From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Methionine and Kynurenine Activate Oncogenic Kinases in Glioblastoma, and Methionine Deprivation Compromises Proliferation

PURPOSE

We employed a metabolomics-based approach with the goal to better understand the molecular signatures of glioblastoma cells and tissues, with an aim toward identifying potential targetable biomarkers for developing more effective and novel therapies.

EXPERIMENTAL DESIGN

We used liquid chromatography coupled with mass spectrometry (LC-MS/Q-TOF and LC-MS/QQQ) for the discovery and validation of metabolites from primary and established glioblastoma cells, glioblastoma tissues, and normal human astrocytes.

RESULTS

We identified tryptophan, methionine, kynurenine, and 5-methylthioadenosine as differentially regulated metabolites (DRM) in glioblastoma cells compared with normal human astrocytes (NHAs). Unlike NHAs, glioblastoma cells depend on dietary methionine for proliferation, colony formation, survival, and to maintain a deregulated methylome (SAM:SAH ratio). In methylthioadenosine phosphorylase (MTAP)-deficient glioblastoma cells, expression of MTAP transgene did not alter methionine dependency, but compromised tumor growth in vivo We discovered that a lack of the kynurenine-metabolizing enzymes kynurenine monooxygenase and/or kynureninase promotes the accumulation of kynurenine, which triggers immune evasion in glioblastoma cells. In silico analysis of the identified DRMs mapped the activation of key oncogenic kinases that promotes tumorigenesis in glioblastoma. We validated this result by demonstrating that the exogenous addition of DRMs to glioblastoma cells in vitro results in oncogene activation as well as the simultaneous downregulation of Ser/Thr phosphatase PP2A.

CONCLUSIONS

We have connected a four-metabolite signature, implicated in the methionine and kynurenine pathways, to the promotion and maintenance of glioblastoma. Together, our data suggest that these metabolites and their respective metabolic pathways serve as potential therapeutic targets for glioblastoma. Clin Cancer Res; 22(14); 3513-23. ©2016 AACR.

Metabolomics, Lipidomics and Pharmacometabolomics of Human Hypertension

Hypertension is a common but complex human disease, which can lead to a heart attack, stroke, kidney disease or other complications. Since the pathogenesis of hypertension is heterogeneous and multifactorial, it is crucial to establish a comprehensive metabolomic approach to elucidate the molecular mechanism of hypertension. Although there have been limited metabolomic, lipidomic and pharmacometabolomic studies investigating this disease to date, metabolomic studies on hypertension have provided greater insights into the identification of disease-specific biomarkers, predicting treatment outcome and monitor drug safety and efficacy. Therefore, we discuss recent updates on the applications of metabolomics technology in human hypertension with a focus on metabolic biomarker discovery.

Magnetic Resonance Spectroscopy: Principles and Techniques: Lessons for Clinicians

Magnetic resonance spectroscopy (MRS) provides a non-invasive 'window' on biochemical processes within the body. Its use is no longer restricted to the field of research, with applications in clinical practice increasingly common. MRS can be conducted at high magnetic field strengths (typically 11-14 T) on body fluids, cell extracts and tissue samples, with new developments in whole-body magnetic resonance imaging (MRI) allowing clinical MRS at the end of a standard MRI examination, obtaining functional information in addition to anatomical information. We discuss the background physics the busy clinician needs to know before considering using the technique as an investigative tool. Some potential applications of hepatic and cerebral MRS in chronic liver disease are also discussed.

A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry

For the first time it is possible to simultaneously collect targeted and nontargeted metabolomics data from plasma based on GC with high scan speed tandem mass spectrometry (GC-MS/MS). To address the challenge of getting broad metabolome coverage while quantifying known biomarker compounds in high-throughput GC-MS metabolomics, we developed a novel GC-MS/MS metabolomics method using a high scan speed (20 000 Da/second) GC-MS/MS that enables simultaneous data acquisition of both nontargeted full scan and targeted quantitative tandem mass spectrometry data. The combination of these two approaches has hitherto not been demonstrated in metabolomics. This method allows reproducible quantification of at least 37 metabolites using multiple reaction monitoring (MRM) and full mass spectral scan-based detection of 601 reproducible metabolic features from human plasma. The method showed good linearity over normal concentrations in plasma (0.06-343 to 0.86-4800 μM depending on the metabolite) and good intra- and interbatch precision (0.9-16.6 and 2.6-29.6% relative standard deviation). Based on the parameters determined for this method, targeted quantification using MRM can be expanded to cover at least 508 metabolites while still collecting full scan data. The new simultaneous targeted and nontargeted metabolomics method enables more sensitive and accurate detection of predetermined metabolites and biomarkers of interest, while still allowing detection and identification of unknown metabolites. This is the first validated GC-MS/MS metabolomics method with simultaneous full scan and MRM data collection, and clearly demonstrates the utility of GC-MS/MS with high scanning rates for complex analyses.

