Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Multilayered visual metabolomics analysis framework for enhanced exploration of functional components in wolfberry

Wolfberry, regarded as a nutritious fruit, has garnered significant attention in the food industry due to potential health benefits. However, the tissue-specific distribution and dynamic accumulation patterns of nutritional metabolites such as flavonoids are still unclear. In this study, a novel spatial metabolomics framework was developed, incorporating instrumental optimization, metabolite identification, molecular network analysis, metabolic pathway mapping, and machine learning-based imaging. Using DESI-MSI, this approach enabled rapid, non-destructive, in situ analysis of wolfberry metabolites with enhanced sensitivity and spatial resolution. Detailed insights into chemical and spatial changes during ripening were obtained, with a focus on flavonoids. The visualization of the flavonoid biosynthetic pathway highlighted the impact of C-3 hydroxylation on flavonoid redistribution. Furthermore, a classification model achieved a prediction accuracy exceeding 99 %, consistent with metabolic network analyses. This framework provides a powerful tool for plant metabolomics, facilitating the exploration of functional components and metabolic pathways.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation

RATIONALE

Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation.

ARCHITECTURES

Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search.

CONCLUSIONS

In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

Mass spectral database-based methodologies for the annotation and discovery of natural products

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.

An ensemble model of machine learning regression techniques and color spaces integrated with a color sensor: application to color-changing biochemical assays

Non-destructive color sensors are widely applied for rapid analysis of various biological and healthcare point-of-care applications. However, existing red, green, blue (RGB)-based color sensor systems, relying on the conversion to human-perceptible color spaces like hue, saturation, lightness (HSL), hue, saturation, value (HSV), as well as cyan, magenta, yellow, key (CMYK) and the CIE L*a*b* (CIELAB) exhibit limitations compared to spectroscopic methods. The integration of machine learning (ML) techniques presents an opportunity to enhance data analysis and interpretation, enabling insights discovery, prediction, process automation, and decision-making. In this study, we utilized four different regression models integrated with an RGB sensor for colorimetric analysis. Colorimetric protein concentration assays, such as the bicinchoninic acid (BCA) assay and the Bradford assay, were chosen as model studies to evaluate the performance of the ML-based color sensor. Leveraging regression models, the sensor effectively interprets and processes color data, facilitating precision color detection and analysis. Furthermore, the incorporation of diverse color spaces enhances the sensor's adaptability to various color perception models, promising precise measurement, and analysis capabilities for a range of applications.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation

MOTIVATION

A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule.

RESULTS

We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule.

AVAILABILITY AND IMPLEMENTATION

The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Heterogeneous Transformation of Ginsenoside Rb1 with Ethanol Using Heteropolyacid-Loaded Mesoporous Silica and Identification by HPLC-MS

Rare ginsenosides with major pharmacological effects are barely present in natural ginseng and are required to be obtained by transformation. In the current study, ginsenoside Rb1 was chemically transformed with the involvement of ethanol molecules to prepare rare ginsenosides using the synthesized heterogeneous catalyst 12-HPW@MeSi. A total of 16 transformation products were obtained and identified using high-performance liquid chromatography coupled with multistage tandem mass spectrometry and high-resolution mass spectrometry. Ethanol molecules were involved in the production of 6 transformation products by adding to the C-20(21), C-20(22), or C-24(25) double bonds on the aglycone to produce ethoxyl groups at the C-25 and C-20 positions. Transformation pathways of ginsenoside Rb1 are summarized, which involve deglycosylation, elimination, cycloaddition, epimerization, and addition reactions. In addition, 12-HPW@MeSi was recyclable through a simple centrifugation, maintaining an 85.1% conversion rate of Rb1 after 3 cycles. This work opens up an efficient and recycled process for the preparation of rare ginsenosides with the involvement of organic molecules.

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma

The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.

Mechanism of formation and ion mobility separation of protomers and deprotomers of diaminobenzoic acids and aminophthalic acids

