Metabolites

26 pages, 1131 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

The Bovine Metabolome

by Aidin Foroutan, Carolyn Fitzsimmons, Rupasri Mandal, Hamed Piri-Moghadam, Jiamin Zheng, AnChi Guo, Carin Li, Le Luo Guan and David S. Wishart

Metabolites 2020, 10(6), 233; https://doi.org/10.3390/metabo10060233 - 5 Jun 2020

Cited by 79 | Viewed by 5552

Abstract

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including [...] Read more.

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including serum, ruminal fluid, liver, Longissimus thoracis (LT) muscle, semimembranosus (SM) muscle, and testis tissues. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–tandem mass spectrometry (LC–MS/MS), and inductively coupled plasma–mass spectrometry (ICP–MS), we were able to identify and quantify more than 145 metabolites in each of these biofluids/tissues. Combining these results with previous work done by our team on other bovine biofluids, as well as previously published literature values for other bovine tissues and biofluids, we were able to generate quantitative reference concentration data for 2100 unique metabolites across five different bovine biofluids and seven different tissues. These experimental data were combined with computer-aided, genome-scale metabolite inference techniques to add another 48,628 unique metabolites that are biochemically expected to be in bovine tissues or biofluids. Altogether, 51,801 unique metabolites were identified in this study. Detailed information on these 51,801 unique metabolites has been placed in a publicly available database called the Bovine Metabolome Database. Full article

(This article belongs to the Section Animal Metabolism)

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18 pages, 1088 KiB

Open AccessEditor’s ChoiceReview

Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics

by Raúl González-Domínguez, Álvaro González-Domínguez, Ana Sayago and Ángeles Fernández-Recamales

Metabolites 2020, 10(6), 229; https://doi.org/10.3390/metabo10060229 - 3 Jun 2020

Cited by 83 | Viewed by 10458

Abstract

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these [...] Read more.

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research. Full article

(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)

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35 pages, 1844 KiB

Open AccessEditor’s ChoiceReview

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

by Tara Eicher, Garrett Kinnebrew, Andrew Patt, Kyle Spencer, Kevin Ying, Qin Ma, Raghu Machiraju and Ewy A. Mathé

Metabolites 2020, 10(5), 202; https://doi.org/10.3390/metabo10050202 - 15 May 2020

Cited by 71 | Viewed by 13791

Abstract

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical [...] Read more.

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study. Full article

(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)

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23 pages, 1537 KiB

Open AccessEditor’s ChoiceReview

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

by Susumu Muroya, Shuji Ueda, Tomohiko Komatsu, Takuya Miyakawa and Per Ertbjerg

Metabolites 2020, 10(5), 188; https://doi.org/10.3390/metabo10050188 - 11 May 2020

Cited by 91 | Viewed by 7425

Abstract

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and [...] Read more.

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)–mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality. Full article

(This article belongs to the Special Issue Metabolomic Applications in Animal Science)

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24 pages, 401 KiB

Open AccessEditor’s ChoiceReview

Metabolite Changes during Postharvest Storage: Effects on Fruit Quality Traits

by Delphine M. Pott, José G. Vallarino and Sonia Osorio

Metabolites 2020, 10(5), 187; https://doi.org/10.3390/metabo10050187 - 8 May 2020

Cited by 103 | Viewed by 7497

Abstract

Metabolic changes occurring in ripe or senescent fruits during postharvest storage lead to a general deterioration in quality attributes, including decreased flavor and ‘off-aroma’ compound generation. As a consequence, measures to reduce economic losses have to be taken by the fruit industry and [...] Read more.

Metabolic changes occurring in ripe or senescent fruits during postharvest storage lead to a general deterioration in quality attributes, including decreased flavor and ‘off-aroma’ compound generation. As a consequence, measures to reduce economic losses have to be taken by the fruit industry and have mostly consisted of storage at cold temperatures and the use of controlled atmospheres or ripening inhibitors. However, the biochemical pathways and molecular mechanisms underlying fruit senescence in commercial storage conditions are still poorly understood. In this sense, metabolomic platforms, enabling the profiling of key metabolites responsible for organoleptic and health-promoting traits, such as volatiles, sugars, acids, polyphenols and carotenoids, can be a powerful tool for further understanding the biochemical basis of postharvest physiology and have the potential to play a critical role in the identification of the pathways affected by fruit senescence. Here, we provide an overview of the metabolic changes during postharvest storage, with special attention to key metabolites related to fruit quality. The potential use of metabolomic approaches to yield metabolic markers useful for chemical phenotyping or even storage and marketing decisions is highlighted. Full article

