| Literature DB >> 31319583 |
Cindy Kaufmann1,2, Hanspeter Stalder1,2, Xaver Sidler3, Sandra Renzullo1,2, Corinne Gurtner2,4, Alexander Grahofer5, Matthias Schweizer6,7.
Abstract
In 2015, a new pestivirus was described in pig sera in the United States. This new "atypical porcine pestivirus" (APPV) was later associated with congenital tremor (CT) in newborn piglets. The virus appears to be distributed worldwide, but the limited knowledge of virus diversity and the use of various diagnostic tests prevent direct comparisons. Therefore, we developed an APPV-specific real-time RT-PCR assay in the 5'UTR of the viral genome to investigate both retro- and prospectively the strains present in Switzerland and their prevalence in domestic pigs. Overall, 1080 sera obtained between 1986 and 2018 were analyzed, revealing a virus prevalence of approximately 13% in pigs for slaughter, whereas it was less than 1% in breeding pigs. In the prospective study, APPV was also detected in piglets displaying CT. None of the samples could detect the Linda virus, which is another new pestivirus recently reported in Austria. Sequencing and phylogenetic analysis revealed a broad diversity of APP viruses in Switzerland that are considerably distinct from sequences reported from other isolates in Europe and overseas. This study indicates that APPV has already been widely circulating in Switzerland for many years, mainly in young animals, with 1986 being the earliest report of APPV worldwide.Entities:
Keywords: APPV; Switzerland; atypical porcine pestivirus; congenital tremor; epidemiology; phylogenetic analysis; prevalence; real-time RT-PCR
Year: 2019 PMID: 31319583 PMCID: PMC6669711 DOI: 10.3390/v11070653
Source DB: PubMed Journal: Viruses ISSN: 1999-4915 Impact factor: 5.048
Real-time RT-PCR primers and probes.
| Virus | Primer | Name | Start 5′ | Sequence 5′-3′ | bp | Reference Gene |
|---|---|---|---|---|---|---|
| APPV | Forward primer | APPV-F1-5utr | 168 | GGGCAGACGTCACYGAGTAGTACA | 24 | KX929062 |
| APPV | Reverse primer | APPV-R2-5utr | 340 | TCCGCCGGCACTCTATCA | 18 | |
| APPV | Probe (MGB) | APPV-MGB3-5utr | 214 | TGTAGGGTCTACTGAGGCT | 19 | |
| Linda | Forward primer | Linda-F1 | 210 | ACCCACTGGCGATGCCT | 17 | KY436034 |
| Linda | Reverse primer | Linda-R2 | 337 | TCCGCCGGCATCCTATC | 17 | |
| Sendai | Forward primer | Sendai-F5 | 8553 | GTCATGGATGGGCAGGAGTC | 20 | M30202 |
| Sendai | Reverse primer | Sendai-R6 | 8788 | CGTTGAAGAGCCTTACCCAGA | 21 | |
| Sendai | Probe | Sendai-P7 | 8720 | CAAAATTAGGAACGGAGGATTGTCCCCTC | 29 |
PCR primers for the production of the control fragments (Bold/italics = T7 promoter sequence).
| Virus | Primer | Name | Start 5′ | Sequence 5′-3′ | bp | Reference Gene |
|---|---|---|---|---|---|---|
| Linda | Control fragment | Linda-control | 200 | GGTAAGGATCACCCACTGGCGATGCCTTGT | 148 | KY436034 |
| Linda | Forward primer (incl T7) | Linda-frwT7-contr | 200 | ACTG | 48 | |
| Linda | Reverse Primer | Linda-rev-contr | 367 | GATATTCTTTATACTGGCTGTCACAGGGCAT | 37 | |
| APPV | Forward Primer (incl. T7) | APPV-F8-T7-5utr | 130 | ACTG | 51 | KX929062 |
| APPV | Reverse Primer | APPV-rev-9 | 890 | TCACAATTGGGTTTCCATTGGTA | 23 | |
| BVDV | Forward Primer (incl. T7) | BVD386 | 80 | ACTG | 43 | NC_001461 |
| BVDV | Reverse Primer | BVD387 | 444 | ACCCCGACGGGTTTTTGT | 18 |
Figure 1qRT-PCR standard curve of in vitro transcribed APPV RNA in the 5′UTR. A tenfold dilution series within a range of 1 × 109 molecules/reaction to 1 × 10−1 molecules/reaction was performed. Targeting the FAM reporter, an efficiency of 86.5% was achieved. The slope of the standard curve was −3.7 with a standard deviation (SD) between 1 and 2.5.
Figure 2Virus prevalence of APPV in Switzerland from fattening farms. Relative number of APP virus prevalence in Switzerland from 1986 until 2015 with an overall viral prevalence of 13% in fattening pigs.
Figure 3Molecular phylogenetic analysis of the nucleotide sequence of the full ORF of APPV (A) or of a fragment in the 5′UTR (B). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model the evolutionary rate differences among the sites. The trees were drawn to scale, with branch lengths measured in the number of substitutions per site. All positions containing gaps and missing data were eliminated. The percentage of trees in which the associated taxa clustered together is shown next to the branches. In the final dataset, there were a total of 10,810 positions encompassing the full open reading frame (ORF) (A) and 165 positions in the 5′UTR of the virus genome (B). Each dot of a specific color was assigned to a specific country including China (CHN) in yellow, South Korea (KOR in pink, USA (USA) in green, Germany (DEU) in light blue, Austria (AUT) in dark blue, Spain (ESP) in middle shade blue, Switzerland (SUI) in red, and the Netherlands (NLD) in purple. The strains are all marked with the IOC-code of each specific country, the year of sample collection, and the accession number. Swiss isolates are additionally marked with the abbreviation code of the canton (see Table S3).