Expression quantitative trait Loci for extreme host response to influenza a in pre-collaborative cross mice

An updated systematic review of the role of host genetics in susceptibility to influenza

The World Health Organization has identified studies of the role of host genetics on susceptibility to severe influenza as a priority. A systematic review was conducted in June 2011 to summarise the evidence on the role of host genetics in susceptibility to influenza, and this report updates that previously published review. Animal studies suggest that genetic control of susceptibility to severe influenza in mice is complex and not controlled by a single locus, but there is encouraging evidence that some of the host genetic determinants of susceptibility to severe disease may be common across influenza subtypes. Although a number of studies on genetic susceptibility to influenza in humans have been published recently, all are underpowered and unreplicated, so do not provide robust statistical evidence of an association between the identified genetic loci and susceptibility. One study does however present convincing functional evidence for an important role for IFITM3 in susceptibility to severe influenza in mice, and some evidence that this may also be important in human A/H1N1/pdm2009 infection.

The Collaborative Cross as a resource for modeling human disease: CC011/Unc, a new mouse model for spontaneous colitis

Inflammatory bowel disease (IBD) is an immune-mediated condition driven by improper responses to intestinal microflora in the context of environmental and genetic background. GWAS in humans have identified many loci associated with IBD, but animal models are valuable for dissecting the underlying molecular mechanisms, characterizing environmental and genetic contributions and developing treatments. Mouse models rely on interventions such as chemical treatment or introduction of an infectious agent to induce disease. Here, we describe a new model for IBD in which the disease develops spontaneously in 20-week-old mice in the absence of known murine pathogens. The model is part of the Collaborative Cross and came to our attention due to a high incidence of rectal prolapse in an incompletely inbred line. Necropsies revealed a profound proliferative colitis with variable degrees of ulceration and vasculitis, splenomegaly and enlarged mesenteric lymph nodes with no discernible anomalies of other organ systems. Phenotypic characterization of the CC011/Unc mice with homozygosity ranging from 94.1 to 99.8% suggested that the trait was fixed and acted recessively in crosses to the colitis-resistant C57BL/6J inbred strain. Using a QTL approach, we identified four loci, Ccc1, Ccc2, Ccc3 and Ccc4 on chromosomes 12, 14, 1 and 8 that collectively explain 27.7% of the phenotypic variation. Surprisingly, we also found that minute levels of residual heterozygosity in CC011/Unc have significant impact on the phenotype. This work demonstrates the utility of the CC as a source of models of human disease that arises through new combinations of alleles at susceptibility loci.

Heritability and coefficient of genetic variation analyses of phenotypic traits provide strong basis for high-resolution QTL mapping in the Collaborative Cross mouse genetic reference population

Most biological traits of human importance are complex in nature; their manifestation controlled by the cumulative effect of many genetic factors interacting with one another and with the individual's life history. Because of this, mouse genetic reference populations (GRPs) consisting of collections of inbred lines or recombinant inbred lines (RIL) derived from crosses between inbred lines are of particular value in analysis of complex traits, since massive amounts of data can be accumulated on the individual lines. However, existing mouse GRPs are derived from inbred lines that share a common history, resulting in limited genetic diversity, and reduced mapping precision due to long-range gametic disequilibrium. To overcome these limitations, the Collaborative Cross (CC) a genetically highly diverse collection of mouse RIL was established. The CC, now in advanced stages of development, will eventually consist of about 500 RIL derived from reciprocal crosses of eight divergent founder strains of mice, including three wild subspecies. Previous studies have shown that the CC indeed contains enormous diversity at the DNA level, that founder haplotypes are inherited in expected frequency, and that long-range gametic disequilibrium is not present. We here present data, primarily from our own laboratory, documenting extensive genetic variation among CC lines as expressed in broad-sense heritability (H(2)) and by the well-known "coefficient of genetic variation," demonstrating the ability of the CC resource to provide unprecedented mapping precision leading to identification of strong candidate genes.

The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits

Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.

