Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

Whole-Blood Metabolomics of a Rat Model of Repetitive Concussion

Mild traumatic brain injury (mTBI) and repetitive mTBI (RmTBI) are silent epidemics, and so far, there is no objective diagnosis. The severity of the injury is solely based on the Glasgow Coma Score (GCS) scale. Most patients suffer from one or more behavioral abnormalities, such as headache, amnesia, cognitive decline, disturbed sleep pattern, anxiety, depression, and vision abnormalities. Additionally, most neuroimaging modalities are insensitive to capture structural and functional alterations in the brain, leading to inefficient patient management. Metabolomics is one of the established omics technologies to identify metabolic alterations, mostly in biofluids. NMR-based metabolomics provides quantitative metabolic information with non-destructive and minimal sample preparation. We employed whole-blood NMR analysis to identify metabolic markers using a high-field NMR spectrometer (800 MHz). Our approach involves chemical-free sample pretreatment and minimal sample preparation to obtain a robust whole-blood metabolic profile from a rat model of concussion. A single head injury was given to the mTBI group, and three head injuries to the RmTBI group. We found significant alterations in blood metabolites in both mTBI and RmTBI groups compared with the control, such as alanine, branched amino acid (BAA), adenosine diphosphate/adenosine try phosphate (ADP/ATP), creatine, glucose, pyruvate, and glycerphosphocholine (GPC). Choline was significantly altered only in the mTBI group and formate in the RmTBI group compared with the control. These metabolites corroborate previous findings in clinical and preclinical cohorts. Comprehensive whole-blood metabolomics can provide a robust metabolic marker for more accurate diagnosis and treatment intervention for a disease population.

Metabolic profile of blood serum in experimental arterial hypertension

The etiology of essential hypertension is intricate, since it employs simultaneously various body systems related to the regulation of blood pressure in one way or another: the sympathetic nervous system, renin-angiotensin-aldosterone and hypothalamic-pituitary-adrenal systems, renal and endothelial mechanisms. The pathogenesis of hypertension is influenced by a variety of both genetic and environmental factors, which determines the heterogeneity of the disease in human population. Hence, there is a need to perform research on experimental models - inbred animal strains, one of them being ISIAH rat strain, which is designed to simulate inherited stress-induced arterial hypertension as close as possible to primary (or essential) hypertension in humans. To determine specific markers of diseases, various omics technologies are applied, including metabolomics, which makes it possible to evaluate the content of low-molecular compounds - amino acids, lipids, carbohydrates, nucleic acids fragments - in biological samples available for clinical analysis (blood and urine). We analyzed the metabolic profile of the blood serum of male ISIAH rats with a genetic stress-dependent form of arterial hypertension in comparison with the normotensive WAG rats. Using the method of nuclear magnetic resonance spectroscopy (NMR spectroscopy), 56 metabolites in blood serum samples were identified, 18 of which were shown to have significant interstrain differences in serum concentrations. Statistical analysis of the data obtained showed that the hypertensive status of ISIAH rats is characterized by increased concentrations of leucine, isoleucine, valine, myo-inositol, isobutyrate, glutamate, glutamine, ornithine and creatine phosphate, and reduced concentrations of 2-hydroxyisobutyrate, betaine, tyrosine and tryptophan. Such a ratio of the metabolite concentrations is associated with changes in the regulation of glucose metabolism (metabolic markers - leucine, isoleucine, valine, myo-inositol), of nitric oxide synthesis (ornithine) and catecholamine pathway (tyrosine), and with inflammatory processes (metabolic markers - betaine, tryptophan), all of these changes being typical for hypertensive status. Thus, metabolic profiling of the stress-dependent form of arterial hypertension seems to be an important result for a personalized approach to the prevention and treatment of hypertensive disease.

Early Perturbations in Red Blood Cells in Response to Murine Malarial Parasite Infection: Proof-of-Concept 1H NMR Metabolomic Study

BACKGROUND

The major focus of metabolomics research has been confined to the readily available biofluids-urine and blood serum. However, red blood cells (RBCs) are also readily available, and may be a source of a wealth of information on vertebrates. However, the comprehensive metabolomic characterization of RBCs is minimal although they exhibit perturbations in various physiological states. RBCs act as the host of malarial parasites during the symptomatic stage. Thus, understanding the changes in RBC metabolism during infection is crucial for a better understanding of disease progression.

METHODS

The metabolome of normal RBCs obtained from Swiss mice was investigated using 1H NMR spectroscopy. Several 1 and 2-dimensional 1H NMR experiments were employed for this purpose. The information from this study was used to investigate the changes in the RBC metabolome during the early stage of infection (~1% infected RBCs) by Plasmodium bergheii ANKA.

