scispace - formally typeset
Search or ask a question

Showing papers in "Metabolites in 2020"


Journal ArticleDOI
TL;DR: This work introduces MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: efficient parameter optimization for peak picking; automated batch effect correction; and more accurate pathway activity prediction that offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
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 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 20~100X 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.

320 citations


Journal ArticleDOI
TL;DR: This review addresses recent developments in using machine learning for processing MS spectra and shows how machine learning generates new biological insights in metabolomics research because of the ability to supply quantitative predictions.
Abstract: The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

131 citations


Journal ArticleDOI
TL;DR: 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.
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 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.

111 citations


Journal ArticleDOI
TL;DR: The field of CA inhibitors (CAIs), which selectively inhibit the cancer-associated CA isoforms, are reviewed, with particular focus on the identification of lead compounds and various inhibitor classes, and the measurement of CA inhibitory on-/off-target effects.
Abstract: The tumor microenvironment is crucial for the growth of cancer cells, triggering particular biochemical and physiological changes, which frequently influence the outcome of anticancer therapies. The biochemical rationale behind many of these phenomena resides in the activation of transcription factors such as hypoxia-inducible factor 1 and 2 (HIF-1/2). In turn, the HIF pathway activates a number of genes including those involved in glucose metabolism, angiogenesis, and pH regulation. Several carbonic anhydrase (CA, EC 4.2.1.1) isoforms, such as CA IX and XII, actively participate in these processes and were validated as antitumor/antimetastatic drug targets. Here, we review the field of CA inhibitors (CAIs), which selectively inhibit the cancer-associated CA isoforms. Particular focus was on the identification of lead compounds and various inhibitor classes, and the measurement of CA inhibitory on-/off-target effects. In addition, the preclinical data that resulted in the identification of SLC-0111, a sulfonamide in Phase Ib/II clinical trials for the treatment of hypoxic, advanced solid tumors, are detailed.

101 citations


Journal ArticleDOI
TL;DR: The review firstly revisits models that have been elucidated to describe the molecular machinery employed by plants in coping with environmental stresses, indicating the lack of biological basis to accurately postulate the mechanisms of action of plant biostimulants.
Abstract: Adverse environmental conditions due to climate change, combined with declining soil fertility, threaten food security. Modern agriculture is facing a pressing situation where novel strategies must be developed for sustainable food production and security. Biostimulants, conceptually defined as non-nutrient substances or microorganisms with the ability to promote plant growth and health, represent the potential to provide sustainable and economically favorable solutions that could introduce novel approaches to improve agricultural practices and crop productivity. Current knowledge and phenotypic observations suggest that biostimulants potentially function in regulating and modifying physiological processes in plants to promote growth, alleviate stresses, and improve quality and yield. However, to successfully develop novel biostimulant-based formulations and programs, understanding biostimulant-plant interactions, at molecular, cellular and physiological levels, is a prerequisite. Metabolomics, a multidisciplinary omics science, offers unique opportunities to predictively decode the mode of action of biostimulants on crop plants, and identify signatory markers of biostimulant action. Thus, this review intends to highlight the current scientific efforts and knowledge gaps in biostimulant research and industry, in context of plant growth promotion and stress responses. The review firstly revisits models that have been elucidated to describe the molecular machinery employed by plants in coping with environmental stresses. Furthermore, current definitions, claims and applications of plant biostimulants are pointed out, also indicating the lack of biological basis to accurately postulate the mechanisms of action of plant biostimulants. The review articulates briefly key aspects in the metabolomics workflow and the (potential) applications of this multidisciplinary omics science in the biostimulant industry.

94 citations


Journal ArticleDOI
TL;DR: In this paper, a review of the application of metabolomics and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods, and it is imperative to bring the power of metabolic analyses to bear on the study of plant resistance/susceptibility.
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 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.

