Mayo Research Featured on the Cover of Human Molecular Genetics

Jan 12, 2015 | Suzanne Ferguson | @suzannerferguson

The work of Dr. Xing Li, a bioinformatics scientist in the Division of Biomedical Statistics and Informatics at Mayo Clinic, was recently featured on the cover of Human Molecular Genetics. Dr. Li developed a software program to integrate the multi-dimensional patient genomic information to pinpoint the molecular mechanism in human diseases.

Bioinformatics, an interdisciplinary field that combines computer science, statistics, mathematics, and engineering to study and process biomedical data, plays a critical role in scientific discovery and biomedical research by helping to solve real clinical problems and unmet needs in healthcare. Because there is so much information in science experiments today, bioinformatics specialists are needed to simplify the understanding of all the data available.

Working with colleagues in the Todd and Karen Wanek Family Program for Hypoplastic Left Heart Syndrome (HLHS), Dr. Li applied his tool to study dilated cardiomyopathy (DCM), a disease in which the heart becomes weakened and enlarged, and cannot pump blood efficiently. DCM is a result of a mutation of the gene Rbm20 and carries the risk of progressive heart failure and sudden death. Although genetic research has established the linkage between the gene and early-onset DCM, the underlying basis of cellular and molecular dysfunction is still undetermined, making innovative treatments difficult to develop.

Dr. Li and other researchers approached this problem in a new way. Using an ordinary skin sample, the cells from a patient with DCM were bioengineered into heart cells. These heart cells were then harvested to release the RNA (or blueprints of each cell that informs them what to do and when to do it). The blueprints that control the function of the heart cells can now be compared to instruction manuals of others who do not have the heart disease. By comparing these samples Dr. Li was able to identify on which page there may be a problem in the manual, thus informing the scientists where to begin looking for possible solutions or therapies.

To integrate this analysis, Dr. Li had to create the software package to incorporate and visualize the gene-gene interaction networks, known as multi-dimensional interactome, in human disease. The inner network shows the gene interaction and hub genes. In other words, it illustrates the scientific data so that it can be visualized and used in the laboratory more quickly to help scientists make decisions on where to focus their research to improve heart function.

This new strategy revealed interactions between hub genes with time-dependent expression properties to further enhance the hypothesis of the disease-causing mechanism for mutations of the gene Rbm20 [1]. The strategy provides a comprehensive analysis of gigabytes of data into a simplified format allowing researchers to pinpoint potential targets that were previously buried in the sea of data and work to develop new, powerful therapeutic strategies.

In addition to appearing as the cover story in the journal of Human Molecular Genetics, Dr. Li’s work was presented in the official annual conference of International Society for Computational Biology (ISMB 2014). It is also applied in the project for building a congenital heart disease centric interactome – which is published in the peer-review journal Physiological Genomics and is recommended as the featured article by the editor [2] – and the study of myocardial infarction published in the Journal of Molecular and Cellular Cardiology [3].

The Todd and Karen Wanek Family Program for Hypoplastic Left Heart Syndrome (HLHS) is a collaborative network of specialists bonded by the vision of delaying or preventing heart failure for individuals affected by congenital heart defects including HLHS. The specialized team is addressing the various aspects of these defects by using research and clinical strategies ranging from basic science, to diagnostic imaging to regenerative therapies.