Metabolomics and atherosclerosis

Exploring the adverse effects of 1,3,6,8-tetrabromo-9H-carbazole in atherosclerotic model mice by metabolomic profiling integrated with mechanism studies in vitro

Given its wide distribution in the environment and latent toxic effects, 1,3,6,8-tetrabromo-9H-carbazole (1368-BCZ) is an emerging concern that has gained increasing attention globally. 1368-BCZ exposure is reported to have potential cardiovascular toxicity. Although atherosclerosis is a cardiovascular disease and remains a primary cause of mortality worldwide, no evidence has been found regarding the impact of 1368-BCZ on atherosclerosis. Therefore, we aimed to explore the deleterious effects of 1368-BCZ on atherosclerosis and the underlying mechanisms. Serum samples from 1368-BCZ-treated atherosclerotic model mice were subjected to metabolomic profiling to investigate the adverse influence of the pollutant. Subsequently, the molecular mechanism associated with the metabolic pathway of atherosclerotic mice that was identified following 1368-BCZ exposure was validated in vitro. Serum metabolomics analysis revealed that 1368-BCZ significantly altered the tricarboxylic acid cycle, causing a disturbance in energy metabolism. In vitro, we further validated general markers of energy metabolism based on metabolome data: 1368-BCZ dampened adenosine triphosphate (ATP) synthesis and increased reactive oxygen species (ROS) production. Furthermore, blocking the aryl hydrocarbon receptor (AhR) reversed the high production of ROS induced by 1368-BCZ. It is concluded that 1368-BCZ decreased the ATP synthesis by disturbing the energy metabolism, thereby stimulating the AhR-mediated ROS production and presumably causing aggravated atherosclerosis. This is the first comprehensive study on the cardiovascular toxicity and mechanism of 1368-BCZ based on rodent models of atherosclerosis and integrated with in vitro models.

Early hyperlipidemia triggers metabolomic reprogramming with increased SAH, increased acetyl-CoA-cholesterol synthesis, and decreased glycolysis

To identify metabolomic reprogramming in early hyperlipidemia, unbiased metabolome was screened in four tissues from ApoE-/- mice fed with high fat diet (HFD) for 3 weeks. 30, 122, 67, and 97 metabolites in the aorta, heart, liver, and plasma, respectively, were upregulated. 9 upregulated metabolites were uremic toxins, and 13 metabolites, including palmitate, promoted a trained immunity with increased syntheses of acetyl-CoA and cholesterol, increased S-adenosylhomocysteine (SAH) and hypomethylation and decreased glycolysis. The cross-omics analysis found upregulation of 11 metabolite synthetases in ApoE‾/‾ aorta, which promote ROS, cholesterol biosynthesis, and inflammation. Statistical correlation of 12 upregulated metabolites with 37 gene upregulations in ApoE‾/‾ aorta indicated 9 upregulated new metabolites to be proatherogenic. Antioxidant transcription factor NRF2-/- transcriptome analysis indicated that NRF2 suppresses trained immunity-metabolomic reprogramming. Our results have provided novel insights on metabolomic reprogramming in multiple tissues in early hyperlipidemia oriented toward three co-existed new types of trained immunity.

Altered choline level in atherosclerotic lesions: Upregulation of choline transporter-like protein 1 in human coronary unstable plaque

Inflammatory activity and hypoxia in atherosclerotic plaques are associated with plaque instability and thrombotic complications. Recent studies show that vascular cell metabolism affects atherogenesis and thrombogenicity. This study aimed to identify the metabolites in macrophage-rich unstable plaques that modulate atherogenesis and serve as potential markers of plaque instability. Atherosclerotic plaques were induced by balloon injury in the iliofemoral arteries of rabbits fed on a conventional or 0.5% cholesterol diet. At 3 months post-balloon injury, the arteries and cardiac tissues were subjected to histological, quantitative real-time polymerase chain reaction, and metabolomic analyses. The identified metabolite-related proteins were immunohistochemically analyzed in stable and unstable plaques from human coronary arteries. The factors modulating the identified metabolites were examined in macrophages derived from human peripheral blood mononuclear cells. Metabolomic analysis revealed that choline and guanine levels in macrophage-rich arteries were upregulated compared with those in non-injured arteries and cardiac tissues. Vascular choline levels, but not guanine levels, were positively correlated with the areas immunopositive for macrophages and tumor necrosis factor (TNF)-α and matrix metalloproteinase (MMP) 9 mRNA levels in injured arteries. In human coronary arteries, choline transporter-like protein (CTL) 1 was mainly localized to macrophages within plaques. The area that was immunopositive for CTL1 in unstable plaques was significantly higher than that in stable plaques. Intracellular choline levels were upregulated upon stimulation with TNF-α but were downregulated under hypoxia in cultured macrophages. Administration of choline upregulated the expression of TNF-α and CTL1 mRNA in cultured macrophages. The transfection of CTL1 small interfering RNA decreased CTL1, TNF-α, and MMP9 mRNA levels in cultured macrophages. These results suggest that choline metabolism is altered in macrophage-rich atherosclerotic lesions and unstable plaques. Thus, CTL1 may be potential markers of plaque instability.

