TMPyP4 Porphyrin Distorts RNA G-quadruplex Structures of the Disease-associated r(GGGGCC)n Repeat of the C9orf72 Gene and Blocks Interaction of RNA-binding Proteins
Bibliographic record
Abstract
Certain DNA and RNA sequences can form G-quadruplexes, which can affect genetic instability, promoter activity, RNA splicing, RNA stability, and neurite mRNA localization. Amyotrophic lateral sclerosis and frontotemporal dementia can be caused by expansion of a (GGGGCC)n repeat in the C9orf72 gene. Mutant r(GGGGCC)n- and r(GGCCCC)n-containing transcripts aggregate in nuclear foci, possibly sequestering repeat-binding proteins such as ASF/SF2 and hnRNPA1, suggesting a toxic RNA pathogenesis, as occurs in myotonic dystrophy. Furthermore, the C9orf72 repeat RNA was recently demonstrated to undergo the noncanonical repeat-associated non-AUG translation (RAN translation) into pathologic dipeptide repeats in patient brains, a process that is thought to depend upon RNA structure. We previously demonstrated that the r(GGGGCC)n RNA forms repeat tract length-dependent G-quadruplex structures that bind the ASF/SF2 protein. Here we show that the cationic porphyrin (5,10,15,20-tetra(N-methyl-4-pyridyl) porphyrin (TMPyP4)), which can bind some G-quadruplex-forming sequences, can bind and distort the G-quadruplex formed by r(GGGGCC)8, and this ablates the interaction of either hnRNPA1 or ASF/SF2 with the repeat. These findings provide proof of concept that nucleic acid binding small molecules, such as TMPyP4, can distort the secondary structure of the C9orf72 repeat, which may beneficially disrupt protein interactions, which may ablate either protein sequestration and/or RAN translation into potentially toxic dipeptides. Disruption of secondary structure formation of the C9orf72 RNA repeats may be a viable therapeutic avenue, as well as a means to test the role of RNA structure upon RAN translation.
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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".