Epistatic effects on carcass composition and meat quality in pigs

The analysis of epistasis is not yet a routine, but it has been shown by few studies in livestock animals that interaction effects contribute with considerable proportions to the phenotypic variance. Therefore the objective of this study was to evaluate the importance of epistatic effects in the Bonn Duroc × Pietrain resource population (DuPi) for carcass composition and meat quality traits. This population was investigated so far for single quantitative trait loci (QTL) considering additive, dominance and imprinting effects. In the first approach, 585 F2 pigs of DuPi were used to perform a two dimensional QTL scan. All animals were genotyped using 125 genetic markers (microsatellites and SNP) spread across the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F2 animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program. A number of 56 interacting QTL was observed for 19 different traits. These interacting QTL pairs explained up to 8% of the phenotypic variance. Based on these results a variety of networks among chromosomal regions throughout the porcine genome were identified. Moreover, considering interactions between loci allowed to detect several novel QTL and trait-specific relationships of loci within and across chromosomes. In a second step the causes of an epistatic QTL pair between Sus scrofa chomsosome (SSC) 8 and 15 influencing pH value 1 h post mortem in M. long. dorsi were investigated. Gene expression data was obtained from loin tissue of 74 F2 which were selected from 585 animals. Gene expression profiles, genotypes and phenotypes of these pigs were investigated jointly applying three alternative models. Method A considered the phenotypic differences in pH values between groups of pigs with extreme values. Method B was based on differences between the genotype combinations of relevant epistatic QTL pairs between SSC8 and SSC15. Finally, method C was a linear model comprising the epistatic QTL genotypes as fixed effects. Overall method A, B and C revealed 1182, 480 and 1823 differentially expressed or associated genes, respectively. By means of a functional analysis it was possible to set up networks which contained mainly interactions between genes located within the specific regions on SSC8 and SSC15 and allowed a meaningful biological discussion. Expression QTL (eQTL) analyses were performed for functional and positional transcripts in order to assume regulations patterns. This approach showed that combining phenotype, genotype and transcriptome data helped to uncover the involved molecules of observed epistasis. In conclusion, this study revealed the importance of epistasis for the expression of complex traits. Furthermore, it was possible to uncover potential biological causes of observed epistatic QTL pairs applying different statistical models as well as bioinformatic tools.Epistatische Effekte auf die Schlachtkörperzusammensetzung und Fleischqualität beim Schwein Epistasie wird bisher nur selten in Untersuchungen komplexer Merkmale berücksichtigt. Dabei wurde bereits in einer Vielzahl von Studien gezeigt, dass die zu beobachtenden Variationen von quantitativen Merkmalen nicht alleine durch additive Effekte erklärt werden können. Daher war das Ziel dieser Studie, die Bedeutung von epistatischen Effekten auf Schlachtkörper- und Fleischqualitätsmerkmale innerhalb der Bonner Duroc × Piétain Ressourcenpopulation (DuPi) zu untersuchen. Bisherige Studien in der DuPi Population berücksichtigten nur einfache Quantitative Trait Loci (QTL), die additive, Dominanz oder Imprintingeffekte beinhalteten. In der ersten Analyse wurden 585 Schweine der F2-Generation verwendet um epistatische QTL Paare zu identifizieren. Diese Tiere sind mit 125 genetischen Markern genotypisiert worden, die sich gleichmäßig über alle 18 Autosomen verteilten. Als phänotypische Informationen wurden 26 verschiedene Schlachtkörper- und Fleischqualitätsmerkmale erfasst. Die Koppelungsanalyse wurde in einer zweistufigen Prozedur innerhalb des Programms Qxpak, basierend auf einem Maximum Likelihood Ansatzes, durchgeführt. Insgesamt konnten 56 interagierende QTL für 19 verschiedene Merkmale beobachtet werden. Für Schlachtkörpermerkmale konnten 17 und für Fleischqualitätsmerkmale 39 epistatische QTL Paare identifiziert werden. Diese interagierenden QTL Paare erklärten bis zu 8% der phänotypischen Varianz. Auf Grundlage dieser Ergebnisse konnten verschiedene Netzwerkstrukturen zwischen den verschiedenen Chromosomensegmenten identifiziert werden. Die Berücksichtigung der Beziehung zwischen zwei Genorten ermöglichte es einige neue QTL zu identifizieren, sowie merkmalsbezogene Beziehungen innerhalb eines Chromosoms und zwischen Chromosomen zu charakterisieren. In einer zweiten Untersuchung wurde versucht, die biologischen Gründe des epistatischen QTL Paares zwischen den porcinen Chromosomen (SSC) 8 und 15 aufzuklären. Für die Analyse standen die Muskeltranskriptionsprofile von 74 ausgewählten F2 Tieren der DuPi Population zur Verfügung. Die Interaktion zwischen SSC8 und 15 war assoziiert mit früh post mortalem pH Wert im M. long. dorsi. Genexpressionsprofile, Genotypen und Phänotypen dieser Tiere wurden mit drei verschiedenen statistischen Ansätzen und Modellen untersucht. Methode A berücksichtigte phänotypische Unterschiede des pH Wertes zwischen zwei Tiergruppen mit extremen Werten, Methode B basierte auf den Unterschieden zwischen den Genotypgruppen des relevanten epistatischen QTL Paares und Methode C berücksichtigte die Genotypen des epistatischen QTL Paares als fixen Effekt innerhalb eines linearen Modells. Insgesamt ließen sich mit Methode A, B und C 1182 und 480 unterschiedlich exprimierte Gene sowie 1823 linear assoziierte Gene identifizieren. Durch funktionale Analysen war es möglich Netzwerke zu erstellen, die nur Gene beinhalteten, die innerhalb der epistatischen Regionen lagen. Die daraus erzielten Ergebnisse erlaubten eine biologisch sinnvolle Diskussion möglicher Kandidatengene der epistatischen Regionen. Des Weiteren wurden Expressions-QTL Analysen durchgeführt um eine Aussage über die Genregulation zu treffen. Schlussfolgernd konnte gezeigt werden, dass Epistasie eine bedeutende Rolle bei der Ausprägung von komplexen Merkmalen beim Schwein hat. Es war des Weiteren möglich biologische Ursachen beobachteter epistatischer Beziehungen mit Hilfe verschiedener statistischer Methoden zu identifizieren

