Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Linking the humification of organic amendments with size aggregate distribution: Insights into molecular composition using FT-ICR-MS

Soil organic matter (SOM) plays a pivotal role in enhancing physical and biological characteristics of soil. Humic substances constitute a substantial proportion of SOM and their increase can improve crop yields and promote agricultural sustainability. While previous research has primarily assessed the influence that humic acids (HAs) derived from natural water have on soil structure, our study focuses on the impact of HAs on soil aggregation under different fertilizer regimes. During the summer cropping season, maize was cultivated under organic and synthetic fertilizer treatments. The organic fertilizer treatment utilized barley (Hordeum vulgare L.) and hairy vetch (Vicia villosa R.) as an organic amendment five days prior to maize planting. The synthetic treatment included a synthetic fertilizer (NPK) applied at South Korea's recommended rates. The organic treatment resulted in significant improvements in the soil aggregates and stability (mean weight diameter, MWD; p < 0.05) compared to the synthetic fertilizer application. These improvements could be primarily attributed to the increased quantity and quality of HAs in the soil derived from the organic amendment. The amount of extracted HAs in the organic treatment was nearly twice that of the synthetic treatment. Additionally, the organic treatment had a 140 % larger MWD and a 40 % increase in total phenolic content compared to the synthetic treatment. The organic treatment also had an increased macronutrient uptake (p < 0.001), an 11 % increase in aboveground maize biomass, and a 21 % increase in grain yield relative to the synthetic treatment. Thus, the enhancement of HA properties through the incorporation of fresh organic manure can both directly and indirectly increase crop productivity.

Climate-Affected Australian Tropical Montane Cloud Forest Plants: Metabolomic Profiles, Isolated Phytochemicals, and Bioactivities

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds.

Effects of Extraction Process Factors on the Composition and Antioxidant Activity of Blackthorn (Prunus spinosa L.) Fruit Extracts

This study aimed at establishing the optimal conditions for the classic extraction of phenolic compounds from Prunus spinosa L. fruits. The effects of different parameters, i.e., ethanol concentration in the extraction solvent (mixture of ethanol and water), operation temperature, and extraction time, on process responses were evaluated. Total phenolic content (TPC), total anthocyanin content (TAC), antioxidant capacity (AC), and contents of protocatechuic acid (PA), caffeic acid (CA), vanillic acid (VA), rutin hydrate (RH), and quercetin (Q) of fruit extracts were selected as process responses. A synergistic effect of obtaining high values of TPC, TAC, AC, PA, and VA was achieved for the extraction in 50% ethanol at 60 °C for 30 min. At a higher level of process temperature, the extraction of protocatechuic acid and vanillic acid was enhanced, but the flavonoids, i.e., rutin hydrate and quercetin, were degraded. A lower temperature should be used to obtain a higher amount of flavonoids. TPC, TAC, AC, and phenolic acid contents (PA, CA, and VA) in the extract samples obtained at an ethanol concentration of 50-100%, a temperature of 30-60 °C, and an extraction time of 30 min were strongly directly correlated.

Population divergence in heat and drought responses of a coastal plant: from metabolic phenotypes to plant morphology and growth

Studying intraspecific variation in multistress responses is central for predicting and managing the population dynamics of wild plant species under rapid global change. Yet, it remains a challenging goal in this field to integrate knowledge on the complex biochemical underpinnings for the targeted 'non-model' species. Here, we studied divergence in combined drought and heat responses among Northern and Southern European populations of the dune plant Cakile maritima, by combining comprehensive plant phenotyping with metabolic profiling via FT-ICR-MS and UPLC-TQ-MS/MS. We observed pronounced constitutive divergence in growth phenology, leaf functional traits, and defence chemistry (glucosinolates and alkaloids) among population origins. Most importantly, the magnitude of growth reduction under drought was partly weaker in southern plants and associated with divergence in plastic growth responses (leaf abscission) and the modulation of primary and specialized metabolites with known central functions not only in plant abiotic but also in biotic stress responses. Our study indicates that divergent selection has shaped the constitutive and drought-/heat-induced expression of numerous morphological and biochemical functional traits to mediate higher abiotic stress resistance in southern Cakile populations, and highlights that metabolomics can be a powerful tool to explore the underlying mechanisms of local adaptation in 'non-model' species.

