Sarah Rowe

Host Genetic Variation Affects Resistance to Infection with a Highly Pathogenic H5N1 Influenza A Virus in Mice

ABSTRACT Despite the prevalence of H5N1 influenza viruses in global avian populations, comparatively few cases have been diagnosed in humans. Although viral factors almost certainly play a role in limiting human infection and disease, host genetics most likely contribute substantially. To model host factors in the context of influenza virus infection, we determined the lethal dose of a highly pathogenic H5N1 virus (A/Hong Kong/213/03) in C57BL/6J and DBA/2J mice and identified genetic elements associated with survival after infection. The lethal dose in these hosts varied by 4 logs and was associated with differences in replication kinetics and increased production of proinflammatory cytokines CCL2 and tumor necrosis factor alpha in susceptible DBA/2J mice. Gene mapping with recombinant inbred BXD strains revealed five loci or Qivr (quantitative trait loci for influenza virus resistance) located on chromosomes 2, 7, 11, 15, and 17 associated with resistance to H5N1 virus. In conjunction with gene expression profiling, we identified a number of candidate susceptibility genes. One of the validated genes, the hemolytic complement gene, affected virus titer 7 days after infection. We conclude that H5N1 influenza virus-induced pathology is affected by a complex and multigenic host component.

Dissection of the Molecular Basis for Hypervirulence of an In Vivo—Selected Phenotype of the Widely Disseminated M1T1 Strain of Group A Streptococcus Bacteria

Group A streptococci (GAS) may engage different sets of virulence strategies, depending on the site of infection and host context. We previously isolated 2 phenotypic variants of a globally disseminated M1T1 GAS clone: a virulent wild-type (WT) strain, characterized by a SpeB(+)/SpeA(-)/Sda1(low) phenotype, and a hypervirulent animal-passaged (AP) strain, better adapted to survive in vivo, with a SpeB(-)/SpeA(+)/Sda1(high) phenotype. This AP strain arises in vivo due to the selection of bacteria with mutations in covS, the sensor part of a key 2-component regulatory system, CovR/S. To determine whether covS mutations explain the hypervirulence of the AP strain, we deleted covS from WT bacteria (DeltaCovS) and were able to simulate the hypervirulence and gene expression phenotype of naturally selected AP bacteria. Correction of the covS mutation in AP bacteria reverted them back to the WT phenotype. Our data confirm that covS plays a direct role in regulating GAS virulence.

Microevolution of group A streptococci in vivo: capturing regulatory networks engaged in sociomicrobiology, niche adaptation, and hypervirulence.

The onset of infection and the switch from primary to secondary niches are dramatic environmental changes that not only alter bacterial transcriptional programs, but also perturb their sociomicrobiology, often driving minor subpopulations with mutant phenotypes to prevail in specific niches. Having previously reported that M1T1 Streptococcus pyogenes become hypervirulent in mice due to selection of mutants in the covRS regulatory genes, we set out to dissect the impact of these mutations in vitro and in vivo from the impact of other adaptive events. Using a murine subcutaneous chamber model to sample the bacteria prior to selection or expansion of mutants, we compared gene expression dynamics of wild type (WT) and previously isolated animal-passaged (AP) covS mutant bacteria both in vitro and in vivo, and we found extensive transcriptional alterations of pathoadaptive and metabolic gene sets associated with invasion, immune evasion, tissue-dissemination, and metabolic reprogramming. In contrast to the virulence-associated differences between WT and AP bacteria, Phenotype Microarray analysis showed minor in vitro phenotypic differences between the two isogenic variants. Additionally, our results reflect that WT bacterias rapid host-adaptive transcriptional reprogramming was not sufficient for their survival, and they were outnumbered by hypervirulent covS mutants with SpeB−/Sdahigh phenotype, which survived up to 14 days in mice chambers. Our findings demonstrate the engagement of unique regulatory modules in niche adaptation, implicate a critical role for bacterial genetic heterogeneity that surpasses transcriptional in vivo adaptation, and portray the dynamics underlying the selection of hypervirulent covS mutants over their parental WT cells.

