Plant phytohormone pathways are regulated by an intricate network of signaling components and modulators, many of which still remain unknown. Here, we report a forward chemical genetics approach for the identification of functional SA agonists in Arabidopsis thaliana that revealed Neratinib (Ner), a covalent pan-HER kinase inhibitor drug in humans, as a modulator of SA signaling. Instead of a protein kinase, chemoproteomics unveiled that Ner covalently modifies a surface-exposed cysteine residue of Arabidopsis epoxide hydrolase isoform 7 (AtEH7), thereby triggering its allosteric inhibition. Physiologically, the Ner application induces jasmonate metabolism in an AtEH7-dependent manner as an early response. In addition, it modulates PATHOGENESIS RELATED 1 (PR1) expression as a hallmark of SA signaling activation as a later effect. AtEH7, however, is not the exclusive target for this physiological readout induced by Ner. Although the underlying molecular mechanisms of AtEH7-dependent modulation of jasmonate signaling and Ner-induced PR1-dependent activation of SA signaling and thus defense response regulation remain unknown, our present work illustrates the powerful combination of forward chemical genetics and chemical proteomics for identifying novel phytohormone signaling modulatory factors. It also suggests that marginally explored metabolic enzymes such as epoxide hydrolases may have further physiological roles in modulating signaling.

Introduction Liverworts are a group of non-vascular plants that possess unique metabolism not found in other plants. Many liverwort metabolites have interesting structural and biochemical characteristics, however the fluctuations of these metabolites in response to stressors is largely unknown. Objectives To investigate the metabolic stress-response of the leafy liverwort Radula complanata. Methods Five phytohormones were applied exogenously to in vitro cultured R. complanata and an untargeted metabolomic analysis was conducted. Compound classification and identification was performed with CANOPUS and SIRIUS while statistical analyses including PCA, ANOVA, and variable selection using BORUTA were conducted to identify metabolic shifts.Results It was found that R. complanata was predominantly composed of carboxylic acids and derivatives, followed by benzene and substituted derivatives, fatty acyls, organooxygen compounds, prenol lipids, and flavonoids. The PCA revealed that samples grouped based on the type of hormone applied, and the variable selection using BORUTA (Random Forest) revealed 71 identified and/or classified features that fluctuated with phytohormone application. The stress-response treatments largely reduced the production of the selected primary metabolites while the growth treatments resulted in increased production of these compounds. 4-(3-Methyl-2-butenyl)-5-phenethylbenzene-1,3-diol was identified as a biomarker for the growth treatments while GDP-hexose was identified as a biomarker for the stress-response treatments. Conclusion Exogenous phytohormone application caused clear metabolic shifts in Radula complanata that deviate from the responses of vascular plants. Further identification of the selected metabolite features can reveal metabolic biomarkers unique to liverworts and provide more insight into liverwort stress responses.

Enzyme-based synthetic chemistry provides a green way to synthesize industrially important chemical scaffolds and provides incomparable substrate specificity and unmatched stereo-, regio-, and chemoselective product formation. However, using biocatalysts at an industrial scale has its challenges, like their narrow substrate scope, limited stability in large-scale one-pot reactions, and low expression levels. These limitations can be overcome by engineering and fine-tuning these biocatalysts using advanced protein engineering methods. A detailed understanding of the enzyme structure and catalytic mechanism and its structure–function relationship, cooperativity in binding of substrates, and dynamics of substrate–enzyme–cofactor complexes is essential for rational enzyme engineering for a specific purpose. This Review covers all these aspects along with an in-depth categorization of various industrially and pharmaceutically crucial bisubstrate enzymes based on their reaction mechanisms and their active site and substrate/cofactor-binding site structures. As the bisubstrate enzymes constitute around 60% of the known industrially important enzymes, studying their mechanism of actions and structure–activity relationship gives significant insight into deciding the targets for protein engineering for developing industrial biocatalysts. Thus, this Review is focused on providing a comprehensive knowledge of the bisubstrate enzymes’ structure, their mechanisms, and protein engineering approaches to develop them into industrial biocatalysts.

