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Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.

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SummaryDuring a compatible interaction, the sebacinoid root-associated fungi Piriformospora indica and Sebacina vermifera induce modification of root morphology and enhance shoot growth in Arabidopsis thaliana.The genomic traits common in these two fungi were investigated and compared with those of other root-associated fungi and saprotrophs. The transcriptional responses of the two sebacinoid fungi and of Arabidopsis roots to colonization at three different symbiotic stages were analyzed by custom-designed microarrays.We identified key genomic features characteristic of sebacinoid fungi, such as expansions for gene families involved in hydrolytic activities, carbohydrate-binding and protein-protein interaction. Additionally, we show that colonization of Arabidopsis correlates with the induction of salicylic acid catabolism and accumulation of jasmonate and glucosinolates (GSLs). Genes involved in root developmental processes were specifically induced by S. vermifera at later stages during interaction.Using different Arabidopsis indole-GSLs mutants and measurement of secondary metabolites, we demonstrate the importance of the indolic glucosinolate pathway in the growth restriction of P. indica and S. vermifera and we identify indole-phytoalexins and specifically indole-carboxylic acids derivatives as potential key players in the maintenance of a mutualistic interaction with root endophytes.

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