Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

Metabolic Fingerprint of Chronic Obstructive Lung Diseases: A New Diagnostic Perspective

Chronic obstructive lung disease (COLD) is a group of airway diseases, previously known as emphysema and chronic bronchitis. The heterogeneity of COLD does not allow early diagnosis and leads to increased morbidity and mortality. The increasing number of COLD incidences stresses the need for precision medicine approaches that are specific to the patient. Metabolomics is an emerging technology that allows for the discrimination of metabolic changes in the cell as a result of environmental factors and specific genetic background. Thus, quantification of metabolites in human biofluids can provide insights into the metabolic state of the individual in real time and unravel the presence of, or predisposition to, a disease. In this article, the advantages of and potential barriers to putting metabolomics into clinical practice for COLD are discussed. Today, metabolomics is mostly lab-based, and research studies with novel COLD-specific biomarkers are continuously being published. Several obstacles in the research and the market field hamper the translation of these data into clinical practice. However, technological and computational advances will facilitate the clinical interpretation of data and provide healthcare professionals with the tools to prevent, diagnose, and treat COLD with precision in the coming decades.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Understanding glycaemic control and current approaches for screening antidiabetic natural products from evidence-based medicinal plants

Type 2 Diabetes Mellitus has reached epidemic proportions as a result of over-nutrition and increasingly sedentary lifestyles. Current therapies, although effective, are not without limitations. These limitations, the alarming increase in the prevalence of diabetes, and the soaring cost of managing diabetes and its complications underscores an urgent need for safer, more efficient and affordable alternative treatments. Over 1200 plant species are reported in ethnomedicine for treating diabetes and these represents an important and promising source for the identification of novel antidiabetic compounds. Evaluating medicinal plants for desirable bioactivity goes hand-in-hand with methods in analytical biochemistry for separating and identifying lead compounds. This review aims to provide a comprehensive summary of current methods used in antidiabetic plant research to form a useful resource for researchers beginning in the field. The review summarises the current understanding of blood glucose regulation and the general mechanisms of action of current antidiabetic medications, and combines knowledge on common experimental approaches for screening plant extracts for antidiabetic activity and currently available analytical methods and technologies for the separation and identification of bioactive natural products. Common in vivo animal models, in vitro models, in silico methods and biochemical assays used for testing the antidiabetic effects of plants are discussed with a particular emphasis on in vitro methods such as cell-based bioassays for screening insulin secretagogues and insulinomimetics. Enzyme inhibition assays and molecular docking are also highlighted. The role of metabolomics, metabolite profiling, and dereplication of data for the high-throughput discovery of novel antidiabetic agents is reviewed. Finally, this review also summarises sample preparation techniques such as liquid-liquid extraction, solid phase extraction, and supercritical fluid extraction, and the critical function of nuclear magnetic resonance and high resolution liquid chromatography-mass spectrometry for the dereplication, putative identification and structure elucidation of natural compounds from evidence-based medicinal plants.

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

Roots of the Resurrection Plant Tripogon loliiformis Survive Desiccation Without the Activation of Autophagy Pathways by Maintaining Energy Reserves