RESULTS

We identified over 40 metabolites in RBCs. Several of these metabolites were quantitated using 1H NMR spectroscopy. The results indicate changes in the choline/membrane components and other metabolites during the early stage of malaria.

CONCLUSIONS

The paper reports the comprehensive characterization of the metabolome of mouse RBCs. Changes during the early stage of malarial infection suggest significant metabolic alteration, even at low parasite content (~1%).

GENERAL SIGNIFICANCE

This study should be of use in maximizing the amount of information available from metabolomic experiments on the cellular components of blood. The technique can be directly applied to real-time investigation of infectious diseases that target RBCs.

Decompression Mechanism of Radish Seed in Prehypertension Rats through Integration of Transcriptomics and Metabolomics Methods

Radish seed (RS), the dried ripe seed of Raphanus sativus L., is widely used in traditional Chinese medicine (TCM) to reduce blood pressure. However, the molecular and pharmacological mechanisms underlying its therapeutic effects are still unclear. In this study, we analyzed the effects of RS in a rat model of prehypertension and assessed the mechanistic basis by integrating transcriptomics and metabolomics. RS administration significantly reduced blood pressure in prehypertensive male Wistar rats, negatively regulated endothelin-1, increased nitric oxide levels, and reduced the exfoliation of endothelium cells. In vitro vascular ring experiments further confirmed the effects of RS on vascular endothelial cells. Furthermore, we identified 65 differentially expressed genes (DEGs; P _adj < 0.05 and fold change (FC) > 2) and 52 metabolites (VIP > 1, P < 0.05 and FC ≥ 2 or ≤0.5) in the RS intervention group using RNA-seq and UPLC-MS/MS, respectively. A network of the DEGs and the metabolites was constructed,q which indicated that RS regulates purine metabolism, linoleic acid metabolism, arachidonic acid metabolism, circadian rhythm, and phosphatidylinositol signaling pathway, and its target genes are Pik3c2a, Hspa8, Dnaja1, Arntl, Ugt1a1, Dbp, Rasd1, and Aldh1a3. Thus, the antihypertensive effects of RS can be attributed to its ability to improve vascular endothelial dysfunction by targeting multiple genes and pathways. Our findings provide new insights into the pathological mechanisms underlying prehypertension, along with novel targets for the prevention and treatment of hypertension.

Mass spectrometry-based identification of new serum biomarkers in patients with latent infection pulmonary tuberculosis

Noninvasive and simple indicators for diagnosing latent tuberculosis (TB) infection (LTBI) and tracking progression from latent infection to active TB infection are still desperately needed. The aim of this study was to screen and identify possible biomarkers for diagnosing LTBI and monitoring the progression from latent infection to active TB infection, as well as to investigate the underlying processes and functions. To assess changes in metabolite composition associated with active tuberculosis infection in humans, whole blood supernatants were collected from patients with LTBI, drug-susceptible TB patients, drug-resistant TB patients, and healthy controls. The metabolites in all serum samples were extracted by oscillatory, deproteinization, and then detected by liquid chromatography-tandem mass spectrometry/MS analysis. Normalization by Pareto-scaling method, the difference analysis was carried out by Metaboanalyst 4.0 software, and 1-way analysis of variance analysis among groups showed that P-value < 0.05 was regarded as a different metabolite. To clarify the dynamic changes and functions of differential metabolites with disease progression, and explore its significance and mechanism as a marker by further cluster analysis, functional enrichment analysis, and relative content change analysis of differential metabolites. 65 metabolites were substantially different in four groups. Differential metabolites such as Inosine and Prostaglandin E1 may be important blood indicators for diagnosing mycobacterium tuberculosis latent infection, which were all tightly connected to amino acid metabolism, Biosynthesis of various secondary metabolites, Nucleotide metabolism, Endocrine system, Immune system, Lipid metabolism, and Nervous system. This study screened and identified Inosine, 16, 16-dimethyl-6-keto Prostaglandin E1, Theophylline, and Cotinine as potential serum biomarkers for diagnosing latent TB infection, and Cotinine as potential biomarkers for monitoring disease progression from healthy population to LTBI and then to active TB including drug-resistant TB infection and sensitive TB infection. Furthermore, this research provides a preliminary experimental basis to further investigate the development of metabolomics-based diagnosis of LTBI and monitoring the progress from latent infection to active TB infection.

Machine learning integration of multimodal data identifies key features of blood pressure regulation

BACKGROUND

Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP).

METHODS

We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB).

FINDINGS

Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts.

INTERPRETATION

We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies.

FUNDING

Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.