Systems analysis of host-parasite interactions

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug‐resistant parasites necessitates that the research community take an active role in understanding host–parasite infection biology in order to develop improved therapeutics. Recent advances in next‐generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host–parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high‐throughput ‐omic data will undoubtedly generate extraordinary insight into host–parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host–parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies. WIREs Syst Biol Med 2015, 7:381–400. doi: 10.1002/wsbm.1311

For further resources related to this article, please visit the WIREs website.

Plasma metabonomic profiling of lumbar disc herniation and its traditional Chinese medicine subtypes in patients by using gas chromatography coupled with mass spectrometry

Lumbar disc herniation (LDH) is a commonly occurring disease, threatening human health and life quality. Lack of a gold standard of diagnosis has hindered the efficiency and efficacy of clinical therapy against LDH. Traditional Chinese medicine (TCM) has provided an experience-based but subjective diagnosis system for LDH, demanding objective evidence and explanation. In this study, we adopted a metabonomics approach using gas chromatography-mass spectrometry (GC-MS) to profile metabolic characteristics of LDH and its TCM subtypes. Plasma samples of 41 LDH patients and 25 healthy controls were collected. LDH patients were classified into two main subtypes, the reality syndrome and deficiency syndrome, according to TCM theory. By using multivariate statistical analysis and metabolism network analysis, we found diverse perturbations of metabolites in amino acid metabolism and carbohydrate metabolism, in which the amino acids (glutamic acid, aspartic acid, glycine, etc.) were up-regulated and a key carbohydrate metabolite (glucose 1-phosphate) was down-regulated. Few differences were found between the two TCM subtypes. Our findings reveal the metabolic disorders of LDH for the first time and demonstrate the feasibility of the metabonomics approach for LDH research but not for its TCM subtypes.

Defining the metabolome: size, flux, and regulation

Renewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as "untargeted" or "discovery" metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates.

A UPLC/MS-based metabolomics investigation of the protective effect of ginsenosides Rg1 and Rg2 in mice with Alzheimer's disease

Background

Alzheimer's disease (AD) is a progressive brain disease, for which there is no effective drug therapy at present. Ginsenoside Rg1 (G-Rg1) and G-Rg2 have been reported to alleviate memory deterioration. However, the mechanism of their anti-AD effect has not yet been clearly elucidated.

Methods

Ultra performance liquid chromatography tandem MS (UPLC/MS)-based metabolomics was used to identify metabolites that are differentially expressed in the brains of AD mice with or without ginsenoside treatment. The cognitive function of mice and pathological changes in the brain were also assessed using the Morris water maze (MWM) and immunohistochemistry, respectively.

Results

The impaired cognitive function and increased hippocampal Aβ deposition in AD mice were ameliorated by G-Rg1 and G-Rg2. In addition, a total of 11 potential biomarkers that are associated with the metabolism of lysophosphatidylcholines (LPCs), hypoxanthine, and sphingolipids were identified in the brains of AD mice and their levels were partly restored after treatment with G-Rg1 and G-Rg2. G-Rg1 and G-Rg2 treatment influenced the levels of hypoxanthine, dihydrosphingosine, hexadecasphinganine, LPC C 16:0, and LPC C 18:0 in AD mice. Additionally, G-Rg1 treatment also influenced the levels of phytosphingosine, LPC C 13:0, LPC C 15:0, LPC C 18:1, and LPC C 18:3 in AD mice.

Conclusion

These results indicate that the improvements in cognitive function and morphological changes produced by G-Rg1 and G-Rg2 treatment are caused by regulation of related brain metabolic pathways. This will extend our understanding of the mechanisms involved in the effects of G-Rg1 and G-Rg2 on AD.

Metabolomics in the pharmaceutical industry

Metabolomics has roots in the pharmaceutical industry that go back nearly three decades. Initially focused on applications in toxicology and disease pathology, more recent academic and commercial efforts have helped advance metabolomics as a tool to reveal the molecular basis of biological processes and pharmacological responses to drugs. This article will discuss areas where metabolomic technologies and applications are poised to have the greatest impact in the discovery and development of pharmaceuticals.

