Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding

BackgroundSalmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonise the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively few have used correlation of shedding traits with gene expression patterns to identify genes whose variable expression among different individuals may be associated with differences in Salmonella clearance and resistance. Here, we aimed to identify porcine genes and gene co-expression networks that differentiate distinct responses to Salmonella challenge with respect to faecal Salmonella shedding.ResultsPeripheral blood transcriptome profiles from 16 pigs belonging to extremes of the trait of faecal Salmonella shedding counts recorded up to 20 days post-inoculation (low shedders (LS), n = 8; persistent shedders (PS), n = 8) were generated using RNA-sequencing from samples collected just before (day 0) and two days after (day 2) Salmonella inoculation. Weighted gene co-expression network analysis (WGCNA) of day 0 samples identified four modules of co-expressed genes significantly correlated with Salmonella shedding counts upon future challenge. Two of those modules consisted largely of innate immunity related genes, many of which were significantly up-regulated at day 2 post-inoculation. The connectivity at both days and the mean gene-wise expression levels at day 0 of the genes within these modules were higher in networks constructed using LS samples alone than those using PS alone. Genes within these modules include those previously reported to be involved in Salmonella resistance such as SLC11A1 (formerly NRAMP1), TLR4, CD14 and CCR1 and those for which an association with Salmonella is novel, for example, SIGLEC5, IGSF6 and TNFSF13B.ConclusionsOur analysis integrates gene co-expression network analysis, gene-trait correlations and differential expression to provide new candidate regulators of Salmonella shedding in pigs. The comparatively higher expression (also confirmed in an independent dataset) and the significantly higher connectivity of genes within the Salmonella shedding associated modules in LS compared to PS even before Salmonella challenge may be factors that contribute to the decreased faecal Salmonella shedding observed in LS following challenge.

[1]  Daniel R. Zerbino,et al.  Ensembl 2014 , 2013, Nucleic Acids Res..

[2]  J. Lunney,et al.  Genetic control of host resistance to porcine reproductive and respiratory syndrome virus (PRRSV) infection. , 2010, Virus research.

[3]  P. Wigley Genetic resistance to Salmonella infection in domestic animals. , 2004, Research in veterinary science.

[4]  John C. Marioni,et al.  Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection , 2012, Proceedings of the National Academy of Sciences.

[5]  M. Wick Innate Immune Control of Salmonella enterica Serovar Typhimurium: Mechanisms Contributing to Combating Systemic Salmonella Infection , 2011, Journal of Innate Immunity.

[6]  G. Salvat,et al.  Salmonella carrier-state in hens: study of host resistance by a gene expression approach. , 2006, Microbes and infection.

[7]  M. Campbell,et al.  PANTHER: a library of protein families and subfamilies indexed by function. , 2003, Genome research.

[8]  M. Begon,et al.  Genetic Diversity in Cytokines Associated with Immune Variation and Resistance to Multiple Pathogens in a Natural Rodent Population , 2011, PLoS genetics.

[9]  V. Pascual,et al.  Assessing the human immune system through blood transcriptomics , 2010, BMC Biology.

[10]  S. Horvath,et al.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks , 2006, BMC Genomics.

[11]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[12]  P. Wigley,et al.  In vivo and in vitro studies of genetic resistance to systemic salmonellosis in the chicken encoded by the SAL1 locus. , 2002, Microbes and infection.

[13]  F. Jiggins,et al.  Genetic variation in Drosophila melanogaster pathogen susceptibility , 2006, Parasitology.

[14]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[15]  Eleazar Eskin,et al.  Gene networks associated with conditional fear in mice identified using a systems genetics approach , 2011, BMC Systems Biology.

[16]  A. Vignal,et al.  Genetic control of resistance to salmonellosis and to Salmonella carrier-state in fowl: a review , 2010, Genetics Selection Evolution.

[17]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[18]  J. Steibel,et al.  Characterizing differential individual response to porcine reproductive and respiratory syndrome virus infection through statistical and functional analysis of gene expression , 2013, Front. Gene..

[19]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[20]  E. Birney,et al.  Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.

[21]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[22]  J. Lunney,et al.  Porcine differential gene expression in response to Salmonella enterica serovars Choleraesuis and Typhimurium. , 2007, Molecular immunology.

[23]  Rui Luo,et al.  Is My Network Module Preserved and Reproducible? , 2011, PLoS Comput. Biol..

[24]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[25]  A. Kommadath,et al.  MicroRNA Buffering and Altered Variance of Gene Expression in Response to Salmonella Infection , 2014, PloS one.

[26]  Dan Nettleton,et al.  Distinct Peripheral Blood RNA Responses to Salmonella in Pigs Differing in Salmonella Shedding Levels: Intersection of IFNG, TLR and miRNA Pathways , 2011, PloS one.

[27]  M. van Schriek,et al.  Mapping markers linked to porcine salmonellosis susceptibility. , 2009, Animal genetics.

[28]  Lior Pachter,et al.  Sequence Analysis , 2020, Definitions.

[29]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[30]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[31]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[32]  J. Dekkers,et al.  Use of bioinformatic SNP predictions in differentially expressed genes to find SNPs associated with Salmonella colonization in swine. , 2011, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[33]  D. Maes,et al.  Non-typhoidal Salmonella infections in pigs: a closer look at epidemiology, pathogenesis and control. , 2008, Veterinary microbiology.

[34]  A. Clark,et al.  Genetic Basis of Natural Variation in D. melanogaster Antibacterial Immunity , 2004, Science.

[35]  Ross Lazarus,et al.  Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes , 2011, BMC Systems Biology.

[36]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[37]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[38]  D. Monack,et al.  Shedding light on Salmonella carriers. , 2012, Trends in microbiology.

[39]  Hiroaki Kitano,et al.  The PANTHER database of protein families, subfamilies, functions and pathways , 2004, Nucleic Acids Res..

[40]  P. Gros,et al.  Macrophage NRAMP1 and its role in resistance to microbial infections , 1998, Inflammation Research.

[41]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[42]  Lucinda K. Southworth,et al.  Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules , 2009, PLoS genetics.

[43]  A. Kommadath,et al.  Gene coexpression network analysis identifies genes and biological processes shared among anterior pituitary and brain areas that affect estrous behavior in dairy cows. , 2013, Journal of dairy science.

[44]  J. Estellé,et al.  The peripheral blood transcriptome reflects variations in immunity traits in swine: towards the identification of biomarkers , 2013, BMC Genomics.

[45]  Maria Keays,et al.  ArrayExpress update—trends in database growth and links to data analysis tools , 2012, Nucleic Acids Res..

[46]  Y. Kato,et al.  Deletion polymorphism of SIGLEC14 and its functional implications. , 2009, Glycobiology.

[47]  Bronwen L. Aken,et al.  Analyses of pig genomes provide insight into porcine demography and evolution , 2012, Nature.

[48]  D. Malo,et al.  Genetic regulation of host responses to Salmonella infection in mice , 2002, Genes and Immunity.

[49]  D. Nettleton,et al.  Correlating blood immune parameters and a CCT7 genetic variant with the shedding of Salmonella enterica serovar Typhimurium in swine. , 2009, Veterinary microbiology.

[50]  S. Lamont,et al.  Analyses of Five Gallinacin Genes and the Salmonella enterica Serovar Enteritidis Response in Poultry , 2006, Infection and Immunity.

[51]  Peter Langfelder,et al.  When Is Hub Gene Selection Better than Standard Meta-Analysis? , 2013, PloS one.

[52]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[53]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.