The study aims to serve as a proof of concept model of individualized medicine in Infectious diseases that will also advance our understanding of innate immunity and chlamydial pathogenesis. The individual differences in susceptibility to various infectious diseases have been attributed to numerous genetic polymorphisms or genetic loci. Such population based phenotype to genotype correlations do not provide information regarding the causal immune pathways or predict how a certain individual will respond to a particular infection given their whole genotype. We have previously determined that the difference in susceptibility to Chlamydia in inbred strains of mice is determined by a genetic locus containing polymorphisms in the novel p47GTPase genes and several other modifying loci that result in differential immunological phenotypes affecting various arms of the immune system. We hypothesize that these genetic loci represent key modulators of immune responses and gene transcription. The causal relationship of the components of the immune pathway will be analyzed in Aim 1 by cross examination of gene expression data, immunological phenotype of a set of recombinant inbred mice infected with Chlamydia.
In Aim 2 the data will be integrated by a multiscale computational analysis of host genotype, quantitative trait loci mapping, and Bayesian network to construct a model of how a mouse with a particular genotype will respond to chlamydial infection and validated experimentally in vivo. The completion of the study is likely to reveal innate immune pathways important to Chlamydia research as well as for the p47GTPases and have broad implications. Chlamydia is the most commonly reported cause of sexually transmitted disease that impact reproductive health of women. Also, p47GTPases have recently emerged as a central innate immune component in the pathogenesis of intracellular pathogens and putatively inflammatory disorders such as Crohn's disease.

Public Health Relevance

The study aims to serve as a proof of concept study to predict the outcome of infectious diseases based on the genetic information of the host. The information will also enhance our understanding of the immune responses to Chlamydia.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Exploratory/Developmental Grants (R21)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Hiltke, Thomas J
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University of Tennessee Health Science Center
Other Basic Sciences
Schools of Medicine
United States
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Miyairi, Isao; Ziebarth, Jesse; Laxton, Jonathan D et al. (2012) Host genetics and Chlamydia disease: prediction and validation of disease severity mechanisms. PLoS One 7:e33781
Ziebarth, Jesse D; Bhattacharya, Anindya; Chen, Anlong et al. (2012) PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res 40:D216-21
Miyairi, Isao; Laxton, Jonathan D; Wang, Xiaofei et al. (2011) Chlamydia psittaci genetic variants differ in virulence by modulation of host immunity. J Infect Dis 204:654-63
Miyairi, Isao; Ramsey, Kyle H; Patton, Dorothy L (2010) Duration of untreated chlamydial genital infection and factors associated with clearance: review of animal studies. J Infect Dis 201 Suppl 2:S96-103
Bao, Lei; Xia, Xuefeng; Cui, Yan (2010) Expression QTL modules as functional components underlying higher-order phenotypes. PLoS One 5:e14313