Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets

Analysis and Identification of Polyphenols in Fragaria, Rubus, and Ribes Species by Nontargeted UHPLC-PDA-MS Metabolite Profiling

Profiling of secondary metabolites within Fragaria sp. (strawberry), Rubus sp. (raspberries and blackberries), and Ribes sp. (blackcurrants and redcurrants) is of key importance with respect to the nutritional (antioxidant) benefits associated with a range of polyphenol classes within such fruits and how they vary between growth environment and season, as well as their deployment as quantitative trait loci (QTL) within breeding populations. This chapter presents an ultra-high-performance liquid chromatography (UHPLC) approach applying aqueous functionalized C18 chromatography combined with mass spectrometry (MS) for the characterization and relative quantification of a wide range of polyphenols such as the pelargonidins, being rich in strawberry; cyanidins, procyanidins, and anthocyanins, being rich in raspberry; and delphinidins and prodelphinidins, being particularly rich in blackcurrant, as well as a range of colorless flavonoids, and amino and organic acids.

GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types

Glycans are sugar-based polymers found to modify biomolecules, including lipids and proteins, as well as occur unconjugated as free polysaccharides. Due to their ubiquitous cellular presentation, glycans mediate crucial biological processes and are frequently sought after as biomarkers for a wide range of diseases. Identification of glycans present in samples acquired with mass spectrometry (MS) is a cornerstone of glycomics research; thus, the ability to rapidly identify glycans in each acquisition is integral to glycomics analysis pipelines. Here we introduce GlyCombo (https://github.com/Protea-Glycosciences/GlyCombo), an open-source, freely available software tool designed to rapidly assign monosaccharide combinations to glycan precursor masses including those subjected to MS2 in LC-MS/MS experiments. GlyCombo was evaluated across six diverse data sets, demonstrating MS vendor, derivatization, and glycan-type neutrality. Compositional assignments using GlyCombo are shown to be faster than the current predominant approach, GlycoMod, a closed-source web application. Two unique features of GlyCombo, multiple adduct search and off-by-one error anticipation, reduced unassigned MS2 scans in a benchmark data set by 40%. Finally, the comprehensiveness of glycan feature identification is exhibited in Skyline, a software that requires predefined transitions that are derived from GlyCombo output files.

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.

From sound check to encore: A journey through high-resolution mass spectrometry-based food analyses and metabolomics

This manuscript presents a comprehensive review of high-resolution mass spectrometry in the field of food analysis and metabolomics. We have followed the historical evolution of metabolomics, its associated techniques and technologies, and its increasing role in food science and research. The review provides a critical comparison and synthesis of tentative identification guidelines proposed for over 15 years, offering a condensed resource for researchers in the field. We have also examined a wide range of recent metabolomics studies, showcasing various methodologies and highlighting key findings as a testimony of the versatility of the field and the possibilities it offers. In doing so, we have also carefully provided a compilation of the software tools that may be employed in this type of studies. The manuscript also explores the prospects of high-resolution mass spectrometry and metabolomics in food science. By covering the history, guidelines, applications, and tools of metabolomics, this review attempts to become a comprehensive guide for researchers in a rapidly evolving field.

Data-Driven Characterization of Metabolome Reprogramming during Early Development of Sorghum Seedlings

Specialized metabolites are produced via discrete metabolic pathways. These small molecules play significant roles in plant growth and development, as well as defense against environmental stresses. These include damping off or seedling blight at a post-emergence stage. Targeted metabolomics was followed to gain insights into metabolome changes characteristic of different developmental stages of sorghum seedlings. Metabolites were extracted from leaves at seven time points post-germination and analyzed using ultra-high performance liquid chromatography coupled to mass spectrometry. Multivariate statistical analysis combined with chemometric tools, such as principal component analysis, hierarchical clustering analysis, and orthogonal partial least squares-discriminant analysis, were applied for data exploration and to reduce data dimensionality as well as for the selection of potential discriminant biomarkers. Changes in metabolome patterns of the seedlings were analyzed in the early, middle, and late stages of growth (7, 14, and 29 days post-germination). The metabolite classes were amino acids, organic acids, lipids, cyanogenic glycosides, hormones, hydroxycinnamic acid derivatives, and flavonoids, with the latter representing the largest class of metabolites. In general, the metabolite content showed an increase with the progression of the plant growth stages. Most of the differential metabolites were derived from tryptophan and phenylalanine, which contribute to innate immune defenses as well as growth. Quantitative analysis identified a correlation of apigenin flavone derivatives with growth stage. Data-driven investigations of these metabolomes provided new insights into the developmental dynamics that occur in seedlings to limit post-germination mortality.

Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics

Untargeted metabolomics is an established approach in toxicology for characterising endogenous metabolic responses to xenobiotic exposure. Detecting the xenobiotic and its biotransformation products as part of the metabolomics analysis provides an opportunity to simultaneously gain deep insights into its fate and metabolism, and to associate the internal relative dose directly with endogenous metabolic responses. This integration of untargeted exposure and response measurements into a single assay has yet to be fully demonstrated. Here we assemble a workflow to discover and analyse pharmaceutical-related measurements from routine untargeted UHPLC-MS metabolomics datasets, derived from in vivo (rat plasma and cardiac tissue, and human plasma) and in vitro (human cardiomyocytes) studies that were principally designed to investigate endogenous metabolic responses to drug exposure. Our findings clearly demonstrate how untargeted metabolomics can discover extensive biotransformation maps, temporally-changing relative systemic exposure, and direct associations of endogenous biochemical responses to the internal dose.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structures and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

Long-term supplementation with anthocyanin-rich or -poor Rubus idaeus berries does not influence microvascular architecture nor cognitive outcome in the APP/PS-1 mouse model of Alzheimer's disease

Disruption of microvascular architecture is a common pathogenic mechanism in the progression of Alzheimer's disease (AD). Given the anti-angiogenic activity of berry (poly)phenols, we investigated whether long-term feeding of Rubus idaeus (raspberries) could ameliorate cerebral microvascular pathology and improve cognition in the APP/PS-1 mouse model of AD. Male C57Bl/6J mice (50 wild type, 50 APP/PS-1) aged 4-months were fed for 24-weeks, with a normal diet enriched with either 100 mg/day glucose (control diet) or supplemented with glucose and freeze-dried anthocyanin-rich (red) or -poor (yellow) raspberries (100 mg/day) and assessed/sampled post intervention. Cerebral microvascular architecture of wild-type mice was characterised by regularly spaced capillaries with uniform diameters, unlike APP/PS-1 transgenic mice which showed dysregulated microvascular architecture. Long-term feeding of raspberries demonstrated limited modulation of microbiota and no substantive effect on microvascular architecture or cognition in either mice model although changes were evident in endogenous cerebral and plasmatic metabolites.