Aminobenzoic acids are well-established candidates for understanding the formation of isomeric ions in positive mode electrospray ionization as they yield both N- and O-protomers (prototropic isomers) at the amine and carbonyl sites, respectively. In the present work, a combination of ion mobility-mass spectrometry and density functional theory calculations to determine the protonation and deprotonation behaviour of four diamino benzoic acid and four aminophthalic acid isomers is presented. The additional COOH group on the ring of aminophthalic acids provides experimental evidence regarding the mechanism of intramolecular NH₃⁺ → O proton transfer, which has been the subject of debate in recent years. To determine the proton acceptor O atom, ion mobility spectra of the fragments of protomers were used as a new method for the confidential assignment of the O-protomer structure, confirming only short-distance intramolecular NH₃⁺ → O proton transfer. Additionally, the substitution pattern both influences the basicity of the protonation sites and enables these molecules to form internal hydrogen bonds with the protonated or deprotonated sites. The formation of the hydrogen bonds in the deprotonated aminophthalic acids changed the charge distribution and subsequently their ion mobility-derived collision cross sections in nitrogen (CCS_N2) leading to separation of the four isomers studied. Finally, an interesting effect of the substitution pattern was observed as a synergistic electron-donating effect of the amine groups of 3,5-diaminobenzoic acid on enhancing the basicity of the carbon atom C2 of the ring and previously unreported formation of a C-protomer within aminobenzoic acid systems.

An end-to-end deep learning framework for translating mass spectra to de-novo molecules

Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

MS-FINDER and SIRIUS for phenolic compound identification from high-resolution mass spectrometry data

LC-HR-MS/MS is the predominant analytical technique in phenolic compound (PC) research. However, the manual interpretation of mass spectra is a heavy nontrivial time-consuming task and depends on mass spectrometry and phenolic compounds fragmentation deep knowledge. We think this manual approach should be partially translated into a practical software that allows users to perform such complicated analyses. In silico fragmentation software have been tested for small molecule identification, MS-FINDER and SIRIUS stood out at identification contests and challenges. We evaluated both software to identify PC from two data categories: 1^st MS/MS spectra from 18 phenolic compound standards (PCS) and 2^nd phenolic compounds from 8 food samples (FPC) (coffee, green tea, cranberry juice, grape juice, orange juice, apple juice, soy extract and parsley extract). MS-FINDER and SIRIUS were able to correctly identifymore than 90% of the PCS by LC-HR-MS/MS. The main FPC were also correctly identified by MS-FINDER (70%) and SIRIUS (38%). We highlight that these software were unable to differentiate PC isomers. This task is only possible by using additional information, such as chromatographic behavior and manual analysis of the relative intensity of fragments in the MS/MS spectra. Therefore, the combination of initial screening by using MS-FINDER and SIRIUS with manual analyses of additional information is a powerful and efficient approach for identifying phenolic compounds.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]^-, 155 [M + H]⁺, and 138 [M + Na]⁺ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a Δ_CCS threshold of 2.8% and a mass error of 10 ppm.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS²). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC₅₀) values from the CompTox database and validated for 219 chemicals and 420 MS² spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC₅₀ values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC₅₀ values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log K_OW.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Higher-Energy Collisional Dissociation Mass Spectral Networks for the Rapid, Semi-automated Characterization of Known and Unknown Ribonucleoside Modifications

Higher-energy collisional dissociation (HCD) of modified ribonucleosides generates characteristic and highly reproducible nucleoside-specific tandem mass spectra (MS/MS). Here, we demonstrate the capability of HCD spectra in combination with spectral matching for the semi-automated characterization of ribonucleosides. This process involved the generation of an HCD spectral library and the establishment of a mass spectral network for rapid detection with high sensitivity and specificity in a retention time-independent fashion. Systematic spectral matching analysis of the MS/MS spectra of tRNA hydrolysates from different organisms has helped us to uncover evidence for the existence of novel ribonucleoside modifications such as s²Cm and OHyW-14. Such an untargeted label-free approach has the potential to be integrated with other methods, including those that use isotope labeling, to simplify the characterization of unknown modified ribonucleosides. These findings suggest the compilation of a universal spectral network, for the characterization of known and unknown ribonucleosides, could accelerate discoveries in the epitranscriptome.

Pathway-Based Metabolomics Analysis Reveals Biosynthesis of Key Flavor Compounds in Mango

Mango is a tropical fruit with global demand as a result of its high sensory quality and nutritional attributes. Improving fruit quality at the consumer level could increase demand, but fruit quality is a complex trait requiring a deep understanding of flavor development to uncover key pathways that could become targets for improving sensory quality. Here, a pathway-based metabolomics (untargeted and targeted) approach was used to explore biosynthetic mechanisms of key flavor compounds with five core metabolic pathways (butanoate metabolism, phenylalanine biosynthesis and metabolism, terpenoid backbone biosynthesis, linoleic and linolenic acid pathway, and carbon fixation and sucrose metabolism) in three mango cultivars. The relationships between flavor precursors and flavor compounds were identified using correlation analysis. With these novel strategies, differentially regulated metabolic flux through the pathways was first elucidated, demonstrating possible mechanisms of key flavor formation and regulation in mango fruits.