IntroductionThe demand to develop efficient and reliable analytical methods for the quality control of nutraceuticals is on the rise, together with an increase in the legal requirements for safe and consistent levels of its active principles.ObjectiveTo establish a reliable model for the quality control of widely used Senna preparations used as laxatives and assess its phyto-equivalency.MethodsA comparative metabolomics approach via NMR and MS analyses was employed for the comprehensive measurement of metabolites and analyzed using chemometrics.ResultsUnder optimized conditions, 30 metabolites were simultaneously identified and quantified including anthraquinones, bianthrones, acetophenones, flavonoid conjugates, naphthalenes, phenolics, and fatty acids. Principal component analysis (PCA) was used to define relative metabolite differences among Senna preparations. Furthermore, quantitative 1H NMR (qHNMR) was employed to assess absolute metabolites levels in preparations. Results revealed that 6-hydroxy musizin or tinnevellin were correlated with active metabolites levels, suggesting the use of either of these naphthalene glycosides as markers for official Senna drugs authentication.ConclusionThis study provides the first comparative metabolomics approach utilizing NMR and UPLC–MS to reveal for secondary metabolite compositional differences in Senna preparations that could readily be applied as a reliable quality control model for its analysis.

IntroductionThe production of marine drugs in its normal habitats is often low and depends greatly on ecological conditions. Chemical synthesis of marine drugs is not economically feasible owing to their complex structures. Biotechnology application via elicitation represents a promising tool to enhance metabolites yield that has yet to be explored in soft corals.ObjectivesStudy the elicitation impact of salicylic acid (SA) and six analogues in addition to a systemic acquired resistance inducer on secondary metabolites accumulation in the soft coral Sarcophyton ehrenbergi along with the symbiont zooxanthellae and if SA elicitation effect is extended to other coral species S. glaucum and Lobophyton pauciliforum.MethodsPost elicitation in the three corals and zooxanthella, metabolites were extracted and analyzed via UHPLC-MS coupled with chemometric tools.ResultsMultivariate data analysis of the UHPLC-MS data set revealed clear segregation of SA, amino-SA, and acetyl-SA elicited samples. An increased level ca. 6- and 8-fold of the diterpenes viz., sarcophytonolide I, sarcophine and a C28-sterol, was observed in SA and amino-SA groups, respectively. Post elicitation, the level of diepoxy-cembratriene increased 1.5-fold and 2.4-fold in 1 mM SA, and acetyl-SA (aspirin) treatment groups, respectively. S. glaucum and Lobophyton pauciliforum showed a 2-fold increase of diepoxy-cembratriene levels.ConclusionThese results suggest that SA could function as a general and somewhat selective diterpene inducing signaling molecule in soft corals. Structural consideration reveals initial structure–activity relationship (SAR) in SA derivatives that seem important for efficient diterpene and sterol elicitation.

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little “arm twisting” in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

Univariate hypotheses tests such as Student’s t test or variance analysis (ANOVA) can help to answer a variety of questions in metabolomics data analysis. The statistical power of these tests depends on the setup of the experiment, the experimental design and the analytical variance of the actual observations. In this paper, we demonstrate how a well-designed pilot study prior to an experiment with the aim to find differences between e.g. several genotypes, can help to determine the variance at multiple levels ranging from biological variance, sample preparation to instrumental variances. Next, we illustrate how these variances can be used to obtain several parameters (e.g. minimum statistically significant effect, number of required replicates and error probabilities) which influence the design of the actual study. In particular, we are going to sketch how technical replicates can improve the performance of a test, when they are correctly used in the statistical analysis, e.g. with a hierarchical model. Finally, we demonstrate the process of evaluating the trade-off between different experimental designs with different replication strategies. The choice of an experimental design beyond the gut feeling can be influenced by factors such as costs, sample availability and the accuracy of of the tests. We use metabolite profiles of the model plant Arabidopsis thaliana measured on an UPLC-ESI/QqTOF-MS as real-world dataset, but the approach is equally applicable to other sample types and measurement methods like NMR based metabolomics.

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Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.