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Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine

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Dreher,  Felix
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Herwig,  Ralf
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Reiner, G., Dreher, F., Drungowski, M., Hoeltig, D., Bertsch, N., Selke, M., et al. (2014). Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine. Mammalian Genome, 25(11-12), 600-617. doi:10.1007/s00335-014-9536-9.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0026-AA86-E
Abstract
Actinobacillus (A.) pleuropneumoniae is among the most important pathogens in pig. The agent causes severe economic losses due to decreased performance, the occurrence of acute or chronic pleuropneumonia, and an increase in death incidence. Since therapeutics cannot be used in a sustainable manner, and vaccination is not always available, new prophylactic measures are urgently needed. Recent research has provided evidence for a genetic predisposition in susceptibility to A. pleuropneumoniae in a Hampshire × German Landrace F2 family with 170 animals. The aim of the present study is to characterize the expression response in this family in order to unravel resistance and susceptibility mechanisms and to prioritize candidate genes for future fine mapping approaches. F2 pigs differed distinctly in clinical, pathological, and microbiological parameters after challenge with A. pleuropneumoniae. We monitored genome-wide gene expression from the 50 most and 50 least susceptible F2 pigs and identified 171 genes differentially expressed between these extreme phenotypes. We combined expression QTL analyses with network analyses and functional characterization using gene set enrichment analysis and identified a functional hotspot on SSC13, including 55 eQTL. The integration of the different results provides a resource for candidate prioritization for fine mapping strategies, such as TF, TFRC, RUNX1, TCN1, HP, CD14, among others.