De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4

Discovery and isolation of novel capsaicinoids and their TRPV1-related activity

Chilis contain capsaicin and other structurally related molecules known as capsaicinoids. Capsaicin's target protein, the transient receptor potential cation channel subfamily V member 1 (TRPV1), has been linked to many post-activation effects, including changes in metabolism and pain sensation. Capsaicinoids also bind to TRPV1, but current studies often disregard non-capsaicin interactions. To fill in these gaps, we screened 40 different chili varieties derived from four Capsicum species by means of untargeted metabolomics and a rat TRPV1 (rTRPV1) calcium influx activation assay. The resulting capsaicinoid profiles were specific to each variety but only partially corresponded with species delimitations. Based on rTRPV1 activation elicited by crude chili extracts, capsaicinoids act in an additive manner and a capsaicinoid profile can serve as a gauge of this activation. In addition, we isolated eighteen capsaicinoids, including five previously unreported ones, and confirmed their structure by NMR and MS/MS. We then tested rTRPV1 activation by 23 capsaicinoids and three related compounds. This testing revealed that even slight deviations from the structure of capsaicin reduce the ability to activate the target, with a mere single hydroxylation on the acyl tail reducing potency towards rTRPV1 by more than 100-fold. In addition, we tested how rTRPV1 activity changes in the presence of capsaicin together with non-activating capsaicin analogs and weakly activating capsaicinoids and found both classes of molecules to positively modulate the effects of capsaicin. This demonstrates that even such compounds have measurable pharmacological effects, making a case for the use and study of natural chili extracts.

Exploring associations between metabolites and gene transcripts of common bean (Phaseolus vulgaris L.) in response to rust (Uromyces appendiculatus) infection

Common bean (Phaseolus vulgaris L.) faces escalating challenges resulting from the increasing prevalence of fungal pathogens such as rust caused by Uromyces appendiculatus, threatening yields and quality of the crop. Understanding P. vulgaris' disease response mechanisms is pivotal for the crop's resilience and food security. Current scientific understanding of underlying molecular mechanisms of P. vulgaris to U. appendiculatus is limited, particularly with respect to specialised molecular data, including metabolite profiles and gene expression. There is a significant knowledge gap in explicating precise metabolomic and transcriptional changes that occur in P. vulgaris upon interaction with U. appendiculatus, which limits strategies aimed at enhancing pathogen resistance. In this study, biological stress response strategies of common bean to the rust pathogen were elucidated through a combined metabolomic and transcriptomic profiling approach. Our findings revealed that U. appendiculatus triggered diverse levels of 30 known metabolites, primarily flavonoids, lipids, nucleosides, and phenylpropanoids among others. Transcriptome sequencing detected over 3000 differentially expressed genes, including multiple transcription factor families such as heat shock proteins (HSPs), cytochrome P450 monooxygenases (CYP), terpene synthases and WRKY transcription factors (TFs) among others. Integrative metabolome and transcriptome analysis showed that rust infection enriched metabolomic pathways, biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum, and purine metabolism among others. The metabolome and transcriptome integration approach employed in this study provides insights on molecular mechanisms underlying U. appendiculatus response in P. vulgaris' key developmental stages.

FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events

Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.

Untargeted Metabolomics and Targeted Phytohormone Profiling of Sweet Aloes (Euphorbia neriifolia) from Guyana: An Assessment of Asthma Therapy Potential in Leaf Extracts and Latex

