NMR-based metabolomics of urine for the atherosclerotic mouse model using apolipoprotein-E deficient mice

Cross-comparative metabolomics reveal sex-age specific metabolic fingerprints and metabolic interactions in acute myocardial infarction

The elucidation of metabolic perturbations and gender-age-specific metabolic characteristics associated with acute myocardial infarction (AMI) is essential for clinical risk stratification and disease management. A comprehensive cross-comparative metabolomics analysis was performed on the sera from 445 healthy controls, 347 AMI patients without cardiovascular disease (CVD), 79 AMI with CVD (AMICVD) patients including 27 deaths. Machine-learning-based integrated biomarker profiling and global network analysis were used to create a multi-biomarker for distinguishing the different AMI outcomes. The changes of most metabolites were dependent on AMI, but gender and age also give additional contributions to the changes of histidine, malonate, O-acetyl-glycoprotein and trimethylamine N-oxide. The altered metabolic pathways included gut dysbiosis, increased amino acid metabolism, glucose metabolism and ketone metabolism, and inactivation of tricarboxylic acid cycle. Enhanced histidine metabolism and microbiota dysbiosis may be one of the key factors during the developing of AMI into AMICVD. For the differential diagnosis of AMI events, three sets of specific multi-biomarkers provided relatively high accuracy with the areas under the curve more than 0.8 and hazard ratio more than 1 in the discovery set, and the results were reproduced and confirmed by the validation set. First use of cross-comparative metabolomics and machine-learning-based integrated biomarker analysis gives great capability to discriminate the different AMI outcomes. Also, the multi-biomarkers seem to be a valid and accurate auxiliary diagnosis biomarker in addition to standard stratification based on clinical parameters.

Metabolomic Profile in Venous Thromboembolism (VTE)

Venous thromboembolism (VTE) is a condition comprising deep venous thrombosis (DVT) and pulmonary embolism (PE). The prevalence of this disease is constantly increasing and it is also a chief reason for morbidity. Therefore, the primary prevention of VTE remains a highly important public health issue. At present, its diagnosis generally relies on subjective clinical examination and ultrasound imaging. D-dimer is also used as a biomarker, but it is considered to be poorly specific and only moderately sensitive. There are also no reliable methods that could accurately guide the type of treatment and potentially identify patients who may benefit from more aggressive therapies without the risk of bleeding. The application of metabolomics profiling in the area of vascular diseases may become a turning point in early diagnosis and patient management. Among the most described metabolites possibly related to VTE are carnitine species, glucose, phenylalanine, 3-hydroxybutarate, lactic acid, tryptophan and some monounsaturated and polyunsaturated fatty acids. The cell response to acute PE was suggested to involve the uncoupling between glycolysis and oxidative phosphorylation. Despite technological advancement in the identification of metabolites and their alteration in thrombosis, we still do not understand the mechanisms and pathways responsible for the occurrence of observed alterations.

A UPLC-Q-TOF/MS-Based Metabolomics Study on the Effect of Corallodiscus flabellatus (Craib) B. L. Burtt Extract on Alzheimer's Disease

A UPLC-Q-TOF/MS-based metabolomics study was carried out to explore the intervening mechanism of Corallodiscus flabellatus (Craib) B. L. Burtt (CF) extract on Alzheimer's disease (AD). The AD model group consisted of senescence-accelerated mouse prone 8 (SAMP8) mice, and the control group consisted of senescence-accelerated mouse resistant 1 (SAMR1) mice. UPLC-Q-TOF/MS detection, multivariate statistical analysis, and pathway enrichment were jointly performed to research the change in metabolite profiling in the urine of AD mice. The result suggested that the metabolite profiling of SAMP8 mice significantly changed at the sixth month compared with SAMR1 mice of the same age, and the principal component analysis (PCA) score scatter plots of the CF group closely resembled those of the control and positive drug (huperzine A, HA) group. A total of 28 metabolites were considered potential biomarkers associated with the metabolism of beta-alanine, glycine, serine, threonine, cysteine, methionine, arginine, proline, and purines in AD mice. Furthermore, the CF group was clustered with the control and positive group and was clearly separated from the model group in the heat map. In conclusion, significant anti-AD effects were firstly observed in mice after treatment with the CF extract, and the urinary metabolomics approach assisted with dissecting the underlying mechanism.

