BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Compositional profiling of protein hydrolysates by high resolution liquid chromatography-mass spectrometry and chemometric analysis

Protein hydrolysates have attracted growing research and commercial attention due to their numerous nutritional, functional, and biological activities. However, only a limited range of proximate properties are determined routinely due to their substantial structural complexity and compositional variability. From both a manufacturing and functional perspective, it is of critical importance to monitor the compositional variations and identify potential similar or disparate features between different protein hydrolysates. In the current study, a single-approached method employing reverse phase ultra-high performance liquid chromatography coupled to high resolution electrospray ionization tandem mass spectrometry (RP-UHPLC-HR-ESI-MS/MS) was developed, optimized, and cross-validated for comprehensive structural and compositional profiling of a range of protein hydrolysates of varying raw materials, including soy, cotton, wheat, rice, and meat. Untargeted chemometric analysis and feature-based molecular network demonstrated potential for large-scale compositional assessment of protein hydrolysates without the need of prior component annotation. Signature features were identified to differentiate soy hydrolysates prepared from different batches of raw material and by different manufacturing processes. A hybrid approach combining de novo sequencing and target-decoy database homology search for peptide annotation is also described. Short peptides of 2 to 5 amino acids represented the most abundant components in soy protein hydrolysates (SPHs). A simple yet reliable integrated workflow for comprehensive structural and compositional profiling of protein hydrolysates was developed to enable an eventual correlation between their structure and function.

Waterlogging alone and combined with other abiotic stresses provides unique metabolic signatures at the plant-rhizosphere interface: A multi-omics perspective on root metabolome, root exudation and rhizomicrobiome

Despite the growing evidence on unique and unpredictable impact of stress combination over plants, waterlogging-combined stresses effects are still underexplored. Under those conditions, besides the impairment of plant aerial parts, the root system is particularly vulnerable, leading to consequences on plant survival. Here, we report on the short-term exposure of soil-grown Arabidopsis thaliana L. to waterlogging alone and combined with cold, heat, and salinity to inspect their antagonistic, additive or synergistic effects in the rhizosphere. To this aim, root metabolic changes, exudation profiles, and microbial diversity were investigated using a combination of metabolomics and metagenomics, and their interaction was analysed through multi-omics data integration. In roots, waterlogging strongly affected metabolism compared to other single stresses, causing a down-accumulation of targeted classes of compounds including, phenylpropanoids, sterols, terpenoids, and alkaloids. Additive and synergistic effects were reported in roots under waterlogging combined with heat and cold stresses, respectively. Regarding root exudates, flavonoids, terpenoids, and alkaloids were the main classes of compounds affected. Waterlogging caused a down-accumulation of all classes except for coumarins, and mixed trends were observed in waterlogging-combined stresses, with waterlogging-salinity stresses resulting in an ameliorating effect. Even though microbial communities' alpha- and beta-diversity remained stable, suggesting their resilience under short-term exposure, specific taxa modulation was recorded under each condition. Overall, these results contribute to understanding the hierarchical impact of waterlogging on root metabolism and exudation, influencing rhizosphere interactions. This multi-omics approach advances our understanding of plant stress responses and microbial dynamics, paving the way for future studies on adaptive mechanisms.

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.

Latest Developments in Direct and Non-Direct LC-MS Methods Based on Liquid Electron Ionization (LEI)

Mass spectrometry (MS) enables precise identification and quantification of molecules, particularly when combined with chromatography. The advent of atmospheric pressure ionization (API) techniques allowed the efficient coupling of liquid chromatography with MS (LC-MS), extending analyses to nonvolatile and thermolabile compounds. API techniques present limitations such as low informative capacity and reproducibility of mass spectra, increasing instrument complexity and costs. Other challenges include analyzing poorly polar molecules and matrix effects (ME), which negatively impact quantitative analyses, necessitating extensive sample purification or using expensive labeled standards. These limitations prompted the exploration of alternative solutions, leading to the development of the Liquid Electron Ionization (LEI) interface. The system has demonstrated excellent robustness and reproducibility. LEI has been employed to analyze various compounds, including pesticides, drugs of abuse, phenols, polycyclic aromatic hydrocarbons (PAHs), phthalates, and many others. Its versatility has been validated with single quadrupole, triple quadrupole, and QToF detectors, operating in electron ionization (EI) or chemical ionization (CI) modes and with both reverse phase liquid chromatography (RPLC) and normal phase liquid chromatography (NPLC). LEI has also been successfully integrated with the Microfluidic Open Interface (MOI), Membrane Introduction Mass Spectrometry (MIMS), and Microfluidic Water-Assisted Trap Focusing (M-WATF), broadening its application scope and consistently demonstrating promising results in terms of sensitivity and identification power. The most recent advancement is the development of Extractive-Liquid Sampling Electron Ionization-Mass Spectrometry (E-LEI-MS), a surface sampling and real-time analysis technique based on the LEI concept. This review article offers a comprehensive and up-to-date picture of the potential of LEI.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

