Regulation of transcription termination by FUS and TDP-43
Bibliographic record
Abstract
ABSTRACT The carboxy-terminal domain (CTD) of the RNA polymerase II (RNAPII) subunit POLR2A is a platform for modifications specifying the recruitment of factors that regulate transcription, mRNA processing, and chromatin remodelling. We previously found that symmetrical dimethylation (me2s) of a CTD Arginine residue (R1810 in human) causes recruitment of the Tudor domain of SMN, which interacts with Senataxin. SMN is mutated in spinal muscular atrophy (SMA), and Senataxin is sometimes mutated in Amyotrophic Lateral Sclerosis (ALS). R1810me2s and SMN, like Senataxin, are important for resolving R-loops (DNA:RNA hybrids) at transcription terminators. FUS and TDP-43 (TARDBP) are DNA/RNA binding proteins that are sometimes mutated in ALS and FTD (Frontotemporal dementia). Here we show that TDP-43 and, to some extent, FUS are recruited by the R1810me2s-SMN pathway. Defects in FUS and TDP-43 recruitment influence RNAPII termination and R-loop accumulation, leading to elevated DNA damage at terminators that may contribute to neurodegenerative disorders like ALS and FTD.
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 | 0.000 |
| 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".