Metabolic Profiling and Metabolites Fingerprints in Human Hypertension: Discovery and Potential

Early detection of pathogenesis through biomarkers holds the key to controlling hypertension and preventing cardiovascular complications. Metabolomics profiling acts as a potent and high throughput tool offering new insights on disease pathogenesis and potential in the early diagnosis of clinical hypertension with a tremendous translational promise. This review summarizes the latest progress of metabolomics and metabolites fingerprints and mainly discusses the current trends in the application in clinical hypertension. We also discussed the associated mechanisms and pathways involved in hypertension's pathogenesis and explored related research challenges and future perspectives. The information will improve our understanding of the development of hypertension and inspire the clinical application of metabolomics in hypertension and its associated cardiovascular complications.

How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?

The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.

A strategy for advancing for population-based scientific discovery using the metabolome: the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group

Metabolomic Epidemiology is a growing area of research within the metabolomics research community. In response to this, we describe the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group. The overall mission of this group is to promote the growth and understanding of metabolomic epidemiology as an independent research discipline and to drive collaborative efforts that can shape the field. In this article we define metabolomic epidemiology and identify the key challenges that need to be addressed in order to advance population-based scientific discovery in metabolomics.

The Role and Mechanism of Intestinal Flora in Blood Pressure Regulation and Hypertension Development

Significance: Hypertension (HTN) has a complex etiology that is characterized by genetic and environmental factors. It has become a global health burden leading to cardiovascular diseases and kidney diseases, ultimately progressing to premature death. Accumulating evidence indicated that gut microbiome was associated with metabolic disorders and inflammation, which were closely linked to HTN. Recent Advances: Recent studies using bacterial genomic analysis and fecal microbiota transplantation as well as many lines of seminal evidence demonstrated that aberrant gut microbiome was significantly associated with HTN. The intestinal microbiome of both patients and animals with HTN had decreased bacterial diversity, disordered microbial structure and functions, and altered end products of fermentation. Gut dysbiosis and metabolites of the gut microbiota play an important role in blood pressure (BP) control, and they are therefore responsible for developing HTN. Critical Issues: This study aimed at focusing on the recent advances in understanding the role played by gut bacteria and the mechanisms underlying the pathological milieu that induced elevated BP and led to HTN pathogenesis. Potential intervention strategies targeting the correction of gut dysbiosis to improve HTN development were summarized. Future Directions: Larger numbers of fecal transplants from participants with HTN should be carried out to examine the magnitude of BP changes with the replacement of the gut microbiome. The proposed mechanisms for the gut in regulating BP remain to be verified. Whether intervention strategies using probiotics, dietary interventions, bacteriophages, and fecal transplants are feasible for individuals with HTN remains to be explored. Antioxid. Redox Signal. 34, 811-830.

Metabolomics profiling in hypertension and blood pressure regulation: a review

Hypertension is a chronic health condition in which blood pressure is usually elevated beyond normal levels. It can progress with serious complications if left undetected and untreated. Incidence of hypertension is on the increase worldwide with debilitating consequences on the health systems of many countries. It is a multifactorial disorder that requires a multi-pronged approach to address it. One such approach is the use of metabolomics or metabolite profiling to understand its underlying cause and possibly control it. Changes in metabolites profiles have been used to accurately predict so many disease conditions in addition to identifying possible biomarkers and pathways associated in their pathogenicity. This will enable their early detection, diagnosis and treatment as well as likely complications that may arise and also assist in development of biomarkers for clinical uses. The objective of this review therefore is to present some of the current knowledge on the application of metabolomics profiling in hypertension and blood pressure control.

Metabolomics of Interstitial Fluid, Plasma and Urine in Patients with Arterial Hypertension: New Insights into the Underlying Mechanisms

There is growing evidence that lymphatic system plays a pivotal role in the pathogenesis of hypertension. Here, for the first time, the metabolome of interstitial fluid is analyzed in patients with arterial hypertension. Due to ethical issues to obtain human interstitial fluid samples, this study included only oncological patients after axillary lymph node dissection (ALND). These patients were matched into hypertensive (n = 29) and normotensive (n = 35) groups with similar oncological status. Simultaneous evaluation of interstitial fluid, plasma, and urine was obtained by combining high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy with chemometric analysis. Orthogonal partial least squares discriminant analysis (OPLS-DA) provided a clear differentiation between the hypertension and normotensive group, with the discrimination visible in each biofluid. In interstitial fluid nine potential metabolomic biomarkers for hypertension could be identified (creatinine, proline, pyroglutamine, glycine, alanine, 1-methylhistidine, the lysyl group of albumin, threonine, lipids), seven distinct markers in plasma (creatinine, mannose, isobutyrate, glycine, alanine, lactate, acetate, ornithine), and seven respectively in urine (methylmalonate, citrulline, phenylacetylglycine, fumarate, citrate, 1-methylnicotinamide, trans-aconitate). Biomarkers in plasma and urine allowed for the identification of specific biochemical pathways involved in hypertension, as previously suggested. Analysis of the interstitial fluid metabolome provided additional biomarkers compared to plasma or urine. Those biomarkers reflected primarily alterations in the metabolism of lipids and amino acids, and indicated increased levels of oxidative stress/inflammation in patients with hypertension.