Being sessile, plants must regulate energy balance, potentially via source-sink relations, to compromise growth with survival in stressful conditions. Crops are sensitive, possibly because they allocate their energy resources toward growth and yield rather than stress tolerance. In contrast, resurrection plants tightly regulate sugar metabolism and use a series of physiological adaptations to suppress cell death in their vegetative tissue to regain full metabolic capacity from a desiccated state within 72 h of watering. Previously, we showed that shoots of the resurrection plant Tripogon loliiformis, initiate autophagy upon dehydration as one strategy to reinstate homeostasis and suppress cell death. Here, we describe the relationship between energy status, sugar metabolism, trehalose-mediated activation of autophagy pathways and investigate whether shoots and roots utilize similar desiccation tolerance strategies. We show that despite containing high levels of trehalose, dehydrated Tripogon roots do not display elevated activation of autophagy pathways. Using targeted and non-targeted metabolomics, transmission electron microscopy (TEM) and transcriptomics we show that T. loliiformis engages a strategy similar to the long-term drought responses of sensitive plants and continues to use the roots as a sink even during sustained stress. Dehydrating T. loliiformis roots contained more sucrose and trehalose-6-phosphate compared to shoots at an equivalent water content. The increased resources in the roots provides sufficient energy to cope with stress and thus autophagy is not required. These results were confirmed by the absence of autophagosomes in roots by TEM. Upregulation of sweet genes in both shoots and roots show transcriptional regulation of sucrose translocation from leaves to roots and within roots during dehydration. Differences in the cell's metabolic status caused starkly different cell death responses between shoots and roots. These findings show how shoots and roots utilize different stress response strategies and may provide candidate targets that can be used as tools for the improvement of stress tolerance in crops.

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID's compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID's performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID's compound identification abilities; (3) the development of new scoring functions that improves CFM-ID's accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.

Recent developments in metabolomics-based research in understanding transgenic grass metabolism

BACKGROUND

Transgenic herbicide-resistant (HR) turfgrass together with its associated, broad spectrum herbicides promise cheap, selective and efficient weed control by excluding infested weeds resulting in turf lawn with high uniformity and aesthetic value. The concept of this "weeding program" initiated from modern biotechnology has been widely implemented in several principal crops including maize, soybean, canola and cotton as early as the 1990s. Transgenic HR turfgrass classified as a genetically modified organism (GMO) has undoubtedly caused public concern with respect to its biosafety and legalities similar to well-established HR crops. Nevertheless, applying metabolomics-based approaches which focuses on the identification of the global metabolic state of a biological system in response to either internal or external stimuli can also provide a comprehensive characterization of transgenic grass metabolism and its involvement in biosecurity and public perception.

AIM OF REVIEW

This review summaries the recent applications of metabolomics applied to HR crops to predict the molecular and physiological phenotypes of HR turfgrass species, glyphosate-resistant Kentucky bluegrass (Poa pratensis L.) and glufosinate-resistant creeping bentgrass (Agrotis stonifera L.). Additionally, this review also presents background knowledge with respect to the application of metabolomics, transformation of HR crops and its biosafety concerns, turfgrass botanical knowledge and its economic and aesthetic value.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to demonstrate the molecular and physiological phenotypes of HR turfgrass based on several lines of evidence primarily derived from metabolomics data applied to HR crops to identify alterations on HR turfgrass metabolism as a result of genetic modification that confers resistant traits.

De novo structure determination of 3-((3-aminopropyl)amino)-4-hydroxybenzoic acid, a novel and abundant metabolite in Acinetobacter baylyi ADP1

INTRODUCTION

Metabolite identification remains a major bottleneck in the understanding of metabolism. Many metabolomics studies end up with unknown compounds, leaving a landscape of metabolites and metabolic pathways to be unraveled. Therefore, identifying novel compounds within a metabolome is an entry point into the 'dark side' of metabolism.

OBJECTIVES

This work aimed at elucidating the structure of a novel metabolite that was first detected in the soil bacterium Acinetobacter baylyi ADP1 (ADP1).

METHODS

We used high resolution multi-stage tandem mass spectrometry for characterizing the metabolite within the metabolome. We purified the molecule for 1D- and 2D-NMR (¹H, ¹³C, ¹H-¹H-COSY, ¹H-¹³C-HSQC, ¹H-¹³C-HMBC and ¹H-¹⁵N-HMBC) analyses. Synthetic standards were chemically prepared from MS and NMR data interpretation.