Plasma metabolite profiling and chemometric analyses of lung cancer along with three controls through gas chromatography-mass spectrometry

Lung cancer has been the most common death causing cancer in the world for several decades. This study is focused on the metabolite profiling of plasma from lung cancer (LC) patients with three control groups including healthy non-smoker (NS), smokers (S) and chronic obstructive pulmonary disease patients (COPD) samples using gas chromatography-mass spectrometry (GC-MS) in order to identify the comparative and distinguishing metabolite pattern for lung cancer. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) and Fiehn Retention Time Lock (RTL) libraries. Mass Profiler Professional (MPP) Software was used for the alignment and for all the statistical analysis. 32 out of 1,877 aligned metabolites were significantly distinguished among three controls and lung cancer using p-value ≤ 0.001. Partial Least Square Discriminant Analysis (PLSDA) model was generated using statistically significant metabolites which on external validation provide high sensitivity (100%) and specificity (78.6%). Elevated level of fatty acids, glucose and acids were observed in lung cancer in comparison with control groups apparently due to enhanced glycolysis, gluconeogenesis, lipogenesis and acidosis, indicating the metabolic signature for lung cancer.

Enhancing metabolomics research through data mining

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.

BIOLOGICAL SIGNIFICANCE

Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.

Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes

This paper highlights the metabolomic roles in systems biology towards the elucidation of metabolic mechanisms in obesity and type 2 diabetes.

Metabonomic profiling of bladder cancer

Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites, and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical, and therapeutic clinical trials. An introduction to metabonomics, typical metabonomic workflow, and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.

Haystack, a web-based tool for metabolomics research

Background

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species.

Results

To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins.

Conclusion

Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

Aspartic acid in the hippocampus: a biomarker for postoperative cognitive dysfunction

This study established an aged rat model of cognitive dysfunction using anesthesia with 2% isoflurane and 80% oxygen for 2 hours. Twenty-four hours later, Y-maze test results showed that isoflurane significantly impaired cognitive function in aged rats. Gas chromatography-mass spectrometry results showed that isoflurane also significantly increased the levels of N,N-diethylacetamide, n-ethylacetamide, aspartic acid, malic acid and arabinonic acid in the hippocampus of isoflurane-treated rats. Moreover, aspartic acid, N,N-diethylacetamide, n-ethylacetamide and malic acid concentration was positively correlated with the degree of cognitive dysfunction in the isoflurane-treated rats. It is evident that hippocampal metabolite changes are involved in the formation of cognitive dysfunction after isoflurane anesthesia. To further verify these results, this study cultured hippocampal neurons in vitro, which were then treated with aspartic acid (100 μmol/L). Results suggested that aspartic acid concentration in the hippocampus may be a biomarker for predicting the occurrence and disease progress of cognitive dysfunction.

Metabolomics Reveals Inflammatory-Linked Pulmonary Metabolic Alterations in a Murine Model of House Dust Mite-Induced Allergic Asthma

Although the house dust mite (HDM) is a major environmental aeroallergen that promotes the pathogenesis and severity of allergic asthma, it remains elusive if HDM exposures can induce global metabolism aberrations during allergic airway inflammation. Using an integrated gas and liquid chromatography mass spectrometry-based metabolomics and multiplex cytokine profile analysis, metabolic alterations and cytokine changes were investigated in the bronchoalveolar lavage fluid (BALF), serum, and lung tissues in experimental HDM-induced allergic asthma. Allergic pulmonary HDM exposures lead to pronounced eosinophilia, neutrophilia, and increases in inflammatory cytokines. Metabolomics analysis of the BALF, serum, and lung tissues revealed distinctive compartmental metabolic signatures, which included depleted carbohydrates, increased energy metabolites, and consistent losses of sterols and phosphatidylcholines. Pearson correlation analysis uncovered strong associations between specific metabolic alterations and inflammatory cells and cytokines, linking altered pulmonary metabolism to allergic airway inflammation. The clinically prescribed glucocorticoid prednisolone could modulate airway inflammation but was ineffective against the reversal of many HDM-induced metabolic alterations. Collectively, metabolomics reveal comprehensive pulmonary metabolic signatures in HDM-induced allergic asthma, with specific alterations in carbohydrates, lipids, sterols, and energy metabolic pathways. Altered pulmonary metabolism may be a major underlying molecular feature involved during HDM-induced allergic airway inflammation, linked to inflammatory cells and cytokines changes.

