Comprehensive Bioinformatics Study Predicts Molecular Causes of Many Genetic Diseases
It is widely known that genetic mutations cause disease. What are largely unknown are the mechanisms by which these mutations wreak havoc at the molecular level, giving rise to clinically observable symptoms in patients. Now, a new study using bioinformatics, led by scientists at the Buck Institute for Age Research, reports the ability to predict the molecular cause of many inherited genetic diseases.
These predictions involve tens of thousands of genetic disease-causing mutations and have led to the creation of a Web-based tool available to academic researchers who study disease. The research is published online in the February 9, 2010, edition of Human Mutation.
“We now have a quantitative model of function using bioinformatic methods that can predict things like the stability of the protein and how its stability is disrupted when a mutation occurs,” said Buck Institute faculty member Sean Mooney, who led the research team. “Traditionally, people have used a very time-consuming process based on evolutionary information about protein structure to predict molecular activity,” Mooney said, “I think we’re the first group to really quantitatively describe the universe of molecular functions that cause human genetic disease.”
The research was done in the contexts of inherited single gene diseases, complex diseases such as cardiovascular and developmental disorders and mutations in cancerous tumors. The study focused on amino acid substitutions (AAS), which are genetically driven changes in proteins that can give rise to disease, and utilized a series of complex mathematical algorithms to predict activity stemming from the mutations.
As a first step, researchers used available databases of known sites of protein function and built mathematical algorithms to predict new sites of protein function said Mooney. They then applied the algorithms to proteins that have disease-associated mutations assigned to them and looked for statistical co-occurrences of mutations that fell in or near those functional sites. Because the computer algorithms are imperfect, researchers compared that information against a data set of neutral AAS, ones that don’t cause human disease, said Mooney.
“We looked for statistical differences between the percentage of mutations that fell into the same functional site from both non-disease and disease-associated AAS and looked to see if there was a statistically significant enrichment or depletion of protein activity based on the type of AAS. That data was used to hypothesize the molecular mechanism of genetic disease,” said Mooney.
Mooney says 40,000 AAS were analyzed, which represents one of the most comprehensive studies of mutations. Describing the results, he used the analogy of a car as a protein — a big molecular machine. “We are predicting how this machine will break down,” said Mooney. “We’ve known the car isn’t working properly because it has some defect; now we can hypothesize that the symptom stems from a broken water pump.”
The Web tool, designed to enhance the functional profiling of novel AAS, has been made available at http://www..mutdb.org/profile. Mooney identified three different areas of research that could be furthered by use of the tool. Scientists who manage databases of clinically observed mutations for research purposes could develop hypotheses about what those mutations are causing on a molecular level; they also may be able to use the tool to correlate molecular activity to the clinical severity or subtype of a disease.
Mooney says cancer researchers re-sequencing tumors could use the tool to identify mutations that drive the progression of the malignancy. He also expects non-clinical researchers who work with mutations in proteins to use the tool to gain insight into what is causing the mutations.
“We are happy to collaborate with scientists, to share data and help them better identify hypotheses about the specific mutations they might be interested in,” said Mooney.
The project involved collaborations with several organizations. Scientists from Cardiff University in the UK supplied the Human Gene Mutation Database. Researchers at the Indiana University School of Informatics and Computing helped develop the statistical methods for measuring enrichment and depletion of the mutations. Scientists at the National Center for Biomedical Ontology at Stanford University mapped the disease names and provided a standard vocabulary for the work. Researchers at the Department of Biological Sciences at the University of Maryland collected the genetic data from the National Library of Medicine and formatted them for this study. All the analysis was done by scientists at the Buck Institute and Cardiff University.
Other Buck Institute researchers involved in the study include Uday S. Evani, Vidhya G. Krishnan, Kishore K. Kamati, and Angshuman Bagchi. Other collaborators include lead author Matthew Mort of the Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK, as well as David N. Cooper from Cardiff University; Peter H. Baenziger, Brandon Peters, Rakesh Sathyesh, and Bin Xue, Center for Computational Biology and Bioinformatics, Division of Hereditary Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine; Biao Li, Predrag Radivojac, and Fuxiao Xin, School of Informatics and Computing, Indiana University, Bloomington; Yanan Sun and Maricel Kann, Department of Biological Sciences, University of Maryland, Baltimore; and Nigam Shah of the National Center for Biomedical Ontology, Stanford University, Stanford, CA.
The research was funded from awards from the National Science Foundation, a grant from the IU Biomedical Research Council, Indiana University, the Showalter Trust and the Indiana Genomics Initiative (INGEN). INGEN is supported in part by the Lilly Endowment.