RESULTS

We determined the de novo structure of a previously unreported metabolite: 3-((3-aminopropyl)amino)-4-hydroxybenzoic acid. The proposed structure was validated by comparison to a synthetic standard. With a concentration in the millimolar range, this compound appears as a major metabolite in ADP1, which we anticipate to participate to an unsuspected metabolic pathway. This novel metabolite was also detected in another γ-proteobacterium.

CONCLUSION

Structure elucidation of this abundant and novel metabolite in ADP1 urges to decipher its biosynthetic pathway and cellular function.

High Throughput Complementary Analysis and Quantitation of Metabolites by MALDI- and Silicon Nanopost Array-Laser Desorption/Ionization-Mass Spectrometry

Silicon nanopost array (NAPA) structures have been shown to be effective substrates for laser desorption/ionization-mass spectrometry (LDI-MS) and have been used to analyze a variety of samples including peptides, metabolites, drugs, explosives, and intact cells, as well as to image lipids and metabolites in tissue sections. However, no direct comparison has yet been conducted between NAPA-MS and the most commonly used LDI-MS technique, matrix-assisted laser desorption/ionization (MALDI)-MS. In this work, we compare the utility of NAPA-MS to that of MALDI-MS using two common matrices for the analysis of metabolites in cellular extracts and human urine. Considerable complementarity of molecular coverage was observed between the two techniques. Of 178 total metabolites assigned from cellular extracts, 68 were uniquely detected by NAPA-MS and 62 were uniquely detected by MALDI-MS. NAPA-MS was found to provide enhanced coverage of low-molecular weight compounds such as amino acids, whereas MALDI afforded better detection of larger, labile compounds including nucleotides. In the case of urine, a sample largely devoid of higher-mass labile compounds, 88 compounds were uniquely detected by NAPA-MS and 13 by MALDI-MS. NAPA-MS also favored more extensive alkali metal cation adduction relative to MALDI-MS, with the [M + 2Na/K - H]⁺ species accounting for as much as 97% of the total metabolite ion signal in positive mode. The capability of NAPA-MS for targeted quantitation of endogenous metabolites in urine via addition of isotopically labeled standards was also examined. Both NAPA-MS and MALDI-MS provided quantitative results in good agreement with one another and the concentrations reported in the literature, as well as good sample-to-sample reproducibility (RSD < 10%).

Advances in mass spectrometry based single-cell metabolomics

Single cell metabolomics using mass spectrometry can contribute to understanding biological activities in health and disease.

Targeted discovery and identification of novel nucleoside biomarkers in Apostichopus japonicus viscera using metabonomics

In this study, we investigated the metabonomic profiles of Apostichopus japonicus using an LC-MS-based method in conjunction with multivariate data analysis. Based on the PLS-DA model, 85 differential metabolites (VIP value >1.0) were obtained from viscera and body wall samples. The MS/MS and NMR experiments were used for the qualitative identification of the characteristic peaks. Sphingoid-based nucleoside analogues were the main components in Chinese A. japonicus viscera. Our findings demonstrate that A. japonicus viscera contain a large number of compounds that may have applications as nutraceuticals or pharmaceuticals.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

ChemFrag: Chemically meaningful annotation of fragment ion mass spectra

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule-based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.

Integration of non-targeted metabolomics and automated determination of elemental compositions for comprehensive alkaloid profiling in plants

Plants produce a large array of specialized metabolites to protect themselves. Among these allelochemicals, alkaloids display highly diverse and complex structures that are directly related to their biological activities. Plant alkaloid profiling traditionally requires extensive and time-consuming sample preparation and analysis. Herein, we developed a rapid and efficient approach for the comprehensive profiling of alkaloids in plants using ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics. Using automated compound extraction and elemental composition assignment, our method achieved >83% correct alkaloid identification and even >90% for medium to high intensity peaks. This represented a significant improvement in identification rate compared to generic methods used for EC determination with no a priori, such as in untargeted metabolomics studies. The developed approach was then applied to identify specific alkaloids of Aconitum lycoctonum L. and A. napellus L. (Ranunculaceae) using different parts of the plant (leaf, perianth and pollen). Significant differences in alkaloid profiles between the two species were highlighted and discussed under taxonomic and evolutionary perspectives. Taken together, the presented approach constitutes a valuable chemotaxonomic tool in the search for known and unknown alkaloids from plants.

