New insights into cardiovascular and lipid metabolomics

Metabolomics is the study of metabolite profiles in biological samples, particularly urine, saliva, blood plasma and fat biopsies. The metabolome is now considered by some to be the most predictive phenotype: consequently, the comprehensive and quantitative study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets and enabling appropriate treatments. A wealth of information about metabolites has been accumulated with global profiling tools and several candidate technologies for metabolomic studies are now available. Many high-throughput metabolomics methodologies are currently under development and have yet to be applied in clinical practice on a routine basis. In the cardiovascular field, few recent metabolomic studies have been reported so far. This minireview provides an updated overview of alternative technical approaches for metabolomics studies and reviews initial applications of metabolomics that relate to both cardiovascular disease and lipid metabolism research.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

荷瘤抑郁样模型小鼠的血清代谢组学研究

目的：从代谢组学角度开展肿瘤共病抑郁的研究,将在复杂疾病发生发展机制以及药物治疗新靶点方面做出有益探索。本实验采用代谢组学方法检测荷瘤抑郁样模型小鼠血清小分子代谢物的变化及抗抑郁药氟西汀的影响,探讨代谢组学在肿瘤共病抑郁研究中的应用。方法：制备移植性荷瘤小鼠模型,氟西汀处理组对荷瘤小鼠连续28d灌胃给药,观察各组行为学反应。采用液相色谱串联四级杆飞行时间质谱（LC—QToF／MS）获取荷瘤小鼠与正常小鼠的血清代谢轮廓,并用正交信号校正的偏最小二乘法（OPLS）进行多元统计分析,结合单维水平筛选结果,得到荷瘤小鼠区别于正常对照小鼠的特征性差异代谢物,并观察氟西汀对上述代谢物的影响。结果：行为学反应结果显示,与正常小鼠相比,荷瘤小鼠表现抑郁相关性行为改变,氟西汀对荷瘤引起的行为学变化有显著改善作用。代谢组学分析结果显示,荷瘤小鼠血清中乙酰肉碱和油酰胺的含量较正常小鼠显著降低,氟西汀处理后可增加荷瘤小鼠血清中的乙酰肉碱和油酰胺的含量。结论：基于代谢组学分析得到的特征性代谢产物的下调可能与荷瘤小鼠的抑郁样状态有关,氟西汀对这两种潜在生物标志物有明显的调节作用。代谢组学研究为疾病特异性生物标志物的筛选及药物药效的评价提供了新的思路与方法。     

A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice

Defining the functional status of host-associated microbial ecosystems has proven challenging owing to the vast number of predicted genes within the microbiome and relatively poor understanding of community dynamics and community–host interaction. Metabolomic approaches, in which a large number of small molecule metabolites can be defined in a biological sample, offer a promising avenue to ‘fingerprint'' microbiota functional status. Here, we examined the effects of the human gut microbiota on the fecal and urinary metabolome of a humanized (HUM) mouse using an optimized ultra performance liquid chromatography–mass spectrometry-based method. Differences between HUM and conventional mouse urine and fecal metabolomic profiles support host-specific aspects of the microbiota''s metabolomic contribution, consistent with distinct microbial compositions. Comparison of microbiota composition and metabolome of mice humanized with different human donors revealed that the vast majority of metabolomic features observed in donor samples are produced in the corresponding HUM mice, and individual-specific features suggest ‘personalized'' aspects of functionality can be reconstituted in mice. Feeding the mice a defined, custom diet resulted in modification of the metabolite signatures, illustrating that host diet provides an avenue for altering gut microbiota functionality, which in turn can be monitored via metabolomics. Using a defined model microbiota consisting of one or two species, we show that simplified communities can drive major changes in the host metabolomic profile. Our results demonstrate that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.     

Validation of Metabolic Alterations in Microscale Cell Culture Lysates Using Hydrophilic Interaction Liquid Chromatography (HILIC)-Tandem Mass Spectrometry-Based Metabolomics

