The Batten disease protein CLN3 is important for stress granules dynamics and translational activity
Bibliographic record
Abstract
The assembly of membrane-less organelles such as stress granules (SGs) is emerging as central in helping cells rapidly respond and adapt to stress. Following stress sensing, the resulting global translational shutoff leads to the condensation of stalled mRNAs and proteins into SGs. By reorganizing cytoplasmic contents, SGs can modulate RNA translation, biochemical reactions, and signaling cascades to promote survival until the stress is resolved. While mechanisms for SG disassembly are not widely understood, the resolution of SGs is important for maintaining cell viability and protein homeostasis. Mutations that lead to persistent or aberrant SGs are increasingly associated with neuropathology and a hallmark of several neurodegenerative diseases. Mutations in CLN3 are causative of juvenile neuronal ceroid lipofuscinosis, a rare neurodegenerative disease affecting children also known as Batten disease. CLN3 encodes a transmembrane lysosomal protein implicated in autophagy, endosomal trafficking, metabolism, and response to oxidative stress. Using a HeLa cell model lacking CLN3, we now show that CLN 3 KO is associated with an altered metabolic profile, reduced global translation, and altered stress signaling. Furthermore, loss of CLN3 function results in perturbations in SG dynamics, resulting in assembly and disassembly defects, and altered expression of the key SG nucleating factor G3BP1. With a growing interest in SG-modulating drugs for the treatment of neurodegenerative diseases, novel insights into the molecular basis of CLN3 Batten disease may reveal avenues for disease-modifying treatments for this debilitating childhood 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) | 0.000 | 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) | 0.002 | 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".