Genome-wide characterization of transcriptional regulatory elements and mechanisms and associated genetic variations involved in the pathogenesis of atherosclerotic disease

Malin Andersen, PhD student Dr María Jesús Iglesias, post doc Jorge Andrade, PhD student

Prof Jens Lagergren (SBC, KTH),
Assoc Prof Per Eriksson (GV, Karolinska University Hospital)
Dr Dick Wågsäter (GV, Karolinska University Hospital)
Prof Anders Hamsten (GV, Karolinska University Hospital)
Prof Mathias Uhlen (KTH),
Dr Cristina Al-Khalili Szigyarto (KTH).

Cardiovascular disease (CVD) is the number one killer in the western world today. Multiple genetic and environmental factors interact. The challenge is to single out pathological bottle necks, the mechanisms, genes and regulatory networks where targeted intervention is likely to have the greatest benefit. Inflammation is an important part of the underlying atherosclerotic disease The aim of the project is to identify transcriptional regulatory mechanisms (transcription factors, target regions and elements) involved in gene regulation focused on mechanisms and pathways in the pathogenesis of cardiovascular disease, and identify gene variations affecting disease susceptibility. The results will contribute to the mapping of regulatory elements to the human genome sequence involved in this disease, and we will in parallel be able to generate new datasets including refined binding motifs, all which will improve our tools and algorithms for in silico prediction of regulatory elements and mechanisms.
For this purpose we use the chromatin immunoprecipitation technique in combination with both microarray based analysis. Selection of transcription factors to study is based on tissue expression patterns derived from the Human proteome atlas project, from current knowledge, previous experiments, previous studies, and from putative transcription factor binding sites (TFBS) predicted by in silico methods in regions of candidate gene loci. This is integrated with a continuous development, testing and calibration of bioinformatics algorithms and methods for prediction of regulatory regions and networks. The will hopefully lead to an increased understanding of patterns of transcriptional mechanisms and networks and in clinically useful results for one of the most common diseases today, at the same time as we can improve general bioinformatics methods. In parallel, computational GRID based implementations of algorithms and software is being developed for proteomic sequence analyses and genetic biostatistical analyses.

Last updated: 2008-01-10