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An unbiased systems genetics approach to mapping genetic loci modulating susceptibility to severe streptococcal sepsis.

Abdeltawab NF, Aziz RK, Kansal R, Rowe SL, Su Y, Gardner L, Brannen C, Nooh MM, Attia RR, Abdelsamed HA, Taylor WL, Lu L, Williams RW, Kotb M - PLoS Pathog. (2008)

Bottom Line: We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens.By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL) on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%-30% of variance.This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections.

View Article: PubMed Central - PubMed

Affiliation: Mid-South Center for Biodefense and Security, The University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

ABSTRACT
Striking individual differences in severity of group A streptococcal (GAS) sepsis have been noted, even among patients infected with the same bacterial strain. We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens. Inasmuch as the bacteria produce additional virulence factors that participate in the pathogenesis of this complex disease, we sought to identify additional gene networks modulating GAS sepsis. Accordingly, we applied a systems genetics approach using a panel of advanced recombinant inbred mice. By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL) on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%-30% of variance. This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections. We evaluated candidate genes within this QTL using multiple parameters that included linkage, gene ontology, variation in gene expression, cocitation networks, and biological relevance, and identified interleukin1 alpha and prostaglandin E synthases pathways as key networks involved in modulating GAS sepsis severity. The association of GAS sepsis with multiple pathways underscores the complexity of traits modulating GAS sepsis and provides a powerful approach for analyzing interactive traits affecting outcomes of other infectious diseases.

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Differential susceptibility to GAS sepsis among different BXD strains and their parental strains.(A) Rank-ordered bar chart of survival expressed as mean values of coefficient of mean corrected relative survival indices (cRSI) across 30 different BXD strains and their parental strains (DBA/2J and C57Bl/6J) at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. Total number of mice is 489 belonging to 32 strains; number of mice used per strain is indicated. (B) Bar chart of GAS bacteremia expressed as coefficient of mean values of corrected log blood CFUs 24 h post injection (corrected log CFU/ml blood). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. (C) Bar chart of GAS dissemination to spleen expressed as coefficient of corrected bacterial load in spleen (log CFU/spleen). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001.
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ppat-1000042-g001: Differential susceptibility to GAS sepsis among different BXD strains and their parental strains.(A) Rank-ordered bar chart of survival expressed as mean values of coefficient of mean corrected relative survival indices (cRSI) across 30 different BXD strains and their parental strains (DBA/2J and C57Bl/6J) at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. Total number of mice is 489 belonging to 32 strains; number of mice used per strain is indicated. (B) Bar chart of GAS bacteremia expressed as coefficient of mean values of corrected log blood CFUs 24 h post injection (corrected log CFU/ml blood). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. (C) Bar chart of GAS dissemination to spleen expressed as coefficient of corrected bacterial load in spleen (log CFU/spleen). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001.

Mentions: We infected mice (n = 5–26 mice per strain), belonging to 32 strains (30 different BXD strains and their parental strains, B6 and D2), intravenously (i.v.) with the same bacterial dose (2±1.5×107 CFU/mouse). Survival was expressed as normalized corrected relative survival indices (cRSI) calculated for each of the 32 strains as detailed in materials and methods. We observed significant difference in relative susceptibility to GAS sepsis across the BXD panel (P≤0.0001), Figure 1A. This wide range of susceptibility across the various strains, together with the finding that some of the strains showed phenotypes considerably more exaggerated than the parental strains (B6 and D2), is an illustration of how different combination of polymorphic genes can manifest quite differently according to the overall genetic context.


An unbiased systems genetics approach to mapping genetic loci modulating susceptibility to severe streptococcal sepsis.

Abdeltawab NF, Aziz RK, Kansal R, Rowe SL, Su Y, Gardner L, Brannen C, Nooh MM, Attia RR, Abdelsamed HA, Taylor WL, Lu L, Williams RW, Kotb M - PLoS Pathog. (2008)

Differential susceptibility to GAS sepsis among different BXD strains and their parental strains.(A) Rank-ordered bar chart of survival expressed as mean values of coefficient of mean corrected relative survival indices (cRSI) across 30 different BXD strains and their parental strains (DBA/2J and C57Bl/6J) at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. Total number of mice is 489 belonging to 32 strains; number of mice used per strain is indicated. (B) Bar chart of GAS bacteremia expressed as coefficient of mean values of corrected log blood CFUs 24 h post injection (corrected log CFU/ml blood). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. (C) Bar chart of GAS dissemination to spleen expressed as coefficient of corrected bacterial load in spleen (log CFU/spleen). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2277464&req=5

ppat-1000042-g001: Differential susceptibility to GAS sepsis among different BXD strains and their parental strains.(A) Rank-ordered bar chart of survival expressed as mean values of coefficient of mean corrected relative survival indices (cRSI) across 30 different BXD strains and their parental strains (DBA/2J and C57Bl/6J) at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. Total number of mice is 489 belonging to 32 strains; number of mice used per strain is indicated. (B) Bar chart of GAS bacteremia expressed as coefficient of mean values of corrected log blood CFUs 24 h post injection (corrected log CFU/ml blood). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001. (C) Bar chart of GAS dissemination to spleen expressed as coefficient of corrected bacterial load in spleen (log CFU/spleen). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P≤0.0001.
Mentions: We infected mice (n = 5–26 mice per strain), belonging to 32 strains (30 different BXD strains and their parental strains, B6 and D2), intravenously (i.v.) with the same bacterial dose (2±1.5×107 CFU/mouse). Survival was expressed as normalized corrected relative survival indices (cRSI) calculated for each of the 32 strains as detailed in materials and methods. We observed significant difference in relative susceptibility to GAS sepsis across the BXD panel (P≤0.0001), Figure 1A. This wide range of susceptibility across the various strains, together with the finding that some of the strains showed phenotypes considerably more exaggerated than the parental strains (B6 and D2), is an illustration of how different combination of polymorphic genes can manifest quite differently according to the overall genetic context.

Bottom Line: We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens.By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL) on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%-30% of variance.This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections.

View Article: PubMed Central - PubMed

Affiliation: Mid-South Center for Biodefense and Security, The University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

ABSTRACT
Striking individual differences in severity of group A streptococcal (GAS) sepsis have been noted, even among patients infected with the same bacterial strain. We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens. Inasmuch as the bacteria produce additional virulence factors that participate in the pathogenesis of this complex disease, we sought to identify additional gene networks modulating GAS sepsis. Accordingly, we applied a systems genetics approach using a panel of advanced recombinant inbred mice. By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL) on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%-30% of variance. This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections. We evaluated candidate genes within this QTL using multiple parameters that included linkage, gene ontology, variation in gene expression, cocitation networks, and biological relevance, and identified interleukin1 alpha and prostaglandin E synthases pathways as key networks involved in modulating GAS sepsis severity. The association of GAS sepsis with multiple pathways underscores the complexity of traits modulating GAS sepsis and provides a powerful approach for analyzing interactive traits affecting outcomes of other infectious diseases.

Show MeSH
Related in: MedlinePlus