By standard convention, in order to increase the efficacy of metabolite detection from cell culture lysates, metabolite extracts from a large quantity of cells are utilized for multiple reaction monitoring-based metabolomic studies. Metabolomics from a small number of cell extracts offers a potential economical alternative to increased cell numbers, in turn increasing the utility of cell culture-based metabolomics. However, the effect of reduced cell numbers on targeted metabolomic profiling is relatively unstudied. Considering the limited knowledge available of the feasibility and accuracy of microscale cell culture metabolomics, the present study analyzes differences in metabolomic profiles of different cell numbers of three pancreatic cancer cell lines. Specifically, it examines the effects of reduced cell numbers on metabolite profiles by obtaining extracts either directly from microscale culture plates or through serial dilution of increased numbers of cellular metabolite extracts. Our results indicate reduced cell numbers only modestly affect the number of metabolites detected (93% of metabolites detected in cell numbers as low as 10⁴ cells and 97% for 10⁵ cells), independent of the method used to obtain the cells. However, metabolite peak intensities were differentially affected by the reduced cell numbers, with some peak intensities inversely proportional to the cell numbers. To help eliminate such potential inverse relationships, peak intensities for increased cell numbers were excluded from the comparative analysis. Overall, metabolite profiles from microscale culture plates were observed to differ from the serial dilution samples, which may be attributable to the medium-to-cell-number ratios. Finally, findings identify perturbations in metabolomic profiling for cellular extracts from reduced cell numbers, which offer future applications in microscale metabolomic evaluations.     

Identification of novel toxicity-associated metabolites by metabolomics and mass isotopomer analysis of acetaminophen metabolism in wild-type and Cyp2e1-null mice

CYP2E1 is recognized as the most important enzyme for initiation of acetaminophen (APAP)-induced toxicity. In this study, the resistance of Cyp2e1-null mice to APAP treatment was confirmed by comparing serum aminotransferase activities and blood urea nitrogen levels in wild-type and Cyp2e1-null mice. However, unexpectedly, profiling of major known APAP metabolites in urine and serum revealed that the contribution of CYP2E1 to APAP metabolism decreased with increasing APAP doses administered. Measurement of hepatic glutathione and hydrogen peroxide levels exposed the importance of oxidative stress in determining the consequence of APAP overdose. Subsequent metabolomic analysis was capable of constructing a principal components analysis (PCA) model that delineated a relationship between urinary metabolomes and the responses to APAP treatment. Urinary ions high in wild-type mice treated with 400 mg/kg APAP were elucidated as 3-methoxy-APAP glucuronide (VII) and three novel APAP metabolites, including S-(5-acetylamino-2-hydroxyphenyl)mercaptopyruvic acid (VI, formed by a Cys-APAP transamination reaction in kidney), 3,3'-biacetaminophen (VIII, an APAP dimer), and a benzothiazine compound (IX, originated from deacetylated APAP), through mass isotopomer analysis, accurate mass measurement, tandem mass spectrometry fragmentation, in vitro reactions, and chemical treatments. Dose-, time-, and genotype-dependent appearance of these minor APAP metabolites implied their association with the APAP-induced toxicity and potential biomarker application. Overall, the oxidative stress elicited by CYP2E1-mediated APAP metabolism might significantly contribute to APAP-induced toxicity. The combination of genetically modified animal models, mass isotopomer analysis, and metabolomics provides a powerful and efficient technical platform to characterize APAP-induced toxicity through identifying novel biomarkers and unraveling novel mechanisms.     

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.     

Metabolomic analysis of bronchoalveolar lavage fluid from cystic fibrosis patients

Metabolite profiles of bronchoalveolar lavage fluid (BALF) from pediatric patients with cystic fibrosis (CF) were correlated to the degree of airway inflammation using nuclear magnetic resonance spectroscopy-based metabolomics. BALF was collected from 11 children with CF during clinically indicated bronchoscopy. The spectra from BALF with high levels of neutrophilic airway inflammation displayed signals from numerous metabolites, whereas the spectra from subjects with low levels of inflammation were very sparse. The metabolites identified in samples taken from subjects with high inflammation include known markers of inflammation such as amino acids and lactate, as well as many novel signals. Statistical analysis highlighted the most important metabolites that distinguished the high- from the low-inflammation groups. This first demonstration of metabolomics of human BALF shows that clear distinctions in the metabolic profiles can be observed between subjects experiencing high versus low inflammation and is a first step toward the goal of discovering novel biomarkers of airway inflammation.     

