Metabolomics with LC-QTOF-MS permits the prediction of disease stage in aortic abdominal aneurysm based on plasma metabolic fingerprint

Mendelian randomization demonstrates a causal link between peripheral circulating acylcarnitines and intracranial aneurysms

Intracranial aneurysm (IA) is the most prevalent type of cerebral vascular disease causing life-threatening subarachnoid hemorrhages (SAH). A long-term vascular structure remodeling is considered as the main pathophysiological feature of IAs. However, the causal factors triggering the pathophysiological process are not clear. Recently, the abnormalities of peripheral circulating proteins and metabolites have been found in IAs patients and associated with the ruptures. We comprehensively investigated the potential causal relationship between blood metabolites and proteins and IAs using the mendelian randomization (MR) analysis. We applied two-sample MR to explore the potential causal association between peripheral circulating metabolites (191 blood metabolites) and proteins (1398 proteins) and IAs using data from the FinnGen study and the GWAS datasets published by Bakker et al. We identified palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine as causal contributors of IAs and ruptures. Further two-step mediation MR analysis suggested that hypertension as one of the contributors of IAs and ruptures mediated the causal relationship between palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine and IAs. Together, our study demonstrates that blood metabolic palmitoylcarnitine, stearoylcarnitine and 2-tetradecenoylcarnitine are causally linked to the formation and rupture of IAs. Hypertension partially mediates the causal effects.

Targeted proteomics and metabolomics for biomarker discovery in abdominal aortic aneurysm and post-EVAR sac volume

BACKGROUND AND AIMS

Abdominal aortic aneurysm (AAA) patients undergo uniform surveillance programs both leading up to, and following surgery. Circulating biomarkers could play a pivotal role in individualizing surveillance. We applied a multi-omics approach to identify relevant biomarkers and gain pathophysiological insights.

MATERIALS AND METHODS

In this cross-sectional study, 108 AAA patients and 200 post-endovascular aneurysm repair (post-EVAR) patients were separately investigated. We performed partial least squares regression and ingenuity pathway analysis on circulating concentrations of 96 proteins (92 Olink Cardiovascular-III panel, 4 ELISA-assays) and 199 metabolites (measured by LC-TQMS), and their associations with CT-based AAA/sac volume.

RESULTS

The median (25th-75th percentile) maximal diameter was 50.0 mm (46.0, 53.0) in the AAA group, and 55.4 mm (45.0, 64.2) in the post-EVAR group. Correcting for clinical characteristics in AAA patients, the aneurysm volume Z-score differed 0.068 (95 %CI: (0.042, 0.093)), 0.066 (0.047, 0.085) and -0.051 (-0.064, -0.038) per Z-score valine, leucine and uPA, respectively. After correcting for clinical characteristics and orthogonalization in the post-EVAR group, the sac volume Z-score differed 0.049 (0.034, 0.063) per Z-score TIMP-4, -0.050 (-0.064, -0.037) per Z-score LDL-receptor, -0.051 (-0.062, -0.040) per Z-score 1-OG/2-OG and -0.056 (-0.066, -0.045) per Z-score 1-LG/2-LG.

CONCLUSIONS

The branched-chain amino acids and uPA were related to AAA volume. For post-EVAR patients, LDL-receptor, monoacylglycerols and TIMP-4 are potential biomarkers for sac volume. Additionally, distinct markers for sac change were identified.

Targeting metabolism in aortic aneurysm and dissection: from basic research to clinical applications

Aortic aneurysm and dissection (AAD) are a group of insidious and lethal cardiovascular diseases that characterized by seriously threatening the life and health of people, but lack effective nonsurgical interventions. Alterations in metabolites are increasingly recognized as universal features of AAD because metabolic abnormalities have been identified not only in arterial tissue but also in blood and vascular cells from both patients and animal models with this disease. Over the past few decades, studies have further supported this notion by linking AAD to various types of metabolites such as those derived from gut microbiota or involved in TCA cycle or lipid metabolism. Many of these altered metabolites may contribute to the pathogenesis of AAD. This review aims to illustrate the close association between body metabolism and the occurrence and development of AAD, as well as summarize the significance of metabolites correlated with the pathological process of AAD. This provides valuable insight for developing new therapeutic agents for AAD. Therefore, we present a brief overview of metabolism in AAD biology, including signaling pathways involved in these processes and current clinical studies targeting AAD metabolisms. It is necessary to understand the metabolic mechanisms underlying AAD to provides significant knowledge for AAD diagnosis and new therapeutics for treatment.

Multi-omics in thoracic aortic aneurysm: the complex road to the simplification

BACKGROUND

Thoracic aortic aneurysm (TAA) is a serious condition that affects the aorta, characterized by the dilation of its first segment. The causes of TAA (e.g., age, hypertension, genetic syndromes) are heterogeneous and contribute to the weakening of the aortic wall. This complexity makes treating this life-threatening aortopathy challenging, as there are currently no etiological therapy available, and pharmacological strategies, aimed at avoiding surgical aortic replacement, are merely palliative. Recent studies on novel therapies for TAA have focused on identifying biological targets and etiological mechanisms of the disease by using advanced -omics techniques, including epigenomics, transcriptomics, proteomics, and metabolomics approaches.

METHODS

This review presents the latest findings from -omics approaches and underscores the importance of integrating multi-omics data to gain more comprehensive understanding of TAA.

RESULTS

Literature suggests that the alterations in TAA mediators frequently involve members of pro-fibrotic process (i.e., TGF-β signaling pathways) or proteins associated with cell/extracellular structures (e.g., aggrecans). Further analyses often reported the importance in TAA of processes as inflammation (PCR, CD3, leukotriene compounds), oxidative stress (chromatin OXPHOS, fatty acids), mitochondrial respiration and glycolysis/gluconeogenesis (e.g., PPARs and HIF1a). Of note, more recent metabolomics studies added novel molecular markers to the list of TAA-specific detrimental mediators (proteoglycans).

CONCLUSION

It is increasingly clear that integrating data from different -omics branches, along with clinical data, is essential as well as complicated both to reveal hidden relevant information and to address complex diseases such as TAA. Importantly, recent progresses in metabolomics highlighted novel potential and unprecedented marks in TAA diagnosis and therapy.