92 citations


Journal ArticleDOI
TL;DR: A better understanding of the phytochemical landscape shaped by plant–AMF interactions would enable us to harness this symbiotic association to enhance plant performance, particularly under non-optimal growing conditions.
Abstract: Arbuscular mycorrhizal fungi (AMF) is among the most ubiquitous plant mutualists that enhance plant growth and yield by facilitating the uptake of phosphorus and water. The countless interactions that occur in the rhizosphere between plants and its AMF symbionts are mediated through the plant and fungal metabolites that ensure partner recognition, colonization, and establishment of the symbiotic association. The colonization and establishment of AMF reprogram the metabolic pathways of plants, resulting in changes in the primary and secondary metabolites, which is the focus of this review. During initial colonization, plant-AMF interaction is facilitated through the regulation of signaling and carotenoid pathways. After the establishment, the AMF symbiotic association influences the primary metabolism of the plant, thus facilitating the sharing of photosynthates with the AMF. The carbon supply to AMF leads to the transport of a significant amount of sugars to the roots, and also alters the tricarboxylic acid cycle. Apart from the nutrient exchange, the AMF imparts abiotic stress tolerance in host plants by increasing the abundance of several primary metabolites. Although AMF initially suppresses the defense response of the host, it later primes the host for better defense against biotic and abiotic stresses by reprogramming the biosynthesis of secondary metabolites. Additionally, the influence of AMF on signaling pathways translates to enhanced phytochemical content through the upregulation of the phenylpropanoid pathway, which improves the quality of the plant products. These phytometabolome changes induced by plant-AMF interaction depends on the identity of both plant and AMF species, which could contribute to the differential outcome of this symbiotic association. A better understanding of the phytochemical landscape shaped by plant-AMF interactions would enable us to harness this symbiotic association to enhance plant performance, particularly under non-optimal growing conditions.

86 citations


Journal ArticleDOI
TL;DR: 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.
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 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.

82 citations


Journal ArticleDOI
TL;DR: The results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including serum, ruminal fluid, liver, Longissimus thoracis muscle, semimembranosus muscle, and testis tissues, are described.
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 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.

73 citations


Journal ArticleDOI
TL;DR: MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.
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 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.

66 citations


Journal ArticleDOI
TL;DR: This review discusses common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis and examples of the applications of corresponding methods to clinical and basic research.
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 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.

Journal ArticleDOI
TL;DR: Findings from the literature highlight the importance of research on lipidomics of lipoproteins to enhance the 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.
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 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.

Journal ArticleDOI
TL;DR: 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 are provided.
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 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.

Journal ArticleDOI
TL;DR: The Pentose Phosphate Pathway is one of the key metabolic pathways occurring in living cells to produce energy and maintain cellular homeostasis and its possible correlation with cancer is discussed.
Abstract: The Pentose Phosphate Pathway (PPP) is one of the key metabolic pathways occurring in living cells to produce energy and maintain cellular homeostasis. Cancer cells have higher cytoplasmic utilization of glucose (glycolysis), even in the presence of oxygen; this is known as the “Warburg Effect”. However, cytoplasmic glucose utilization can also occur in cancer through the PPP. This pathway contributes to cancer cells by operating in many different ways: (i) as a defense mechanism via the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) to prevent apoptosis, (ii) as a provision for the maintenance of energy by intermediate glycolysis, (iii) by increasing genomic material to the cellular pool of nucleic acid bases, (iv) by promoting survival through increasing glycolysis, and so increasing acid production, and (v) by inducing cellular proliferation by the synthesis of nucleic acid, fatty acid, and amino acid. Each step of the PPP can be upregulated in some types of cancer but not in others. An interesting aspect of this metabolic pathway is the shared regulation of the glycolytic and PPP pathways by intracellular pH (pHi). Indeed, as with glycolysis, the optimum activity of the enzymes driving the PPP occurs at an alkaline pHi, which is compatible with the cytoplasmic pH of cancer cells. Here, we outline each step of the PPP and discuss its possible correlation with cancer.

Journal ArticleDOI
TL;DR: Notame is introduced, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis, and an overview of lab protocols and statistical methods used for the analysis of nutritional metabolomics data is provided.
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 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.

Journal ArticleDOI
TL;DR: Findings indicating that NO, through its interaction with heme and non-heme metal containing proteins, together with components of the electron transport chain, functions not only as a regulator of cell respiration, but also a modulator of intracellular cell metabolism are reviewed.
Abstract: Nitric Oxide (NO) is a soluble endogenous gas with various biological functions like signaling, and working as an effector molecule or metabolic regulator. In response to inflammatory signals, immune myeloid cells, like macrophages, increase production of cytokines and NO, which is important for pathogen killing. Under these proinflammatory circumstances, called "M1", macrophages undergo a series of metabolic changes including rewiring of their tricarboxylic acid (TCA) cycle. Here, we review findings indicating that NO, through its interaction with heme and non-heme metal containing proteins, together with components of the electron transport chain, functions not only as a regulator of cell respiration, but also a modulator of intracellular cell metabolism. Moreover, diverse effects of NO and NO-derived reactive nitrogen species (RNS) involve precise interactions with different targets depending on concentration, temporal, and spatial restrictions. Although the role of NO in macrophage reprogramming has been in evidence for some time, current models have largely minimized its importance. It has, therefore, been hiding in plain sight. A review of the chemical properties of NO, past biochemical studies, and recent publications, necessitates that mechanisms of macrophage TCA reprogramming during stimulation must be re-imagined and re-interpreted as mechanistic results of NO exposure. The revised model of metabolic rewiring we describe here incorporates many early findings regarding NO biochemistry and brings NO out of hiding and to the forefront of macrophages immunometabolism.