Metabolomic analysis of antimicrobial mechanisms of ε-poly-L-lysine on Saccharomyces cerevisiae

ε-Poly-L-lysine (ε-PL), a naturally occurring amino acid homopolymer, has been widely used as a food preservative. However, its antimicrobial mechanism has not been fully understood. This study investigated the antimicrobial mode of action of ε-PL on a yeast, Saccharomyces cerevisiae. When treated with ε-PL at the concentration of 500 μg/mL, cell mortality was close to 100% and the phospholipid bilayer curvature, pores, and micelles on the surface of S. cerevisiae were clearly observed by scanning electron microscopy (SEM). At the level of 200 μg/mL, ε-PL significantly inhibited the cell growth of S. cerevisiae. When treated with 50 μg/mL ε-PL, the yeast cell was able to grow but the cell cycle was prolonged. A significant increase in cell membrane permeability was induced by ε-PL at higher concentrations. Metabolomics analysis revealed that the ε-PL stress led to the inhibition of primary metabolic pathways through the suppression of the tricarboxylic acid cycle and glycolysis. It is therefore proposed that the microbiostatic effect of ε-PL at lower levels on S. cerevisiae is achieved by inducing intracellular metabolic imbalance via disruption of cell membrane functions. Moreover, the results suggested that the antimicrobial mechanism of ε-PL on S. cerevisiae can in fact change from microbiostatic to microbicidal when the concentration of ε-PL increased, and the mechanisms of these two modes of action were completely different.

Approaches to uremia

The development of dialysis was a dramatic step forward in medicine, allowing people who would soon have died because of lack of kidney function to remain alive for years. We have since found, however, that the "artificial kidney" does not live up fully to its name. Dialysis keeps patients alive but not well. Part of the residual illness that dialysis patients experience is caused by retained waste solutes that dialysis does not remove as well as native kidney function does. New means are available to identify these toxic solutes, about which we currently know remarkably little, and knowledge of these solutes would help us to improve therapy. This review summarizes our current knowledge of toxic solutes and highlights methods being explored to identify additional toxic solutes and to enhance the clearance of these solutes to improve patient outcomes.

Exo-metabolome of Pseudovibrio sp. FO-BEG1 analyzed by ultra-high resolution mass spectrometry and the effect of phosphate limitation

Oceanic dissolved organic matter (DOM) is an assemblage of reduced carbon compounds, which results from biotic and abiotic processes. The biotic processes consist in either release or uptake of specific molecules by marine organisms. Heterotrophic bacteria have been mostly considered to influence the DOM composition by preferential uptake of certain compounds. However, they also secrete a variety of molecules depending on physiological state, environmental and growth conditions, but so far the full set of compounds secreted by these bacteria has never been investigated. In this study, we analyzed the exo-metabolome, metabolites secreted into the environment, of the heterotrophic marine bacterium Pseudovibrio sp. FO-BEG1 via ultra-high resolution mass spectrometry, comparing phosphate limited with phosphate surplus growth conditions. Bacteria belonging to the Pseudovibrio genus have been isolated worldwide, mainly from marine invertebrates and were described as metabolically versatile Alphaproteobacteria. We show that the exo-metabolome is unexpectedly large and diverse, consisting of hundreds of compounds that differ by their molecular formulae. It is characterized by a dynamic recycling of molecules, and it is drastically affected by the physiological state of the strain. Moreover, we show that phosphate limitation greatly influences both the amount and the composition of the secreted molecules. By assigning the detected masses to general chemical categories, we observed that under phosphate surplus conditions the secreted molecules were mainly peptides and highly unsaturated compounds. In contrast, under phosphate limitation the composition of the exo-metabolome changed during bacterial growth, showing an increase in highly unsaturated, phenolic, and polyphenolic compounds. Finally, we annotated the detected masses using multiple metabolite databases. These analyses suggested the presence of several masses analogue to masses of known bioactive compounds. However, the annotation was successful only for a minor part of the detected molecules, underlining the current gap in knowledge concerning the biosynthetic ability of marine heterotrophic bacteria.

