Location: Animal Parasitic Diseases LaboratoryTitle: Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection
|SCHROYEN, MARTINE - IOWA STATE UNIVERSITY|
|STEIBEL, JUAN - MICHIGAN STATE UNIVERSITY|
|CHOI, IG SEO|
|KOLTES, JAMES - IOWA STATE UNIVERSITY|
|EISLEY, CHRIS - IOWA STATE UNIVERSITY|
|FRITZ-WATERS, ERIC - IOWA STATE UNIVERSITY|
|REECY, JAMES - IOWA STATE UNIVERSITY|
|DEKKERS, JACK - IOWA STATE UNIVERSITY|
|ROWLAND, ROBERT - KANSAS STATE UNIVERSITY|
|ERNST, CATHY - MICHIGAN STATE UNIVERSITY|
|TUGGLE, C - IOWA STATE UNIVERSITY|
Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2015
Publication Date: 7/10/2015
Citation: Schroyen, M., Steibel, J.P., Choi, I., Koltes, J.E., Eisley, C., Fritz-Waters, E., Reecy, J., Dekkers, J.C., Rowland, R.R., Lunney, J.K., Ernst, C., Tuggle, C.K. 2015. Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection. Biomed Central (BMC) Genomics. 16:516.
Interpretive Summary: Our goal is to select for pigs which are resistant to Porcine Reproductive and Respiratory Syndrome (PRRS), the most economically important infection threatening pig production worldwide. We used molecular tests and sophisticated analytic programs to find differences in blood from PRRS susceptible versus resistant pigs (based on differences in growth rates and viremia levels after PRRS viral infection). We discovered a cluster of genes whose response pattern correlated with changes in weight gain. These genes can now be used as targets for therapeutics or vaccines for PRRS control.
Technical Abstract: Background Observed variability in pig response to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recently demonstrated genetic control of such responses, suggest that it may be possible to reduce the economic impact of this disease by selecting more disease-resistant pigs. To find underlying physiological and immunological differences between susceptible and resistant pigs, we measured responses in an initial cohort of 600 infected pigs, focusing here on those 100 differing most markedly in their post-infection growth rates and viremia. Microarrays were used to measure expression in RNA extracted from whole blood at 0, 4 and 7 days post infection (dpi). We sought to determine whether gene expression varied with weight gain and viral load phenotypes, and to determine the effect of the WUR10000125 (WUR) genotype, which has been associated with a significant portion of the genetic variability in response to PRRSv infection. Results Linear modeling of blood gene differential expression (DE) provided only limited power to differentiate pigs according to growth or viral load extremes, or between animals with different WUR genotypes. However, network based approaches provided the means to identify molecular pathway differences between extreme classes. Several interesting clusters of genes were found when comparing Weighted Gene Co-expression Network Analysis (WGCNA) at day 4 to baseline. The expression pattern of one such cluster of genes, found to be correlated with weight gain and WUR genotype, contained numerous immune response genes such as cytokines, chemokines, interferon type I stimulated genes, apoptotic genes and genes regulating complement activation. In addition, Partial Correlation and Information Theory (PCIT) found differentially hubbed (DH) genes between the phenotypic divergent groups. GO enrichment revealed that the target genes of the hubs identified by PCIT are enriched in adaptive immune pathways. Conclusion Pigs differing most markedly, in viremia and in growth after infection with PRRSv, exhibit distinct expression patterns of particular genes as measured in the blood. Distinct WUR genotypes are also associated with distinct expression patterns. Differences can be quite subtle, and can escape detection using conventional DE expression analyses. Co-expression, analyses such as WGCNA and PCIT, reveal network differences between such phenotypic extremes.