Background/Objectives:Euphorbia neriifolia is a succulent plant from the therapeutically rich family of Euphorbia comprising 2000 species globally. E. neriifolia is used in Indigenous Guyanese asthma therapy. Methods: To investigate E. neriifolia's therapeutic potential, traditionally heated leaf, simple leaf, and latex extracts were evaluated for phytohormones and therapeutic compounds. Full scan, data-dependent acquisition, and parallel reaction monitoring modes via liquid chromatography Orbitrap mass spectrometry were used for screening. Results: Pathway analysis of putative features from all extracts revealed a bias towards the phenylpropanoid, terpenoid, and flavonoid biosynthetic pathways. A total of 850 compounds were annotated using various bioinformatics tools, ranging from confidence levels 1 to 3. Lipids and lipid-like molecules (34.35%), benzenoids (10.24%), organic acids and derivatives (12%), organoheterocyclic compounds (12%), and phenylpropanoids and polyketides (10.35%) dominated the contribution of compounds among the 13 superclasses. Semi-targeted screening revealed 14 out of 16 literature-relevant therapeutic metabolites detected, with greater upregulation in traditional heated extracts. Targeted screening of 39 phytohormones resulted in 25 being detected and quantified. Simple leaf extract displayed 4.4 and 45 times greater phytohormone levels than traditional heated leaf and latex extracts, respectively. Simple leaf extracts had the greatest nucleotide and riboside cytokinin and acidic phytohormone levels. In contrast, traditional heated extracts exhibited the highest free base and glucoside cytokinin levels and uniquely contained methylthiolated and aromatic cytokinins while lacking acidic phytohormones. Latex samples had trace gibberellic acid levels, the lowest free base, riboside, and nucleotide levels, with absences of aromatic, glucoside, or methylthiolated cytokinin forms. Conclusions: In addition to metabolites with possible therapeutic value for asthma treatment, we present the first look at cytokinin phytohormones in the species and Euphorbia genus alongside metabolite screening to present a comprehensive assessment of heated leaf extract used in Indigenous Guyanese asthma therapy.

Natural variation in root exudate composition in the genetically structured Arabidopsis thaliana in the Iberian Peninsula

Plant root exudates are involved in nutrient acquisition, microbial partnerships, and inter-organism signaling. Yet, little is known about the genetic and environmental drivers of root exudate variation at large geographical scales, which may help understand the evolutionary trajectories of plants in heterogeneous environments. We quantified natural variation in the chemical composition of Arabidopsis thaliana root exudates in 105 Iberian accessions. We identified up to 373 putative compounds using ultra-high-performance liquid chromatography coupled with mass spectrometry. We estimated the broad-sense heritability of compounds and conducted a genome-wide association (GWA) study. We associated variation in root exudates to variation in geographic, environmental, life history, and genetic attributes of Iberian accessions. Only 25 of 373 compounds exhibited broad-sense heritability values significantly different from zero. GWA analysis identified polymorphisms associated with 12 root exudate compounds and 26 known genes involved in metabolism, defense, signaling, and nutrient transport. The genetic structure influenced root exudate composition involving terpenoids. We detected five terpenoids related to plant defense significantly varying in mean abundances in two genetic clusters. Our study provides first insights into the extent of root exudate natural variation at a regional scale depicting a diversified evolutionary trajectory among A. thaliana genetic clusters chiefly mediated by terpenoid composition.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions

INTRODUCTION

Tree bacterial diseases are a threat in forestry due to their increasing incidence and severity. Understanding tree defence mechanisms requires evaluating metabolic changes arising during infection. Metabolite extraction affects the chemical diversity of the samples and, therefore, the biological relevance of the data. Metabolite extraction has been standardized for several biological models. However, little information is available regarding how it influences wood extract's chemical diversity.

OBJECTIVES

This study aimed to develop a methodological approach to obtain extracts from different tree species with the highest reproducibility and chemical diversity possible, to ensure proper coverage of the trees' metabolome.

METHODS

A full factorial design was used to evaluate the effect of solvent type, extraction temperature and number of extraction cycles on the metabolic profile, chemical diversity and antibacterial activity of four tree species.

RESULTS

Solvent, temperature and their interaction significantly affected the extracts' chemical diversity, while the number of extraction cycles positively correlated with yield and antibacterial activity. Although 60% of the features were recovered in all the tested conditions, differences in the presence and abundance of specific chemical classes per tree were observed, including organooxygen compounds, prenol lipids, carboxylic acids, and flavonoids.