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

Strategy for NMR metabolomic analysis of urine in mouse models of obesity--from sample collection to interpretation of acquired data

The mouse model of monosodium glutamate induced obesity was used to examine and consequently optimize the strategy for analysis of urine samples by NMR spectroscopy. A set of nineteen easily detectable metabolites typical in obesity-related studies was selected. The impact of urine collection protocol, choice of (1)H NMR pulse sequence, and finally the impact of the normalization method on the detected concentration of selected metabolites were investigated. We demonstrated the crucial effect of food intake and diurnal rhythms resulting in the choice of a 24-hour fasting collection protocol as the most convenient for tracking obesity-induced increased sensitivity to fasting. It was shown that the Carr-Purcell-Meiboom-Gill (CPMG) experiment is a better alternative to one-dimensional nuclear Overhauser enhancement spectroscopy (1D-NOESY) for NMR analysis of mouse urine due to its ability to filter undesirable signals of proteins naturally present in rodent urine. Normalization to total spectral area provided comparable outcomes as did normalization to creatinine or probabilistic quotient normalization in the CPMG-based model. The optimized approach was found to be beneficial mainly for low abundant metabolites rarely monitored due to their overlap by strong protein signals.

Metabolomic analyses for atherosclerosis, diabetes, and obesity

Insulin resistance associated with type 2 diabetes mellitus (T2DM), obesity, and atherosclerosis is a global health problem. A portfolio of abnormalities of metabolic and vascular homeostasis accompanies T2DM and obesity, which are believed to conspire to lead to accelerated atherosclerosis and premature death. The complexity of metabolic changes in the diseases presents challenges for a full understanding of the molecular pathways contributing to the development of these diseases. The recent advent of new technologies in this area termed “Metabolomics” may aid in comprehensive metabolic analysis of these diseases. Therefore, metabolomics has been extensively applied to the metabolites of T2DM, obesity, and atherosclerosis not only for the assessment of disease development and prognosis, but also for the biomarker discovery of disease diagnosis. Herein, we summarize the recent applications of metabolomics technology and the generated datasets in the metabolic profiling of these diseases, in particular, the applications of these technologies to these diseases at the cellular, animal models, and human disease levels. In addition, we also extensively discuss the mechanisms linking the metabolic profiling in insulin resistance, T2DM, obesity, and atherosclerosis, with a particular emphasis on potential roles of increased production of reactive oxygen species (ROS) and mitochondria dysfunctions.

NMR-based metabolomics of urine in a mouse model of Alzheimer's disease: identification of oxidative stress biomarkers

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia among elderly patients. A biomarker for the disease could make diagnosis easier and more accurate, and accelerate drug discovery. In this study, NMR-based metabolomics analysis in conjunction with multivariate statistics was applied to examine changes in urinary metabolites in transgenic AD mice expressing mutant tau and β-amyloid precursor protein. These mice showed significant changes in urinary metabolites throughout the progress of the disease. Levels of 3-hydroxykynurenine, homogentisate and allantoin were significantly higher compared to control mice in 4 months (prior to onset of AD symptoms) and reverted to control values by 10 months of age (early/middle stage of AD), which highlights the relevance of oxidative stress to this neurodegenerative disorder even prior the onset of dementia. The level of these changed metabolites at very early period may provide an indication of disease risk at asymptomatic stage.

Integration of datasets from different analytical techniques to assess the impact of nutrition on human metabolome

Bacteria colonizing the human intestinal tract exhibit a high phylogenetic diversity that reflects their immense metabolic potentials. The catalytic activity of gut microbes has an important impact on gastrointestinal (GI) functions and host health. The microbial conversion of carbohydrates and other food components leads to the formation of a large number of compounds that affect the host metabolome and have beneficial or adverse effects on human health. Metabolomics is a metabolic-biology system approach focused on the metabolic responses understanding of living systems to physio-pathological stimuli by using multivariate statistical data on human body fluids obtained by different instrumental techniques. A metabolomic approach based on an analytical platform could be able to separate, detect, characterize and quantify a wide range of metabolites and its metabolic pathways. This approach has been recently applied to study the metabolic changes triggered in the gut microbiota by specific diet components and diet variations, specific diseases, probiotic and synbiotic food intake. This review describes the metabolomic data obtained by analyzing human fluids by using different techniques and particularly Gas Chromatography Mass Spectrometry Solid-phase Micro Extraction (GC-MS/SPME), Proton Nuclear Magnetic Resonance (¹H-NMR) Spectroscopy and Fourier Transform Infrared (FTIR) Spectroscopy. This instrumental approach has a good potential in the identification and detection of specific food intake and diseases biomarkers.

Metabolomic profiling for identification of novel potential biomarkers in cardiovascular diseases

Metabolomics involves the identification and quantification of metabolites present in a biological system. Three different approaches can be used: metabolomic fingerprinting, metabolic profiling, and metabolic footprinting, in order to evaluate the clinical course of a disease, patient recovery, changes in response to surgical intervention or pharmacological treatment, as well as other associated features. Characteristic patterns of metabolites can be revealed that broaden our understanding of a particular disorder. In the present paper, common strategies and analytical techniques used in metabolomic studies are reviewed, particularly with reference to the cardiovascular field.