Dataset Information

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

ABSTRACT: BACKGROUND: Salmonella 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. RESULTS: Peripheral 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. CONCLUSIONS: Our 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.

SUBMITTER: Kommadath A

PROVIDER: S-EPMC4070558 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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

Kommadath Arun A Bao Hua H Arantes Adriano S AS Plastow Graham S GS Tuggle Christopher K CK Bearson Shawn M D SM Guan Le Luo le L Stothard Paul P

BMC genomics 20140609

<h4>Background</h4>Salmonella 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 difference ...[more]

PMID: 24912583

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Project description:BackgroundFat deposition is an important economic trait in livestock and poultry production. However, the relationship between various genes and signal pathways of fat deposition is still unclear to a large extent. The purpose of this study is to analyze the potential molecular targets and related molecular pathways in bovine subcutaneous adipose tissue.ResultsWe downloaded the GSE116775 microarray dataset from Gene Expression Omnibus (GEO). The weighted gene co-expression network (WGCNA) was used to analyze the gene expression profile, and the key gene modules with the highest correlation with subcutaneous adipose tissue were identified, and the functional enrichment of the key modules was analyzed. Then, the "real" Hub gene was screened by in-module analysis and protein-protein interaction network (PPI), and its expression level in tissue samples and adipocytes was verified. The study showed that a total of nine co-expression modules were identified, and the number of genes in these modules ranged from 101 to 1,509. Among them, the blue module is most closely related to subcutaneous adipose tissue, containing 1,387 genes. These genes were significantly enriched in 10 gene ontologies including extracellular matrix organization, biological adhesion, and collagen metabolic process, and were mainly involved in pathways including ECM-receptor interaction, focal adhesion, cAMP signaling pathway, PI3K-AKT signaling pathway, and regulation of lipolysis in adipocytes. In the PPI network and coexpression network, five genes (CAV1, ITGA5, COL5A1, ABL1, and HSPG2) were identified as "real" Hub genes. Analysis of Hub gene expression by dataset revealed that the expression of these Hub genes was significantly higher in subcutaneous adipose tissue than in other tissues. In addition, real-time fluorescence quantitative PCR (qRT-PCR) analysis based on tissue samples and adipocytes also confirmed the above results.ConclusionIn this study, five key genes related to subcutaneous adipose tissue were discovered, which laid a foundation for further study of the molecular regulation mechanism of subcutaneous adipose tissue development and adipose deposition.

Project description:BackgroundSurface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.MethodsThe microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the "limma" R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.ResultsA total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.ConclusionTo the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.

Project description:BACKGROUND:Aroma is an important organoleptic quality for fruit and has a large influence on consumer preference. Kiwifruit esters undergo rapid and substantial changes contributing to the flavor during fruit ripening. Part of enzymes and their coding genes have been indicated potential candidates for flavor-related esters synthesis. However, there still exist obvious gaps in the biosynthetic pathways of esters and the mechanisms regulating ester biosynthesis in kiwifruit remain unknown. RESULTS:Using gas chromatography-mass spectrometry (GC-MS), volatile compounds of kiwifruit were quantified in response to ethylene (ETH, 100??l/l, 24?h, 20?°C) and 1-methylcyclopropene (1-MCP, 1??l/l, 24?h, 20?°C). The results indicated that esters showed the most substantial changes enhanced by ethylene and were inhibited by 1-MCP. Correlations between RNA-seq results and concentrations of esters, constructed using Weighted Gene Co-Expression Network Analysis (WGCNA) indicated that three structural genes (fatty acid desaturase, AdFAD1; aldehyde dehydrogenase, AdALDH2; alcohol acyltransferase, AdAT17) had similar expression patterns that paralled the changes in total ester content, and AdFAD1 transcripts exhibited the highest correlation. In order to search for potential regulators for ester biosynthesis, 14 previously reported ethylene-responsive transcription factors (TFs) were included in the correlation analysis with esters and their biosynthetic genes. Using dual-luciferase assay, the in vivo regulatory activities of TFs on ester biosynthetic gene promoters were investigated and the results indicated that AdNAC5 and AdDof4 (DNA binding with one finger) trans-activated and trans-suppressed the AdFAD1 promoter. CONCLUSIONS:The present study advanced the molecular basis of ripening-related ester biosynthesis in kiwifruit by identifying three biosynthetic related genes AdFAD1, AdALDH2 and AdAT17 by transcriptome analysis, and highlighted the function of two TFs by transactivation studies.

Dataset Information

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

Publications

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

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