Fibulin-4 deficiency differentially affects cytoskeleton structure and dynamics as well as TGFβ signaling
Bibliographic record
Abstract
Fibulin-4 is an extracellular matrix (ECM) protein essential for elastogenesis and mutations in this protein lead to aneurysm formation. In this study, we isolated vascular smooth muscle cells (VSMCs) from mice with reduced fibulin-4 protein expression (Fibulin-4R/R) and from mice with a smooth muscle cell specific deletion of the Fibulin-4 gene (Fibulin-4f/−/SM22Cre+). We subsequently analyzed and compared the molecular consequences of reduced Fibulin-4 expression versus total ablation of Fibulin-4 expression with regard to effects on the SMC specific contractile machinery, cellular migration and TGFβ signaling. Analysis of the cytoskeleton showed that while Fibulin-4f/−/SM22Cre+ VSMCs lack smooth muscle actin (SMA) fibers, Fibulin-4R/R VSMCs were able to form SMA fibers. Furthermore, Fibulin-4f/−/SM22Cre+ VSMCs showed a decreased pCofilin to Cofilin ratio, suggesting increased actin depolymerization, while Fibulin-4R/R VSMCs did not display this decrease. Yet, both Fibulin-4 mutant VSMCs showed decreased migration. We found increased activation of TGFβ signaling in Fibulin-4R/R VSMCs. However, TGFβ signaling was not increased in Fibulin-4f/−/SM22Cre+ VSMCs. From these results we conclude that both reduction and absence of Fibulin-4 leads to structural and functional impairment of the SMA cytoskeleton. However, while reduced levels of Fibulin-4 result in increased TGFβ activation, complete absence of Fibulin-4 does not result in increased TGFβ activation. Since both mouse models show thoracic aortic aneurysm formation, we conclude that not only hampered TGFβ signaling, but also SMA cytoskeleton dynamics play an important role in aortic aneurysmal disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".