A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Exploring the chemical subspace of RPLC: A data driven approach

BACKGROUND

The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC.

RESULTS

We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace.

SIGNIFICANCE AND NOVELTY

The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

Ebola virus-like particles reprogram cellular metabolism

Ebola virus can trigger a release of pro-inflammatory cytokines with subsequent vascular leakage and impairment of clotting finally leading to multiorgan failure and shock after entering and infecting patients. Ebola virus is known to directly target endothelial cells and macrophages, even without infecting them, through direct interactions with viral proteins. These interactions affect cellular mechanics and immune processes, which are tightly linked to other key cellular functions such as metabolism. However, research regarding metabolic activity of these cells upon viral exposure remains limited, hampering our understanding of its pathophysiology and progression. Therefore, in the present study, an untargeted cellular metabolomic approach was performed to investigate the metabolic alterations of primary human endothelial cells and M1 and M2 macrophages upon exposure to Ebola virus-like particles (VLP). The results show that Ebola VLP led to metabolic changes among endothelial, M1, and M2 cells. Differential metabolite abundance and perturbed signaling pathway analysis further identified specific metabolic features, mainly in fatty acid-, steroid-, and amino acid-related metabolism pathways for all the three cell types, in a host cell specific manner. Taken together, this work characterized for the first time the metabolic alternations of endothelial cells and two primary human macrophage subtypes after Ebola VLP exposure, and identified the potential metabolites and pathways differentially affected, highlighting the important role of those host cells in disease development and progression. KEY MESSAGES: • Ebola VLP can lead to metabolic alternations in endothelial cells and M1 and M2 macrophages. • Differential abundance of metabolites, mainly including fatty acids and sterol lipids, was observed after Ebola VLP exposure. • Multiple fatty acid-, steroid-, and amino acid-related metabolism pathways were observed perturbed.

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.

Metabolite Profiling of Tartary Buckwheat Extracts in Rats Following Co-Administration of Ethanol Using UFLC-Q-Orbitrap High-Resolution Mass Spectrometry

Tartary buckwheat, a gluten-free pseudocereal, has received considerable attention owing to its unique nutritional ingredients and beneficial health effects such as anti-tumor, anti-oxidation, anti-inflammation and hepatoprotective activities. Pharmacokinetic and metabolite profiling have been preliminarily assessed for Tartary buckwheat extracts. However, its metabolites have not yet been characterized in vivo after co-administration with ethanol when Tartary buckwheat extracts are used for the treatment of alcoholic liver disease. In this paper, a Q-Exactive orbitrap high-resolution mass spectrometer was employed to identify the metabolites of Tartary buckwheat extracts in rat biological samples. Compared with previous metabolite profiling results, a total of 26 novel metabolites were found in rat biological samples, including 11, 10, 2 and 5 novel metabolites in rat plasma, bile, urine and feces, respectively, after oral co-administration of 240 mg/kg Tartary buckwheat extracts with ethanol (42%, v/v). The major metabolic pathways of the constituents in Tartary buckwheat extracts involved hydroxylation, methylation, glucuronidation, acetylation and sulfation. Quercetin and its metabolites may be the pharmacological material basis of Tartary buckwheat for the protective effect against alcoholic liver injury. The research enriched in vivo metabolite profiling of Tartary buckwheat extracts, which provided experimental data for a comprehensive understanding and rational use of Tartary buckwheat against alcoholic liver disease.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.

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.

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation

INTRODUCTION

Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.

OBJECTIVE

The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.

METHODS

We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study.

RESULTS

We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag.

CONCLUSION

MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

Signature Fragment Ions of Biotinylated Peptides

The use of biotin or biotin-containing reagents is an essential component of many protein purification and labeling technologies. Owing to its small size and high affinity to the avidin family of proteins, biotin is a versatile molecular handle that permits both enrichment and purity that is not easily achieved by other reagents. Traditionally, the use of biotinylation to enrich for proteins has not required the detection of the site of biotinylation. However, newer technologies for discovery of protein-protein interactions, such as APEX and BioID, as well as some of the click chemistry-based labeling approaches have underscored the importance of determining the exact residue that is modified by biotin. Anti-biotin antibody-based enrichment of biotinylated peptides (e.g., BioSITe) coupled to LC-MS/MS permit large-scale detection and localization of sites of biotinylation. As with any chemical modification of peptides, understanding the fragmentation patterns that result from biotin modification is essential to improving its detection by LC-MS/MS. Tandem mass spectra of biotinylated peptides has not yet been studied systematically. Here, we describe the various signature fragment ions generated with collision-induced dissociation of biotinylated peptides. We focused on biotin adducts attached to peptides generated by BioID and APEX experiments, including biotin, isotopically heavy biotin, and biotin-XX-phenol, a nonpermeable variant of biotin-phenol. We also highlight how the detection of biotinylated peptides in high-throughput studies poses certain computational challenges for accurate quantitation which need to be addressed. Our findings about signature fragment ions of biotinylated peptides should be helpful in the confirmation of biotinylation sites.

