Genetic control of susceptibility towards influenza infections
The role of genetic determinants has been well documented for viral, bacterial, and parasite pathogens in humans. In the case of influenza, the value of the mouse model has been demonstrated for recent and historical (1918) influenza A/H1N1 subtypes, as well as the highly pathogenic bird high pathogenicity subtype A/H5N1.
We are characterizing factors of genetic host susceptibility for several influenza A virus subtypes. In particular, the pathogenicity, course of disease and immune response is being studied in different mouse strains that vary in their genetic background, and the results will be used for the mapping of quantitative trait loci (QTL).
Within the QTL regions identified, the candidate genomic regions will be further narrowed down using back- and inter-crosses of selected strains, in silico mapping, SNP analysis, and comparison to homologous regions in the human genome. Once the region has been narrowed down to 2 cM or less, congenic mouse lines will be established to confirm the genetic contribution of the candidate region. We will then try to identify potential candidate genes in this region by infecting appropriate mouse mutants.
Regulatory networks in regulatory T-cells
When encountering an infection, the immune system must activate its defense mechanism in a very controlled manner. In order to orchestrate this response in such a way that the invader is killed but the remaining host tissue is left intact, a complex interaction of effector T cells and regulatory T cells is needed. The gene regulatory network system controlling T-cell function consists of many components and complex interactions including feed-forward, feed-back loops, epistatic interactions as well as alternative and redundant pathways. Therefore, a whole-genome approach to acquire a comprehensive data set and subsequent modeling of the regulatory interactions is mandatory to understand this system.
In this project, we describe, at a genome-wide level, the expression profiles in regulatory T cells that are derived from recombinant inbred mouse strains. Each of these mouse strains has a slightly different genetic background and, therefore, the transcriptional expression profiles of many genes will vary from strain to strain. Since the genotypes of all strains are known, it is possible, at a genome-wide scale, to describe the upstream regulatory locus for all transcripts differing in expression levels between strains.
In this way, single regulatory interactions can be described. But more importantly, differences in expression profiles of whole sets of target genes that are controlled by the same genomic locus can be identified as groups of co-regulated genes belonging to the same regulatory network. Thus, many regulatory interactions, master regulatory gene loci, and regulatory networks can be identified and interaction networks be derived.
In subsequent stages of this project, we will built computer models of gene regulatory pathways of regulatory T cells from our data and already existing models. These models will then be extended and refined as we generate more data.