1a.Objectives (from AD-416):
The specific objectives of this agreement are to Identify differentially expressed (DE) genes in blood in response to PRRSV infection; Determine putative gene sets and pathways that predict a pig's ability to clear PRRSV infection and maintain weight gain; and Validate utility of gene sets and pathways for prediction of responsiveness to PRRSV infections in multiple populations. Predictive blood tests of pigs with improved PRRS disease resistance and growth maintenance; increased understanding of mechanisms involved in pig responses to PRRSV infection; scientific publications.
1b.Approach (from AD-416):
The university's Data storage will be centralized at ISU via the PRRS Host Genetics Consortium (PHGC) database and for this project the microarray data through ANEXdb. A tool will be developed to accept two-color array data as well as updated annotations for the Pigoligoarray oligonucleotides. For the first objective the microarray data analysis, lists of important DE genes will be annotated using DAVID tools, clustered and drawn into networks to generate informative networks that describe the common response to PRRSV over time in high viral load and low viral load animals. These networks will be further reinforced and expanded using orthogonal data such as cytokine expression and the second objective data. Will overlay the first and second objective networks to potentially clarify modules and hubs responding in vivo, and attempt to understand regulatory networks across datasets. For Classifier development will use all the first and second objective expression data (using gene sets clustered by expression pattern within and across Objectives., separately or in combination with QPCR, cytokine or other phenotypic data) to create and evaluate several classifiers using machine learning algorithms implemented in Weka. Initially, 24-48 genes will be used from each sample using sets of RNAs from PHGC samples representing the 4 different virus/weight categories to construct the classifiers. The performance of the resulting classifiers (as measured by classification accuracy, sensitivity, specificity, and area under ROC curve), will be evaluated using rigorous statistical cross-validation. Genes predicted to be good classifiers will be tested by coPIs using QPCR on additional samples for the iterative refinement process to improve classifier predictions.
By comparing anti-viral responses of resistant versus susceptible pigs an attempt is being made to identify biomarkers expressed by pigs infected with porcine reproductive and respiratory syndrome virus (PRRSV), a major swine pathogen which causes $664 million per year losses to the U.S. pig industry. ARS Researchers at Beltsville, Maryland (BARC) have partnered with Iowa State University (ISU) scientists to identify genes expressed at different levels in resistant versus susceptible pigs. Microarray data were evaluated at several times from blood samples collected as part of the PRRS Host Genetics Consortium (PHGC). To coordinate the PHGC effort data storage has been centralized at ISU (www.animalgenome.org/lunney/). Michigan State University and ISU scientists used sophisticated statistical and bioinformatic tools to generate informative gene networks and biomarkers that differentiate PRRSV response patterns of resistant versus susceptible pigs. Future work will validate, with specific assays, which of these genes and pathways are consistently over- or under-expressed in pigs that exhibit greater resistance to this important virus.