Identification of HnRNPC as a novel Tau exon 10 splicing factor using RNA antisense purification mass spectrometry
Bibliographic record
Abstract
Alternative splicing in Tau exon 10 generates 3 R- and 4 R-Tau proteoforms, which have equal abundance in healthy adult human brain. Aberrant alternative splicing in Tau exon 10 leads to distortion of the balanced 3 R- and 4 R-Tau expression levels, which is a causal factor to trigger toxic Tau aggregation, neuron dysfunction and patient death in a group of neurodegenerative diseases known as tauopathies. Hence, identification of regulators upstream of the Tau exon 10 splicing events are crucial to understanding pathogenic mechanisms driving tauopathies. In this study, we used RNA Antisense Purification with Mass Spectrometry (RAP-MS) analysis to identify RNA-binding proteins (RBPs) that interact with the Tau pre-mRNA near exon 10. Among the newly identified RBP candidates, we show that knockdown of hnRNPC induces Tau exon 10 skipping whereas overexpression of hnRNPC promotes Tau exon 10 inclusion. In addition, we show that hnRNPC interacts with the poly-uridine (U-tract) sequences in introns 9 and 10 of Tau pre-mRNA. Mutation of these U-tract motifs abolished binding of hnRNPC with Tau pre-mRNA fragment and blocked its impact on Tau exon 10 inclusion. These findings indicate that hnRNPC binds and utilizes these U-tract motifs located in introns 9 and 10 of Tau pre-mRNA to promote Tau exon 10 inclusion. Intriguingly, high hnRNPC expression level is associated with progressive supranuclear palsy (PSP), a sporadic tauopathy with pathological accumulation of Tau species that contain exon 10, which suggests a putative therapeutic role of hnRNPC for PSP treatment. [Figure: see text].
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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) | 0.000 | 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 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".