A two‐hybrid screen identifies an unconventional role for the intermediate filament peripherin in regulating the subcellular distribution of the SNAP25‐interacting protein, SIP30
Bibliographic record
Abstract
Abstract Peripherin is a type III intermediate filament protein, the expression of which is associated with the acquisition and maintenance of a terminally differentiated neuronal phenotype. Peripherin up‐regulation occurs during acute neuronal injury and in degenerating motor neurons of amyotrophic lateral sclerosis. The functional role(s) of peripherin during normal, injurious, and disease conditions remains unknown, but may be related to differential expression of spliced isoforms. To better understand peripherin function, we performed a yeast two‐hybrid screen on a mouse brain cDNA library using an assembly incompetent peripherin isoform, Per‐61, as bait. We identified new peripherin interactors with roles in vesicular trafficking, signal transduction, DNA/RNA processing, protein folding, and mitochondrial metabolism. We focused on the interaction of Per‐61 and the constitutive isoform, Per‐58, with SNAP25 interacting protein 30 (SIP30), a neuronal protein involved in SNAP receptor‐dependent exocytosis. We found that peripherin and SIP30 interacted through coiled‐coil domains and colocalized in cytoplasmic aggregates in SW13vim(−) cells. Interestingly, Per‐61 and Per‐58 differentially altered the subcellular distribution of SIP30 and SNAP25 in primary motor neurons. Our findings suggest a novel role of peripherin in vesicle trafficking. image The functional roles of the intermediate filament peripherin remain enigmatic. We performed a two‐hybrid screen and identified that peripherin interacts with SIP30 (a), a vesicle trafficking protein. Per‐58 and toxic Per‐61 isoforms coaggregated with SIP30 in SW13vim(−) cells and altered SIP30 and SNAP25 subcellular localization in primary motor neurons (b). Our results support a role for peripherin in vesicle trafficking.
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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 | 0.000 | 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".