Literature DB >> 21072409

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers.

Haja N Kadarmideen1, Nathan S Watson-Haigh, Nicholas M Andronicos.   

Abstract

This study reports on the molecular systems biology of gastrointestinal nematode (GIN) infection and potential biomarkers for GIN resistance in sheep. Microarray gene expression data were obtained for 3 different tissues at 4 time points from sheep artificially challenged with two types of nematodes, Haemonchus contortus (HC) and Trichostrongylus colubriformis (TC). We employed an integrated systems biology approach, integrating 3 main methods: standard differential gene expression analyses, weighted gene co-expression network analyses (WGCNA) and quantitative genetic analyses of gene expression traits of key biomarkers. Using standard differential gene expression analyses we identified differentially expressed genes (DE) which responded differently in sheep challenged with HC compared to those challenged with TC. These interaction genes (e.g. MRPL51, SMEK2, CAT, MAPK1IP1 and SLC25A20A) were enriched in Wnt receptor signalling pathway (p = 0.0132) and positive regulation of NFκβ transcription factor activity (p = 0.00208). We report FCER1A, a gene encoding a high-affinity receptor for the Fc region of immunoglobulin E, which is linked to innate immunity to GIN in sheep. Using weighted gene co-expression network analysis (WGCNA) methods, we identified gene modules that were correlated with the length of infection (disease modules). Hub genes (with high intramodular connectivity) were filtered further to identify biomarkers that are related to the length of infection (e.g. CAT, FBX033, COL15A1, IGFBP7, FBLN1 and IgCgamma). The biomarkers we found in HC networks were significantly associated with functions such as T-cell and B-cell regulations, TNF-alpha, interleukin and cytokine production. In TC networks, biomarkers were significantly associated with functions such as protein catabolic process, heat shock protein binding, protein targeting and localization, cytokine receptor binding, TNF receptor binding, apoptosis and IGF binding. These results provide specific gene targets for therapeutic interventions and provide insights into GIN infections in sheep which may be used to infer the same in related host species. This is also the first study to apply the concept of estimating breeding values of animals to expression traits and reveals 11 heritable candidate biomarkers (0.05 to 0.92) that could be used in selection of animals for GIN resistance.

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Year:  2010        PMID: 21072409     DOI: 10.1039/c0mb00190b

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  27 in total

1.  Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data.

Authors:  Haja N Kadarmideen; Nathan S Watson-Haigh
Journal:  Bioinformation       Date:  2012-09-21

2.  Genetic architecture of gene expression in ovine skeletal muscle.

Authors:  Lisette J A Kogelman; Keren Byrne; Tony Vuocolo; Nathan S Watson-Haigh; Haja N Kadarmideen; James W Kijas; Hutton V Oddy; Graham E Gardner; Cedric Gondro; Ross L Tellam
Journal:  BMC Genomics       Date:  2011-12-15       Impact factor: 3.969

3.  Characterization of the abomasal transcriptome for mechanisms of resistance to gastrointestinal nematodes in cattle.

Authors:  Robert W Li; Manuela Rinaldi; Anthony V Capuco
Journal:  Vet Res       Date:  2011-11-30       Impact factor: 3.683

4.  Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma.

Authors:  Vinay Randhawa; Vishal Acharya
Journal:  BMC Med Genomics       Date:  2015-07-16       Impact factor: 3.063

5.  An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

Authors:  Lisette J A Kogelman; Daria V Zhernakova; Harm-Jan Westra; Susanna Cirera; Merete Fredholm; Lude Franke; Haja N Kadarmideen
Journal:  Genome Med       Date:  2015-10-20       Impact factor: 11.117

6.  Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.

Authors:  Jael Sanyanda Wekesa; Yushi Luan; Jun Meng
Journal:  J Comput Biol       Date:  2020-04-17       Impact factor: 1.479

7.  FOSL2 Is Involved in the Regulation of Glycogen Content in Chicken Breast Muscle Tissue.

Authors:  Xiaojing Liu; Lu Liu; Jie Wang; Huanxian Cui; Guiping Zhao; Jie Wen
Journal:  Front Physiol       Date:  2021-07-06       Impact factor: 4.566

8.  An f2 pig resource population as a model for genetic studies of obesity and obesity-related diseases in humans: design and genetic parameters.

Authors:  Lisette J A Kogelman; Haja N Kadarmideen; Thomas Mark; Peter Karlskov-Mortensen; Camilla S Bruun; Susanna Cirera; Mette J Jacobsen; Claus B Jørgensen; Merete Fredholm
Journal:  Front Genet       Date:  2013-03-18       Impact factor: 4.599

9.  Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping.

Authors:  Duy Ngoc Do; Anders Bjerring Strathe; Tage Ostersen; Just Jensen; Thomas Mark; Haja N Kadarmideen
Journal:  PLoS One       Date:  2013-08-19       Impact factor: 3.240

10.  Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses.

Authors:  Lisette J A Kogelman; Sameer D Pant; Merete Fredholm; Haja N Kadarmideen
Journal:  Front Genet       Date:  2014-07-09       Impact factor: 4.599

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