Peak AAA fatty acid homolog contaminants present in the dietary supplement l-Tryptophan associated with the onset of eosinophilia-myalgia syndrome

The eosinophilia-myalgia syndrome (EMS) outbreak that occurred in the USA and elsewhere in 1989 was caused by the ingestion of Showa Denko K.K. (SD) L-tryptophan (L-Trp). "Six compounds" detected in the L-Trp were reported as case-associated contaminants. Recently the final and most statistically significant contaminant, "Peak AAA" was structurally characterized. The "compound" was actually shown to be two structural isomers resulting from condensation reactions of L-Trp with fatty acids derived from the bacterial cell membrane. They were identified as the indole C-2 anteiso (AAA₁-343) and linear (AAA₂-343) aliphatic chain isomers. Based on those findings, we utilized a combination of on-line HPLC-electrospray ionization mass spectrometry (LC-MS), as well as both precursor and product ion tandem mass spectrometry (MS/MS) to facilitate identification of a homologous family of condensation products related to AAA₁-343 and AAA₂-343. We structurally characterized eight new AAA₁-XXX/AAA₂-XXX contaminants, where XXX represents the integer molecular ions of all the related homologs, differing by aliphatic chain length and isomer configuration. The contaminants were derived from the following fatty acids of the bacterial cell membrane, 5-methylheptanoic acid (anteiso-C8:0) for AAA₁-315; n-octanoic acid (n-C8:0) for AAA₂-315; 6-methyloctanoic acid (anteiso-C9:0) for AAA₁-329; n-nonanoic acid (n-C9:0) for AAA₂-329; 10-methyldodecanoic acid (anteiso-C13:0) for AAA₁-385; n-tridecanoic acid (n-C13:0) for AAA₂-385; 11-methyltridecanoic acid (anteiso-C14:0) for AAA₁-399; and n-tetradecanoic acid (n-C14:0) for AAA₂-399. The concentration levels for these contaminants were estimated to be 0.1-7.9 μg / 500 mg of an individual SD L-Trp tablet or capsule The structural similarity of these homologs to case-related contaminants of Spanish Toxic Oil Syndrome (TOS) is discussed.

Intestinal phase-II metabolism of quercetin in HT29 cells, 3D human intestinal tissues and in healthy volunteers: a qualitative comparison using LC-IMS-MS and LC-HRMS

Flavonoids are a large class of dietary molecules, among which quercetin is the most ubiquitous, which undergo an extensive intestinal phase-II metabolism. We compared the in vivo metabolism of quercetin in healthy volunteers with two in vitro models, HT29 cells and 3 D human intestinal tissues. Supernatants of the in vitro experiments and the human intestinal fluids (HIF) were analyzed by LC-IMS-MS and LC-HRMS in a qualitative way. Quercetin glucuronides, sulfates and their methyl conjugates were detected in all three systems. The metabolic profiles were found to be different, both in terms of the metabolites produced and their relative proportions. In particular, quercetin sulfates were almost absent in supernatants from HT29 cells incubations while they were a major metabolite in HIF and also found in 3 D intestinal tissues incubations. IMS provided structural information as well as a third dimension of characterization, while HRMS brought increased sensitivity and MS/MS confirmation. HT29 cells are a useful tool to generate phase-II metabolites but do not represent the in vivo situation. 3 D intestinal tissues appear as a more relevant tool to study the intestinal phase-II metabolism of flavonoids.