Gut Parabacteroides merdae protects against cardiovascular damage by enhancing branched-chain amino acid catabolism

Obesity, dyslipidemia and gut dysbiosis are all linked to cardiovascular diseases. A Ganoderma meroterpene derivative (GMD) has been shown to alleviate obesity and hyperlipidemia through modulating the gut microbiota in obese mice. Here we show that GMD protects against obesity-associated atherosclerosis by increasing the abundance of Parabacteroides merdae in the gut and enhancing branched-chain amino acid (BCAA) catabolism. Administration of live P. merdae to high-fat-diet-fed ApoE-null male mice reduces atherosclerotic lesions and enhances intestinal BCAA degradation. The degradation of BCAAs is mediated by the porA gene expressed in P. merdae. Deletion of porA from P. merdae blunts its capacity to degrade BCAAs and leads to inefficacy in fighting against atherosclerosis. We further show that P. merdae inhibits the mTORC1 pathway in atherosclerotic plaques. In support of our preclinical findings, an in silico analysis of human gut metagenomic studies indicates that P. merdae and porA genes are depleted in the gut microbiomes of individuals with atherosclerosis. Our results provide mechanistic insights into the therapeutic potential of GMD through P. merdae in treating obesity-associated cardiovascular diseases.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes

PURPOSE

Modic changes (MC) on magnetic resonance imaging (MRI) have been associated with the development and severity of low back pain (LBP). The etiology of MC remains elusive, but it has been suggested that altered metabolism may be a risk factor. As such, this study aimed to identify metabolomic biomarkers for MC phenotypes of the lumbar spine via a combined metabolomic-genomic approach.

METHODS

A population cohort of 3,584 southern Chinese underwent lumbar spine MRI. Blood samples were genotyped with single-nucleotide polymorphisms (SNP) arrays (n = 2,482) and serum metabolomics profiling using magnetic resonance spectroscopy (n = 757), covering 130 metabolites representing three molecular windows, were assessed. Genome-wide association studies (GWAS) were performed on each metabolite, to construct polygenic scores for predicting metabolite levels in subjects who had GWAS but not metabolomic data. Associations between predicted metabolite levels and MC phenotypes were assessed using linear/logistic regression and least absolute shrinkage and selection operator (LASSO). Two-sample Mendelian randomization analysis tested for causal relationships between metabolic biomarkers and MC.

RESULTS

20.4% had MC (10.6% type 1, 67.2% type 2, 22.2% mixed types). Significant MC metabolomic biomarkers were mean diameter of very-low-density lipoprotein (VLDL)/low-density lipoprotein (LDL) particles and cholesterol esters/phospholipids in large LDL. Mendelian randomization indicated that decreased VLDL mean diameter may lead to MC.

