CK1α, FAM83H, and FAM83B contribute to bundling of neurofilaments and are sequestered in cellular and mice models of ARSACS
Bibliographic record
Abstract
Abstract Autosomal recessive spastic ataxia of the Charlevoix-Saguenay (ARSACS) is a rare neurodegenerative disorder characterized by mutations in the SACS gene that encodes for the sacsin protein. Sacsin dysfunction in ARSACS results in neurofilament bundling, a phenotype observed in various cellular models of ARSACS. The mechanisms underlying bundling in ARSACS remain unclear. With neurofilament phosphorylation controlling several processes of intermediate filament dynamics, its dysregulation may play a role in ARSACS. Accordingly, we investigated the interaction between CK1α (Casein Kinase 1α) and its adaptor proteins FAM83H or FAM83B (FAMily with sequence similarity 83). Here, we report that these target proteins are upregulated in ARSACS patient fibroblasts and co-localize at the sites of intermediate filament bundling. In the Sacs -/- cerebellum, FAM83B compensated for the lack of FAM83H expression, suggesting a cell-type specific activity of CK1α that depends on the relative expression of its adaptor proteins. Further, CK1α inhibition with D4475 or knockdown of the target proteins with the CRISPR system caused neurofilament bundling, a phenotype that was only partially remedied by CK1α activation with SSTC3. Our findings suggest that CK1α, FAM83H, and FAM83B contribute to neurofilament bundling in ARSACS; however, the inability of CK1α to resolve neurofilament bundling may reflect an error in a priming phosphorylation event in ARSACS. Future research is needed to understand the hierarchical phosphorylation cascade to CK1α activity and its contribution to ARSACS pathology. Highlights CK1α and its cell-specific adaptors are upregulated in ARSACS and targeted to the sites of bundles Knockdown of CK1α, FAM83H, and FAM83B cause neurofilament bundling Activation of CK1α partially remediates bundling
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".