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Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection.

Schroyen M, Steibel JP, Koltes JE, Choi I, Raney NE, Eisley C, Fritz-Waters E, Reecy JM, Dekkers JC, Rowland RR, Lunney JK, Ernst CW, Tuggle CK - BMC Genomics (2015)

Bottom Line: Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data.GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways.Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Science, Iowa State University, Ames, IA, USA. schroyen@iastate.edu.

ABSTRACT

Background: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined.

Results: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data. The expression pattern of one such cluster of genes 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) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways.

Conclusion: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

No MeSH data available.


Related in: MedlinePlus

GO term enrichment for correlates of hub genes that contrast Des coef and WUR genotype. These hub genes are the top 10 genes with the most extreme wiring between Des coef (a and b) and WUR genotype (c and d) groups according to the PCIT analyses. On the Y axis, the average fold change of the correlates belonging to a GO term is shown. Colors represent specific GO term groups as shown in the legend
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Fig6: GO term enrichment for correlates of hub genes that contrast Des coef and WUR genotype. These hub genes are the top 10 genes with the most extreme wiring between Des coef (a and b) and WUR genotype (c and d) groups according to the PCIT analyses. On the Y axis, the average fold change of the correlates belonging to a GO term is shown. Colors represent specific GO term groups as shown in the legend

Mentions: A PCIT analysis explores differences in connectivity strength between gene expression patterns in two contrasting groups of animals, as measured by connections drawn between genes in a correlation network. Genes shown to have significant differences in connections between groups with a distinct set of genes are called hub genes and could identify a difference in gene regulation between these groups. In this study, the phenotypic contrasts provided to PCIT were Hg versus Lg, Hv versus Lv, High Des coef (>0.5) versus Low Des coef (<0.5), and AA versus AB WUR genotype. Additional file 8: Table S5 shows for each contrast the top 10 differentially connected hub genes, their annotation and the difference in number of correlates between the two contrasts. For these PCIT analyses, Information Theory was used to determine the significance of correlation in these PCIT analyses. This approach considers the total number of correlations calculated for the entire dataset and only the partial correlation values that achieve the significance threshold for the entire dataset are retained to define the connectivity between a target and hub gene. To explore the effect of these differences, all correlates of the 10 extreme DH genes were combined and the resulting gene list examined for GO term enrichment. In Fig. 5a, significant GO annotation terms are shown for the correlates that were only present in the Lv group or more strongly connected to at least one of the top 10 extreme hub genes in the Lv group when compared to the Hv group. The height of the bars is the average log2 fold change between 4dpi and 0dpi of all correlates annotated with the respective GO term. Figure 5b shows the enriched annotation of correlates of the hub genes in the Hv group. Several immune-related pathways were significantly over-represented in the Lv group compared to the Hv group. Similar results for GO terms of correlates tightly connected to the Lg and Hg group are shown in Fig. 5c and d, with more enrichment of immune-related pathways in the Lg group compared to the Hg group. Animals with a low Des coef (n = 34) were contrasted with the animals with a high Des coef (n = 38) in Fig. 6a and b. The GO terms enriched for the correlates of the top 10 hub genes in AA and AB animals is shown in Fig. 6c and d. For these last two contrasts, GO terms did not reveal major correlation network differences between more and less favorable animals.Fig. 5


Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection.

Schroyen M, Steibel JP, Koltes JE, Choi I, Raney NE, Eisley C, Fritz-Waters E, Reecy JM, Dekkers JC, Rowland RR, Lunney JK, Ernst CW, Tuggle CK - BMC Genomics (2015)

GO term enrichment for correlates of hub genes that contrast Des coef and WUR genotype. These hub genes are the top 10 genes with the most extreme wiring between Des coef (a and b) and WUR genotype (c and d) groups according to the PCIT analyses. On the Y axis, the average fold change of the correlates belonging to a GO term is shown. Colors represent specific GO term groups as shown in the legend
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4496889&req=5

Fig6: GO term enrichment for correlates of hub genes that contrast Des coef and WUR genotype. These hub genes are the top 10 genes with the most extreme wiring between Des coef (a and b) and WUR genotype (c and d) groups according to the PCIT analyses. On the Y axis, the average fold change of the correlates belonging to a GO term is shown. Colors represent specific GO term groups as shown in the legend
Mentions: A PCIT analysis explores differences in connectivity strength between gene expression patterns in two contrasting groups of animals, as measured by connections drawn between genes in a correlation network. Genes shown to have significant differences in connections between groups with a distinct set of genes are called hub genes and could identify a difference in gene regulation between these groups. In this study, the phenotypic contrasts provided to PCIT were Hg versus Lg, Hv versus Lv, High Des coef (>0.5) versus Low Des coef (<0.5), and AA versus AB WUR genotype. Additional file 8: Table S5 shows for each contrast the top 10 differentially connected hub genes, their annotation and the difference in number of correlates between the two contrasts. For these PCIT analyses, Information Theory was used to determine the significance of correlation in these PCIT analyses. This approach considers the total number of correlations calculated for the entire dataset and only the partial correlation values that achieve the significance threshold for the entire dataset are retained to define the connectivity between a target and hub gene. To explore the effect of these differences, all correlates of the 10 extreme DH genes were combined and the resulting gene list examined for GO term enrichment. In Fig. 5a, significant GO annotation terms are shown for the correlates that were only present in the Lv group or more strongly connected to at least one of the top 10 extreme hub genes in the Lv group when compared to the Hv group. The height of the bars is the average log2 fold change between 4dpi and 0dpi of all correlates annotated with the respective GO term. Figure 5b shows the enriched annotation of correlates of the hub genes in the Hv group. Several immune-related pathways were significantly over-represented in the Lv group compared to the Hv group. Similar results for GO terms of correlates tightly connected to the Lg and Hg group are shown in Fig. 5c and d, with more enrichment of immune-related pathways in the Lg group compared to the Hg group. Animals with a low Des coef (n = 34) were contrasted with the animals with a high Des coef (n = 38) in Fig. 6a and b. The GO terms enriched for the correlates of the top 10 hub genes in AA and AB animals is shown in Fig. 6c and d. For these last two contrasts, GO terms did not reveal major correlation network differences between more and less favorable animals.Fig. 5

Bottom Line: Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data.GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways.Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Science, Iowa State University, Ames, IA, USA. schroyen@iastate.edu.

ABSTRACT

Background: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined.

Results: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data. The expression pattern of one such cluster of genes 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) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways.

Conclusion: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

No MeSH data available.


Related in: MedlinePlus