Most common diseases, including allergy, cancer and diabetes, are complex. The genetic susceptibility of an individual to such a disease is not the result of a single causative gene, but rather altered interactions between multiple genes. In many cases, DNA microarray studies have implicated hundreds of genes. Moreover, there is considerable individual variability. A clinical consequence of all this is variable response to medication, which increases both suffering and cost. Physicians should ideally be able to personalize medication routinely, based on measurements of but a few protein markers. The identification of such markers is thus an important goal, but also a formidable challenge, and one that requires understanding complex pathogenic mechanisms and how they vary across populations. It is possible that the recent advances in high-throughput genomics, computer science, bioinformatics and systems biology outlined below could contribute to such understanding (Fox JL. Nature Biotechnol 2007).
In this project we hypothesize that markers for individualized medication can be identified by network-based analysis of gene expression arrays. Our approach is outlined as follows:
- Disease-associated genes are identified and organized into putative interaction networks*.
- Networks are dissected to find modules of genes with distinct biological functions.
- Modules are further decomposed to elucidate putative pathways and individual genes with key probable regulatory functions.
- The transcriptomal modules are expanded to include other layers ranging from DNA to protein. This is done by adding data from complementary high-throughput experiments. The ultimate aim is to obtain multi-layer modules (MLM) that include information about all layers and regulatory elements.
- Protein markers for individual variations are extracted from those modules.
- Markers are tested diagnostically for personalized medication in patients.
*In the initial analysis these networks include all forms of interactions between gene products.
The project is facilitated by the recent development of high-throughput methods to analyse SNPs, proteins and different regulatory elements such as microRNAs and DNA methylation. Moreover, because the layers and elements are interdependent, an analysis of dependencies can be used for step-wise cross-validation (for example, altered mRNA expression due to regulatory SNPs).
On the other hand, this project is faced with several noteworthy challenges: a) the heterogeneity of complex diseases, and in many cases very little is known about causal mechanisms; b) the difficulties in finding representative study models; c) methodological problems involved in the development of computational and bioinformatics methods to build modules; d) experimental validation of disease mechanisms that may involve great numbers of genes, many of which have unknown or poorly defined functions.
The effort is based on ongoing multi-disciplinary collaborations between clinically active researchers and leading experts in genomics, systems biology, computer science, bioinformatics and statistics.