Determination of antiviral action of long non-coding RNA loc107051710 during infectious bursal disease virus infection due to enhancement of interferon production
Bibliographic record
Abstract
The functions and profiles of lncRNAs during infectious bursal disease virus (IBDV) infection have not been determined, yet. The objectives of this study were to determine the antiviral action of loc107051710 lncRNA during IBDV infection by investigating the relationship between loc107051710 and IRF8, Type I IFN, STATs, and ISGs. DF-1 cells were either left untreated as non-infected controls (n = 1) or infected with IBDV (n = 3). RNA sequencing was applied for analysis of mRNAs and lncRNAs expression. Differentially expressed genes were verified by RT-qPCR. Then identification, of 230 significantly different expressed genes (182 mRNAs and 48 lncRNA) by pairwise comparison of the infected and control groups, was carried out. The functions of differentially expressed lncRNAs were investigated by selection of lncRNAs and mRNAs significantly enriched in the aforementioned biological processes and signaling pathways for construction of lncRNA-mRNA co-expression networks. The techniques of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways were applied. It was suggested that these differentially expressed genes were involved in the interaction between the host and IBDV. Loc107051710 was found to have potential antiviral effects. RT-qPCR and western blot were applied and revealed that loc107051710 was required for induction of IRF8, type I IFN, STAT, and ISG expression, and its knockdown promoted IBDV replication. By fluorescence in situ hybridization, it was found that loc107051710 was translocated from the nucleus to the cytoplasm after infection with IBDV. Overall, loc107051710 promoted the production of IFN-α and IFN-β by regulating IRF8, thereby promoting the antiviral activity of ISGs.
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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 | 0.000 |
| 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".