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Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level., (Copyright © 2017 John Wiley & Sons, Ltd.)

Metabolomics allows for the investigation of the small molecules found within living systems. Based on the design of the experiments, it is not uncommon for these analyses to include matrices of thousands of variables. In order to handle such large datasets, many have turned to multivariate statistical analyses to analyze and understand their data. Herein, we present protocols for using R to analyze metabolomic data using some of the more common multivariate statistical techniques.

The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC-MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold=0.6). The plasma metabolites identified by GC-MS and the significantly altered metabolites (P<0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The work is aimed at elucidation of metabolite profile of probiotic yoghurts prepared from cow milk (CPY) and goat milk (GPY) using gas chromatography–mass spectrometry‐based untargeted metabolomics approach. A total of 192 metabolites including 49 differentially expressed metabolites (DEMs) were observed, wherein amino‐acids, peptides and analogues constituted 41.93% of the metabolite sub‐class. Acetic acid, glycine, Thr‐Ala, octanoic acid, l‐kynurenine and 2‐amino adipic acid were among the top 15 DEMs. A significant relationship with seven key metabolic pathways including glycolysis, lysine degradation, purine metabolism, fatty acid biosynthesis, tryptophan metabolism, nicotinate and nicotinamide metabolism and glycine, serine and threonine metabolism has been established based on their impact values. [ABSTRACT FROM AUTHOR]

The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture. [ABSTRACT FROM AUTHOR]

The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture.

Shiraia bambusicola is a fungus with high economic value widely used in medicine, agriculture, and food. We wished to understand the genes and metabolites changes involved in the different developmental stages of S. bambusicola. So, to reveal key genes and metabolites in the main active metabolite, the were analyzed in different developmental stages of S. bambusicola fruiting body. A total of 29,137 Unigenes were annotated. In the whole growth process, differentially expressed genes were involved in the pathways of cytochrome P450, transcription factors, transporters, and so on, while in the early stage of growth, genes enriching to synthesis pathways of basic substances. In the middle stage of growth, genes with more prominent changes were involved in the pathways of the cell cycle, cancer mechanisms, and aminobenzoate degradation; in the later stage of growth, differentially expressed genes that enriched synthesis pathways of secondary metabolites. A total of 612 metabolites were detected from different growth stages of S. bambusicola. Among them, coumarins, alkaloids, rutin, liquiritigenin, quercetin, and other medically relevant metabolites were detected for the first time. We have identified 31 secondary metabolites, relevantly only accumulated in the early and middle stage, but not detected in the later stage, such as flavonols, coumarins, nucleotides and its derivates and hydroxycinnamoyl derivatives. The differential genes and metabolites of the same group were enriched in 127 pathways, and more significantly in ubiquinone and other terpenoid quinone biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and phenylpropanoid biosynthesis. The correlation networks of several significantly enriched pathways were analyzed, and the relationships within and between these pathways, genes, and metabolites, were analyzed. The synthetic pathway of hypocrellin has been speculated upon. We believe that hypocrellin is synthesized in S. bambusicola via the shikimic acid pathway followed by phenylalanine, tyrosine, and tryptophan biosynthesis pathway, then the ubiquinone and other terpenoid quinone biosynthesis pathway, and finally a series of polymerization and modification reactions. Several genes and metabolites involved in the biosynthesis of hypocrellin have been identified. This study provides a reference for further research on S. bambusicola, by providing a basis for its use and development. [ABSTRACT FROM AUTHOR]

