Material-Driven Fibronectin Assembly Rescues Matrix Defects due to Mutations in Collagen IV in Fibroblasts
Bibliographic record
Abstract
Abstract Basement membranes (BMs) provide structural support to tissues and influence cell signaling. Mutations in COL4A1/COL4A2, a major BM component, cause eye, kidney and cerebrovascular disease, including stroke. Common variants in these genes are risk factors for intracerebral hemorrhage in the general population. However, the contribution of the matrix to the disease mechanism(s) and its effects on the biology of cells harboring a collagen IV mutation remain poorly understood. To shed light on this, we engineered controlled microenvironments using polymer biointerfaces coated with ECM proteins laminin or fibronectin (FN), to investigate the cellular phenotype of primary fibroblasts harboring a COL4A2 +/G702D mutation. FN nanonetworks assembled on poly(ethyl acrylate) (PEA) induced increased deposition and assembly of collagen IV in COL4A2 +/G702D cells, which was associated with reduced ER size and enhanced levels of protein chaperones such as BIP, suggesting increased protein folding capacity of cells. FN nanonetworks on PEA also partially rescued the reduced stiffness of the deposited matrix and cells, and enhanced cell adhesion through β 1 -mediated signaling and actin-myosin contractility, effectively rescuing some of the cellular phenotypes associated with COL4A1/4A2 mutations. Collectively, these results suggest that biomaterials are able to shape the matrix and cellular phenotype of the COL4A2 +/G702D mutation in patient-derived cells.
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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".