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites

The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography-mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions

Drought is one of the major abiotic stresses causing severe damage and losses in economically important crops worldwide. Drought decreases the plant water status, leading to a disruptive metabolic reprogramming that negatively affects plant growth and yield. Seaweed extract-based biostimulants show potential as a sustainable strategy for improved crop health and stress resilience. However, cellular, biochemical, and molecular mechanisms governing the agronomically observed benefits of the seaweed extracts on plants are still poorly understood. In this study, a liquid chromatography–mass spectrometry-based untargeted metabolomics approach combined with computational metabolomics strategies was applied to unravel the molecular ‘stamps’ that define the effects of seaweed extracts on greenhouse-grown maize (Zea mays) under drought conditions. We applied mass spectral networking, substructure discovery, chemometrics, and metabolic pathway analyses to mine and interpret the generated mass spectral data. The results showed that the application of seaweed extracts induced alterations in the different pathways of primary and secondary metabolism, such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, and amino acids pathways. These metabolic changes involved increasing levels of phenylalanine, tryptophan, coumaroylquinic acid, and linolenic acid metabolites. These metabolic alterations are known to define some of the various biochemical and physiological events that lead to enhanced drought resistance traits. The latter include root growth, alleviation of oxidative stress, improved water, and nutrient uptake. Moreover, this study demonstrates the use of molecular networking in annotating maize metabolome. Furthermore, the results reveal that seaweed extract-based biostimulants induced a remodeling of maize metabolism, subsequently readjusting the plant towards stress alleviation, for example, by increasing the plant height and diameter through foliar application. Such insights add to ongoing efforts in elucidating the modes of action of biostimulants, such as seaweed extracts. Altogether, our study contributes to the fundamental scientific knowledge that is necessary for the development of a biostimulants industry aiming for a sustainable food security.

A Multilevel Assessment of Plasticity in Response to High-Altitude Environment for Agama Lizards

Upslope range shifting has been documented in diverse species in response to global warming. Plasticity, which refers to the ability of organisms to alter their phenotypes in changing environments, is crucial for the survival of those that newly migrated to a high-altitude environment. The scope and mechanisms of plasticity across biological levels, however, have rarely been examined. We used two agama lizards (genus Phrynocephalus) as model systems and a transplant experiment to comprehensively assess their plasticity on multiple organization levels. Two low-altitude (934 m) agama species, Phrynocephalus axillaris (oviparous) and P. forsythii (viviparous), were transplanted to a high-altitude site (3,400 m). After acclimation for 6 weeks in seminatural enclosures, plasticity was measured from bite force, tail display behavior, gene expression, and metabolome. Both lizards were capable of acclimating to the high-altitude environment without sacrificing their performance in bite force, but they also showed high plasticity in tail display behavior by either decreasing the intensity of a specific display component (P. forsythii) or by the trade-off between display components (P. axillaris). Genes and metabolites associated with lipids, especially fatty acid metabolism, exhibited significant differentiation in expression, compared to individuals from their native habitats. Improved fatty acid storage and metabolism appeared to be a common response among animals at high altitudes. Despite distinct reproductive modes that may differ in response to physiological pressure, the two lizards demonstrated high concordance in plasticity when they faced a novel environment at high altitudes. Taken together, lizards likely acclimate to high-altitude environments by reducing behavioral activity and increasing energy efficiency after range shifting. Our results provide new insights into our understanding of phenotypic plasticity and its importance in today’s changing climate.

Perturbations in cardiac metabolism in a human model of acute myocardial ischaemia

INTRODUCTION

Acute myocardial ischaemia and the transition from reversible to irreversible myocardial injury are associated with abnormal metabolic patterns. Advances in metabolomics have extended our capabilities to define these metabolic perturbations on a metabolome-wide scale.

OBJECTIVES

This study was designed to identify cardiac metabolic changes in serum during the first 5 min following early myocardial ischaemia in humans, applying an untargeted metabolomics approach.

METHODS

Peripheral venous samples were collected from 46 patients in a discovery study (DS) and a validation study (VS) (25 for DS, 21 for VS). Coronary sinus venous samples were collected from 7 patients (4 for DS, 3 for VS). Acute myocardial ischaemia was induced by transient coronary occlusion during percutaneous coronary intervention (PCI). Plasma samples were collected at baseline (prior to PCI) and at 1 and 5 min post-coronary occlusion. Samples were analyzed by Ultra Performance Liquid Chromatography-Mass Spectrometry in an untargeted metabolomics approach.

RESULTS

The study observed changes in the circulating levels of metabolites at 1 and 5 min following transient coronary ischaemia. Both DS and VS identified 54 and 55 metabolites as significant (P < 0.05) when compared to baseline levels, respectively. Fatty acid beta-oxidation and anaerobic respiration, lysoglycerophospholipids, arachidonic acid, docosahexaenoic acid, tryptophan metabolism and sphingosine-1-phosphate were identified as mechanistically important.

CONCLUSION

Using an untargeted metabolomics approach, the study identified important cardiac metabolic changes in peripheral and coronary sinus plasma, in a human model of controlled acute myocardial ischaemia. Distinct classes of metabolites were shown to be involved in the rapid cardiac response to ischemia and provide insights into diagnostic and interventional targets.