Development of a root exudate collection protocol for metabolomics analysis using Nuclear Magnetic Resonance

Large amounts of root exudates are released by plant roots into the soil. Due to their importance in regulating the rhizosphere properties, it is necessary to unravel the precise composition and function of exudates at the root-soil interface. However, obtaining root exudates without inducing artefacts is a difficult task. To analyse the low molecular weight molecules secreted by pea roots, a protocol of root exudate collection was developed to perform a metabolomics analysis using Nuclear Magnetic Resonance (NMR). To date a few NMR studies are dedicated to root exudates. Plant culture, exudates collection and sample preparation methods had thus to be adapted to the NMR approach. Here, pea seedlings were hydroponically grown. The obtained NMR fingerprints show that osmotic stress increases the quantity of the exudates but not their diversity. We therefore selected a protocol reducing the harvest time and using an ionic solvent and applied it to the analysis of faba bean exudates. NMR analysis of the metabolic profiles allowed to discriminate between pea and faba bean according to their exudate composition. This protocol is therefore very promising for studying the composition of root exudates from different plant species as well as their evolution in response to different environmental conditions or pathophysiological events.

Comprehensive Lipid Profile of Eight Echinoderm Species by RPLC-Triple TOF-MS/MS

Echinoderms are of broad interest for abundant bioactive lipids. The comprehensive lipid profiles in eight echinoderm species were obtained by UPLC-Triple TOF-MS/MS with characterization and semi-quantitative analysis of 961 lipid molecular species in 14 subclasses of 4 classes. Phospholipids (38.78-76.83%) and glycerolipids (6.85-42.82%) were the main classes in all investigated echinoderm species, with abundant ether phospholipids, whereas the proportion of sphingolipids was higher in sea cucumbers. Two sulfated lipid subclasses were detected in echinoderms for the first time; sterol sulfate was rich in sea cucumbers, whereas sulfoquinovosyldiacylglycerol existed in the sea star and sea urchins. Furthermore, PC(18:1/24:2), PE(16:0/14:0), and TAG(50:1e) could be used as lipid markers to distinguish eight echinoderm species. In this study, the differentiation of eight echinoderms was achieved by lipidomics and revealed the uniqueness of the natural biochemical fingerprints of echinoderms. The findings will help evaluate the nutritional value in the future.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Production and Characterization of Hydrothermal Extracts of the Needles from Four Conifer Tree Species: Scots Pine, Norway Spruce, Common Juniper, and European Larch

Coniferous trees are the most dominant trees in Finland with a great economic value for pulp, paper, and timber making. Thus, their utilization also results in large quantities of residues, especially bark and needles. Tree needles are a rich source of bioactive compounds, which have a considerable utilization potential in different pharmaceutical or techno-chemical applications. In this study, hydrothermal extraction (HTE) of the needles from four conifer tree species, namely, Scots pine, Norway spruce, common juniper, and European larch, was performed. Besides water, ethanol was also used as a solvent to enhance extraction efficiency and selectivity. All of the HTE experiments were conducted with a customized high-pressure reactor operated at 120 °C and 5 bar. The obtained needle extracts were then analyzed using a direct-infusion ultrahigh-resolution Fourier transform ion cyclotron (FT-ICR) mass spectrometry. The FT-ICR analysis of water and ethanol extracts allowed identification of over 200 secondary plant metabolites, including monosaccharides, organic acids, terpenoids, a variety of phenolic compounds, and nitrogen alkaloids. The use of ethanol as the extraction solvent considerably enhanced the recovery of lipids, especially terpenoids, some polyphenols, and other unsaturated hydrocarbon species.

Advances in Mass Spectrometry-Metabolomics Based Approaches

Highly selective and sensitive analytical techniques are necessary for microbial metabolomics due to the complexity of the microbial sample matrix. Hence, mass spectrometry (MS) has been successfully applied in microbial metabolomics due to its high precision, versatility, sensitivity, and wide dynamic range. The different analytical tools using MS have been employed in microbial metabolomics investigations and can contribute to the discovery or accelerate the search for bioactive substances. The coupling with chromatographic and electrophoretic separation techniques has resulted in more efficient technologies for the analysis of microbial compounds occurring in trace levels. This book chapter describes the current advances in the application of mass spectrometry-based metabolomics in the search for new biologically active agents from microbial sources; the development of new approaches for in silico annotation of natural products; the different technologies employing mass spectrometry imaging to deliver more comprehensive analysis and elucidate the metabolome involved in ecological interactions as they enable visualization of the spatial dispersion of small molecules. We also describe other ambient ionization techniques applied to the fingerprint of microbial natural products and modern techniques such as ion mobility mass spectrometry used to microbial metabolomic analyses and the dereplication of natural microbial products through MS.