CONCLUSIONS

This large-scale study is the first to address metabolomics in subject with/without lumbar MC. Causality studies implicate VLDL related to MC, noting a metabolic etiology. Our study substantiates the field of "spino-metabolomics" and illustrates the power of integrating metabolomics-genomics-imaging phenotypes to discover biomarkers for spinal disorders, paving the way for more personalized spine care for patients.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

The potential of using itaconate as treatment for inflammation-related heart diseases

Intracellular metabolites can cause critical changes in biological functions. Itaconate is perhaps the most fascinating substance in macrophages. Lipopolysaccharide can activate aconitate decarboxylase 1 and induces the generation of itaconate from the tricarboxylic acid cycle by decarboxylation of cis-aconitate. It has been reported that itaconate has beneficial effects on inflammation and oxidation. The mechanisms involved in these effects include the suppression of succinate dehydrogenase, the activation of nuclear factor E2-related factor 2 by alkylation of Kelch-like ECH-associated protein 1, suppression of aerobic glycolysis through regulation of glyceraldehyde-3-phosphate dehydrogenase and fructose-bisphosphate aldolase A, and suppression of IκBζ translation through activating transcription factor 3 activation. All of these findings elucidated the possible therapeutic implications of itaconate in inflammation-related diseases. In this review, we highlight that itaconate is a crucial molecule of the immunomodulatory response in macrophages and can regulate between immune response and cardiovascular metabolism. Furthermore, these discoveries suggest that itaconate is a very novel therapeutic molecule for the treatment of inflammation-related heart diseases.

From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions

While a molecular assessment of the perturbations and injury arising from diseases is essential in their diagnosis and treatment, understanding changes due to preventative strategies is also imperative. Currently, complex diseases such as cardiovascular disease (CVD), the leading cause of death worldwide, suffer from a limited understanding of how the molecular mechanisms taking place following preventive measures (e.g., exercise) differ from changes occurring due to the injuries caused from the disease (e.g., myocardial infarction (MI)). Therefore, this manuscript assesses lipidomic changes before and one hour after exercise treadmill testing (ETT) and before and one hour after a planned myocardial infarction (PMI) in two separate patient cohorts. Strikingly, unique lipidomic perturbations were observed for these events, as could be expected from their vastly different stresses on the body. The lipidomic results were then combined with previously published metabolomic characterizations of the same patients. This integration provides complementary insights into the exercise and PMI events, thereby giving a more holistic understanding of the molecular changes associated with each.

Effects of Long-Term Calcium Supplementation on Rats Bone Mineral Density and Cardiovascular Based on Metabonomics

Calcium supplements were necessary for those people with low calcium intake and high risk of osteoporosis. Recent cohort studies have shown that long-term calcium supplements may raise the risk of cardiovascular disease, but its mechanism is still unclear. In this study, metabonomics were employed to evaluate the changes of metabolism in rats with long-term calcium supplementation and further seek the potential markers of cardiovascular risk. SD rats were divided into two groups including normal control group (calcium intake, 0.50 g/kg bw) and high calcium supplement group (calcium intake, 2.50 g/kg bw). After 6 mo, the cardiovascular system and bone mineral density were observed. UPLC-MS was used to analyze serum metabonomics in rats. The results showed that the contents of total cholesterol and low-density lipoprotein cholesterol in the high calcium group were significantly higher than those in normal control group (p<0.05). The interventricular septum thickness (IVS), left ventricular mass (LVM), left ventricular posterior wall thickness (LVPW) in the high calcium group were higher than those in normal control group (p<0.05). Serum metabonomics analysis showed that there were persistent changes in many metabolites such as sphingosine and its derivatives (p<0.01) in the comparison between the high calcium group and the normal group. These results indicated that long term calcium supplementation can lead to dyslipidemia in rats, such as the rise of cholesterol and low-density lipoprotein, which might induce myocardial hypertrophy. Long-term calcium supplementation can cause the changes of the amount of sphingosine and its derivatives in the body, which many have potential risk to cardiovascular diseases such as myocardial hypertrophy and atherosclerosis.

Investigation of the metabolic difference between ST-elevated myocardial infarction and non-ST-elevated myocardial infarction via LC/Q-TOF/MS/MS