Untargeted metabolomics approach to discriminate mistletoe commercial products

Mistletoe (Viscum album L.) is used in German-speaking European countries in the field of integrative oncology linking conventional and complementary medicine therapies to improve quality of life. Various companies sell extracts, fermented or not, for injection by subcutaneous or intra-tumoral route with a regulatory status of anthroposophic medicinal products (European Medicinal Agency (EMA) assessment status). These companies as well as anthroposophical physicians argue that complex matrices composed of many molecules in mixture are necessary for activity and that the host tree of the mistletoe parasitic plant is the main determining factor for this matrix composition. The critical point is that parenteral devices of European mistletoe extracts do not have a standard chemical composition regulated by EMA quality guidelines, because they are not drugs, regulatory speaking. However, the mechanism of mistletoe's anticancer activity and its effectiveness in treating and supporting cancer patients are not fully understood. Because of this lack of transparency and knowledge regarding the matrix chemical composition, we undertook an untargeted metabolomics study of several mistletoe extracts to explore and compare their fingerprints by LC-(HR)MS(/MS) and 1H-NMR. Unexpectedly, we showed that the composition was primarily driven by the manufacturer/preparation method rather than the different host trees. This differential composition may cause differences in immunostimulating and anti-cancer activities of the different commercially available mistletoe extracts as illustrated by structure-activity relationships based on LC-MS/MS and 1H-NMR identifications completed by docking experiments. In conclusion, in order to move towards an evidence-based medicine use of mistletoe, it is a priority to bring rigor and quality, chemically speaking.

Maternal intermittent fasting during pregnancy induces fetal growth restriction and down-regulated placental system A amino acid transport in the rat

During Ramadan, many pregnant Muslim women fast between dawn and sunset. Although the impacts of prolonged maternal intermittent fasting (IF) on fetal growth and placental function are under-researched, reported effects include reduced placental weight and birth weight. In the present study, pregnant Wistar rats were used to model repeated cycles of IF on fetal development and placental function and to examine sex-specific effects. In the IF group, food was withdrawn daily from 17:00 to 09:00 over 21 days of gestation, while the control group received food ad libitum. Both groups had free water access. IF dams consumed less food, had significantly reduced weight compared with controls, with reduced plasma glucose and amino acids. Both fetal sexes were significantly lighter in the IF group with reduced fetal plasma amino acids. Placental weights and morphology were unchanged. The profile of placental metabolites was altered in the IF group with sex-specific responses evident. Transplacental flux of 14C-methylaminoisobutyric acid (14C-MeAIB), a system A amino acid transporter substrate, was significantly reduced in both fetal sexes in the IF group. Sodium-dependent 14C-MeAIB uptake into isolated placental plasma membrane vesicles was unchanged. The gene expression of system A transporter Slc38a1, Slc38a2 and Slc38a4 was up-regulated in IF male placentas only. No changes were observed in placental SNAT1 and SNAT2 protein expression. Maternal IF results in detrimental impacts on maternal physiology and fetal development with changes in the placental and fetal metabolite profiles. Reduced placental system A transporter activity may be responsible for fetal growth restriction in both sexes.

TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars

The total number of low-molecular-weight compounds in the plant kingdom, most of which are secondary metabolites, is hypothesized to be over one million, although only a limited number of plant compounds have been characterized. Untargeted analysis, especially using mass spectrometry (MS), has been useful for understanding the plant metabolome; however, due to the limited availability of authentic compounds for MS-based identification, the identities of most of the ion peaks detected by MS remain unknown. Accurate mass values of peaks obtained by high accuracy mass measurement and, if available, MS/MS fragmentation patterns provide abundant annotation for each peak. Here, we carried out an untargeted analysis of compounds in the mature fruit of 25 tomato cultivars using liquid chromatography-Orbitrap MS for accurate mass measurement, followed by manual curation to construct the metabolome database TOMATOMET (http://metabolites.in/tomato-fruits/). The database contains 7,118 peaks with accurate mass values, in which 1,577 ion peaks are annotated as members of a chemical group. Remarkably, 71% of the mass values are not found in the accurate masses detected previously in Arabidopsis thaliana, Medicago truncatula or Jatropha curcas, indicating significant chemical diversity among plant species that remains to be solved. Interestingly, substantial chemical diversity exists also among tomato cultivars, indicating that chemical profiling from distinct cultivars contributes towards understanding the metabolome, even in a single organ of a species, and can prioritize some desirable metabolic targets for further applications such as breeding.

Assessing the impact of nitrogen supplementation in oats across multiple growth locations and years with targeted phenotyping and high-resolution metabolite profiling approaches

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The response to nitrogen of 4 winter oat varieties in three field trials was analysed.

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A novel high-resolution method was developed to profile metabolite changes.

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Conditions that enhance yield do not necessarily result in higher nutritional value.

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Choice of variety is of equally high importance as the nitrogen levels applied.

Oats (Avena sativa L.) are a healthy food, being high in dietary fibre (e.g. β-glucans), antioxidants, minerals, and vitamins. Understanding the effect of variety and crop management on nutritional quality is important. The response of four oat varieties to increased nitrogen levels was investigated across multiple locations and years with respect to yield, grain quality and metabolites (assessed via GC- and LC- MS). A novel high-resolution UHPLC-PDA-MS/MS method was developed, providing improved metabolite enrichment, resolution, and identification. The combined phenotyping approach revealed that, amino acid levels were increased by nitrogen supplementation, as were total protein and nitrogen containing lipid levels, whereas health-beneficial avenanthramides were decreased. Although nitrogen addition significantly increased grain yield and β-glucan content, supporting increasing the total nitrogen levels recommended within agricultural guidelines, oat varietal choice as well as negative impacts upon health beneficial secondary metabolites and the environmental burdens associated with nitrogen fertilisation, require further consideration.

Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars

The first step in crop introduction-or breeding programmes-requires cultivar identification and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemical maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) high-definition mass spectrometer analytical platform. The generated data were processed and analysed using multivariate statistical methods. Principal component analysis (PCA) models were computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of the samples and pointing to underlying differential metabolic profiles of these cultivars. Further multivariate analyses were performed to profile differential signatory markers, which included carboxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Cytoglobin protects cancer cells from apoptosis by regulation of mitochondrial cardiolipin

Cytoglobin is important in the progression of oral squamous cell carcinoma but the molecular and cellular basis remain to be elucidated. In the current study, we develop a new cell model to study the function of cytoglobin in oral squamous carcinoma and response to cisplatin. Transcriptomic profiling showed cytoglobin mediated changes in expression of genes related to stress response, redox metabolism, mitochondrial function, cell adhesion, and fatty acid metabolism. Cellular and biochemical studies show that cytoglobin expression results in changes to phenotype associated with cancer progression including: increased cellular proliferation, motility and cell cycle progression. Cytoglobin also protects cells from cisplatin-induced apoptosis and oxidative stress with levels of the antioxidant glutathione increased and total and mitochondrial reactive oxygen species levels reduced. The mechanism of cisplatin resistance involved inhibition of caspase 9 activation and cytoglobin protected mitochondria from oxidative stress-induced fission. To understand the mechanism behind these phenotypic changes we employed lipidomic analysis and demonstrate that levels of the redox sensitive and apoptosis regulating cardiolipin are significantly up-regulated in cells expressing cytoglobin. In conclusion, our data shows that cytoglobin expression results in important phenotypic changes that could be exploited by cancer cells in vivo to facilitate disease progression.

Lipopolysaccharide perception in Arabidopsis thaliana: Diverse LPS chemotypes from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris trigger differential defence-related perturbations in the metabolome

Lipopolysaccharides (LPSs) are microbe-associated molecular pattern molecules (MAMPs) from Gram-negative bacterial pathogens that potentially contain three different MAMPs (the O-polysaccharide chain, the oligosaccharide core and lipid A). LPSs was purified from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris and electrophoretically profiled. Outcomes of the interactions of the three different LPS chemotypes with Arabidopsis thaliana, as reflected in the induced defence metabolites, profiled at 12 h and 24 h post elicitation, were investigated. Plants were pressure-infiltrated with LPS solutions and methanol-based extractions at different time points were performed for untargeted metabolomics using ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Multivariate data modelling and chemometric analysis were applied to generate interpretable biochemical information from the multidimensional data sets. The three LPSs triggered differential metabolome changes in the plants as apparent from chromatographically distinct MS chromatograms. Unsupervised and supervised multivariate data models exhibited time- and treatment-related variations, and revealed discriminating metabolite variables. Heat map models comparatively displayed the up-regulated pathways affecting the metabolomes and Venn diagrams indicated up-regulated and shared metabolites among the three LPS treatments. The altered metabolomes reflect the up-regulation of metabolites from not only the glucosinolate pathway, but also from the shikimate-phenylpropanoid-flavonoid -, terpenoid - and indolic/alkaloid pathways, as well as oxygenated fatty acids. Distinct phytochemical profiles, especially at the earlier time point, suggest differences in the perception of the three LPS chemotypes, associated with the molecular patterns within the tripartite lipoglycans.

Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research

Metabolomics-the endpoint of the omics cascade-is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Lipopolysaccharides trigger synthesis of the allelochemical sorgoleone in cell cultures of Sorghum bicolor

The use of plant cell suspension culture systems has demonstrated to be highly suitable for metabolomics investigations of inducible defense responses. Here we report on sorghum cell suspension cultures that were elicited with purified lipopolysaccharides from the sorghum pathogen Burkholderia andropogonis, to activate metabolic pathways involved in the chemical defenses of the plant. Metabolomic analysis using liquid chromatography coupled to mass spectrometry identified a resorcinol phenolic lipid, annotated as sorgoleone, as one of the biomarkers associated with the LPS-induced response. Sorgoleone is a semiochemical and an allelochemical, synthesized by specialized root hair cells and the major component of the hydrophobic root exudate of sorghum. Its detection in undifferentiated cells might indicate a previously undescribed role for this phytochemical in plant defense responses.

Deep annotation of untargeted LC-MS metabolomics data with Binner

MOTIVATION

When metabolites are analyzed by electrospray ionization (ESI)-mass spectrometry, they are usually detected as multiple ion species due to the presence of isotopes, adducts and in-source fragments. The signals generated by these degenerate features (along with contaminants and other chemical noise) obscure meaningful patterns in MS data, complicating both compound identification and downstream statistical analysis. To address this problem, we developed Binner, a new tool for the discovery and elimination of many degenerate feature signals typically present in untargeted ESI-LC-MS metabolomics data.

RESULTS

Binner generates feature annotations and provides tools to help users visualize informative feature relationships that can further elucidate the underlying structure of the data. To demonstrate the utility of Binner and to evaluate its performance, we analyzed data from reversed phase LC-MS and hydrophilic interaction chromatography (HILIC) platforms and demonstrated the accuracy of selected annotations using MS/MS. When we compared Binner annotations of 75 compounds previously identified in human plasma samples with annotations generated by three similar tools, we found that Binner achieves superior performance in the number and accuracy of annotations while simultaneously minimizing the number of incorrectly annotated principal ions. Data reduction and pattern exploration with Binner have allowed us to catalog a number of previously unrecognized complex adducts and neutral losses generated during the ionization of molecules in LC-MS. In summary, Binner allows users to explore patterns in their data and to efficiently and accurately eliminate a significant number of the degenerate features typically found in various LC-MS modalities.

AVAILABILITY AND IMPLEMENTATION

Binner is written in Java and is freely available from http://binner.med.umich.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts, and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a buffer modification workflow (BMW) in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3-acetate buffer and in solvent with the buffer modified with 15NH3-formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Overall, it identified 26% of ∼27 000 liver LC-MS features as putative metabolites, of which ∼2600 showed HMDB or KEGG database formula match. This workflow is well suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies

Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.

CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network

Motivation

The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample.

Results

Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.

