The vast majority of human genes require RNA splicing for their expression, and at least 15% of the mutations that cause human diseases do so by disrupting splicing. The long term objective of this project remains: to understand the basis of RNA splicing specificity - the nature of the sequences in primary transcripts which are recognized by the RNA splicing machinery and used in the selection of splice sites in vertebrates and other organisms. This long-term objective is focused on four shorter-term aims: 1) to systematically identify sequences that can act as intronic splicing enhancer (ISE) and silencer (ISS) elements, to refine our knowledge of exonic splicing enhancer (ESE) and silencer (ESS) elements, and to determine rules for the context-dependent activity of these elements;2) to identify the trans-acting factors responsible for the splicing regulatory activity of a significant proportion of the exonic splicing regulatory elements identified previously;3) to develop and apply high-throughput technologies to map changes in expression of spliced isoforms that occur genome-wide during development and in response to external stimuli, using the murine hematopoietic system as a model;4) to determine functional relationships - additivity, sub-additivity, synergism, etc. - between different classes of splicing regulatory elements, to develop associated scoring systems to improve algorithms that simulate splicing and predict splicing phenotypes of mutations or polymorphisms in human genes. In addressing these questions, we will use a synergistic combination of computational methods with molecular genetic and functional genomic approaches. Knowledge of splicing regulatory sequences and proteins will aid in understanding the changes that occur in the expression of RNA versions (isoforms) of genes as cells proliferate, and may identify specific protein or RNA targets for therapuetic intervention in hyperproliferative diseases of the blood such as leukemias, lymphomas and autoimmune diseases. The ability to accurately simulate splicing will enable improved genome annotation and will facilitate identification of specific genes, mutations and polymorphisms associated with human diseases.
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