The biological interpretation of metabolomic data can be misled by the extraction method used

The field of metabolomics is getting more and more popular and a wide range of different sample preparation procedures are in use by different laboratories. Chemical extraction methods using one or more organic solvents as the extraction agent are the most commonly used approach to extract intracellular metabolites and generate metabolite profiles. Metabolite profiles are the scaffold supporting the biological interpretation in metabolomics. Therefore, we aimed to address the following fundamental question: can we obtain similar metabolomic results and, consequently, reach the same biological interpretation by using different protocols for extraction of intracellular metabolites? We have used four different methods for extraction of intracellular metabolites using four different microbial cell types (Gram negative bacterium, Gram positive bacterium, yeast, and a filamentous fungus). All the quenched samples were pooled together before extraction, and, therefore, they were identical. After extraction and GC?CMS analysis of metabolites, we did not only detect different numbers of compounds depending on the extraction method used and regardless of the cell type tested, but we also obtained distinct metabolite levels for the compounds commonly detected by all methods (P-value?<?0.001). These differences between methods resulted in contradictory biological interpretation regarding the activity of different metabolic pathways. Therefore, our results show that different solvent-based extraction methods can yield significantly different metabolite profiles, which impact substantially in the biological interpretation of metabolomics data. Thus, development of alternative extraction protocols and, most importantly, standardization of sample preparation methods for metabolomics should be seriously pursued by the scientific community.     

Metabolomic Profiles Are Gender,Disease and Time Specific in the Interleukin-10 Gene-Deficient Mouse Model of Inflammatory Bowel Disease

Metabolomic profiling can be used to study disease-induced changes in inflammatory bowel diseases (IBD). The aim of this study was to investigate the difference in the metabolomic profile of males and females as they developed IBD. Using the IL-10 gene-deficient mouse model of IBD and wild-type mice, urine at age 4, 6, 8, 12, 16, and 20 weeks was collected and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Multivariate data analysis was employed to assess differences in metabolomic profiles that occurred as a consequence of IBD development and severity (at week 20). These changes were contrasted to those that occurred as a consequence of gender. Our results demonstrate that both IL-10 gene-deficient and wild-type mice exhibit gender-related changes in urinary metabolomic profile over time. Some male-female separating metabolites are common to both IL-10 gene-deficient and control wild-type mice and, therefore, appear to be related predominantly to gender maturation. In addition, we were able to identify gender-separating metabolites that are unique for IL-10 gene-deficient and wild-type mice and, therefore, may be indicative of a gender-specific involvement in the development and severity of the intestinal inflammation. The comparison of the gender-separating metabolomic profile from IL-10 gene-deficient mice and wild-type mice during the development of IBD allowed us to identify changes in profile patterns that appear to be imperative in the development of intestinal inflammation, but yet central to gender-related differences in IBD development. The knowledge of metabolomic profile differences by gender and by disease severity has potential clinical implications in the design of both biomarkers of disease as well as the development of optimal therapies.     

Metabolomics in the fields of oncology: a review of recent research

The study of all endogenously produced metabolites, known as metabolomics, is the youngest of the "omics" sciences. It is becoming increasingly clear that, of all of the "omics" techniques, metabolomic approaches will become increasingly useful in disease diagnosis and have potential power to improve our understanding of the underlying mechanisms of cancer. The primary aim of the review is to discuss the relationship between metabolomics and tumors are elucidated in detail. Then the review is also to introduce the technologies of metabolomics, especially emphasizing the application of metabolomics in the fields of oncology.     

Metabolomic approach by 1H NMR spectroscopy of serum for the assessment of chronic liver failure in patients with cirrhosis

Assessment of chronic liver failure (CLF) in cirrhotic patients is needed to make therapeutic decisions. A biological score is usually performed, using the Model for End-Stage Liver Disease (MELD), to evaluate CLF. Nevertheless, MELD does not take into account metabolic perturbations produced by liver-function impairment. In contrast, metabolomics can investigate many metabolic perturbations within biological systems. The purpose of this study was to assess whether metabolomic profiles of serum, obtained by proton NMR spectroscopy from cirrhotic patients, are affected by the severity of CLF. An orthogonal projection to latent-structure analysis was performed to compare MELD scores and NMR spectra of 124 patients with cirrhosis. The statistical model obtained showed a good explained variance (R(2)X = 0.87 and R(2)Y = 0.86) and a good predictability (Q(2)Y = 0.64). Metabolomic profiles showed significant differences regarding various metabolites depending of severity of CLF: levels of high-density lipoprotein and phosphocholine resonances were significantly higher in patients with mild CLF compared to severe CLF. Other metabolites such as lactate, pyruvate, glucose, amino acids, and creatinine were significantly higher in patients with severe CLF than mild CLF. Our conclusion is that metabolomic NMR analysis provides new insights into metabolic processes related to the severity of hepatic function impairment in cirrhosis.     