Availability and implementation

https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-Omics Analysis of Diabetic Heart Disease in the db/db Model Reveals Potential Targets for Treatment by a Longevity-Associated Gene

Characterisation of animal models of diabetic cardiomyopathy may help unravel new molecular targets for therapy. Long-living individuals are protected from the adverse influence of diabetes on the heart, and the transfer of a longevity-associated variant (LAV) of the human BPIFB4 gene protects cardiac function in the db/db mouse model. This study aimed to determine the effect of LAV-BPIFB4 therapy on the metabolic phenotype (ultra-high-performance liquid chromatography-mass spectrometry, UHPLC-MS) and cardiac transcriptome (next-generation RNAseq) in db/db mice. UHPLC-MS showed that 493 cardiac metabolites were differentially modulated in diabetic compared with non-diabetic mice, mainly related to lipid metabolism. Moreover, only 3 out of 63 metabolites influenced by LAV-BPIFB4 therapy in diabetic hearts showed a reversion from the diabetic towards the non-diabetic phenotype. RNAseq showed 60 genes were differentially expressed in hearts of diabetic and non-diabetic mice. The contrast between LAV-BPIFB4- and vehicle-treated diabetic hearts revealed eight genes differentially expressed, mainly associated with mitochondrial and metabolic function. Bioinformatic analysis indicated that LAV-BPIFB4 re-programmed the heart transcriptome and metabolome rather than reverting it to a non-diabetic phenotype. Beside illustrating global metabolic and expressional changes in diabetic heart, our findings pinpoint subtle changes in mitochondrial-related proteins and lipid metabolism that could contribute to LAV-BPIFB4-induced cardio-protection in a murine model of type-2 diabetes.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Rhizobacteria-induced systemic tolerance against drought stress in Sorghum bicolor (L.) Moench

Induction of systemic tolerance in sorghum [Sorghum bicolor (L.) Moench] against drought stress was studied by screening a large collection of rhizobacterial isolates for their potential to exhibit this essential plant growth-promoting trait. This was done by means of a greenhouse assay that measured the relative change in both plant height and -biomass (roots and shoots) between rhizobacteria-primed versus non-primed (naïve) plants under drought stress conditions. In order to elucidate the metabolomic changes in S. bicolor that conferred the drought stress tolerance after treatment (priming) with selected isolates, untargeted ultra-high performance liquid chromatography-high definition mass spectrometry (UHPLC-HDMS)-based metabolomics was carried out. Intracellular metabolites were methanol-extracted from rhizobacteria-primed and naïve S. bicolor roots and shoots. Extracts were analysed on a UHPLC-HDMS system and the generated data were chemometrically mined to determine signatory metabolic profiles and bio-markers related to induced systemic tolerance. The metabolomic results showed significant treatment-related differential metabolic reprogramming between rhizobacteria-primed and naïve plants, correlating to the ability of the selected isolates to protect S. bicolor against drought stress. The selected isolates, identified by means of 16S rRNA gene sequencing as members of the genera Bacillus and Pseudomonas, were screened for 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity by means of an in vitro assay and the presence of the acdS gene was subsequently confirmed by PCR for strain N66 (Pseudomonas sp.). The underlying key metabolic changes in the enhanced drought stress tolerance observed in rhizobacteria-primed S. bicolor plants included (1) augmented antioxidant capacity; (2) growth promotion and root architecture modification as a result of the upregulation of the hormones gibberellic acid, indole acetic acid and cytokinin; (3) the early activation of induce systemic tolerance through the signalling hormones brassinolides, salicylic acid and jasmonic acid and signalling molecules sphingosine and psychosine; (4) the production of the osmolytes proline, glutamic acid and choline; (5) the production of the epicuticular wax docosanoic acid and (6) ACC deaminase activity resulting in lowered ethylene levels. These results unravelled key molecular details underlying the PGPR-induced systemic tolerance in sorghum plants, providing insights for the plant priming for abiotic stress.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping

In this study we assess multiple sample preparation methods for UHPLC-MS metabolic phenotyping analysis of human urine and plasma. All methods are discussed in terms of metabolite and lipid coverage and reproducibility.

Unravelling the Metabolic Reconfiguration of the Post-Challenge Primed State in Sorghum bicolor Responding to Colletotrichum sublineolum Infection

Priming is a natural phenomenon that pre-conditions plants for enhanced defence against a wide range of pathogens. It represents a complementary strategy, or sustainable alternative that can provide protection against disease. However, a comprehensive functional and mechanistic understanding of the various layers of priming events is still limited. A non-targeted metabolomics approach was used to investigate metabolic changes in plant growth-promoting rhizobacteria (PGPR)-primed Sorghum bicolor seedlings infected with the anthracnose-causing fungal pathogen, Colletotrichum sublineolum, with a focus on the post-challenge primed state phase. At the 4-leaf growth stage, the plants were treated with a strain of Paenibacillus alvei at 10⁸ cfu mL⁻¹. Following a 24 h PGPR application, the plants were inoculated with a C. sublineolum spore suspension (10⁶ spores mL⁻¹), and the infection monitored over time: 1, 3, 5, 7 and 9 days post-inoculation. Non-infected plants served as negative controls. Intracellular metabolites from both inoculated and non-inoculated plants were extracted with 80% methanol-water. The extracts were chromatographically and spectrometrically analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to high-definition mass spectrometry. The acquired multidimensional data were processed to create data matrices for chemometric modelling. The computed models indicated time-related metabolic perturbations that reflect primed responses to the fungal infection. Evaluation of orthogonal projection to latent structure-discriminant analysis (OPLS-DA) loading shared and unique structures (SUS)-plots uncovered the differential stronger defence responses against the fungal infection observed in primed plants. These involved enhanced levels of amino acids (tyrosine, tryptophan), phytohormones (jasmonic acid and salicylic acid conjugates, and zeatin), and defence-related components of the lipidome. Furthermore, other defence responses in both naïve and primed plants were characterised by a complex mobilisation of phenolic compounds and de novo biosynthesis of the flavones, apigenin and luteolin and the 3-deoxyanthocyanidin phytoalexins, apigeninidin and luteolinidin, as well as some related conjugates.