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra

Improvements in Fourier transform mass spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have developed a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using detected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas; instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 Da was constructed, and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and ¹⁵N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs, and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

Phenolic Metabolites in Plasma and Thigh Meat of Chickens Supplemented with Grape Byproducts

Grape byproducts are rich sources of polyphenols with powerful antioxidant and health-promoting effects. The impact of supplementing chicken diets with grape byproducts on plasma and thigh meat concentrations of phenolic metabolites was evaluated by analyzing samples by high-performance liquid chromatography quadrupole time of flight mass spectrometry. Chickens were fed three experimental diets: Control diet, Control+8% grape pomace, and Control+0.1% grape seed extract. In plasma, 32 phenolic metabolites were identified, some of which were conjugated catechin/epicatechin metabolites exclusively identified in chickens fed diets enriched in grape byproducts. Also, these chickens showed significantly higher plasmatic concentrations of 21 phenolic metabolites. In thigh meat, 14 phenolic metabolites were identified, but no differences were found between diets. Higher plasmatic tocopherol was found when supplementing diets with grape byproducts, while no changes were observed in meat. Thus, supplementing chicken diets with grape byproducts leads to a significant increase in the circulation of phenolic metabolites and tocopherol.

Application of metabolomics and molecular networking in investigating the chemical profile and antitrypanosomal activity of British bluebells (Hyacinthoides non-scripta)

Bulb, leaf, scape and flower samples of British bluebells (Hyacinthoides non-scripta) were collected regularly for one growth period. Methanolic extracts of freeze-dried and ground samples showed antitrypanosomal activity, giving more than 50% inhibition, for 20 out of 41 samples. High-resolution mass spectrometry was used in the dereplication of the methanolic extracts of the different plant parts. The results revealed differences in the chemical profile with bulb samples being distinctly different from all aerial parts. High molecular weight metabolites were more abundant in the flowers, shoots and leaves compared to smaller molecular weight ones in the bulbs. The anti-trypanosomal activity of the extracts was linked to the accumulation of high molecular weight compounds, which were matched with saponin glycosides, while triterpenoids and steroids occurred in the inactive extracts. Dereplication studies were employed to identify the significant metabolites via chemotaxonomic filtration and considering their previously reported bioactivities. Molecular networking was implemented to look for similarities in fragmentation patterns between the isolated saponin glycoside at m/z 1445.64 [M + formic-H]- equivalent to C64H104O33 and the putatively found active metabolite at m/z 1283.58 [M + formic-H]- corresponding to scillanoside L-1. A combination of metabolomics and bioactivity-guided approaches resulted in the isolation of a norlanostane-type saponin glycoside with antitrypanosomal activity of 98.9% inhibition at 20 µM.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

Exometabolomic Profiling of Bacterial Strains as Cultivated Using Arabidopsis Root Extract as the Sole Carbon Source

The ability of microorganisms to use root-derived metabolites as growth substrates is a key trait for success in the rhizospheric niche. However, few studies describe which specific metabolites are consumed or to what degree microbial strains differ in their substrate consumption patterns. Here, we present a liquid chromatography-mass spectrometry (MS) exometabolomic study of three bacterial strains cultivated using either glucose or Arabidopsis thaliana root extract as the sole carbon source. Two of the strains were previously isolated from field-grown Arabidopsis roots, the other is Escherichia coli, included as a comparison. When cultivated on root extract, a set of 62 MS features were commonly taken up by all three strains, with m/z values matching components of central metabolism (including amino acids and purine or pyrimidine derivatives). Escherichia coli took up very few MS features outside this commonly consumed set, whereas the root-inhabiting strains took up a much larger number of MS features, many with m/z values matching plant-specific metabolites. These measurements define the metabolic niche that each strain potentially occupies in the rhizosphere. Furthermore, we document many MS features released by these strains that could play roles in cross-feeding, antibiosis, or signaling. We present our methodological approach as a foundation for future studies of rhizosphere exometabolomics.

Identification of the acid-induced degradation products of omeprazole and 5-hydroxyomeprazole by high-resolution mass spectrometry

RATIONALE

Omeprazole is used to treat gastric disorders and is one of the most commonly consumed drugs in the western world. It forms several metabolites but is mostly excreted unchanged and as 5-hydroxyomeprazole. Since omeprazole is widely prescribed, its excretion from the body has a potential environmental effect. After excretion it will enter the wastewater system and if not adequately removed during wastewater treatment will be discharged into rivers in the wastewater effluent. It is important to consider not only the parent drug, but also the main metabolite (5-hydroxyomeprazole) and their degradation products to fully understand the fate of this drug during wastewater treatment. In order to do this potential degradation products need to be determined.

METHODS

Acid was used to artificially accelerate the degradation of omeprazole and 5-hydroxyomeprazole. A Q-Exactive Orbitrap mass spectrometer with an electrospray ionisation source was used to determine precursor and product ion data for the degradation products.