Plasma Lipidomics Analysis Reveals the Potential Role of Lysophosphatidylcholines in Abdominal Aortic Aneurysm Progression and Formation

Abdominal aortic aneurysm (AAA) is hallmarked by irreversible dilation of the infrarenal aorta. Lipid deposition in the aortic wall and the potential importance of a lipid disorder in AAA etiology highlight the need to explore lipid variation during AAA development. This study aimed to systematically characterize the lipidomics associated with AAA size and progression. Plasma lipids from 106 subjects (36 non-AAA controls and 70 AAA patients) were comprehensively analyzed using untargeted lipidomics. An AAA animal model was established by embedding angiotensin-II pump in ApoE^-/- mice for four weeks and blood was collected at 0, 2 and 4 weeks for lipidomic analysis. Using a false-discovery rate (FDR) < 0.05, a group of lysophosphatidylcholines (lysoPCs) were specifically decreased in AAA patients and mice. LysoPCs were principally lower in the AAA patients with larger diameter (diameter > 50 mm) than those with a smaller size (30 mm < diameter < 50 mm), and levels of lysoPCs were also found to be decreased with modelling time and aneurysm formation in AAA mice. Correlation matrices between lipids and clinical characteristics identified that the positive correlation between lysoPCs and HDL-c was reduced and negative correlations between lysoPCs and CAD rate, lysoPCs and hsCRP were converted to positive correlations in AAA compared to control. Weakened positive correlations between plasma lysoPCs and circulating HDL-c in AAA suggested that HDL-lysoPCs may elicit instinctive physiological effects in AAA. This study provides evidence that reduced lysoPCs essentially underlie the pathogenesis of AAA and that lysoPCs are promising biomarkers for AAA development.

In Vitro Animal Model for Estimating the Time since Death with Attention to Early Postmortem Stage

Estimating the postmortem interval (PMI) has remained the subject of investigations in forensic medicine for many years. Every kind of death results in changes in metabolites in body tissues and fluids due to lack of oxygen, altered circulation, enzymatic reactions, cellular degradation, and cessation of anabolic production of metabolites. Metabolic changes may provide markers determining the time since death, which is challenging in current analytical and observation-based methods. The study includes metabolomics analysis of blood with the use of an animal model to determine the biochemical changes following death. LC-MS is used to fingerprint postmortem porcine blood. Metabolites, significantly changing in blood after death, are selected and identified using univariate statistics. Fifty-one significant metabolites are found to help estimate the time since death in the early postmortem stage. Hypoxanthine, lactic acid, histidine, and lysophosphatidic acids are found as the most promising markers in estimating an early postmortem stage. Selected lysophosphatidylcholines are also found as significantly increased in blood with postmortal time, but their practical utility as PMI indicators can be limited due to a relatively low increasing rate. The findings demonstrate the great potential of LC-MS-based metabolomics in determining the PMI due to sudden death and provide an experimental basis for applying this attitude in investigating various mechanisms of death. As we assume, our study is also one of the first in which the porcine animal model is used to establish PMI metabolomics biomarkers.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Rosmarinic Acid Suppresses Abdominal Aortic Aneurysm Progression in Apolipoprotein E-deficient Mice

An abdominal aortic aneurysm is a life-threatening cardiovascular disorder caused by dissection and rupture. No effective medicine is currently available for the > 90% of patients whose aneurysms are below the surgical threshold. The present study investigated the impact of rosmarinic acid, salvianolic acid C, or salvianolic acid B on experimental abdominal aortic aneurysms. Abdominal aortic aneurysms were induced in apolipoprotein E-deficient mice via infusion of angiotensin II for 4 wks. Rosmarinic acid, salvianolic acid C, salvianolic acid B, or doxycycline as a positive control was provided daily through intraperitoneal injection. Administration of rosmarinic acid was found to decrease the thickness of the aortic wall, as determined by histopathological assay. Rosmarinic acid also exhibited protection against elastin fragmentation in aortic media and down-regulated cell apoptosis and proliferation in the aortic adventitia. Infiltration of macrophages, T lymphocytes, and neutrophils in aortic aneurysms was found, especially at the aortic adventitia. Rosmarinic acid, salvianolic acid C, or salvianolic acid B inhibited the infiltration on macrophages specifically, but these compounds did not influence T lymphocytes and neutrophils. Expression of matrix metalloproteinase 9 and macrophage migration inhibitory factor significantly increased in aortic aneurysms. Rosmarinic acid and salvianolic acid C decreased the expression of matrix metalloproteinase-9 in media, and rosmarinic acid also tended to reduce migration inhibitory factor expression. Further then, partial least squares-discriminate analysis was used to classify metabolic changes among different treatments. Rosmarinic acid affected most of the metabolites in the biosynthesis of the citrate cycle, fatty acid pathway significantly. Our present study on mice demonstrated that rosmarinic acid inhibited multiple pathological processes, which were the key features important in abdominal aortic aneurysm formation. Further study on rosmarinic acid, the novel candidate for aneurysmal therapy, should be undertaken to determine its potential for clinical use.

HPLC–(Q)-TOF-MS-Based Study of Plasma Metabolic Profile Differences Associated with Age in Pediatric Population Using an Animal Model

A deep knowledge about the biological development of children is essential for appropriate drug administration and dosage in pediatrics. In this sense, the best approximation to study organ maturation is the analysis of tissue samples, but it requires invasive methods. For this reason, surrogate matrices should be explored. Among them, plasma emerges as a potential alternative since it represents a snapshot of global organ metabolism. In this work, plasma metabolic profiles from piglets of different ages (newborns, infants, and children) obtained by HPLC–(Q)-TOF-MS at positive and negative ionization modes were studied. Improved clustering within groups was achieved using multiblock principal component analysis compared to classical principal component analysis. Furthermore, the separation observed among groups was better resolved by using partial least squares-discriminant analysis, which was validated by bootstrapping and permutation testing. Thanks to univariate analysis, 13 metabolites in positive and 21 in negative ionization modes were found to be significant to discriminate the three groups of piglets. From these features, an acylcarnitine and eight glycerophospholipids were annotated and identified as metabolites of interest. The findings indicate that there is a relevant change with age in lipid metabolism in which lysophosphatidylcholines and lysophoshatidylethanolamines play an important role.

The metabolic analysis in human aortic tissues of aortic dissection

Background

The metabolic profile of human aortic tissues is of great importance. Among the analytical platforms utilized in metabolomics, LC‐MS provides broad metabolome coverage. The non‐targeted metabolomics can comprehensively detect the entire metabolome of an organism and find the metabolic characteristics that have significant changes in the experimental group and the control group and elucidate the metabolic pathway concerning the recognized metabolites. Employing non‐targeted metabolomics is helpful to develop biomarkers for disease diagnosis and disease pathology research; for instance, Aortic aneurysm (AA) and Aortic dissection (AD).

Aim

This study sought to describe the non‐targeted analysis of 18 aortic tissue samples, comparing between AA and AD.

Material & Methods

Our experimental flow included dividing the samples into (AA, nine samples) and (AD, nine samples), SCIEX quadrupole timeofflight tandem mass spectrometer (TripleTOF) 6600+ mass spectrometer data refinement, MetDNA database analysis, and pathway analysis. We performed an initial validation by setting quality control parameters to evaluate the stability of the analysis system during the computer operation. We then used the repeatability of the control samples to examine the stability of the instrument during the entire analysis process to ensure the reliability of the results.