(This article belongs to the Special Issue Fruit Metabolism and Metabolomics)

14 pages, 7327 KiB

Open AccessEditor’s ChoiceArticle

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

by Zhiqiang Pang, Jasmine Chong, Shuzhao Li and Jianguo Xia

Metabolites 2020, 10(5), 186; https://doi.org/10.3390/metabo10050186 - 7 May 2020

Cited by 346 | Viewed by 23114

Abstract

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain [...] Read more.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment. Full article

(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)

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13 pages, 233 KiB

Open AccessEditor’s ChoiceReview

A Review of Lipidomics of Cardiovascular Disease Highlights the Importance of Isolating Lipoproteins

by Ming Ding and Kathryn M. Rexrode

Metabolites 2020, 10(4), 163; https://doi.org/10.3390/metabo10040163 - 23 Apr 2020

Cited by 76 | Viewed by 5759

Abstract

Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of [...] Read more.

Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of lipids with CVD based on lipidomics. The main findings of 16 cohort studies were that, independent of total and high-density lipoprotein cholesterol (HDL-c), ceramides (d18:1/16:0, d18:1/18:0, and d18:1/24:1) and phosphatidylcholines (PCs) containing saturated and monounsaturated fatty acyl chains are positively associated with risks of CVD outcomes, while PCs containing polyunsaturated fatty acyl chains (PUFA) are inversely associated with risks of CVD outcomes. Lysophosphatidylcholines (LPCs) may be positively associated with risks of CVD outcomes. Interestingly, the distributions of the identified lipids vary across lipoproteins: LPCs are primarily contained in HDLs, ceramides are mainly contained in low-density lipoproteins (LDLs), and PCs are distributed in both HDLs and LDLs. Thus, the potential mechanism behind previous findings may be related to the effect of the identified lipids on the biological functions of HDLs and LDLs. Only eight studies on the lipidomics of HDL and non-HDL particles and CVD outcomes have been conducted, which showed that higher triglycerides (TAGs), lower PUFA, lower phospholipids, and lower sphingomyelin content in HDLs might be associated with a higher risk of coronary heart disease (CHD). However, the generalizability of these studies is a major concern, given that they used case–control or cross-sectional designs in hospital settings, included a very small number of participants, and did not correct for multiple testing or adjust for blood lipids such as HDL-c, low-density lipoprotein cholesterol (LDL-c), or TAGs. Overall, findings from the literature highlight the importance of research on lipidomics of lipoproteins to enhance our understanding of the mechanism of the association between the identified lipids and the risk of CVD and allow the identification of novel lipid biomarkers in HDLs and LDLs, independent of HDL-c and LDL-c. Lipidomic techniques show the feasibility of this exciting research direction, and the lack of high-quality epidemiological studies warrants well-designed prospective cohort studies. Full article

(This article belongs to the Special Issue Integrative-Metabolomics in Epidemiological Studies)

35 pages, 15010 KiB

Open AccessEditor’s ChoiceProtocol

“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

by Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R. Häkkinen, Soile Rummukainen, Ambrin Farizah Babu, Taisa Sallinen, Olli Kärkkäinen, Jussi Paananen, David Broadhurst, Carl Brunius and Kati Hanhineva

Metabolites 2020, 10(4), 135; https://doi.org/10.3390/metabo10040135 - 31 Mar 2020

Cited by 77 | Viewed by 13108

Abstract

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an [...] Read more.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting. Full article

(This article belongs to the Special Issue Metabolomics Data Processing and Data Analysis—Current Best Practices)

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16 pages, 3543 KiB

Open AccessEditor’s ChoiceReview

Antifungal Drugs

by Jiří Houšť, Jaroslav Spížek and Vladimír Havlíček

Metabolites 2020, 10(3), 106; https://doi.org/10.3390/metabo10030106 - 12 Mar 2020

Cited by 157 | Viewed by 20030

Abstract

We reviewed the licensed antifungal drugs and summarized their mechanisms of action, pharmacological profiles, and susceptibility to specific fungi. Approved antimycotics inhibit 1,3-β-d-glucan synthase, lanosterol 14-α-demethylase, protein, and deoxyribonucleic acid biosynthesis, or sequestrate ergosterol. Their most severe side effects are hepatotoxicity, [...] Read more.

