Translational control of SEPT9 isoforms is perturbed in disease
Bibliographic record
Abstract
A common feature of the mammalian septin gene family is complex genomic architecture with multiple alternate splice variants. Septin 9 has 18 distinct transcripts encoding 15 polypeptides, with two transcripts (SEPT9_v4 and v4*) encoding the same polypeptide. We have previously reported that the ratio of these distinct transcripts is altered in neoplasia, with the v4 transcript being the usual form in normal cells but v4* becoming predominant in tumours. This led us to ask what the functional differences between these two transcripts might be. The 5'-UTRs of v4 and v4* have distinct 5' ends encoded by exons 1beta (v4) and 1zeta and 2 (v4*) and a common 3' region and initiating ATG encoded within exon 3. Here we show that the two mRNAs are translated with different efficiencies and that cellular stress can alter this. A putative internal ribosome entry site can be identified in the common region of the v4 and v4* 5'-UTRs and translation is modulated by an upstream open-reading frame in the unique region of the v4 5'-UTR. Germline mutations in hereditary neuralgic amyotrophy (HNA) map to the region which is common to the two UTRs. These mutations dramatically enhance the translational efficiency of the v4 5'-UTR, leading to elevated SEPT9_v4 protein under hypoxic conditions. Our data provide a mechanistic insight into how the HNA mutations can alter the fine control of SEPT9_v4 protein and its regulation under physiologically relevant conditions and are consistent with the episodic and stress-induced nature of the clinical features of HNA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".