Results

Our study found 138 novel metabolites involved in galactose metabolism.

Discussion

138 novel metabolites found in this study will be further studied in the future.

Conclusion

Our study found 138 novel metabolites between AA and AD, which will provide viable clinical data for future studies aimed to implement galactose markers in aortic tissue analysis.

Metabolomic Profiling of Angiotensin-II-Induced Abdominal Aortic Aneurysm in Ldlr-/- Mice Points to Alteration of Nitric Oxide, Lipid, and Energy Metabolisms

Aneurysm is the second-most common disease affecting the aorta worldwide after atherosclerosis. While several clinical metabolomic studies have been reported, no study has reported deep metabolomic phenotyping in experimental animal models of aortic aneurysm. We performed a targeted metabolomics study on the blood and aortas of an experimental mice model of aortic aneurysm generated by high-cholesterol diet and angiotensin II in Ldlr^−/− mice. The mice model showed a significant increase in media/lumen ratio and wall area, which is associated with lipid deposition within the adventitia, describing a hypertrophic remodeling with an aneurysm profile of the abdominal aorta. Altered aortas showed increased collagen remodeling, disruption of lipid metabolism, decreased glucose, nitric oxide and lysine metabolisms, and increased polyamines and asymmetric dimethylarginine (ADMA) production. In blood, a major hyperlipidemia was observed with decreased concentrations of glutamine, glycine, taurine, and carnitine, and increased concentrations of the branched amino acids (BCAA). The BCAA/glycine and BCAA/glutamine ratios discriminated with very good sensitivity and specificity between aneurysmatic and non-aneurysmatic mice. To conclude, our results reveal that experimental induction of aortic aneurysms causes a profound alteration in the metabolic profile in aortas and blood, mainly centered on an alteration of NO, lipid, and energetic metabolisms.

Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets

Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.

Metabolomic Profile of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is characterized by structural deterioration of the aortic wall, leading to aortic dilation and rupture. The aim was to compare 183 low molecular weight metabolites in AAA patients and aorta-healthy controls and to explore if low molecular weight metabolites are linked to AAA growth. Blood samples were collected from male AAA patients with fast (mean 3.3 mm/year; range 1.3-9.4 mm/year; n = 39) and slow growth (0.2 mm/year; range -2.6-1.1 mm/year; n = 40), and from controls with non-aneurysmal aortas (n = 79). Targeted analysis of 183 metabolites in plasma was performed with AbsoluteIDQ p180 kit. The samples were measured on a QTRAP 4500 coupled to an Agilent 1260 series HPLC. The levels of only four amino acids (histidine, asparagine, leucine, isoleucine) and four phosphatidylcholines (PC.ae.C34.3, PC.aa.C34.2, PC.ae.C38.0, lysoPC.a.C18.2) were found to be significantly lower (p < 0.05) after adjustment for confounders among the AAA patients compared with the controls. There were no differences in the metabolites distinguishing the AAA patients with slow or fast growth from the controls, or distinguishing the patients with slow growth from those with fast growth. The current study describes novel significant alterations in amino acids and phosphatidylcholines metabolism associated with AAA occurrence, but no associations were found with AAA growth rate.

The Ability of Metabolomics to Discriminate Non-Small-Cell Lung Cancer Subtypes Depends on the Stage of the Disease and the Type of Material Studied

Identification of the NSCLC subtype at an early stage is still quite sophisticated. Metabolomics analysis of tissue and plasma of NSCLC patients may indicate new, and yet unknown, metabolic pathways active in the NSCLC. Our research characterized the metabolomics profile of tissue and plasma of patients with early and advanced NSCLC stage. Samples were subjected to thorough metabolomics analyses using liquid chromatography-mass spectrometry (LC-MS) technique. Tissue and/or plasma samples from 137 NSCLC patients were analyzed. Based on the early stage tissue analysis, more than 200 metabolites differentiating adenocarcinoma (ADC) and squamous cell lung carcinoma (SCC) subtypes as well as normal tissue, were identified. Most of the identified metabolites were amino acids, fatty acids, carnitines, lysoglycerophospholipids, sphingomyelins, plasmalogens and glycerophospholipids. Moreover, metabolites related to N-acyl ethanolamine (NAE) biosynthesis, namely glycerophospho (N-acyl) ethanolamines (GP-NAE), which discriminated early-stage SCC from ADC, have also been identified. On the other hand, the analysis of plasma of chronic obstructive pulmonary disease (COPD) and NSCLC patients allowed exclusion of the metabolites related to the inflammatory state in lungs and the identification of compounds (lysoglycerophospholipids, glycerophospholipids and sphingomyelins) truly characteristic to cancer. Our results, among already known, showed novel, thus far not described, metabolites discriminating NSCLC subtypes, especially in the early stage of cancer. Moreover, the presented results also indicated the activity of new metabolic pathways in NSCLC. Further investigations on the role of NAE biosynthesis pathways in the early stage of NSCLC may reveal new prognostic and diagnostic targets.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review

OBJECTIVE

This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making.

BACKGROUND

Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants.

METHODS

PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods.

RESULTS

Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses.

CONCLUSIONS

Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Metabolomics for prediction of hypertension in pregnancy: a systematic review and meta-analysis protocol

INTRODUCTION

Hypertension is a very important cause of maternal morbidity and mortality worldwide, despite efforts on prevention. The lack of a tool to provide effective and early prediction of hypertension for a high-risk group may contribute to improving maternal and fetal outcomes. Metabolomics has figured out as a promised technology to contribute to the improvement of hypertension in pregnancy prediction.

METHODS AND ANALYSIS

Our primary outcome is hypertensive disorders of pregnancy. A detailed systematic literature search will be performed in electronic databases PubMed, EMBASE, Scopus, Web of Science, Latin America and Caribbean Health Sciences Literature, Scientific Electronic Library Online, Health Technology Assessment and Database of Abstracts of Reviews of Effects using controlled terms 'pre-eclampsia', 'hypertensive disorders', 'metabolomics' and 'prediction' (and their variations). Studies from the latest 20 years will be included, except case reports, reviews, cross-sectional studies, letter to editors, expert opinions, commentaries papers or non-human research. If possible, we will perform a meta-analysis. Two peer-reviewers will independently perform the search and in cases of discordance, a third reviewer will be consulted.

ETHICS AND DISSEMINATION

As a systematic review, ethics approval is not required. The results of this review will present the current use and performance of metabolomics for predicting gestational hypertension. Such data could potentially guide future studies and interventions to improve existing prediction models.

PROSPERO REGISTRATION NUMBER

CRD42018097409.