Bidirectional Cross-Talk between Biliary Epithelium and Th17 Cells Promotes Local Th17 Expansion and Bile Duct Proliferation in Biliary Liver Diseases

There is no effective treatment for autoimmune biliary diseases. Therefore, understanding their immunopathology is crucial. The biliary epithelial cells (BEC), expressing TLR-4, are constantly exposed to gut microbes and bacterial wall LPS, and in settings of inflammation, the immune infiltrate is dense within the peribiliary region of human liver. By dual immunohistochemistry, we affirm human intrahepatic T cell infiltrate includes CCR6+CD4+ and AhR+CD4+ T cells with potential for plasticity to Th17 phenotype. Mechanistically, we demonstrate that Th1 and Th17 inflammatory cytokines and LPS enhance human primary BEC release of the CCR6 ligand CCL20 and BEC secretion of Th17-polarizing cytokines IL-6 and IL-1β. Cell culture assays with human BEC secretome showed that secretome polarizes CD4 T cells toward a Th17 phenotype and supports the survival of Th17 cells. BEC secretome did not promote Th1 cell generation. Additionally, we give evidence for a mutually beneficial feedback of the type 17 cell infiltrate on BEC, showing that treatment with type 17 cytokines increases BEC proliferation, as monitored by Ki67 and activation of JAK2-STAT3 signaling. This study identifies human BEC as active players in determining the nature of the intrahepatic immune microenvironment. In settings of inflammation and/or infection, biliary epithelium establishes a prominent peribiliary type 17 infiltrate via recruitment and retention and enhances polarization of intrahepatic CD4 cells toward Th17 cells via type 17 cytokines, and, reciprocally, Th17 cells promote BEC proliferation for biliary regeneration. Altogether, we provide new insight into cross-talk between Th17 lymphocytes and human primary biliary epithelium in biliary regenerative pathologies.

Comparative Metabolomics between Mycobacterium tuberculosis and the MTBVAC Vaccine Candidate

MTBVAC is a live attenuated M. tuberculosis vaccine constructed by genetic deletions in the phoP and fadD26 virulence genes. The MTBVAC vaccine is currently in phase 2 clinical trials with newborns and adults in South Africa, one of the countries with the highest incidence. Although MTBVAC has been extensively characterized by genomics, transcriptomics, lipidomics, and proteomics, its metabolomic profile is yet unknown. Accordingly, in this study we aim to identify differential metabolites between M. tuberculosis and MTBVAC. To this end, an untargeted metabolomics approach based on liquid chromatography coupled to high-resolution mass spectrometry was implemented in order to explore the main metabolic differences between M. tuberculosis and MTBVAC. As an outcome, we identified a set of 34 metabolites involved in diverse bacterial biosynthetic pathways. A consistent increase in the phosphatidylinositol species was observed in the vaccine candidate relative to its parental strain. This phenotype resulted in an increased production of phosphatidylinositol mannosides, a novel PhoP-regulated phenotype in the most widespread lineages of M. tuberculosis. This study represents a step ahead in our understanding of the MTBVAC vaccine, and some of the differential metabolites identified in this work might be used as potential vaccination biomarkers.

Differential Metabolic Reprogramming in Paenibacillus alvei-Primed Sorghum bicolor Seedlings in Response to Fusarium pseudograminearum Infection

Metabolic changes in sorghum seedlings in response to Paenibacillus alvei (NAS-6G6)-induced systemic resistance against Fusarium pseudograminearum crown rot were investigated by means of untargeted ultra-high performance liquid chromatography-high definition mass spectrometry (UHPLC-HDMS). Treatment of seedlings with the plant growth-promoting rhizobacterium P. alvei at a concentration of 1 × 108 colony forming units mL-1 prior to inoculation with F. pseudograminearum lowered crown rot disease severity significantly at the highest inoculum dose of 1 × 106 spores mL-1. Intracellular metabolites were subsequently methanol-extracted from treated and untreated sorghum roots, stems and leaves at 1, 4 and 7 days post inoculation (d.p.i.) with F. pseudograminearum. The extracts were analysed on an UHPLC-HDMS platform, and the data chemometrically processed to determine metabolic profiles and signatures related to priming and induced resistance. Significant treatment-related differences in primary and secondary metabolism post inoculation with F. pseudograminearum were observed between P. alvei-primed versus naïve S. bicolor seedlings. The differential metabolic reprogramming in primed plants comprised of a quicker and/or enhanced upregulation of amino acid-, phytohormone-, phenylpropanoid-, flavonoid- and lipid metabolites in response to inoculation with F. pseudograminearum.

Multiple metabolic pathways are predictive of ricin intoxication in a rat model

INTRODUCTION

Exposure to ricin can be lethal and treatments that are under development have short windows of opportunity for administration after exposure. It is therefore essential to achieve early detection of ricin exposure to provide the best prognosis for exposed individuals. Ricin toxin can be detected in clinical samples via several antibody-based techniques, but the efficacy of these can be limited due to the rapid processing and cellular uptake of toxin in the body and subsequent low blood ricin concentrations. Other diagnostic tools that perform, in an orthogonal manner, are therefore desirable.

OBJECTIVES

To determine time-dependent metabolic changes in Sprague-Dawley rats following intravenous exposure to ricin.

METHODS

Sprague-Dawley rats were intravenously exposed to ricin and multiple blood samples were collected from each animal for up to 48 h following exposure in two independent studies. Plasma samples were analysed applying HILIC and C₁₈ reversed phase UHPLC-MS assays followed by univariate and multivariate analysis.

RESULTS

In Sprague-Dawley rats we have demonstrated that metabolic changes measured in blood can distinguish between rats exposed intravenously to ricin and controls prior to the onset of behavioral signs of intoxication after 24 h. A total of 37 metabolites were significantly altered following exposure to ricin when compared to controls. The arginine/proline, bile acid and triacylglyceride metabolic pathways were highlighted as being important with two triacylglycerides at 8 h post exposure giving an AUROC score of 0.94. At 16 h and 24 h the AUROC score increased to 0.98 and 1.0 with the number of metabolites in the panel increasing to 5 and 7, respectively.