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

BACKGROUND

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as 'unknown compounds'. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

AIM OF REVIEW

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

The role of the Human Metabolome Database in inborn errors of metabolism

Metabolomics holds considerable promise to advance our understanding of human disease, including our understanding of inborn errors of metabolism (IEM). The application of metabolomics in IEM research has already led to the discovery of several novel IEMs and the identification of novel IEM biomarkers. However, with hundreds of known IEMs and more than 700 associated IEM metabolites, it is becoming increasingly challenging for clinical researchers to keep track of IEMs, their associated metabolites, and their corresponding metabolic mechanisms. Furthermore, when using metabolomics to assist in IEM biomarker discovery or even in IEM diagnosis, it is becoming much more difficult to properly identify metabolites from the complex NMR and MS spectra collected from IEM patients. To that end, comprehensive, open access metabolite databases that provide up-to-date referential information about metabolites, metabolic pathways, normal/abnormal metabolite concentrations, and reference NMR or MS spectra for compound identification are essential. Over the last few years, a number of compound databases, including the Human Metabolome Database (HMDB), have been developed to address these challenges. First described in 2007, the HMDB is now the world's largest and most comprehensive metabolomic resource for human metabolic studies. The latest release of the HMDB contains 114,100 metabolite entries (with 247 being relevant to IEMs), thousands of metabolite concentrations (with 600 being relevant to IEMs), and ~33,000 metabolic and disease-associated pathways (with 202 being relevant to IEMs). Here we provide a summary of the HMDB and offer some guidance on how it can be used in metabolomic studies of IEMs.

Metabolomic "Dark Matter" Dependent on Peroxisomal β-Oxidation in Caenorhabditis elegans

Peroxisomal β-oxidation (pβo) is a highly conserved fat metabolism pathway involved in the biosynthesis of diverse signaling molecules in animals and plants. In Caenorhabditis elegans, pβo is required for the biosynthesis of the ascarosides, signaling molecules that control development, lifespan, and behavior in this model organism. Via comparative mass spectrometric analysis of pβo mutants and wildtype, we show that pβo in C. elegans and the satellite model P. pacificus contributes to life stage-specific biosynthesis of several hundred previously unknown metabolites. The pβo-dependent portion of the metabolome is unexpectedly diverse, e.g., intersecting with nucleoside and neurotransmitter metabolism. Cell type-specific restoration of pβo in pβo-defective mutants further revealed that pβo-dependent submetabolomes differ between tissues. These results suggest that interactions of fat, nucleoside, and other primary metabolism pathways can generate structural diversity reminiscent of that arising from combinatorial strategies in microbial natural product biosynthesis.

HMDB 4.0: the human metabolome database for 2018

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

LC-MS-Based Metabolomics of Biofluids Using All-Ion Fragmentation (AIF) Acquisition

The field of liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compounds in biological samples. However, compound identification remains a major challenge, which is crucial in interpreting the biological function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an in-house spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative standard deviation between ion ratios of a metabolite in a biofluid vs. its analytical standard is used as an additional metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compounds. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.

LC-MS Untargeted Analysis

LC-MS untargeted analysis is a valuable tool in the field of metabolic profiling (metabonomics/metabolomics), and the applications of this technology have grown rapidly over the past decade. LC-MS offers advantages over other analytical platforms such as speed, sensitivity, relative ease of sample preparation, and large dynamic range. As with any analytical approach, there are still drawbacks and challenges to overcome, but advances are constantly being made regarding both column chemistries and instrumentation. There are numerous untargeted LC-MS approaches which can be used in this ever-growing research field; these can be optimized depending on sample type and the nature of the study or biological question. Some of the main LC-MS approaches for the untargeted analysis of biological samples will be described in detail in the following protocol.

