1a.Objectives (from AD-416):
The specific objectives will be. 1)Identify differentially expressed (DE) genes in blood in response to PRRSV infection;. 2)Determine putative gene sets and pathways that predict a pig’s ability to clear PRRSV infection and maintain weight gain; and. 3)Validate utility of gene sets and pathways for prediction of responsiveness to PRRSV infections in multiple populations.
1b.Approach (from AD-416):
The proposed studies will identify functional genomic determinants, and potential “classifier genes” that predict resistance/susceptibility of commercial U.S. swine to PRRSV infection and associated growth losses. Such gene-specific information will directly complement the genetic variation and QTL mapping data being developed in the PHGC, to provide an integrated view of the molecular genetic architecture of PRRSV resistance. All information will be disseminated to swine researchers and breeders, as well as genetics companies and genotyping services so that these recommended genes can be targeted in future breeding programs to increase resistance to PRRS, the most economically important disease affecting the U.S. pork industry.
By comparing anti-viral responses of resistant versus susceptible pigs we are identifying 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 Michigan State University (MSU), Iowa State University (ISU), Washington State University and Purdue University scientists to identify genes expressed at different levels in resistant versus susceptible pigs. Microarray data was evaluated at several timepoints 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 at BARC, Purdue and WSU, which of these genes and pathways are consistently over- or under-expressed in pigs that exhibit greater resistance to this important virus. As this work progresses systems biology based analyses will be used to develop predictive gene expression pathways and classifier genes that identify pigs which resist PRRSV infection and grow normally.