TDP-43 regulates the alternative splicing of hnRNP A1 to yield an aggregation-prone variant in amyotrophic lateral sclerosis
Bibliographic record
Abstract
See Fratta and Isaacs (doi:10.1093/brain/awy091) for a scientific commentary on this article.The RNA binding proteins TDP-43 (encoded by TARDBP) and hnRNP A1 (HNRNPA1) are each mutated in certain amyotrophic lateral sclerosis cases and are often mislocalized in cytoplasmic aggregates within motor neurons of affected patients. Cytoplasmic inclusions of TDP-43, which are accompanied by a depletion of nuclear TDP-43, are observed in most amyotrophic lateral sclerosis cases and nearly half of frontotemporal dementia cases. Here, we report that TDP-43 binds HNRNPA1 pre-mRNA and modulates its splicing, and that depletion of nuclear TDP-43 results in increased inclusion of a cassette exon in the HNRNPA1 transcript, and consequently elevated protein levels of an isoform containing an elongated prion-like domain, referred to as hnRNP A1B. Combined in vivo and in vitro approaches demonstrated greater fibrillization propensity for hnRNP A1B, which drives protein aggregation and is toxic to cells. Moreover, amyotrophic lateral sclerosis patients with documented TDP-43 pathology showed neuronal hnRNP A1B cytoplasmic accumulation, indicating that TDP-43 mislocalization may contribute to neuronal vulnerability and loss via altered HNRNPA1 pre-mRNA splicing and function. Given that TDP-43 and hnRNP A1 each bind, and thus modulate, a third of the transcriptome, our data suggest a much broader disruption in RNA metabolism than previously considered.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".