QTL mapping in outbred populations: successes and challenges

Quantitative trait locus (QTL) mapping in animal populations has been a successful strategy for identifying genomic regions that play a role in complex diseases and traits. When conducted in an F2 intercross or backcross population, the resulting QTL is frequently large, often encompassing 30 Mb or more and containing hundreds of genes. To narrow the locus and identify candidate genes, additional strategies are needed. Congenic strains have proven useful but work less well when there are multiple tightly linked loci, frequently resulting in loss of phenotype. As an alternative, we discuss the use of highly recombinant outbred models for directly fine-mapping QTL to only a few megabases. We discuss the use of several currently available models such as the advanced intercross (AI), heterogeneous stocks (HS), the diversity outbred (DO), and commercially available outbred stocks (CO). Once a QTL has been fine-mapped, founder sequence and expression QTL mapping can be used to identify candidate genes. In this regard, the large number of alleles found in outbred stocks can be leveraged to identify causative genes and variants. We end this review by discussing some important statistical considerations when analyzing outbred populations. Fine-resolution mapping in outbred models, coupled with full genome sequence, has already led to the identification of several underlying causative genes for many complex traits and diseases. These resources will likely lead to additional successes in the coming years.

A novel outbred mouse model of 2009 pandemic influenza and bacterial co-infection severity

Influenza viruses pose a significant health risk and annually impose a great cost to patients and the health care system. The molecular determinants of influenza severity, often exacerbated by secondary bacterial infection, are largely unclear. We generated a novel outbred mouse model of influenza virus, Staphylococcus aureus, and co-infection utilizing influenza A/CA/07/2009 virus and S. aureus (USA300). Outbred mice displayed a wide range of pathologic phenotypes following influenza virus or co-infection ranging broadly in severity. Influenza viral burden positively correlated with weight loss although lung histopathology did not. Inflammatory cytokines including IL-6, TNF-α, G-CSF, and CXCL10 positively correlated with both weight loss and viral burden. In S. aureus infection, IL-1β, G-CSF, TNF-α, and IL-6 positively correlated with weight loss and bacterial burden. In co-infection, IL-1β production correlated with decreased weight loss suggesting a protective role. The data demonstrate an approach to identify biomarkers of severe disease and to understand pathogenic mechanisms in pneumonia.

Moving H5N1 studies into the era of systems biology

The dynamics of H5N1 influenza virus pathogenesis are multifaceted and can be seen as an emergent property that cannot be comprehended without looking at the system as a whole. In past years, most of the high-throughput studies on H5N1-host interactions have focused on the host transcriptomic response, at the cellular or the lung tissue level. These studies pointed out that the dynamics and magnitude of the innate immune response and immune cell infiltration is critical to H5N1 pathogenesis. However, viral-host interactions are multidimensional and advances in technologies are creating new possibilities to systematically measure additional levels of 'omic data (e.g. proteomic, metabolomic, and RNA profiling) at each temporal and spatial scale (from the single cell to the organism) of the host response. Natural host genetic variation represents another dimension of the host response that determines pathogenesis. Systems biology models of H5N1 disease aim at understanding and predicting pathogenesis through integration of these different dimensions by using intensive computational modeling. In this review, we describe the importance of 'omic studies for providing a more comprehensive view of infection and mathematical models that are being developed to integrate these data. This review provides a roadmap for what needs to be done in the future and what computational strategies should be used to build a global model of H5N1 pathogenesis. It is time for systems biology of H5N1 pathogenesis to take center stage as the field moves toward a more comprehensive view of virus-host interactions.

Prioritizing genes responsible for host resistance to influenza using network approaches

Background

The genetic make-up of humans and other mammals (such as mice) affects their resistance to influenza virus infection. Considering the complexity and moral issues associated with experiments on human subjects, we have only acquired partial knowledge regarding the underlying molecular mechanisms. Although influenza resistance in inbred mice has been mapped to several quantitative trait loci (QTLs), which have greatly narrowed down the search for host resistance genes, only few underlying genes have been identified.

Results

To prioritize a list of promising candidates for future functional investigation, we applied network-based approaches to leverage the information of known resistance genes and the expression profiles contrasting susceptible and resistant mouse strains. The significance of top-ranked genes was supported by different lines of evidence from independent genetic associations, QTL studies, RNA interference (RNAi) screenings, and gene expression analysis. Further data mining on the prioritized genes revealed the functions of two pathways mediated by tumor necrosis factor (TNF): apoptosis and TNF receptor-2 signaling pathways. We suggested that the delicate balance between TNF’s pro-survival and apoptotic effects may affect hosts’ conditions after influenza virus infection.