CONCLUSIONS

These data demonstrate that metabolites may be a useful tool to diagnose and detect ricin exposure, thus increasing the effectiveness of supportive therapy and future ricin-specific medical treatments.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

The potential of nanoflow liquid chromatography-nano electrospray ionisation-mass spectrometry for global profiling the faecal metabolome

Faeces are comprised of a wide array of metabolites arising from the circulatory system as well as the human microbiome. A global metabolite analysis (metabolomics) of faecal extracts offers the potential to uncover new compounds which may be indicative of the onset of bowel diseases such as colorectal cancer (CRC). To date, faecal metabolomics is still in its infancy and the compounds of low abundance present in faecal extracts poorly characterised. In this study, extracts of faeces from healthy subjects were profiled using a sensitive nanoflow-nanospray LC-MS platform which resulted in highly repeatable peak retention times (<2% CV) and intensities (<15% CV). Analysis of the extracts revealed wide coverage of the faecal metabolome including detection of low abundant signalling compounds such as sex steroids and eicosanoids, alongside highly abundant pharmaceuticals and tetrapyrrole metabolites. A small pilot study investigating differences in metabolomics profiles of faecal samples obtained from 7 CRC, 25 adenomatous polyp and 26 healthy groups revealed that secondary bile acids, conjugated androgens, eicosanoids, phospholipids and an unidentified haem metabolite were potential classes of metabolites that discriminated between the CRC and control sample groups. However, much larger follow up studies are needed to confirm which components of the faecal metabolome are associated with actual CRC disease rather than dietary influences. This study reveals the potential of nanospray-nanoflow LC-MS profiling of faecal samples from large scale cohort studies for uncovering the role of the faecal metabolome in colorectal disease formation.

Rapid UHPLC-MS metabolite profiling and phenotypic assays reveal genotypic impacts of nitrogen supplementation in oats

INTRODUCTION

Oats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels.

OBJECTIVES

Oats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated.

METHODS

Four winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha-1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach.

RESULTS

Application of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed.

CONCLUSIONS

Although the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism.

Time-resolved decoding of metabolic signatures of in vitro growth of the hemibiotrophic pathogen Colletotrichum sublineolum

Metabolomics has emerged as a powerful approach to comprehensively interrogate cellular biochemistry. As such, we applied an untargeted liquid chromatography-mass spectrometry metabolomic strategy to elucidate metabolome changes in the anthracnose-causing hemibiotrophic sorghum pathogen, Colletotrichum sublineolum. An in vitro batch culture study model with different carbon sources, glucose, arabinose and rhamnose, were used to support fungal growth over a period of twelve days. Metabolites representing the intracellular and extracellular (secreted) metabolomes were extracted with methanol and subjected to LC-MS analyses. Chemometric modelling revealed a metabolic variation trajectory, comprising three distinct stages that metabolically describe the adaptation of the fungus to diminishing nutrients. Selected marker gene expression indicated stage one (0-3 d.p.i) as corresponding to the early logarithmic phase. Stage two can be interpreted as an intermediate transitionary stage with stage three corresponding to the stationary phase (9-12 d.p.i). Stage one was characterised by up-regulation of endo-metabolites such as ferricrocin, fatty acids and flavone-conjugates, while stage three was characterised by the secretion of phytotoxins, including colletotrichin and colletotric acid. Ultimately, results from our in vitro model reveal previously unknown insights into the dynamic aspects of metabolome reprogramming in the growth phases of Colletotrichum spp as determined by nutrients obtainable from plant cell walls.

Nitrogen deprivation in Fusarium oxysporum promotes mycotoxin production via intermediates in the Krebs cycle and unreported methylmalonyl-CoA mutase activity

INTRODUCTION

Fusarium oxysporum has a high affinity for lignin and cellulose-based substrates and is known to grow in a wide range of environments. It is these properties and its ability to produce mycotoxins that have contributed to its pathogenicity in cereal crops that can affect human and animal health when ingested.

OBJECTIVES

Identify the mechanisms of mycotoxin production and map the functional output of F. oxysporum under varying growth conditions.

METHODS

Liquid and gas-based chromatography coupled with mass spectrometry was used to identify and map the untargeted metabolic pathway of F. oxysporum grown using nitrogen limited and organic/inorganic nitrogen supplemented media.

RESULTS

Over 1300 metabolites were identified, relating to 42 metabolic pathways. Of these, 520 metabolites merged at pyruvate (glycolysis), succinate (Krebs cycle) and aspartate-glutamate metabolic pathways. CoA depletion at the growth stage triggered the initiation of fatty acid and branched amino acid degradation. This in turn activated propionyl CoA carnitine acetyltransferase enzymes, resulting in nitrogen preservation (urea, putrescine and organic acids end-products). CoA then transferred into the TCA cycle via previously unreported β-alanine and propionyl CoA metabolic pathways, the latter likely being a novel methylmalonyl-CoA mutase activity for F. oxysporum.

CONCLUSIONS

The lower supplementation of inorganic nitrogen compounds (≤ 50 mM) and the elimination of nitrates/organic nitrogen sources resulted in TCA autophagy events that boosted mycotoxin-based metabolism and decreased overall F. oxysporum growth. Such knowledge of functional mycotoxin production can be used to supplement agricultural crops and reduce the risk of mycotoxin contamination in human and animal food supplies.

Genome guided investigation of antibiotics producing actinomycetales strain isolated from a Macau mangrove ecosystem

Actinomycetes are a heterogeneous group of gram positive filamentous bacteria that have been found to produce a wide range of valuable bioactive secondary metabolites, particularly antibiotics. Moreover, actinomycetes isolated from unexplored environments show an unprecedented potential to generate novel active compounds. Hence, in order to search for novel antibiotics, we isolated and characterized actinomycetes strains from plant samples collected from a mangrove in Macau. Within the class of actinobacteria, fourteen actinomycetes isolates have been isolated and identified belonging to the genus of Streptomyces, Micromonospora, Mycobacterium, Brevibacterium, Curtobacterium and Kineococcus based on their 16S rRNA sequences. Further whole genome sequencing analysis of one of the isolated Streptomyces sp., which presented 99.13% sequence similarity with Streptomyces parvulus strain 2297, showed that it consisted of 118 scaffolds, 8,348,559 base pairs and had a 72.28% G + C content. In addition, genome-mining revealed that the isolated Streptomyces sp. contains 109 gene clusters responsible for the biosynthesis of known and/or novel secondary metabolites, including different types of terpene, T1pks, T2pks, T3pks, Nrps, indole, siderophore, bacteriocin, thiopeptide, phosphonate, lanthipeptide, ectoine, butyrolactone, T3pks-Nrps, and T1pks-Nrps. Meanwhile, the small molecules present in ethyl acetate extract of the fermentation broth of this strain were analyzed by LC-MS. Predicted secondary metabolites of melanin and desferrioxamine B were identified and both of them were firstly found to be produced by the Streptomyces parvulus strain. Our study highlights that combining genome mining is an efficient method to detect potentially promising natural products from mangrove-derived actinomycetes.