CONCLUSIONS

Each tree species has a unique metabolic profile, which means that no single protocol is universally effective. Extraction at 50 °C for three cycles using 80% methanol or chloroform/methanol/water showed the best results and is suggested for studying wood metabolome. These observations highlight the need to tailor extraction protocols to each tree species to ensure comprehensive metabolome coverage for metabolic profiling.

Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics

Several database search methods have been employed in untargeted metabolomics utilizing high-resolution mass spectrometry to comprehensively annotate acquired product ion spectra. Recent technical advancements in in silico analyses have facilitated the sorting of the degree of coincidence between a query product ion spectrum, and the molecular structures in the database. However, certain search results may be false positives, necessitating a method for controlling the false discovery rate (FDR). This study proposes 4 simple methods for controlling the FDR in compound search results. Instead of preparing a decoy compound database, a decoy spectral dataset was created from the measured product-ion spectral dataset (target). Target and decoy product ion spectra were searched against an identical compound database to obtain target and decoy hits. FDR was estimated based on the number of target and decoy hits. In this study, 3 decoy generation methods, polarity switching, mirroring, and spectral sampling, were compared. Additionally, the second-rank method was examined using second-ranked hits in the target search results as decoy hits. The performances of these 4 methods were evaluated by annotating product ion spectra from the MassBank database using the SIRIUS 5 CSI:FingerID scoring method. The results indicate that the FDRs estimated using the second-rank method were the closest to the true FDR of 0.05. Using this method, a compound search was performed on 4 human metabolomic data-dependent acquisition datasets with an FDR of 0.05. The FDR-controlled compound search successfully identified several compounds not present in the Human Metabolome Database.

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

Advancing the Effect-Directed Identification in Combined Pollution: Using Pathways to Link Effects and Toxicants

The difficulty in associating diverse pollutants with mixture effects has led to significant challenges in identifying toxicants in combined pollution. In this study, pathways were used to link effects and toxicants. By pathways evaluated by the concentration-dependent transcriptome, individual effects were extended to molecular mechanisms encompassing 135 pathways corresponding to 6 biological processes. Accordingly, mechanism-based identification of toxicants was achieved by constructing a pathway toxicant database containing 2413 chemical-pathway interactions and identifying pathway active fragments of 72 pathways. The developed method was applied to two different wastewaters, industrial wastewater OB and municipal wastewater HL. Although lethality and teratogenesis were both observed at the individual level, different molecular mechanisms were revealed by pathways, with cardiotoxicity- and genotoxicity-related pathways significantly enriched in OB, and neurotoxicity- and environmental information processing-related pathways significantly enriched in HL. Further suspect and nontargeted screening generated 59 and 86 causative toxicants in OB and HL, respectively, among which 29 toxicants were confirmed, that interacted with over 90% of enriched pathways and contributed over 50% of individual effects. After upgrading treatments based on causative toxicants, consistent removal of toxicants, pathway effects, and individual effects were observed. Mediation by pathways enables mechanism-based identification, supporting the assessment and management of combined pollution.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Data-Driven Compound Identification in Atmospheric Mass Spectrometry

Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, the authors review the current state of data-driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry.

Suspect screening strategy for pesticide application history based on characteristics of trace metabolites

Pesticides are widely used to protect crops but can also threaten public health as they can remain in the environment for a long time. Additionally, some transformation products (TPs) of unknown toxicity, stability, or bioaccumulation properties can further be formed from the hydrolysis, photolysis and biodegradation of pesticides. The identification and quantification of those TPs can be challenging for environmental regulation and risk assessment due to a limited understanding about them. In this study, a suspect screening strategy for pesticide application history was developed and then used to organic products (tea). Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) was used to screen and identify the TPs in crops and their toxicity was subsequently predicted with the open-source software (ECOSAR and admetSAR). Finally, the SIRIUS software was applied and 142 TPs from 20 pesticides were identified in tea plants based on the fragmentation-degradation relationship. Of these, standards (level 1) and 53 were considered as tentatively identified (levels 2a and 2b). Some TPs were also found to be present in tea plants and soil after 65 days, thus indicating higher persistency or stability than parent pesticides. While others from diafenthiuron and neonicotinoids had higher predicted toxicity of daphnid, and demonstrated positive for honeybee toxicity. Suspect screening is a powerful tool to screen pesticide TPs on the complex matrix of crops. Such screening can provide potential evidence of pesticide application, especially in cases of illegal practices in organic farming.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Intrapopulation adaptive variance supports thermal tolerance in a reef-building coral