Plasma Metabolomics Analysis Identifies Abnormal Energy, Lipid, and Amino Acid Metabolism in Abdominal Aortic Aneurysms

Background

Abdominal aortic aneurysm (AAA) is a complicated aortic dilatation disease. Metabolomics is an emerging system biology method. This aim of this study was to identify abnormal metabolites and metabolic pathways associated with AAA and to discover potential biomarkers that could affect the size of AAAs.

Material/Methods

An untargeted metabolomic method was used to analyze the plasma metabolic profiles of 39 patients with AAAs and 30 controls. Multivariate analysis methods were used to perform differential metabolite screening and metabolic pathway analysis. Cluster analysis and univariate analysis were performed to identify potential metabolites that could affect the size of an AAA.

Results

Forty-five different metabolites were identified with an orthogonal projection to latent squares-discriminant analysis model and the differences between them in the patients with AAAs and the control group were compared. A variable importance in the projection score >1 and P<0.05 were considered statistically significant. In patients with AAAs, the pathways involving metabolism of alanine, aspartate, glutamate, D-glutamine, D-glutamic acid, arginine, and proline; tricarboxylic acid cycling; and biosynthesis of arginine are abnormal. The progression of an AAA may be related to 13 metabolites: citric acid, 2-oxoglutarate, succinic acid, coenzyme Q1, pyruvic acid, sphingosine-1-phosphate, platelet-activating factor, LysoPC (16: 00), lysophosphatidylcholine (18: 2(9Z,12Z)/0: 0), arginine, D-aspartic acid, and L- and D-glutamine.

Conclusions

An untargeted metabolomic analysis using ultraperformance liquid chromatography-tandem mass spectrometry identified metabolites that indicate disordered metabolism of energy, lipids, and amino acids in AAAs.

An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies

Human Biomonitoring (HB), the process for determining whether and to what extent chemical substances penetrated our bodies, serves as a useful tool to quantify human exposure to pollutants. In cases of nutrition and physiologic status, HB plays a critical role in the identification of excess or deficiency of essential nutrients. In pollutant HB studies, levels of substances measured in body fluids (blood, urine, and breast milk) or tissues (hair, nails or teeth) aid in the identification of potential health risks or associated adverse effects. However, even as a widespread practice in several countries, most HB studies reflect exposure to a single compound or mixtures which are measured at a single time point in lifecycle. On the other hand, throughout an individual's lifespan, the contact with different physical, chemical, and social stressors occurs at varying intensities, differing times and durations. Further, the interaction between stressors and body receptors leads to dynamic responses of the entire biological system including proteome, metabolome, transcriptome, and adductome. Bearing this in mind, a relatively new vision in exposure science, defined as the exposome, is postulated to expand the traditional practice of measuring a single exposure to one or few chemicals at one-time point to an approach that addresses measures of exposure to multiple stressors throughout the lifespan. With the exposome concept, the science of exposure advances to an Environment-Wide Association Perspective, which might exhibit a stronger relationship with good health or disease conditions for an individual (phenotype). Thus, this critical review focused on the current progress of HB and exposome investigations, anticipating some challenges, strategies, and future needs to be taken into account for designing future surveys.

Identification of the metabolic fingerprints in women with polycystic ovary syndrome using the multiplatform metabolomics technique

In addition to chronic anovulation and clinical signs of hyperandrogenism women with polycystic ovary syndrome (PCOS) are insulin resistant and therefore, develop central obesity with its long term consequences such as dyslipidaemia, hypertension, atherosclerosis and type 2 diabetes mellitus (T2DM), which all lead to the development of cardiovascular disease (CVD). Due to the polysymptomatic nature of this syndrome and lack of consensus on its diagnostic criteria there is a strong need of finding a reliable biochemical or molecular marker, which would facilitate making the accurate diagnosis of PCOS. Therefore, the aim of our study was to perform a metabolomics analysis with the use of two complementary techniques: gas chromatography and liquid chromatography coupled with mass spectrometry, of the serum samples from women with PCOS (n = 30) and to compare them with healthy age and BMI matched controls (n = 30). Obtained results were subjected to one-dimensional statistical analysis (student's t-test or its non-parametric equivalent U Mann-Whitney test) and multivariate statistical analysis (the principal component analysis [PCA], variable importance into projection [VIP] and selectivity ratio [SR]). The results of our study showed that women with PCOS are characterised by metabolic disorders of the amino acids, carbohydrates, steroid hormones, lipids and purines. Compared to control subjects, women with PCOS had increased serum levels of phospholipids, aromatic amino acids, organic acids, hormones and sphinganine and decreased total cholesterol. Among the identified compounds, total cholesterol, phenylalanine and dehydroepiandrosterone sulfate, uric and lactic acid were the compounds with the strongest discriminating power.

LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates

In the process of anticancer drug development, research on the mechanism of action remains a major obstacle. In the present study, a cell metabolic profiling based discriminatory model was designed to give general direction on anticancer candidate mechanisms. Firstly, ultra-performance liquid chromatography in tandem with high-definition mass spectrometry was applied to obtain a comprehensive metabolic view of 12 human tumor cells. Secondly, multivariate data analysis was used to assess the metabolites' variations, and 42 metabolites were identified as the main contributors to the discrimination of different groups. Then a metabolite-based prediction model was constructed for the first time and verified by cross validation (R 2 = 0.909 and Q 2 = 0.869) and a permutation test (R 2 = 0.0871 and Q 2 = -0.4360). To validate if the model can be applied for mechanism prediction, 4 independent sample sets were used to train the model and the data dots of different drugs were located in different regions. Finally, the model was applied to predict the anticancer mechanism of two natural compounds and the results were consistent with several other studies. Overall, this is the first experimental evidence which reveals that a metabolic profiling based prediction model has good performance in anticancer mechanism research, and thus it may be a new method for rapid mechanism screening.

Maternal Plasma Metabolomic Profiles in Spontaneous Preterm Birth: Preliminary Results

Objective

To profile maternal plasma metabolome in spontaneous preterm birth.

Method

In this retrospective case-control study, we have examined plasma of patient with preterm birth (between 22 and 36 weeks of pregnancy (n = 57)), with threatened preterm labor (between 23 and 36 weeks of pregnancy (n = 49)), and with term delivery (n = 25). Plasma samples were analysed using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) in positive and negative polarity modes.

Results

We found 168 differentially expressed metabolites that were significantly distinct between study groups. We determined 51 metabolites using publicly available databases that could be subdivided into one of the five groups: amino acids, fatty acids, lipids, hormones, and bile acids. PLS-DA models, verified by SVM classification accuracy, differentiated preterm birth and term delivery groups.

