The Shape of Silence: A Role for the Nuclear Envelope in Transcriptional Regulation
Bibliographic record
Abstract
The lipid drug edelfosine integrates into the nuclear envelope (NE) of S. cerevisiae (budding yeast) and results in its morphological deformation. As biologists we are guided by a central tenant that form and function are inextricably linked. This is true for the thumb on a hand and a protein produced in a cell. We hypothesized that this will also be true for the organelles inside a eukaryotic cell, wherein changes in nuclear shape would lead to changes in nuclear functions, like alterations in gene expression and protein localization. Using live cell imaging combined with yeast genetics and biochemical approaches, we found that edelfosine treatment led to dispersion of the membrane‐associated SIR complex and triggered processing of the membrane‐sensing transcription factors, Mga2 and Spt23. By a complementary approach of RNA‐seq followed by transcriptomic analysis, we identified that targets of Mga2 as well as subtelomeric regions were upregulated in response to edelfosine, while ribosomal protein targets of Rap1 were downregulated. Our work suggests that disruption of the nuclear membrane is sufficient to trigger changes in membrane‐associated transcription factors and chromatin remodellers. Our data indicate that nuclear membrane integrity is linked to transcriptional regulation. The NE could be a novel target for chemotherapeutics as lipid drugs like edelfosine do not cause DNA damage and would likely not pose mutagenic threats.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".