Porcine β-defensin 2 inhibits proliferation of pseudorabies virus in vitro and in transgenic mice
Bibliographic record
Abstract
BACKGROUND: Porcine β-defensin 2 (PBD-2), produced by host cells, is an antimicrobial cysteine-rich cationic peptide with multi-functions. Previous studies have demonstrated that PBD-2 can kill various bacteria, regulate host immune responses and promote growth of piglets. However, the antiviral role of PBD-2 is rarely investigated. This study aimed to reveal the antiviral ability of PBD-2 against pseudorabies virus (PRV), the causative pathogen of Aujeszky's disease, in PK-15 cells and in a PBD-2 expressing transgenic (TG) mouse model. METHODS: In this study, the cytotoxicity of PBD-2 on PK-15 cells was measured by CCK-8 assay. PK-15 cells were incubated with PRV pre-treated with different concentrations of PBD-2 and PRV titers in cell culture supernatants were determined by real-time quantitative PCR (RT-qPCR). TG mice and wild-type (WT) mice were intraperitoneally injected with PRV and the survival rate was recorded for 10 days. Meanwhile, tissue lesions in brain, spleen and liver of infected mice were observed and the viral loads of PRV in brain, liver and lung were analyzed by RT-qPCR. RESULTS: PBD-2 at a maximum concentration of 80 μg/mL displayed no significant cytotoxicity on PK-15 cells. A threshold concentration of PBD-2 at 40 μg/mL was required to inhibit PRV proliferation in PK-15 cells. The survival rate in PBD-2 TG mice was 50% higher than that of WT mice. In addition, TG mice showed alleviated tissue lesions in brain, spleen and liver compared with their WT littermates after PRV challenge, while viral loads of PRV in brain, liver and lung of TG mice were significantly lower than that of WT mice. CONCLUSIONS: PBD-2 could inhibit PRV proliferation in PK-15 cells and protect mice from PRV infection, which confirmed the antiviral ability of PBD-2 both in vitro and in vivo. The application of PBD-2 in developing anti-viral drugs or disease-resistant animals can be further investigated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".