Mass Spectrometry Based Imaging of Labile Glucosides in Plants

Mass spectrometry based imaging is a powerful tool to investigate the spatial distribution of a broad range of metabolites across a variety of sample types. The recent developments in instrumentation and computing capabilities have increased the mass range, sensitivity and resolution and rendered sample preparation the limiting step for further improvements. Sample preparation involves sectioning and mounting followed by selection and application of matrix. In plant tissues, labile small molecules and specialized metabolites are subject to degradation upon mechanical disruption of plant tissues. In this study, the benefits of cryo-sectioning, stabilization of fragile tissues and optimal application of the matrix to improve the results from MALDI mass spectrometry imaging (MSI) is investigated with hydroxynitrile glucosides as the main experimental system. Denatured albumin proved an excellent agent for stabilizing fragile tissues such as Lotus japonicus leaves. In stem cross sections of Manihot esculenta, maintaining the samples frozen throughout the sectioning process and preparation of the samples by freeze drying enhanced the obtained signal intensity by twofold to fourfold. Deposition of the matrix by sublimation improved the spatial information obtained compared to spray. The imaging demonstrated that the cyanogenic glucosides (CNglcs) were localized in the vascular tissues in old stems of M. esculenta and in the periderm and vascular tissues of tubers. In MALDI mass spectrometry, the imaged compounds are solely identified by their m/z ratio. L. japonicus MG20 and the mutant cyd1 that is devoid of hydroxynitrile glucosides were used as negative controls to verify the assignment of the observed masses to linamarin, lotaustralin, and linamarin acid.

Dark matter in host-microbiome metabolomics: Tackling the unknowns-A review

The "dark matter" in metabolomics (unknowns) represents an exciting frontier with significant potential for discovery in relation to biochemistry, yet it also presents one of the largest challenges to overcome. This focussed review takes a close look at the current state-of-the-art and future challenges in tackling the unknowns with specific focus on the human gut microbiome and host-microbe interactions. Metabolomics, like metabolism itself, is a very dynamic discipline, with many workflows and methods under development, both in terms of chemical analysis and post-analysis data processing. Here, we look at developments in the mutli-omic analyses and the use of mass spectrometry to investigate the exchange of metabolites between the host and the microbiome as well as the environment within the microbiome. A case study using HuMiX, a microfluidics-based human-microbial co-culture system that enables the co-culture of human and microbial cells under controlled conditions, is used to highlight opportunities and current limitations. Common definitions, approaches, databases and elucidation techniques from both the environmental and metabolomics fields are covered, with perspectives on how to merge these, as the boundaries blur between the fields. While reflecting on the number of unknowns remaining to be conquered in typical complex samples measured with mass spectrometry (often orders of magnitude above the "knowns"), we provide an outlook on future perspectives and challenges in elucidating the relevant "dark matter".

Metabolomic Profiles of a Midge (Procladius villosimanus, Kieffer) Are Associated with Sediment Contamination in Urban Wetlands

Metabolomic techniques are powerful tools for investigating organism-environment interactions. Metabolite profiles have the potential to identify exposure or toxicity before populations are disrupted and can provide useful information for environmental assessment. However, under complex environmental scenarios, metabolomic responses to exposure can be distorted by background and/or organismal variation. In the current study, we use LC-MS (liquid chromatography-mass spectrometry) and GC-MS (gas chromatography-mass spectrometry) to measure metabolites of the midge Procladius villosimanus inhabiting 21 urban wetlands. These metabolites were tested against common sediment contaminants using random forest models and metabolite enrichment analysis. Sediment contaminant concentrations in the field correlated with several P. villosimanus metabolites despite natural environmental and organismal variation. Furthermore, enrichment analysis indicated that metabolite sets implicated in stress responses were enriched, pointing to specific cellular functions affected by exposure. Methionine metabolism, sugar metabolism and glycerolipid metabolism associated with total petroleum hydrocarbon and metal concentrations, while mitochondrial electron transport and urea cycle sets associated only with bifenthrin. These results demonstrate the potential for metabolomics approaches to provide useful information in field-based environmental assessments.

Significance estimation for large scale metabolomics annotations by spectral matching

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.

Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?