RESULTS

Both starting materials quickly degrade under acidic conditions and the main degradation product formed in each case was a re-arranged monomer. Other species identified were doubly and singly charged dimers with varying numbers of sulphur atoms in the dimer bridge. Careful inspection of the accurate mass, isotope pattern, isotope abundance and product ion spectra was used to interpret the data.

CONCLUSIONS

The resultant degradants from omeprazole and 5-hydroxyomeprazole were analogous to each other, differing only by an oxygen atom. This investigation determined the degradation products of omeprazole and 5-hydroxyomeprazole and proposed structures based on the accurate mass and isotope information. The product ions from the degradation products are also reported.

Promises and pitfalls of untargeted metabolomics

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Structure elucidation of metabolite x17299 by interpretation of mass spectrometric data

INTRODUCTION

A major bottleneck in metabolomic studies is metabolite identification from accurate mass spectrometric data. Metabolite x17299 was identified in plasma as an unknown in a metabolomic study using a compound-centric approach where the associated ion features of the compound were used to determine the true molecular mass.

OBJECTIVES

The aim of this work is to elucidate the chemical structure of x17299, a new compound by de novo interpretation of mass spectrometric data.

METHODS

An Orbitrap Elite mass spectrometer was used for acquisition of mass spectra up to MS4 at high resolution. Synthetic standards of N,N,N-trimethyl-l-alanyl-l-proline betaine (l,l-TMAP), a diastereomer, and an enantiomer were chemically prepared.

RESULTS

The planar structure of x17299 was successfully proposed by de novo mechanistic interpretation of mass spectrometric data without any laborious purification and nuclear magnetic resonance spectroscopic analysis. The proposed structure was verified by deuterium exchanged mass spectrometric analysis and confirmed by comparison to a synthetic standard. Relative configuration of x17299 was determined by direct chromatographic comparison to a pair of synthetic diastereomers. Absolute configuration was assigned after derivatization of x17299 with a chiral auxiliary group followed by its chromatographic comparison to a pair of synthetic standards.

CONCLUSION

The chemical structure of metabolite x17299 was determined to be l,l-TMAP.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.

New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE)

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.

Qualitative Alterations of Bacterial Metabolome after Exposure to Metal Nanoparticles with Bactericidal Properties: A Comprehensive Workflow Based on (1)H NMR, UHPLC-HRMS, and Metabolic Databases

Metal nanoparticles (NPs) have proven to be more toxic than bulk analogues of the same chemical composition due to their unique physical properties. The NPs, lately, have drawn the attention of researchers because of their antibacterial and biocidal properties. In an effort to shed light on the mechanism through which the bacteria elimination is achieved and the metabolic changes they undergo, an untargeted metabolomic fingerprint study was carried out on Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria species. The (1)H NMR spectroscopy, in conjunction with high resolution mass-spectrometry (HRMS) and an unsophisticated data processing workflow were implemented. The combined NMR/HRMS data, supported by an open-access metabolomic database, proved to be efficacious in the process of assigning a putative annotation to a wide range of metabolite signals and is a useful tool to appraise the metabolome alterations, as a consequence of bacterial response to NPs. Interestingly, not all the NPs diminished the intracellular metabolites; bacteria treated with iron NPs produced metabolites not present in the nonexposed bacteria sample, implying the activation of previously inactive metabolic pathways. In contrast, copper and iron-copper NPs reduced the annotated metabolites, alluding to the conclusion that the metabolic pathways (mainly alanine, aspartate, and glutamate metabolism, beta-alanine metabolism, glutathione metabolism, and arginine and proline metabolism) were hindered by the interactions of NPs with the intracellular metabolites.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

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Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).

Mapping an atlas of tissue-specific Drosophila melanogaster metabolomes by high resolution mass spectrometry

Metabolomics can provide exciting insights into organismal function, but most work on simple models has focussed on the whole organism metabolome, so missing the contributions of individual tissues. Comprehensive metabolite profiles for ten tissues from adult Drosophila melanogaster were obtained here by two chromatographic methods, a hydrophilic interaction (HILIC) method for polar metabolites and a lipid profiling method also based on HILIC, in combination with an Orbitrap Exactive instrument. Two hundred and forty two polar metabolites were putatively identified in the various tissues, and 251 lipids were observed in positive ion mode and 61 in negative ion mode. Although many metabolites were detected in all tissues, every tissue showed characteristically abundant metabolites which could be rationalised against specific tissue functions. For example, the cuticle contained high levels of glutathione, reflecting a role in oxidative defence; the alimentary canal (like vertebrate gut) had high levels of acylcarnitines for fatty acid metabolism, and the head contained high levels of ether lipids. The male accessory gland uniquely contained decarboxylated S-adenosylmethionine. These data thus both provide valuable insights into tissue function, and a reference baseline, compatible with the FlyAtlas.org transcriptomic resource, for further metabolomic analysis of this important model organism, for example in the modelling of human inborn errors of metabolism, aging or metabolic imbalances such as diabetes.