Using MetaboAnalyst to introduce undergraduates to lipidomic analysis and lipid remodeling in barley roots

Lipidomics is a discipline that focuses on the identification and quantification of lipids. Although a part of the larger omics field, lipidomics requires specific approaches for the analysis and biological interpretation of datasets. This article presents a series of activities for introducing undergraduate microbiology students to lipidomic analysis through tools from the web-based platform MetaboAnalyst. The students perform a complete lipidomic workflow, which includes experiment design, data processing, data normalization, and statistical analysis of molecular phospholipid species obtained from barley roots exposed to Fusarium macroconidia. The input data are provided by the teacher, but students also learn about the methods through which they were originally obtained (untargeted liquid chromatography coupled with mass spectrometry). The ultimate aim is for students to understand the biological significance of phosphatidylcholine acyl editing. The chosen methodology allows users who are not proficient in statistics to make a comprehensive analysis of quantitative lipidomic datasets. We strongly believe that virtual activities based on the analysis of such datasets should be incorporated more often into undergraduate courses, in order to improve students' data-handling skills for omics sciences.

The potential of metabolomics in meat science: Current applications, future trends, and challenges

Metabolites are the final products of metabolism and provide insights into the biochemical balance of tissue systems. A cascade of reactions involving proteins, carbohydrates, and lipids affects meat color, tenderness, and flavor, specifically, metabolites that are key biomolecules in biochemical reactions associated with attainment of acceptable meat quality. Bioinformatics platforms, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) databases and MetaboAnalyst, are utilized to help understanding the role of differentially abundant metabolites and characterizing their roles in cellular function/metabolism. However, the inability to identify all metabolites using a single platform and limited metabolite libraries specifically for meat/food remains a challenge. Therefore, the advances in metabolite separation, easy-to-use data processing, increased resolution of mass-spectrometry, and data analysis will help to make inferences or develop biomarkers related to meat quality. This review discusses how metabolomics can be exploited to characterize meat quality, the challenges, and current trends. SIGNIFICANCE: Metabolites are the final products of metabolism and provide insights into the biochemical balance of tissue systems. They play an important role in quality traits (i.e., color, texture, and flavor) and nutritive value of foods. Visual appearance of fresh foods, such as muscle foods, are utilized by consumers to assess the quality at the retail market before making purchases. Similarly, tenderness and flavor of meats influence eating satisfaction and re-purchase decisions. Inconsistencies in meat quality lead to huge economic losses to food industry. For instance, consumers often associate a bright-cherry red color with freshness, and the US beef industry loses $3.74 billion annually due to discoloration during storage. Both pre-and post-harvest factors influence the extent of meat quality changes. Metabolomics offer robust tools to get a snapshot of small molecules such as acids, amino acids, glycolytic- and tricarboxylic acids, fatty acids, and sugars present in post-mortem muscle tissue and their role in meat quality. Further, using bioinformatics platforms enables characterizing the role of differentially present metabolites in meat quality as well as identifying biomarkers for desirable quality traits such as tender meat or color-stable carcasses. Innovative applications of metabolomics can be exploited to elucidate the underpinnings of meat quality and to develop novel strategies to enhance marketability of retail fresh meats.

Downloading and Analysis of Metabolomic and Lipidomic Data from Metabolomics Workbench Using MetaboAnalyst 5.0

Advances in computational and data processing technology have enabled the development of many novel tools for analyzing metabolomic and lipidomic data. These advances involved the catalyst for the creation of publicly accessible complex web-based databases such as the Metabolomics Workbench. Open Source internet-based software packages such as MetaboAnalyst 5.0 enable researchers to perform a wide range of analyte identification and statistical analyses of their own and other researchers' data in order to identify biomarkers and classify compounds. In this paper, we set forth a protocol for obtaining experimental data of interest from a public data repository (Metabolomics Workbench), converting the data into a format suitable for submission to MetaboAnalyst 5.0, and then uploading the data to the MetaboAnalyst server for identification and statistical analysis.

An Open-Source Pipeline for Processing Direct Infusion Mass Spectrometry Data of the Human Plasma Metabolome

Direct infusion mass spectrometry (DIMS) is growing in popularity as an effective method for the screening of biological samples in clinical metabolomics. Being quick to execute, DIMS generally requires special skills when interpreting the results of measurements. By inspecting the similarities between two-dimensional electrospray ionization with quadrupole time-of-flight (ESI-QTOF) and matrix-assisted laser desorption/ionization (MALDI) mass spectra, the pipeline for processing QTOF mass spectra using open-source packages (MALDIquant, MSnbase and MetaboAnalystR) was tested. Previously, all algorithmic workflows have relied on the application of software either provided by a vendor or privately developed by enthusiasts. Here, we computationally examined two ways of interpreting the DIMS results of human blood metabolomic profiling. The studied spectra were acquired using ESI-QTOF maXis Impact II (Bruker Daltonics, Billerica, MA, USA), then pre-processed using COMPASS/DataAnalysis commercial software and mapped onto the metabolites using in-lab-developed MatLab scripts. Alternatively, in this work we used the open-source packages MALDIquant, for spectrum pre-processing, and MetaboAnalystR, for data interpretation, instead of the low-availability commercial and home-made tools. Using a set of 100 plasma samples (20 from volunteers with normal body mass index and 80 from patients at different stages of obesity), we observed a high degree of concordance in annotated metabolic pathways between the proprietary DataAnalysis/MatLab pipeline and our freely available solution.

Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS

Metabolic syndrome (MetS) is a disorder characterized by a group of factors that can increase the risk of chronic diseases, including cardiovascular diseases and type 2 diabetes mellitus (T2D). Metabolomics has provided new insight into disease diagnosis and biomarker identification. This cross-sectional investigation used an untargeted metabolomics-based technique to uncover metabolomic alterations and their relationship to pathways in normoglycemic and prediabetic MetS participants to improve disease diagnosis. Plasma samples were collected from drug-naive prediabetic MetS patients (n = 26), normoglycemic MetS patients (n = 30), and healthy (normoglycemic lean) subjects (n = 30) who met the inclusion criteria for the study. The plasma samples were analyzed using highly sensitive ultra-high-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). One-way ANOVA analysis revealed that 59 metabolites differed significantly among the three groups (p < 0.05). Glutamine, 5-hydroxy-L-tryptophan, L-sorbose, and hippurate were highly associated with MetS. However, 9-methyluric acid, sphinganine, and threonic acid were highly associated with prediabetes/MetS. Metabolic pathway analysis showed that arginine biosynthesis and glutathione metabolism were associated with MetS/prediabetes, while phenylalanine, D-glutamine and D-glutamate, and lysine degradation were highly impacted in MetS. The current study sheds light on the potential diagnostic value of some metabolites in metabolic syndrome and the role of their alteration on some of the metabolic pathways. More studies are needed in larger cohorts in order to verify the implication of the above metabolites on MetS and their diagnostic value.

Profiling Destruxin Synthesis by Specialist and Generalist Metarhizium Insect Pathogens during Coculture with Plants

Metarhizium is a genus of endophytic, insect-pathogenic fungi that is used as a biological control agent. The dual lifestyles of these fungi combine the parasitism of insect pests with the symbiotic association with plant roots. A major class of secreted metabolites by Metarhizium are cyclic depsipeptides called destruxins (DTXs). As prominent insecticidal compounds, their role during plant interactions is still largely unknown. Here, we examined the metabolomic profile of Metarhizium, with special emphasis on DTX production, using untargeted, liquid chromatography-tandem mass spectrometry (LC-MS/MS). Four Metarhizium species, two insect generalists (M. robertsii and M. brunneum), and two insect specialists (M. flavoviride and M. acridum) were inoculated onto agar plate cultures containing either bean (Phaseolus vulgaris) or corn (Zea mays) and grown for four and seven days. After methanol extraction, feature-based molecular networking (FBMN) was used to obtain DTX identification as defined by the Global Natural Products Social Molecular Networking (GNPS). A total of 25 DTX analogs were identified, with several DTX-like compounds in coculture that could not be identified. Metarhizium species differed in the amount and type of DTXs they produced, with the insect specialists producing far fewer amounts and types of DTXs than the insect generalists. The production of these metabolites varied between cultures of different ages and plant hosts. Conditions that influence the production of DTXs are discussed. As the genetic arsenal of natural products relates to the lifestyle of the organism, uncovering conditions with an ecological context may reveal strategies for producing novel compounds or precursors suitable for synthetic biology. IMPORTANCE The development of an intimate and beneficial association between fungi and plants requires an exchange of a complex mixture of chemical cues. These compounds are a means of communication, promoting or limiting the interaction, but can have numerous other biological and ecological functions. Determining how the metabolome, or a subset thereof, is linked to plant host preference and colonization has implications for future functional studies and may uncover novel therapeutic compounds whose production is elicited only under cocultivation. In this study, we performed an untargeted metabolomic analysis of plate cocultures with individual plant-fungal pairs. The identification of a major group of fungal metabolites, the destruxins, was examined for their role in plant specificity. The diversity of these metabolites and the production of numerous unidentified, structural analogs are evidence of the sensitivity of the methodology and the potential for future mining of this living data set.