Conclusions

Maternal plasma metabolites are different between term and preterm parturitions. Part of them may be related with preterm labor, while others may be affected by gestational age or the beginning of labor. Metabolite profile can classify preterm or term delivery groups raising the potential of metabolome as a biomarker to identify high-risk pregnancies. Metabolomic studies are also a tool to detect individual compounds that may be further tested in targeted researches.

An exploratory LC-MS-based metabolomics study reveals differences in aqueous humor composition between diabetic and non-diabetic patients with cataract

Cataract is the leading cause of blindness worldwide. Epidemiological studies revealed up to a fivefold increased prevalence of cataracts in diabetic subjects. Metabolomics is nowadays frequently implemented to understand pathophysiological processes responsible for disease occurrence and progression. It has also been used recently to study the metabolic composition of aqueous humor (AH). AH is a transparent fluid which fills the anterior and posterior chambers of the eye. It supplies nutrients and removes metabolic waste from avascular tissues in the eye. The aim of this study was to use metabolomics to compare the AH of diabetic and non-diabetic patients undergoing cataract surgery. Several antioxidants (methyltetrahydrofolic acid, taurine, niacinamide, xanthine, and uric acid) were found decreased (-22 to -61%, p-value 0.05-0.003) in AH of diabetics. Also amino acids (AA) and derivatives were found decreased (-21 to -36%, p-value 0.05-0.01) while glycosylated AA increased (+75-98%, p-value 0.03-0.009) in this group of patients. Metformin was detected in AH of people taking this drug. To our knowledge, this is the first metabolomics study aiming to assess differences in AH composition between diabetic and non-diabetic patients with cataract. An increased oxidative stress and perturbations in amino acid metabolism in AH may be responsible for earlier cataract onset in diabetic patients.

Untargeted metabolomics for plasma biomarker discovery for early chronic kidney disease diagnosis in pediatric patients using LC-QTOF-MS

Pediatric chronic kidney disease (CKD) is a clinical syndrome characterized by renal hypofunction occurring due to gradual and irreversible kidney damage that can further progress over time.

Application of Metabolic Profiling to Abdominal Aortic Aneurysm Research

Abdominal aortic aneurysm (AAA) is a complex disease posing diagnostic and therapeutic challenges. Metabonomics may aid in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. A systematic review of the diversity and outcomes of existing AAA metabonomic research has been performed. Original research studies applying metabonomics to human aneurysmal disease are included. Seven relevant articles were identified: four studies were based on plasma/serum metabolite profiling, and three studies examined aneurysmal tissue. Aminomalonic acid, guanidinosuccinic acid, and glycerol emerge as potential plasma biomarkers of large aneurysm. Lipid profiling improves predictive models of aneurysm presence. Patterns of metabolite variation associated with AAA relate to carbohydrate and lipid metabolism. Perioperative perturbations in metabolites suggest differential systemic inflammatory responses to surgery, generating hypotheses for adjunctive perioperative therapy. Significant limitations include small study sizes, lack of correction for multiple testing false discovery rates, and single time-point sampling. Metabolic profiling carries the potential to identify biomarkers of AAA and elucidate pathways underlying aneurysmal disease. Statistically and methodologically robust studies are required for validation, addressing the hiatus in understanding mechanisms of aneurysm growth and developing effective treatment strategies.

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

Objective

Our basic understanding of ascending thoracic aortic aneurysm (ATAA) pathogenesis is still very limited, hampering early diagnosis, risk prediction, and development of treatment options. “Omics”-technologies, ideal to reveal tissue alterations from the normal physiological state due to disease have hardly been applied in the field. Using a metabolomic approach, with this study the authors seek to define tissue differences between controls and various forms of ATAAs.

Methods

Using a targeted FIA-MS/MS metabolomics approach, we analysed and compared the metabolic profiles of ascending thoracic aortic wall tissue of age-matched controls (n = 8), bicuspid aortic valve-associated aneurysms (BAV-A; n = 9), tricuspid aortic valve-associated aneurysms (TAV-A; n = 14), and tricuspid aortic valve-associated aortic dissections (TAV-Diss; n = 6).

Results

With sphingomyelin (SM) (OH) C22:2, SM C18:1, SM C22:1, and SM C24:1 only 4 out of 92 detectable metabolites differed significantly between controls and BAV-A samples. Between controls and TAV-Diss samples only phosphatidylcholine (PC) ae C32:1 differed. Importantly, our analyses revealed a general increase in the amount of total sphingomyelin levels in BAV-A and TAV-Diss samples compared to controls.

Conclusions

Significantly increased levels of sphingomyelins in BAV-A and TAV-Diss samples compared to controls may argue for a repression of sphingomyelinase activity and the sphingomyelinase-ceramide pathway, which may result in an inhibition of tissue regeneration; a potential basis for disease initiation and progression.

A metabolomic approach shows sphingosine 1-phosphate and lysophospholipids as mediators of the therapeutic effect of liver growth factor in emphysema

Tobacco smoke exposure is the principal cause of lung tissue destruction, which in turn results in emphysema that leads into shortness of breath. Liver growth factor (LGF, a cell and tissue regenerating factor with therapeutic activity in several organs) has antifibrotic and antioxidant properties that could be useful to promote lung tissue regenerating capacity in damaged lungs. The current study has examined differences in metabolite profiles (fingerprints) of plasma from mice (strain C57BL/6J, susceptible to develop emphysema) exposed to tobacco smoke during six months. One group of mice received a treatment with Liver Growth Factor (LGF) after emphysema was established, whereas the other group did not receive the treatment. Age and sex-matched mice not exposed to smoke were also maintained with or without treatment as controls. Metabolic fingerprints (untargeted analysis) of plasma after protein precipitation were obtained by LC-QTOF-MS. The signals were processed and a large number of possible metabolites were found (23944). Multivariate data analysis provided models that highlighted the differences between control and smoke exposed mice in both conditions. Accurate masses of features (possible compounds) representing significant differences were searched using online public databases. Lipid mediators, related to intracellular signaling in inflammation, were found among the metabolites putatively identified as markers of the different conditions and among them, sphingosine, sphingosine 1-phosphate and lysophospholipids point at the relevance of such metabolites in the regulation of the processes related to tissue regeneration mediated by LGF. These results also suggest that metabolomic fingerprinting could potentially guide the characterization of relevant metabolites leading the regeneration of lungs in emphysema disease.

Metabolomic study of the intervention effects of Shuihonghuazi Formula, a Traditional Chinese Medicinal formulae, on hepatocellular carcinoma (HCC) rats using performance HPLC/ESI-TOF-MS

ETHNOPHARMACOLOGICAL RELEVANCE

Metabolomics is the comprehensive assessment of endogenous metabolites of a biological system in a holistic context, and its property consists with the global view of Traditional Chinese Medicine (TCM). Shuihonghuazi Formula (SHHZF) has been used for liver cancer early treatment in clinical for more than thirty years, but its mechanism remains unclear completely. This paper was designed to explore the therapeutic effects of SHHZF on liver cancer and its metabolomic characters.

