Alternative Splicing and Tissue-specific Elastin Misassembly Act as Biological Modifiers of Human Elastin Gene Frameshift Mutations Associated with Dominant Cutis Laxa
Bibliographic record
Abstract
Elastin is the extracellular matrix protein in vertebrates that provides elastic recoil to blood vessels, the lung, and skin. Because the elastin gene has undergone significant changes in the primate lineage, modeling elastin diseases in non-human animals can be problematic. To investigate the pathophysiology underlying a class of elastin gene mutations leading to autosomal dominant cutis laxa, we engineered a cutis laxa mutation (single base deletion) into the human elastin gene contained in a bacterial artificial chromosome. When expressed as a transgene in mice, mutant elastin was incorporated into elastic fibers in the skin and lung with adverse effects on tissue function. In contrast, only low levels of mutant protein incorporated into aortic elastin, which explains why the vasculature is relatively unaffected in this disease. RNA stability studies found that alternative exon splicing acts as a modifier of disease severity by influencing the spectrum of mutant transcripts that survive nonsense-mediated decay. Our results confirm the critical role of the C-terminal region of tropoelastin in elastic fiber assembly and suggest tissue-specific differences in the elastin assembly pathway.
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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".