Metabolomics as a diagnostic tool for hepatology: validation in a naturally occurring canine model

Human hepatopathies are a diagnostic challenge, with many distinct diseases having similar clinical signs and laboratory findings. Naturally occurring canine hepatic disease provides an excellent model for human diseases and similar diagnostic dilemmas exist; differentiating canine congenital portosystemic vascular anomalies (PVA) from acquired hepatopathies is difficult and traditionally requires invasive diagnostic procedures. The emerging post-genomic science of metabolomics is concerned with detecting global changes of populations of endogenous low molecular weight metabolites in biological samples and offers the possibility of identifying surrogate profiles of disease. Metabolomics couples sensitive metabolite analysis with sophisticated pattern recognition techniques. In this study, a metabolomic strategy has been employed to assess metabolite changes in the plasma of dogs with congenital PVA and acquired hepatic disease. Plasma samples were collected from 25 dogs, comprising 9 dogs with congenital PVA, 6 with acquired hepatopathy and 10 with non-hepatic disorders. Low molecular weight metabolites were analyzed by liquid chromatography-mass spectrometry (LC-MS). Following identification of metabolites, multivariate data analysis was used to compare profiles amongst groups. The analysis demonstrated significant disturbances in the plasma bile acid and phospholipid profiles of dogs with portovascular anomalies. In contrast to traditional laboratory parameters, the metabolomic strategy was able to produce a clear segregation between all three study groups. In conclusion, this study demonstrates the potential of metabolomics as a diagnostic tool for naturally occurring hepatic disease. With further validation, this approach will improve diagnostic capabilities, provide an insight into pathogenetic mechanisms, and ultimately inform therapeutic decision making in clinical hepatology. This work was supported in part by grants from The Royal Society, Petplan Charitable Trust and The Waltham Foundation.     

Characteristic gut microbiota and metabolic changes in patients with pulmonary tuberculosis

Intestinal flora provides an important contribution to the development of pulmonary tuberculosis (PTB). We performed a cross-sectional study in 52 healthy controls (HCs) and 83 patients with untreated active PTB to assess the differences in their microbiomic and metabolic profiles in faeces via V3-V4 16S rRNA gene sequencing and gas chromatography–mass spectrometry. Patients with PTB had considerable reductions in phylogenetic alpha diversity and the production of short-chain fatty acids, dysbiosis of the intestinal flora and alterations in the faecal metabolomics composition compared with HCs. Significant alterations in faecal metabolites were associated with changes in the relative abundance of specific genera. Our study describes the imbalance of the gut microbiota and altered faecal metabolomics profiles in patients with PTB; the results indicate that the gut microbiota and faecal metabolomic profiles can be used as potential preventive and therapeutic targets for PTB.     

Plasma Metabolomics in Human Pulmonary Tuberculosis Disease: A Pilot Study

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution.     

Fishy Aroma of Social Status: Urinary Chemo-Signalling of Territoriality in Male Fathead Minnows (Pimephales promelas)

Chemical structures of several urinary reproductive pheromones in fish have been identified, and their role in the chemical communication of reproductive condition is well characterized. On the contrary, the role of chemical communication in signalling of social/territorial status in fish is poorly understood. Fathead minnows are an example of a fish species whose life history traits appear conducive to evolution of chemical communication systems that confer information about social/territorial status. Male reproduction in this species is dependent upon their ability to acquire and defend a high quality nesting territory, and to attract a female to the nest. We hypothesized that fathead minnow males use visual and urine-derived chemical cues to signal territorial status. To test this hypothesis, effects of territorial acquisition on male-specific secondary sex characteristics (SSCs) and urine volumes were first assessed. Second, frequencies of male urination in varying social contexts were examined. Finally, nuclear magnetic resonance-based metabolomics was used to identify urinary metabolites that were differentially excreted in the urine of territorial versus non-territorial males. The expression of SSCs, sperm, and urine volumes increased with territory acquisition, and either remained unchanged or decreased in non-territorial males. Frequency of male urination increased significantly in the presence of females (but not males), suggesting that females are the main target of the urinary signals. Territorial and non-territorial males had distinct urinary metabolomic profiles. An unforeseen finding was that one could discern future territorial status of males, based on their initial metabolomic profiles. Bile acids and volatile amines were identified as potential chemical signals of social status in the fathead minnow. The finding that trimethylamine (a fishy smelling volatile amine) may be a social cue is particularly interesting, because it is known to bind trace amine-associated receptors, indicating that these receptors may play role in chemical signalling of social status in fish.     

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.