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.

Coordinated Modulation of Energy Metabolism and Inflammation by Branched-Chain Amino Acids and Fatty Acids

As important metabolic substrates, branched-chain amino acids (BCAAs) and fatty acids (FAs) participate in many significant physiological processes, such as mitochondrial biogenesis, energy metabolism, and inflammation, along with intermediate metabolites generated in their catabolism. The increased levels of BCAAs and fatty acids can lead to mitochondrial dysfunction by altering mitochondrial biogenesis and adenosine triphosphate (ATP) production and interfering with glycolysis, fatty acid oxidation, the tricarboxylic acid cycle (TCA) cycle, and oxidative phosphorylation. BCAAs can directly activate the mammalian target of rapamycin (mTOR) signaling pathway to induce insulin resistance, or function together with fatty acids. In addition, elevated levels of BCAAs and fatty acids can activate the canonical nuclear factor-κB (NF-κB) signaling pathway and inflammasome and regulate mitochondrial dysfunction and metabolic disorders through upregulated inflammatory signals. This review provides a comprehensive summary of the mechanisms through which BCAAs and fatty acids modulate energy metabolism, insulin sensitivity, and inflammation synergistically.

Dietary Supplementation of Vine Tea Ameliorates Glucose and Lipid Metabolic Disorder via Akt Signaling Pathway in Diabetic Rats

A traditional Chinese tea with many pharmacological effects, vine tea (VT) is considered a potential dietary supplement to improve type 2 diabetes (T2D). To investigate the effect and mechanism of VT on glucose and lipid metabolic disorders in T2D rats, Wistar rats fed a normal diet served as the normal control, while rats fed a high-fat diet combined with low-dose streptozotocin (STZ)-induced T2D were divided into three groups: The model group (MOD); the positive control group (MET, metformin at 200 mg/kg/d); and the VT-treated group (VT500, allowed to freely drink 500 mg/L VT). After four weeks of intervention, biochemical metrics indicated that VT significantly ameliorated hyperglycemia, hyperlipidemia and hyperinsulinemia in T2D rats. Metabolomics research indicated that VT regulated the levels of metabolites closely related to glucose and lipid metabolism and promoted glycogen synthesis. Furthermore, VT had a significant influence on the expression of key genes involved in the Akt signaling pathway, inhibited gluconeogenesis through the Akt/Foxo1/Pck2 signaling pathway, and reduced fatty acid synthesis via the SREBP1c/Fasn signaling pathways. In conclusion, VT has great potential as a dietary supplement to ameliorate glucose and lipid metabolic disorders via the Akt signaling pathway in T2D rats.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

An Overview of Metabolic Phenotyping in Blood Pressure Research

PURPOSE OF THE REVIEW

This review presents the analytical techniques, processing and analytical steps used in metabolomics phenotyping studies, as well as the main results from epidemiological studies on the associations between metabolites and high blood pressure.

RECENT FINDINGS

A variety of metabolomic approaches have been applied to a range of epidemiological studies to uncover the pathophysiology of high blood pressure. Several pathways have been suggested in relation to blood pressure including the possible role of the gut microflora, inflammatory, oxidative stress, and lipid pathways. Metabolic changes have also been identified associated with blood pressure lowering effects of diets high in fruits and vegetables and low in meat intake. However, the current body of literature on metabolic profiling and blood pressure is still in its infancy, not fully consistent and requires careful interpretation. Metabolic phenotyping is a promising approach to uncover metabolic pathways associated with high blood pressure and throw light into the complex pathophysiology of hypertension.

Circulating MicroRNA-21 Expression as a Novel Serum Biomarker for Oral Sub-Mucous Fibrosis and Oral Squamous Cell Carcinoma