Mass Spectrometry-Based Untargeted Metabolomics and Lipidomics Platforms to Analyze Cell Culture Extracts

Metabolites represent the most downstream level of the cellular organization. Hence, an in vitro untargeted metabolomics approach is extremely valuable to deepen the understanding of how endogenous metabolites in cells are altered under a given biological condition. This chapter describes a robust liquid chromatography-high-resolution mass spectrometry-based metabolomics and lipidomics platform applied to cell culture extracts. The analytical workflow includes an optimized sample preparation procedure to cover a wide range of metabolites using liquid-liquid extraction and validated instrumental operation procedures with the implementation of comprehensive quality assurance and quality control measures to ensure high reproducibility. The lipidomics platform is based on reversed-phase liquid chromatography for the separation of slightly polar to apolar metabolites and covers a broad range of lipid classes, while the metabolomics platform makes use of two hydrophilic interaction liquid chromatography methods for the separation of polar metabolites, such as organic acids, amino acids, and sugars. The chapter focuses on the analysis of cultured HepaRG cells that are derived from a human hepatocellular carcinoma; however, the sample preparation and analytical platforms can easily be adapted for other types of cells.

Analysis of Brain Metabolites Using Two Complementary Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry Methods

Metabolomics continues to progress, but obstacles remain. The preservation of metabolites in the target tissue and gathering information on the current metabolic state of the organism of interest proves challenging. Robustness, reproducibility, and reliable quantification are necessary for confident metabolite identification and should always be considered for effective biomarker discovery. Recent advancements in analytical platforms, techniques, and data analysis make metabolomics a promising omics for significant research. However, there is no single approach to effectively capturing the metabolome. Coupling separation techniques may improve the power of the analysis and facilitate confident metabolite identification, especially when performing untargeted metabolomics. In this chapter, we will present an untargeted metabolomic analysis of brain tissue from C57BL/6 mice using two UHPLC-MS methods based on reversed-phase and HILIC chromatography.

Evaluation and optimization of analytical procedure and sample preparation for polar Streptomyces albus J1074 metabolome profiling

Metabolomics is an essential discipline in omics technology that promotes research on the biology of microbial systems. Streptomyces albus J1074 is a model organism used in fundamental research and industrial microbiology. Nevertheless, a comprehensive and standardized method for analyzing the metabolome of S. albus J1074 is yet to be developed. Thus, we comprehensively evaluated and optimized the analytical procedure and sample preparation for profiling polar metabolites using hydrophilic interaction liquid chromatography (HILIC) coupled with high-resolution mass spectrometry (HRMS). We systematically examined the HILIC columns, quenching solutions, sample-to-quenching ratios, and extraction methods. Then, the optimal protocol was used to investigate the dynamic intracellular polar metabolite profile of the engineered S. albus J1074 strains during spinosad (spinosyn A and spinosyn D) fermentation. A total of 3648 compounds were detected, and 83 metabolites were matched to the standards. The intracellular metabolomic profiles of engineered S. albus J1074 strains (ADE-AP and OE3) were detected; furthermore, their metabolomes in different stages were analyzed to reveal the reasons for their differences in their spinosad production, as well as the current metabolic limitation of heterologous spinosad production in S. albus J1074. The HILIC-HRMS method is a valuable tool for investigating polar metabolomes, and provides a reference methodology to study other Streptomyces metabolomes.

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A HILIC-HRMS method was developed for polar metabolome profiling.

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Sample preparation protocol for Streptomyces albus J1074 intracellular metabolites was studied for the first time.

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This study revealed the possible reasons for different production of spinosad of engineered S. albus J1074 strains.

Fine Structure in Isotopic Peak Distributions Measured Using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: A Comparison between an Infinity ICR Cell and a Dynamically Harmonized ICR Cell

The fine structure of isotopic peak distributions of glutathione in mass spectra is measured using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at 12 and 15 T magnetic field, with an infinity cell and a dynamically harmonized cell (DHC) respectively. The resolved peaks in the fine structure of glutathione consist of ²H, ¹³C, ¹⁵N, ¹⁷O, ¹⁸O, ³³S, ³⁴S, ³⁶S, and combinations of them. The positions of the measured fine structure peaks agree with the simulated isotopic distributions with the mass error less than 250 ppb in broadband mode for the infinity cell and no more than 125 ppb with the DHC after internal calibration. The 15 T FT-ICR MS with DHC cell also resolved around 30 isotopic peaks in broadband with a resolving power (RP) of 2 M. In narrowband (m/z 307-313), our current highest RP of 13.9 M in magnitude mode was observed with a 36 s transient length by the 15 T FT-ICR MS with the DHC and 2ω detection on the 15 T offers slightly higher RP (14.8 M) in only 18 s. For the 12 T FT-ICR MS with the infinity cell, the highest RP achieved was 15.6 M in magnitude mode with a transient length of 45 s. Peak decay was observed for low abundance peaks, which could be due to the suppression effects from the most abundant peak, as result of ion cloud Coulombic interactions (space-charge).

