Bibenzyls are a specialized metabolite class found throughout the plant kingdom. One of the most prolific producers of bibenzyls are liverworts, specifically plants of the Radula genera. These plants possess an incredible diversity of bibenzyls, prenylated bibenzyls, and a few (bis)bibenzyls, several of which have medicinal properties, including perrottetinene, an analog of tetrahydrocannabinol from cannabis. To provide insight into the bibenzyls’ biosynthesis in planta, exogenous phytohormones were applied to in vitro grown Radula complanata and bibenzyl metabolite production was monitored with targeted and untargeted metabolomics. The targeted metabolomic analysis of six prenylated bibenzyls revealed that production of these metabolites was largely reduced when plants were treated with abscisic acid (AA), salicylic acid (SA), 1-naphthaleneacetic acid (NAA), or 6-benzylaminopurine (BAP). The reduction of these metabolites in the BAP and NAA treatment suggests that prenylated bibenzyl production is negatively correlated with vegetative plant growth. The reduction of bibenzyls at low AA and SA concentrations and mild increase at higher AA and SA concentrations suggest that their production is regulated by these stress hormones. In addition, six other bibenzyl metabolites were tentatively identified from the untargeted analysis. These results provide insight into the influence of phytohormones on the bioactive bibenzyl content of R. complanata.

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data.

Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead-end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab note-books (ELN) and laboratory information managementsystems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research.

1. Plants produce thousands of compounds, collectively called the metabolome, which mediate interactions with other organisms. The metabolome of an individual plant may change according to the number and nature of these interactions. We tested the hypothesis that tree diversity level affects the metabolome of four subtropical tree species in a biodiversity–ecosystem functioning experiment, BEF-China. We postulated that the chemical diversity of leaves, roots and root exudates increases with tree diversity. We expected that the strength of this diversity effect differs among leaf, root and root exudates samples. Considering their role in plant competition, we expected to find the strongest effects in root exudates. 2. Roots, root exudates and leaves of four tree species (Cinnamomum camphora, Cyclobalanopsis glauca, Daphniphyllum oldhamii and Schima superba) were sampled from selected plots in BEF-China. The exudate metabolomes were normalized over their non-purgeable organic carbon level. Multivariate analyses were applied to identify the effect of both neighbouring (local) trees and plot diversity on tree metabolomes. The species- and sample-specific metabolites were assigned to major compound classes using the ClassyFire tool, whereas potential metabolites related to diversity effects were annotated manually. 3. Individual tree species showed distinct leaf, root and root exudate metabolomes. The main compound class in leaves was the flavonoids, whereas carboxylic acids, prenol lipids and specific alkaloids were most prominent in root exudates and roots. Overall, plot diversity had a stronger effect on metabolome profiles than the local diversity. Leaf metabolomes responded more often to tree diversity level than exudates, whereas root metabolomes varied the least. We found no uniform or general pattern of alterations in metabolite richness or diversity in response to variation in tree diversity. The response differed among species and tissues. 4. Synthesis. Classification of metabolites supported initial ecological interpretation of differences among species and organs. Particularly, the metabolomes of leaves and root exudates respond to differences in tree diversity. These responses were neither linear nor uniform and individual metabolites showed different dynamics. More controlled interaction experiments are needed to dissect the causes and consequences of the observed shifts in plant metabolomes.

1. A major aim of ecology is to upscale attributes of individuals to understand processes at population, community and ecosystem scales. Such attributes are typically described using functional traits, that is, standardised characteristics that impact fitness via effects on survival, growth and/or reproduction. However, commonly used functional traits (e.g. wood density, SLA) are becoming increas-ingly criticised for not being truly mechanistic and for being questionable pre-dictors of ecological processes.2. This Special Feature reviews and studies how the metabolome (i.e. the thousands of unique metabolites that underpin physiology) can enhance trait-based ecology and our understanding of plant and ecosystem functioning.3. In this Editorial, we explore how the metabolome relates to plant functional traits, with reference to life-history trade-offs governing fitness between generations and plasticity shaping fitness within generations. We also identify solutions to challenges of acquiring, interpreting and contextualising metabolome data, and propose a roadmap for integrating the metabolome into ecology. 4. We next summarise the seven studies composing the Special Feature, which use the metabolome to examine mechanisms behind plant community assembly, plant-organismal interactions and effects of plants and soil micro-organisms on ecosystem processes. 5. Synthesis. We demonstrate the potential of the metabolome to improve mechanistic and predictive power in ecology by providing a high-resolution coupling between physiology and fitness. However, applying metabolomics to ecological questions is currently limited by a lack of conceptual, technical and data frameworks, which needs to be overcome to realise the full potential of the metabolome for ecology.

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.