Data-mining software enables the reconstruction of a putative gene network containing several recently identified potential risk factors. Further analysis of this network could help researchers understand the biological bases for the connection between pathogen infection and Kawasaki disease.
© iStockphoto/dra_schwartz
Kawasaki disease (KD) is the most common cause of heart disease among children in the developed world, and has the potential to inflict severe coronary damage if not treated promptly.
However, remarkably little is known about the pathology of this inflammatory disorder, and although there is strong evidence for a hereditary component, research suggests that KD arises from the interplay of both genetic and environmental factors.
“All the epidemiology points to an infectious agent,” says Martin Hibberd of the A*STAR Genome Institute of Singapore. “There’s probably a bacterium or virus initiating the disease, but we have not consistently found a specific one of these in every case.” Hibberd has been studying KD for over a decade, but without a clear causative agent, he and his colleagues have been forced to speculate about the pathology of KD.
Fortunately, an ongoing collaboration between Hibberd’s group and members of the International Kawasaki Disease Genetics Consortium has now achieved important progress on this front. They used a multi-pronged approach to zoom in on genes of interest, working with KD patients and controls from four different countries in an effort to identify small genomic sequence variations, ‘single-nucleotide polymorphisms’ (SNPs), that might be associated with susceptibility to KD.
Between these two datasets, they characterized 40 SNPs with a potentially significant link to the disease, and subsequently identified eight genes containing sequence variations that may represent meaningful risk factors. These initial findings contained some promising leads, such as the gene encoding AT-binding transcription factor 1 (ZFHX3). “ZFHX3 has since been identified in two recent publications as influencing heart disease,” says Hibberd, “and this may show us how KD can lead to heart involvement.”
To establish additional biological context for their data, the researchers further analyzed their results with software that combs through published data to glean information about functional relationships between genes. This enabled them to fit several of their genes into a larger network of interacting factors, revealing a number of potential mechanisms by which an infectious agent could trigger the catastrophic inflammatory response observed in KD patients.
Having identified a number of promising starting points, Hibberd and his co-workers now intend to dig deeper to get at the roots of KD. “We are now looking for a more detailed understanding of the host response pathways involved,” he says, “and we are also finely mapping the genes we identified to try to find the specific genetic variations that are causing the disease.”
The A*STAR-affiliated authors in this highlight are from the Genome Institute of Singapore.
