Loss of fibulin-4 disrupts collagen synthesis and maturation: implications for pathology resulting from<i>EFEMP2</i>mutations
Bibliographic record
Abstract
Homozygous recessive mutations in either EFEMP2 (encoding fibulin-4) or FBLN5 (encoding fibulin-5), critical genes for elastogenesis, lead to autosomal recessive cutis laxa types 1B and 1A, respectively. Previously, fibulin-4 was shown to bind lysyl oxidase (LOX), an elastin/collagen cross-linking enzyme, in vitro. Consistently, reported defects in humans with EFEMP2 mutations are more severe and broad in range than those due to FBLN5 mutations and encompass both elastin-rich and collagen-rich tissues. However, the underlying disease mechanism in EFEMP2 mutations has not been fully addressed. Here, we show that fibulin-4 is important for the integrity of aortic collagen in addition to elastin. Smooth muscle-specific Efemp2 loss in mouse (termed SMKO) resulted in altered fibrillar collagen localization with larger, poorly organized fibrils. LOX activity was decreased in Efemp2-null cells, and collagen cross-linking was diminished in SMKO aortas; however, elastin cross-linking was unaffected and the level of mature LOX was maintained to that of wild-type aortas. Proteomic screening identified multiple proteins involved in procollagen processing and maturation as potential fibulin-4-binding partners. We showed that fibulin-4 binds procollagen C-endopeptidase enhancer 1 (Pcolce), which enhances proteolytic cleavage of the procollagen C-terminal propeptide during procollagen processing. Interestingly, however, procollagen cleavage was not affected by the presence or absence of fibulin-4 in vitro. Thus, our data indicate that fibulin-4 serves as a potential scaffolding protein during collagen maturation in the extracellular space. Analysis of collagen in other tissues affected by fibulin-4 loss should further increase our understanding of underlying pathologic mechanisms in patients with EFEMP2 mutations.
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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 | 0.001 |
| 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".