Journal ArticleDOI
TL;DR: In this paper, the authors systematically searched three electronic databases (up to 31 October 2020) for identifying placebo-controlled or head-to-head RCTs that used SGLT-2 inhibitors for treatment of NAFLD.
Abstract: Recent randomized controlled trials (RCTs) tested the efficacy of sodium-glucose cotransporter-2 (SGLT-2) inhibitors to specifically treat nonalcoholic fatty liver disease (NAFLD). We systematically searched three electronic databases (up to 31 October 2020) for identifying placebo-controlled or head-to-head RCTs that used SGLT-2 inhibitors for treatment of NAFLD. No published RCTs with paired liver biopsy data were available for the meta-analysis. Primary outcome measures were changes in serum liver enzyme levels and liver fat content on imaging techniques. Overall, we included a total of twelve RCTs testing the efficacy of dapagliflozin (n = six RCTs), empagliflozin (n = three RCTs), ipragliflozin (n = two RCTs) or canagliflozin (n = one RCT) to specifically treat NAFLD for a median period of 24 weeks with aggregate data on 850 middle-aged overweight or obese individuals with NAFLD (90% with type 2 diabetes). Compared to placebo/reference therapy, treatment with SGLT-2 inhibitors significantly decreased serum alanine aminotransferase (weighted mean differences (WMD): −10.0 IU/L, 95%CI −12.2 to −7.79 IU/L; I2 = 10.5%) and gamma-glutamyltransferase levels (WMD: −14.49 IU/L, 95%CI −19.35 to −9.63 IU/L, I2 = 38.7%), as well as the absolute percentage of liver fat content on magnetic resonance-based techniques (WMD: −2.05%, 95%CI −2.61 to −1.48%; I2 = 0%). In conclusion, SGLT-2 inhibitors seem to be a promising treatment option for NAFLD.

Journal ArticleDOI
TL;DR: Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software, and validate annotation accuracy against lipid standards and other lipidomicsSoftware.
Abstract: Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Journal ArticleDOI
TL;DR: The potential of reversing chemoresistance by metabolic regulation is discussed and the proposed metabolic mechanisms of drug resistance include the increase of glucose and glutamine demand and promotion of NADPH from the pentose phosphate pathway.
Abstract: Metabolic reprogramming is one of the hallmarks of tumors. Alterations of cellular metabolism not only contribute to tumor development, but also mediate the resistance of tumor cells to antitumor drugs. The metabolic response of tumor cells to various chemotherapy drugs can be analyzed by metabolomics. Although cancer cells have experienced metabolic reprogramming, the metabolism of drug resistant cancer cells has been further modified. Metabolic adaptations of drug resistant cells to chemotherapeutics involve redox, lipid metabolism, bioenergetics, glycolysis, polyamine synthesis and so on. The proposed metabolic mechanisms of drug resistance include the increase of glucose and glutamine demand, active pathways of glutaminolysis and glycolysis, promotion of NADPH from the pentose phosphate pathway, adaptive mitochondrial reprogramming, activation of fatty acid oxidation, and up-regulation of ornithine decarboxylase for polyamine production. Several genes are associated with metabolic reprogramming and drug resistance. Intervening regulatory points described above or targeting key genes in several important metabolic pathways may restore cell sensitivity to chemotherapy. This paper reviews the metabolic changes of tumor cells during the development of chemoresistance and discusses the potential of reversing chemoresistance by metabolic regulation.