Background: Microbial communities and their metabolic components in the gut are of vital importance for immune homeostasis and have an influence on the susceptibility of the host to a number of immune-mediated diseases like acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, little is known about the functional connections between microbiome and metabolome in aGVHD due to the complexity of the gastrointestinal environment. Method: Initially, gut microbiota and fecal metabolic phenotype in aGVHD murine models were unleashed by performing 16S ribosomal DNA gene sequencing and ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS)-based metabolomics. Findings: The group with aGVHD experienced a significant drop in Lachnospiraceae_unclassified but an increase in the relative abundance of Clostridium XI, Clostridium XIVa and Enterococcus. Meanwhile, a lower content of tyrosine was observed in the gut of aGVHD mice. The correlation analysis revealed that tyrosine-related metabolites were inversely correlated with Clostridium XIVa, besides, Blautia and Enterococcus also displayed the negative tendency in aGVHD condition. Apart from exploring the importance and function of tyrosine, different tyrosine diets were offered to mice during transplantation. Additional tyrosine supplements can improve overall survival, ameliorate symptoms at the early stage of aGVHD and change the structure and composition of gut microbiota and fecal metabolic phenotype. In addition, aGVHD mice deprived from tyrosine displayed worse manifestations than the vehicle diet group. Interpretation: The results demonstrated the roles and mechanisms of gut microbiota, indispensable metabolites and tyrosine in the progression of aGVHD, which can be an underlying biomarker for aGVHD diagnosis and treatment. Funding: This research was funded by the International Cooperation and Exchange Program (81520108002), the National Key R&D Program of China, Stem Cell and Translation Research (2018YFA0109300), National Natural Science Foundation of China (81670169, 81670148, 81870080 and 91949115) and Natural Science Foundation of Zhejiang Province (LQ19H080006).

Dahl salt-sensitive (SS) rats develop similar prediabetes lesion characteristics, such as impaired glucose tolerance (IGT), when compared with the salt resistant rat. In this study, we evaluate the risk of high glucose intake during prediabetes and reveal the metabolic pathways relevant to the pathophysiology of prediabetes to diabetes using the SS rat model and compared this with the salt-resistant consomic SS.13BN rat model. SS rats were fed with normal chow ±10% glucose solution ad libitum for five weeks. The same experimental treatment was performed on the SS.13BN rats. Metabolites derived from the serum and liver tissue were measured through biochemical and metabolomics analyses. Multivariate, pathway enrichment, and metabolic correlation network analyses were performed based on the metabolomics data. Biochemical analysis revealed that serum triglyceride (TG) significantly increased with a significant decrease in serum total cholesterol (TC) after high glucose intake in the SS rat. Metabolic pathway analysis revealed that high glucose intake interfered with galactose, glyoxylate, and dicarboxylate metabolism, most evidently in the SS rat. Hepatic l -lactic acid content increased in the SS rat after high glucose intake, whereas the opposite was observed in SS.13BN rats. Metabolic correlation network analysis based on serum metabolites revealed that urea and l -valine had higher metabolic centrality in the SS rat. Our findings revealed that high glucose intake can significantly stimulate hypertriglyceridemia and reduce serum TC level. The profoundly altered metabolic pathway included galactose, glyoxylate, and dicarboxylate metabolism. l -lactic acid was screened as a biomarker in liver, whereas l -valine and urea were screened as hub metabolites in serum. • High glucose intake induced accumulation of triglycerides and decrease of total cholesterol level in serum. • Disorder of hepatic l -lactic acid metabolism might predispose diabetes in salt sensitive subjects. • Metabolomics uncovers distinct metabolic pathways of salt sensitive prediabetic rats. • l -valine and urea are screened as hub metabolites of correlation networks. [ABSTRACT FROM AUTHOR]