Computed Mass-Fragmentation Energy Profiles of Some Acetalized Monosaccharides for Identification in Mass Spectrometry

Our study found that quantum calculations can differentiate fragmentation energies into isomeric structures with asymmetric carbon atoms, such as those of acetalized monosaccharides. It was justified by the good results that have been published in recent years on the discrimination of structural isomers and diastereomers by correlating the calculated mass energy fragmentation profiles with their mass spectra. Based on the quantitative structure–fragmentation relationship (QSFR), this technique compares the intensities of primary ions from the experimental spectrum using the mass energy profiles calculated for the candidate structures. Maximum fit is obtained for the true structure. For a preliminary assessment of the accuracy of the identification of some di-O-isopropylidene monosaccharide diastereomers, we used fragmentation enthalpies (ΔfH) and Gibbs energies (ΔfG) as the energetic descriptors of fragmentation. Four quantum chemical methods were used: RM1, PM7, DFT ΔfH and DFT ΔfG. The mass energy database shows that the differences between the profiles of the isomeric candidate structures could be large enough to be distinguished from each other. This database allows the optimization of energy descriptors and quantum computing methods that can ensure the correct identification of these isomers.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Two New Fumarprotocetraric Acid Lactones Identified and Characterized by UHPLC-PDA/ESI/ORBITRAP/MS/MS from the Antarctic Lichen Cladonia metacorallifera

Lichens are symbiotic organisms between algae and fungi, which are makers of secondary compounds named as lichen substances. Hyphenated techniques have significantly helped natural product chemistry, especially UHPLC/ESI/MS/MS in the identification, separation, and tentative characterization of secondary metabolites from natural sources. Twenty-five compounds were detected from the Antarctic lichen Cladonia metacorallifera for the first time using UHPLC-PDA/ESI/Orbitrap/MS/MS. Compounds 5 and 7 are reported as new compounds, based on their MS/MS fragmentation routes, and considered as fumarprotocetraric acid derivatives. Besides, ten known phenolic identified as orsellinic acid, ethyl 4-carboxyorsellinate, psoromic acid isomer, succinprotocetraric acid, siphulellic acid, connorstictic acid, cryptostictic acid, lecanoric acid, lobaric acid and gyrophoric acid are noticed for the first time in the Cladonia genus.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Metabolomic analysis elucidates how shade conditions ameliorate the deleterious effects of greening (Huanglongbing) disease in citrus

Under tropical and subtropical environments, citrus leaves are exposed to excess sunlight, inducing photoinhibition. Huanglongbing (HLB, citrus greening), a devastating phloem-limited disease putatively caused by Candidatus Liberibacter asiaticus, exacerbates this challenge with additional photosynthetic loss and excessive starch accumulation. A combined metabolomics and physiological approach was used to elucidate whether shade alleviates the deleterious effects of HLB in field-grown citrus trees, and to understand the underlying metabolic mechanisms related to shade-induced morpho-physiological changes in citrus. Using metabolite profiling and multinomial logistic regression, we identified pivotal metabolites altered in response to shade. A core metabolic network associated with shade conditions was identified through pathway enrichment analysis and metabolite mapping. We measured physio-biochemical responses and growth and yield characteristics. With these, the relationships between metabolic network and the variables measured above were investigated. We found that moderate-shade alleviates sink limitation by preventing excessive starch accumulation and increasing foliar sucrose levels. Increased growth and fruit yield in shaded compared with non-shaded trees were associated with increased photosystem II efficiency and leaf carbon fixation pathway metabolites. Our study also shows that, in HLB-affected trees under shade, the signaling of plant hormones (auxins and cytokinins) and nitrogen supply were downregulated with reducing new shoot production likely due to diminished needs of cell damage repair and tissue regeneration under shade. Overall, our findings provide the first glimpse of the complex dynamics between cellular metabolites and leaf physiological functions in citrus HLB pathosystem under shade, and reveal the mechanistic basis of how shade ameliorates HLB disease.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.

The evolving landscape of untargeted metabolomics

AIMS

Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics.

DATA SYNTHESIS

Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available.

