The role of metabolites and metabolomics in clinically applicable biomarkers of disease

Serum metabolite profiling of ST-segment elevation myocardial infarction using liquid chromatography quadrupole time-of-flight mass spectrometry

ST segment elevation myocardial infarction (STEMI) is one of the most common global causes of cardiovascular disease-related death. Several metabolites may change during STEMI. Hence, analysis of metabolites in body fluid may be considered as a rapid and accurate test for initial diagnosis. This study has therefore attempted to determine the variation in metabolites identified in the serum of STEMI patients (n = 20) and 15 controls. Samples collected from the Cardiology Department, Medical Faculty, Ataturk University, were extracted by liquid-liquid extraction and analysed using liquid chromatography quadrupole time-of-flight mass spectrometry. The METLIN database was used for the identification and characterization of metabolites. According to Q-TOF/MS measurements, 231 m/z values, which were significantly different between groups (P < 0.01 and fold analysis >1.5) were detected. Metabolite identification was achieved via the Human Metabolome database. According to the multivariate data analysis, leucine, isoleucine, l-proline, l-alanine, glycine, fumaric acid, citrate, succinate and carnitine levels were decreased, whereas levels of propionic acid, maleic acid, butyric acid, urea, oleic acid, palmitic acid, lysoPC [18:2(9Z)], glycerol, phoshpatidylethanolamine, caffeine and l-lactic acid were increased in STEMI patients compared with controls. In conclusion, malonic acid, maleic acid, fumaric acid and palmitic acid can be used as biomarkers for early risk stratification of patients with STEMI.

Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods

Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Analysis of serum changes in response to a high fat high cholesterol diet challenge reveals metabolic biomarkers of atherosclerosis

Atherosclerotic plaques are characterized by an accumulation of macrophages, lipids, smooth muscle cells, and fibroblasts, and, in advanced stages, necrotic debris within the arterial walls. Dietary habits such as high fat and high cholesterol (HFHC) consumption are known risk factors for atherosclerosis. However, the key metabolic contributors to diet-induced atherosclerosis are far from established. Herein, we investigate the role of a 2-year HFHC diet challenge in the metabolic changes of development and progression of atherosclerosis. We used a non-human primate (NHP) model (baboons, n = 60) fed a HFHC diet for two years and compared metabolomic profiles in serum from animals on baseline chow with serum collected after the challenge diet using two-dimensional gas chromatography time-of-flight mass-spectrometry (2D GC-ToF-MS) for untargeted metabolomic analysis, to quantify metabolites that contribute to atherosclerotic lesion formation. Further, clinical biomarkers associated with atherosclerosis, lipoprotein measures, fat indices, and arterial plaque formation (lesions) were quantified. Using two chemical derivatization (i.e., silylation) approaches, we quantified 321 metabolites belonging to 66 different metabolic pathways, which revealed significantly different metabolic profiles of HFHC diet and chow diet fed baboon sera. We found heritability of two important metabolites, lactic acid and asparagine, in the context of diet-induced metabolic changes. In addition, abundance of cholesterol, lactic acid, and asparagine were sex-dependent. Finally, 35 metabolites correlated (R2, 0.068-0.271, P < 0.05) with total lesion burden assessed in three arteries (aortic arch, common iliac artery, and descending aorta) which could serve as potential biomarkers pending further validation. This study demonstrates the feasibility of detecting sex-specific and heritable metabolites in NHPs with diet-induced atherosclerosis using untargeted metabolomics allowing understanding of atherosclerotic disease progression in humans.

Evaluation of salivary metabolomics in oral leukoplakia and oral squamous cell carcinoma

BACKGROUND

Metabolomics is the study of metabolome which describes the full repertoire of small molecules, and the analysis of salivary metabolomics may help in identifying tumor-specific biomarkers for early diagnosis and prediction of tumor progression. The aim of the study was to evaluate the clinical utility of salivary metabolites in oral leukoplakia and oral squamous cell carcinoma.

METHODS

Salivary metabolomic profile of patients diagnosed with oral leukoplakia (n = 21) and oral squamous cell carcinoma (n = 22) was compared with apparently normal controls (n = 18) using Q-TOF-liquid chromatography-mass spectrometry. MassHunter profile software and Metlin database were used for metabolite identification. ANOVA to identify the regulation of metabolites between the three groups, t test (P < 0.05) to signify the changes between two groups, and chi-square test (P < 0.05) to indicate the presence or absence of metabolites in the study participants of the three groups were performed.

