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Current State of the Art of High-Throughput Metabolomics

Aihua Zhang, Qiang Yang, Hui Sun, and Xijun Wang

Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Functional Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heping Road 24, Harbin, 150040, China

1.1 Introduction

At present, a series of systems biology disciplines, such as genomics, proteomics, transcriptomics, metabolomics, and lipidomics have provided new methods for researchers to explore the pathogenesis of diseases. As an important component of systems biology, metabolomics can discover the metabolite biomarkers and various related metabolic pathways in bio-samples [1]. Metabolomics is usually used with nuclear magnetic resonance (NMR), mass spectrometry (MS), and chromatography, and it plays an important role in clinic, disease treatment, drug metabolism, plant research, agricultural, and nutrition research [2]. Due to the important position of metabolomics in the life science, a large number of domestic and foreign scholars have improved the innovation research of techniques and methods of metabolomics [3]. In this chapter, we show the recent advancements and current application as well as future development of metabolomics.

1.2 Metabolomics

In 1999, Dr. Jeremy Nicholson proposed the concept of metabolomics [4]. Metabolomics is a method to quantitatively analyze all the metabolites in bio-sample and to reveal the relationship between metabolites and pathological changes. It mainly focuses on the small-molecule metabolites with relative molecular weight of less than 1000 [5]. Its research process mainly includes sample preparation and analysis, data processing and analysis, and metabolic mechanism analysis. Metabolomics studies used analytical tools including NMR spectroscopy, liquid chromatography-mass spectrometry (LC-MS), and gas-chromatography-mass spectrometry (GC-MS) [6-9]. Due to the very complex and huge original data volume of metabolome, it cannot be analyzed by conventional methods. Instead, it often uses pattern recognition analysis, such as principal component analysis (Figure 1.1) and loading (molecules) analysis (Figure 1.2), for screening metabolites (Figure 1.3) and for exploring the metabolic pathway (Figures 1.4 and 1.5) and the metabolic network change mechanism (Figure 1.6). Thus metabolomics analysis has formed a series analysis approaches including data extraction, data preprocessing, supervised and unsupervised pattern recognition, and biological information exploration [10-12].

Figure 1.1 Principal component analysis in high-throughput metabolomics.

1.3 Mass Spectrometry Technology

MS has become a widely used analytical tool for chemists and is increasingly used for metabolomics analysis. It plays a key role in the identification of structural information for metabolite molecules. With the advance of technology, MS combination with separation technology is gradually improving [13,14]. Due to the high specificity and sensitivity, low sample consumption, fast analysis, and the advantages of identification, mass spectrometer is widely used in metabolomics science, and it opens up a new gate for the rapid analysis of complex samples [15-17].

Figure 1.2 The loading ion analysis for metabolomics.

Figure 1.3 The variable importance in projection for screening metabolites from large biological data sets.

Figure 1.4 Metabolic pathway analysis for the biologically meaningful metabolite sets.

1.4 Recent Advancements and Applications

MS-based metabolomics methods have been used to reveal the disease diagnosis, drug effects, metabolic mechanism, toxins, and various diseases including cardiovascular disease, cancer, natural product discovery, toxicological effects, and nutrition [18-24].

1.4.1 Disease Diagnoses and Metabolism

The disease causes the pathophysiological changes and eventually result in corresponding metabolites and pathway changes. Some scholars have established a diagnostic method for diseases by detection of metabolites. Cholesterol sulfate and phospholipids are considered as novel biomarkers for atherosclerosis [25]. The levels of norvaline, 1,5-anhydroglucitol, and L-aspartic acid are linked with macroalbuminuric diabetic kidney disease [26,27]. Abdulwahab et al. used MS-based metabolomics and revealed new associations between 37 proteins and T2DM and found the significant up-regulation of immunoglobulin [28]. Yang et al. found that there were 33 distinct metabolites in the urine of femoral head necrosis patients [29]. Lin et al. used MS to detect plasma metabolites and found that the alanine, aspartic acid, and carbamate were significantly different among all groups of respiratory distress patients [30]. Øvrehus et al. had found that early hypertensive nephrosclerosis showed disturbances in dopamine intrarenal biosynthesis [31]. Metabolic pathways such as sphingolipid, vitamin D-related compounds, and steroid precursors were discovered in glaucoma patients [32]. Dong et al. discovered the 31 metabolites between nonalcoholic fatty liver disease and nonalcoholic steatohepatitis [33]. Based on the metabolic method, Wang et al. found that the changes of acute ischemic stroke were mainly related to amino acid-related metabolism [34]. There are 30 different metabolites of gout patients, mainly involving tricarboxylic acid cycle, amino acid metabolism, and lipid metabolism [35]. A total of 12 metabolites, mainly related to fatty acid metabolism, exerted significant changes in ischemic stroke [36]. Metabolomics research is of great significance for the disease diagnosis, to better understand the disease pathogenesis and to provide new evidence for the primary prevention of disease [37-43]. Cancer is a major health problem in the world, so understanding the metabolic causes of cancer and its influencing factors is important for disease treatment. Yang et al. used MS to explore the metabolic changes of ovarian and had identified 18 metabolites closely related to ovarian cancer [44]. Some scholars used LC/MS to study the metabolic changes of pancreatic cancer tissue and found that seven metabolites may be potential biomarkers [45]. Metabolic pathways are closely related to tumor growth, metastasis, and immune escape mechanism [46,47].

Figure 1.5 Metabolite enrichment analysis for the biologically meaningful metabolite sets based on the libraries.

Figure 1.6 Metabolic networks for the biologically meaningful metabolite sets performed with MetaboAnalyst online tool (www.metaboanalyst.ca).

1.4.2 Metabolic Mechanisms

The disorder of metabolic mechanism is one of the crucial factors affecting the disease, for clarifying the metabolic mechanism plays a crucial role in the treatment of the disease. Breviscapine mainly improves the metabolism of phospholipids by regulating the level of serotonin [48]. A total of 19 metabolites were found with the hypoglycemic effect of Crassostrea gigas polysaccharide and mainly involved in carbohydrate metabolism, amino acid metabolism, and purine metabolism [49]. Aloe emodin has a therapeutic effect on hyperlipidemia by regulating metabolic disorders [50]. Pang et al. had found that methotrexate could regulate the inflammatory-related metabolic networks [51]. Metabolic mechanism of disease research in metabolomics-based approach provided a new method for disease treatment [52-56].

1.4.3 Drug Efficacy

The efficacy evaluation is an essential requirement for discovering the effective constituents and therapeutic targets. Therefore, based on the metabolomics and other omics techniques, the innovative methods such as chinmedomics, fectomics, and functiomics could be established and used for evaluating the efficacy; discovering the chemicalome and metabolome; screening of active compounds, fectome and functiome; revealing the effective mechanism of drug, herbal medicine, and traditional medicine; etc. and seek to elucidate the therapeutic properties with modern techniques. Some scholars have found that the process of drug treatments could be uncovered by metabolic methods [57]. Bao et al. used UPLC/MS metabolomic method to rapidly identify the anticancer compounds in Forsythia and found that betulinic acid was the most effective anticancer compound. The forsythia extract can exert anti-inflammatory effect through acting on different metabolic pathways [58,59]. A total of 27 potential biomarkers were discovered and related with effective mechanism of Xanthii Fructus for allergic rhinitis, which mainly involved glycerophospholipid and branched-chain amino acid metabolism [60]. Gross saponins of Tribulus terrestris fruit could regulate the multiple metabolic...