the advantages of the system by testing new hypotheses that may ultimately lead to effective therapy options for this devastating and currently untreatable neurological disorder.
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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) | 0.000 | 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 | 0.001 | 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 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".