Comprehensive Bioinformatics Analysis the circRNAs of Viral Infection Associated Pathway in HepG2 Expressing ORF3 of Genotype IV Swine Hepatitis E Virus
Bibliographic record
Abstract
The open reading frame 3 (ORF3) protein of the swine hepatitis E virus (SHEV) is a critical virulence factor implicated in viral infection, yet its precise mechanisms remain poorly understood. Circular RNAs (circRNAs) have emerged as key regulators of gene expression during viral infections by functioning as miRNA sponges. This study aimed to identify key circRNAs and construct a potential circRNA-miRNA-mRNA regulatory network associated with the viral infection pathway in HepG2 cells expressing genotype IV SHEV ORF3. Based on our previous high-throughput circRNA and transcriptome sequencing data from HepG2 cells with adenovirus-mediated ORF3 overexpression, we screened for differentially expressed circRNAs and mRNAs linked to viral infection pathways. Using bioinformatic tools, we predicted miRNAs targeted by these mRNAs and those that could bind to the circRNAs, ultimately constructing a competing endogenous RNA (ceRNA) network with Cytoscape. We identified 31 differentially expressed circRNAs and 7 mRNAs (HSPA8, HSPA1B, EGR2, CXCR4, SOCS3, NOTCH3, and ZNF527) related to viral infection. A potential ceRNA network comprising 32 circRNAs, 23 miRNAs, and the 7 mRNAs was constructed. Core circRNAs, including ciRNA203, circRNA14936, and circRNA5562, may act as miRNA sponges to regulate the expression of these mRNAs. This network suggests a novel mechanism by which SHEV ORF3 might modulate host cell functions to facilitate viral infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".