CONCLUSIONS

Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Metabolic profiling of epidermal and mesophyll tissues under water-deficit stress in Opuntia ficus-indica reveals stress-adaptive metabolic responses

Cactus pear (Opuntia ficus-indica) is a high productivity species within the Cactaceae grown in many semiarid parts of the world for food, fodder, forage, and biofuels. O. ficus-indica utilises obligate crassulacean acid metabolism (CAM), an adaptation that greatly improves water-use efficiency (WUE) and reduces crop water usage. To better understand CAM-related metabolites and water-deficit stress responses of O. ficus-indica, comparative metabolic profiling was performed on mesophyll and epidermal tissues collected from well-watered and water-deficit stressed cladodes at 50% relative water content (RWC). Tissues were collected over a 24-h period to identify metabolite levels throughout the diel cycle and analysed using a combination of acidic/basic ultra-high-performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS) and gas chromatography/mass spectrometry (GC/MS) platforms. A total of 382 metabolites, including 210 (55%) named and 172 (45%) unnamed compounds, were characterised across both tissues. Most tricarboxylic acid (TCA) cycle and glycolysis intermediates were depleted in plants undergoing water-deficit stress indicative of CAM idling or post-idling, while the raffinose family oligosaccharides (RFO) accumulated in both mesophyll and epidermal tissues as osmoprotectants. Levels of reduced glutathione and other metabolites of the ascorbate cycle as well as oxylipins, stress hormones such as traumatic acid, and nucleotide degradation products were increased under water-deficit stress conditions. Notably, tryptophan accumulation, an atypical response, was significantly (24-fold) higher during all time points in water-deficit stressed mesophyll tissue compared with well-watered controls. Many of the metabolite increases were indicative of a highly oxidising environment under water-deficit stress. A total of 34 unnamed metabolites also accumulated in response to water-deficit stress indicating that such compounds might play important roles in water-deficit stress tolerance.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Marine Community Metabolomes Carry Fingerprints of Phytoplankton Community Composition

Phytoplankton transform inorganic carbon into thousands of biomolecules that represent an important pool of fixed carbon, nitrogen, and sulfur in the surface ocean. Metabolite production differs between phytoplankton, and the flux of these molecules through the microbial food web depends on compound-specific bioavailability to members of a wider microbial community. Yet relatively little is known about the diversity or concentration of metabolites within marine plankton. Here, we compare 313 polar metabolites in 21 cultured phytoplankton species and in natural planktonic communities across environmental gradients to show that bulk community metabolomes reflect the chemical composition of the phytoplankton community. We also show that groups of compounds have similar patterns across space and taxonomy, suggesting that the concentrations of these compounds in the environment are controlled by similar sources and sinks. We quantify several compounds in the surface ocean that represent substantial understudied pools of labile carbon. For example, the N-containing metabolite homarine was up to 3% of particulate carbon and is produced in high concentrations by cultured Synechococcus, and S-containing gonyol accumulated up to 2.5 nM in surface particles and likely originates from dinoflagellates or haptophytes. Our results show that phytoplankton composition directly shapes the carbon composition of the surface ocean. Our findings suggest that in order to access these pools of bioavailable carbon, the wider microbial community must be adapted to phytoplankton community composition.

IMPORTANCE Microscopic phytoplankton transform 100 million tons of inorganic carbon into thousands of different organic compounds each day. The structure of each chemical is critical to its biological and ecosystem function, yet the diversity of biomolecules produced by marine microbial communities remained mainly unexplored, especially small polar molecules which are often considered the currency of the microbial loop. Here, we explore the abundance and diversity of small biomolecules in planktonic communities across ecological gradients in the North Pacific and within 21 cultured phytoplankton species. Our work demonstrates that phytoplankton diversity is an important determinant of the chemical composition of the highly bioavailable pool of organic carbon in the ocean, and we highlight understudied yet abundant compounds in both the environment and cultured organisms. These findings add to understanding of how the chemical makeup of phytoplankton shapes marine microbial communities where the ability to sense and use biomolecules depends on the chemical structure.

Mass spectrometry based untargeted metabolomics for plant systems biology

Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.

Maize specialized metabolome networks reveal organ-preferential mixed glycosides

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Integration of LC/MS-based molecular networking and classical phytochemical approach allows in-depth annotation of the metabolome of non-model organisms - The case study of the brown seaweed Taonia atomaria

