Project description:Immune traits (ITs) are potentially relevant criteria to characterize individualM-bM-^@M-^Ys immunocompetence. Thus, porcine ITs related to innate and adaptive immunity were studied by functional genomics approaches, with no initial focus on resistance to specific pathogens. Peripheral blood transcriptome was analysed in 60 days old Large White pigs (N=445) 3 weeks after vaccination against Mycoplasma hyopneumoniae. Groups of 4 to 10 animals classified in the extreme tails of CD4-/CD8+and TCRM-NM-3M-NM-4+ cell counts, phagocytosis, in vitro production of IL2, IL10, TNF and IFNG, and anti-Mycoplasma antibodies distributions were selected for transcriptome studies with a porcine generic array enriched with immunity-related genes (SLA-RI/NRSP8-13K). Among the ITs studied, transcriptome analysis revealed differentially expressed genes for CD4-/CD8+cell counts, phagocytosis, and in vitro production of IL2 and IL10. A subset of these genes was confirmed by real time qPCR. Gene set enrichment analysis showed a significant over-representation of immune response functions. An independent set of 74 no overlapping animals was employed for validation and a total of 5 potential gene biomarkers were found for prediction of immunocompetence. These biomarkers performed with 79% sensitivity (95% CI 61% to 97%) and 86% specificity (95% CI 72% to 100%). Considering the observed transcriptome differences in animals with extreme ITs levels, we conclude that gene expression profiling appears promising as a tool for biological monitoring of genetic variance in pigs. Test set: Two-condition experiment (High vs Low) for a total of 8 ITS. Three ITs were measured from total blood and referred to as in vivo ITs. They included the seric levels of IgG directed against Mycoplasma hyopneumoniae (IgG-Mh), and the cell counts for M-NM-1M-NM-2 T lymphocytes CD4- CD8+ (CD4- CD8+, or M-NM-3M-NM-4 T lymphocytes (TCRM-NM-3M-NM-4+)) . Additionally, five ITs were measured after in vitro tests, and were further referred to as in vitro ITs. They include the phagocytosis capacity (PHAG), and the in vitro production of TNFM-NM-1, IFNM-NM-3, IL2 and IL10 after in vitro stimulation of total blood diluted five times. For each IT included in the present report, pigs were selected at the higher and lower 10% of the Gaussian distribution in order to generate two extreme groups defined as High (H) and Low (L). Groups of 4 to 10 animals classified in the extreme tails of different ITs were selected. A two-channel microarray experiments were carried out using a common reference hybridization design. The standard reference RNA sample was prepared by pooling of total RNAs from different porcine tissues. As the reference is common to all the arrays, this design allowed an indirect comparison between the conditions of interest (H vs L groups). Additionally, in order to provide potential gene biomarkers to produce new insight into pig immunocompetence, the top 50 genes most differentially expressed genes when comparing H vs L groups in CD4-/CD8+ cell counts, phagocytosis, and in vitro production of IL2 and IL10 phenotypes were assessed across 74 animals (experimental animals non included) that were analyzed with the same SLA-RI/NRSP8-13K microarray. Validation set: A total of 74 animals were analyzed in order to validate potential gene biomarkers found in the following immune traits: CD4- CD8+ lymphocyte counts, phagocytosis capacity, IL2 and IL10 production. As no differentially gene expressions were detected between animals with High and Low levels of gamma delta T lymphocyte counts, IgG-Mh, and in vitro production of TNF-alpha or IFN-gamma immune traits, some of them were used in the validation set (n=50). None of experimental animals with extreme CD4- CD8+ lymphocyte counts, phagocytosis capacity, IL2 and IL10 production levels were included were included in the validation set.

