TDP-43 toxic gain of function links ALS, FTD and Alzheimer's Disease through splicing dysregulation
Bibliographic record
Abstract
Loss of nuclear TDP-43 splicing activity is a common feature across neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but its relevance to Alzheimer's disease (AD) remains unclear. Here, we show that TDP-43 pathology in AD is broadly associated with splicing abnormalities, including aberrant splicing of amyloid precursor protein (APP). TDP-43 drives the formation of elongated APP isoforms, disrupting alternative splicing across ALS, FTLD-TDP and AD, providing a compelling mechanism for a long-standing observation of APP isoform dysregulation. We further establish a mechanistic link between TDP-43, APP splicing, and Aβ pathology. Surprisingly, the disruption to alternative APP splicing is mediated by a toxic gain of cytoplasmic TDP-43 function, rather than loss of its nuclear role. Using proximity proteomics and base editing in human iPSC-derived neurons, we show that TDP-43 pathology causes cytoplasmic co-sequestration of splicing regulators SCAF11, SRSF5, and TIAL1. Knockdown of these regulators also results in APP mis-splicing and increased Aβ burden, without affecting other TDP-43 targets such as STMN2 or UNC13A. Together, our findings suggest that TDP-43-mediated splicing dysfunction upstream of APP contributes to the pathogenesis of seemingly disparate neurodegenerative diseases, uniting AD and ALS/FTLD-TDP through a shared molecular mechanism.
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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".