RESULTS

Significant upregulation of 1-methylhistidine, inositol 1,3,4-triphosphate, d-glycerate-2-phosphate, 4-nitroquinoline-1-oxide, 2-oxoarginine, norcocaine nitroxide, sphinganine-1-phosphate, and pseudouridine in oral leukoplakia and OSCC was noted. Downregulated compounds in the diseased groups included l-homocysteic acid, ubiquinone, neuraminic acid, and estradiol valerate.

CONCLUSION

A range of salivary metabolites were significantly altered in oral leukoplakia and oral squamous cell carcinoma. Further, it is necessary to evaluate the clinical utility of the individual metabolites in preventing malignant transformation of oral leukoplakia and to improve prognosis of oral squamous cell carcinoma.

Metabolic dysfunction following weight regain compared to initial weight gain in a high-fat diet-induced obese mouse model

Diet-induced weight loss and regain leads to physiological and metabolic changes, some of which are potentially harmful. However, the specific metabolic processes and dysfunctions associated with weight regain, and how they differ from initial weight gain, remain unclear. Thus, we examined the metabolic profiles of mice following weight regain compared to initial weight gain. Mice were fed a normal diet or a high-fat diet or were cycled between the two diets to alternate between obese and lean states. Liver samples were collected and hepatic metabolites were profiled using nuclear magnetic resonance (NMR). The identified metabolites associated with weight regain were quantified using gas chromatography/mass spectrometry (GC/MS) and lipid profiles were assessed using ultra-high-performance liquid chromatography-quadrupole time-of-flight MS (UPLC-QTOF-MS). In addition, changes in expression of pro-inflammatory cytokines and gluconeogenic enzymes were investigated using polymerase chain reaction (PCR) and western blotting, respectively. Hepatic levels of several amino acids were reduced in mice during weight regain compared with initial weight gain. In addition, gluconeogenic enzyme levels were increased following weight regain, indicating an up-regulation of gluconeogenesis. Lipidomic profiling revealed that levels of ceramide and sphingomyelin, which are related to obesity-induced inflammation, were significantly increased during weight regain compared to initial weight gain. Moreover, tumor necrosis factor-α (TNF-α) and transforming growth factor-β1 (TGF-β1) levels were significantly up-regulated during weight regain. In this study, weight regains lead to an up-regulation of gluconeogenesis and aggravated inflammation. Additionally, weight regain can worsen the metabolic dysfunction associated with obesity.

Metabolomic and lipidomic plasma profile changes in human participants ascending to Everest Base Camp

At high altitude oxygen delivery to the tissues is impaired leading to oxygen insufficiency (hypoxia). Acclimatisation requires adjustment to tissue metabolism, the details of which remain incompletely understood. Here, metabolic responses to progressive environmental hypoxia were assessed through metabolomic and lipidomic profiling of human plasma taken from 198 human participants before and during an ascent to Everest Base Camp (5,300 m). Aqueous and lipid fractions of plasma were separated and analysed using proton (1H)-nuclear magnetic resonance spectroscopy and direct infusion mass spectrometry, respectively. Bayesian robust hierarchical regression revealed decreasing isoleucine with ascent alongside increasing lactate and decreasing glucose, which may point towards increased glycolytic rate. Changes in the lipid profile with ascent included a decrease in triglycerides (48-50 carbons) associated with de novo lipogenesis, alongside increases in circulating levels of the most abundant free fatty acids (palmitic, linoleic and oleic acids). Together, this may be indicative of fat store mobilisation. This study provides the first broad metabolomic account of progressive exposure to environmental hypobaric hypoxia in healthy humans. Decreased isoleucine is of particular interest as a potential contributor to muscle catabolism observed with exposure to hypoxia at altitude. Substantial changes in lipid metabolism may represent important metabolic responses to sub-acute exposure to environmental hypoxia.

NMR metabolic profiling of follicular fluid for investigating the different causes of female infertility: a pilot study

INTRODUCTION

Several metabolomics studies have correlated follicular fluid (FF) metabolite composition with oocyte competence to fertilization, embryo development and pregnancy but there is a scarcity of research examining the metabolic effects of various gynaecological diseases.