Microbial endophytes: application towards sustainable agriculture and food security

Microbial endophytes are ubiquitous and exist in each recognised plant species reported till date. Within the host plant, the entire community of microbes lives non-invasively within the active internal tissues without causing any harm to the plant. Endophytes interact with their host plant via metabolic communication enables them to generate signal molecules. In addition, the host plant's genetic recombination with endophytes helps them to imitate the host's physicochemical functions and develop identical active molecules. Therefore, when cultured separately, they begin producing the host plant phytochemicals. The fungal species Penicillium chrysogenum has portrayed the glory days of antibiotics with the invention of the antibiotic penicillin. Therefore, fungi have substantially supported social health by developing many bioactive molecules utilised as antioxidant, antibacterial, antiviral, immunomodulatory and anticancerous agents. But plant-related microbes have emanated as fountainheads of biologically functional compounds with higher levels of medicinal perspective in recent years. Researchers have been motivated by the endless need for potent drugs to investigate alternate ways to find new endophytes and bioactive molecules, which tend to be a probable aim for drug discovery. The current research trends with these promising endophytic organisms are reviewed in this review paper. KEY POINTS: • Identified 54 important bioactive compounds as agricultural relevance • Role of genome mining of endophytes and "Multi-Omics" tools in sustainable agriculture • A thorough description and graphical presentation of agricultural significance of plant endophytes.

Present-Day Practice of Non-Target Chemical Analysis

Abstract

We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Compositional analysis of essential oil and solvent extracts of Norway spruce sprouts by ultrahigh-resolution mass spectrometry

INTRODUCTION

Coniferous trees, especially their needles and bark, are a rich source of bioactive compounds. The developing needles of Norway spruce (Picea abies), also known as spruce sprouts, are enriched with vitamin C and other antioxidants, and thus they are used as a dietary supplement and have been traditionally used to treat various inflammatory disorders such as rheumatism and gout. Their chemical composition is only limitedly known, however.

OBJECTIVES

The main objective of this work was to have a deeper understanding on the chemical composition of spruce sprouts to assess their full potential in different pharmaceutical, nutraceutical, or technochemical applications.

MATERIALS AND METHODS

Ultrahigh-resolution Fourier-transform ion cyclotron (FT-ICR) mass spectrometry, coupled to direct-infusion electrospray ionisation (ESI) or atmospheric pressure photoionisation (APPI) techniques, was used for in-depth compositional analysis of solvent extracts and essential oil of spruce sprouts.

RESULTS

A combined use of ESI and APPI techniques offered a great complementary insight into the rich chemistry of different spruce sprout extracts, allowing detection of thousands of chemical constituents with over 200 secondary metabolites tentatively identified. These compounds belonged to different classes such as organic acids, terpenes, flavonoids, stilbenes, sterols, and nitrogen alkaloids.

CONCLUSION

Spruce sprouts have a complex metabolite profile that differs considerably from that of the old, developed needles.

Feature Selection Pipelines with Classification for Non-targeted Metabolomics Combining the Neural Network and Genetic Algorithm

Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants

Comprehensive metabolomic investigations provide a large set of stress-related metabolites and metabolic pathways, advancing crops under heat stress conditions. Metabolomics-assisted breeding, including mQTL and mGWAS boosted our understanding of improving numerous quantitative traits under heat stress. During the past decade, metabolomics has emerged as a fascinating scientific field that includes documentation, evaluation of metabolites, and chemical methods for cell monitoring programs in numerous plant species. A comprehensive metabolome profiling allowed the investigator to handle the comprehensive data groups of metabolites and the equivalent metabolic pathways in an extraordinary manner. Metabolomics, together with transcriptomics, plays an influential role in discovering connections between stress and genes/metabolite, phenotyping, and biomarkers documentation. Further, it helps to decode several metabolic systems connected with heat stress (HS) tolerance in plants. Heat stress is a critical environmental factor that is globally affecting the growth and productivity of plants. Thus, there is an urgent need to exploit modern breeding and biotechnological tools like metabolomics to develop cultivars with improved HS tolerance. Several studies have reported that amino acids, carbohydrates, nitrogen metabolisms, etc. and metabolites involved in the biosynthesis and catalyzing actions play a game-changing role in HS response and help plants to cope with the HS. The use of metabolomics-assisted breeding (MAB) allows a well-organized transmission of higher yield and HS tolerance at the metabolome level with specific properties. Progressive metabolomics systematic techniques have accelerated metabolic profiling. Nonetheless, continuous developments in bioinformatics, statistical tools, and databases are allowing us to produce ever-progressing, comprehensive insights into the biochemical configuration of plants and by what means this is inclined by genetic and environmental cues. Currently, assimilating metabolomics with post-genomic platforms has allowed a significant division of genetic-phenotypic connotation in several plant species. This review highlights the potential of a state-of-the-art plant metabolomics approach for the improvement of crops under HS. The development of plants with specific properties using integrated omics (metabolomics and transcriptomics) and MAB can provide new directions for future research to enhance HS tolerance in plants to achieve a goal of "zero hunger".

Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Naphthenic Acids: Formation, Role in Emulsion Stability, and Recent Advances in Mass Spectrometry-Based Analytical Methods

Naphthenic acids (NAs) are compounds naturally present in most petroleum sources comprised of complex mixtures with a highly variable composition depending on their origin. Their occurrence in crude oil can cause severe corrosion problems and catalysts deactivation, decreasing oil quality and consequently impacting its productivity and economic value. NAs structures also allow them to behave as surfactants, causing the formation and stabilization of emulsions. In face of the ongoing challenge of treatment of water-in-oil (W/O) or oil-in-water (O/W) emulsions in the oil and gas industry, it is important to understand how NAs act in emulsified systems and which acids are present in the interface. Considering that, this review describes the properties of NAs, their role in the formation and stability of oil emulsions, and the modern analytical methods used for the qualitative analysis of such acids.

Mapping the Chemistry of Hair Strands by Mass Spectrometry Imaging-A Review

Hair can record chemical information reflecting our living conditions, and, therefore, strands of hair have become a potent analytical target within the biological and forensic sciences. While early efforts focused on analyzing complete hair strands in bulk, high spatial resolution mass spectrometry imaging (MSI) has recently come to the forefront of chemical hair-strand analysis. MSI techniques offer a localized analysis, requiring fewer de-contamination procedures per default and making it possible to map the distribution of analytes on and within individual hair strands. Applying the techniques to hair samples has proven particularly useful in investigations quantifying the exposure to, and uptake of, toxins or drugs. Overall, MSI, combined with optimized sample preparation protocols, has improved precision and accuracy for identifying several elemental and molecular species in single strands of hair. Here, we review different sample preparation protocols and use cases with a view to make the methodology more accessible to researchers outside of the field of forensic science. We conclude that—although some challenges remain, including contamination issues and matrix effects—MSI offers unique opportunities for obtaining highly resolved spatial information of several compounds simultaneously across hair surfaces.

Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies

Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge crosslinks between formaldehyde and amine/thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artifactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared with fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.

A pioneer study on human 3-nitropropionic acid intoxication: Contributions from metabolomics

The neurotoxin 3-nitropropionic acid (3-NPA) is an inhibitor of succinate dehydrogenase, an enzyme participating both in the citric acid cycle and the mitochondrial respiratory chain. In human intoxications, it produces symptoms such as vomiting and stomach ache in mild cases, and dystonia, coma, and sometimes death in severe cases. We report the results from a liquid chromatography-Orbitrap mass spectrometry metabolomics study mapping the metabolic impacts of 3-NPA intoxication in plasma, urine, and cerebrospinal fluid (CSF) samples of a Norwegian boy initially suspected to suffer from a mitochondrial disease. In addition to the identification of 3-NPA, our findings included a large number of annotated/identified altered metabolites (80, 160, and 62 in plasma, urine, and CSF samples, respectively) belonging to different compound classes, for example, amino acids, fatty acids, and purines and pyrimidines. Our findings indicated protective mechanisms to attenuate the toxic effects of 3-NPA (e.g., decreased oleamide), occurrence of increased oxidative stress in the patient (such as increased free fatty acids and hypoxanthine) and energy turbulence caused by the intoxication (e.g., increased succinate). To our knowledge, this is the first case of 3-NPA intoxication reported in Norway and the first published metabolomics study of human 3-NPA intoxication worldwide. The unexpected identification of 3-NPA illustrates the importance for health care providers to consider intake-related intoxications during diagnostic evaluations, treatment and follow-up examinations for neurotoxicity and a wide range of metabolic derangements.

Metabolomics for Crop Breeding: General Considerations

The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.