Background: Circulating miRNAs (miRs) in the biofluids such as serum and plasma act as potential biomarkers for early diagnosis, treatment and prognosis. In the present study, an attempt made to see the expression of miR-21 in serum of 20 cases of Oral sub-mucous fibrosis (OSMF), 20 cases of Oral squamous cell carcinoma and 40 healthy volunteers. The expression of miR-21 was evaluated in relation to different demographical and clinicopathological features such as sex, tobacco, pan-masala, alcohol, smoking and clinical staging respectively with an aim to identify correlation with oral pre-cancer and cancer stages. Materials and Methods: The relative expression level of miR-21 was determined by quantitative real-time RT-PCR (qRT-PCR) in the sera of 20 OSCC, 20 OSMF patients and 40 healthy subjects as a control. Association between expression of miR-21 and OSCC clinical stages and demographical parameters such as sex, pan-masala, tobacco, smoking, alcohol have also been analyzed in detail. Results: The results obtained by t-test revealed significant increase in the expression level of miR-21 in OSCC as compared to OSMF. The study also revealed the positive correlation between higher miR-21 expression and pan-masala chewers as shown by t-test. The statistical test, ANOVA has also indicated a positive correlation between up-regulation of miR-21 in the clinical stages of the OSCC. Conclusion: The results of present study indicated up-regulation of circulating miR-21 in serum of OSCC as compared to OSMF (p=0.001), this study also elucidated the positive correlation between miR-21 expression in OSCC/OSMF patients, only one demographical parameter (Pan-masala) and negative correlation for other parameters such as sex, tobacco, smoking, alcohol etc. Other findings suggested a significant increase (p=0.000) in the expression of miR-21 in clinical staging (I-IV) of oral cancer. More studies are needed to validate it as potential diagnostic and prognostic biomarker for OSMF and OSCC for better management.

Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting ¹H nuclear magnetic resonance technology, and pattern-recognition multi-variable statistical analysis was used to screen out different metabolites. One-way analysis of variance, and Student-Newman-Keuls and the Kruskal-Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure.

Essential hypertension: A filtered serum based metabolomics study

Despite the easy and reliable methods of blood pressure measurement, the screening of essential hypertension (EH) is usually ignored due to delayed onset of symptoms. A probe into the biochemical changes in hypertension would serve as a welcome asset to provide insight into the mechanistic aspects of EH. Filtered serum samples from 64 EH patients and 59 healthy controls (HC) were analysed using 800 MHz nuclear magnetic resonance (NMR) spectroscopy. Application of principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) following receiver operating characteristic (ROC) curve of NMR data reveals significantly perturbed metabolites: alanine, arginine, methionine, pyruvate, adenine, and uracil. This set of metabolites correctly classified 99% of cases from HC and also showed excellent correlation in both isolated elevated diastolic blood pressure (DBP) cases and combined elevated systolic-diastolic blood pressure cases. Proton NMR metabolomics of EH may prove helpful in defining associated biomarkers and serve as an alternate diagnostic tool with judicious clinical assessment.

Gut microbiota dysbiosis contributes to the development of hypertension

BACKGROUND

Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice.

RESULTS

Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella-dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella, and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated.

CONCLUSIONS

Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

Rationale, design, methods and baseline characteristics of the Asklepios Study

The Asklepios Study is a longitudinal population study focusing on the interplay between ageing, cardiovascular haemodynamics and inflammation in (preclinical) cardiovascular disease. The 2524 participants (1301 women) are a representative cohort of 35-55-year-old individuals, free from overt cardiovascular disease at study initiation, randomly sampled from the twinned Belgian communities of Erpe-Mere and Nieuwerkerken. Baseline examinations (all single-observer, single-device, single-site, single 2-year consecutive timeframe) include: questionnaires, conventional risk factors and biochemistry. Additional phenotypes under study include: (a) vascular structure and function: carotid and femoral atherosclerosis (intima-media thickness, plaque), arterial distension and pressure curves (brachial, carotid, femoral; wall-tracking and applanation tonometry); (b) cardiac structure and function. A novel aspect of the study is 'integrated' non-invasive biomechanical assessment of cardiac, arterial and ventriculovascular function through a combination of modeling, fundamental hydraulical measurements and system identification techniques. Integrated phenotypes result from combining at least two sets of curves (flow/pressure/distension). The value of this 'integrated' haemodynamic phenotype in the detection, prediction and prevention of clinical cardiovascular pathology (atherosclerosis progression, atherothrombosis, development of heart failure) will be tested. A second novel aspect is the systematic determination of peripheral blood leukocyte telomere length as a marker for biological ageing. During follow-up, baseline examinations will be repeated and the incidence of cardiovascular events will be monitored. Sex-specific baseline risk factor and biochemical data are provided in the current analyses. The primary aim is to build a combined dataset that will act as a tool to answer a cluster of questions about ageing, haemodynamics and the emergence of cardiovascular disease, especially the incidence of atherothrombotic events and the development of adverse haemodynamic profiles (arterial stiffening, heart failure). The study will reassess current risk factors and provide a long-term base for the detection of novel (epi)genetic and non-genetic risk factors and for more performant risk stratification modalities. Within these broader goals, a constant will be to strive towards more fundamental mechanistic-haemodynamic insights into cardiovascular disease processes.

Utility of Novel Plasma Metabolic Markers in the Diagnosis of Pediatric Tuberculosis: A Classification and Regression Tree Analysis Approach

Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism.

