Application of industrial scale genomics to discovery of therapeutic targets in heart failure

Gene expression profile of human endothelial cells exposed to sustained fluid shear stress

Biomechanical forces can modulate endothelial phenotype through changes in gene expression. We hypothesized that physiological laminar shear stresses (LSS) act as differentiative stimuli on endothelial cells (EC) to alter gene expression, creating an antioxidant, anti-apoptotic and anti-proliferative environment. The transcriptional profile of cultured human umbilical vein endothelial cells (HUVEC) exposed to LSS was evaluated by GeneCalling; 107 genes demonstrated at least a twofold change in expression at 24 h (LSS vs. static). These flow-responsive genes represent a limited number of functional clusters that include transcription factors, antioxidants, signaling molecules, cell cycle regulators, and genes involved in cellular differentiation. Immunohistochemistry and in situ hybridization confirmed that many of these flow-responsive genes, including the novel basic helix-loop-helix transcription factor Hath6, are expressed in EC in vivo. Thus these data identify a limited set of flow-responsive genes expressed in the endothelium that may be responsible for the establishment and maintenance of the flow-adapted endothelial phenotype in vivo.

Staking out novelty on the genomic frontier

Recent advances in genomics include global assessment and classification of genome content, high-throughput biological pathway construction, systematic identification of previously unpredicted genes and the in vitro creation of novel motifs with biological function not found in nature (extra-genomic gene discovery). The ability to make global surveys of transcriptomes has given rise to fields such as pharmacogenomics and toxicogenomics. These applications of genomics technologies, with conventional drug assessment methodologies, will lead to more tolerable drugs and a better understanding of clinical populations. Integration of pathway mapping, using proteomics married to expression, will also significantly affect how new therapeutics are discovered as cross-biological cross-pathway interactions lead to novel drug targets and better predictions of drug tolerance.

Genes and their polymorphisms in mono- and multifactorial cardiomyopathies: towards pharmacogenomics in heart failure

Cardiomyopathies are diseases of the myocardium associated with cardiac dysfunction, and are classified as dilated cardiomyopathy (DCM), hypertropic cardiomyopathy (HCM) and restrictive cardiomyopathy. Heart failure and sudden death are the two major complications. Also, since DCM is the primary indication for heart transplantation and HCM the primary cause of sudden death in young athletes, the socioeconomic impact of these diseases is important. Recently, the role of the genetic background in both monogenic and multifactorial cardiomyopathies has been studied, which has led to a better understanding of the underlying mechanisms that promote the development and progression of these diseases. Preliminary data suggest interactions between pharmacological treatment and genetic polymorphisms, which appear to be the first steps towards the application of pharmacogenetics in heart failure.

Microbial computational genomics of gene regulation

Escherichia coli is a free-living bacterium that condensates a large legacy of knowledge as a result of years of experimental work in molecular biology. It represents a point of departure for analyses and comparisons with the ever-increasing number of finished microbial genomes. For years, we have been gathering knowledge from the literature on transcriptional regulation and operon organization in E. coli K-12, and organizing it in a relational database, RegulonDB. RegulonDB contains information of 20­25 % of the expected total sets of regulatory interactions at the level of transcription initiation. We have used this knowledge to generate computational methods to predict the missing sets in the genome of E. coli, focusing on prediction of promoters, regulatory sites, regulatory proteins, operons, and transcription units. These predictions constitute separate pieces of a single puzzle. By putting them all together, we shall be able to predict the complete set of regulatory interactions and transcription unit organization of E. coli. Orthologous genes in other genomes of known co-regulated sets of genes in E. coli, along with their corresponding predicted operons, and their predicted transcriptional regulators, shall permit the extension of the previous goal to many more microbial genomes.