OBJECTIVES

In this study we aimed to analyze and correlate the metabolic profile of FF from women who were following in vitro fertilization (IVF) treatments with their different infertility pathologies.

METHODS

We selected 53 women undergoing IVF who were affected by: tubal diseases, unexplained infertility, endometriosis, polycystic ovary syndrome (PCOS). FF of the study participants was collected at the time of oocytes retrieval. Metabolomic analysis of FF was performed by nuclear magnetic resonance (NMR) spectroscopy.

RESULTS

FF presents some significant differences in various infertility pathologies. Although it was not possible to discriminate between FF of control participants and women with tubal diseases and unexplained infertility, comparison of FF metabolic profile from control women with patients with endometriosis and PCOS revealed significant differences in some metabolites that can be correlated to the causes of infertility.

CONCLUSION

NMR-based metabolic profiling may be successfully applied to find diagnostic biomarkers for PCOS and endometriosis and it might be also used to predict oocyte developmental potential and subsequent outcome.

Targeted Metabolomic Profiling of Peritoneal Dialysis Effluents Shows Anti-oxidative Capacity of Alanyl-Glutamine

Readily available peritoneal dialysis (PD) effluents from PD patients in the course of renal replacement therapy are a potentially rich source for molecular markers for predicting clinical outcome, monitoring the therapy, and therapeutic interventions. The complex clinical phenotype of PD patients might be reflected in the PD effluent metabolome. Metabolomic analysis of PD effluent might allow quantitative detection and assessment of candidate PD biomarkers for prognostication and therapeutic monitoring. We therefore subjected peritoneal equilibration test effluents from 20 stable PD patients, obtained in a randomized controlled trial (RCT) to evaluate cytoprotective effects of standard PD solution (3.86% glucose) supplemented with 8 mM alanyl-glutamine (AlaGln) to targeted metabolomics analysis. One hundred eighty eight pre-defined metabolites, including free amino acids, acylcarnitines, and glycerophospholipids, as well as custom metabolic indicators calculated from these metabolites were surveyed in a high-throughput assay requiring only 10 μl of PD effluent. Metabolite profiles of effluents from the cross-over trial were analyzed with respect to AlaGln status and clinical parameters such as duration of PD therapy and history of previous episodes of peritonitis. This targeted approach detected and quantified 184 small molecules in PD effluent, a larger number of detected metabolites than in all previous metabolomic studies in PD effluent combined. Metabolites were clustered within substance classes regarding concentrations after a 4-h dwell. PD effluent metabolic profiles were differentiated according to PD patient sub-populations, revealing novel changes in small molecule abundance during PD therapy. AlaGln supplementation of PD fluid altered levels of specific metabolites, including increases in alanine and glutamine but not glutamate, and reduced levels of small molecule indicators of oxidative stress, such as methionine sulfoxide. Our study represents the first application of targeted metabolomics to PD effluents. The observed metabolomic changes in PD effluent associated with AlaGln-supplementation during therapy suggested an anti-oxidant effect, and were consistent with the restoration of important stress and immune processes previously noted in the RCT. High-throughput detection of PD effluent metabolomic signatures and their alterations by therapeutic interventions offers new opportunities for metabolome-clinical correlation in PD and for prescription of personalized PD therapy.

Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles

The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations between these factors and 34 clinical covariates. Our results suggest that liver function, followed by kidney function and insulin resistance show the strongest associations with the plasma metabolite profile. The association of specific phenotypes with several components may suggest multiple independent metabolic mechanisms, which is further supported by the composition of the associated factors.

Quantitative determination of potential urine biomarkers of respiratory illnesses using new targeted metabolomic approach