Uncovering the Bioactive Potential of a Cyanobacterial Natural Products Library Aided by Untargeted Metabolomics

The Blue Biotechnology and Ecotoxicology Culture Collection (LEGE-CC) holds a vast number of cyanobacteria whose chemical richness is still largely unknown. To expedite its bioactivity screening we developed a natural products library. Sixty strains and four environmental samples were chromatographed, using a semiautomatic HPLC system, yielding 512 fractions that were tested for their cytotoxic activity against 2D and 3D models of human colon carcinoma (HCT 116), and non-cancerous cell line hCMEC/D3. Six fractions showed high cytotoxicity against 2D and 3D cell models (group A), and six other fractions were selected by their effects on 3D cells (group B). The metabolome of each group was organized and characterized using the MolNetEnhancer workflow, and its processing with MetaboAnalyst allowed discrimination of the mass features with the highest fold change, and thus the ones that might be bioactive. Of those, mass features without precedented identification were mostly found in group A, indicating seven possible novel bioactive molecules, alongside in silico putative annotation of five cytotoxic compounds. Manual dereplication of group B tentatively identified nine pheophytin and pheophorbide derivatives. Our approach enabled the selection of 7 out of 60 cyanobacterial strains for anticancer drug discovery, providing new data concerning the chemical composition of these cyanobacteria.

MALDI-ToF MS: A Rapid Methodology for Identifying and Subtyping Listeria monocytogenes

Listeria monocytogenes is a major food-borne pathogen and causative agent of a fatal disease, listeriosis. Stringent regulatory guidelines and zero tolerance policy toward this bacterium necessitate rapid, accurate, and reliable methods of identification and subtyping. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) has recently become a method of choice for routine identification of pathogens in clinical settings and has largely replaced biochemical assays. Identification relies on well-curated databases such as SARAMIS. Extensive use of SARAMIS to generate consensus mass spectra, in conjunction with statistical analysis, such as partial least square-discriminant analysis and hierarchical cluster analysis, is useful in subtyping bacteria. While MALDI-ToF MS has been extensively used for pathogen detection, its application in bacterial subtyping has been limited. The protocol describes a MALDI-ToF MS workflow as a single tool for simultaneous identification and subtyping of L. monocytogenes directly from solid culture medium.

Polycystic ovary syndrome in adolescents: Q-TOF LC/MS analysis of human plasma metabolome

Polycystic ovary syndrome (PCOS) is a hormonal disorder common among women of reproductive age. Women with PCOS may have infrequent or prolonged menstrual periods or excess male hormone levels. Metabolomics provide information on early biochemical changes in patients. Our aim was to find potential biomarkers on metabolome level to notice PCOS in adolescents and propose treatment opportunities based on our findings on metabolome level. In this study, Q-TOF LC/MS based analysis of the plasma samples of 15 healthy adolescents as control group (Group C) were compared with the plasma samples of 15 adolescents having PCOS (Group T). Raw chromatograms were processed on XCMS using Isotopologue Parameter Optimization (IPO) to optimize XCMS parameters. Finally, 2288 peaks were found but 84 of them had fold changes >1.5 based on normalized peak areas and they were statistically different (p < 0.05) between the groups. These peaks were subjected to MetaboAnalyst 4.0 - MS Peaks to Pathways utility for putative identification. The final list based on putative identification were evaluated through a clinical perspective and the statistically proved variation on the metabolite profiles of Group T and Group C presented that PCOS directly affected the lipid metabolism in the body or occurred as a result of a deformation in the lipid metabolism. Lower amount of Gamma-Tocopherol and higher amount of Coenzyme Q9, which is a product of incomplete Coenzyme Q10 biosynthesis, in the plasma samples of adolescent PCOS patients encouraged us to suggest larger randomized placebo controlled studies for Gamma-Tocopherol and Coenzyme Q10 supplements on the disease situation since our findings on metabolome level were in an accordance with the previous clinical findings.

Identification of Putative Biomarkers Specific to Foodborne Pathogens Using Metabolomics

Metabolomics is one of the more recently developed "omics" that measures low molecular weight (typically < 1500 Da) compounds in biological samples. Metabolomics has been widely explored in environmental, clinical, and industrial biotechnology applications. However, its application to the area of food safety has been limited but preliminary work has demonstrated its value. This chapter describes an untargeted (nontargeted) metabolomics workflow using gas chromatography coupled to mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for extraction of polar metabolites from media, analyzing the extracts using GC-MS and, finally, chemometric data analysis using the software "SIMCA" to identify potential pathogen-specific biomarkers.

Integrated pathway-based and network-based analysis of GC-MS rice metabolomics data under diazinon stress to infer affected biological pathways

Diazinon insecticide is widely applied in rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography-mass spectrometry (GC-MS) and subsequently three different methods, MetaboAnalyst, MetaboNetwork, and analysis of reporter reactions, as a potential multivariate method were used to find the underlying changes in metabolism with stronger evidence in order to link differentially expressed metabolites to biological pathways. Results clearly showed the similarity of acetylcholinesterase (AChE) of rice plants to that of animals in terms of its inhibitability by diazinon and emphasized that subsequent accumulation of AChE mainly affects the metabolism of osmolites and tricarboxylic acid intermediates subsequent accumulation of ACh mainly affects the metabolism of osmolites and TCA intermediates.