A Targeted Metabolomics MRM-MS Study on Identifying Potential Hypertension Biomarkers in Human Plasma and Evaluating Acupuncture Effects

The critical role of metabolic abnormality in hypertension is increasingly recognized, but its biomarkers are not clearly identified. In this study, 47 chemical compounds recorded by literature were employed as target metabolites of essential hypertension (EH). We detected their content in the plasma of EH patients and healthy subjects by using the Multiple Reaction Monitoring-Mass Spectrometry (MRM-MS). After screening the most altered compounds, acupuncture was used to treat patients for 3 months and these plasma metabolites were tested again. The results showed that oleic acid (OA) and myoinositol (MI) were the most important differential metabolites between the hypertensive plasma and the healthy plasma. They were also closely correlated with 24-hour blood pressure and nocturnal dipping. Moreover, plasma OA and MI could be restored to normal levels by acupuncture, accompanying with reduction of 24-hour systolic and diastolic blood pressure [from 145.10 ± 9.28 mm Hg to 140.70 ± 9.59 mm Hg (P < 0.0001), and 88.35 ± 7.92 mm Hg to 85.86 ± 7.95 mm Hg (P = 0.0024), respectively] and improvement of circadian blood pressure rhythm. This study demonstrated that plasma OA and MI were potential hypertension biomarkers and they could be used to preliminarily assess the treating effects such as acupuncture.

Urine metabolomics of women from small villages exposed to high environmental cadmium levels

The present study aimed to identify urine metabolites in women exposed to high cadmium (Cd) levels. Twenty-one women exposed to environmental Cd and 12 age-matched controls were categorized as high exposure (urine Cd ≥ 15 μg/g creatinine; n = 9) or low exposure (15 μg/g creatinine > urine Cd > 5 μg/g creatinine; n = 12). Low-molecular weight metabolites in urine were analyzed by gas chromatography and mass spectrometry after derivatization. An orthogonal partial least-squares discriminant analysis model was constructed, and metabolites from the dimensional model were selected according to the variable importance in projection (>1). Metabolites differing significantly in abundance between different exposure groups were identified by searching mass spectral databases, and related pathways were analyzed using the Kyoto Encyclopedia of Genes and Genomes. Approximately 110 significantly different metabolites were detected with variable importance in projection > 1, and 48 of them were found to differ markedly in abundance among the 3 groups. Twenty-seven matched with known metabolites, including 22 significantly increased and 5 markedly decreased in the high-exposure group (p < 0.01). Kyoto Encyclopedia of Genes and Genomes results indicated that carbohydrate, amino acid, bone, and intestinal flora metabolism and the tricarboxylic acid cycle were affected by Cd exposure. The present study identified metabolites that differed in abundance in response to Cd exposure. Further studies may connect these biomarkers to early damage caused by Cd.

A Nested Case-Control Study of Association between Metabolome and Hypertension Risk

We aimed to explore novel small metabolites that associated with hypertension risk in a population-based nested case-control study. Among 460 individuals with optimal blood pressure (<120/80 mmHg) at baseline, 55 progressed to hypertension during 5 years of follow-up. Twenty-nine cases of incident hypertension and 29 controls, matched for age, sex, and baseline systolic blood pressure, were included in this study. Serum metabolites were measured by gas chromatography-tandem mass spectrometry. t-test and logistic regression analysis were applied to investigate the association between metabolites and incident hypertension. Among the 241 metabolites identified in this study, baseline levels of 26 metabolites were significantly different between hypertension and control groups. After adjusting for body mass index, smoking, and drinking, 16 out of the 26 metabolites were still associated with hypertension risk including four amino acids. Amino acids were negatively associated with risk of future hypertension, with odds ratio (OR) ranging from 0.33 to 0.53. Two of these amino acids were essential amino acids including threonine and phenylalanine. Higher level of lyxose, a fermentation product of gut microbes, was associated with higher risk of hypertension. Our study identified multiple metabolites that associated with hypertension risk. These findings implied that low amino acid levels and gut microbiome might play an important role in the pathogenesis of hypertension.

(1)H NMR-Based Analysis of Serum Metabolites in Monocrotaline-Induced Pulmonary Arterial Hypertensive Rats

AIMS

To study the changes of the metabolic profile during the pathogenesis in monocrotaline (MCT) induced pulmonary arterial hypertension (PAH).

METHODS

Forty male Sprague-Dawley (SD) rats were randomly divided into 5 groups (n = 8, each). PAH rats were induced by a single dose intraperitoneal injection of 60 mg/kg MCT, while 8 rats given intraperitoneal injection of 1 ml normal saline and scarified in the same day (W0) served as control. Mean pulmonary arterial pressure (mPAP) was measured through catherization. The degree of right ventricular hypertrophy and pulmonary hyperplasia were determined at the end of first to fourth weeks; nuclear magnetic resonance (NMR) spectra of sera were then acquired for the analysis of metabolites. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to discriminate different metabolic profiles.