The diagnosis of asthma and chronic obstructive pulmonary disease (COPD) can be challenging due to the overlap in their clinical presentations in some patients. There is a need for a more objective clinical test that can be routinely used in primary care settings. Through an untargeted ¹H NMR urine metabolomic approach, we identified a set of endogenous metabolites as potential biomarkers for the differentiation of asthma and COPD. A subset of these potential biomarkers contains 7 highly polar metabolites of diverse physicochemical properties. To the best of our knowledge, there is no liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that evaluated more than two of the target metabolites in a single analytical run. The target metabolites belong to the families of monosaccharides, organic acids, amino acids, quaternary ammonium compounds and nucleic acids, rendering hydrophilic interaction liquid chromatography (HILIC) an ideal technology for their quantification. Since a clinical decision is to be made from patients data, a fully validated analytical method is required for biomarker validation. Method validation for endogenous metabolites is a daunting task since current guidelines were designed for exogenous compounds. As such, innovative approaches were adopted to meet the validation requirements. Herein, we describe a sensitive HILIC-MS/MS method for the quantification of the 7 endogenous urinary metabolites. Detection was achieved in the multiple reaction monitoring (MRM) mode with polarity switching, using quadrupole-linear ion trap instrument (QTRAP 6500) as well as single ion monitoring in the negative-ion mode. The method was fully validated according to the regulatory guidelines. Linearity was established between 6 and 21000 ng/mL and quality control samples demonstrated acceptable intra- and inter-day accuracy (85.7%-112%), intra- and inter-day precision (CV% <11.5%) as well as stability under various storage and sample processing conditions. To illustrate the method's applicability, the validated method was applied to the analysis of a small set of urine samples collected from asthma and COPD patients. Preliminary modelling of separation was generated using partial least square discriminant analysis (R² 0.752 and Q² 0.57). The adequate separation between patient samples confirms the diagnostic potential of these target metabolites as a proof-of-concept for the differentiation between asthma and COPD. However, more patient urine samples are needed in order to increase the statistical power of the analytical model.

Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma

Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted ¹H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

Non-invasive urinary metabolomic profiles discriminate biliary atresia from infantile hepatitis syndrome

INTRODUCTION

Neonatal cholestatic disorders are a group of hepatobiliary diseases occurring in the first 3 months of life. The most common causes of neonatal cholestasis are infantile hepatitis syndrome (IHS) and biliary atresia (BA). The clinical manifestations of the two diseases are too similar to distinguish them. However, early detection is very important in improving the clinical outcome of BA. Currently, a liver biopsy is the only proven and effective method used to differentially diagnose these two similar diseases in the clinic. However, this method is invasive. Therefore, sensitive and non-invasive biomarkers are needed to effectively differentiate between BA and IHS. We hypothesized that urinary metabolomics can produce unique metabolite profiles for BA and IHS.

OBJECTIVES

The aim of this study was to characterize urinary metabolomic profiles in infants with BA and IHS, and to identify differences among infants with BA, IHS, and normal controls (NC).

METHODS

Urine samples along with patient characteristics were obtained from 25 BA, 38 IHS, and 38 NC infants. A non-targeted gas chromatography-mass spectrometry (GC-MS) metabolomics method was used in conjunction with orthogonal partial least squares discriminant analysis (OPLS-DA) to explore the metabolomic profiles of BA, IHS, and NC infants.

RESULTS

In total, 41 differentially expressed metabolites between BA vs. NC, IHS vs. NC, and BA vs. IHS were identified. N-acetyl-D-mannosamine and alpha-aminoadipic acid were found to be highly accurate at distinguishing between BA and IHS.

CONCLUSIONS

BA and IHS infants have specific urinary metabolomic profiles. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be used to discriminate BA from IHS.

Metabolomics in Radiation-Induced Biological Dosimetry: A Mini-Review and a Polyamine Study

In this study, we elucidate that polyamine metabolite is a powerful biomarker to study post-radiation changes. Metabolomics in radiation biodosimetry, the application of a metabolomics analysis to the field of radiobiology, promises to increase the understanding of biological responses by ionizing radiation (IR). Radiation exposure triggers a complex network of molecular and cellular responses that impacts metabolic processes and alters the levels of metabolites. Such metabolites have potential as biomarkers for radiation dosimetry. Among metabolites, polyamine is one of many potential biomarkers to estimate radiation response. In addition, this review provides an opportunity for the understanding of a radiation metabolomics in biodosimetry and a polyamine case study.

Robust volcano plot: identification of differential metabolites in the presence of outliers

BACKGROUND

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.

RESULTS

We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites.

