Gene Expression and miR Profiles of Human Corneal Fibroblasts in Response to Dexamethasone
Bibliographic record
Abstract
PURPOSE: Dexamethasone (DEX) is commonly used as a therapeutic agent for various ocular inflammatory diseases; however, its effect on resident naive cells is unknown. In this study, genome microarray and microRNA (miR) analyses were used to evaluate the global gene and miR expression of human corneal fibroblasts (HKFs) in response to treatment with DEX. METHODS: Primary HKFs from three donors were treated with DEX for 16 hours. Treated and untreated cells were snap frozen for microarray and miR array analyses. Genes with a more than threefold change were classified into gene families using the DAVID web-based classification database, and six of these genes were validated using quantitative real-time PCR. Five miRs were also validated using miR-detection assays. RESULTS: Of the 41,093 genes examined, 261 were upregulated and 123 were downregulated greater than threefold after DEX treatment. Real-time PCR confirmed upregulation of six genes, including oculocutaneous albinism II (OCA2), angiopoietin-like 7 (ANGPTL7), neuron navigator 2 (NAV2), neurofilament light chain polypeptide (NEFL), solute carrier family 16/member 12 (SLC16A12), and serum amyloid A1 (SAA1). Expression of several miR including miR-16, -21, and -29C were upregulated, whereas miR-100 was downregulated in fibroblasts by DEX. CONCLUSIONS: DEX can greatly change the global gene and miR profile of HKFs. DEX not only downregulates inflammatory genes, but can also induce expression of angiogenic and inflammatory genes. In addition, DEX may exert posttranscriptional gene regulation through miRs. These data support a complex role for DEX-induced changes in resident cells that may have implications in the clinical management of corneal inflammation with topical glucocorticoids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".