Project description: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 60K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so, additionally, the effect of the WUR10000125 (WUR) genotype was examined. 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 4dpi contrasted with 0dpi 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. 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. three timepoints from 100 animals are hibridized following a blocked reference design; Blood gene expression of pigs 4 days post infection

Project description: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 60K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so, additionally, the effect of the WUR10000125 (WUR) genotype was examined. 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 4dpi contrasted with 0dpi 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. 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. three timepoints from 100 animals are hibridized following a blocked reference design; Blood gene expression of pigs 7 days post infection

Project description: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 60K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so, additionally, the effect of the WUR10000125 (WUR) genotype was examined. 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 4dpi contrasted with 0dpi 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. 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.

Project description:In pigs, adipose tissue is one of the principal organs involved in the regulation of lipid metabolism. It is particulary involved in the overall fatty acid synthesis with consequences in other lipid-target organs such as muscles and the liver. With this in mind, we have used massive, parallel high-throughput sequencing technologies to characterize the porcine adipose tissue transcriptome architecture in six Iberian x Landrace crossbred pigs showing extreme phenotypes for intramuscular fatty acid composition (three per group). High-throughput RNA sequencing was used to generate a whole characterization of adipose tissue (backfat) transcriptome. A total of 4,130 putative unannotated protein-coding sequences were identified in the 20% of reads which mapped in intergenic regions. Furthermore, 36% of the unmapped reads were represented by interspersed repeats, SINEs being the most abundant elements. Differential expression analyses identified 396 candidate genes among divergent animals for intramuscular fatty acid composition. Sixty-two percent of these genes (247/396) presented higher expression in the group of pigs with higher content of intramuscular SFA and MUFA, while the remaining 149 showed higher expression in the group with higher content of PUFA. Pathway analysis related these genes to biological functions and canonical pathways controlling lipid and fatty acid metabolisms. In concordance with the phenotypic classification of animals, the major metabolic pathway differentially modulated between groups was de novo lipogenesis, the group with more PUFA being the one that showed lower expression of lipogenic genes. These results will help in the identification of genetic variants at loci that affect fatty acid composition traits. The implications of these results range from the improvement of porcine meat quality traits to the application of the pig as an animal model of human metabolic diseases. The supplementary files contains the five arrays reported in the paper. Backfat from five animals were assayed with high-density oligonucleotide microarray chips (GeneChipM-BM-. Porcine) from Affymetrix, in order to validate RNA-Seq data.

Project description:In pigs, adipose tissue is one of the principal organs involved in the regulation of lipid metabolism. It is particulary involved in the overall fatty acid synthesis with consequences in other lipid-target organs such as muscles and the liver. With this in mind, we have used massive, parallel high-throughput sequencing technologies to characterize the porcine adipose tissue transcriptome architecture in six Iberian x Landrace crossbred pigs showing extreme phenotypes for intramuscular fatty acid composition (three per group). High-throughput RNA sequencing was used to generate a whole characterization of adipose tissue (backfat) transcriptome. A total of 4,130 putative unannotated protein-coding sequences were identified in the 20% of reads which mapped in intergenic regions. Furthermore, 36% of the unmapped reads were represented by interspersed repeats, SINEs being the most abundant elements. Differential expression analyses identified 396 candidate genes among divergent animals for intramuscular fatty acid composition. Sixty-two percent of these genes (247/396) presented higher expression in the group of pigs with higher content of intramuscular SFA and MUFA, while the remaining 149 showed higher expression in the group with higher content of PUFA. Pathway analysis related these genes to biological functions and canonical pathways controlling lipid and fatty acid metabolisms. In concordance with the phenotypic classification of animals, the major metabolic pathway differentially modulated between groups was de novo lipogenesis, the group with more PUFA being the one that showed lower expression of lipogenic genes. These results will help in the identification of genetic variants at loci that affect fatty acid composition traits. The implications of these results range from the improvement of porcine meat quality traits to the application of the pig as an animal model of human metabolic diseases. The supplementary files contains the five arrays reported in the paper.

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The peripheral blood transcriptome reflects variations in immunity traits in swine: toward identification of biomarkers

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