IDENTIFICATION OF GENE NETWORKS AND CLASSIFIER GENES INVOLVED IN PIG RESPONSES TO PRRSV INFECTION AND GROWTH MAINTENANCE
Animal Parasitic Diseases
2011 Annual Report
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.
This Agreement reports functional genomic analyses to determine response pathways that differ in porcine respiratory and the reproductive syndrome (PRRS) resistant versus susceptible PRRS Host Genetics Consortium (PHGC) pigs. ARS Researchers at Beltsville, MD have partnered with Iowa State University (ISU) scientists to use PHGC samples to assess whole blood gene expression responses to identify genes and pathways that are associated with pigs that clear PRRS virus (PRRSV) and that grow well despite PRRSV infection. Data storage will be centralized at ISU via the PHGC database with the microarray data being stored through the gene expression based ANEXdb. Michigan State University scientists have performed the first series of Pigoligoarrays and biostatistical analyses are underway. Detailed gene expression analyses will be done at ISU in the coming year using sophisticated statistical tools analyses to generate informative networks and identify classifier genes that differentiate the PRRSV response patterns in pigs representing different virus/weight categories. As this work progresses we expect to develop predictive gene expression pathways and classifier genes that identify pigs which resist PRRSV infection and grow normally. (NP103 2c)
This project was monitored through regular email and phone contact, scheduled conference calls, and a yearly Consortium meeting with the participating labs discussing project plans, experimental design, and reviewing data and presentation options.