Journal ArticleDOI
TL;DR: A comprehensive metabolomics framework is proposed, along with an appropriate checklist refined from current guidelines and previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research.
Abstract: Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Journal ArticleDOI
TL;DR: This study shows that succinate can suppress secretion of inflammatory mediators IL-6, tumor necrosis factor (TNF) and nitric oxide (NO), as well as inhibit Il1b mRNA expression of inflammatory macrophages in a SUCNR1-independent manner, and observes that macrophage S UCNR1 deficiency led to an enhanced inflammatory response without addition of exogenous succinate.
Abstract: Immunometabolism revealed the crucial role of cellular metabolism in controlling immune cell phenotype and functions. Macrophages, key immune cells that support progression of numerous inflammatory diseases, have been well described as undergoing vast metabolic rewiring upon activation. The immunometabolite succinate particularly gained a lot of attention and emerged as a crucial regulator of macrophage responses and inflammation. Succinate was originally described as a metabolite that supports inflammation via distinct routes. Recently, studies have indicated that succinate and its receptor SUCNR1 can suppress immune responses as well. These apparent contradictory effects might be due to specific experimental settings and particularly the use of distinct succinate forms. We therefore compared the phenotypic and functional effects of distinct succinate forms and receptor mouse models that were previously used for studying succinate immunomodulation. Here, we show that succinate can suppress secretion of inflammatory mediators IL-6, tumor necrosis factor (TNF) and nitric oxide (NO), as well as inhibit Il1b mRNA expression of inflammatory macrophages in a SUCNR1-independent manner. We also observed that macrophage SUCNR1 deficiency led to an enhanced inflammatory response without addition of exogenous succinate. While our study does not reveal new mechanistic insights into how succinate elicits different inflammatory responses, it does indicate that the inflammatory effects of succinate and its receptor SUCNR1 in macrophages are clearly context dependent.

Journal ArticleDOI
TL;DR: The current state of the art in metabolic modelling is described, future perspectives and current challenges in the field are discussed and model predictive capabilities are discussed.
Abstract: Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Journal ArticleDOI
TL;DR: The present review compares the different definitions of obesity phenotypes and presents several possible factors underlying them (adipose tissue distribution and cellularity, contaminant accumulation on the adipose tissue, dysbiosis and metabolic endotoxemia imposing on to the endocannabinoid tone and inflammasome, and nutrient intake and dietary patterns) having inflammatory activation at the center.
Abstract: The concept of heterogeneity among obese individuals in their risk for developing metabolic dysfunction and associated complications has been recognized for decades. At the origin of the heterogeneity idea is the acknowledgement that individuals with central obesity are more prone to developing type 2 diabetes and cardiovascular disease than those with peripheral obesity. There have been attempts to categorize subjects according to their metabolic health and degree of obesity giving rise to different obese and non-obese phenotypes that include metabolically unhealthy normal-weight (MUHNW), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Individuals belonging to the MHO phenotype are obese according to their body mass index although exhibiting fewer or none metabolic anomalies such as type 2 diabetes, dyslipidemia, hypertension, and/or unfavorable inflammatory and fribinolytic profiles. However, some authors claim that MHO is only transient in nature. Additionally, the phenotype categorization is controversial as it lacks standardized definitions possibly blurring the distinction between obesity phenotypes and confounding the associations with health outcomes. To add to the discussion, the factors underlying the origin or protection from metabolic deterioration and cardiometabolic risk for these subclasses are being intensely investigated and several hypotheses have been put forward. In the present review, we compare the different definitions of obesity phenotypes and present several possible factors underlying them (adipose tissue distribution and cellularity, contaminant accumulation on the adipose tissue, dysbiosis and metabolic endotoxemia imposing on to the endocannabinoid tone and inflammasome, and nutrient intake and dietary patterns) having inflammatory activation at the center.

Journal ArticleDOI
TL;DR: The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine.
Abstract: Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual's metabolic profile has been referred to as his or her "metabotype." As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual's pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Journal ArticleDOI
TL;DR: An overview of the metabolic changes during postharvest storage, with special attention to key metabolites related to fruit quality is provided, and the potential use of metabolomic approaches to yield metabolic markers useful for chemical phenotyping or even storage and marketing decisions is highlighted.
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 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.

