Molecular basis of the autoregulatory mechanism of motor neuron-related splicing factor 30
Bibliographic record
Abstract
Motor neuron-related splicing factor 30 (SPF30, also known as SMNDC1) is a paralog of the survival motor neuron protein that regulates the expression of various genes by affecting mRNA splicing. SPF30 has an autoregulatory mechanism that controls its expression. However, the detailed molecular mechanisms determining cellular levels of SPF30 remain unclear. Here, we demonstrated that SPF30 expression was controlled via the negative autoregulatory feedback, whereby increased SPF30 expression caused the inclusion of cassette exon within intron 2 and/or the generation of a newly spliced variant with exon 4a (produced by splicing 17 bases upstream of the canonical intron 3 and exon 4 junction). Altered transcripts with cassette exon or exon 4a were subjected to nonsense-mediated mRNA decay, leading to reduced SPF30 mRNA levels. Conversely, the loss of SPF30 protein resulted in a drastic reduction in the alternative exon 4a splice site usage compared to cassette exon inclusion, suggesting that alternative splicing at exon 4a contributes more to adjusting SPF30 expression levels. An in vivo splicing assay designed to reflect the usage of alternative exon 4a splice site demonstrated that a short stretch of sequence within exon 4 of SPF30 mRNA was required for the alternative splicing at exon 4a. In addition, the C terminal region of SPF30, particularly the latter part of α-helix and a kink-like structure, was crucial for the autoregulatory mechanism by enabling binding to exon 4a-containing RNA. These results reveal the molecular basis of the autoregulatory mechanism underlying SPF30 gene expression.
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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 | 0.001 |
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".