RESULTS

The prominent changes of metabolic profiles were seen during these four weeks. Twenty specific metabolites were identified, which were mainly involved in lipid metabolism, glycolysis, energy metabolism, ketogenesis, and methionine metabolism. Profiles of correlation between these metabolites in each stage changed markedly, especially in the fourth week. Highly activated methionine and betaine metabolism pathways were selected by the pathway enrichment analysis.

CONCLUSIONS

Metabolic dysfunction is involved in the development and progression of PAH.

Metabolic differentiation and classification of abnormal Savda Munziq's pharmacodynamic role on rat models with different diseases by nuclear magnetic resonance-based metabonomics

Background:

Abnormal Savda Munziq (ASMq) is a traditional Uyghur herbal preparation used as a therapy for abnormal Savda-related diseases. In this study, we investigate ASMq's dynamic effects on abnormal Savda rat models under different disease conditions.

Materials and Methods:

Abnormal Savda rat models with hepatocellular carcinoma (HCC), type 2 diabetes mellitus (T2DM), and asthma dosed of ASMq. Serum samples of each animal tested by nuclear magnetic resonance spectroscopy and analyzed by orthogonal projection to latent structure with discriminant analysis.

Results:

Compared with healthy controls, HCC rats had higher concentrations of amino acids, fat-related metabolites, lactate, myoinositol, and citrate, but lower concentrations of α-glucose, β-glucose, and glutamine. Following ASMq treatment, the serum acetone very low-density lipoprotein (VLDL), LDL, unsaturated lipids, acetylcysteine, and pyruvate concentration decreased, but α-glucose, β-glucose, and glutamine concentration increased (P < 0.05). T2DM rats had higher concentrations of α- and β-glucose, but lower concentrations of isoleucine, leucine, valine, glutamine, glycoprotein, lactate, tyrosine, creatine, alanine, carnitine, and phenylalanine. After ASMq treated T2DM groups showed reduced α- and β-glucose and increased creatine levels (P < 0.05). Asthma rats had higher acetate, carnitine, formate, and phenylalanine levels, but lower concentrations of glutamine, glycoprotein, lactate, VLDL, LDL, and unsaturated lipids. ASMq treatment showed increased glutamine and reduced carnitine, glycoprotein, formate, and phenylalanine levels (P < 0.05).

Conclusion:

Low immune function, decreased oxidative defense, liver function abnormalities, amino acid deficiencies, and energy metabolism disorders are common characteristics of abnormal Savda-related diseases. ASMq may improve the abnormal metabolism and immune function of rat models with different diseases combined abnormal Savda.

Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis

BACKGROUND

Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

METHODS

Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

RESULTS

From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

CONCLUSION

The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Metabolomics in hypertension

Hypertension is the most prevalent chronic medical condition and a major risk factor for cardiovascular morbidity and mortality. In the majority of hypertensive cases, the underlying cause of hypertension cannot be easily identified because of the heterogeneous, polygenic and multi-factorial nature of hypertension. Metabolomics is a relatively new field of research that has been used to evaluate metabolic perturbations associated with disease, identify disease biomarkers and to both assess and predict drug safety and efficacy. Metabolomics has been increasingly used to characterize risk factors for cardiovascular disease, including hypertension, and it appears to have significant potential for uncovering mechanisms of this complex disease. This review details the analytical techniques, pre-analytical steps and study designs used in metabolomics studies, as well as the emerging role for metabolomics in gaining mechanistic insights into the development of hypertension. Suggestions as to the future direction for metabolomics research in the field of hypertension are also proposed.

Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for young hypertensive men

The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC-MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold=0.6). The plasma metabolites identified by GC-MS and the significantly altered metabolites (P<0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases.

Analysis of urinary metabolomic profiling for unstable angina pectoris disease based on nuclear magnetic resonance spectroscopy

The NMR-based metabolomics approach showed good performance in identifying diagnostic urinary biomarkers, providing new insights into the metabolic process related to UAP.

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.