Journal ArticleDOI
TL;DR: Future treatment strategies in neurodegenerative disorders should focus on energy metabolism and compensation age-related NAD deficit in neurons, which summarizes the complex interrelationships between metabolic processes on the systemic and cellular levels and provides directions on how to reduce the risk of neurodegenersation and protect the elderly against neuro degenerative diseases.
Abstract: There is a growing body of evidencethat indicates that the aging of the brain results from the decline of energy metabolism. In particular, the neuronal metabolism of glucose declines steadily, resulting in a growing deficit of adenosine triphosphate (ATP) production-which, in turn, limits glucose access. This vicious circle of energy metabolism at the cellular level is evoked by a rising deficiency of nicotinamide adenine dinucleotide (NAD) in the mitochondrial salvage pathway and subsequent impairment of the Krebs cycle. A decreasing NAD level also impoverishes the activity of NAD-dependent enzymes that augments genetic errors and initiate processes of neuronal degeneration and death.This sequence of events is characteristic of several brain structures in which neurons have the highest energy metabolism. Neurons of the cerebral cortex and basal ganglia with long unmyelinated axons and these with numerous synaptic junctions are particularly prone to senescence and neurodegeneration. Unfortunately, functional deficits of neurodegeneration are initially well-compensated, therefore, clinical symptoms are recognized too late when the damages to the brain structures are already irreversible. Therefore, future treatment strategies in neurodegenerative disorders should focus on energy metabolism and compensation age-related NAD deficit in neurons. This review summarizes the complex interrelationships between metabolic processes on the systemic and cellular levels and provides directions on how to reduce the risk of neurodegeneration and protect the elderly against neurodegenerative diseases.

Journal ArticleDOI
TL;DR: It remains unclear whether WAT autophagy alteration in obese and diabetic patients are the cause or the consequence of WAT malfunction, and current data regarding these issues are discussed, focusing on evidence from human studies.
Abstract: White adipose tissue (WAT) is a highly adaptive endocrine organ that continuously remodels in response to nutritional cues. WAT expands to store excess energy by increasing adipocyte number and/or size. Failure in WAT expansion has serious consequences on metabolic health resulting in altered lipid, glucose, and inflammatory profiles. Besides an impaired adipogenesis, fibrosis and low-grade inflammation also characterize dysfunctional WAT. Nevertheless, the precise mechanisms leading to impaired WAT expansibility are yet unresolved. Autophagy is a conserved and essential process for cellular homeostasis, which constitutively allows the recycling of damaged or long-lived proteins and organelles, but is also highly induced under stress conditions to provide nutrients and remove pathogens. By modulating protein and organelle content, autophagy is also essential for cell remodeling, maintenance, and survival. In this line, autophagy has been involved in many processes affected during WAT maladaptation, including adipogenesis, adipocyte, and macrophage function, inflammatory response, and fibrosis. WAT autophagy dysregulation is related to obesity and diabetes. However, it remains unclear whether WAT autophagy alteration in obese and diabetic patients are the cause or the consequence of WAT malfunction. In this review, current data regarding these issues are discussed, focusing on evidence from human studies.

Journal ArticleDOI
TL;DR: This review discusses the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome, and focuses on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body.
Abstract: In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these "omics" are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.

Journal ArticleDOI
TL;DR: An extensive metabolomics analysis on the plasma of 52 female subjects delivered data for about 1750 blood compounds spanning 20 super-pathways, subdivided into 113 sub-pathway of lipids, pointing to a few disrupted metabolic pathways containing many unexplored compounds.
Abstract: The latest worldwide prevalence rate projects that over 65 million patients suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), an illness with known effects on the functioning of the immune and nervous systems. We performed an extensive metabolomics analysis on the plasma of 52 female subjects, equally sampled between controls and ME/CFS patients, which delivered data for about 1750 blood compounds spanning 20 super-pathways, subdivided into 113 sub-pathways. Statistical analysis combined with pathway enrichment analysis points to a few disrupted metabolic pathways containing many unexplored compounds. The most intriguing finding concerns acyl cholines, belonging to the fatty acid metabolism sub-pathway of lipids, for which all compounds are consistently reduced in two distinct ME/CFS patient cohorts. We compiled the extremely limited knowledge about these compounds and regard them as promising in the quest to explain many of the ME/CFS symptoms. Another class of lipids with far-reaching activity on virtually all organ systems are steroids; androgenic, progestin, and corticosteroids are broadly reduced in our patient cohort. We also report on lower dipeptides and elevated sphingolipids abundance in patients compared to controls. Disturbances in the metabolism of many of these molecules can be linked to the profound organ system symptoms endured by ME/CFS patients.

Journal ArticleDOI
TL;DR: Breath concentrations of volatile organic compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.
Abstract: The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.