Scientists from Golm in the COSMOS

Resolving the Babylonian speech confusion in the area of metabolomics

October 25, 2012
It is not common that scientists from the United States of America look longingly at Europe. In many fields of science, innovations come from the other side of the pond. But in the area of metabolomics, the scientific analysis and investigation of metabolites, the picture looks quite different and Europeans are leading the way. What right now still hinders fast progress and fruitful cooperation among the different researchers, is the lack of a central database and community standards that scientists can use to exchange and compare their results. The EU-funded project COSMOS is supposed to bridge the gap and establish an e-infrastructure for metabolomics. It is supported by the bioinformaticians from the Max Planck Institute of Molecular Plant Physiology (MPI-MP). 

Insightful information about the current state of cells or tissues can be gained from metabolic profiles. These profiles comprise all metabolites that can be found in a cell at a given time, such as sugar molecules, hormones, or fatty acids, and scientists can draw conclusions about the future development of the cell from them. Will the plant accumulate a lot of biomass? Is a tumor malign and likely to metastasize? All these questions can be answered from metabolic profiles.

Therefore, metabolomics is not only a valuable diagnostic tool in human medicine, but is also widely implemented in the field of plant science, where it helps to facilitate the understanding of molecular processes in the cells. In the 1990s the MPI-MP in Potsdam, Germany, was one of the first institutes to really explore the use of metabolomics techniques. Prof. Dr. Lothar Willmitzer, one of the institute’s directors, is considered a pioneer in this area and Dr. Joachim Kopka and his team developed one of the first important databases for metabolomics data: the Golm Metabolome Database (GMD).

By now, many big research institutes and universities concentrate on metabolomics. What’s missing is a fruitful exchange between the individual workgroups. Everyone is doing their own thing. The reason for this is the many different technologies and measurement techniques that can be used to generate a metabolic profile. “There is an entire zoo of solutions with which I can generate data, but the individual datasets can hardly be compared,” explains Dr. Dirk Walther, a bioinformatician at the MPI-MP. That results in situations where scientists find themselves unable to use, evaluate, compare, or even reproduce data from their collaborators and colleagues. Fruitful synergies are not possible.

The COSMOS initiative – short for coordination of standards in metabolomics – aims to put things right. Under the auspices of the European Bioinformatics Institute (EBI), the scientists aim to create a database in which researchers from all disciplines can enter their data in standardized formats and upload information about the course of the experiment. The first, and maybe the most difficult, step is to agree on community standards. At the kick-off meeting in Barcelona, one of the participants hit the nail on the head when he said that “standards are like toothbrushes: a wonderful idea, but nobody wants to use one of the others.” Once that obstacle is resolved, the giant amount of data would finally be centrally accessible and easily comparable.

In other disciplines, such an e-infrastructure has long been established. A scientist who wants to publish an article about the decoding of a certain gene is asked by the publishing journal to first enter his data in a centralized database. This process was established to ensure that other scientists can evaluate and use the data. In the near future, metabolomics will profit from such a system as well.

-OMICS-Technologies

The suffix “-omic” is used by life scientists to state that they are focusing their attention on a group of similar elements. Before they targeted the cell’s metabolites, they concentrated their analyses on individual molecular groups: for example, either the genetic material (genomics) or the proteins (proteomic).

[CS]

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