An Unbiased Systems Genetics Approach to Mapping Genetic Loci Modulating Susceptibility to Severe Streptococcal Sepsis

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.

Susceptibility to severe streptococcal sepsis: use of a large set of isogenic mouse lines to study genetic and environmental factors

Variation in responses to pathogens is influenced by exposure history, environment and the hosts genetic status. We recently demonstrated that human leukocyte antigen class II allelic differences are a major determinant of the severity of invasive group A streptococcal (GAS) sepsis in humans. While in-depth controlled molecular studies on populations of genetically well-characterized humans are not feasible, it is now possible to exploit genetically diverse panels of recombinant inbred BXD mice to define genetic and environmental risk factors. Our goal in this study was to standardize the model and identify genetic and nongenetic covariates influencing invasive infection outcomes. Despite having common ancestors, the various BXD strains (n strains=33, n individuals=445) showed marked differences in survival. Mice from all strains developed bacteremia but exhibited considerable differences in disease severity, bacterial dissemination and mortality rates. Bacteremia and survival showed the expected negative correlation. Among nongenetic factors, age – but not sex or weight – was a significant predictor of survival (P=0.0005). To minimize nongenetic variability, we limited further analyses to mice aged 40–120 days and calculated a corrected relative survival index that reflects the number of days an animal survived post-infection normalized to all significant covariates. Genetic background (strain) was the most significant factor determining susceptibility (P⩽0.0001), thus underscoring the strong effect of host genetic variation in determining susceptibility to severe GAS sepsis. This model offers powerful unbiased forward genetics to map specific quantitative trait loci and networks of pathways modulating the severity of GAS sepsis.

Selective Targeting of Leukemic Cell Growth in Vivo and in Vitro Using a Gene Silencing Approach to Diminish S-Adenosylmethionine Synthesis

We exploited the fact that leukemic cells utilize significantly higher levels of S-adenosylmethionine (SAMe) than normal lymphocytes and developed tools that selectively diminished their survival under physiologic conditions. Using RNA interference gene silencing technology, we modulated the kinetics of methionine adenosyltransferase-II (MAT-II), which catalyzes SAMe synthesis from ATP and l-Met. Specifically, we silenced the expression of the regulatory MAT-IIβ subunit in Jurkat cells and accordingly shifted the \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(K_{m{\ }L-\mathrm{Met}}\) \end{document} of the enzyme 10–15-fold above the physiologic levels of l-Met, thereby reducing enzyme activity and SAMe pools, inducing excessive apoptosis and diminishing leukemic cell growth in vitro and in vivo. These effects were reversed at unphysiologically high l-Met (>50 μm), indicating that diminished leukemic cell growth at physiologic l-Met levels was a direct result of the increase in MAT-II \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(K_{m{\ }L-\mathrm{Met}}\) \end{document} due to MAT-IIβ ablation and the consequent reduction in SAMe synthesis. In our NOD/Scid IL-2Rγnull humanized mouse model of leukemia, control shRNA-transduced Jurkat cells exhibited heightened engraftment, whereas cells lacking MAT-IIβ failed to engraft for up to 5 weeks post-transplant. These stark differences in malignant cell survival, effected by MAT-IIβ ablation, suggest that it may be possible to use this approach to disadvantage leukemic cell survival in vivo with little to no harm to normal cells.

Bioinformatics analysis of immune response to group A streptococcal sepsis integrating quantitative trait loci mapping with genome-wide expression studies

