Now that the human genome has been sequenced, the race is on to discover the functions of potential genes. But in mammals, a single gene can produce numerous protein isoforms (multiple molecular forms of given proteins) through a process called "alternative pre-mRNA splicing" (AS). Defects in AS are believed to account for several well-known diseases, such as cystic fibrosis, thalassemia, spinal muscular atrophy and several types of cancer. However, little is known about the biological mechanisms that control AS.

The overall goal of our project is to annotate and analyze the splice isoforms of cancer-related genes to understand in a more complete manner the mechanism of oncogenesis. This information will have tremendous value since novel markers will be identified for cancer diagnosis, and novel targets will become available for drug discovery programs. A rigorous assessment of the physiological impact of tumor-specific alterations in alternative splicing is crucial to understand the contribution of splice isoforms to specific types of cancer. To assess the functional importance of each isoform that belongs to cancer-related genes, strategies capable of decreasing the abundance of specific mRNA isoforms or specifically reprogramming splice site selection will be of tremendous value and may turn out to have important clinical relevance. For example, shifting the balance of isoforms in favor of variants displaying dominant negative activity may help reverse the malignant phenotype of cancer cells.

Our research group uses various biological approaches to control gene expression at the RNA level in mammalian cells. We are using TOSS (targeted oligonucleotide silencing of splicing), siRNA (small interfering RNA) as well as other modified oligonucleotides to control the fate of a specific gene. Using such molecules, we are targeting regulatory elements or regions in pre-mRNA identified as differentially spliced between normal and cancerous cells. By modulating the ratio of the different splicing isoforms we are seeking for the re-establishment of normal splicing patterns.

Concomitantly to optimize the efficiency and specificity of such tools, we are focusing in the assessment of thermodynamic parameters that could be critical for the best design. In that way, bioinformatic predictions are put forward and are validated experimentally.