Transcriptome analyses of human corneal endothelial cell lines derived from patients with Fuchs endothelial corneal dystrophy
Bibliographic record
Abstract
Fuchs Endothelial Corneal Dystrophy (FECD) is an age-related disorder that affects about 4% of the population over the age of 40 years and is genetically associated with CTG repeat expansion in Transcription factor-4 (TCF4) gene. Although both genetic variants as well as environmental exposures like ultraviolent-A light (UVA) have been reported to cause FECD, there are no pharmacological treatments due to the lack of understanding of disease pathogenesis. To characterize the corneal endothelial (CE) cells in FECD individuals with or without CTG repeat expansions in TCF4, we performed RNA-sequencing (RNAseq) of three normal CE cell lines and seven FECD cell lines, including 3 generated from donors with TCF4 repeat expansions (FECD-R) and 4 from non-expanded donors (FECD-NR). Analysis of transcriptomic profiles in all 7 FECD cell lines compared to normal, revealed 214 differentially expressed genes or DEGs with 193 upregulated and 21 downregulated genes. Ingenuity Pathway Analysis (IPA) detected hepatic fibrosis and endothelial-mesenchymal transition pathways as top canonical pathways, which is consistent with extracellular matrix (ECM) deposition in the form of guttae seen in FECD. We identified and further validated transcriptional regulator genes like MAFB, TFAP2B, and POU6F2 to be differentially downregulated in FECD. Furthermore, 48 common DEGs were detected in both FECD-R and FECD-NR, and their upstream regulators included β-estradiol, TGF-β1, Aryl hydrocarbon receptor (AHR), and transcription regulators like CEBPA, CEBPB and SMARCA4. There were 29 genes identified to be differentially regulated only in FECD-R, compared to normal cells. In addition, other canonical pathways like tryptophan and melatonin degradation, Wnt signaling, AHR signaling, mitochondrial dysfunction, and estrogen receptor signaling were also highly enriched in FECD. Our findings support the previously proposed underlying mechanisms of disease progression, including role of transcriptional regulation of endothelial cells, mitochondrial dysfunction, Wnt- and estrogen receptor signaling, as well as the phenotypic clinical FECD hallmark of ECM deposits. Further investigation focusing on differentially expressed genes related to these pathways may be beneficial for elucidating disease-causing mechanisms and developing novel therapies for FECD.
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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 | 0.001 |
| 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".