A Mass Spectrometry Database for Sea Cucumber Triterpene Glycosides

Sea cucumber triterpene glycosides are a class of secondary metabolites that possess distinctive chemical structures and exhibit a variety of biological and pharmacological activities. The application of MS-based approaches for the study of triterpene glycosides allows rapid evaluation of the structural diversity of metabolites in complex mixtures. However, the identification of the detected triterpene glycosides can be challenging. The objective of this study is to establish the first spectral library containing the mass spectra of sea cucumber triterpene glycosides using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. The library contains the electrospray ionization tandem mass spectra and retention times of 191 triterpene glycosides previously isolated from 15 sea cucumber species and one starfish at the Laboratory of the Chemistry of Marine Natural Products of the G.B. Elyakov Pacific Institute of Bioorganic Chemistry. In addition, the chromatographic behavior and some structure-related neutral losses in tandem MS are discussed. The obtained data will accelerate the accurate dereplication of known triterpene glycosides and the annotation of novel compounds, as we demonstrated by the processing of LC-MS/MS data of Eupentacta fraudatrix extract.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Identification of Novel Natural Product Inhibitors against Matrix Metalloproteinase 9 Using Quantum Mechanical Fragment Molecular Orbital-Based Virtual Screening Methods

Matrix metalloproteinases (MMPs) are calcium-dependent zinc-containing endopeptidases involved in multiple cellular processes. Among the MMP isoforms, MMP-9 regulates cancer invasion, rheumatoid arthritis, and osteoarthritis by degrading extracellular matrix proteins present in the tumor microenvironment and cartilage and promoting angiogenesis. Here, we identified two potent natural product inhibitors of the non-catalytic hemopexin domain of MMP-9 using a novel quantum mechanical fragment molecular orbital (FMO)-based virtual screening workflow. The workflow integrates qualitative pharmacophore modeling, quantitative binding affinity prediction, and a raw material search of natural product inhibitors with the BMDMS-NP library. In binding affinity prediction, we made a scoring function with the FMO method and applied the function to two protein targets (acetylcholinesterase and fibroblast growth factor 1 receptor) from DUD-E benchmark sets. In the two targets, the FMO method outperformed the Glide docking score and MM/PBSA methods. By applying this workflow to MMP-9, we proposed two potent natural product inhibitors (laetanine 9 and genkwanin 10) that interact with hotspot residues of the hemopexin domain of MMP-9. Laetanine 9 and genkwanin 10 bind to MMP-9 with a dissociation constant (KD) of 21.6 and 0.614 μM, respectively. Overall, we present laetanine 9 and genkwanin 10 for MMP-9 and demonstrate that the novel FMO-based workflow with a quantum mechanical approach is promising to discover potent natural product inhibitors of MMP-9, satisfying the pharmacophore model and good binding affinity.

Structure-fragmentation study of pentacyclic triterpenoids using electrospray ionization quadrupole time-of-flight tandem mass spectrometry (ESI-QTOFMS/MS)

RATIONALE

Pentacyclic triterpenoids are secondary plant metabolites widespread in fruit peel, leaves, and stem bark. Due to their important biological activities, these compounds are widely screened using advance analytical techniques like electrospray ionization (ESI) mass spectrometry. Over the past few decades, the practice of ESI has been refined into a versatile ionization technique for a wide variety of analytes differing in their chemical makeup, size, complexity, and bimolecular stability.

METHODS

The structure-fragmentation relationships (SFRs) of 16 pentacyclic triterpenoids were studied using a positive ion ESI quadrupole time-of-flight mass spectrometry (ESI-QqTOFMS/MS) hybrid instrument.

RESULTS

ESI-QqTOFMS (positive ion mode) showed the presence of the protonated molecules [M + H]⁺ of most of analyzed compounds. Low-energy collision-induced dissociation MS/MS analysis of these molecules indicated multiple losses of water molecules and the loss of the formic acid moiety [M + H - HCOOH]⁺ and other substituents as the predominant pathway for further fragmentation. Key product ions were identified which resulted from the retro-Diels-Alder cleavage of the ring system. SFRs of all the compounds analyzed were also developed.

CONCLUSIONS

We developed a fragmentation pattern of pentacyclic triterpenoids using the ESI-QqTOFMS/MS technique. It was concluded that the formation of key product ions and loss of characteristic neutrals can give detailed insight into structural information about the basic structure and attached substituents.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

The year 2020 in natural product bioinformatics: an overview of the latest tools and databases

Covering: 2020 Bioinformatic approaches to document and analyse chemical structures, biosynthetic gene clusters and analytical data play an important role in the study of natural products. Every year, such a large number of new algorithms, tools and databases are released, that it is difficult to keep track of all the latest developments. The aim of this short article is to provide a concise overview of and reference to the major tools, methods and databases that have been released in the past year.