CONCLUSION

Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma

Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum samples obtained before and after RFA therapy. Significant variations were observed in metabolites in the glycerolipid, tricarboxylic acid (TCA) cycle, fatty acid, and amino acid pathways between recurrent and non-recurrent patients. Observed differences in metabolites associated with recurrence did not coincide before and after treatment except for fatty acids. Based on the comparison of serum metabolomes between recurrent and non-recurrent patients, key discriminatory metabolites were defined by a random forest (RF) test. Two combinations of these metabolites before and after RFA treatment showed outstanding performance in predicting HCV-related HCC recurrence, they were further confirmed by an external validation set. Our study showed that the determined combination of metabolites may be potential biomarkers for the prediction of HCC recurrence before and after RFA treatment.

The dynamic metabolomic changes throughout mouse epididymal lumen fluid potentially contribute to sperm maturation

Epididymal lumen fluids are directly responsible for sperm maturation. However, very little is known about the molecular details of small molecule metabolites in the epididymal lumen fluids until now. Here we identified and compared the metabolic profiles of mouse caput and cauda epididymal lumen fluids using GC-MS technique. Among 236 metabolites identified in caput and cauda epididymis, 36 were significantly enriched in caput epididymis while 18 were significantly enriched in cauda epididymis. Pathway analysis identified ascorbate and aldarate metabolism and beta-alanine metabolism as most relevant pathways in caput and cauda epididymis, respectively. Ascorbate, dehydroascorbic acid and beta-alanine associated with these two pathways were firstly reported in mouse epididymal lumen fluids and might play important roles in sperm maturation.

Clinical applications of infrared and Raman spectroscopy: state of play and future challenges

This review examines the state-of-the-art of clinical applications of infrared absorption and Raman spectroscopy, outstanding challenges, and progress towards translation.

Metabolomics as a Tool to Understand Pathophysiological Processes

Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.

Retention and effective diffusion of model metabolites on porous graphitic carbon

The study of metabolites in biological samples is of high interest for a wide range of biological and pharmaceutical applications. Reversed phase liquid chromatography is a common technique used for the separation of metabolites, but it provides little retention for polar metabolites. An alternative to C18 bonded phases, porous graphitic carbon has the ability to provide significant retention for both non-polar and polar analytes. The goal of this work is to study the retention and effective diffusion properties of porous graphitic carbon, to see if it is suitable for the wide injection bands and long run times associated with long, packed capillary-scale separations. The retention of a set of standard metabolites was studied for both stationary phases over a wide range of mobile phase conditions. This data showed that porous graphitic carbon benefits from significantly increased retention (often >100 fold) under initial gradient conditions for these metabolites, suggesting much improved ability to focus a wide injection band at the column inlet. The effective diffusion properties of these columns were studied using peak-parking experiments with the standard metabolites under a wide range of retention conditions. Under the high retention conditions, which can be associated with retention after injection loading for gradient separations, Deff/Dm∼0.1 for both the C18-bonded and porous graphitic carbon columns. As C18 bonded particles are widely, and successfully utilized for long gradient separations without issue of increasing peak width from longitudinal diffusion, this suggests that porous graphitic carbon should be amenable for long runtime gradient separations as well.

Metabolic changes in rat serum after administration of suberoylanilide hydroxamic acid and discriminated by SVM

Suberoylanilide hydroxamic acid (SAHA) exerts marked anticancer effects via promotion of apoptosis, cell cycle arrest, and prevention of oncogene expression. In this study, serum metabolomics and artificial intelligence recognition were used to investigate SAHA toxicity. Forty rats (220 ± 20 g) were randomly divided into control and three SAHA groups (low, medium, and high); the experimental groups were treated with 12.3, 24.5, or 49.0 mg kg-1 SAHA once a day via intragastric administration. After 7 days, blood samples from the four groups were collected and analyzed by gas chromatography-mass spectrometry, and pathological changes in the liver were examined using microscopy. The results showed that increased levels of urea, oleic acid, and glutaconic acid were the most significant indicators of toxicity. Octadecanoic acid, pentadecanoic acid, glycerol, propanoic acid, and uric acid levels were lower in the high SAHA group. Microscopic observation revealed no obvious damage to the liver. Based on these data, a support vector machine (SVM) discrimination model was established that recognized the metabolic changes in the three SAHA groups and the control group with 100% accuracy. In conclusion, the main toxicity caused by SAHA was due to excessive metabolism of saturated fatty acids, which could be recognized by an SVM model.

High-resolution metabolomics determines the mode of onset of type 2 diabetes in a 3-year prospective cohort study

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5β-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H‑H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.