Acute coronary syndrome (ACS) is a clinical condition caused by a disturbance in myocardial blood flow. ACS can be basically divided into two forms: ST elevation myocardial infarction (STEMI) due to complete occlusion of the coronary artery and non-ST elevation myocardial infarction (NSTEMI) due to partial occlusion of the coronary artery. In this study, we aimed to monitor the metabolite profile of STEMI and NSTEMI patients and compare the results via untargeted metabolomics approach. Serum samples were collected from STEMI and NSTEMI patients, and each group consists of 20 participants. Extraction was achieved by acetonitrile, and chromatographic separation was performed by LC/Q-TOF/MS/MS accompanied with dual AJS ESI positive ion mode. METLIN, MATLAB 2017a-PLS Toolbox7.2, and Human Metabolome Database were utilized for bioinformatics evaluation of obtained findings. In our results, 203 m/z ratio was detected and 163 m/z ratio passed the significance criteria (fold analysis > 1.5 and p < 0.05). Twenty-five metabolites including BCAAs, LysoPC species, lactic acid, succinate, malonic acid, maleic acid, butyric acid, carnitine, and betaine were identified. In conclusion, new biomarker candidates were identified to differentiate the diagnosis of STEMI and NSTEMI. Identified metabolites are indicative of alterations in oxidative stress, hypoxia, TCA cycle, and amino acid metabolism.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

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.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Glycosphingolipids promote pro-atherogenic pathways in the pathogenesis of hyperglycemia-induced accelerated atherosclerosis

INTRODUCTION

Three out of four people with diabetes will die of cardiovascular disease. However, the molecular mechanisms by which hyperglycemia promotes atherosclerosis, the major underlying cause of cardiovascular disease, are not clear.

OBJECTIVES

Three distinct models of hyperglycemia-associated accelerated atherosclerosis were used to identify commonly altered metabolites and pathways associated with the disease.

METHODS

Normoglycemic apolipoprotein-E-deficient mice served as atherosclerotic control. Hyperglycemia was induced by multiple low-dose streptozotocin injections, or by introducing a point-mutation in one copy of insulin-2 gene. Glucosamine-supplemented mice, which experience accelerated atherosclerosis to a similar extent as hyperglycemia-induced models without alterations in glucose or insulin levels, were also included in the analysis. Untargeted plasma metabolomics were used to investigate hyperglycemia-associated accelerated atherosclerosis in three disease models. The effect of specific significantly altered metabolites on pro-atherogenic processes was investigated in cultured human vascular cells.

RESULTS

Hyperglycemic and glucosamine-supplemented mice showed distinct metabolomic profiles compared to controls. Meta-analysis of three disease models revealed 62 similarly altered metabolite features (FDR-adjusted p < 0.05). Identification of shared metabolites revealed alterations in glycerophospholipid and sphingolipid metabolism, and pro-atherogenic processes including inflammation and oxidative stress. Post-multivariate and pathway analyses indicated that the glycosphingolipid pathway is strongly associated with hyperglycemia-induced accelerated atherosclerosis in these atherogenic mouse models. Glycosphingolipids induced oxidative stress and inflammation in cultured human vascular cells.

CONCLUSION

Glycosphingolipids are strongly associated with hyperglycemia-induced accelerated atherosclerosis in three distinct models. They also promote pro-atherogenic processes in cultured human cells. These results suggest glycosphingolipid pathway may be a potential therapeutic target to block or slow atherogenesis in diabetic patients.

Effects of Diacetyl Flavoring Exposure in Mice Metabolism

Diacetyl is a flavoring that imparts a buttery flavor to foods, but the use or exposure to diacetyl has been related to some diseases. We investigated the effect of oral intake of diacetyl in male and female C57/Bl mice. We performed a target metabolomics assay using ultraperformance liquid chromatography paired with triple quadrupole mass spectrometry (UPLC-MS/MS) for the determination and quantification of plasmatic metabolites. We observed alterations in metabolites present in the urea and tricarboxylic acid (TCA) cycles. Peroxynitrite plasmatic levels were evaluated by a colorimetric method, final activity of superoxide dismutase (SOD) was evaluated by an enzymatic method, and mouse behavior was evaluated. Majority of the assay showed differences between control and treatment groups, as well as between genders. This may indicate the involvement of sex hormones in the regulation of a normal metabolic profile, and the implication of sex differences in metabolite disease response.

Mass spectrometry for the evaluation of cardiovascular diseases based on proteomics and lipidomics

The identification and quantification of proteins and lipids is of major importance for the diagnosis, prognosis and understanding of the molecular mechanisms involved in disease development. Owing to its selectivity and sensitivity, mass spectrometry has become a key technique in analytical platforms for proteomic and lipidomic investigations. Using this technique, many strategies have been developed based on unbiased or targeted approaches to highlight or monitor molecules of interest from biomatrices. Although these approaches have largely been employed in cancer research, this type of investigation has been met by a growing interest in the field of cardiovascular disorders, potentially leading to the discovery of novel biomarkers and the development of new therapies. In this paper, we will review the different mass spectrometry- based proteomic and lipidomic strategies applied in cardiovascular diseases, especially atherosclerosis. Particular attention will be given to recent developments and the role of bioinformatics in data treatment. This review will be of broad interest to the medical community by providing a tutorial of how mass spectrometric strategies can support clinical trials.

Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis

Coronary atherosclerosis (CAS) is the pathogenesis of coronary heart disease, which is a prevalent and chronic life-threatening disease. Initially, this disease is not always detected until a patient presents with seriously vascular occlusion. Therefore, new biomarkers for appropriate and timely diagnosis of early CAS is needed for screening to initiate therapy on time. In this study, we used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAS detection. Score plots from partial least-squares discriminant analysis clearly separated early-stage CAS patients from controls. Meanwhile, the levels of 24 metabolites increased greatly and those of 18 metabolites decreased markedly in early CAS patients compared with the controls, which suggested significant metabolic dysfunction in phospholipid, sphingolipid, and fatty acid metabolism in the patients. Furthermore, binary logistic regression showed that nine metabolites could be used as a combinatorial biomarker to distinguish early-stage CAS patients from controls. The panel of nine metabolites was then tested with an independent cohort of samples, which also yielded satisfactory diagnostic accuracy (AUC = 0.890). In conclusion, our findings provide insight into the pathological mechanism of early-stage CAS and also supply a combinatorial biomarker to aid clinical diagnosis of early-stage CAS.

Plasma Metabolic Profile Determination in Young ST-segment Elevation Myocardial Infarction Patients with Ischemia and Reperfusion: Ultra-performance Liquid Chromatography and Mass Spectrometry for Pathway Analysis

Background:

This study was to establish a disease differentiation model for ST-segment elevation myocardial infarction (STEMI) youth patients experiencing ischemia and reperfusion via ultra-performance liquid chromatography and mass spectrometry (UPLC/MS) platform, which searches for closely related characteristic metabolites and metabolic pathways to evaluate their predictive value in the prognosis after discharge.

Methods:

Forty-seven consecutive STEMI patients (23 patients under 45 years of age, referred to here as “youth,” and 24 “elderly” patients) and 48 healthy control group members (24 youth, 24 elderly) were registered prospectively. The youth patients were required to provide a second blood draw during a follow-up visit one year after morbidity (n = 22, one lost). Characteristic metabolites and relative metabolic pathways were screened via UPLC/MS platform base on the Kyoto encyclopedia of genes and genomes (KEGG) and Human Metabolome Database. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of characteristic metabolites in the prognosis after discharge.

Results:

We successfully established an orthogonal partial least squares discriminated analysis model (R₂X = 71.2%, R₂Y = 79.6%, and Q₂ = 55.9%) and screened out 24 ions; the sphingolipid metabolism pathway showed the most drastic change. The ROC curve analysis showed that ceramide [Cer(d18:0/16:0), Cer(t18:0/12:0)] and sphinganine in the sphingolipid pathway have high sensitivity and specificity on the prognosis related to major adverse cardiovascular events after youth patients were discharged. The area under curve (AUC) was 0.671, 0.750, and 0.711, respectively. A follow-up validation one year after morbidity showed corresponding AUC of 0.778, 0.833, and 0.806.

Conclusions:

By analyzing the plasma metabolism of myocardial infarction patients, we successfully established a model that can distinguish two different factors simultaneously: pathological conditions and age. Sphingolipid metabolism is the top most altered pathway in young STEMI patients and as such may represent a valuable prognostic factor and potential therapeutic target.

