Quantitative Analysis of Porcine Reproductive and Respiratory Syndrome (PRRS) Viremia Profiles from Experimental Infection: A Statistical Modelling Approach

• Published in: PloS one Vol. 8; no. 12; p. e83567
• Main Authors: Islam, Zeenath U, Bishop, Stephen C, Savill, Nicholas J, Rowland, Raymond R. R, Lunney, Joan K, Trible, Benjamin, Doeschl-Wilson, Andrea B, Meng, Xiang-Jin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.12.2013; Public Library of Science (PLoS)
• Subjects: Analysis; Animal lactation; Animals; Antibodies, Viral - metabolism; Antibody Formation; Bayes Theorem; Bayesian analysis; Bayesian theory; Biological models (mathematics); Biology; Blood; control methods; cross immunity; Cross-protection; data collection; Disease control; Disease Progression; Experimental infection; Functions (mathematics); Growth models; Hogs; Homology; hosts; Immunology; Infection; Infections; Livestock; Mathematical models; Medical research; Models, Animal; Models, Statistical; Mutation; neutralizing antibodies; Parameter estimation; Porcine reproductive and respiratory syndrome; Porcine Reproductive and Respiratory Syndrome - blood; Porcine Reproductive and Respiratory Syndrome - immunology; Porcine Reproductive and Respiratory Syndrome - virology; Porcine reproductive and respiratory syndrome virus; Porcine respiratory and reproductive syndrome virus - physiology; Pork industry; PRRS; PRRS virus; Quantitative analysis; Statistical analysis; Statistical inference; Statistical modelling; Statistical models; Swine; Swine Diseases - immunology; Swine Diseases - virology; Vaccines; Veterinary colleges; Veterinary medicine; Viral diseases; Viremia; Viremia - immunology; Viremia - pathology; Virology; Virus Replication; Viruses; Scotland; Edinburgh Scotland; Kansas; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0083567

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically significant viral diseases facing the global swine industry. Viremia profiles of PRRS virus challenged pigs reflect the severity and progression of infection within the host and provide crucial information for subsequent control measures. In this study we analyse the largest longitudinal PRRS viremia dataset from an in-vivo experiment. The primary objective was to provide a suitable mathematical description of all viremia profiles with biologically meaningful parameters for quantitative analysis of profile characteristics. The Wood's function, a gamma-type function, and a biphasic extended Wood's function were fit to the individual profiles using Bayesian inference with a likelihood framework. Using maximum likelihood inference and numerous fit criteria, we established that the broad spectrum of viremia trends could be adequately represented by either uni- or biphasic Wood's functions. Three viremic categories emerged: cleared (uni-modal and below detection within 42 days post infection(dpi)), persistent (transient experimental persistence over 42 dpi) and rebound (biphasic within 42 dpi). The convenient biological interpretation of the model parameters estimates, allowed us not only to quantify inter-host variation, but also to establish common viremia curve characteristics and their predictability. Statistical analysis of the profile characteristics revealed that persistent profiles were distinguishable already within the first 21 dpi, whereas it is not possible to predict the onset of viremia rebound. Analysis of the neutralizing antibody(nAb) data indicated that there was a ubiquitous strong response to the homologous PRRSV challenge, but high variability in the range of cross-protection of the nAbs. Persistent pigs were found to have a significantly higher nAb cross-protectivity than pigs that either cleared viremia or experienced rebound within 42 dpi. Our study provides novel insights into the nature and degree of variation of hosts' responses to infection as well as new informative traits for subsequent genomic and modelling studies.