Salivary microbiota and metabolome associated with celiac disease

This study aimed to investigate the salivary microbiota and metabolome of 13 children with celiac disease (CD) under a gluten-free diet (treated celiac disease [T-CD]). The same number of healthy children (HC) was used as controls. The salivary microbiota was analyzed by an integrated approach using culture-dependent and -independent methods. Metabolome analysis was carried out by gas chromatography-mass spectrometry-solid-phase microextraction. Compared to HC, the number of some cultivable bacterial groups (e.g., total anaerobes) significantly (P < 0.05) differed in the saliva samples of the T-CD children. As shown by community-level catabolic profiles, the highest Shannon's diversity and substrate richness were found in HC. Pyrosequencing data showed the highest richness estimator and diversity index values for HC. Levels of Lachnospiraceae, Gemellaceae, and Streptococcus sanguinis were highest for the T-CD children. Streptococcus thermophilus levels were markedly decreased in T-CD children. The saliva of T-CD children showed the largest amount of Bacteroidetes (e.g., Porphyromonas sp., Porphyromonas endodontalis, and Prevotella nanceiensis), together with the smallest amount of Actinobacteria. T-CD children were also characterized by decreased levels of some Actinomyces species, Atopobium species, and Corynebacterium durum. Rothia mucilaginosa was the only Actinobacteria species found at the highest level in T-CD children. As shown by multivariate statistical analyses, the levels of organic volatile compounds markedly differentiated T-CD children. Some compounds (e.g., ethyl-acetate, nonanal, and 2-hexanone) were found to be associated with T-CD children. Correlations (false discovery rate [FDR], <0.05) were found between the relative abundances of bacteria and some volatile organic compounds (VOCs). The findings of this study indicated that CD is associated with oral dysbiosis that could affect the oral metabolome.

Untargeted screening of secondary metabolites in fungal cultures and samples from mouldy indoor environments by time-of-flight mass spectrometry

Nowadays, complaints about poor indoor air quality have become common. The variety of indoor air health problems include chronic fatigue, allergy, skin and eye irritation, and can be caused by several factors including fungi and their metabolites present in a building. The objective of this study was to establish a method for untargeted analysis of secondary fungal metabolites in indoor environments. As a detection technique, time-of-flight mass spectrometry was chosen, as it provided mass accuracy and higher sensitivity in full scan acquisition mode compared to tandem mass spectrometers. The method was first applied to fungal cultures, namely Penicillium brevicompactum and Chaetomium murorum, which were isolated from mouldy houses and grown on building materials under laboratory conditions for 7-21 days. Following the proposed strategy based on accurate mass measurement and post-acquisition data processing using principal component analysis, roquefortine C, brevianamide A and mycophenolic acid were identified in Penicillium sp., while chaetoglobosin A was found to be produced by Chaetomium sp. Subsequently, samples from mouldy inhabited buildings were analysed using the developed method. The actual presence of meleagrin was demonstrated in mouldy indoor environment. Applying the method to air and dust samples collected in these mouldy buildings, no metabolites were detected possibly due to generally low concentrations in these types of samples.

Development of a Standard Reference Material for metabolomics research

The National Institute of Standards and Technology (NIST), in collaboration with the National Institutes of Health (NIH), has developed a Standard Reference Material (SRM) to support technology development in metabolomics research. SRM 1950 Metabolites in Human Plasma is intended to have metabolite concentrations that are representative of those found in adult human plasma. The plasma used in the preparation of SRM 1950 was collected from both male and female donors, and donor ethnicity targets were selected based upon the ethnic makeup of the U.S. population. Metabolomics research is diverse in terms of both instrumentation and scientific goals. This SRM was designed to apply broadly to the field, not toward specific applications. Therefore, concentrations of approximately 100 analytes, including amino acids, fatty acids, trace elements, vitamins, hormones, selenoproteins, clinical markers, and perfluorinated compounds (PFCs), were determined. Value assignment measurements were performed by NIST and the Centers for Disease Control and Prevention (CDC). SRM 1950 is the first reference material developed specifically for metabolomics research.

Metabolic changes during the pu-erh tea pile-fermentation revealed by a liquid chromatography tandem mass-spectrometry-based metabolomics approach

In the current study, liquid chromatography-mass spectrometry combined with multivariate statistical analyses was employed to investigate the time-varying biochemical changes during the pile-fermentation process with the emphasis on the active ingredients to clarify the manufacturing process of ripened pu-erh tea as a whole. The metabolite profiles of different manufacturing processes were unique and could be distinguished with the aid of principal component analysis. Furthermore, partial least-squares discriminant analysis revealed a pairwise discrimination between the raw material group and pile-fermentation process groups or the final product group, and 48 differential metabolites with variable importance in the projection value greater than 1 were identified, which was confirmed by the subsequent hierarchical cluster analysis. These results highlight our current understanding of the exact changing process of the bioactive compounds during the pile fermentation, and the global change of these bioactive compounds provides the special flavor, taste, and health promoting effects of ripened pu-erh tea.

