Oxygen limitation (hypoxia), arising as a key stress factor due to flooding, negatively affects plant development. Consequently, maintaining root growth under such stress is crucial for plant survival, yet we know little about the root system\'s adaptions to low‐oxygen conditions and its regulation by phytohormones. In this study, we examine the impact of hypoxia and, herein, the regulatory role of group VII ETHYLENE‐RESPONSE FACTOR (ERFVII) transcription factors on root growth in Arabidopsis. We found lateral root (LR) elongation to be actively maintained by hypoxia via ERFVII factors, as erfVII seedlings possess hypersensitivity towards hypoxia regarding their LR growth. Pharmacological inhibition of abscisic acid (ABA) biosynthesis revealed ERFVII‐driven counteraction of hypoxia‐induced inhibition of LR formation in an ABA‐dependent manner. However, postemergence LR growth under hypoxia mediated by ERFVIIs was independent of ABA. In roots, ERFVIIs mediate, among others, the induction of ABA‐degrading ABA 8′‐hydroxylases CYP707A1 expression. RAP2.12 could activate the pCYC707A1:LUC reporter gene, indicating, combined with single mutant analyses, that this transcription factor regulates ABA levels through corresponding transcript upregulation. Collectively, hypoxia‐induced adaptation of the Arabidopsis root system is shaped by developmental reprogramming, whereby ERFVII‐dependent promotion of LR emergence, but not elongation, is partly executed through regulation of ABA degradation.

Arbuscular mycorrhizal (AM) symbiosis modulates plant‐herbivore interactions. Still, how it shapes the overall plant defence strategy and the mechanisms involved remain unclear. We investigated how AM symbiosis simultaneously modulates plant resistance and tolerance to a shoot herbivore, and explored the underlying mechanisms. Bioassays with Medicago truncatula plants were used to study the effect of the AM fungus Rhizophagus irregularis on plant resistance and tolerance to Spodoptera exigua herbivory. By performing molecular and chemical analyses, we assessed the impact of AM symbiosis on herbivore‐triggered phosphate (Pi)‐ and jasmonate (JA)‐related responses. Upon herbivory, AM symbiosis led to an increased leaf Pi content by boosting the mycorrhizal Pi‐uptake pathway. This enhanced both plant tolerance and herbivore performance. AM symbiosis counteracted the herbivore‐triggered JA burst, reducing plant resistance. To disentangle the role of the mycorrhizal Pi‐uptake pathway in the plant\'s response to herbivory, we used the mutant line ha1‐2, impaired in the H+‐ATPase gene HA1, which is essential for Pi‐uptake via the mycorrhizal pathway. We found that mycorrhiza‐triggered enhancement of herbivore performance was compromised in ha1‐2 plants. AM symbiosis thus affects the defence pattern of M. truncatula by altering resistance and tolerance simultaneously. We propose that the mycorrhizal Pi‐uptake pathway is involved in the modulation of the plant defence strategy.

Downy mildew in hop (Humulus lupulus L.) is caused by Pseudoperonospora humuli and generates significant losses in quality and yield. To identify the biochemical processes that confer natural downy mildew resistance (DMR), a metabolome- and genomewide association study was performed. Inoculation of a high density genotyped F1 hop population (n = 192) with the obligate biotrophic oomycete P. humuli led to variation in both the levels of thousands of specialized metabolites and DMR. We observed that metabolites of almost all major phytochemical classes were induced 48 hr after inoculation. But only a small number of metabolites were found to be correlated with DMR and these were enriched with phenylpropanoids. These metabolites were also correlated with DMR when measured from the non-infected control set. A genome-wide association study revealed co-localization of the major DMR loci and the phenylpropanoid pathway markers indicating that the major contribution to resistance is mediated by these metabolites in a heritable manner. The application of three putative prophylactic phenylpropanoids led to a reduced degree of leaf infection in susceptible genotypes, confirming their protective activity either directly or as precursors of active compounds.

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

The large WRKY transcription factor family is mainly involved in regulating plant immune responses. Arabidopsis WRKY33 is a key transcriptional regulator of hormonal and metabolic processes towards Botrytis cinerea strain 2100 infection and is essential for resistance. In contrast to B. cinerea strain 2100, the strain B05.10 is virulent on wild‐type (WT) Col‐0 Arabidopsis plants highlighting the genetic diversity within this pathogen species. We analysed how early WRKY33‐dependent responses are affected upon infection with strain B05.10 and found that most of these responses were strongly dampened during this interaction. Ectopic expression of WRKY33 resulted in complete resistance towards this strain indicating that virulence of B05.10, at least partly, depends on suppressing WRKY33 expression/protein accumulation. As a consequence, the expression levels of direct WRKY33 target genes, including those involved in the biosynthesis of camalexin, were also reduced upon infection. Concomitantly, elevated levels of the phytohormone abscisic acid (ABA) were observed. Molecular and genetic studies revealed that ABA negatively influences defence to B05.10 and effects jasmonic acid/ethylene (JA/ET) and salicylic acid (SA) levels. Susceptibility/resistance was determined by the antagonistic effect of ABA on JA, and this crosstalk required suppressing WRKY33 functions at early infection stages. This indicates that B. cinerea B05.10 promotes disease by suppressing WRKY33‐mediated host defences.

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

BackgroundUntargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.ResultsWe implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.ConclusionsIPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.

Phytochelatin synthases (PCS) play key roles in plant metal tolerance. They synthesize small metal‐binding peptides, phytochelatins, under conditions of metal excess. Respective mutants are strongly cadmium and arsenic hypersensitive. However, their ubiquitous presence and constitutive expression had long suggested a more general function of PCS besides metal detoxification. Indeed, phytochelatin synthase1 from Arabidopsis thaliana (AtPCS1) was later implicated in non‐host resistance. The two different physiological functions may be attributable to the two distinct catalytic activities demonstrated for AtPCS1, that is the dipeptidyl transfer onto an acceptor molecule in phytochelatin synthesis, and the proteolytic deglycylation of glutathione conjugates. In order to test this hypothesis and to possibly separate the two biological roles, we expressed a phylogenetically distant PCS from Caenorhabditis elegans in an AtPCS1 mutant. We confirmed the involvement of AtPCS1 in non‐host resistance by showing that plants lacking the functional gene develop a strong cell death phenotype when inoculated with the potato pathogen Phytophthora infestans. Furthermore, we found that the C. elegans gene rescues phytochelatin synthesis and cadmium tolerance, but not the defect in non‐host resistance. This strongly suggests that the second enzymatic function of AtPCS1, which remains to be defined in detail, is underlying the plant immunity function.