Expression and Impact of Fibronectin, Tenascin-C, Osteopontin, and Type XIV Collagen in Fuchs Endothelial Corneal Dystrophy
Bibliographic record
Abstract
Purpose: Fuchs endothelial corneal dystrophy (FECD) is characterized by Descemet's membrane (DM) abnormalities, namely an increased thickness and a progressive appearance of guttae and fibrillar membranes. The goal of this study was to identify abnormal extracellular matrix (ECM) proteins expressed in FECD DMs and to evaluate their impact on cell adhesion and migration. Methods: Gene expression profiles from in vitro (GSE112039) and ex vivo (GSE74123) healthy and FECD corneal endothelial cells were analyzed to identify deregulated matrisome genes. Healthy and end-stage FECD DMs were fixed and analyzed for guttae size and height. Immunostaining of fibronectin, tenascin-C, osteopontin, and type XIV collagen was performed on ex vivo specimens, as well as on tissue-engineered corneal endothelium reconstructed using healthy and FECD cells. An analysis of ECM protein expression according to guttae and fibrillar membrane was performed using immunofluorescent staining and phase contrast microscopy. Finally, cell adhesion was evaluated on fibronectin, tenascin-C, and osteopontin, and cell migration was studied on fibronectin and tenascin-C. Results: SPP1 (osteopontin), FN1 (fibronectin), and TNC (tenascin-C) genes were upregulated in FECD ex vivo cells, and SSP1 was upregulated in both in vitro and ex vivo FECD conditions. Osteopontin, fibronectin, tenascin-C, and type XIV collagen were expressed in FECD specimens, with differences in their location. Corneal endothelial cell adhesion was not significantly affected by fibronectin or tenascin-C but was decreased by osteopontin. The combination of fibronectin and tenascin-C significantly increased cell migration. Conclusions: This study highlights new abnormal ECM components in FECD, suggests a certain chronology in their deposition, and demonstrates their impact on cell behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".