Conclusions

This study considerably cuts down the list of candidate genes responsible for host resistance to influenza and proposed novel pathways and mechanisms. Our study also demonstrated the efficacy of network-based methods in prioritizing genes for complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-816) contains supplementary material, which is available to authorized users.

Systems virology: host-directed approaches to viral pathogenesis and drug targeting

Systems biology approaches are required to advance our understanding of virus–host interactions, how these interactions cause disease and, ultimately, how to improve diagnostics, therapeutics and vaccines.

Over the past decade, the field of systems virology has evolved from using first-generation microarrays to the integration of multidimensional data sets. This has resulted in significant findings, including the identification of gene expression signatures that are predictive of viral pathogenesis and vaccine efficacy, insights into how viruses disrupt cellular metabolism, and the mapping of virus–host interactomes.

To fulfil its initial promise of revolutionizing our understanding of virus–host interactions, the field of systems virology must move beyond just the listing of molecules that are differentially expressed following viral infection; it must now look to define the relationships between key host molecules and their interactions with viral components.

Several key computational challenges must be addressed in order to move into this new phase of systems virology, including consideration of nonlinear relationships such as the dynamics of the system, the integration of multidimensional data sets and the identification of causal relationships.

Virologists, computer scientists and mathematicians must combine their skills and expertise in applying systems approaches to untangle the complex question of how viruses kill.

Katze and colleagues provide an overview of the evolution of systems virology and the insights obtained from using such methodologies to study virus–host interactions. Combining systems, mathematical and computational approaches with traditional virology research will offer a better understanding of how viruses cause disease and will help in the development of therapeutics.

High-throughput molecular profiling and computational biology are changing the face of virology, providing a new appreciation of the importance of the host in viral pathogenesis and offering unprecedented opportunities for better diagnostics, therapeutics and vaccines. Here, we provide a snapshot of the evolution of systems virology, from global gene expression profiling and signatures of disease outcome, to geometry-based computational methods that promise to yield novel therapeutic targets, personalized medicine and a deeper understanding of how viruses cause disease. To realize these goals, pipettes and Petri dishes need to join forces with the powers of mathematics and computational biology.

Controlling complexity: the clinical relevance of mouse complex genetics

Experimental animal models are essential to obtain basic knowledge of the underlying biological mechanisms in human diseases. Here, we review major contributions to biomedical research and discoveries that were obtained in the mouse model by using forward genetics approaches and that provided key insights into the biology of human diseases and paved the way for the development of novel therapeutic approaches.

Modeling host genetic regulation of influenza pathogenesis in the collaborative cross

Genetic variation contributes to host responses and outcomes following infection by influenza A virus or other viral infections. Yet narrow windows of disease symptoms and confounding environmental factors have made it difficult to identify polymorphic genes that contribute to differential disease outcomes in human populations. Therefore, to control for these confounding environmental variables in a system that models the levels of genetic diversity found in outbred populations such as humans, we used incipient lines of the highly genetically diverse Collaborative Cross (CC) recombinant inbred (RI) panel (the pre-CC population) to study how genetic variation impacts influenza associated disease across a genetically diverse population. A wide range of variation in influenza disease related phenotypes including virus replication, virus-induced inflammation, and weight loss was observed. Many of the disease associated phenotypes were correlated, with viral replication and virus-induced inflammation being predictors of virus-induced weight loss. Despite these correlations, pre-CC mice with unique and novel disease phenotype combinations were observed. We also identified sets of transcripts (modules) that were correlated with aspects of disease. In order to identify how host genetic polymorphisms contribute to the observed variation in disease, we conducted quantitative trait loci (QTL) mapping. We identified several QTL contributing to specific aspects of the host response including virus-induced weight loss, titer, pulmonary edema, neutrophil recruitment to the airways, and transcriptional expression. Existing whole-genome sequence data was applied to identify high priority candidate genes within QTL regions. A key host response QTL was located at the site of the known anti-influenza Mx1 gene. We sequenced the coding regions of Mx1 in the eight CC founder strains, and identified a novel Mx1 allele that showed reduced ability to inhibit viral replication, while maintaining protection from weight loss.

