Researchers Introduce Yeast Augmented Network Analysis -- Novel Approach to Identify Drug Targets for Human Disease
Research led by Gennaro D’Urso, Ph.D., associate professor of molecular and cellular pharmacology, introduces the Yeast Augmented Network Analysis, a novel genetic approach to identifying new compounds for the treatment of human diseases. The study, funded by the Muscular Dystrophy Association, was featured in F1000Research, an open access journal that offers immediate publication, open peer review and full data sharing.
For the study, “Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases,” D’Urso and his team considered spinal muscular atrophy, an often fatal disease that destroys the nerves controlling voluntary muscle movement and the number one genetic cause of infant death in the United States.
More than 90 percent of spinal muscular atrophy cases are caused by deletion of SMN1, a gene encoding the survival motor neuron, a protein involved in the assembly of spliceosomal small nuclear ribonucleoprotein particles, which are essential to forming mature mRNA. In contrast, X-linked spinal muscular atrophy is caused by mutations in UBA1, a gene encoding the ubiquitin-activating enzyme 1.
By combining both experimental and bioinformatics data, the team identified potential new therapeutic targets for the debilitating disease.
“Our yeast system, with its small but complex eukaryotic genome and complete deletion library, is unique in allowing unbiased genome wide screening of deletions that alter human disease gene activity,” D’Urso said. “Moreover, YANA can be applied to any human gene, regardless of the phenotype or availability of endogenous mutations.”
Though many disease-associated genes have been identified, most genetic diseases remain untreatable. One path to treatment, as the study suggests, is to develop extensive genetic networks in which human disease genes function (or dysfunction) and to target therapies for the genes identified in those networks.
Gene networks, D’Urso says, are based on the principle that networks contain proteins that interact physically or functionally and that these interactions govern most, if not all, cellular functions. Given that network interactions may be conserved between human and model organisms, the researchers hypothesized that human disease networks could be identified in the fission yeast Schizosaccharomyces pombe, a simple, genetically tractable model organism coupled to existing protein-protein interaction databases.
That knowledge can then be translated to human cells to identify new drug targets.
“This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks,” D’Urso said. “Our approach could speed up the identification of new targets for the treatment of thousands of human diseases.”
To that end, D’Urso has formed the IOMICS Pathways Initiative, a non-profit corporation with the objective of generating an open access comprehensive collection of gene network data for the more than 3,000 genes associated with all 5,000 plus human genetic diseases.
“IOMICS is identifying gene networks linked to aging and human diseases, including cardiovascular disease, diabetes, neurodegenerative diseases and cancer,” D’Urso said. “By screening yeast deletion mutants that alter the activity of human genes expressed in yeast, IOMICS identifies functional gene network interactions that can be exploited for treating these diseases. Our network analysis of a gene linked to spinal muscular atrophy has already identified a new potential therapeutic target. This is only the beginning.”
University of Miami co-authors of the study include David J. Wiley, Ph.D., assistant scientist in the Department of Molecular and Cellular Pharmacology; Xiaodong Cai, associate professor of electrical and computer engineering; and Lisa Baumbach, Ph.D., voluntary associate professor of neurology. Funded by an NIH grant, Christine Beattie, Ph.D., professor of neuroscience at Ohio State University, also contributed to the study.