Plasma metabolites associated with healthy Nordic dietary indexes and risk of type 2 diabetes-a nested case-control study in a Swedish population

Background

Epidemiologic evidence on the association of a healthy Nordic diet and future type 2 diabetes (T2D) is limited. Exploring metabolites as biomarkers of healthy Nordic dietary patterns may facilitate investigation of associations between such patterns and T2D.

Objectives

We aimed to identify metabolites related to a priori-defined healthy Nordic dietary indexes, the Baltic Sea Diet Score (BSDS) and Healthy Nordic Food Index (HNFI), and evaluate associations with the T2D risk in a case-control study nested in a Swedish population-based prospective cohort.

Design

Plasma samples from 421 case-control pairs at baseline and samples from a subset of 151 healthy controls at a 10-y follow-up were analyzed with the use of untargeted liquid chromatography-mass spectrometry metabolomics. Index-related metabolites were identified through the use of random forest modelling followed by partial correlation analysis adjustment for lifestyle confounders. Metabolite patterns were derived via principal component analysis (PCA). ORs of T2D were estimated via conditional logistic regression. Reproducibility of metabolites was assessed by intraclass correlation (ICC) in healthy controls. Associations were also assessed for 10 metabolites previously identified as linking a healthy Nordic diet with T2D.

Results

In total, 31 metabolites were associated with BSDS and/or HNFI (-0.19 ≤ r ≤ 0.21, 0.10 ≤ ICC ≤ 0.59). Two PCs were determined from index-related metabolites: PC1 strongly correlated to the indexes (r = 0.27 for BSDS, r = 0.25 for HNFI, ICC = 0.45) but showed no association with T2D risk. PC2 was weakly associated with the indexes, but more strongly with foods not part of the indexes, e.g., pizza, sausages, and hamburgers. PC2 was also significantly associated with T2D risk. Predefined metabolites were confirmed to be reflective of consumption of whole grains, fish, or vegetables, but not related to T2D risk.

Conclusions

Our study did not support an association between healthy Nordic dietary indexes and T2D. However, foods such as hamburger, sausage, and pizza not covered by the indexes appeared to be more important for T2D risk in the current population.

Metabolomic characterization of hypertension and dyslipidemia

BACKGROUND

Hypertension and dyslipidemia are two main risk factors for cardiovascular diseases (CVD). Moreover, their coexistence predisposes individuals to a considerably increased risk of CVD. However, the regulatory mechanisms involved in hypertension and dyslipidemia as well as their interactions are incompletely understood.

OBJECTIVES

The aims of our study were to identify metabolic biomarkers and pathways for hypertension and dyslipidemia, and compare the metabolic patterns between hypertension and dyslipidemia.

METHODS

In this study, we performed metabolomic investigations into hypertension and dyslipidemia based on a "healthy" UK population. Metabolomic data from the Husermet project were acquired by gas chromatography-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. Both univariate and multivariate statistical methods were used to facilitate biomarker selection and pathway analysis.

RESULTS

Serum metabolic signatures between individuals with and without hypertension or dyslipidemia exhibited considerable differences. Using rigorous selection criteria, 26 and 46 metabolites were identified as potential biomarkers of hypertension and dyslipidemia respectively. These metabolites, mainly involved in fatty acid metabolism, glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, are implicated in insulin resistance, vascular remodeling, macrophage activation and oxidised LDL formation. Remarkably, hypertension and dyslipidemia exhibit both common and distinct metabolic patterns, revealing their independent and synergetic biological implications.

CONCLUSION

This study identified valuable biomarkers and pathways for hypertension and dyslipidemia, and revealed common and different metabolic patterns between hypertension and dyslipidemia. The information provided in this study could shed new light on the pathologic mechanisms and offer potential intervention targets for hypertension and dyslipidemia as well as their related diseases.

Comparison of modified Matyash method to conventional solvent systems for polar metabolite and lipid extractions

In the last decade, metabolomics has experienced significant advances in the throughput and robustness of analytical methodologies. Yet the preparation of biofluids and low-mass tissue samples remains a laborious and potentially inconsistent manual process, and a significant bottleneck for high-throughput metabolomics. To address this, we have compared three different sample extraction solvent systems in three diverse sample types with the purpose of selecting an optimum protocol for subsequent automation of sample preparation. We have investigated and re-optimised the solvent ratios in the recently introduced methyl tert-butyl ether (MTBE)/methanol/water solvent system (here termed modified Matyash; 2.6/2.0/2.4, v/v/v) and compared it to the original Matyash method (10/3/2.5, v/v/v) and the conventional chloroform/methanol/water (stepwise Bligh and Dyer, 2.0/2.0/1.8, v/v/v) using two biofluids (human serum and urine) and one tissue (whole Daphnia magna). This is the first report of the use of the Matyash method for extracting metabolites from the US National Institutes of Health (NIH) model organism D. magna. Extracted samples were analysed by non-targeted direct infusion mass spectrometry metabolomics or LC-MS metabolomics. Overall, the modified Matyash method yielded a higher number of peaks and putatively annotated metabolites compared to the original Matyash method (1-29% more peaks and 1-30% more metabolites) and the Bligh and Dyer method (4-20% more peaks and 1-41% more metabolites). Additionally the modified Matyash method was superior when considering metabolite intensities. The reproducibility of the modified Matyash method was higher than other methods (in 10 out of 12 datasets, compared to the original Matyash method; and in 8 out of 12 datasets, compared to the Bligh and Dyer method), based upon the observation of a lower mRSD of peak intensities. In conclusion, the modified Matyash method tended to provide a higher yield and reproducibility for most sample types in this study compared to two widely used methods.