TDP-43 loss of function drives aberrant splicing in Parkinson’s disease
Bibliographic record
Abstract
Introductory paragraph While mRNA splicing dysregulation is a well-established contributor to neurodegeneration in disorders such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), its role in Parkinson’s disease (PD) remains underexplored. Here, we analyse transcriptomic data from >500 post-mortem human brain samples from individuals with and without PD to show that splicing alterations are frequently detected. Differentially spliced genes were significantly more enriched for those causally-implicated in both PD and ALS than genes that were differentially expressed. Furthermore, we observed a strong association between these splicing alterations and dysfunction of the RNA-binding protein (RBP), TAR DNA-binding protein 43 (TDP-43). Strikingly, genes and exon junctions affected by TDP-43 knockdown overlapped significantly with those dysregulated across brain regions in PD. In brains from individuals with the LRRK2 c.6055G>A (p.G2019S) mutation, the most common genetic cause of PD, we also observed significant enrichment of TDP-43-dependent splicing changes. This finding was corroborated in human pluripotent stem cell-derived midbrain dopaminergic neurons and a LRRK2 p.G2019S knock-in mouse model, where reduced nuclear TDP-43 levels evidenced the well-recognised loss-of-function mechanism contributing to splicing dysregulation. By leveraging our RNA-based analyses we predicted TDP-43-dependent novel peptide sequences and validated their existence within human LRRK2 mutation mDNs, while also demonstrating an overall loss of protein and mRNA expression in mis-spliced genes. Collectively, our findings reveal that PD is marked by extensive splicing dysregulation dependent on TDP-43, making TDP-43 a promising new therapeutic target in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".