Untargeted LC-MS based metabolomics is a useful approach in many research areas such as medicine, systems biology, environmental sciences or even ecology. In such an approach, annotation of metabolomes of non-model organisms remains a significant challenge. In this study, an analytical workflow combining a classical phytochemical approach, using the isolation and the full characterization of the chemical structure of natural products, together with the use of MS/MS-based molecular networking with various levels of restrictiveness was developed. This protocol was applied to the marine brown seaweed Taonia atomaria, a cosmopolitan algal species, and allowed to annotate more than 200 metabolites. First, the algal organic crude extracts were fractionated by flash-chromatography and the chemical structure of eight of the main chemical constituents of this alga were fully characterized by means of spectroscopic methods (1D and 2D NMR, HRMS). These compounds were further used as chemical standards. In a second step, the main fractions of the algal extracts were analyzed by UHPLC-MS/MS and the resulting data were uploaded to the Global Natural Products Social Molecular Networking platform (GNPS) to create several molecular networks (MNs). A first MN (MN-1) was built with restrictive parameters and allowed the creation of clusters composed by nodes with highly similar MS/MS spectra. Then, using database hits and chemical standards as "seed" nodes and/or similarity between MS/MS fragmentation pattern, the main clusters were easily annotated as common glycerolipids and phospholipids, much rare lipids -such as acylglycerylhydroxymethyl-N,N,N-trimethyl-ß-alanines or fulvellic acid derivatives- but also new glycerolipids bearing a terpene moiety. Lastly, the use of less and less constrained MNs allowed to further increase the number of annotated metabolites.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Chemical repertoire and biosynthetic machinery of the Aspergillus flavus secondary metabolome: A review

Filamentous fungi represent a rich source of extrolites, including secondary metabolites (SMs) comprising a great variety of astonishing structures and interesting bioactivities. State-of-the-art techniques in genome mining, genetic manipulation, and secondary metabolomics have enabled the scientific community to better elucidate and more deeply appreciate the genetic and biosynthetic chemical arsenal of these microorganisms. Aspergillus flavus is best known as a contaminant of food and feed commodities and a producer of the carcinogenic family of SMs, aflatoxins. This fungus produces many SMs including polyketides, ribosomal and nonribosomal peptides, terpenoids, and other hybrid molecules. This review will discuss the chemical diversity, biosynthetic pathways, and biological/ecological role of A. flavus SMs, as well as their significance concerning food safety and security.

Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops

BACKGROUND

The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites.

AIM OF REVIEW

The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.

Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

Motivation

Untargeted mass spectrometry (MS/MS) is a powerful method for detecting metabolites in biological samples. However, fast and accurate identification of the metabolites’ structures from MS/MS spectra is still a great challenge.

Results

We present a new analysis method, called SubFragment-Matching (SF-Matching) that is based on the hypothesis that molecules with similar structural features will exhibit similar fragmentation patterns. We combine information on fragmentation patterns of molecules with shared substructures and then use random forest models to predict whether a given structure can yield a certain fragmentation pattern. These models can then be used to score candidate molecules for a given mass spectrum. For rapid identification, we pre-compute such scores for common biological molecular structure databases. Using benchmarking datasets, we find that our method has similar performance to CSI: FingerID and those very high accuracies can be achieved by combining our method with CSI: FingerID. Rarefaction analysis of the training dataset shows that the performance of our method will increase as more experimental data become available.

Availability and implementation

SF-Matching is available from http://www.bork.embl.de/Docu/sf_matching.

Supplementary information

Supplementary data are available at Bioinformatics online.

ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra

Motivation

Metabolite identification is an important task in metabolomics to enhance the knowledge of biological systems. There have been a number of machine learning-based methods proposed for this task, which predict a chemical structure of a given spectrum through an intermediate (chemical structure) representation called molecular fingerprints. They usually have two steps: (i) predicting fingerprints from spectra; (ii) searching chemical compounds (in database) corresponding to the predicted fingerprints. Fingerprints are feature vectors, which are usually very large to cover all possible substructures and chemical properties, and therefore heavily redundant, in the sense of having many molecular (sub)structures irrelevant to the task, causing limited predictive performance and slow prediction.

Results

We propose ADAPTIVE, which has two parts: learning two mappings (i) from structures to molecular vectors and (ii) from spectra to molecular vectors. The first part learns molecular vectors for metabolites from given data, to be consistent with both spectra and chemical structures of metabolites. In more detail, molecular vectors are generated by a model, being parameterized by a message passing neural network, and parameters are estimated by maximizing the correlation between molecular vectors and the corresponding spectra in terms of Hilbert-Schmidt Independence Criterion. Molecular vectors generated by this model are compact and importantly adaptive (specific) to both given data and task of metabolite identification. The second part uses input output kernel regression (IOKR), the current cutting-edge method of metabolite identification. We empirically confirmed the effectiveness of ADAPTIVE by using a benchmark data, where ADAPTIVE outperformed the original IOKR in both predictive performance and computational efficiency.

Availability and implementation

The code will be accessed through http://www.bic.kyoto-u.ac.jp/pathway/tools/ADAPTIVE after the acceptance of this article.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches

MOTIVATION

Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.