Metabolomic study on the faecal extracts of atherosclerosis mice and its application in a Traditional Chinese Medicine

The intestinal microbiota and their metabolites are closely related to the formation of atherosclerosis (AS). In this study, a metabolomic approach based on the reversed-phase liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) platform was established to analyze the metabolic profiling of fecal extracts from AS mice model. The established metabolomic platform was also used for clearing the effective mechanism of a Traditional Chinese Medicine (TCM) named Sishen granule (SSKL). Totally, sixteen potential biomarkers in faeces of AS mice were identified and 5 of them could be reversed by SSKL. Through functional analysis of these biomarkers and the established network, lipid metabolism, cholesterol metabolism, energy cycle, and inflammation reaction were considered as the most relevant pathological changes in gastrointestinal tract of AS mice. The metabolomic study not only revealed the potential biomarkers in AS mice' faeces but also supplied a systematic view of the pathological changes in gastrointestinal metabolite in AS mice. This metabolomic study also demonstrated that SSKL had the therapeutic effectiveness on AS through partly reversing the lipid metabolism, inflammation and energy metabolism.

Stability of energy metabolites-An often overlooked issue in metabolomics studies: A review

Recent advances in analytical chemistry have set the stage for metabolite profiling to help understand complex molecular processes in physiology. Despite ongoing efforts, there are concerns regarding metabolomics workflows, since it has been shown that internal (enzyme activity, blood contamination, and the dynamic nature of metabolite concentrations) as well as external factors (storage, handling, and analysis method) may affect the metabolome profile. Many metabolites are intrinsically instable, particularly some of those associated with central carbon metabolism. While enzymatic conversions have been studied in great detail, nonenzymatic, chemical conversions received comparatively little attention. This review aims to give an in-depth overview of nonenzymatic energy metabolite degradation/interconversion chemistry focusing on a selected range of metabolites. Special attention will be given to qualitative (degradation pathways) as well as quantitative aspects, that may affect the acquisition of accurate data in the context of metabolomics studies. Problems related to the use of isotopically labeled internal standards hindering the quantitative analysis of common metabolites will be presented with an experimental example. Finally, general conclusions and perspectives are given.

1H-NMR-based metabolomic analysis of cerebrospinal fluid from adult bilateral moyamoya disease: comparison with unilateral moyamoya disease and atherosclerotic stenosis

Although metabolomics has been increasingly used to observe metabolic pattern and disease-specific metabolic markers, metabolite profiling for moyamoya disease (MMD) has not yet been done in adults. This study investigated cerebrospinal fluid (CSF) metabolites specific to bilateral MMD (B-MMD) and compared them to those of unilateral MMD (U-MMD) or atherosclerotic stenosis with hydrogen-1 nuclear magnetic resonance spectroscopy to identify metabolic biomarkers associated with MMD in adults.CSF samples of B-MMD (n = 29), U-MMD (n = 11), and atherosclerotic cerebrovascular disease (ACVD) (n = 8) were recruited. Principal component analysis, partial least square discriminant analysis, and orthogonal projections to latent structure discriminant analysis (OPLS-DA) were done for the comparisons. Diagnostic performance was acquired by prediction of 1 left-out sample from the distinction model constructed with the rest of the samples.B-MMD showed an increase in glutamine (P < 0.001) and taurine (P = 0.004), and a decrease in glucose (P < 0.001), citrate (P = 0.002), and myo-inositol (P = 0.006) than those in ACVD. U-MMD showed a higher level of glutamine (P = 0.005) and taurine (P = 0.034), and a lower level of glutamate (P < 0.004) than those in ACVD. No difference at the metabolite level was observed between B-MMD and U-MMD. Cross-validation with the OPLS-DA model showed a high accuracy for the prediction of MMD.The results of the study suggest that a metabolomics approach may be helpful in confirming MMD and providing a better understanding of MMD pathogenesis. Elevated glutamine in the CSF may be associated with MMD pathogenesis, which was different from ACVD.

Intervention Trials with the Mediterranean Diet in Cardiovascular Prevention: Understanding Potential Mechanisms through Metabolomic Profiling