Genetic background and high-salt diet are considered key factors contributing to the development of hypertension and its associated metabolic disorders. Metabolomics is an emerging powerful tool to analyze the low-molecular weight metabolites in plasma and tissue. This study integrated metabolomics and correlation network analysis to investigate the metabolic profiles of plasma and muscle of Dahl salt-sensitive (SS) rats and SS.13BN rats (control) under normal and high-salt diet. The hub metabolites, which could play important roles in the metabolic changes, were identified by correlation network analysis. The results of the network analysis were further confirmed by pathway analysis and enzyme activity analysis. The results indicated a higher amino acid levels in both plasma and muscle of SS rats fed with high-salt diet. Alanine was found as a hub metabolite with the highest score of three centrality indices and also as the significant differential metabolite in plasma of SS rats after high-salt diet. Valine and lysine were found as hub metabolites and differential metabolites in muscle of SS rats after high-salt diet. Amino acid levels increased in both plasma and muscle of SS rats fed with a high salt diet. Moreover, alanine in plasma and valine and lysine in muscle as hub metabolites could play important roles in the response to high-salt diet. [ABSTRACT FROM AUTHOR]

Optimal drug proportions and mechanism deciphering of multicomponent drugs are critical for developing novel therapies to cope with complex diseases, such as stroke. In the present study, orthogonal experimental design was applied to explore the optimal proportion of the four component herbs in Huang-Lian-Jie-Du-Decoction (HLJDD) on the treatment of ischemic stroke. The treatment efficacies and mechanisms were assessed using global and amino acids (AAs) targeted metabolomics, as well as correlation network analysis. The global NMR metabolomics results revealed that AAs metabolism was significantly perturbed in middle cerebral artery occlusion rats. The levels of 23 endogenous AAs were then subjected to HPLCQTOF-MS/MS analysis. These results complemented with neurobehavioral evaluations, cerebral infarct assessments, biochemical evaluations, histological inspections and immunohistochemistry observations strongly demonstrated that HLJDD with optimal proportion of 6 (Rhizoma coptidis): 4 (Radix scutellariae): 1 (Cortex phellodendr): 3 (Fructus Gardeniae) had the best efficacy on ischemic stroke, which could be ascribed to its modulation on AA metabolism. This integrated metabolomics approach showed the potential and applicable in deciphering the complex mechanisms of traditional Chinese medicine formulae on the treatment of complicated diseases, which provided new means to assess the treatment effects of herb combinations and to further development of drugs or therapies based on these formulae. [ABSTRACT FROM AUTHOR]

A high-protein, low-carbohydrate diet has been regarded as a dietary intervention for weight loss in the obese population. We integrated metabolomics profiles and correlation-based network analysis to reveal the difference in metabolism under diets with different protein:carbohydrate ratios. Rats were fed a control diet (moderate-protein moderate-carbohydrate: MPMC; 20 % protein, 56 % carbohydrate) or HPLC diet (high-protein low-carbohydrate: 45 % protein, 30 % carbohydrate) for 6 weeks. The fat content was equal for both diets. HPLC feeding induced weight loss and reduced adipose weight and plasma triglyceride. Compared to the MPMC diet, HPLC significantly increased plasma α-tocopherol, pyruvate, 2-oxoisocaproate, and β-hydroxybutyrate, and reduced linoleate, palmitate, α-glycerophosphate and pyroglutamic acid. The HPLC-associated urinary metabolite profile was signified with an increase in palmitate and stearate and a reduction of citrate, 2-ketoglutarate, malate, and pantothenate. Pathway analysis implicated a significant alteration of the TCA cycle in urine. Biomarker screening demonstrated that individual metabolites, including plasma urea, pyruvate, and urinary citrate, robustly distinguished the HPLC group from the MPMC group. Correlation-based network analysis enabled to demonstrate that the correlation of plasma metabolite was strengthened after the HPLC diet, while the energy-metabolism relatives 2-ketoglutarate and fumarate correlated positively with phenylalanine, methionine, and serine. The correlation network between plasma-urinary metabolites revealed a negative correlation of plasma valine with urinary β-hydroxybutyrate in MPMC rats. In HPLC rats, plasma 2-oxoisocaproate negatively correlated with urinary pyruvate and glycine. This study using metabolomics analysis revealed the systemic metabolism in response to diet treatment and identified the significantly distinct profiles associated with a HPLC diet. [ABSTRACT FROM AUTHOR]

