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Verticillium longisporum is a soil‐borne vascular pathogen causing economic loss in rape. Using the model plant Arabidopsis this study analyzed metabolic changes upon fungal infection in order to identify possible defense strategies of Brassicaceae against this fungus. Metabolite fingerprinting identified infection‐induced metabolites derived from the phenylpropanoid pathway. Targeted analysis confirmed the accumulation of sinapoyl glucosides, coniferin, syringin and lignans in leaves from early stages of infection on. At later stages, the amounts of amino acids increased. To test the contribution of the phenylpropanoid pathway, mutants in the pathway were analyzed. The sinapate‐deficient mutant fah1‐2 showed stronger infection symptoms than wild‐type plants, which is most likely due to the lack of sinapoyl esters. Moreover, the coniferin accumulating transgenic plant UGT72E2‐OE was less susceptible. Consistently, sinapoyl glucose, coniferyl alcohol and coniferin inhibited fungal growth and melanization in vitro, whereas sinapyl alcohol and syringin did not. The amount of lignin was not significantly altered supporting the notion that soluble derivatives of the phenylpropanoid pathway contribute to defense. These data show that soluble phenylpropanoids are important for the defense response of Arabidopsis against V. longisporum and that metabolite fingerprinting is a valuable tool to identify infection‐relevant metabolic markers.

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite ( http://marvis.gobics.de ), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

In Arabidopsis, the fatty acid moiety of sphingolipids is mainly α-hydroxylated. The consequences of a reduction in this modification were analysed. Mutants of both Fatty Acid Hydroxylase genes (AtFAH1 and AtFAH2) were analysed for sphingolipid profiles. To elucidate further consequences of the mutations, metabolic analyses were performed and the influence on pathogen defence was determined. Ceramide and glucosylceramide profiles of double-mutant plants showed a reduction in sphingolipids with α-hydroxylated fatty acid moieties, and an accumulation of sphingolipids without these moieties. In addition, the free trihydroxylated long-chain bases and ceramides were increased by five- and ten-fold, respectively, whereas the amount of glucosylceramides was decreased by 25%. Metabolite analysis of the double mutant revealed salicylates as enriched metabolites. Infection experiments supported the metabolic changes, as the double mutant showed an enhanced disease-resistant phenotype for infection with the obligate biotrophic pathogen Golovinomyces cichoracearum. In summary, these results suggest that fatty acid hydroxylation of ceramides is important for the biosynthesis of complex sphingolipids. Its absence leads to the accumulation of long-chain bases and ceramides as their precursors. This increases salicylate levels and resistance towards obligate biotrophic fungal pathogens, confirming a role of sphingolipids in salicylic acid-dependent defence reactions.

Six transcription regulatory genes of the Verticillium plant pathogen, which reprogrammed nonadherent budding yeasts for adhesion, were isolated by a genetic screen to identify control elements for early plant infection. Verticillium transcription activator of adhesion Vta2 is highly conserved in filamentous fungi but not present in yeasts. The Magnaporthe grisea ortholog conidiation regulator Con7 controls the formation of appressoria which are absent in Verticillium species. Vta2 was analyzed by using genetics, cell biology, transcriptomics, secretome proteomics and plant pathogenicity assays. Nuclear Vta2 activates the expression of the adhesin‐encoding yeast flocculin genes FLO1 and FLO11. Vta2 is required for fungal growth of Verticillium where it is a positive regulator of conidiation. Vta2 is mandatory for accurate timing and suppression of microsclerotia as resting structures. Vta2 controls expression of 270 transcripts, including 10 putative genes for adhesins and 57 for secreted proteins. Vta2 controls the level of 125 secreted proteins, including putative adhesins or effector molecules and a secreted catalase‐peroxidase. Vta2 is a major regulator of fungal pathogenesis, and controls host‐plant root infection and H₂O₂ detoxification. Verticillium impaired in Vta2 is unable to colonize plants and induce disease symptoms. Vta2 represents an interesting target for controlling the growth and development of these vascular pathogens.