Schizophrenia: A review of potential biomarkers

OBJECTIVES

Understanding the biological process and progression of schizophrenia is the first step to developing novel approaches and new interventions. Research on new biomarkers is extremely important when the goal is an early diagnosis (prediction) and precise theranostics. The objective of this review is to understand the research on biomarkers and their effects in schizophrenia to synthesize the role of these new advances.

METHODS

In this review, we search and review publications in databases in accordance with established limits and specific objectives. We look at particular endpoints such as the category of biomarkers, laboratory techniques and the results/conclusions of the selected publications.

RESULTS

The investigation of biomarkers and their potential as a predictor, diagnosis instrument and therapeutic orientation, requires an appropriate methodological strategy. In this review, we found different laboratory techniques to identify biomarkers and their function in schizophrenia.

CONCLUSION

The consolidation of this information will provide a large-scale application network of schizophrenia biomarkers.

The oxylipin and endocannabidome responses in acute phase Plasmodium falciparum malaria in children

BACKGROUND

Oxylipins and endocannabinoids are low molecular weight bioactive lipids that are crucial for initiation and resolution of inflammation during microbial infections. Metabolic complications in malaria are recognized contributors to severe and fatal malaria, but the impact of malaria infection on the production of small lipid derived signalling molecules is unknown. Knowledge of immunoregulatory patterns of these molecules in malaria is of great value for better understanding of the disease and improvement of treatment regimes, since the action of these classes of molecules is directly connected to the inflammatory response of the organism.

METHODS

Detection of oxylipins and endocannabinoids from plasma samples from forty children with uncomplicated and severe malaria as well as twenty controls was done after solid phase extraction followed by chromatography mass spectrometry analysis. The stable isotope dilution method was used for compound quantification. Data analysis was done with multivariate (principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA®) and univariate approaches (receiver operating characteristic (ROC) curves, t tests, correlation analysis).

RESULTS

Forty different oxylipin and thirteen endocannabinoid metabolites were detected in the studied samples, with one oxylipin (thromboxane B2, TXB2) in significantly lower levels and four endocannabinoids (OEA, PEA, DEA and EPEA) at significantly higher levels in infected individuals as compared to controls according to t test analysis with Bonferroni correction. Three oxylipins (13-HODE, 9-HODE and 13-oxo-ODE) were higher in severe compared to uncomplicated malaria cases according to the results from multivariate analysis. Observed changes in oxylipin levels can be connected to activation of cytochrome P450 (CYP) and 5-lipoxygenase (5-LOX) metabolic pathways in malaria infected individuals compared to controls, and related to increased levels of all linoleic acid oxylipins in severe patients compared to uncomplicated ones. The endocannabinoids were extremely responsive to malaria infection with majority of this class of molecules found at higher levels in infected individuals compared to controls.

CONCLUSIONS

It was possible to detect oxylipin and endocannabinoid molecules that can be potential biomarkers for differentiation between malaria infected individuals and controls and between different classes of malaria. Metabolic pathways that could be targeted towards an adjunctive therapy in the treatment of malaria were also pinpointed.

Development of a validated LC- MS/MS method for the quantification of 19 endogenous asthma/COPD potential urinary biomarkers

Obstructive airways inflammatory diseases sometimes show overlapping symptoms that hinder their early and correct diagnosis. Current clinical tests are tedious and are of inadequate specificity in special population such as the elderly and children. Therefore, we are developing tandem mass spectrometric (MS/MS) methods for targeted analysis of urine biomarkers. Recently, proton-nuclear magnetic resonance (¹H-NMR) analysis proposed 50 urinary metabolites as potential diagnostic biomarkers among asthma and chronic obstructive pulmonary disease (COPD) patients. Metabolites are divided into 3 groups based on chemical nature. For group 1 (amines and phenols, 19 urinary metabolites), we developed and validated a high performance liquid chromatographic (HPLC)-MS/MS method using differential isotope labeling (DIL) with dansyl chloride. Method development included the optimization of the derivatization reaction, the MS/MS conditions, and the chromatographic separation. Linearity varied from 2 to 4800 ng/mL and the use of ¹³C₂-labeled derivatives allowed for the correction of matrix effects as well as the unambiguous confirmation of the identity of each metabolite in the presence of interfering isomers in urine. Despite the challenges associated with method validation, the method was fully validated as per the food and drug administration (FDA) and the European medicines agency (EMA) recommendations. Validation criteria included linearity, precision, accuracy, dilution integrity, selectivity, carryover, and stability. Challenges in selectivity experiments included the isotopic contributions of the analyte towards its internal standard (IS), that was addressed via the optimization of the IS concentration. In addition, incurred sample analysis was performed to ensure that results from patient samples are accurate and reliable. The method was robust and reproducible and is currently being applied in a cohort of asthma and COPD patient urine samples for biomarker discovery purposes.

