Scientists from Helmholtz Zentrum München have developed a breakthrough methodology for understanding metabolism related variations from experimental genomics data. Using proposed bioinformatics strategy they were able to demonstrate that in most cases identified disease-specific metabolism variations are not independent, e.g. deregulated genes from different pathways are linked to each other via one or two step of consecutive metabolic reactions. The methodology may prompt new drug development strategies to affect/change metabolism of the disease cells.

The results of the German research team, which consists of scientists from Helmholtz Zentrum München (National German Research Center for Environmental Health) have been published on 18 December 2008, in the open access journal "Genome Biology".

In the post-genomic era the targets of many clinical experimental studies are complex cell disorders. A standard experimental strategy is to compare the genetic/proteomics signature of cells in normal and anomalous states. As a result, a set of genes with differential activity is delivered. In the next step, the interpretation of identified genes in a model context is required. A widely accepted strategy is to infer biological processes that are most relevant to the analyzed gene list. The inference is based on prior knowledge of individual gene properties, such as gene biological functions or interactions. This common approach is usually referred as enrichment analysis. The major limitation of such type of analyses is an ability to understand the connections between genes from different and seems to be unrelated pathways.

In comparison to previously developed methods, KEGG spider provides a robust analytical framework for interpretation of gene lists in the context of a global gene metabolic network. The information of gene pair wise relations is widely exploited (gene A is related to gene B via metabolite C) and the inferred network model is not limited to the size of one metabolic pathway.

Examples of analysis of disease-specific genes by KEGG spider suggest that the split of metabolic reactions to canonical pathways is to some degree artificial. In most cases, metabolism-related genes were from several KEGG canonical pathways. However, the analysis with KEGG spider reveals that, if to consider the topology of the global gene metabolic network, these genes form a non-interrupted (maximum one or two gene is missing) disease-specific pathway, which run through several canonical pathways. These results also support a hypothesis that disease-specific metabolism variations in most cases are not independent, e.g. deregulated genes from different pathways are linked to each other via one or two step consecutive metabolic reactions. Of about twenty examples of analysis of disease-specific genes presented in the paper may serve as support for this hypothesis.

Antonov A.V., Dietmann S., Mewes G.W. KEGG spider: interpretation of genomics data in the context of the global gene metabolic network. Genome Biology 2008, 9:R179 doi:10.1186/gb-2008-9-12-r179.

About the Institute of Bioinformatics and Systems Biology (Munich Information Center for Protein Sequences)
The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) provides genome-related information and analyses in a systematic way. Institute supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis experimental data. For further information, please visit