Aberrant Splicing of tau Pre-mRNA Caused by Intronic Mutations Associated with the Inherited Dementia Frontotemporal Dementia with Parkinsonism Linked to Chromosome 17
Bibliographic record
Abstract
Frontotemporal dementia accounts for a significant fraction of dementia cases. Frontotemporal dementia with parkinsonism linked to chromosome 17 is associated with either exonic or intronic mutations in the tau gene. This highlights the involvement of aberrant pre-mRNA splicing in the pathogenesis of neurodegenerative disorders. Little is known about the molecular mechanisms of the splicing defects underlying these diseases. To establish a model system for studying the role of pre-mRNA splicing in neurodegenerative diseases, we have constructed a tau minigene that reproduces tau alternative splicing in both cultured cells and in vitro biochemical assays. We demonstrate that mutations in a nonconserved intronic region of the human tau gene lead to increased splicing between exon 10 and exon 11. Systematic biochemical analyses indicate the importance of U1 snRNP and, to a lesser extent, U6 snRNP in differentially recognizing wild-type versus intron mutant tau pre-mRNAs. Gel mobility shift assays with purified U1 snRNP and oligonucleotide-directed RNase H cleavage experiments support the idea that the intronic mutations destabilize a stem-loop structure that sequesters the 5' splice site downstream of exon 10 in tau pre-mRNA, leading to increases in U1 snRNP binding and in splicing between exon 10 and exon 11. Thus, mutations in nonconserved intronic regions that increase rather than decrease alternative splicing can be an important pathogenic mechanism for the development of human diseases.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".