Metabolome Exploration by High-Resolution Mass Spectrometry Methodologies of Two New Yeast Species: Starmerella reginensis and Starmerella kourouensis

Nature is harnessed since ancient times to fulfill human needs, and yeast culture has been mastered for bakery, brewery, or the preparation of beverages. In this context, the two recently discovered yeast species Starmerella reginensis and Starmerella kourouensis, belonging to a genus related to fermentative activities in the literature, were explored via untargeted metabolomics approaches. Ultrahigh-performance liquid chromatography hyphenated with tandem mass spectrometry and a deep investigation of molecular networks and spectral data allowed the annotation of, respectively, 439 and 513 metabolites for S. reginensis and S. kourouensis, with approximatively 30% compound annotations and 40% chemical class annotations for both yeast strains. These analyses and Fourier transform ion cyclotron resonance mass spectrometry accurate metabolic profiles unveiled a rich content of alkaloids, lipids, amino acids, and terpenoids for S. reginensis. S. kourouensis presents a similar profile with more sulfated compounds. In short, these results enrich the current knowledge about Starmerella yeast secondary metabolites and reveal their significant structural diversity of small molecules.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Nontargeted identification of chlorinated disinfection byproducts formed from natural organic matter using Orbitrap mass spectrometry and a halogen extraction code

Natural organic matter is a major source of precursors of hazardous chlorinated disinfection byproducts (Cl-DBPs) formed during water treatment, but the majority of Cl-DBPs are still unidentified. In this study, we used a self-written halogen extraction code to identify halogen isotopic patterns in combination with the R package MFAssignR, to identify Cl-DBPs from Orbitrap mass spectra. One hundred and eighty-nine Cl-DBPs were detected during chlorination of a Suwannee River natural organic matter solution, and the structures of 20 of these compounds are reported for the first time. Kendrick mass defect analysis and structural identification confirmed that chlorinated carboxylic acids are common and likely to form during chlorination. A toxicity prediction using quantitative structure-activity relationship models indicated that most of the chlorinated carboxylic acids may be highly toxic. Our analytical strategy can identify Cl-DBPs accurately from complex mixtures and may also be applicable to the identification of other halogenated disinfection byproducts formed during water treatment.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Analysis of Differentiated Chemical Components between Zijuan Purple Tea and Yunkang Green Tea by UHPLC-Orbitrap-MS/MS Combined with Chemometrics

Zijuan tea (Camellia sinensis var. assamica cv. Zijuan) is a unique purple tea. Recently, purple tea has drawn much attention for its special flavor and health benefits. However, the characteristic compounds of purple tea compared with green tea have not been reported yet. The present study employed a non-targeted metabolomics approach based on ultra-high performance liquid chromatography (UHPLC)-Orbitrap-tandem mass spectrometry (MS/MS) for comprehensive analysis of characteristic metabolites between Zijuan purple tea (ZJT) and Yunkang green tea (YKT). Partial least squares-discriminant analysis (PLS-DA) indicated that there are significant differences in chemical profiles between ZJT and YKT. A total of 66 major differential metabolites included catechins, proanthocyanins, flavonol and flavone glycosides, phenolic acids, amino acids and alkaloids were identified in ZJT. Among them, anthocyanins are the most characteristic metabolites. Nine glycosides of anthocyanins and six glycosides of proanthocyanins were found to be significantly higher in ZJT than that in YKT. Subsequently, pathway analysis revealed that ZJT might generate anthocyanins and proanthocyanins through the flavonol and flavone glycosides. Furthermore, quantitative analysis showed absolutely higher concentrations of total anthocyanins in ZJT, which correlated with the metabolomics results. This study presented the comprehensive chemical profiling and the characterized metabolites of ZJT. These results also provided chemical evidence for potential health functions of ZJT.

Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

Methods of Lipidomic Analysis: Extraction, Derivatization, Separation, and Identification of Lipids

Lipidomics refers to the large-scale study of pathways and networks of cellular lipids in biological systems. A lipidomic analysis often involves the identification and quantification of the thousands of cellular lipid molecular species within a complex biological sample and therefore requires a well optimized method for lipid profiling. In this chapter, the methods for lipidomic analysis, including sample collection and preparation, lipid derivatization and separation, mass spectrometric identification of lipids, data processing and interpretation, and quality control, are overviewed.

Synergizing the potential of bacterial genomics and metabolomics to find novel antibiotics

