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.

The rapid annotation and identification by mass spectrometry techniques of flavonoids remains a challenge, due to their structural diversity and the limited availability of reference standards. This study applies a workflow to characterize two isoflavonoids, the orobol-C-glycosides analogs, using high-energy collisional dissociation (HCD)- and collision-induced dissociation (CID)-type fragmentation patterns, and also to evaluate the antioxidant effects of these compounds by ferric reducing antioxidant power (FRAP), 2,2′-azino-bis(3-ethylbenzothiazolin acid) 6-sulfonic acid (ABTS), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) methods. By the CID-type fragmentation, in positive mode and at all high-resolution mass spectrometry (HRMS) multiple stage, there were shown differences in the annotation of the compounds, mainly concerning some ratios of relative abundance. At CID-MS2 20 eV, the compounds could be efficiently characterized, because they present distinct base peaks [M + H]+ and [M + H–H2O]+ for the orobol-8-C- and orobol-6-C-glycoside, respectively. Similarly, by the HCD-type fragmentation, in HRMS2 stage, differences between orobol analogs in both mode of ionization were observed. However, the HR HCD-MS2 at 80 eV, in positive mode, generated more ions and each isomer presented different base peaks ions, [0,2X]+ for the orobol-8-C-glycoside and [0,3X]+ for the orobol-6-C-glycoside. By the DPPH, the 8-C-derivative showed a very close value compared with the standard rutin and, in the ABTS method, a higher radical-scavenging activity. In both methods, the EC50 of orobol-8-C-glycoside was almost twice better compared with orobol-6-C-glycoside. In FRAP, both C-glycosides showed a good capacity as Fe+3 reducing agents. We could realize that combined MS techniques, highlighting the positive mode of ionization, can be used to evaluate the isoflavones analogs being useful to differentiate between the isomeric flavones; therefore, these data are important to mass spectrometry dereplication studies become more efficient.

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.

Long‐chain ferulic acid esters, such as eicosyl ferulate (1), show a complex and analytically valuable fragmentation behavior under negative‐ion electrospay collision‐induced dissociation ((‐)‐ESI‐CID) mass spectrometry, as studied by use of a high‐resolution (Orbitrap) mass spectrometer. In a strong contrast to the very simple fragmentation of the [M + H]+ ion, which is discussed briefly, the deprotonated molecule, [M ‐ H]‐, exhibits a rich secondary fragmentation chemistry. It first loses a methyl radical (MS2) and the ortho‐quinoid [M ‐ H ‐ Me]‐• radical anion thus formed then dissociates by loss of an extended series of neutral radicals, CnH2n+1• (n = 0‐16) from the long alkyl chain, in competition with the expulsion of CO and CO2 (MS3). The further fragmentation (MS4) of the [M ‐ H ‐ Me ‐ C3H7]‐ ion, discussed as an example, and the highly specific losses of alkyl radicals from the [M ‐ H ‐ Me ‐ CO]‐• and [M ‐ H ‐ Me ‐ CO2]‐• ions provide some mechanistic and structural insights.

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule‐based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.

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.

Representative compounds with a 1,3‐dihydroxybenzene substructure belonging to different important polyphenol classes (stilbenes, flavones, isoflavones, flavonols, flavanones, flavanols, phloroglucinols, anthraquinones and bisanthraquinones) were investigated based on detailed high‐resolution tandem mass spectrometry measurements with an Orbitrap system under negative ion electrospray conditions. The mass spectral behaviour of these compound classes was compared among each other not only with respect to previously described losses of CO, CH2CO and C3O2 but also concerning the loss of CO2 and successive specific fragmentations. Furthermore, some unusual fragmentations such as the loss of a methyl radical during mass spectral decomposition are discussed. The obtained results demonstrate both similarities and differences in their mass spectral fragmentation under MSn conditions, allowing a characterization of the corresponding compound type.

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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