With their new approach, the bioformatics expert and systems biologist Professor Nikolaus Rajewsky from the Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch and Dr. Kevin Chen from the Center for Comparative Functional Genomics, New York University, New York (USA) are now able to look more systematically for structures in the human genome which are likely to be deleterious and are the causes of human disease.
(idw/ehj.vt) – Looking at single inherited variations in the human genome, the researchers try to gain new insights into gene regulation and their impact on health and disease. They are especially interested in the development of gene regulation throughout millions of years of human evolution.
To do this, they combined two methods in genome research – the search for single nucleotide polymorphisms (SNPs), which can influence the correct expression of genes, with population genetics. This strategy enables the researchers not only to look at gene regulation, but also to detect those variations in the genome which can cause human disease. Their findings have now been published as a research highlight in Nature Genetics* (Vol. 38, No. 11, October 29, 2006). Gene regulation is a basic process which regulates human development and is important for health and disease. Genes are sections of the hereditary molecule DNA which contains the blue print for proteins, the building blocks and molecular machineries of life.
The DNA molecule is made up of 3,2 billion nucleotides. However, each human individual has a different sequence of nucleotides. One individual, for example, can have an Adenin (A) nucleotide whereas, at the same position, another individual has a cytosin (C) nucleotide.
Eleven Million Variations
Researchers estimate that, all in all, the human genome has eleven million of such single nucleotide variants or polymorphisms (SNPs). Their aim is, to detect these SNPs and find out their function in health and disease.
Various consortia have measured how often these millions of SNPs occur in different human populations. Based on this data, Professor Rajewsky and Dr. Chen were able to develop their new approach.
Professor Rajewsky, who until recently has been working at New York University, and Dr. Chen looked at microRNAs to research gene regulation. MicroRNAs are thought to regulate thousands of human genes, as Professor Rajewsky and other labs have previously shown. New studies suggest that they also play a key role in development, cancer, and metabolism.
MicroRNAs bind to specific short regions of DNA, called cis regulatory sites or microRNA targets. They are widely scattered over the genome and, at these binding sites, block the messenger RNA (mRNA) molecule. Thus, microRNAs repress the production of proteins and, therefore, also influence which genes and proteins are expressed, since mRNA is the transcribed DNA.
Professor Rajewsky and Dr. Chen combined information from SNP databases about bioinformatically predicted microRNA targets with population genetics. This way they were able to demonstrate that 80 per cent of the predicted microRNA targets are likely to be functional and good candidates to better understand human disease. At the same time, they could estimate how many functional microRNA targets exist specifically in humans, which may cause disease specific for humans. Finally, the researchers believe that their approach can be applied to other genetic regulatory mechanisms in the future.
*Natural Selection on Human MicroRNA Binding Sites Inferred from Single Nucleotide Polymorphism Data
Kevin Chen1 and Nikolaus Rajewsky1,2
1Center for Comparative Functional Genomics, Department of Biology, New York University, New York NY 10003, USA.
2 Max Delbrück Centrum für Molekulare Medizin, RobertRössleStrasse 10, BerlinBuch, Germany
Correspondence should be addressed to Nikolaus Rajewsky rajewsky@mdcberlin.de
Further Informations:
Barbara Bachtler
Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch
Robert-Rössle-Straße 10; 13125 Berlin; Germany
Phone: +49 (0) 30 94 06 – 38 96
Fax: +49 (0) 30 94 06 – 38 33