Host responses to an infectious agent are highly variable across the human population, however, it is not entirely clear how various factors such as pathogen dose, demography, environment and host genetic polymorphisms contribute to variable host responses and infectious outcomes. In this study, a new in vivo experimental model was used that recapitulates many of the genetic characteristics of an outbred population, such as humans. By controlling viral dose, environment and demographic variables, we were able to focus on the role that host genetic variation plays in influenza virus infection. Both the range of disease phenotypes and the combinations of sets of disease phenotypes at 4 days post infection across this population exhibited a large amount of diversity, reminiscent of the variation seen across the human population. Multiple host genome regions were identified that contributed to different aspects of the host response to influenza infection. Taken together, these results emphasize the critical role of host genetics in the response to infectious diseases. Given the breadth of host responses seen within this population, several new models for unique host responses to infection were identified.

Deconstructing Mus gemischus: advances in understanding ancestry, structure, and variation in the genome of the laboratory mouse

The laboratory mouse is an artificial construct with a complex relationship to its natural ancestors. In 2002, the mouse became the first mammalian model organism with a reference genome. Importantly, the mouse genome sequence was assembled from data on a single inbred laboratory strain, C57BL/6. Several large-scale genetic variant discovery efforts have been conducted, resulting in a catalog of tens of millions of SNPs and structural variants. High-density genotyping arrays covering a subset of those variants have been used to produce hundreds of millions of genotypes in laboratory stocks and a small number of wild mice. These landmark resources now enable us to determine relationships among laboratory mice, assign local ancestry at fine scale, resolve important controversies, and identify a new set of challenges-most importantly, the troubling scarcity of genetic data on the very natural populations from which the laboratory mouse was derived. Our aim with this review is to provide the reader with an historical context for the mouse as a model organism and to explain how practical decisions made in the past have influenced both the architecture of the laboratory mouse genome and the design and execution of current large-scale resources. We also provide examples on how the accomplishments of the past decade can be used by researchers to streamline the use of mice in their experiments and correctly interpret results. Finally, we propose future steps that will enable the mouse community to extend its successes in the decade to come.

Systems approaches to influenza-virus host interactions and the pathogenesis of highly virulent and pandemic viruses

Influenza virus research has recently undergone a shift from a virus-centric perspective to one that embraces the full spectrum of virus-host interactions and cellular signaling events that determine disease outcome. This change has been brought about by the increasing use and expanding scope of high-throughput molecular profiling and computational biology, which together fuel discovery in systems biology. In this review, we show how these approaches have revealed an uncontrolled inflammatory response as a contributor to the extreme virulence of the 1918 pandemic and avian H5N1 viruses, and how this response differs from that induced by the 2009 H1N1 viruses responsible for the most recent influenza pandemic. We also discuss how new animal models, such as the Collaborative Cross mouse systems genetics platform, are key to the necessary systematic investigation of the impact of host genetics on infection outcome, how genome-wide RNAi screens have identified hundreds of cellular factors involved in viral replication, and how systems biology approaches are making possible the rational design of new drugs and vaccines against an ever-evolving respiratory virus.

Distinct gene loci control the host response to influenza H1N1 virus infection in a time-dependent manner

Background

There is strong but mostly circumstantial evidence that genetic factors modulate the severity of influenza infection in humans. Using genetically diverse but fully inbred strains of mice it has been shown that host sequence variants have a strong influence on the severity of influenza A disease progression. In particular, C57BL/6J, the most widely used mouse strain in biomedical research, is comparatively resistant. In contrast, DBA/2J is highly susceptible.

Results

To map regions of the genome responsible for differences in influenza susceptibility, we infected a family of 53 BXD-type lines derived from a cross between C57BL/6J and DBA/2J strains with influenza A virus (PR8, H1N1). We monitored body weight, survival, and mean time to death for 13 days after infection. Qivr5 (quantitative trait for influenza virus resistance on chromosome 5) was the largest and most significant QTL for weight loss. The effect of Qivr5 was detectable on day 2 post infection, but was most pronounced on days 5 and 6. Survival rate mapped to Qivr5, but additionally revealed a second significant locus on chromosome 19 (Qivr19). Analysis of mean time to death affirmed both Qivr5 and Qivr19. In addition, we observed several regions of the genome with suggestive linkage. There are potentially complex combinatorial interactions of the parental alleles among loci. Analysis of multiple gene expression data sets and sequence variants in these strains highlights about 30 strong candidate genes across all loci that may control influenza A susceptibility and resistance.