Large observational epidemiologic studies and randomized trials support the benefits of a Mediterranean dietary pattern on cardiovascular disease (CVD). Mechanisms postulated to mediate these benefits include the reduction of low-grade inflammation, increased adiponectin concentrations, decreased blood coagulation, enhanced endothelial function, lower oxidative stress, lower concentrations of oxidized LDL, and improved apolipoprotein profiles. However, the metabolic pathways through which the Mediterranean diet influences CVD risk remain largely unknown. Investigating specific mechanisms in the context of a large intervention trial with the use of high-throughput metabolomic profiling will provide more solid public health messages and may help to identify key molecular targets for more effective prevention and management of CVD. Although metabolomics is not without its limitations, the techniques allow for an assessment of thousands of metabolites, providing wide-ranging profiling of small molecules related to biological status. Specific candidate plasma metabolites that may be associated with CVD include branched-chain and aromatic amino acids; the glutamine-to-glutamate ratio; some short- to medium-chain acylcarnitines; gut flora metabolites (choline, betaine, and trimethylamine N-oxide); urea cycle metabolites (citrulline and ornithine); and specific lipid subclasses. In addition to targeted metabolites, the role of a large number of untargeted metabolites should also be assessed. Large intervention trials with the use of food patterns for the prevention of CVD provide an unparalleled opportunity to examine the effects of these interventions on plasma concentrations of specific metabolites and determine whether such changes mediate the benefits of the dietary interventions on CVD risk.

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. ¹H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. ¹H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of ¹H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment.

This review describes the theoretical basis of ¹H NMR; compares MS and ¹H NMR and provides a tabular overview of recent ¹H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. ¹H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

Systems Biology of Human Atherosclerosis

Systems biology describes a holistic and integrative approach to understand physiology and pathology. The “omic” disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the “net” biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism’s context. Systems biology relies on the existence of “nodes” and “edges.” Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.

Technology platform development for targeted plasma metabolites in human heart failure

Background

Heart failure is a multifactorial disease associated with staggeringly high morbidity and motility. Recently, alterations of multiple metabolites have been implicated in heart failure; however, the lack of an effective technology platform to assess these metabolites has limited our understanding on how they contribute to this disease phenotype. We have successfully developed a new workflow combining specific sample preparation with tandem mass spectrometry that enables us to extract most of the targeted metabolites. 19 metabolites were chosen ascribing to their biological relevance to heart failure, including extracellular matrix remodeling, inflammation, insulin resistance, renal dysfunction, and cardioprotection against ischemic injury.

Results

In this report, we systematically engineered, optimized and refined a protocol applicable to human plasma samples; this study contributes to the methodology development with respect to deproteinization, incubation, reconstitution, and detection with mass spectrometry. The deproteinization step was optimized with 20% methanol/ethanol at a plasma:solvent ratio of 1:3. Subsequently, an incubation step was implemented which remarkably enhanced the metabolite signals and the number of metabolite peaks detected by mass spectrometry in both positive and negative modes. With respect to the step of reconstitution, 0.1% formic acid was designated as the reconstitution solvent vs. 6.5 mM ammonium bicarbonate, based on the comparable number of metabolite peaks detected in both solvents, and yet the signal detected in the former was higher. By adapting this finalized protocol, we were able to retrieve 13 out of 19 targeted metabolites from human plasma.

Conclusions

We have successfully devised a simple albeit effective workflow for the targeted plasma metabolites relevant to human heart failure. This will be employed in tandem with high throughput liquid chromatography mass spectrometry platform to validate and characterize these potential metabolic biomarkers for diagnostic and therapeutic development of heart failure patients.

Systems mapping of metabolic genes through control theory

The formation of any complex phenotype involves a web of metabolic pathways in which one chemical is transformed through the catalysis of enzymes into another. Traditional approaches for mapping quantitative trait loci (QTLs) are based on a direct association analysis between DNA marker genotypes and end-point phenotypes, neglecting the mechanistic processes of how a phenotype is formed biochemically. Here, we propose a new dynamic framework for mapping metabolic QTLs (mQTLs) responsible for phenotypic formation. By treating metabolic pathways as a biological system, robust differential equations have proven to be a powerful means of studying and predicting the dynamic behavior of biochemical reactions that cause a high-order phenotype. The new framework integrates these differential equations into a statistical mixture model for QTL mapping. Since the mathematical parameters that define the emergent properties of the metabolic system can be estimated and tested for different mQTL genotypes, the framework allows the dynamic pattern of genetic effects to be quantified on metabolic capacity and efficacy across a time-space scale. Based on a recent study of glycolysis in Saccharomyces cerevisiae, we design and perform a series of simulation studies to investigate the statistical properties of the framework and validate its usefulness and utilization in practice. This framework can be generalized to mapping QTLs for any other dynamic systems and may stimulate pharmacogenetic research toward personalized drug and treatment intervention.

