ALBERT

Description: Background: Tardigrades are multicellular organisms, resistant to extreme environmental changes suchas heat, drought, radiation and freezing. They outlast these conditions in an inactive form(tun) to escape damage to cellular structures and cell death. Tardigrades are apparentlyable to prevent or repair such damage and are therefore a crucial model organism for stresstolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated byremoving the surrounding water to induce tun formation. During this process and thesubsequent rehydration, metabolites were measured in a time series by GC-MS.Additionally expressed sequence tags are available, especially libraries generated from theactive and inactive state. The aim of this integrated analysis is to trace changes intardigrade metabolism and identify pathways responsible for their extreme resistanceagainst physical stress. Results: In this study we propose a novel integrative approach for the analysis of metabolicnetworks to identify modules of joint shifts on the transcriptomic and metabolic levels. Wederive a tardigrade-specific metabolic network represented as an undirected graph with3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profilesare used to score the network nodes showing a significant change over time. The edges arescored according to information on enzymes from the EST data. Using this combinedinformation, we identify a key subnetwork (functional module) of concerted changes inmetabolic pathways, specific for de- and rehydration. The module is enriched in reactionsshowing significant changes in metabolite levels and enzyme abundance during thetransition. It resembles the cessation of a measurable metabolism (e.g. glycolysis andamino acid anabolism) during the tun formation, the production of storage metabolites andbioprotectants, such as DNA stabilizers, and the generation of amino acids and cellularcomponents from monosaccharides as carbon and energy source during rehydration. Conclusions: The functional module identifies relationships among changed metabolites (e.g.spermidine) and reactions and provides first insights in important altered metabolicpathways. With sparse and diverse data available, the presented integrated metabolitenetwork approach is suitable to integrate all existing data and analyse it in a combinedmanner.