Identification of intracellular Spiroplasma melliferum metabolites by the HPLC-MS method

In contrast to the abundance of systems-oriented approaches describing changes on the transcriptome or proteome level, relatively few studies have employed the metabolome. The goal of the presented research was to identify as many intracellular metabolites as possible in a Spiroplasma melliferum extract by flow injection time-of-flight mass spectrometry. The Mollicutes class bacterium S. melliferum is a member of a unique category of bacteria that have in common the absence of a cell wall, a reduced genome, and simplified metabolic pathways. Metabolite identification was confirmed by fragmentation of previously detected ions by target mass spectrometry. The selected liquid chromatography approach, hydrophilic interaction chromatography with amino and silica columns, effectively separates highly polar cellular metabolites prior to their detection on a high accuracy mass spectrometer in positive and negative acquisition mode for each column. Here we present reliable measurement of 76 metabolites, including components of sugar, amino acid, and nucleotide metabolism. We have identified about a third of the possible intracellular S. melliferum metabolites predicted by genome annotation.

Combination of injection volume calibration by creatinine and MS signals' normalization to overcome urine variability in LC-MS-based metabolomics studies

It is essential to choose one preprocessing method for liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.

Metabolomic analyses of faeces reveals malabsorption in cirrhotic patients

BACKGROUND

The study of faeces offers a unique opportunity to observe cooperation between the microbiome and the metabolism of mammalian hosts, an essential element in the study of the human metabolome. In the present study, a global metabolomics approach was used to identify metabolites differentially excreted in the faeces of cirrhotic patients compared to controls.

METHODS

Seventeen cirrhotic patients and 24 healthy individuals were recruited. Faecal metabolites were detected through non-targeted reversed-phase ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry.

RESULTS

A total of 9215 peaks were detected. Using unequal variance t-tests, 2393 peaks were observed with P≤0.05, approximately 74.0% of which were due to decreased faecal metabolite concentrations in liver cirrhosis vs. healthy controls. Integrating multivariate data analyses, we identified six major groups of metabolites. Relative levels of identified metabolites were as follows: strong increase in lysophosphatidylcholines, aromatic amino acids, fatty acids, and acylcarnitines, and a dramatic decrease in bile acids and bile pigments.

CONCLUSION

With severe hepatic injury in patients with liver cirrhosis, malabsorption occurs along with disorders of fatty acid metabolism, potentially due to changes in gut microflora.

Identification of drug targets by chemogenomic and metabolomic profiling in yeast

OBJECTIVE

To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets.

BASIC METHODS

We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found.

RESULTS AND CONCLUSION

The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets.

Urinary metabolomic markers of aminoglycoside nephrotoxicity in newborn rats

BACKGROUND

Aminoglycoside exposure is a common cause of acute kidney injury (AKI). Delay in the diagnosis of AKI using conventional biomarkers has been one of the important obstacles in applying early effective interventions. We tested the hypothesis that urinary metabolomics could identify novel early biomarkers for toxic renal injury.

METHODS

Three-day-old rats were divided into three groups; they received a single daily injection of vehicle (0.9% NaCl solution) or gentamicin at a dose of 10 or 20 mg/kg/d for 7 d. Urine and blood were collected after 3 and 7 d of injections. Urinary metabolites were evaluated using high-performance liquid chromatography and gas chromatography/mass spectrometry.

RESULTS

A distinct urinary metabolic profile characterized by glucosuria, phosphaturia, and aminoaciduria was identified preceding changes in serum creatinine. At both the gentamicin doses, urinary tryptophan was significantly (P < 0.05) increased (fold change: 1.91 and 2.31 after 3 d; 1.81 and 1.93 after 7 d). Similarly, kynurenic acid, a tryptophan metabolite, showed a significant (P < 0.05) decrease (fold change: 0.26 and 0.24 after 3 d; 0.21 and 0.52 after 7 d), suggesting an interruption of the normal tryptophan metabolism pathway.