Abstract: Large-scale “omics” data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson''s correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based “eigen-molecules” in the correlation networks, and tests differential correlation between the 2 groups based on Fisher''s z-test. We illustrated its utility by demonstrating biologically relevant, differentially correlated molecules in transcriptome coexpression and metabolite-to-metabolite correlation networks. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates. The package can be downloaded from the following website: http://diffcorr.sourceforge.net/. [Copyright &y& Elsevier]

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) is the best MS technology for obtaining exact mass measurements owing to its great resolution and accuracy, and several outstanding FT-ICR/MS-based metabolomics approaches have been reported. A reliable annotation scheme is needed to deal with direct-infusion FT-ICR/MS metabolic profiling. Correlation analyses can help us not only uncover relations between the ions but also annotate the ions originated from identical metabolites (metabolite derivative ions). In the present study, we propose a procedure for metabolite annotation on direct-infusion FT-ICR/MS by taking into consideration the classification of metabolite-derived ions using correlation analyses. Integrated analysis based on information of isotope relations, fragmentation patterns by MS/MS analysis, co-occurring metabolites, and database searches (KNApSAcK and KEGG) can make it possible to annotate ions as metabolites and estimate cellular conditions based on metabolite composition. A total of 220 detected ions were classified into 174 metabolite derivative groups and 72 ions were assigned to candidate metabolites in the present work. Finally, metabolic profiling has been able to distinguish between the growth stages with the aid of PCA. The constructed model using PLS regression for OD600 values as a function of metabolic profiles is very useful for identifying to what degree the ions contribute to the growth stages. Ten phospholipids which largely influence the constructed model are highly abundant in the cells. Our analyses reveal that global modification of those phospholipids occurs as E. coli enters the stationary phase. Thus, the integrated approach involving correlation analyses, metabolic profiling, and database searching is efficient for high-throughput metabolomics. [ABSTRACT FROM AUTHOR]

Network analysis of correlations between abundances of metabolites across tomato genotypes can help in unraveling the biological basis of organoleptic variation in tomato. We illustrate how to construct and interpret simple correlations networks using metabolic data collected on a diverse set of tomato genotypes. Various types of correlations are calculated and displayed in the form of networks. Interpretations on the basis of network analyses are compared to interpretations following principal components analysis. [ABSTRACT FROM AUTHOR]

Sexual dimorphism exists in the onset and development of type 1 diabetes (T1D), but its potential pathological mechanism is poorly understood. In the present study, we examined sex-specific changes in the gut microbiome and host metabolome of T1D mice via 16S rRNA gene sequencing and nuclear magnetic resonance (NMR)-based metabolomics approach, and aimed to investigate potential mechanism of the gut microbiota-host metabolic interaction in the sexual dimorphism of T1D. Our results demonstrate that female mice had a greater shift in the gut microbiota than male mice during the development of T1D; however, host metabolome was more susceptible to T1D in male mice. The correlation network analysis indicates that T1D-induced host metabolic changes may be regulated by the gut microbiota in a sex-specific manner, mainly involving short-chain fatty acids (SCFAs) metabolism, energy metabolism, amino acid metabolism, and choline metabolism. Therefore, our study suggests that sex-dependent "gut microbiota-host metabolism axis" may be implicated in the sexual dimorphism of T1D, and the link between microbes and metabolites might contribute to the prevention and treatment of T1D. • Male mice have more typical type 1 diabetic symptoms than female mice. • The gut microbial shifts are more significant in female mice during T1D progress. • Male mice show larger changes in serum/liver metabolism during T1D progress. • Host metabolism may be modulated by the gut microbiota in a sex-specific manner. [ABSTRACT FROM AUTHOR]