Patient-derived xenografts (PDX) have emerged as an important translational research tool for understanding tumor biology and enabling drug efficacy testing. They are established by transfer of patient tumor into immune compromised mice with the intent of using them as Avatars; operating under the assumption that they closely resemble patient tumors. In this study, we established 27 PDX from 100 resected gastric cancers and studied their fidelity in histological and molecular subtypes. We show that the established PDX preserved histology and molecular subtypes of parental tumors. However, in depth investigation of the entire cohort revealed that not all histological and molecular subtypes are established. Also, for the established PDX models, genetic changes are selected at early passages and rare subclones can emerge in PDX. This study highlights the importance of considering the molecular and evolutionary characteristics of PDX for a proper use of such models, particularly for Avatar trials. Peille et al. establish patient-derived xenografts (PDX) from gastric adenocarcinoma, expanding our knowledge on what subtypes can be developed into PDX models. Their extensive molecular characterization also reveals that PDX are subject to clonal selection in early passages, which is an important consideration for clinical studies.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background The annotation of biomolecular functions is an essential step in the analysis of newly sequenced organisms. Usually, the functions are inferred from predicted genes on the genome using homology search techniques. A high quality genomic sequence is an important prerequisite which, however, is difficult to achieve for certain organisms, such as hybrids or organisms with a large genome. For functional analysis it is also possible to use a de novo transcriptome assembly but the computational requirements can be demanding. Up to now, it is unclear how much of the functional repertoire of an organism can be reliably predicted from unassembled RNA-seq short reads alone. Results We have conducted a study to investigate to what degree it is possible to reconstruct the functional profile of an organism from unassembled transcriptome data. We simulated the de novo prediction of biomolecular functions for Arabidopsis thaliana using a comprehensive RNA-seq data set. We evaluated the prediction performance using several homology search methods in combination with different evidence measures. For the decision on the presence or absence of a particular function under noisy conditions we propose a statistical mixture model enabling unsupervised estimation of a detection threshold. Our results indicate that the prediction of the biomolecular functions from the KEGG database is possible with a high sensitivity up to 94 percent. In this setting, the application of the mixture model for automatic threshold calibration allowed the reduction of the falsely predicted functions down to 4 percent. Furthermore, we found that our statistical approach even outperforms the prediction from a de novo transcriptome assembly. Conclusion The analysis of an organism’s transcriptome can provide a solid basis for the prediction of biomolecular functions. Using RNA-seq short reads directly, the functional profile of an organism can be reconstructed in a computationally efficient way to provide a draft annotation in cases where the classical genome-based approaches cannot be applied.

State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

Amphidiploid fungal Verticillium longisporum strains Vl43 and Vl32 colonize the plant host Brassica napus but differ in their ability to cause disease symptoms. These strains represent two V. longisporum lineages derived from different hybridization events of haploid parental Verticillium strains. Vl32 and Vl43 carry same‐sex mating‐type genes derived from both parental lineages. Vl32 and Vl43 similarly colonize and penetrate plant roots, but asymptomatic Vl32 proliferation in planta is lower than virulent Vl43. The highly conserved Vl43 and Vl32 genomes include less than 1% unique genes, and the karyotypes of 15 or 16 chromosomes display changed genetic synteny due to substantial genomic reshuffling. A 20 kb Vl43 lineage‐specific (LS) region apparently originating from the Verticillium dahliae‐related ancestor is specific for symptomatic Vl43 and encodes seven genes, including two putative transcription factors. Either partial or complete deletion of this LS region in Vl43 did not reduce virulence but led to induction of even more severe disease symptoms in rapeseed. This suggests that the LS insertion in the genome of symptomatic V. longisporum Vl43 mediates virulence‐reducing functions, limits damage on the host plant, and therefore tames Vl43 from being even more virulent. A lineage‐specific region in the Verticillium longisporum Vl43 genome reduces fungal virulence of infected rapeseed host plants.