CONCLUSION

We conclude that urinary metabolomic profiling provides a robust approach for identifying early and novel markers of gentamicin-induced AKI.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Metabolic profiling of plasma from cardiac surgical patients concurrently administered with tranexamic acid: DI-SPME-LC-MS analysis

A metabolic profile of plasma samples from patients undergoing heart surgery with the use of cardiopulmonary bypass (CPB) and concurrent administration of tranexamic acid was determined. Direct immersion solid phase microextraction (DI-SPME), a new sampling and sample preparation tool for metabolomics, was used in this study for the first time to investigate clinical samples. The results showed alteration of diverse compounds involved in different biochemical pathways. The most significant contribution in changes induced by surgery and applied pharmacotherapy was noticed in metabolic profile of lysophospholipids, triacylglycerols, mediators of platelet aggregation, and linoleic acid metabolites. Two cases of individual response to treatment were also reported.

Pharmacometabolomic approach to predict QT prolongation in guinea pigs

Drug-induced torsades de pointes (TdP), a life-threatening arrhythmia associated with prolongation of the QT interval, has been a significant reason for withdrawal of several medicines from the market. Prolongation of the QT interval is considered as the best biomarker for predicting the torsadogenic risk of a new chemical entity. Because of the difficulty assessing the risk for TdP during drug development, we evaluated the metabolic phenotype for predicting QT prolongation induced by sparfloxacin, and elucidated the metabolic pathway related to the QT prolongation. We performed electrocardiography analysis and liquid chromatography-mass spectroscopy-based metabolic profiling of plasma samples obtained from 15 guinea pigs after administration of sparfloxacin at doses of 33.3, 100, and 300 mg/kg. Principal component analysis and partial least squares modelling were conducted to select the metabolites that substantially contributed to the prediction of QT prolongation. QTc increased significantly with increasing dose (r = 0.93). From the PLS analysis, the key metabolites that showed the highest variable importance in the projection values (>1.5) were selected, identified, and used to determine the metabolic network. In particular, cytidine-5'-diphosphate (CDP), deoxycorticosterone, L-aspartic acid and stearic acid were found to be final metabolomic phenotypes for the prediction of QT prolongation. Metabolomic phenotypes for predicting drug-induced QT prolongation of sparfloxacin were developed and can be applied to cardiac toxicity screening of other drugs. In addition, this integrative pharmacometabolomic approach would serve as a good tool for predicting pharmacodynamic or toxicological effects caused by changes in dose.

Early prediction of macrosomia based on an analysis of second trimester amniotic fluid by capillary electrophoresis

AIM

To identify, using capillary electrophoresis and chemometrics, early biomarkers in human amniotic fluid of large-for-gestational-age (LGA) infants.

MATERIALS & METHODS

Second trimester amniotic fluid samples, obtained from mothers undergoing age-related amniocentesis, were analyzed by capillary electrophoresis. Electropherogram data were aligned using correlation-optimized warping. A genetic algorithm using a Bayesian evaluation function and a leave-one-out cross-validation strategy for two birth outcomes: appropriate-for-gestational-age (AGA) versus LGA infants.

RESULTS

LGA (n = 23) was differentiated from AGA (n = 86) with a sensitivity of 100% and a specificity of 98% using only two wavelets. The first wavelet is associated with albumin and the second wavelet with an unknown small molecule.

CONCLUSION

The approach developed herein allows LGA fetuses to be metabolically distinguished from AGA fetuses early in pregnancy and indicates that the birth of a LGA infant is already associated with an altered biochemical profile by the second trimester.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

LC-MS-based Metabolomics of Xenobiotic-induced Toxicities

Xenobiotic exposure, especially high-dose or repeated exposure of xenobiotics, can elicit detrimental effects on biological systems through diverse mechanisms. Changes in metabolic systems, including formation of reactive metabolites and disruption of endogenous metabolism, are not only the common consequences of toxic xenobiotic exposure, but in many cases are the major causes behind development of xenobiotic-induced toxicities (XIT). Therefore, examining the metabolic events associated with XIT generates mechanistic insights into the initiation and progression of XIT, and provides guidance for prevention and treatment. Traditional bioanalytical platforms that target only a few suspected metabolites are capable of validating the expected outcomes of xenobiotic exposure. However, these approaches lack the capacity to define global changes and to identify unexpected events in the metabolic system. Recent developments in high-throughput metabolomics have dramatically expanded the scope and potential of metabolite analysis. Among all analytical techniques adopted for metabolomics, liquid chromatography-mass spectrometry (LC-MS) has been most widely used for metabolomic investigations of XIT due to its versatility and sensitivity in metabolite analysis. In this review, technical platform of LC-MS-based metabolomics, including experimental model, sample preparation, instrumentation, and data analysis, are discussed. Applications of LC-MS-based metabolomics in exploratory and hypothesis-driven investigations of XIT are illustrated by case studies of xenobiotic metabolism and endogenous metabolism associated with xenobiotic exposure.