We reviewed the licensed antifungal drugs and summarized their mechanisms of action, pharmacological profiles, and susceptibility to specific fungi. Approved antimycotics inhibit 1,3-β-d-glucan synthase, lanosterol 14-α-demethylase, protein, and deoxyribonucleic acid biosynthesis, or sequestrate ergosterol. Their most severe side effects are hepatotoxicity, nephrotoxicity, and myelotoxicity. Whereas triazoles exhibit the most significant drug–drug interactions, echinocandins exhibit almost none. The antifungal resistance may be developed across most pathogens and includes drug target overexpression, efflux pump activation, and amino acid substitution. The experimental antifungal drugs in clinical trials are also reviewed. Siderophores in the Trojan horse approach or the application of siderophore biosynthesis enzyme inhibitors represent the most promising emerging antifungal therapies. Full article

(This article belongs to the Section Microbiology and Ecological Metabolomics)

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17 pages, 665 KiB

Open AccessEditor’s ChoiceReview

Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions

by Lorraine Smith, Joran Villaret-Cazadamont, Sandrine P. Claus, Cécile Canlet, Hervé Guillou, Nicolas J. Cabaton and Sandrine Ellero-Simatos

Metabolites 2020, 10(3), 104; https://doi.org/10.3390/metabo10030104 - 12 Mar 2020

Cited by 65 | Viewed by 6510

Abstract

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. [...] Read more.

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells). Full article

(This article belongs to the Special Issue Metabolism and Metabolomics of Liver in Health and Disease)

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23 pages, 1227 KiB

Open AccessEditor’s ChoiceReview

Metabolomics as an Emerging Tool for the Study of Plant–Pathogen Interactions

by Fernanda R. Castro-Moretti, Irene N. Gentzel, David Mackey and Ana P. Alonso

Metabolites 2020, 10(2), 52; https://doi.org/10.3390/metabo10020052 - 29 Jan 2020

Cited by 137 | Viewed by 12774

Abstract

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a [...] Read more.

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a central role in determining the outcome of attempted infections. Metabolomic analyses, for example between healthy, newly infected and diseased or resistant plants, have the potential to reveal perturbations to signaling or output pathways with key roles in determining the outcome of a plant–microbe interaction. However, application of this -omic and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods. Thus, it is imperative to bring the power of metabolomics to bear on the study of plant resistance/susceptibility. This review discusses metabolomics studies that link changes in primary or specialized metabolism to the defense responses of plants against bacterial, fungal, nematode, and viral pathogens. Also examined are cases where metabolomics unveils virulence mechanisms used by pathogens. Finally, how integrating metabolomics with other -omics can advance plant pathology research is discussed. Full article

(This article belongs to the Special Issue Metabolomic and Flux Analysis in Plants)

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23 pages, 2128 KiB

Open AccessEditor’s ChoiceReview

Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function

by Alexander Gardner, Guy Carpenter and Po-Wah So

Metabolites 2020, 10(2), 47; https://doi.org/10.3390/metabo10020047 - 26 Jan 2020

Cited by 92 | Viewed by 9072

Abstract

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current [...] Read more.

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current knowledge of salivary metabolomics. Current applications of salivary metabolomics have largely focused on diagnostic biomarker discovery and the diagnostic value of the current literature base is explored. There is also a small, albeit promising, literature base concerning the use of salivary metabolomics in monitoring athletic performance. Functional roles of salivary metabolites remain largely unexplored. Areas of emerging knowledge include the role of oral host–microbiome interactions in shaping the salivary metabolite profile and the potential roles of salivary metabolites in oral physiology, e.g., in taste perception. Discussion of future research directions describes the need to begin acquiring a greater knowledge of the function of salivary metabolites, a current research direction in the field of the gut metabolome. The role of saliva as an easily obtainable, information-rich fluid that could complement other gastrointestinal fluids in the exploration of the gut metabolome is emphasized. Full article

(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)

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30 pages, 917 KiB

Open AccessEditor’s ChoiceReview

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

by Mohamed A. Salem, Leonardo Perez de Souza, Ahmed Serag, Alisdair R. Fernie, Mohamed A. Farag, Shahira M. Ezzat and Saleh Alseekh

Metabolites 2020, 10(1), 37; https://doi.org/10.3390/metabo10010037 - 15 Jan 2020

Cited by 184 | Viewed by 15534

Abstract

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating [...] Read more.

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery. Full article

(This article belongs to the Special Issue Sample Preparation in Metabolomics)

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