Statistical modelling and analysis of the infection dynamics of PRRSV in vivo infections

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 unior 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. Citation: Islam ZU, Bishop SC, Savill NJ, Rowland RRR, Lunney JK, et al. (2013) Quantitative Analysis of Porcine Reproductive and Respiratory Syndrome (PRRS) Viremia Profiles from Experimental Infection: A Statistical Modelling Approach. PLoS ONE 8(12): e83567. doi:10.1371/journal.pone. 0083567 Editor: Xiang-Jin Meng, Virginia Polytechnic Institute and State University, United States of America Received September 17, 2013; Accepted November 13, 2013; Published December 17, 2013 Copyright: 2013 Islam et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The development of the model fitting code was supported by a Wellcome Trust Project grant to NJS (Grant No. 091078/Z/09/Z, http://www.wellcome. ac.uk/). The characterization of neutralizing antibody responses to PRRSV and association with host factors was supported by the NPB (Grant No. 12–120, http:// www.pork.org/filelibrary/ResearchGrants/Funded2012.pdf). Funding for the project also came from "Genome Canada: Application of Genomics to Improve Swine Health and Welfare" (http://www.swineimprovement.com/) and "CSHB: Development of genetic selection tools to enhance sow health using a novel acclimation challenge model in Canadian commercial herds" (http://www.swinehealth.ca/program-research-proj-11.php). This work was supported by the Roslin Institute Strategic Grant funding from the BBSRC (ISPG 1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors received funding from a commercial source, Genome Canada, and are not related to the organisation in the form of: employment, consultancy, patents, products in development, or marketed products. This source of funding does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. * E-mail: zeenath.islam@roslin.ed.ac.uk