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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2012

Open Access Meeting abstract

Meta-analysis of genes within QTLs of group A streptococcal sepsis and their expression QTLs reveal pathways modulating host differential response to streptococcal sepsis

Nourtan Abdeltawab134, Lu Lu2, Robert Williams2 and Malak Kotb14*

Author affiliations

1 Veteran’s Affairs Medical Center, Cincinnati, OH 45220, USA

2 Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN 38163, USA

3 Faculty of Pharmacy, Cairo University, Cairo, Egypt

4 Department of Molecular Genetics, Microbiology and Immunology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 12):A6  doi:10.1186/1471-2105-13-S12-A6

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/13/S12/A6


Published:31 July 2012

© 2012 Abdeltawab et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Meeting abstract

Complex host–pathogen interactions modulate differential responses to group A streptococcal (GAS) sepsis systemic disease [1,2]. We previously found that host HLA-II allelic variations are associated with differential response to severe GAS sepsis [3]. In addition, using mouse models of GAS sepsis we found other host genetic factors contribute to disease severity by modulating inflammatory responses [4,5]. We applied systems genetics approaches and analyzed variations in disease severity phenotypes using advance recombinant inbred (ARI) BXD strain panel. We mapped quantitative trait loci (QTLs) associated with differential host response to severe GAS sepsis to mouse Chr2 and X [5]. The focus of the current study is to identify regulating genes within QTLs associated with differential GAS sepsis. To do so, we explored differences in expression and nsSNPs of genes within mapped QTLs using expression data sets of relevant tissues. We selected spleen, leukocytes and lung expression data sets deposited in GeneNetwork as most relevant data sets for GAS sepsis disease severity. Collectively, integration of QTL mapping of sepsis phenotypes with expression QTLs uncovered pathways that modulate differential susceptibility to severe GAS sepsis, underscoring the complexity of traits modulating severe GAS sepsis. Approaches used in our study provide a powerful, unbiased genetics approach for analyzing interactive traits modulating the outcomes of infectious diseases.

References

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