"Exciting new approach" for identifying microRNAs

MicroRNAs (miRNAs) are genes which produce important elements that regulate a wide variety of processes in plants, animals and humans. MiRNAs are considered to be promising diagnostic and therapeutic candidates for the treatment of human diseases. Worldwide, scientists are seeking to develop methods to detect which miRNAs are active in tissue samples or to identify novel miRNA genes.

To date, researchers have identified more than 600 human miRNAs, each of which regulates the activity of several hundred proteins, the building and operating materials of life. Marc Friedländer, a PhD student in the laboratory of Nikolaus Rajewsky at the Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Germany, has now developed a software package named miRDeep. Using it, researchers can detect not only which miRNAs are active in a tissue sample, but can also discover previously unknown miRNAs. MiRDeep is based on the analysis of modern high-throughput sequencing technologies and modeling the activity of a key enzyme in the miRNA pathway.

The paper, written in collaboration with Wei Chen of the Max Planck Institute (MPI) for Molecular Genetics, Berlin, has been published in Nature Biotechnology* online (Vol. 26, No. 4, pp. 407 – 415, 008). It also reports more than 250 novel or unannotated miRNA genes, 15 of these are human, which Friedländer and his colleagues were able to identify.

RNA is an abbreviation for ribonucleic acid. It is a chemical relative of DNA and functions as carrier of genetic information, which the cell needs to produce proteins. Besides this messenger RNA there are also miRNAs, small RNA fragments, which bind to certain regions of messenger RNA and thus block the production of proteins. MiRNA genes thus regulate which proteins the body generates.

Researchers want to utilize this process. “For instance, cancer researchers compare cancer cells with healthy cells to find out which miRNAs might play a role in the development of cancer,” Rajewsky said, explaining the significance of miRNAs for basic medical research. “However, many known, but also still unknown miRNAs can only be found in small numbers in cells and are thus overlooked using traditional methods,” he added. With novel “deep sequencing” methods, researchers can detect even these miRNAs. Using these revolutionary high-throughput sequencing technologies, genetic material can be decoded more rapidly and at lower cost.

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“Until now,” Rajewsky explained, “the problem also involved analyzing the immense amount of data generated by deep sequencing. Such a machine can easily decode 100 million letters of DNA in 3.5 days. Moreover, cells produce many other RNAs, not only miRNAs.” Marc Friedländer developed the computer program “miRDeep”.

Using this program, researchers can discover signatures in the sequencing data which are generated in the production of miRNAs in the cell. MiRDeep searches the data for these traces and then computes the probability with which a potential precursor-miRNA will produce a real miRNA. MiRDeep can be downloaded as software package from the website of the Rajewsky research group.

“Due to the good collaboration of bioinformaticians and lab biologists, we have succeeded in testing miRDeep in practice,” Rajewsky said, describing the work of his research team. MDC researchers tested the new program by sequencing even small RNAs of human cancer cells and blood cells in the dog and analyzing these with miRDeep. They detected most of the already known miRNAs, but also 230 miRNAs that were previously unknown.

Various new miRNA genes could then also be validated by the researchers independently in the lab. “We started very early with the analysis of deep sequencing data and were thus able to gain experience, which is necessary considering the complexity and magnitude of the data.” He summed up by saying, “Generally, until now there have been very few published methods for analyzing this data. Right now we are just at the beginning of this exciting research.”

*miRDeep: Discovering miRNAs from deep sequencing data

Marc R. Friedländer1, Wei Chen2, Catherine Adamidi1 , Jonas Maaskola1, Ralf Einspanier3, Signe Knespel1, and Nikolaus Rajewsky1,*

1 Max Delbrück Centrum für Molekulare Medizin, Robert-Rössle-Strasse 10, D-13125 Berlin-Buch, Germany
2 Department of Human Molecular Genetics, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, D-14195 Berlin, Germany
3 Institute of Veterinary Biochemistry, Freie Universität Berlin, Oertzenweg 19b, D-14163 Berlin, Germany

doi:10.1038/nbt1394

Barbara Bachtler
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