Transcriptional Profile and Integrative Analyses of Long Noncoding RNAs in Primary Human Corneal Epithelial Cells in Response to HSV-1 Infection
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) have been demonstrated to have important regulatory functions in diverse cellular processes; however, the role of lncRNAs in the pathogenesis of herpes simplex keratitis (HSK) remains poorly understood. Primary human corneal epithelial cells (HCECs) were infected with herpes simplex virus-1 (HSV-1) and the total RNAs extracted from both the infected group and the mock-infected group subjected to microarray analysis to identify the differential expression of lncRNAs and mRNAs. We also performed bioinformatic analysis including gene ontology (GO) analysis, pathway analysis and co-expression network analysis. Compared with mock-infected group, the expression of thousands of lncRNAs and mRNAs were significantly changed, and the microarray results were validated by qRT-PCR. The most enriched GOs targeted by up-regulated transcripts were defense response, intrinsic component of plasma membrane and cytokine activity，and the most enriched GOs targeted by the down-regulated transcripts were cellular metabolic process, intracellular part and poly (A) RNA binding. Pathway analysis indicated that the most correlated pathways for up- and down-regulated transcripts were cytokine-cytokine receptor interaction and RNA transport, respectively. Our study identified the genome-wide profile of lncRNAs and mRNAs expression in primary corneal epithelial cells with HSV-1 infection. These transcriptomic data together with subsequent bioinformatic analysis will provide us with novel clue to the insight into molecular mechanism and potential therapeutic targets of HSK. Further studies are expected to verify the potentially functional genes and pathways and explore the critical lncRNAs. Long noncoding RNAs: lncRNAs; herpes simplex virus-1: HSV-1; herpes simplex virus keratitis: HSK; human corneal epithelial cells: HCECs
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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 | 0.001 |
| 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".