Profiling the metabolic signature of senescence

Aging is a complex process, which involves changes in different cellular functions that all can be integrated on the metabolite level. This means that different gene regulation pathways that affect aging might lead to similar changes in metabolism and result in a metabolic signature of senescence. In this chapter, we describe how to establish a metabolic signature of senescence by analyzing the metabolome of various longevity mutants of the model organism Caenorhabditis elegans using gas chromatography-mass spectrometry (GC-MS). Since longevity-associated genes exist for other model organisms and humans, this analysis could be universally applied to body fluids or whole tissue samples for studing the relationship between senescence and metabolism.

Biomarkers of primary dysmenorrhea and herbal formula intervention: an exploratory metabonomics study of blood plasma and urine

Primary dysmenorrhea (PDM), a common clinical endocrine disorder affecting young women, is associated with endocrinopathy and metabolic abnormalities. Although some physiological and pathological function parameters have been investigated, little information about the changes of small metabolites in biofluids has been reported, which may cause poor diagnosis and treatment for PDM. The Xiang-Fu-Si-Wu Formula (XFSWF) is a Chinese herbal formula used to treat PDM for hundreds of years. The aim of this study was to establish the metabolic profile of PDM and investigate the action mechanism of XFSWF effect. In this cross-sectional study of 25 patients with PDM and 12 healthy controls, contents of small molecular endogenous metabolites in blood plasma and urine samples were measured by ultra performance liquid chromatography (UPLC) coupled with quadrupole-time-of-flight mass spectrometry (QTOF/MS) and triple quadrupole mass spectrometry (QqQ/MS) based techniques and analyzed by multivariate statistical methods. The levels of LPCs including lypso (16 : 1), lysoPC(20 : 4), lysoPC(18 : 2), lysoPC(16 : 0), lysoPC(18 : 1), lysoPC(10 : 1), estrone, 17-hydroxyprogesterone, myristoylglycine and palmitoylglycine increased significantly (p < 0.05) in PDM, while the levels of phytosphingosine, dihydrocortisol and sphingosine decreased significantly (p < 0.05) compared with the healthy controls. These significant perturbations are involved in glycerophospholipid metabolism and sphingolipid metabolism, as well as steroid hormone biosynthesis. The metabolic deviations recovered to the normal level after XFSWF intervention. The results demonstrated that biofluids metabonomics was a powerful tool in clinical diagnosis and treatment of PDM for providing information on changes in metabolites and neural, endocrinal and immune pathways. XFSWF can be used for the treatment of PDM cases, especially for those adolescents who do not desire a contraceptive method, to reduce the risk of secondary dysmenorrhea.

Rapid and simultaneous determination of twenty amino acids in complex biological and food samples by solid-phase microextraction and gas chromatography-mass spectrometry with the aid of experimental design after ethyl chloroformate derivatization

Amino acids play a vital role as intermediates in many important metabolic pathways such as the biosynthesis of nucleotides, vitamins and secondary metabolites. A sensitive and rapid analytical method has been proposed for the first time for the simultaneous determination of twenty amino acids using solid-phase microextraction (SPME). The protein samples were hydrolyzed by 6M HCl under microwave radiation for 120 min. Then the amino acids were derivatized by ethyl chloroformate (ECF) and the ethoxy carbonyl ethyl esters of amino acids formed were extracted using SPME by direct immersion. Finally the extracted analytes on the SPME fiber were desorbed at 260°C and analyzed by gas chromatography-mass spectrometer (GC-MS) in electron ionization mode. Factors which affect the SPME efficiency were screened by Plackett-Burmann design; most significant factors were optimized with response surface methodology. The optimum conditions for SPME are as follows: pH of 1.7, ionic strength of 733 mg, extraction time of 30 min and fiber of divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). The recovery of all the amino acids was found to be in the range of 89.17-100.98%. The limit of detection (LOD) of all derivatized amino acids in urine, hair and soybean was found to be in the range of 0.20-7.52 μg L(-1), 0.21-8.40 μg L(-1) and 0.18-5.62 μg L(-1), respectively. Finally, the proposed technique was successfully applied for the determination of amino acids in complex biological (hair, urine) and food samples (soybean). The method can find wide applications in the routine analysis of amino acids in any biological as well as food samples.