Using Statistics to Understand Gene-splicing Regulation
Hereditary human diseases are caused by mutations in our genetic material, which are then passed down to our progeny. Researchers have already identified the mutations underlying certain genetic disorders, such as breast cancer and phenylketonuria, but most hereditary diseases remain poorly understood. We need to learn how to identify the genetic variants responsible for hereditary diseases if we are going to carry out early pre-symptomatic detection and develop the appropriate therapies. And, to do this, scientists, such as Canada Research Chair Dr. Jacek Majewski, are working toward a better understanding of the regulation of gene expression.
In complex eukaryotes, such as humans, the path leading from the DNA sequence of a gene and the production of the final protein involves several crucial steps. RNA splicing is one such step. Messenger RNA must be "spliced" in order to remove non-coding segments known as "introns" and provide the final template for translation. The splicing process relies on various signals within the RNA sequence that direct the splicing machinery to appropriate sites. Mutations within splicing signals are known to lead to several genetic disorders, notably cystic fibrosis.
Dr. Majewski is taking a bioinformatics approach to understanding RNA splicing. He focuses on the statistical analysis of the human genomic DNA sequence and its comparison to related mammalian genomes. Conservation of sequences between species implies functional significance. Therefore by identifying conserved regions in the proximity of splice sites, and then applying statistical models, Dr. Majewski hopes to clarify the short nucleotide motifs that serve as binding sites for the proteins involved in splicing.
To validate his statistical results experimentally, Dr. Majewski is collaborating with researchers at the McGill University and Genome Quebec Innovation Centre. The results of his research will be used in mutation screening of candidate genes for human genetic disorders and will help identify genetic changes responsible for such diseases, thus contributing in the long-term to curing hereditary disease.