Investigation of a Fused in Sarcoma Splicing Mutation in a Chinese Amyotrophic Lateral Sclerosis Patient
Bibliographic record
Abstract
ABSTRACT: Objective: Genetic mutations of fused in sarcoma (FUS) causing amyotrophic lateral sclerosis (ALS) may disrupt mRNA splicing events. For example, the FUS c.1394-2delA variant was reported in two western ALS patients, but its molecular mechanism is unclear. In this study, we aim to investigate FUS splice site mutations in Chinese ALS patients. Methods: Sanger sequencing was used to identify FUS splicing mutations in Chinese ALS patients. We combined a deep learning tool (SpliceAI), RNA sequencing, and RT-PCR/RT-qPCR to analyze the effect of FUS c.1394-2delA mutation on RNA splicing and expression. AlphaFold was used to predict the protein structure of mutant FUS. In transfected cell lines, we used immunofluorescence to assess cytoplasmic mislocalization of mutant FUS protein. Results: We identified a de novo FUS splice acceptor site mutation (c.1394-2delA, p. Gly466Valfs*14) in one Chinese sporadic ALS patient, which is linked to exon 14 skipping, and upregulated total FUS mRNA expression. The FUS splice site mutation was predicted to be translated into a truncated protein product at C-terminal. In vitro studies revealed that the FUS mutation increased cytoplasmic mislocalization in both HEK293T and SH-SY5Y cells. Conclusions: We identified a de novo FUS splicing mutation (c.1394-2delA, p. Gly466Valfs*14) in 1 out of 233 Chinese ALS patients. It caused abnormal RNA splicing, upregulated gene expression, truncated FUS translation, and cytosolic mislocalization. Our findings suggested that FUS splice site mutation is rare in Chinese ALS patients and extended our knowledge of molecular mechanisms of the FUS c.1394-2delA mutation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.000 |
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".