MATERIALS AND METHODS

All the rats were given diethylnitrosamine (DEN) at the dosage of 70mg/kg for 14 weeks. From the 7th weeks, SHHZF was given to the rats which lasted for 10 weeks. Therapeutic effects of SHHZF was compared with that of cyclophosphamide (CTX). High performance liquid-chromatography/electrospray-ionization time of flight mass spectrometer (HPLC/ESI-TOF-MS) combined with pattern recognition approaches including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), was integrated to approximate the comprehensive metabolic signature and discover differentiating metabolites by Agilent MPP 12.1. The changes in metabolic profiling in plasma were restored to their baseline values after SHHZF treatment according to the PLS-DA score plots.

RESULTS

The results indicated that 23 ions as "differentiating metabolites". The alterations in those metabolites were associated with perturbations in fatty acid and bile acid metabolism, in response to liver cancer through immune and nervous system. And SHHZF could increase the uptake and utilization of linoleic acid and oleic acid, increase arachidonic acid-like substance content and enhance organism immunity of liver cancer rats. And it also could increase the translation from phosphatidylethanolamine (PE) to phosphatidylcholine (PC), linoleic acid metabolism and inhibits abnormal metabolism of bile acid.

CONCLUSIONS

The mechanism of therapeutic effects of SHHZF on liver cancer by adjusting the activities of PE N-methyl transferase (PEMT), Lysophospholipase D, methylenetetrahydrofolate reductase (MTHFR) and lysophospholipase was elucidated by the method of metabonomics for the first time.

LC-MS-Based Metabolic Fingerprinting of Aqueous Humor

Aqueous humor (AH) is a transparent fluid which fills the anterior and posterior chambers of the eye. It supplies nutrients and removes metabolic waste from avascular tissues in the eye. Proper homeostasis of AH is required to maintain adequate intraocular pressure as well as optical and refractive properties of the eye. Application of metabolomics to study human AH may improve knowledge about the molecular mechanisms of eye diseases. Until now, global analysis of metabolites in AH has been mainly performed using NMR. Among the analytical platforms used in metabolomics, LC-MS allows for the highest metabolome coverage. The aim of this study was to develop a method for extraction and analysis of AH metabolites by LC-QTOF-MS. Different protocols for AH preparation were tested. The best results were obtained when one volume of AH was mixed with one volume of methanol : ethanol (1 : 1). In the final method, 2 µL of extracted sample was analyzed by LC-QTOF-MS. The method allowed for reproducible measurement of over 1000 metabolic features. Almost 250 metabolites were identified in AH and assigned to 47 metabolic pathways. This method is suitable to study the potential role of amino acids, lipids, oxidative stress, or microbial metabolites in development of ocular diseases.

Serum metabolic fingerprinting after exposure of rats to quinolinic acid

Quinolinic acid (QUIN), one of the end metabolites in the kynurenine pathway, plays an important role in the pathogenesis of several diseases. Serum QUIN concentration rises in patients with renal dysfunction, liver cirrhosis, and many other inflammatory diseases. In the present study, osmotic minipumps containing QUIN (0.3 and 1mg/day) were implanted intraperitoneally into rats for 28days. Then, the physiological and toxicological variables were evaluated and LC-QTOF-MS serum metabolic fingerprinting was performed. QUIN significantly decreased the serum concentrations of several amino acids (phenylalanine, valine, tyrosine, and tryptophan), pantothenic acid, branched chain C4 acylcarnitine, total cholesterol, and glucose; increased the serum concentrations of amides (pentadecanoic amide, palmitic amide, oleamide, and stearamide), polyamines (spermine and spermidine), sphingosine, and deoxy-prostaglandin; caused alterations in phospholipids. This is the first report of comprehensive metabolites analysis after chronic intraperitoneal administration of QUIN. Further studies could develop new therapeutics for patients with disorders accompanied by increased serum level of QUIN.

Evidence of deregulated cholesterol efflux in abdominal aortic aneurysm

Previous studies indicated that lipids may be associated with abdominal aortic aneurysm (AAA); however the molecular mechanism involved is unclear. Our study aimed to investigate the expression pattern of cholesterol efflux related proteins in AAA. Liver X receptors (LXRα and LXRβ), ATP-binding-cassette transporter A1 (ABCA1), Apolipoprotein AI (ApoAI), smooth muscle α-actin (α-SM) and vimentin expression levels were evaluated in human AAA, atherosclerotic (ATH) and normal abdominal aortic tissues. We found significant differences in LXRα, LXRβ and ABCA1 mRNA expression levels between AAA, ATH and normal whole aortic tissues and also within the AAA, ATH and normal "intima-media" layers. Specifically, LXRα, LXRβ and ABCA1 mRNA levels were decreased in AAA compared to ATH-whole tissues, as well as in AAA "intima-media" compared to ATH and normal "intima-media" layers. Moreover, immunohistochemical evaluation revealed that LXRα and ABCA1 immunoreactivities (IR) were reduced in the AAA media compared to the normal and ATH media layers and that they were also reduced in the intima layer of AAA and ATH tissues, whereas ApoAI-IR was increased in the AAA and ATH aortic walls compared to normal pointing to possible deregulation of the cholesterol efflux mechanism in AAA. Furthermore, double staining for vimentin and α-SM showed vimentin expression in the intima and inner media layer of AAA with sparse vimentin positive SMCs designating possible SMCs phenotype switch from contractile to synthetic form. In addition, histochemical analysis showed excessive lipid accumulation in the AAA wall, while co-staining using Oil Red O with α-SM or CD68 revealed lipid accumulation in SMCs and macrophages, respectively. Our study provides novel evidence for impaired cholesterol efflux in AAA associated with lipid accumulation in SMCs and macrophages, as well as switch of SMCs phenotype from contractile to synthetic form.

From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS

This tutorial provides a comprehensive description of the GC-MS-based untargeted metabolomics workflow including: ethical approval requirement, sample collection and storage, equipment maintenance and setup, sample treatment, monitoring of analytical variability, data pre-processing including deconvolution by free software such as AMDIS, data processing, statistical analysis and validation, detection of outliers and biological interpretation of the results. For each stage tricks will be suggested, pitfalls will be highlighted and advice will be provided on how to get the best from this methodology and technique. In addition, a step-by-step procedure and an example of our in-house library have been included in the supplementary material to lead the user through the concepts described herein. As a case study, an interesting example from one of our experiments at CEMBIO Research Centre is described, presenting an example of the use of this ready-to use protocol for identification of a metabolite that was not previously included in Fiehn commercial target library.

Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis

Differences in the degree and severity of Acute Coronary Syndrome, associated to differences in the electrocardiogram, together with blood tests of biomarkers classify patients for diagnosis and treatment. Cases where the electrocardiogram and/or biomarkers are not conclusive still appear, and there is a need for complementary biomarkers for routine determinations. Metabolomics approaches with blind fingerprinting could reveal differences in metabolites, which must be confirmed by means of targeted determinations. CE-MS and HILIC-MS are well suited for the determination of highly polar compounds, like those from to the intermediate metabolism, altered due to acute stress induced by myocardial infarction. Serum from patients with ST-elevated and non-ST elevated myocardial infarction was collected at intensive care and emergency units, and fingerprinted with CE-MS. Data pretreatment and analysis showed up carnitine-related compounds and amino acids differentially present in both groups. Acylcarnitines and amino acids were then quantitatively measured with HILIC-MS-QqQ. The significance of the differences and the sensitivity/specificity of each compound were individually evaluated. The ratio of free carnitine to acylcarnitines, together with the ratios of acetylcarnitine to betaine, to threonine, and to citrulline, showed high significance and area under the curve in the respective receiver operating characteristic curves. This study opens new possibilities for defining new sets of biomarkers for refining the diagnosis of the patients with difficult classification.

Metabolomic analysis of clinical plasma from cerebral infarction patients presenting with blood stasis

Blood stasis (BS) is characterized as a disorder of blood circulation. In traditional Korean medicine (TKM), it is viewed as a cause factor of diseases such as multiple sclerosis and stroke. This study investigated differences in the plasma metabolites profiles of subjects displaying BS or non-BS patterns. Thirty-one patients with cerebral infarction diagnosed with BS and an equal number of sex- and age-matched non-BS patients were enrolled. Metabolic profiling was performed using UPLC-MS. The ratio of subjects with a rough pulse and purple coloration of the tongue was higher in patients presenting with BS pattern. Through metabolomics analysis, 82 metabolites that differed significantly between the BS and non-BS pattern were identified, and the two groups were significantly separated using an orthogonal partial least square-discriminant analysis model (P < 0.001). Of these 82 metabolites, acetyl carnitine, leucine, kynurenine, phosphocholine, hexanoyl carnitine, and decanoyl carnitine were present in significantly higher levels in patients with a BS pattern than those with a non-BS pattern. Our results also demonstrated that seven plasma metabolites, including acyl-carnitines and kynurenine, were associated with a BS pattern, suggesting that variant plasma metabolic profiles may serve as a biomarker for diagnosis of BS in patients with cerebral infarction.

Sample collection and preparation of biofluids and extracts for gas chromatography-mass spectrometry

To maximize the utility of gas chromatography-mass spectrometry (GC-MS) in metabonomics research, all stages of the experimental design should be standardized, including sample collection, storage, preparation, and sample separation. Moreover, the prerequisite for any GC-MS analysis is that a compound must be volatile and thermally stable if it is to be analyzed using this technique. Since many metabolites are nonvolatile and polar in nature, they are not readily amenable to analysis by GC-MS and require initial chemical derivatization of the polar functional groups in order to reduce the polarity and to increase the thermal stability and volatility of the analytes. In this chapter, an overview is presented of the optimum approach to sample collection, storage, and preparation for gas chromatography-mass spectrometry-based metabonomics with particular focus on urine samples as example of biofluids.

Metabolomics for laboratory diagnostics

Metabolomics is an emerging approach in a systems biology field. Due to continuous development in advanced analytical techniques and in bioinformatics, metabolomics has been extensively applied as a novel, holistic diagnostic tool in clinical and biomedical studies. Metabolome's measurement, as a chemical reflection of a current phenotype of a particular biological system, is nowadays frequently implemented to understand pathophysiological processes involved in disease progression as well as to search for new diagnostic or prognostic biomarkers of various organism's disorders. In this review, we discussed the research strategies and analytical platforms commonly applied in the metabolomics studies. The applications of the metabolomics in laboratory diagnostics in the last 5 years were also reviewed according to the type of biological sample used in the metabolome's analysis. We also discussed some limitations and further improvements which should be considered taking in mind potential applications of metabolomic research and practice.

A pilot study of plasma metabolomic patterns from patients treated with ketamine for bipolar depression: evidence for a response-related difference in mitochondrial networks

BACKGROUND AND PURPOSE

(R,S)-ketamine produces rapid and significant antidepressant effects in approximately 65% of patients suffering from treatment-resistant bipolar depression (BD). The genetic, pharmacological and biochemical differences between ketamine responders and non-responders have not been identified. The purpose of this study was to employ a metabolomics approach, a global, non-targeted determination of endogenous metabolic patterns, to identify potential markers of ketamine response and non-response.

EXPERIMENTAL APPROACH

Plasma samples from 22 BD patients were analyzed to produce metabolomic patterns. The patients had received ketamine in a placebo-controlled crossover study and the samples were obtained 230 min post-administration at which time the patients were categorized as responders or non-responders. Matching plasma samples from the placebo arm of the study were also analysed. During the study, the patients were maintained on either lithium or valproate.

KEY RESULTS

The metabolomic patterns were significantly different between the patients maintained on lithium and those maintained on valproate, irrespective of response to ketamine. In the patients maintained on lithium, 18 biomarkers were identified. In responders, lysophosphatidylethanolamines (4) and lysophosphatidylcholines (9) were increased relative to non-responders.

CONCLUSIONS AND IMPLICATIONS

The results indicate that the differences between patients who respond to ketamine and those who do not are due to alterations in the mitochondrial β-oxidation of fatty acids. These differences were not produced by ketamine administration. The data indicate that pretreatment metabolomics screening may be a guide to the prediction of response and a potential approach to the individualization of ketamine therapy.

Novel biomarkers of abdominal aortic aneurysm disease: identifying gaps and dispelling misperceptions

Abdominal aortic aneurysm (AAA) is a prevalent and potentially life-threatening disease. Early detection by screening programs and subsequent surveillance has been shown to be effective at reducing the risk of mortality due to aneurysm rupture. The aim of this review is to summarize the developments in the literature concerning the latest biomarkers (from 2008 to date) and their potential screening and therapeutic values. Our search included human studies in English and found numerous novel biomarkers under research, which were categorized in 6 groups. Most of these studies are either experimental or hampered by their low numbers of patients. We concluded that currently no specific laboratory markers allow screeing for the disease and monitoring its progression or the results of treatment. Further studies and studies in larger patient groups are required in order to validate biomarkers as cost-effective tools in the AAA disease.