Deciphering transcriptome profiles of peripheral blood mononuclear cells in response to PRRSV vaccination in pigs

List of DEGs in PBMCs of pigs at 6 hpv of PRRSV vaccination in pigs compared to control. (XLSX 57 kb

Transcriptome profile of lung dendritic cells after in vitro porcine reproductive and respiratory syndrome virus (PRRSV) infection

The porcine reproductive and respiratory syndrome (PRRS) is an infectious disease that leads to high financial and production losses in the global swine industry. The pathogenesis of this disease is dependent on a multitude of factors, and its control remains problematic. The immune system generally defends against infectious diseases, especially dendritic cells (DCs), which play a crucial role in the activation of the immune response after viral infections. However, the understanding of the immune response and the genetic impact on the immune response to PRRS virus (PRRSV) remains incomplete. In light of this, we investigated the regulation of the host immune response to PRRSV in porcine lung DCs using RNA-sequencing (RNA-Seq). Lung DCs from two different pig breeds (Pietrain and Duroc) were collected before (0 hours) and during various periods of infection (3, 6, 9, 12, and 24 hours post infection (hpi)). RNA-Seq analysis revealed a total of 20,396 predicted porcine genes, which included breed-specific differentially expressed immune genes. Pietrain and Duroc infected lung DCs showed opposite gene expression courses during the first time points post infection. Duroc lung DCs reacted more strongly and distinctly than Pietrain lung DCs during these periods (3, 6, 9, 12 hpi). Additionally, cluster analysis revealed time-dependent co-expressed groups of genes that were involved in immune-relevant pathways. Key clusters and pathways were identified, which help to explain the biological and functional background of lung DCs post PRRSV infection and suggest IL-1β1 as an important candidate gene. RNA-Seq was also used to characterize the viral replication of PRRSV for each breed. PRRSV was able to infect and to replicate differently in lung DCs between the two mentioned breeds. These results could be useful in investigations on immunity traits in pig breeding and enhancing the health of pigs