Antibiotic development based on natural products has faced a long lasting decline since the 1970s, while both the speed and the extent of antimicrobial resistance (AMR) development have been severely underestimated. The discovery of antimicrobial natural products of bacterial and fungal origin featuring new chemistry and previously unknown mode of actions is increasingly challenged by rediscovery issues. Natural products that are abundantly produced by the corresponding wild type organisms often featuring strong UV signals have been extensively characterized, especially the ones produced by extensively screened microbial genera such as streptomycetes. Purely synthetic chemistry approaches aiming to replace the declining supply from natural products as starting materials to develop novel antibiotics largely failed to provide significant numbers of antibiotic drug leads. To cope with this fundamental issue, microbial natural products science is being transformed from a 'grind-and-find' study to an integrated approach based on bacterial genomics and metabolomics. Novel technologies in instrumental analytics are increasingly employed to lower detection limits and expand the space of detectable substance classes, while broadening the scope of accessible and potentially bioactive natural products. Furthermore, the almost exponential increase in publicly available bacterial genome data has shown that the biosynthetic potential of the investigated strains by far exceeds the amount of detected metabolites. This can be judged by the discrepancy between the number of biosynthetic gene clusters (BGC) encoded in the genome of each microbial strain and the number of secondary metabolites actually detected, even when considering the increased sensitivity provided by novel analytical instrumentation. In silico annotation tools for biosynthetic gene cluster classification and analysis allow fast prioritization in BGC-to-compound workflows, which is highly important to be able to process the enormous underlying data volumes. BGC prioritization is currently accompanied by novel molecular biology-based approaches to access the so-called orphan BGCs not yet correlated with a secondary metabolite. Integration of metabolomics, in silico genomics and molecular biology approaches into the mainstream of natural product research will critically influence future success and impact the natural product field in pharmaceutical, nutritional and agrochemical applications and especially in anti-infective research.

Biodegradation and metabolic pathway of anthraquinone dyes by Trametes hirsuta D7 immobilized in light expanded clay aggregate and cytotoxicity assessment

Biodegradation and metabolic pathways of three anthraquinone dyes, Reactive Blue 4 (RB4), Remazol Brilliant Blue - R (RBBR), and Acid Blue 129 (AB129) by Trametes hirsuta D7 fungus immobilized in light expanded clay aggregate (LECA) were investigated. Morphological characteristics observed with scanning electron microscope (SEM) showed successful immobilization of the fungus in LECA. Based on UV absorbance measurement, immobilized T. hirsuta D7 effectively degraded 90%, 95%, and 96% of RB4, RBBR and AB129, respectively. Metabolites were identified with high-resolution mass spectrometry (HRMS) and degradation pathway of the dyes by T. hirsuta D7 was proposed. Toxicity assay on human dermal fibroblast (HDF) showed that anthraquinone dyes exhibits significant toxicity of 35%, 40%, and 34% reduction of cell viability by RB4, RBBR, and AB129, respectively. Fungal treatment resulted in an abatement of the toxicity and cell viability was increased up to 94%. The data clearly showed the effectiveness of immobilized T. hirsuta D7 in LECA on detoxification of anthraquinone dyes. This study provides potential and fundamental understanding of wastewater treatment using the newly isolated fungus T. hirsuta D7.

Proteomic and Transcriptomic Techniques to Decipher the Molecular Evolution of Venoms

Nature's library of venoms is a vast and untapped resource that has the potential of becoming the source of a wide variety of new drugs and therapeutics. The discovery of these valuable molecules, hidden in diverse collections of different venoms, requires highly specific genetic and proteomic sequencing techniques. These have been used to sequence a variety of venom glands from species ranging from snakes to scorpions, and some marine species. In addition to identifying toxin sequences, these techniques have paved the way for identifying various novel evolutionary links between species that were previously thought to be unrelated. Furthermore, proteomics-based techniques have allowed researchers to discover how specific toxins have evolved within related species, and in the context of environmental pressures. These techniques allow groups to discover novel proteins, identify mutations of interest, and discover new ways to modify toxins for biomimetic purposes and for the development of new therapeutics.

Review: Metabolomics as a prediction tool for plants performance under environmental stress

Metabolomics as a diagnosis tool for plant performance has shown good features for breeding and crop improvement. Additionally, due to limitations in land area and the increasing climate changes, breeding projects focusing on abiotic stress tolerance are becoming essential. Nowadays no universal method is available to identify predictive metabolic markers. As a result, research aims must dictate the best method or combination of methods. To this end, we will introduce the key aspects to consider regarding growth scenarios and sampling strategies and discuss major analytical and data treatment approaches that are available to find metabolic markers of plant performance.

Rhodiola crenulata protects against Alzheimer's disease in rats: A brain lipidomics study by Fourier-transform ion cyclotron resonance mass spectrometry coupled with high-performance reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography

RATIONALE

Alzheimer's disease (AD) is a chronic, severe, progressive neurodegenerative disorder associated with cognitive and memory impairment that ultimately causes death. Most approved drugs can only alleviate some of the symptoms of AD, but no interventions have been found that reverse the underlying disease mechanisms. Rhodiola crenulata extract (RCE) has been reported to alleviate AD symptoms in rats. However, its underlying mechanism of action is still unclear.

