Web-Based Tool Predicts The Molecular Causes Of Many Genetic Diseases

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Web-Based Tool Predicts The Molecular Causes Of Many Genetic Diseases

http://www.medicalnewstoday.com/articles/178695.php

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 due to be

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

Mooney, PhD, 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 may also 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

(http://www.hgmd.org). 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 land 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.

Contributors to this work:

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 Mort of the Institute of Medical Genetics, School of

Medicine, Cardiff University, Cardiff, UK, as well as N. from

Cardiff University; H. Baenziger, s, 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 land,

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.