Isoform‐specific antibodies reveal distinct subcellular localizations of <scp>C</scp>9orf72 in amyotrophic lateral sclerosis
Bibliographic record
Abstract
OBJECTIVE: A noncoding hexanucleotide repeat expansion in C9orf72 is the most common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). It has been reported that the repeat expansion causes a downregulation of C9orf72 transcripts, suggesting that haploinsufficiency may contribute to disease pathogenesis. Two protein isoforms are generated from three alternatively spliced transcripts of C9orf72; a long form (C9-L) and a short form (C9-S), and their function(s) are largely unknown owing to lack of specific antibodies. METHODS: To investigate C9orf72 protein properties, we developed novel antibodies that recognize either C9-L or C9-S. Multiple techniques, including Western blot, immunohistochemistry, and coimmunoprecipitation, were used to determine the expression levels and subcellular localizations of C9-L and C9-S. RESULTS: Investigation of expression of C9-L and C9-S demonstrated distinct biochemical profiles, region-specific changes, and distinct subcellular localizations in ALS tissues. In particular, C9-L antibody exhibited a diffuse cytoplasmic staining in neurons and labeled large speckles in cerebellar Purkinje cells. In contrast, C9-S antibody gave very specific labeling of the nuclear membrane in healthy neurons, with apparent relocalization to the plasma membrane of diseased motor neurons in ALS. Coimmunoprecipitation experiments revealed an interaction of the C9-isoforms with both Importin β1 and Ran-GTPase, components of the nuclear pore complex. INTERPRETATION: Using these antibodies, we have shown that C9orf72 may be involved in nucleocytoplasmic shuttling and this may have relevance to pathophysiology of ALS/FTLD. Our antibodies have provided improved detection of C9orf72 protein isoforms, which will help elucidate its physiological function and role in ALS/FTLD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".