Prehypertension-associated elevation in circulating lysophosphatidlycholines, Lp-PLA2 activity, and oxidative stress

Prehypertension is a risk factor for atherosclerosis. We investigated alterations in plasma metabolites that are associated with prehypertension. A group of 53 individuals was identified who remained within the range of prehypertension during repeated measurements in a 3-year period. This group was compared with the control group of 53 normotensive subjects who were matched for age and gender. Metabolomic profiles were analyzed with UPLC-LTQ-Orbitrap mass spectrometry. The prehypertensive group showed higher levels of lysophosphatidylcholines (lysoPCs) containing C14:0, C16:1, C16:0, C18:2, C18:1, C18:0, C20:5, C20:4, C20:3, and C22:6, higher circulating Lp-PLA₂ activity, oxidized LDL (ox-LDL), interleukin 6 (IL-6), urinary 8-epi-PGF_2α, and higher brachial-ankle pulse wave velocity (ba-PWV), before and after adjusting for BMI, WHR, smoking, alcohol consumption, serum lipid profiles, glucose, and insulin. LysoPC (16:0) was the most important plasma metabolite for evaluating the difference between control and prehypertensive groups, with a variable important in the projection (VIP) value of 17.173, and it showed a positive and independent association with DBP and SBP. In the prehypertensive group, the levels of lysoPC (16:0) positively and significantly correlated with ox-LDL, Lp-PLA₂ activity, 8-epi-PGF_2α, ba-PWV, and IL-6 before and after adjusting for confounding variables. Prehypertension-associated elevations in lysoPCs, Lp-PLA₂ activity, ox-LDL, urinary 8-epi-PGF_2α, IL-6, and ba-PWV could indicate increased oxidative stress from Lp-PLA₂-catalyzed PC hydrolysis during increased LDL oxidation, thereby enhancing proinflammation and arterial stiffness.

Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids

BACKGROUND

Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits.

METHODS

We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another.

RESULTS

A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci.

CONCLUSIONS

These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.

A metabonomic analysis of serum from rats treated with ricinine using ultra performance liquid chromatography coupled with mass spectrometry

A metabonomic approach based on ultra performance liquid chromatography coupled with mass spectrometry (UPLC/MS) was used to study the hepatotoxicity of ricinine in rats. Potential biomarkers of ricinine toxicity and toxicological mechanism were analyzed by serum metabonomic method. The significant differences in the metabolic profiling of the control and treated rats were clear by using the principal components analysis (PCA) of the chromatographic data. Significant changes of metabolite biomarkers like phenylalanine, tryptophan, cholic acid, LPC and PC were detected in the serum. These biochemical changes were related to the metabolic disorders in amino acids and phospholipids. This research indicates that UPLC/MS-based metabonomic analysis of serum samples can be used to predict the hepatotoxicity and further understand the toxicological mechanism induced by ricinine. This work shows that metabonomics method is a valuable tool in drug mechanism study.

Pattern recognition and biomarker validation using quantitative1H-NMR-based metabolomics

The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as 'metabolic profiling', 'metabonomics' or 'metabolomics'. This review addresses 1H-nuclear magnetic resonance (NMR)-based metabolomic principles and their application in biomedical science, with special emphasis on their potential in translational research in transplantation, oncology, and drug toxicity or discovery. Various steps in metabolomics analysis are described in order to illustrate the types of biological samples, their respective handling and preparation for 1H-NMR analysis; provide a rationale for using pattern-recognition techniques (spectral database concept) versus quantitative 1H-NMR-based metabolomics (metabolite database concept); and identify necessary technological and logistical future developments that will allow 1H-NMR-based metabolomics to become an established tool in biomedical research and patient care.

Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission

PURPOSE

Multiple myeloma remains an incurable disease. New approaches to develop better tools for improving patient prognostication and monitoring treatment efficacy are very much needed. In this study, we aimed to evaluate the potential of metabolomics by (1)H-NMR to provide information on metabolic profiles that could be useful in the management of multiple myeloma.

EXPERIMENTAL DESIGN

Serum samples were collected from multiple myeloma patients at the time of diagnosis and after achieving complete remission. A matched control set of samples was also included in the study. The (1)H-NMR measurements used to obtain the metabolic profile for each patient were followed by the application of univariate and multivariate statistical analyses to determine significant differences.

RESULTS

Metabolic profiles of multiple myeloma patients at diagnosis exhibited higher levels of isoleucine, arginine, acetate, phenylalanine, and tyrosine, and decreased levels of 3-hydroxybutyrate, lysine, glutamine, and some lipids compared with the control set. A similar analysis conducted in multiple myeloma patients after achieving complete remission indicated that some of the metabolic changes (i.e., glutamine, cholesterol, lysine) observed at diagnosis displayed a variation in the opposite direction upon responding to treatment, thus contributing to multiple myeloma patients having a closer metabolic profile to those of healthy individuals after the disappearance of major disease manifestations.

CONCLUSIONS

The results highlight the potential of metabolic profiles obtained by 1H-NMR in identifying multiple myeloma biomarkers that may be useful to objectively discriminate individuals with and without multiple myeloma, and monitor response to treatment.

Metabolomic analyses for atherosclerosis, diabetes, and obesity

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