Coral holobionts are multi-species assemblages, which adds significant complexity to genotype-phenotype connections underlying ecologically important traits like coral bleaching. Small scale heterogeneity in bleaching is ubiquitous in the absence of strong environmental gradients, which provides adaptive variance needed for the long-term persistence of coral reefs. We used RAD-seq, qPCR and LC-MS/MS metabolomics to characterize host genomic variation, symbiont community and biochemical correlates in two bleaching phenotypes of the vertically transmitting coral Montipora capitata. Phenotype was driven by symbiosis state and host genetic variance. We documented 5 gene ontologies that were significantly associated with both the binary bleaching phenotype and symbiont composition, representing functions that confer a phenotype via host-symbiont interactions. We bred these corals and show that symbiont communities were broadly conserved in bulk-crosses, resulting in significantly higher survivorship under temperature stress in juveniles, but not larvae, from tolerant parents. Using a select and re-sequence approach, we document numerous gene ontologies selected by heat stress, some of which (cell signaling, antioxidant activity, pH regulation) have unique selection dynamics in larvae from thermally tolerant parents. These data show that vertically transmitting corals may have an adaptive advantage under climate change if host and symbiont variance interact to influence bleaching phenotype.

Bioprospecting the Metabolome of Plant Urtica dioica L.: A Fast Dereplication and Annotation Workflow in Plant Metabolomics

Plants have a pivotal role in ethnopharmacology, and their preparations are in use globally. However, getting down to the structure requires an effective workflow and mostly requires a time-consuming isolation process. Although bioassay-guided approaches are widely popular, they face a massive problem of rediscovery in recent times, especially in plant metabolomics. Mass spectrometry (MS)-based approach incorporated molecular networking via Global Natural Product Social Molecular Networking (GNPS) is considered here for the benefit of the fast screening of secondary metabolites. This study uses direct crude extracts obtained from various parts of the Urtica dioica plant for the characterization of secondary metabolites. The crude extract of the plant initially displayed promising antioxidant and anti-diabetic activities. Then, we employed mass spectrometry-based dereplication to identify the phytochemical components in the extracts. This led to the discovery of 7 unknown and 17 known secondary metabolites, which were further verified with the SIRIUS 4 platform, a computational tool for the annotation of compounds using tandem MS data. On the other hand, chasing the antioxidant activity of methanolic extract of U. dioica leaves, we employed a bioassay-guided isolation approach. With this method, we isolated and characterized compound 13, a known molecule, which possessed strong antioxidant activity without showing much toxicity in the brine shrimp lethality test at the test concentration of 1 mg/mL. With our results, we advocate the MS-based approach as a good starting point for the dereplication of compounds from the complex crude extracts of plants.

Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example

The complexity of metabolites in traditional Chinese medicine (TCM) hinders the comprehensive profiling and accurate identification of metabolites. In this study, an approach that integrates enhanced column separation, mass spectrometry post-processing and result verification was proposed and applied in the identification of flavonoids in Dalbergia odorifera. Firstly, column chromatography fractionation, followed by liquid chromatography-tandem mass spectrometry was used for systematic separation and detection. Secondly, a three-level data post-processing method was applied to the identification of flavonoids. Finally, fragmentation rules were used to verify the flavonoid compounds. As a result, a total of 197 flavonoids were characterized in D. odorifera, among which seven compounds were unambiguously identified in level 1, 80 compounds were tentatively identified by MS-DIAL and Compound Discoverer in level 2a, 95 compounds were annotated by Compound discoverer and Peogenesis QI in level 2b, and 15 compounds were exclusively annotated by using SIRIUS software in level 3. This study provides an approach for the rapid and efficient identification of the majority of components in herbal medicines.

DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics

The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.