Grape downy mildew is a devastating disease worldwide and new molecular phenotyping tools are required to detect metabolic changes associated to plant disease symptoms. In this purpose, we used UPLC-DAD-MS-based semi-targeted metabolomics to screen downy mildew symptomatic leaves that expressed oil spots (6 dpi, days post-infection) and necrotic lesions (15 dpi) under natural infections in the field. Leaf extract analyses enabled the identification of 47 metabolites belonging to the primary metabolism including 6 amino acids and 1 organic acid, as well as an important diversity of specialized metabolites including 9 flavonols, 11 flavan-3-ols, 3 phenolic acids, and stilbenoids with various degree of polymerization (DP) including 4 stilbenoids DP1, 8 stilbenoids DP2, and 4 stilbenoids DP3. Principal component analysis (PCA) was applied as unsupervised multivariate statistical analysis method to reveal metabolic variables that were affected by the infection status. Univariate and multivariate statistics revealed 33 and 27 metabolites as relevant infection biomarkers at 6 and 15 dpi, respectively. Correlation-based networks highlighted a general decrease of flavonoid-related metabolites, whereas stilbenoid DP1 and DP2 concentrations increased upon downy mildew infection. Stilbenoids DP3 were identified only in necrotic lesions representing late biomarkers of downy mildew infection. [ABSTRACT FROM AUTHOR]

Radiation‐induced brain injury involves acute, early delayed and late delayed damage based on the time‐course and clinical manifestations. The acute symptoms are mostly transient and reversible, whereas the late delayed radiation‐induced changes are progressive and irreversible. Therefore, evaluation of the organ‐specific early response to ionizing radiation exposure is necessary for improving treatment strategies and minimizing possible damage at an early stage after radiation exposure. In the current study, the gas chromatography–mass spectrometry technique based on metabolomics coupled with metabolic correlation network was applied to investigate the early metabolic characterization of rat brain tissues following irradiation. Our findings showed that the metabolic response to irradiation was not just limited to the variations of individual metabolite levels, but also accompanied by alterations of network correlations among various metabolites. Metabolite clustering indicated that energy metabolism disorder and inflammation response were induced following radiation exposure. The correlation networks revealed that the strong positive correlations of differential metabolites were highly reduced and significant negative linkages were highlighted in irradiated groups even without statistical changes in metabolic levels. Our findings provided new insights into our understanding of the radiation‐induced acute brain injury mechanism and clues as to the therapy target for clinical applications. [ABSTRACT FROM AUTHOR]

Lipopolysaccharide (LPS)‐induced inflammation occurs commonly and volatile oil from Angelica sinensis (VOAS) can be used as an anti‐inflammatory agent. The molecular mechanisms that allow the anti‐inflammatory factors to be expressed are still unknown. In this paper, we applied gas chromatography–mass spectrometry (GC–MS) and high‐performance liquid chromatography–time‐of‐flight mass spectrometry (LC‐Q/TOF–MS) based on a metabolomics platform coupled with a network approach to analyze urine samples in three groups of rats: one with LPS‐induced inflammation (MI); one with intervention with VOAS; and normal controls (NC). Our study found definite metabolic footprints of inflammation and showed that all three groups of rats, MI, intervention with VOAS and NC have distinct metabolic profiles in urine. The concentrations of 48 metabolites differed significantly among the three groups. The metabolites in urine were screened by the GC–MS and LC‐Q/TOF–MS methods. The significantly changed metabolites (p < 0.05, variable importance in projection > 1.5) between MI, NC and VOAS were included in the metabolic networks. Finally, hub metabolites were screened, including glycine, arachidonic acid, l‐glutamate, pyruvate and succinate, which have high values of degree (k). the Results suggest that disorders of glycine, arachidonic acid, l‐glutamate, pyruvate and succinate metabolism might play an important part in the predisposition and development of LPS‐induced inflammation. By applying metabolomics with network methods, the mechanisms of diseases are clearly elucidated. [ABSTRACT FROM AUTHOR]

Introduction: Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.Objectives: This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.Methods: We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.Results: We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.Conclusions: Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies. [ABSTRACT FROM AUTHOR]