The heritability of metabolic profiles in newborn twins

Identifying genetic and metabolic biomarkers in neonates has the potential to improve diagnosis and treatment of common complex neonatal diseases, and potentially lead to risk assessment and preventative measures for common adulthood illnesses such as diabetes and cardiovascular disease. There is a wealth of information on using fatty acid, amino acid and organic acid metabolite profiles to identify rare inherited congenital diseases through newborn screening, but little is known about these metabolic profiles in the context of the 'healthy' newborn. Recent studies have implicated many of the amino acid and fatty acid metabolites utilized in newborn screening in common complex adult diseases such as cardiovascular disease, insulin resistance and obesity. To determine the heritability of metabolic profiles in newborns, we examined 381 twin pairs obtained from the Iowa Neonatal Metabolic Screening Program. Heritability was estimated using multilevel mixed-effects linear regression adjusting for gestational age, gender, weight and age at time of sample collection. The highest heritability was for short-chain acylcarnitines, specifically C4 (h²=0.66, P=2 × 10⁻¹⁶), C4-DC (h²=0.83, P<10⁻¹⁶) and C5 (h²=0.61, P=1 × 10⁻⁹). Thyroid stimulating hormone (h²=0.58, P=2 × 10⁻⁵) and immunoreactive trypsinogen (h²=0.52, P=3 × 10⁻⁹) also have a strong genetic component. This is direct evidence for a strong genetic contribution to the metabolic profile at birth and that newborn screening data can be utilized for studying the genetic regulation of many clinically relevant metabolites.

The Use of High-Throughput Technologies to Investigate Vascular Inflammation and Atherosclerosis

The greatest challenge of scientific research is to understand the causes and consequences of disease. In recent years, great efforts have been devoted to unraveling the basic mechanisms of atherosclerosis (the underlying pathology of cardiovascular disease), which remains a major cause of morbidity and mortality worldwide. Because of the complex and multifactorial pathophysiology of cardiovascular disease, different research techniques have increasingly been combined to unravel genetic aspects, molecular pathways, and cellular functions involved in atherogenesis, vascular inflammation, and dyslipidemia to gain a multifaceted picture addressing this complexity. Thanks to the rapid evolution of high-throughput technologies, we are now able to generate large-scale data on the DNA, RNA, and protein levels. With the help of sophisticated computational tools, these data sets are integrated to enhance information extraction and are being increasingly used in a systems biology approach to model biological processes as interconnected and regulated networks. This review exemplifies the use of high-throughput technologies—such as genomics, transcriptomics, proteomics, and epigenomics—and systems biology to explore pathomechanisms of vascular inflammation and atherosclerosis.

Reverse-engineering human regulatory networks

The explosion of genomic, transcriptomic, proteomic, metabolomic, and other omics data is challenging the research community to develop rational models for their organization and interpretation to generate novel biological knowledge. The development and use of gene regulatory networks to mechanistically interpret this data is an important development in molecular biology, usually captured under the banner of systems biology. As a result, the repertoire of methods for the reconstruction of comprehensive and cell-context-specific maps of regulatory interactions, or interactomes, has also exploded in the past few years. In this review, we focus on Network Biology and more specifically on methods for reverse engineering transcriptional, post-transcriptional, and post-translational human interaction networks and show how their interrogation is starting to impact our understanding of cellular pathophysiology and one's ability to predict cellular phenotypes from genome-wide molecular observations.

Metabolomics as a tool for cardiac research

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.

Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potential biomarkers of perinatal asphyxia in a non-human primate model

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC×GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 min of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 min of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC×GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment.

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.

New technologies personalize diagnostics and therapeutics

Atherosclerotic cardiovascular disease remains the leading cause of morbidity and mortality in industrialized societies [10]. Efforts to reduce cardiovascular events in "high-risk" individuals or recurrent events in patients with established atherosclerotic cardiovascular disease emphasize broad-based implementation of guideline-directed therapeutic lifestyle changes and pharmacotherapy. Despite successful implementation of these evidence-based strategies, many individuals are not properly identified before their first event or they continue to experience cardiovascular events despite "optimal" levels of biomarkers. The application of genomics, proteomics, and metabolomics provides opportunities to improve our understanding of the complex process of atherosclerosis and enables personalized diagnostic and therapeutic approaches.