METHODS

A brain lipidomics study was conducted to investigate the protective effects of RCE against AD in rats to identify potential biomarkers of AD using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with high-performance reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). Differences in lipid metabolism profiles were evaluated using multivariate statistical analysis. Finally, the possible mechanism of action of RCE on AD was investigated by analysing metabolic pathways.

RESULTS

The RPLCHILIC/FT-ICR MS results showed 20 lipid components with significant differences between the control and model groups. After administration of RCE, the levels of 10 lipids in AD rats tended to shift toward reference levels. The pathway analysis revealed that the protective effect of RCE against AD might be related to regulation of glycerophospholipid metabolism.

CONCLUSIONS

This study provides a novel perspective on the potential intervention mechanism of RCE in the treatment of AD.

A simplified strategy for molecular formula determination of chemical constituents in traditional Chinese medicines based on accurate mass, A + 1 and A + 2 isotopic peaks using Fourier transform ion cyclotron resonance mass spectrometry

RATIONALE

Recently, isotopic fine structures derived from Fourier transform ion cyclotron resonance mass spectrometry have been used to determine the molecular formula for unknown compounds in many complex systems. However, a simplified strategy for molecular formula determination of chemical constituents in traditional Chinese medicines (TCMs) based on accurate mass, A + 1 and A + 2 isotopic peaks is necessary.

METHODS

Salviae miltiorrhizae was selected as a representative species. First, the chemical constituents were chromatographically separated and their accurate masses were obtained. The A + 1 and A + 2 isotopic peaks of all chemical constituents were then also acquired. Finally, the chemical formulae of the chemical constituents were determined.

RESULTS

In the sample of Salviae miltiorrhizae, the formulae of 38 CHO-containing chemical constituents were quickly determined, and all chemical constituents were identified using their tandem mass spectrometric data. Moreover, the method was validated by comparison of the A + 1 and A + 2 isotopic peaks, their fragmentation patterns and the retention times of six selected standard substances.

CONCLUSIONS

The results demonstrate that the described strategy performs well for molecular formula determination of chemical constituents in TCMs. This also indicates that this method will be meaningful for the structural identification of chemical constituents of TCMs.

Combination of UHPLC-MS/MS-molecular networking approach and FTICR-MS for the metabolic profiling of Saccharomyces cerevisiae

Natural products are a reliable source of bioactive molecules and represent an industrial and pharmaceutical stake. Indeed, the model yeast species Saccharomyces cerevisiae is a well-known eukaryotic organism largely used as a biotechnological tool, but still a topical subject of study. In this work, the exploration of Saccharomyces cerevisiae is taken further through an untargeted metabolomics workflow. The aim is to enrich databases and bring new information about the standard S. cerevisiae strain in a given medium. Analytical methods and bioinformatics tools were combined in a high-throughput methodology useable to dereplicate many types of biological extracts and cartography secondary metabolites. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analyses were carried out and spectral data were pre-processed to build molecular networks. Annotations were attributed to compounds through comparison with databases and manual investigation of networks. Ultra-high-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) brought additional information thanks to a higher dynamic range and enhanced UHPLC-MS/MS results by unveiling ambiguities and bringing accurate molecular formulae. Therefore, accurate and reliable annotated features resulted from the UHPLC-MS/MS data while FTICR-MS provided an overall cartography of metabolites thanks to van Krevelen diagrams. Various small molecules such as amino acids derivatives and indole alkaloids have been determined for the first time in this yeast. The complementarity of FTICR-MS and UHPLC-MS/MS for secondary metabolite annotation brought this new mapping of S. cerevisiae.

Mycotoxins Analysis in Cereals and Related Foodstuffs by Liquid Chromatography-Tandem Mass Spectrometry Techniques

In the entire world, cereals and related foodstuffs are used as an important source of energy, minerals, and vitamins. Nevertheless, their contamination with mycotoxins kept special attention due to harmful effects on human health. The present paper was conducted to evaluate published studies regarding the identification and characterization of mycotoxins in cereals and related foodstuffs by liquid chromatography coupled to (tandem) mass spectrometry (LC-MS/MS) techniques. For sample preparation, published studies based on the development of extraction and clean-up strategies including solid-phase extraction, solid-liquid extraction, and immunoaffinity columns, as well as on methods based on minimum clean-up (quick, easy, cheap, effective, rugged, and safe (QuEChERS)) technology, are examined. LC-MS/MS has become the golden method for the simultaneous multimycotoxin analysis, with different sample preparation approaches, due to the range of different physicochemical properties of these toxic products. Therefore, this new strategy can be an alternative for fast, simple, and accurate determination of multiclass mycotoxins in complex cereal samples.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.