Individuals infected with genetically identical group Astreptococcal (GAS) strains develop starkly different dis-ease progression and outcome [1]. We reported that HLAclass II allelic variation contributes to differences in sys-temic disease severity by modulating host responses tostreptococcal superantigens [2]. Inasmuch as the bacteriaproduce additional virulence factors, we sought to iden-tify additional host gene networks modulating GAS sep-sis. Accordingly, we used two parallel approaches todefine these gene networks, quantitative trait loci (QTL)mapping and genome-wide transcriptome analyses. Tomap QTLs modulating response to severe GAS sepsis, weused advanced recombinant inbred (ARI) strains, whichare genetically diverse strains that have common ancestralparents [3]. We chose to use BXD strains of ARI mice, asparental strains C57Bl/6J (B6) and DBA/2J (D2) show dif-ferential response to GAS sepsis and BXD strains are heav-ily genotyped at 13377 SNPs and microsatellite markers.BXD strains, derived from B6 and D2 parental strains, arehomozygous inbred lines, each of which is genetically dis-tinct. Using 30 different BXD strains (n = 5–26 mice perstrain), we identified significant QTLs on chromosome 2that strongly modulate disease severity [4]. To narrowdown these mapped QTLs, we applied bioinformaticstools including: linkage, interval specific haplotype analy-ses, and gene ontology and we identified multiple candi-date gene networks modulating immune response tosepsis.As a parallel approach, we performed genome-wide tran-scriptome analyses comparing resistant and susceptiblestrains. This comparison revealed 93 genes that were dif-ferentially regulated in mice spleens 36 h post-infection.These genes belonged to gene networks involvingimmune response to sepsis; particularly notable exampleswere prostaglandin (Ptges) and interleukin1 (IL-1) familypathways. Quantitative expression analyses, using realtime PCR, of prostaglandin E synthase (

Integrating neighbor clustering, coexpression clustering and subsystems analysis to define dynamic changes in regulatory networks associated with group A streptococcal sociomicrobiology and niche adaptation

Background Bacterial colonies often consist of heterogeneous communities rather than genetically identical cells with harmonized gene expression profiles[1]. Dramatic changes, such as the onset of infection, may perturb a colony’s sociomicrobiology leading a minor subpopulation with a mutant phenotype to prevail in the host; however, capturing such transitions in real time is difficult. While differential microarray analysis has become a method of choice for comparing the transcriptomes of bacterial subpopulations, current microarray analysis tools are more optimized to the study of eukaryotic organisms. Here, we set out to develop a systems biology model for studying the transcriptional reprogramming underlying the transition of M1T1 group A streptococci[2] from a virulent to a hypervirulent phenotype [3-5]. In addition, we aimed at integrating and optimizing microarray analysis strategies to better understand bacterial regulatory networks.

Biotools for Determining the Genetics of Susceptibility to Infectious Diseases and Expediting Research Translation Into Effective Countermeasures

Infectious diseases, like most human diseases, are affected by complex polymorphic and nonpolymorphic interactive traits that influence host–pathogen interactions and modulate disease phenotype. It is well established that host genetic variability strongly affects susceptibility to infectious diseases and can significantly potentiate the severity of their clinical manifestations. The same individual could be highly susceptible to a particular infection yet completely resistant to another—ultimately these complex genetic variations ensure that some of us will be selected to survive catastrophic biological threats and help protect our species from extinction. As a result of global environmental, social and political changes, we are facing real danger that could result from major natural, deliberate, or accidental biological threats. The best means of protection against these impending threats is to be better prepared. To do so, we need to gain a deeper understanding of how our genotypes modulate our susceptibility and reaction to specific infectious agents, because this information helps us to better understand disease mechanism. Our research has been focused on linking specific genotypes to susceptibility phenotypes and delineating pathways and molecular events that modulate host resistance or susceptibility to specific infectious pathogens. Inasmuch as it is quite difficult to conduct certain infectious disease studies in humans, there has been a critical need for small animal models for infectious diseases. Appreciating the limitations of the existing models, we have developed several novel and complementary mouse models that can be used to gain a better understanding of complex disease mechanisms and reveal the interactive network(s) that lead to eradication of the infection or to serious pathology caused by our overzealous response to it. Recombinant inbred (RI) mouse strains are a powerful tool for identifying quantitative trait loci (QTL) and interactive gene networks modulating infectious disease phenotypes. Data generated using the RI reference population provides a roadmap for the disease that helps focus hypotheses and expedite the process of discovery and forward research translation. Potential diagnostics, therapeutics, and vaccines suggested from the RI mice studies can also be tested in our fully humanized mouse model, where the mouse immune system has been replaced with a human immune system. Together, these models provide valuable preclinical information and allow the screening for vaccine efficacy or adverse effects, to focus and expedite the translation of research into effective countermeasures in major biological threats.