A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines

The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells.

Plasma N-acetylputrescine, cadaverine and 1,3-diaminopropane: potential biomarkers of lung cancer used to evaluate the efficacy of anticancer drugs

Polyamines have been widely investigated as potential biomarkers for various types of cancers, including lung cancer, which is one of the most common causes of death from cancer worldwide. This study was carried out to evaluate the value of polyamines that serve as early diagnostic and cancer progression markers as well as drug evaluation for lung cancer (squamous cell carcinoma of lung, SCCL). SCCL was induced in Wistar rats by intratracheal instillation of 3-methylcholanthrene and treated with three different anti-cancer drugs, Aidi injections, fluorouracil, and a combination of them. After carcinogenesis for 28, 70 and 98 days and therapy for 28 and 56 days, the polyamine levels in plasma of SCCL, healthy and treated rats were determined using a UHPLC-MS/MS assay base on the means of targeted metabolomics. Results showed that increased N-acetylputrescine, cadaverine and 1,3-diaminopropane levels were associated with progression of SCCL. The levels of cadaverine and 1,3-diaminopropane returned to normal after administration of the three different kinds of anticancer drug. In addition, the suitability of using N-acetylputrescine, cadaverine and 1,3-diaminopropane as biomarkers was confirmed by PLS-DA and ROC analysis. It can provide an innovative and effective way for the clinical diagnosis, prevention and treatment of lung cancer, and stimulate a theoretical basis for the design and development of new anticancer drugs. At the same time, this increased the clinical options for polyamines as cancer biomarkers.

Identifying the metabolomic fingerprint of high and low flavonoid consumers

High flavonoid consumption can improve vascular health. Exploring flavonoid–metabolome relationships in population-based settings is challenging, as: (i) there are numerous confounders of the flavonoid–metabolome relationship; and (ii) the set of dependent metabolite variables are inter-related, highly variable and multidimensional. The Metabolite Fingerprint Score has been developed as a means of approaching such data. This study aims to compare its performance with that of more traditional methods, in identifying the metabolomic fingerprint of high and low flavonoid consumers. This study did not aim to identify biomarkers of intake, but rather to explore how systemic metabolism differs in high and low flavonoid consumers. Using liquid chromatography–tandem MS, 174 circulating plasma metabolites were profiled in 584 men and women who had complete flavonoid intake assessment. Participants were randomised to one of two datasets: (a) training dataset, to determine the models for the discrimination variables (n 399); and (b) validation dataset, to test the capacity of the variables to differentiate higher from lower total flavonoid consumers (n 185). The stepwise and full canonical variables did not discriminate in the validation dataset. The Metabolite Fingerprint Score successfully identified a unique pattern of metabolites that discriminated high from low flavonoid consumers in the validation dataset in a multivariate-adjusted setting, and provides insight into the relationship of flavonoids with systemic lipid metabolism. Given increasing use of metabolomics data in dietary association studies, and the difficulty in validating findings using untargeted metabolomics, this paper is of timely importance to the field of nutrition. However, further validation studies are required.

A review of blood sample handling and pre-processing for metabolomics studies

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

Metabolomics allows the discrimination of the pathophysiological relevance of hyperinsulinism in obese prepubertal children

BACKGROUND/OBJECTIVES

Insulin resistance (IR) is the cornerstone of the obesity-associated metabolic derangements observed in obese children. Targeted metabolomics was employed to explore the pathophysiological relevance of hyperinsulinemia in childhood obesity in order to identify biomarkers of IR with potential clinical application.