The vast, diverse universe of organic pollutants is a formidable challenge for environmental sciences, engineering, and regulation. Nontarget screening (NTS) based on high resolution mass spectrometry (HRMS) has enormous potential to help characterize this universe, but is it ready to go for real world applications? In this Feature article we argue that development of mass spectrometers with increasingly high resolution and novel couplings to both liquid and gas chromatography, combined with the integration of high performance computing, have significantly widened our analytical window and have enabled increasingly sophisticated data processing strategies, indicating a bright future for NTS. NTS has great potential for treatment assessment and pollutant prioritization within regulatory applications, as highlighted here by the case of real-time pollutant monitoring on the River Rhine. We discuss challenges for the future, including the transition from research toward solution-centered and robust, harmonized applications.

The future of metabolomics in ELIXIR

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.

Biomarker Research in Parkinson's Disease Using Metabolite Profiling

Biomarker research in Parkinson's disease (PD) has long been dominated by measuring dopamine metabolites or alpha-synuclein in cerebrospinal fluid. However, these markers do not allow early detection, precise prognosis or monitoring of disease progression. Moreover, PD is now considered a multifactorial disease, which requires a more precise diagnosis and personalized medication to obtain optimal outcome. In recent years, advanced metabolite profiling of body fluids like serum/plasma, CSF or urine, known as "metabolomics", has become a powerful and promising tool to identify novel biomarkers or "metabolic fingerprints" characteristic for PD at various stages of disease. In this review, we discuss metabolite profiling in clinical and experimental PD. We briefly review the use of different analytical platforms and methodologies and discuss the obtained results, the involved metabolic pathways, the potential as a biomarker and the significance of understanding the pathophysiology of PD. Many of the studies report alterations in alanine, branched-chain amino acids and fatty acid metabolism, all pointing to mitochondrial dysfunction in PD. Aromatic amino acids (phenylalanine, tyrosine, tryptophan) and purine metabolism (uric acid) are also altered in most metabolite profiling studies in PD.

New frontiers in metabolomics: from measurement to insight

Metabolomics is the newest addition to the "omics" disciplines and has shown rapid growth in its application to human health research because of fundamental advancements in measurement and analysis techniques. Metabolomics has unique and proven advantages in systems biology and biomarker discovery. The next generation of analysis techniques promises even richer and more complete analysis capabilities that will enable earlier clinical diagnosis, drug refinement, and personalized medicine. A review of current advancements in methodologies and statistical analysis that are enhancing and improving the performance of metabolomics is presented along with highlights of some recent successful applications.

Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition

High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.

Labradorins with Antibacterial Activity Produced by Pseudomonas sp

The urgent need for new antibacterial drugs has led to renewed interest in microorganisms, which historically have been the main source of previously discovered antibiotics. The present study describes the discovery of two new antibacterial oxazolylindole type alkaloids, labradorins 5 (1) and 6 (2), which were isolated and characterized from two isolates of Pseudomonas sp., along with four previously known tryptophane derived alkaloids. The structures of 1 and 2 were determined by NMR spectroscopy and MS, and confirmed by synthesis. During bioassay-guided isolation using several human bacterial pathogens, 1 and 2 displayed activity towards Staphylococcus aureus and Acinetobacter baumannii. The minimal inhibitory concentrations (MIC) of compounds 1 and 2 against S. aureus were 12 μg·mL^-1 and 50 μg·mL^-1, respectively, whereas the MICs against A. baumannii were >50 μg·mL^-1. The CC₅₀ values of compound 1 towards a liver cell line (HEP-G2) and a T-cell line (MT4) were 30 μg·mL^-1 and 20 μg·mL^-1, respectively, and for compound 2 were >100 μg·mL^-1 and 20 μg·mL^-1, respectively. Due to the limited potency of compounds 1 and 2, along with their toxicity, the compounds do not warrant further development towards new antibiotics.

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.