Impact of alignment algorithm on the estimation of pairwise genetic similarity of porcine reproductive and respiratory syndrome virus (PRRSV)

The impact of porcine reproductive and respiratory syndrome virus (PRRSV) genotypes, established on the basis of ORF-5 nucleotide sequences, on three production parameters in Ontario sow farms

The porcine reproductive and respiratory syndrome virus (PRRSV) is an enveloped RNA virus, with high mutation rates and genetic variability; which is evident by the large number of discrete strains that co-circulate in swine populations. Veterinary practitioners frequently identify certain discrete PRRSV strains as having a higher clinical impact on production. However, with exception of a few strains, production impact is not well characterized for the majority of PRRSV variants. Predictive analytics, coupled with routine diagnostic sequencing of PRRSV, provide opportunities to study the clinical impact of discrete PRRSV strains on production. Thus, the primary objective of this research was to evaluate clinical impact of discrete PRRSV clades observed in Ontario sow farms. PRRS viruses were classified into discrete clades using Bayesian analysis of the nucleotide sequences of the ORF-5 region of the genome. Production data were gathered through veterinary clinics from herds participating in the ongoing PRRSV surveillance system. Data about pre-weaning mortality, sow mortality, and abortion rates were measured up to 8 weeks post initial PRRSV outbreak. Through conventional regression analysis, results support that clinical impact of the viruses varied among clades over time for abortion rate (p = 0.05) and pre-weaning mortality (p < 0.01). Using predictive modelling approaches based on grouped K-fold cross-validation, it was identified that PRRSV clade designations and other measured factors showed low predictive performance for abortion (R² = 0.07), pre-weaning mortality (R² = 0.09), and sow mortality (R² = 0.04). Clade designation consistently showed moderate importance for abortion and pre-weaning mortality, with clade 2 viruses being identified, on average, as having higher impact. These results demonstrate that the prediction of clinical impact, through production parameters, based on phylogenetic classification of PRRS viruses is possible. However, very high impact outbreaks were difficult to predict across production parameters. More surveillance-derived data are required to continue to improve predictive performance of the models.

Positioning Quebec ORF5 sequences of porcine reproductive and respiratory syndrome virus (PRRSV) within Canada and worldwide diversity

Sequencing of ORF5 gene is widely used and considered essential for diagnostics and control of porcine reproductive and respiratory syndrome (PRRS) in Canada. The objective of this study was to position Quebec ORF5 sequences of PRRS virus within Canada and worldwide diversity. Overall, 76.8% of the 5204 sequences gathered from Quebec (n = 5031), Ontario (n = 151) and Manitoba (n = 18) were classified into one of 34 genetic clusters defined as groupings including ≥15 sequences and having ≥70% rapid bootstrap support value from a maximum likelihood (ML)-phylogeny. Following the addition of PRRSV 2 international reference dataset from Shi et al. (2010), the most predominant lineages in our dataset were wild-type 1 and vaccine-like 5.1 (MLV) and 8.9 (ATP). No strains or only a very few (1 or 2) were assigned to lineages 1.3-1.5, 3, 4, 5.2, 6, 7 or 9. Most wild-type clusters (97%) detected in a dataset from Canada did not include any sequence from the international reference dataset. It might reflect recent subpopulations that were absent at the time of Shi's publication. As an example, cluster #25 first appeared in 2007, but since then had expanded considerably and is now the most prevalent wild-type cluster found in Quebec. A total of 117 RFLP patterns were identified and those were poorly correlated with genetic clusters based on phylogeny. Factors modulating PRRSV diversity such as pig movement that occurred within and between provinces should be further investigated in a perspective of disease control.

Evaluating an automated clustering approach in a perspective of ongoing surveillance of porcine reproductive and respiratory syndrome virus (PRRSV) field strains

Porcine reproductive and respiratory syndrome virus (PRRSV) has a major economic impact on the swine industry. The important genetic diversity needs to be considered for disease management. In this regard, information on the circulating endemic strains and their dispersal patterns through ongoing surveillance is beneficial. The objective of this project was to classify Quebec PRRSV ORF5 sequences in genetic clusters and evaluate stability of clustering results over a three-year period using an in-house automated clustering system. Phylogeny based on maximum likelihood (ML) was first inferred on 3661 sequences collected in 1998-2013 (Run 1). Then, sequences collected between January 2014 and September 2016 were sequentially added into 11 consecutive runs, each one covering a three-month period. For each run, detection of clusters, which were defined as groups of ≥15 sequences having a≥70% rapid bootstrap support (RBS) value, was automated in Python. Cluster stability was described for each cluster and run based on the number of sequences, RBS value, maximum pairwise distance and agreement in sequence assignment to a specific cluster. First and last run identified 29 and 33 clusters, respectively. In the last run, about 77% of the sequences were classified by the system. Most clusters were stable through time, with sequences attributed to one cluster in Run 1 staying in the same cluster for the 11 remaining runs. However, some initial groups were further subdivided into subgroups with time, which is important for monitoring since one specific wild-type cluster increased from 0% in 2007 to 45% of all sequences in 2016. This automated classification system will be integrated into ongoing surveillance activities, to facilitate communication and decision-making for stakeholders of the swine industry.