SUBJECTS/METHODS

One hundred prepubertal obese children (50 girls/50 boys, 50% IR and 50% non-IR in each group), underwent an oral glucose tolerance test for usual carbohydrate and lipid metabolism determinations. Fasting serum leptin, total and high molecular weight-adiponectin and high-sensitivity C-reactive protein (CRP) levels were measured and the metabolites showing significant differences between IR and non-IR groups in a previous metabolomics study were quantified. Enrichment of metabolic pathways (quantitative enrichment analysis) and the correlations between lipid and carbohydrate metabolism parameters, adipokines and serum metabolites were investigated, with their discriminatory capacity being evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Twenty-three metabolite sets were enriched in the serum metabolome of IR obese children (P<0.05, false discovery rate (FDR)<5%). The urea cycle, alanine metabolism and glucose-alanine cycle were the most significantly enriched pathways (P_FDR<0.00005). The high correlation between metabolites related to fatty acid oxidation and amino acids (mainly branched chain and aromatic amino acids) pointed to the possible contribution of mitochondrial dysfunction in IR. The degree of body mass index-standard deviation score (BMI-SDS) excess did not correlate with any of the metabolomic components studied. In the ROC analysis, the combination of leptin and alanine showed a high IR discrimination value in the whole cohort (area under curve, AUC_ALL=0.87), as well as in boys (AUC_M=0.84) and girls (AUC_F=0.91) when considered separately. However, the specific metabolite/adipokine combinations with highest sensitivity were different between the sexes.

CONCLUSIONS

Combined sets of metabolic, adipokine and metabolomic parameters can identify pathophysiological relevant IR in a single fasting sample, suggesting a potential application of metabolomic analysis in clinical practice to better identify children at risk without using invasive protocols.

Metabolomic-guided discovery of Alzheimer's disease biomarkers from body fluid

The rapid increase in the older population has made age-related diseases like Alzheimer's disease (AD) a global concern. Given that there is still no cure for this neurodegenerative disease, the drastic growth in the number of susceptible individuals represents a major emerging threat to public health. The poor understanding of the mechanisms underlying AD is deemed the greatest stumbling block against progress in definitive diagnosis and management of this disease. There is a dire need for biomarkers that can facilitate early diagnosis, classification, prognosis, and treatment response. Efforts have been directed toward discovery of reliable and distinctive AD biomarkers but with very little success. With the recent emergence of high-throughput technology that is able to collect and catalogue vast datasets of small metabolites, metabolomics offers hope for a better understanding of AD and subsequent identification of biomarkers. This review article highlights the potential of using multiple metabolomics platforms as useful means in uncovering AD biomarkers from body fluids. © 2017 Wiley Periodicals, Inc.

Probing Skin for Metabolites and Topical Drugs with Hydrogel Micropatches

Sampling the skin surface is a convenient way to obtain biological specimens bearing clinically relevant information. Hydrogel micropatches enable noninvasive collection of skin excretion specimens, which can subsequently be subjected to rapid mass spectrometric analysis providing insights into the skin metabolome.

Metabolic Profiling of Total Physical Activity and Sedentary Behavior in Community-Dwelling Men

Objective

Physical activity is known to be preventive against various non-communicable diseases. We investigated the relationship between daily physical activity level and plasma metabolites using a targeted metabolomics approach in a population-based study.

Methods

A total of 1,193 participants (male, aged 35 to 74 years) with fasting blood samples were selected from the baseline survey of a cohort study. Information on daily total physical activity, classified into four levels by quartile of metabolic equivalent scores, and sedentary behavior, defined as hours of sitting per day, was collected through a self-administered questionnaire. Plasma metabolite concentrations were quantified by capillary electrophoresis mass spectrometry method. We performed linear regression analysis models with multivariable adjustment and corrected p-values for multiple testing in the original population (n = 808). The robustness of the results was confirmed by replication analysis in a separate population (n = 385) created by random allocation.

Results

Higher levels of total physical activity were associated with various metabolite concentrations, including lower concentrations of amino acids and their derivatives, and higher concentrations of pipecolate (FDR p <0.05 in original population). The findings persisted after adjustment for age, body mass index, smoking, alcohol intake, and energy intake. Isoleucine, leucine, valine, 4-methyl-2-oxoisopentanoate, 2-oxoisopentanoate, alanine, and proline concentrations were lower with a shorter sitting time.

Conclusions

Physical activity is related to various plasma metabolites, including known biomarkers for future insulin resistance or type 2 diabetes. These metabolites might potentially play a key role in the protective effects of higher physical activity and/or less sedentary behavior on non-communicable diseases.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.