Conclusions

We have mapped influenza susceptibility loci to chromosomes 2, 5, 16, 17, and 19. Body weight and survival loci have a time-dependent profile that presumably reflects the temporal dynamic of the response to infection. We highlight candidate genes in the respective intervals and review their possible biological function during infection.

Status and access to the Collaborative Cross population

The Collaborative Cross (CC) is a panel of recombinant inbred lines derived from eight genetically diverse laboratory inbred strains. Recently, the genetic architecture of the CC population was reported based on the genotype of a single male per line, and other publications reported incompletely inbred CC mice that have been used to map a variety of traits. The three breeding sites, in the US, Israel, and Australia, are actively collaborating to accelerate the inbreeding process through marker-assisted inbreeding and to expedite community access of CC lines deemed to have reached defined thresholds of inbreeding. Plans are now being developed to provide access to this novel genetic reference population through distribution centers. Here we provide a description of the distribution efforts by the University of North Carolina Systems Genetics Core, Tel Aviv University, Israel and the University of Western Australia.

Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses

An infection represents a highly dynamic process involving complex biological responses of the host at many levels. To describe such processes at a global level, we recorded gene expression changes in mouse lungs after a non-lethal infection with influenza A virus over a period of 60 days. Global analysis of the large data set identified distinct phases of the host response. The increase in interferon genes and up-regulation of a defined NK-specific gene set revealed the initiation of the early innate immune response phase. Subsequently, infiltration and activation of T and B cells could be observed by an augmentation of T and B cell specific signature gene expression. The changes in B cell gene expression and preceding chemokine subsets were associated with the formation of bronchus-associated lymphoid tissue. In addition, we compared the gene expression profiles from wild type mice with Rag2 mutant mice. This analysis readily demonstrated that the deficiency in the T and B cell responses in Rag2 mutants could be detected by changes in the global gene expression patterns of the whole lung. In conclusion, our comprehensive gene expression study describes for the first time the entire host response and its kinetics to an acute influenza A infection at the transcriptome level.

Transcriptome atlases of mouse brain reveals differential expression across brain regions and genetic backgrounds

Mouse models play a crucial role in the study of human behavioral traits and diseases. Variation of gene expression in brain may play a critical role in behavioral phenotypes, and thus it is of great importance to understand regulation of transcription in mouse brain. In this study, we analyzed the role of two important factors influencing steady-state transcriptional variation in mouse brain. First we considered the effect of assessing whole brain vs. discrete regions of the brain. Second, we investigated the genetic basis of strain effects on gene expression. We examined the transcriptome of three brain regions using Affymetrix expression arrays: whole brain, forebrain, and hindbrain in adult mice from two common inbred strains (C57BL/6J vs. NOD/ShiLtJ) with eight replicates for each brain region and strain combination. We observed significant differences between the transcriptomes of forebrain and hindbrain. In contrast, the transcriptomes of whole brain and forebrain were very similar. Using 4.3 million single-nucleotide polymorphisms identified through whole-genome sequencing of C57BL/6J and NOD/ShiLtJ strains, we investigated the relationship between strain effect in gene expression and DNA sequence similarity. We found that cis-regulatory effects play an important role in gene expression differences between strains and that the cis-regulatory elements are more often located in 5′ and/or 3′ transcript boundaries, with no apparent preference on either 5′ or 3′ ends.

Ten years of the collaborative cross

The February 2012 issues of GENETICS and G3: Genes, Genomes, Genetics present a collection of articles reporting recent advances from the international Collaborative Cross (CC) project. The goal of the CC project is to develop a new resource that will enhance quantitative trait locus (QTL) and systems genetic analyses in mice. The CC consists of hundreds of independently bred, octo-parental recombinant inbred lines (Figure 1). The work reported in these issues represents progress toward completion of the CC, proof-of-principle experiments using incipient inbred CC mice, and new research areas and complementary resources facilitated by the CC project.