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The rise in global temperature also favors the multiplication of pests and pathogens, which calls into question global food security. Plants have developed special coping mechanisms since they are sessile and lack an immune system. These mechanisms use a variety of secondary metabolites as weapons to avoid obstacles, adapt to their changing environment, and survive in less-than-ideal circumstances. Plant secondary metabolites include phenolic compounds, alkaloids, glycosides, and terpenoids, which are stored in specialized structures such as latex, trichomes, resin ducts, etc. Secondary metabolites help the plants to be safe from biotic stressors, either by repelling them or attracting their enemies, or exerting toxic effects on them. Modern omics technologies enable the elucidation of the structural and functional properties of these metabolites along with their biosynthesis. A better understanding of the enzymatic regulations and molecular mechanisms aids in the exploitation of secondary metabolites in modern pest management approaches such as biopesticides and integrated pest management. The current review provides an overview of the major plant secondary metabolites that play significant roles in enhancing biotic stress tolerance. It examines their involvement in both indirect and direct defense mechanisms, as well as their storage within plant tissues. Additionally, this review explores the importance of metabolomics approaches in elucidating the significance of secondary metabolites in biotic stress tolerance. The application of metabolic engineering in breeding for biotic stress resistance is discussed, along with the exploitation of secondary metabolites for sustainable pest management.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics-derived data.This Perspective, from a large group of metabolomics experts, provides best practices and simplified reporting guidelines for practitioners of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics.

Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak–peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.The NetID algorithm annotates untargeted LC-MS metabolomics data by combining known biochemical and metabolomic principles with a global network optimization strategy.

The high spatial resolution of secondary ion mass spectrometry and the high resolving power of the Orbitrap mass spectrometer are combined in a single imaging platform, the 3D OrbiSIMS. The instrument's capabilities for resolving lipids and neurotransmitters in the brain with subcellular spatial resolution, and a drug in a single cell in three dimensions is demonstrated. We report the development of a 3D OrbiSIMS instrument for label-free biomedical imaging. It combines the high spatial resolution of secondary ion mass spectrometry (SIMS; under 200 nm for inorganic species and under 2 μm for biomolecules) with the high mass-resolving power of an Orbitrap (>240,000 at m/z 200). This allows exogenous and endogenous metabolites to be visualized in 3D with subcellular resolution. We imaged the distribution of neurotransmitters—gamma-aminobutyric acid, dopamine and serotonin—with high spectroscopic confidence in the mouse hippocampus. We also putatively annotated and mapped the subcellular localization of 29 sulfoglycosphingolipids and 45 glycerophospholipids, and we confirmed lipid identities with tandem mass spectrometry. We demonstrated single-cell metabolomic profiling using rat alveolar macrophage cells incubated with different concentrations of the drug amiodarone, and we observed that the upregulation of phospholipid species and cholesterol is correlated with the accumulation of amiodarone.

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes. Cataloging microbial genomes from Earth’s environments expands the known phylogenetic diversity of bacteria and archaea.

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries. COSMIC outperforms spectral library search for metabolite annotation and annotates previously unseen structures.

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level. Unknown metabolites are classified from mass spectrometry data.

An array of new techniques allow researchers to catalog the chemical contents of a single cell, or even a single organelle.

Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena.de/sirius/), which provides a fast computational approach for molecular structure identification. SIRIUS 4 integrates CSI:FingerID for searching in molecular structure databases. Using SIRIUS 4, we achieved identification rates of more than 70% on challenging metabolomics datasets.SIRIUS 4 is a fast and highly accurate tool for molecular structure interpretation from mass-spectrometry-based metabolomics data.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC–HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC–HRMS-based metabolomics, with a focus on expanding metabolome coverage.This Review surveys ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC–HRMS), a highly sensitive, high-throughput technique that is used for analyzing a broad range of metabolites.