Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for young hypertensive men

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.     

Metabolomics reveals significant impairments in the immune system of the APP/PS1 transgenic mice of Alzheimer's disease

Inflammatory processes and other failures related to the immune system are common features associated with Alzheimer's disease (AD), in both brain and the peripheral system. Thus, the study of the main organs of the immune system may have a great potential for the elucidation of pathological mechanisms underlying these abnormalities. This is the first metabolomic investigation performed in spleen and thymus from transgenic mice of AD. Tissues were fingerprinted using a metabolomic platform comprising GC‐MS and ultra‐HPLC‐MS. Multivariate statistics demonstrated significant differences in numerous metabolites between the APP/PS1 mice and wild‐type controls, and it was proven that multiple biochemical pathways are disturbed in these organs including abnormal metabolism of phospholipids, energy deficiencies, altered homeostasis of amino acids, oxidative stress, and others. Therefore, these findings highlight the importance of the proper metabolic functioning of peripheral immune system in the development of neurodegenerative disorders such as AD.     

In vivo determination of the volatile metabolites of saprotroph fungi by comprehensive two‐dimensional gas chromatography

In this work, we discuss the use of multiway principal component analysis combined with comprehensive two‐dimensional gas chromatography to study the volatile metabolites of the saprophytic fungus Memnoniella sp. isolated in vivo by headspace solid‐phase microextraction. This fungus has been identified as having the ability to induce plant resistance against pathogens, possibly through its volatile metabolites. Adequate culture media were inoculated, and its headspace was then sampled with a solid‐phase microextraction fiber and chromatographed every 24 h over seven days. The raw chromatogram processing using multiway principal component analysis allowed the determination of the inoculation period, during which the concentration of volatile metabolites was maximized, as well as the discrimination of the appropriate peaks from the complex culture media background. Several volatile metabolites not previously described in the literature on biocontrol fungi were observed, as well as sesquiterpenes and aliphatic alcohols. These results stress that, due to the complexity of multidimensional chromatographic data, multivariate tools might be mandatory even for apparently trivial tasks, such as the determination of the temporal profile of metabolite production and extinction. However, when compared with conventional gas chromatography, the complex data processing yields a considerable improvement in the information obtained from the samples.     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

Metabolic profiling using Fourier-transform ion-cyclotron-resonance mass spectrometry

With the aid of the extreme resolving power of Fourier-transform ion-cyclotron-resonance mass spectrometry (FT-ICR/MS), we have developed a metabolomics platform for high-throughput metabolic profiling and metabolite candidate identification integrating a data-processing system, the Dr.DMASS program (), and a metabolite-species database, KNApSAcK (). We discuss the potential of this FT-ICR/MS-based metabolic profiling scheme as a general metabolomics tool by clarification of plant metabolic disorders and specific metabolite accumulation patterns caused by herbicidal enzyme inhibitors.     

Combination of HPLC with organic and inorganic mass spectrometry to study the metabolic response of the clam Scrobicularia plana to arsenic exposure

Arsenic is a toxic element extensively studied in the marine environment due to differential toxicological effects of inorganic and organic species. In the present work, the bivalve Scrobicularia plana was exposed to As^V (10 and 100 μg/L) for 14 days to evaluate the metabolic perturbations caused by this element. Arsenic speciation and metabolomic analysis were performed in the digestive gland of the bivalve using two complementary analytical platforms based on inorganic and organic mass spectrometry. It has been observed the greater presence of the innocuous specie arsenobetaine produced in this organism as defense mechanism against arsenic toxicity, although significant concentrations of methylated and inorganic arsenic were also present, depending on the level of arsenic in aqueous media. Complementarily, a metabolomic study based on mass spectrometry and statistical discriminant analysis allows a good classification of samples associated to low and high As(V) exposure in relation to controls. About 15 metabolites suffer significant changes of expression by the presence of As(V): amino acids, nucleotides, energy‐related metabolites, free fatty acids, phospholipids and triacylglycerides, which can be related to membrane structural and functional damage. In addition, perturbation of the methylation cycle, associated with the increase of homocysteine and methionine was observed, which enhance the methylation of toxic inorganic arsenic to less toxic dimethylarsenic.     

Multilevel component analysis of time-resolved metabolic fingerprinting data

Genomics-based technologies in systems biology have gained a lot of popularity in recent years. These technologies generate large amounts of data. To obtain information from this data, multivariate data analysis methods are required. Many of the datasets generated in genomics are multilevel datasets, in which the variation occurs on different levels simultaneously (e.g. variation between organisms and variation in time). We introduce multilevel component analysis (MCA) into the field of metabolic fingerprinting to separate these different types of variation. This is in contrast to the commonly used principal component analysis (PCA) that is not capable of doing this: in a PCA model the different types of variation in a multilevel dataset are confounded.

MCA generates different submodels for different types of variation. These submodels are lower-dimensional component models in which the variation is approximated. These models are easier to interpret than the original data. Multilevel simultaneous component analysis (MSCA) is a method within the class of MCA models with increased interpretability, due to the fact that the time-resolved variation of all individuals is expressed in the same subspace.

MSCA is applied on a time-resolved metabolomics dataset. This dataset contains ¹H NMR spectra of urine collected from 10 monkeys at 29 time-points during 2 months. The MSCA model contains a submodel describing the biorhythms in the urine composition and a submodel describing the variation between the animals. Using MSCA the largest biorhythms in the urine composition and the largest variation between the animals are identified.

Comparison of the MSCA model to a PCA model of this data shows that the MSCA model is better interpretable: the MSCA model gives a better view on the different types of variation in the data since they are not confounded.     

Software-assisted serum metabolite quantification using NMR

The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.     

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.     

Metabolomic comparison between natural Huaier and artificial cultured Huaier

Vanderbylia robiniophila (Murrill) B.K. (Huaier) is a kind of higher fungal fruiting body that is parasitic on the trunk of Sophora japonica and Robinia pseudoacacia L.. As a traditional Chinese medicine with a history of more than 1,600 years, Huaier has attracted wide attention for its excellent anticancer activity. A systematic study on the metabolome differences between natural Huaier and artificial cultured Huaier was conducted using liquid chromatography–mass spectrometry in this study. Principal component analysis and orthogonal projection on latent structure-discriminant analysis results showed that cultured Huaier evidently separated and individually separated from natural Huaier, indicating metabolome differences between natural and cultured Huaier. Hierarchical clustering analysis was further performed to cluster the differential metabolites and samples based on their metabolic similarity. The higher contents of amino acids, alkaloids and terpenoids in natural Huaier make it an excellent choice as a traditional Chinese medicine for anticancer or nutritional supplementation. The results of the Bel-7,402 and A549 cell cytotoxicity tests showed that the anticancer activity of natural Huaier was better than that of cultured Huaier. This may be due to the difference in chemical composition, which makes the anticancer activities of natural and cultured Huaier different.