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Host genetics and Chlamydia disease: prediction and validation of disease severity mechanisms.

Miyairi I, Ziebarth J, Laxton JD, Wang X, van Rooijen N, Williams RW, Lu L, Byrne GI, Cui Y - PLoS ONE (2012)

Bottom Line: Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention.We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden.Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America. miyairi-i@ncchd.go.jp

ABSTRACT
Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention. We have previously demonstrated that polymorphisms in immunity-related GTPases (IRG) confer a significant difference in susceptibility to Chlamydia psittaci infection in BXD recombinant mice. Here we combine genetic mapping and network modeling to identify causal pathways underlying this association. We infected a large panel of BXD strains with C. psittaci and assessed host genotype, IRG protein polymorphisms, pathogen load, expression of 32 cytokines, inflammatory cell populations, and weight change. Proinflammatory cytokines correlated with each other and were controlled by a novel genetic locus on chromosome 1, but did not affect disease status, as quantified by weight change 6 days after infection In contrast, weight change correlated strongly with levels of inflammatory cell populations and pathogen load that were controlled by an IRG encoding genetic locus (Ctrq3) on chromosome 11. These data provided content to generate a predictive model of infection using a Bayesian framework incorporating genotypes, immune system parameters, and weight change as a measure of disease severity. Two predictions derived from the model were tested and confirmed in a second round of experiments. First, strains with the susceptible IRG haplotype lost weight as a function of pathogen load whereas strains with the resistant haplotype were almost completely unaffected over a very wide range of pathogen load. Second, we predicted that macrophage activation by Ctrq3 would be central in conferring pathogen tolerance. We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden. Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.

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The correlation network of immune parameters during Chlamydia infection in BXD mice.Correlation network linking BXD genotypes (Ctrq3 and rs13476293), C. psittaci load, inflammatory responses, cytokine profiles, IRGM2 protein expression pattern, and weight change after C. psittaci infection in BXD strains. Positive (red) and negative (blue) correlations between variables with magnitudes of Pearson's correlation coefficient greater than 0.6 (dashed lines) and 0.7 (solid lines) are shown.
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pone-0033781-g003: The correlation network of immune parameters during Chlamydia infection in BXD mice.Correlation network linking BXD genotypes (Ctrq3 and rs13476293), C. psittaci load, inflammatory responses, cytokine profiles, IRGM2 protein expression pattern, and weight change after C. psittaci infection in BXD strains. Positive (red) and negative (blue) correlations between variables with magnitudes of Pearson's correlation coefficient greater than 0.6 (dashed lines) and 0.7 (solid lines) are shown.

Mentions: We constructed a correlation network, including cytokines, genotypes, immune parameters and disease phenotypes (Figure 3). The network nodes clustered into two groups. The first group correlated tightly with the Ctrq3 genotype, IRGM2 expression pattern and several disease-related parameters, including weight change, macrophage activation status (MAS), pathogen load, and neutrophil recruitment. A single cytokine, G-CSF, had a high correlation with weight change and neutrophil level, but was not controlled by Ctrq3 (Figure 1E. no significant QTL). The second group comprised the cytokines, many of which are highly correlated with each other, and the genotype at rs13476293, a marker located at ∼190 Mb on Chr 1, but not directly with disease-related parameters.


Host genetics and Chlamydia disease: prediction and validation of disease severity mechanisms.

Miyairi I, Ziebarth J, Laxton JD, Wang X, van Rooijen N, Williams RW, Lu L, Byrne GI, Cui Y - PLoS ONE (2012)

The correlation network of immune parameters during Chlamydia infection in BXD mice.Correlation network linking BXD genotypes (Ctrq3 and rs13476293), C. psittaci load, inflammatory responses, cytokine profiles, IRGM2 protein expression pattern, and weight change after C. psittaci infection in BXD strains. Positive (red) and negative (blue) correlations between variables with magnitudes of Pearson's correlation coefficient greater than 0.6 (dashed lines) and 0.7 (solid lines) are shown.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3306297&req=5

pone-0033781-g003: The correlation network of immune parameters during Chlamydia infection in BXD mice.Correlation network linking BXD genotypes (Ctrq3 and rs13476293), C. psittaci load, inflammatory responses, cytokine profiles, IRGM2 protein expression pattern, and weight change after C. psittaci infection in BXD strains. Positive (red) and negative (blue) correlations between variables with magnitudes of Pearson's correlation coefficient greater than 0.6 (dashed lines) and 0.7 (solid lines) are shown.
Mentions: We constructed a correlation network, including cytokines, genotypes, immune parameters and disease phenotypes (Figure 3). The network nodes clustered into two groups. The first group correlated tightly with the Ctrq3 genotype, IRGM2 expression pattern and several disease-related parameters, including weight change, macrophage activation status (MAS), pathogen load, and neutrophil recruitment. A single cytokine, G-CSF, had a high correlation with weight change and neutrophil level, but was not controlled by Ctrq3 (Figure 1E. no significant QTL). The second group comprised the cytokines, many of which are highly correlated with each other, and the genotype at rs13476293, a marker located at ∼190 Mb on Chr 1, but not directly with disease-related parameters.

Bottom Line: Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention.We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden.Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America. miyairi-i@ncchd.go.jp

ABSTRACT
Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention. We have previously demonstrated that polymorphisms in immunity-related GTPases (IRG) confer a significant difference in susceptibility to Chlamydia psittaci infection in BXD recombinant mice. Here we combine genetic mapping and network modeling to identify causal pathways underlying this association. We infected a large panel of BXD strains with C. psittaci and assessed host genotype, IRG protein polymorphisms, pathogen load, expression of 32 cytokines, inflammatory cell populations, and weight change. Proinflammatory cytokines correlated with each other and were controlled by a novel genetic locus on chromosome 1, but did not affect disease status, as quantified by weight change 6 days after infection In contrast, weight change correlated strongly with levels of inflammatory cell populations and pathogen load that were controlled by an IRG encoding genetic locus (Ctrq3) on chromosome 11. These data provided content to generate a predictive model of infection using a Bayesian framework incorporating genotypes, immune system parameters, and weight change as a measure of disease severity. Two predictions derived from the model were tested and confirmed in a second round of experiments. First, strains with the susceptible IRG haplotype lost weight as a function of pathogen load whereas strains with the resistant haplotype were almost completely unaffected over a very wide range of pathogen load. Second, we predicted that macrophage activation by Ctrq3 would be central in conferring pathogen tolerance. We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden. Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.

Show MeSH
Related in: MedlinePlus