Livestock 2.0 – genome editing for fitter, healthier, and more productive farmed animals

Abstract The human population is growing, and as a result we need to produce more food whilst reducing the impact of farming on the environment. Selective breeding and genomic selection have had a transformational impact on livestock productivity, and now transgenic and genome-editing technologies offer exciting opportunities for the production of fitter, healthier and more-productive livestock. Here, we review recent progress in the application of genome editing to farmed animal species and discuss the potential impact on our ability to produce food

Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment.

The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators

Untersuchungen zur genetischen Prädisposition von Schweinen gegenüber PRRS

Das Porcine Reproduktive und Respiratorische Syndrom (PRRS) ist eine der wichtigsten viralen Erkrankungen in der weltweiten Schweineindustrie (Balasuriya 2013). Der ätiologische Erreger ist das PRRS Virus (PRRSV) (Balasuriya 2013, Conzelmann et al. 1993). Die Einflussnahme von genetischen Elementen und Funktionen auf die Immunreaktion post PRRSV ist noch unklar. Hauptziele dieser Studie sind, das globale Transkriptomprofil von PRRSV infizierten Lungen-DCs mittels RNA-Sequenzierung (RNA-Seq) von zwei unterschiedlichen Schweinerassen (Piétrain und Duroc) zu charakterisieren und Gründe für die ineffektive Immunreaktion von Schweinen post PRRSV Infektion als auch Veränderungen im Expressionsprofil von unterschiedlichen respiratorischen Zellen nach der Virusinfektion zu ermitteln. Für das in vitro Infektionsmodell wurden sechs weibliche, 30 Tage alte Ferkel von zwei unterschiedlichen Schweinerassen (Piétrain und Duroc) ausgewählt. Alveolarmakrophagen (PAMs), dendritische Zellen (Lungen-DCs) und Epithelzellen aus der Trachea wurden von allen Versuchstieren isoliert. Die Zellcharakterisierung der PAMs und der Lungen-DCs erfolgte mittels der Durchflusszytometrie und die der Tracheaepithelzellen mittels Immunfluoreszenz Assay. Alle respiratorischen Zellen wurden mit dem europäischen PRRSV Stamm Lelystad Virus (LV) infiziert. Nicht-infizierte (0 h) und infizierte (3, 6, 9, 12 und 24 hpi) Lungen-DCs, PAMs und Trachea-Epithelzellen wurden gesammelt. Nach der RNA Isolierung folgte die Qualitätsbestimmung der RNA mittels Bioanalyzer. Zur RNA-Seq wurden nicht-infizierte (0 h) und infizierte (3, 6, 9, 12 und 24 hpi) Lungen-DCs beider Schweinerassen eingesetzt. Das Sequenz-Alignment erfolgte mit dem aktuellen Referenzgenombild Suscrofa 10.2 und mit dem kompletten Genom des LV Stammes. Die Transkriptom-Analyse von PRRSV infizierten Piétrain und Duroc Lungen-DCs charakterisierte 20.396 porcine Gentranskripte. Durch die globale Transkriptomanalyse wurden frühe, temporal verschiedene und konträre Immunreaktionen in PRRSV infizierten Lungen-DCs (Piétrain & Duroc) identifiziert. Das Virus-Sequenz-Alignment zeigte, dass der LV Stamm sowohl Piétrain und Duroc Lungen-DCs infizieren als auch sich dort replizieren kann. Nach der PRRSV Infektion konnten Rassenunterschiede (Piétrain und Duroc) in der Reaktion auf eine PRRSV Infektion festgestellt werden, sowohl beim Viruswachstum als auch in identifizierten mRNA Expressionsprofilen und bei den unterschiedlich exprimierten Genen. Zudem konnten zellspezifischen Reaktionsunterschiede auf PRRSV in den verschiedenen respiratorischen Zelltypen (Lungen-DCs, PAMs und Trachea-Epithelzellen) durch die Expressionsprofil-Analyse der selektierten Kandidatengene charakterisiert werden. Zusätzlich wurden 37 Cluster für Piétrain, 35 für Duroc sowie entscheidende Schlüssel- Cluster und Schlüssel-Signalwege identifiziert.The porcine reproductive and respiratory syndrome (PRRS) is one of the most important viral diseases of the swine industry worldwide (Balasuriya 2013). Its aetiological agent is the PRRS virus (PRRSV) (Balasuriya 2013, Conzelmann et al. 1993). The understanding of the genetic elements and functions, involved in the immune response to PRRSV, remains still unclear. The main objectives of this study are to characterize the global transcriptome profile of PRRSV infected lung DCs, by using the RNA-Sequencing (RNA-Seq), to identify causes of the ineffective immune response to PRRSV as well as to determine changes in the expression profile in different respiratory cells post PRRSV infection. For the in vitro investigations six female 30 days old piglets of two different porcine breeds (Pietrain and Duroc) were selected, pulmonary alveolar macrophages (PAMs), lung dendritic cells (lung DCs) and trachea epithelial cells were isolated and infected with the European prototype PRRSV strain Lelystad virus (LV). The cell characterization of the lung DCs and PAMs was carried out by using a flow cytometric analysis, for trachea-epithelial cells the cell characterization was done by an immunofluorescence assay. Non-infected (0 h) and infected (3, 6, 9, 12 and 24 hpi) lung DCs, PAMs and trachea epithelial cells were collected. After the RNA isolation the RNA quality was measured by Bioanalyzer. Non-infected (0 h) and infected (3, 6, 9, 12 and 24 hpi) lung DCs of both breeds were used for RNA-Seq. The sequence alignment was done with the reference genome build Suscrofa 10.2 and with the complete genome of LV strain. The transcriptome analysis of PRRSV infected lung DCs of Pietrain and Duroc resulted in an amount of 20,396 porcine predicted gene transcripts. Through the global transcriptome analysis early temporal, different and contrary immune reactions in PRRSV infected lung DCs (Pietrain & Duroc) were identified. The virus sequence alignment exhibited that the LV strain was able to infect Pietrain and Duroc lung DCs and to replicate there. Not only breeddifferences (Pietrain und Duroc) post PRRSV infection in the virus growth, also breeddifferences in the detected mRNA expression profiles and in the differently expressed genes were identified. Beside these breed-dependent differences, respiratory cell-type (lung DCs, PAMs, trachea-epithelial cells) dependent differences in the response to PRRSV were characterized. 37 clusters for Pietrain and 35 clusters for Duroc as well as important key clusters and key pathways were identified