Metabolomics reveals metabolite changes in acute pulmonary embolism

Pulmonary embolism (PE) is a common cardiovascular emergency which can lead to pulmonary hypertension (PH) and right ventricular failure as a consequence of pulmonary arterial bed occlusion. The diagnosis of PE is challenging due to nonspecific clinical presentation, which results in relatively high mortality. Moreover, the pathological factors associated with PE are poorly understood. Metabolomics can provide new highlights which can help in the understanding of the processes and even propose biomarkers for its diagnosis. In order to obtain more information about PE and PH, acute PE was induced in large white pigs and plasma was obtained before and after induction of PE. Metabolic fingerprints from plasma were obtained with LC-QTOF-MS (positive and negative ionization) and GC-Q-MS. Data pretreatment and statistical analysis (uni- and multivariate) were performed in order to compare metabolic fingerprints and to select the metabolites that showed higher loading for the classification (28 from LC and 19 from GC). The metabolites found differentially distributed among groups are mainly related to energy imbalance in hypoxic conditions, such as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates, as well as a group of lipidic mediators that could be involved in the transduction of the signals to the cells such as sphingolipids and lysophospholipids, among others. Results presented in this report reveal that combination of LC-MS- and GC-MS-based metabolomics could be a powerful tool for diagnosis and understanding pathophysiological processes due to acute PE.

Comparison of the serum lipidome in patients with abdominal aortic aneurysm and peripheral artery disease

BACKGROUND

Currently, the relationship between circulating lipids and abdominal aortic aneurysm (AAA) is unclear. We conducted a lipidomic analysis to identify serum lipids associated with AAA presence. Secondary analyses assessed the ability of models incorporating lipidomic features to improve stratification of patient groups with and without AAA beyond traditional risk factors.

METHODS AND RESULTS

Serum lipids were profiled via liquid chromatography tandem mass spectrometry analysis of serum from 161 patients with AAA and 168 controls with peripheral artery disease. Binary logistic regression was used to identify AAA-associated lipids. Classification models were created based on a combination of (1) traditional risk factors only or (2) lipidomic features and traditional risk factors. Model performance was assessed using receiver operator characteristic curves. Three diacylglycerols and 7 triacylglycerols were associated with AAA. Combining lipidomic features with traditional risk factors significantly improved stratification of AAA and peripheral artery disease groups when compared with traditional risk factors alone (mean area under the receiver operator characteristic curve [95% confidence interval], 0.760 [0.756-0.763] and 0.719 [0.716-0.723], respectively; P<0.05).

CONCLUSIONS

A group of linoleic acid containing triacylglycerols and diacylglycerols were significantly associated with AAA presence. Inclusion of lipidomic features in multivariate analyses significantly improved prediction of AAA presence when compared with traditional risk factors alone.

Fingerprinting-based metabolomic approach with LC-MS to sleep apnea and hypopnea syndrome: a pilot study

Sleep apnea and hypopnea syndrome (SAHS) is a multicomponent disorder, with associated cardiovascular and metabolic alterations, second in order of frequency among respiratory disorders. Sleep apnea is diagnosed with an overnight sleep test called a polysomnogram, which requires having the patient in hospital. In addition, a more clear classification of patients according to mild and severe presentations would be desirable. The aim of the present study was to assess the relative metabolic changes in SAHS to identify new potential biomarkers for diagnosis, able to evaluate disease severity to establish response to therapeutic interventions and outcomes. For this purpose, metabolic fingerprinting represents a valuable strategy to monitor, in a nontargeted manner, the changes that are at the base of the pathophysiological mechanism of SAHS. Plasma samples of 33 SAHS patients were collected after polysomnography and analyzed with LC coupled to MS (LC-QTOF-MS). After data treatment and statistical analysis, signals differentiating nonsevere and severe patients were detected. Putative identification of 14 statistically significant features was obtained and changes that can be related to the episodes of hypoxia/reoxygenation (inflammation) have been highlighted. Among them, the patterns of variation of platelet activating factor and lysophospholipids, together with some compounds related to differential activity of the gut microflora (bile pigments and pipecolic acid) open new lines of research that will benefit our understanding of the alterations, offering new possibilities for adequate monitoring of the stage of the disease.

From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS

Application of high-throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC-MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC-QTOF-MS.

Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases

In the recent years, there has been an increase in the number of metabolomic approaches used, in parallel with proteomic and functional genomic studies. The wide variety of chemical types of metabolites available has also accelerated the use of different techniques in the investigation of the metabolome. At present, metabolomics is applied to investigate several human diseases, to improve their diagnosis and prevention, and to design better therapeutic strategies. In addition, metabolomic studies are also being carried out in areas such as toxicology and pharmacology, crop breeding, and plant biotechnology. In this review, we emphasize the use and application of metabolomics in human diseases and plant research to improve human health.

New Insights Into Aortic Diseases: A Report From the Third International Meeting on Aortic Diseases (IMAD3)

The current state of research and treatment on aortic diseases was discussed in the "3rd International Meeting on Aortic Diseases" (IMAD3) held on October 4-6, 2012, in Liège, Belgium. The 3-day meeting covered a wide range of topics related to thoracic aortic aneurysms and dissections, abdominal aortic aneurysms, and valvular diseases. It brought together clinicians and basic scientists and provided an excellent opportunity to discuss future collaborative research projects for genetic, genomics, and biomarker studies, as well as clinical trials. Although great progress has been made in the past few years, there are still a large number of unsolved questions about aortic diseases. Obtaining answers to the key questions will require innovative, interdisciplinary approaches that integrate information from epidemiological, genetic, molecular biology, and bioengineering studies on humans and animal models. It is more evident than ever that multicenter collaborations are needed to accomplish these goals.

Method development and validation for rat serum fingerprinting with CE-MS: application to ventilator-induced-lung-injury study

In the search for a noninvasive and reliable rapid screening method to detect biomarkers, a metabolomics fingerprinting approach was developed and applied to rat serum samples using capillary electrophoresis coupled to an electrospray ionization-time of flight-mass spectrometer (CE-TOF-MS). An ultrafiltration method was used for sample pretreatment. To evaluate performance the method was validated with carnitine, choline, ornithine, alanine, acetylcarnitine, betaine, and citrulline, covering the entire electropherogram of pool of rat serum. The linearity for all metabolites was >0.99, with good recovery and precision. Approximately 34 compounds were also confirmed in the pool of rat serum. The method was successfully applied to real serum samples from rats with ventilator-induced lung injury, an experimental rat model for acute lung injury (ALI), giving a total of 1163 molecular features. By use of univariate and multivariate statistics 18 significant compounds were found, of which five were confirmed. The involvement of arginase and nitric oxide synthase has been proved for other lung diseases, meaning the increase of asymmetric dimethyl arginine (ADMA) and ornithine and the decrease of arginine found were in accordance with published literature. Ultimately this fingerprinting approach offers the possibility of identifying biomarkers that could be regularly screened for as part of routine disease control. In this way it might be possible to prevent the development of ALI in patients in critical care units.