Endocrine Fertility Parameters—Genomic Background and Their Genetic Relationship to Boar Taint in German Landrace and Large White

The surgical castration of young male piglets without anesthesia is no longer allowed in Germany from 2021. One alternative is breeding against boar taint, but shared synthesis pathways of androstenone (AND) and several endocrine fertility parameters (EFP) indicate a risk of decreasing fertility. The objective of this study was to investigate the genetic background between AND, skatole (SKA), and six EFP in purebred Landrace (LR) and Large White (LW) populations. The animals were clustered according to their genetic relatedness because of their different origins. Estimated heritabilities (h2) of AND and SKA ranged between 0.52 and 0.34 in LR and LW. For EFP, h2 differed between the breeds except for follicle-stimulating hormone (FSH) (h2: 0.28–0.37). Both of the breeds showed unfavorable relationships between AND and testosterone, 17-β estradiol, and FSH. The genetic relationships (rg) between SKA and EFP differed between the breeds. A genome-wide association analysis revealed 48 significant associations and confirmed a region for SKA on Sus Scrofa chromosome (SSC) 14. For EFP, the results differed between the clusters. In conclusion, rg partly confirmed physiologically expected antagonisms between AND and EFP. Particular attention should be spent on fertility traits that are based on EFP when breeding against boar taint to balance the genetic progress in both of the trait complexes

Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes