An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse

METHYLATION-ASSOCIATED PATHWAYS IN MACULAR TELANGIECTASIA TYPE 2 AND OPHTHALMOLOGIC FINDINGS IN PATIENTS WITH GENETIC METHYLATION DISORDERS

PURPOSE

Serine (Ser) and glycine (Gly) levels were reported to differ between patients with macular telangiectasia type 2 (MacTel) compared with healthy controls. Because they are closely related to methylation metabolism, this report investigates methylation-associated metabolite levels in patients with MacTel and retinal changes in monogenetic methylation disorders.

METHODS

Prospective, monocentric study on patients with MacTel and healthy controls underwent a standardized protocol including a blood draw. Methylation-associated metabolite levels in plasma were determined using targeted quantitative metabolomics. Furthermore, patient records of cystathionine beta-synthase, methylenetetrahydrofolate reductase, and methylmalonic aciduria and homocystinuria type C protein (MMACHC) deficiency were screened for reported retinal changes.

RESULTS

In total, 29 patients with MacTel and 27 healthy controls were included. Patients with MacTel showed lower plasma Ser ( P = 0.02 and P = 0.01) and Gly ( P = 0.11 and P = 0.11) levels than controls. Principal component analyses revealed that methylation-associated metabolite, especially homocysteine, contributed to a distinct clustering of patients with MacTel. No retinal changes were seen in cystathionine beta-synthase (n = 1) and methylenetetrahydrofolate reductase (n = 2) deficiency, while two patients with MMACHC (n = 4) deficiency displayed extensive macular dystrophy.

CONCLUSION

Patients with MacTel show distinct clustering of methylation-associated metabolite compared with controls. Of the three homocystinurias, only MMACHC resulted in macular dystrophy, possibly due to distinct compensatory pathways.

Pharmacomicrobiomics-Guided Precision Oncology: A New Frontier of P4 (Predictive, Personalized, Preventive, and Participatory) Medicine and Microbiome-Based Therapeutics

Pharmacomicrobiomics is a rapidly developing field that promises to make significant contributions to predictive, personalized, preventive, and participatory (P4) medicine. This is becoming evident particularly in the field of precision (P4) oncology by taking seriously the crucial role microbiome plays in health and disease. Several studies have already shown that clinicians can harness insights from the microbiome to better predict treatment response, reduce side effects, and improve overall outcomes for cancer patients. Furthermore, pharmacomicrobiomics will undoubtedly play a crucial role in shaping the future of cancer treatment in the era of P4 oncology as we continue to unravel the intricate relationships between the microbiome and cancer. This perspective and innovation analysis discusses the emerging intersection of P4 medicine and P4 oncology, as seen through a lens of pharmacomicrobiomics. A key promise of pharmacomicrobiomics is the development of personalized microbiome-based therapeutics. In all, we suggest that optimizing cancer treatment and prevention by harnessing pharmacomicrobiomics has vast potentials for precision oncology, and personalized medicine using the right drug, at the right dose, for the right patient, and at the right time.

Identifying subgroups of childhood obesity by using multiplatform metabotyping

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Invited review: Assessment of body condition score and body fat reserves in relation to insulin sensitivity and metabolic phenotyping in dairy cows

The purpose of this article is to review body condition scoring and the role of body fat reserves in relation to insulin sensitivity and metabolic phenotyping. This article summarizes body condition scoring assessment methods and the differences between subcutaneous and visceral fat depots in dairy cows. The mass of subcutaneous and visceral adipose tissue (AT) changes significantly during the transition period; however, metabolism and intensity of lipolysis differ between subcutaneous and visceral AT depots of dairy cows. The majority of studies on AT have focused on subcutaneous AT, and few have explored visceral AT using noninvasive methods. In this systematic review, we summarize the relationship between body fat reserves and insulin sensitivity and integrate omics research (e.g., metabolomics, proteomics, lipidomics) for metabolic phenotyping of cows, particularly overconditioned cows. Several studies have shown that AT insulin resistance develops during the prepartum period, especially in overconditioned cows. We discuss the role of AT lipolysis, fatty acid oxidation, mitochondrial function, acylcarnitines, and lipid insulin antagonists, including ceramide and glycerophospholipids, in cows with different body condition scoring. Nonoptimal body conditions (under- or overconditioned cows) exhibit marked abnormalities in metabolic and endocrine function. Overall, reducing the number of cows with nonoptimal body conditions in herds seems to be the most practical solution to improve profitability, and dairy farmers should adjust their management practices accordingly.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

Independent and Interactive Effects of Genetic Background and Sex on Tissue Metabolomes of Adipose, Skeletal Muscle, and Liver in Mice

Genetics play an important role in the development of metabolic diseases. However, the relative influence of genetic variation on metabolism is not well defined, particularly in tissues, where metabolic dysfunction that leads to disease occurs. We used inbred strains of laboratory mice to evaluate the impact of genetic variation on the metabolomes of tissues that play central roles in metabolic diseases. We chose a set of four common inbred strains that have different levels of susceptibility to obesity, insulin resistance, and other common metabolic disorders. At the ages used, and under standard husbandry conditions, these lines are not overtly diseased. Using global metabolomics profiling, we evaluated water-soluble metabolites in liver, skeletal muscle, and adipose from A/J, C57BL/6J, FVB/NJ, and NOD/ShiLtJ mice fed a standard mouse chow diet. We included both males and females to assess the relative influence of strain, sex, and strain-by-sex interactions on metabolomes. The mice were also phenotyped for systems level traits related to metabolism and energy expenditure. Strain explained more variation in the metabolite profile than did sex or its interaction with strain across each of the tissues, especially in liver. Purine and pyrimidine metabolism and pathways related to amino acid metabolism were identified as pathways that discriminated strains across all three tissues. Based on the results from ANOVA, sex and sex-by-strain interaction had modest influence on metabolomes relative to strain, suggesting that the tissue metabolome remains largely stable across sexes consuming the same diet. Our data indicate that genetic variation exerts a fundamental influence on tissue metabolism.

Multi-omic strategies applied to the study of pharmacoresistance in mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy in adults, and hippocampal sclerosis (HS) is a frequent histopathological feature in patients with MTLE. Pharmacoresistance is present in at least one-third of patients with MTLE with HS (MTLE+HS). Several hypotheses have been proposed to explain the mechanisms of pharmacoresistance in epilepsy, including the effect of genetic and molecular factors. In recent years, the increased knowledge generated by high-throughput omic technologies has significantly improved the power of molecular genetic studies to discover new mechanisms leading to disease and response to treatment. In this review, we present and discuss the contribution of different omic modalities to understand the basic mechanisms determining pharmacoresistance in patients with MTLE+HS. We provide an overview and a critical discussion of the findings, limitations, new approaches, and future directions of these studies to improve the understanding of pharmacoresistance in MTLE+HS. However, it is important to point out that, as with other complex traits, pharmacoresistance to anti-seizure medications is likely a multifactorial condition in which gene-gene and gene-environment interactions play an important role. Thus, studies using multidimensional approaches are more likely to unravel these intricate biological processes.

Molecular Phenomic Approaches to Deconvolving the Systemic Effects of SARS-CoV-2 Infection and Post-acute COVID-19 Syndrome

SARS COV-2 infection causes acute and frequently severe respiratory disease with associated multi-organ damage and systemic disturbances in many biochemical pathways. Metabolic phenotyping provides deep insights into the complex immunopathological problems that drive the resulting COVID-19 disease and is also a source of novel metrics for assessing patient recovery. A multiplatform metabolic phenotyping approach to studying the pathology and systemic metabolic sequelae of COVID-19 is considered here, together with a framework for assessing post-acute COVID-19 Syndrome (PACS) that is a major long-term health consequence for many patients. The sudden emergence of the disease presents a biological discovery challenge as we try to understand the pathological mechanisms of the disease and develop effective mitigation strategies. This requires technologies to measure objectively the extent and sub-phenotypes of the disease at the molecular level. Spectroscopic methods can reveal metabolic sub-phenotypes and new biomarkers that can be monitored during the acute disease phase and beyond. This approach is scalable and translatable to other pathologies and provides as an exemplar strategy for the investigation of other emergent zoonotic diseases with complex immunological drivers, multi-system involvements and diverse persistent symptoms.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot-dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet-induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant's action.

Metabolic Changes of Cholangiocarcinoma Cells in Response to Coniferyl Alcohol Treatment

Cholangiocarcinoma (CCA) is a major cause of mortality in Northeast Thailand with about 14,000 deaths each year. There is an urgent necessity for novel drug discovery to increase effective treatment possibilities. A recent study reported that lignin derived from Scoparia dulcis can cause CCA cell inhibition. However, there is no evidence on the inhibitory effect of coniferyl alcohol (CA), which is recognized as a major monolignol-monomer forming a very complex structure of lignin. Therefore, we aimed to investigate the effect of CA on CCA cell apoptosis. We demonstrated that a half-inhibitory concentration of CA on KKU-100 cells at 48 h and 72 h was 361.87 ± 30.58 and 268.27 ± 18.61 μg/mL, respectively, and on KKU-213 cells 184.37 ± 11.15 and 151.03 ± 24.99 μg/mL, respectively. Furthermore, CA induced CCA cell apoptosis as demonstrated by annexin V/PI staining in correspondence with an increase in the BAX/Bcl-2 ratio. A metabonomic study indicated that CA significantly decreased the intracellular concentrations of glutathione and succinate in KKU-213 cells and increased dihydrogen acetone phosphate levels in KKU-100 cells treated with 200 µg/mL of CA compared to the control group. In conclusion, CA induced cellular metabolic changes which are involved in the antioxidant defense mechanism, glycerophospholipid metabolism and the tricarboxylic acid cycle. CA may serve as a potent anticancer agent for CCA treatment by inducing CCA cellular apoptosis.

Metabolic Fingerprinting Links Oncogenic PIK3CA with Enhanced Arachidonic Acid-Derived Eicosanoids

Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction. VIDEO ABSTRACT.

Nutriome-metabolome relationships provide insights into dietary intake and metabolism

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

The Natural Metabolite 4-Cresol Improves Glucose Homeostasis and Enhances β-Cell Function

Exposure to natural metabolites contributes to the risk of cardiometabolic diseases (CMDs). Through metabolome profiling, we identify the inverse correlation between serum concentrations of 4-cresol and type 2 diabetes. The chronic administration of non-toxic doses of 4-cresol in complementary preclinical models of CMD reduces adiposity, glucose intolerance, and liver triglycerides, enhances insulin secretion in vivo, stimulates islet density and size, and pancreatic β-cell proliferation, and increases vascularization, suggesting activated islet enlargement. In vivo insulin sensitivity is not affected by 4-cresol. The incubation of mouse isolated islets with 4-cresol results in enhanced insulin secretion, insulin content, and β-cell proliferation of a magnitude similar to that induced by GLP-1. In both CMD models and isolated islets, 4-cresol is associated with the downregulated expression of the kinase DYRK1A, which may mediate its biological effects. Our findings identify 4-cresol as an effective regulator of β-cell function, which opens up perspectives for therapeutic applications in syndromes of insulin deficiency.

Twenty years of metabonomics: so what has metabonomics done for toxicology?

In 1999 the journal Xenobiotica published a perspective article detailing the new concept of metabonomics and its application to toxicology. The approach was to apply analytical chemistry techniques, and in particular ¹H NMR spectroscopy, to profile biofluids and tissues to assess the metabolic effects of xenobiotics. Metabonomics has been shown to be sensitive not only to organ specific toxicity but also provides information on the cells, tissues and mechanisms involved, as well as their interactions with the host's sex, age, diet and environment. This review assesses the impact of metabonomics on drug toxicology over the past twenty years and its future prospects. These applications include:Pharmacometabonomics - the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on physiology.The microbiomes role in toxicology - understanding how xenobiotics can be modified by the microbiome dramatically changing their impact on the host.Development of expert systems for toxicity prediction.Data fusion of different omics to better understand the underlying mechanisms of drug toxicity.Metabonomics and exposome - understanding how multiple environmental toxicants might interact with the host organism to produce their overall phenotype. While there has been huge growth in the use of metabonomics within toxicology these applications are set to increase as the tools become more sensitive and robust, as well as the increased use of both experimental and in silico databases to aid prediction of toxicology.

Future prospects for dissecting inter-individual variability in the absorption, distribution and elimination of plant bioactives of relevance for cardiometabolic endpoints

PURPOSE

The health-promoting potential of food-derived plant bioactive compounds is evident but not always consistent across studies. Large inter-individual variability may originate from differences in digestion, absorption, distribution, metabolism and excretion (ADME). ADME can be modulated by age, sex, dietary habits, microbiome composition, genetic variation, drug exposure and many other factors. Within the recent COST Action POSITIVe, large-scale literature surveys were undertaken to identify the reasons and extent of inter-individual variability in ADME of selected plant bioactive compounds of importance to cardiometabolic health. The aim of the present review is to summarize the findings and suggest a framework for future studies designed to investigate the etiology of inter-individual variability in plant bioactive ADME and bioefficacy.

RESULTS

Few studies have reported individual data on the ADME of bioactive compounds and on determinants such as age, diet, lifestyle, health status and medication, thereby limiting a mechanistic understanding of the main drivers of variation in ADME processes observed across individuals. Metabolomics represent crucial techniques to decipher inter-individual variability and to stratify individuals according to metabotypes reflecting the intrinsic capacity to absorb and metabolize bioactive compounds.

CONCLUSION

A methodological framework was developed to decipher how the contribution from genetic variants or microbiome variants to ADME of bioactive compounds can be predicted. Future study design should include (1) a larger number of study participants, (2) individual and full profiling of all possible determinants of internal exposure, (3) the presentation of individual ADME data and (4) incorporation of omics platforms, such as genomics, microbiomics and metabolomics in ADME and efficacy studies.

Are All Breast-fed Infants Equal? Clustering Metabolomics Data to Identify Predictive Risk Clusters for Childhood Obesity

OBJECTIVES

Fetal and early life represent a period of developmental plasticity during which metabolic pathways are modified by environmental and nutritional cues. Little is known on the pathways underlying this multifactorial complex. We explored whether 6 months old breast-fed infants could be clustered into metabolically similar groups and that those metabotypes could be used to predict later obesity risk.

METHODS

Plasma samples were obtained from 183 breast-fed infants aged 6 months participating in the European multicenter Childhood Obesity Project study. We measured amino acids along with polar lipid concentrations (acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, sphingomyelins). We determined the metabotypes using a Bayesian agglomerative clustering method and investigated the properties of these clusters with respect to clinical, programming, and metabolic factors up to 6 years of age.

RESULTS

We identified 20 metabolite clusters comprising 1 to 39 children. Phosphatidylcholines predominantly influenced the clustering process. In the largest clusters (n ≥ 14), large differences existed for birth length (unadjusted P < 0.0001) and length and weight at 6 months (unadjusted P < 0.0001 and P = 0.012, respectively). Infants tended to cluster together by country (unadjusted P < 0.001). The body mass index (BMI) z score at 6 years of age tended to differ (unadjusted P = 0.07).

CONCLUSIONS

Our exploratory study provided evidence that breast-fed infants are not metabolically homogeneous and that variation in metabolic profiles among infants may provide insight into later development and health. This work highlights the potential of metabotypes for identifying inter-individual differences that may form the basis for developing personalized early preventive strategies.

NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor

When using an appropriate data analysis pipeline, protease inhibitor (PI)-containing urine samples are fit-for-purpose for metabolic phenotyping of patients with nephrotic syndrome and proteinuria.

Comparative metabolomics reveals defence-related modification of citrinin by Penicillium citrinum within a synthetic Penicillium-Pseudomonas community

Co-occurring microorganisms have been proved to influence the performance of each other by metabolic means in nature. Here we generated a synthetic fungal-bacterial community comprising Penicillium citrinum and Pseudomonas aeruginosa employing the previously described membrane-separated co-culture device. By applying a newly designed molecular networking routine, new citrinin-related metabolites induced by the fungal-bacterial cross-talk were unveiled in trace amounts. A mechanically cycled co-culture setup with external pumping forces accelerating the chemically interspecies communication was then developed to boost the production of cross-talk-induced metabolites. Multivariate data analysis combined with molecular networking revealed the accumulation of a pair of co-culture-induced molecules whose productions were positively correlated to the exchange rate in the new co-cultures, facilitating the discovery of the previously undescribed antibiotic citrinolide with a novel skeleton. This highly oxidized citrinin adduct showed significantly enhanced antibiotic property against the partner strain P. aeruginosa than its precursor citrinin, suggesting a role in the microbial competition. Thus, we propose competitive-advantage-oriented structural modification driven by microbial defence response mechanism in the interspecies cross-talk might be a promising approach in the search for novel antibiotics. Besides, this study highlights the utility of MS-based metabolomics as an effective tool in the direct biochemical analysis of the community metabolism.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance

The influence of the gut microbiome on metabolic and behavioral traits is widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine ¹H-NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n = 50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behavioral (anxiety and activity) outcomes with 94%–100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT), and behavior while reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity.

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High-fat diet drives phenotypic heterogeneity in metabolism and behavior

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Microbial metabolites, including methylamines, predict phenotypic heterogeneity

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TMAO attenuates ER stress and reduces lipogenesis in adipocytes

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TMAO improves insulin secretion and restores glucose tolerance in vivo

Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

Beyond genomics: understanding exposotypes through metabolomics

BACKGROUND

Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

MAIN TEXT

Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.

CONCLUSIONS

Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Metabolic Profiling: Status, Challenges, and Perspective

Metabolic profiling has advanced greatly in the past decade and evolved from the status of a research topic of a small number of highly specialized laboratories to the status of a major field applied by several hundreds of laboratories, numerous national centers, and core facilities. The present chapter provides our view on the status of the remaining challenges and a perspective of this fascinating research area.

Ion Pair Chromatography for Endogenous Metabolites LC-MS Analysis in Tissue Samples Following Targeted Acquisition

A protocol for the preparation of tissue extracts for the targeted analysis of ca. 150 polar metabolites, including those involved in central carbon metabolism is described, using a reversed-phase ion pair U(H)PLC-MS method. Data collection enabled by multiple-reaction monitoring provides highly specific, sensitive acquisition of metabolic intermediates with a wide range of physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. General comments are given to help the assessment of data quality and system performance.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Ion mobility spectrometry combined with ultra performance liquid chromatography/mass spectrometry for metabolic phenotyping of urine: Effects of column length, gradient duration and ion mobility spectrometry on metabolite detection

The need for rapid and efficient high throughput metabolic phenotyping (metabotyping) in metabolomic/metabonomic studies often requires compromises to be made between analytical speed and metabolome coverage. Here the effect of column length (150, 75 and 30 mm) and gradient duration (15, 7.5 and 3 min respectively) on the number of features detected when untargeted metabolic profiling of human urine using reversed-phase gradient ultra performance chromatography with, and without, ion mobility spectrometry, has been examined. As would be expected, reducing column length from 150 to 30 mm, and gradient duration, from 15 to 3 min, resulted in a reduction in peak capacity from 311 to 63 and a similar reduction in the number of features detected from over ca. 16,000 to ca. 6500. Under the same chromatographic conditions employing UPLC/IMS/MS to provide an additional orthogonal separation resulted in an increase in the number of MS features detected to nearly 20,000 and ca. 7500 for the 150 mm and the 30 mm columns respectively. Based on this limited study the potential of LC/IMS/MS as a tool for improving throughput and increasing metabolome coverage clearly merits further in depth study.

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Ion mobility spectrometry (IMS) significantly increased the number of analytes detected during the LC-MS of urine.

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Nearly ca. 20,000 features were seen for urine using LC-IMS-MS in a 15 min analysis compared to ca. 16,000 by LC-MS alone.

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In a 3 min analysis using a 30 mm column nearly 7600 features were detected with combined IMS and MS.

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For high throughput analysis a 75 mm column and a 3 min analysis was a good compromise between speed and features detected.

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The use of IMS also improved the quality of the mass spectra obtained.

Application of 1H NMR spectroscopy to the metabolic phenotyping of rodent brain extracts: A metabonomic study of gut microbial influence on host brain metabolism

¹H NMR Spectroscopy has been applied to determine the neurochemical profiles of brain extracts from the frontal cortex and hippocampal regions of germ free and normal mice and rats. The results revealed a number of differences between germ free (GF) and conventional (CV) rats or specific pathogen-free (SPF) mice with microbiome-associated metabolic variation found to be both species- and region-dependent. In the mouse, the GF frontal cortex contained lower amounts of creatine, N-acetyl-aspartate (NAA), glycerophosphocholine and lactate, but greater amounts of choline compared to that of specific pathogen free (SPF) mice. In the hippocampus, the GF mice had greater creatine, NAA, lactate and taurine content compared to those of the SPF animals, but lower relative quantities of succinate and an unidentified lipid-related component. The GF rat frontal cortex contained higher relative quantities of lactate, creatine and NAA compared to the CV animals whilst the GF hippocampus was characterized by higher taurine and phosphocholine concentrations and lower quantities of NAA, N-acetylaspartylglutamate and choline compared to the CV animals. Of note is that, in both rat and mouse brain extracts, concentrations of hippocampal taurine were found to be greater in the absence of an established microbiome. The results provide further evidence that brain biochemistry can be influenced by gut microbial status, specifically metabolites involved in energy metabolism demonstrating biochemical dialogue between the microbiome and brain.

Metabolomic approaches in the discovery of potential urinary biomarkers of drug-induced liver injury (DILI)

Drug-induced liver injury (DILI) is a major safety issue during drug development, as well as the most common cause for the withdrawal of drugs from the pharmaceutical market. The identification of DILI biomarkers is a labor-intensive area. Conventional biomarkers are not specific and often only appear at significant levels when liver damage is substantial. Therefore, new biomarkers for early identification of hepatotoxicity during the drug discovery process are needed, thus resulting in lower development costs and safer drugs. In this sense, metabolomics has been increasingly playing an important role in the discovery of biomarkers of liver damage, although the characterization of the mechanisms of toxicity induced by xenobiotics remains a huge challenge. These new-generation biomarkers will offer obvious benefits for the pharmaceutical industry, regulatory agencies, as well as a personalized clinical follow-up of patients, upon validation and translation into clinical practice or approval for routine use. This review describes the current status of the metabolomics applied to the early diagnosis and prognosis of DILI and in the discovery of new potential urinary biomarkers of liver injury.

High-Speed Quantitative UPLC-MS Analysis of Multiple Amines in Human Plasma and Serum via Precolumn Derivatization with 6-Aminoquinolyl-N-hydroxysuccinimidyl Carbamate: Application to Acetaminophen-Induced Liver Failure

A targeted reversed-phase gradient UPLC-MS/MS assay has been developed for the quantification /monitoring of 66 amino acids and amino-containing compounds in human plasma and serum using precolumn derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AccQTag Ultra). Derivatization of the target amines required minimal sample preparation and resulted in analytes with excellent chromatographic and mass spectrometric detection properties. The resulting method, which requires only 10 μL of sample, provides the reproducible and robust separation of 66 analytes in 7.5 min, including baseline resolution of isomers such as leucine and isoleucine. The assay has been validated for the quantification of 33 amino compounds (predominantly amino acids) over a concentration range from 2 to 20 and 800 μM. Intra- and interday accuracy of between 0.05 and 15.6 and 0.78-13.7% and precision between 0.91 and 16.9% and 2.12-15.9% were obtained. A further 33 biogenic amines can be monitored in samples for relative changes in concentration rather than quantification. Application of the assay to samples derived from healthy controls and patients suffering from acetaminophen (APAP, paracetamol)-induced acute liver failure (ALF) showed significant differences in the amounts of aromatic and branched chain amino acids between the groups as well as a number of other analytes, including the novel observation of increased concentrations of sarcosine in ALF patients. The properties of the developed assay, including short analysis time, make it suitable for high-throughput targeted UPLC-ESI-MS/MS metabonomic analysis in clinical and epidemiological environments.

Metabolites as biomarkers of adverse reactions following vaccination: A pilot study using nuclear magnetic resonance metabolomics

An Adverse Event Following Immunization (AEFI) is an adverse reaction to a vaccination that goes above and beyond the usual side effects associated with vaccinations. One serious AEFI related to the smallpox vaccine is myopericarditis. Metabolomics involves the study of the low molecular weight metabolite profile of cells, tissues, and biological fluids, and provides a functional readout of the phenotype. Metabolomics may help identify a particular metabolic signature in serum of subjects who are predisposed to developing AEFIs. The goal of this study was to identify metabolic markers that may predict the development of adverse events following smallpox vaccination. Serum samples were collected from military personnel prior to and following receipt of smallpox vaccine. The study population included five subjects who were clinically diagnosed with myopericarditis, 30 subjects with asymptomatic elevation of troponins, and 31 subjects with systemic symptoms following immunization, and 34 subjects with no AEFI, serving as controls. Two-hundred pre- and post-smallpox vaccination sera were analyzed by untargeted metabolomics using ¹H nuclear magnetic resonance (NMR) spectroscopy. Baseline (pre-) and post-vaccination samples from individuals who experienced clinically verified myocarditis or asymptomatic elevation of troponins were more metabolically distinguishable pre- and post-vaccination compared to individuals who only experienced systemic symptoms, or controls. Metabolomics profiles pre- and post-receipt of vaccine differed substantially when an AEFI resulted. This study is the first to describe pre- and post-vaccination metabolic profiles of subjects who developed an adverse event following immunization. The study demonstrates the promise of metabolites for determining mechanisms associated with subjects who develop AEFI and the potential to develop predictive biomarkers.

Hyphenated MS-based targeted approaches in metabolomics

Review of targeted metabolomics, with a focus on the description of analytical methods.

NMR-based metabonomics study on the effect of Gancao in the attenuation of toxicity in rats induced by Fuzi

ETHNOPHARMACOLOGICAL RELEVANCE

Fuzi, the processed lateral root of Aconitum carmichaelii Debeaux, is a traditional Chinese medicine used for its analgesic, antipyretic, anti-rheumatoid arthritis and anti-inflammation effects; however, it is also well known for its toxicity. Gancao, the root of Glycyrrhiza uralensis Fisch., is often used concurrently with Fuzi to alleviate its toxicity. However, the mechanism of detoxication is still not well clear.

AIM OF THE STUDY

In this study, the effect of Gancao on the metabolic changes induced by Fuzi was investigated by NMR-based metabonomic approaches.

MATERIALS AND METHODS

Fifty male Wistar rats were randomly divided into five groups (group A: control, group B: Fuzi decoction alone, group C: Gancao decoction alone, group D: Fuzi decoction and Gancao decoction simultaneously, group E: Fuzi decoction 5h after Gancao decoction) and urine samples were collected for NMR-based metabolic profiling analysis. Statistical analyses such as unsupervised PCA, t-test, hierarchical cluster, and pathway analysis were used to detect the effects of Gancao on the metabolic changes induced by Fuzi.

RESULTS

The behavioral and biochemical characteristics showed that Fuzi exhibited toxic effects on treated rats (group B) and statistical analyses showed that their metabolic profiles were in contrast to those in groups A and C. However, when Fuzi was administered with Gancao, the metabolic profiles became similar to controls, whereby Gancao reduced the levels of trimethylamine N-oxide, betaine, dimethylglycine, valine, acetoacetate, citrate, fumarate, 2-ketoglutarate and hippurate, and regulated the concentrations of taurine and 3-hydroxybutyrate, resulting in a decrease in toxicity. Furthermore, important pathways that are known to be involved in the effect of Gancao on Fuzi, including phenylalanine, tyrosine and tryptophan biosynthesis, the synthesis and degradation of ketone bodies, and the TCA cycle, were altered in co-treated rats.

CONCLUSIONS

Gancao treatment mitigated the metabolic changes altered by Fuzi administration in rats, demonstrating that dosing with Gancao could reduce the toxicity of Fuzi at the metabolic level. Fuzi and Gancao administered simultaneously resulted in improved toxicity reduction than when Gancao was administrated 5h prior to Fuzi. In summary, co-administration of Gancao with Fuzi reduces toxicity at the metabolic level.

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series

Background

The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits.

Methods

We used systematic metabotyping by ¹H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block.

Results

Despite strong genomic similarities (95–99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing.

Conclusions

These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0352-6) contains supplementary material, which is available to authorized users.

Non-targeted metabolomics identified a common metabolic signature of lethal ventricular tachyarrhythmia (LVTA) in two rat models

Lethal ventricular tachyarrhythmia (LVTA) is the predominant underlying mechanism of sudden cardiac death (SCD). The aim of this study is to characterize the metabolic features of myocardia following LVTA, and identify potential biomarkers to diagnose LVTA. We developed two LVTA rat models induced by aconitine injection or coronary artery ligation, which represent cardiac ion channel disease-related and cardiac ischemia-related SCD, respectively. The myocardial metabolic profile was investigated by gas chromatography-mass spectrometry and proton nuclear magnetic resonance-based metabolomics. Twenty-three aconitine-injected and 14 coronary artery ligation-treated rats developed LVTA SCD. A total of 38 differential metabolites of myocardia were identified in aconitine-induced LVTA rats, of which 31 metabolites showed a similar change in coronary artery ligation-related LVTA rats. Fatty acids (stearic, palmitic, linoleic, elaidic, and myristic) and branched-chain amino acids (valine, leucine, and isoleucine) were the most down-regulated metabolites. Furthermore, elevated ADP, phosphate, lactate, glutamate, aspartate, threonine, choline and arginine were also observed. Major pathways regarding these dysregulated metabolites post LVTA are energy excessive consumption and deficit, ionic imbalance, oxidative stress, cardiac cytotoxicity and membrane injury. Valine, stearic acid and leucine collectively enable a precision of 92.9% to distinguish LVTA from its control, and are correlated with several arrhythmia indices. Our results uncovered a common metabolic feature of LVTA in myocardia in two rat models, which represent cardiac ion channel disease and cardiac ischemia, respectively. l-Valine, l-leucine and stearic acid jointly confer good potential for distinguishing LVTA, which might be potential biomarkers of LVTA-related SCD.

Metabolite profiles of rats in repeated dose toxicological studies after oral and inhalative exposure

The MetaMap(®)-Tox database contains plasma-metabolome and toxicity data of rats obtained from oral administration of 550 reference compounds following a standardized adapted OECD 407 protocol. Here, metabolic profiles for aniline (A), chloroform (CL), ethylbenzene (EB), 2-methoxyethanol (ME), N,N-dimethylformamide (DMF) and tetrahydrofurane (THF), dosed inhalatively for six hours/day, five days a week for 4 weeks were compared to oral dosing performed daily for 4 weeks. To investigate if the oral and inhalative metabolome would be comparable statistical analyses were performed. Best correlations for metabolome changes via both routes of exposure were observed for toxicants that induced profound metabolome changes. e.g. CL and ME. Liver and testes were correctly identified as target organs. In contrast, route of exposure dependent differences in metabolic profiles were noted for low profile strength e.g. female rats dosed inhalatively with A or THF. Taken together, the current investigations demonstrate that plasma metabolome changes are generally comparable for systemic effects after oral and inhalation exposure. Differences may result from kinetics and first pass effects. For compounds inducing only weak changes, the differences between both routes of exposure are visible in the metabolome.

Nuclear Magnetic Resonance (NMR) Spectroscopy For Metabolic Profiling of Medicinal Plants and Their Products

NMR spectroscopy has multidisciplinary applications, including excellent impact in metabolomics. The analytical capacity of NMR spectroscopy provides information for easy qualitative and quantitative assessment of both endogenous and exogenous metabolites present in biological samples. The complexity of a particular metabolite and its contribution in a biological system are critically important for understanding the functional state that governs the organism's phenotypes. This review covers historical aspects of developments in the NMR field, its applications in chemical profiling, metabolomics, and quality control of plants and their derived medicines, foods, and other products. The bottlenecks of NMR in metabolic profiling are also discussed, keeping in view the future scope and further technological interventions.

Allostasis and Resilience of the Human Individual Metabolic Phenotype

The urine metabotype of 12 individuals was followed over a period of 8-10 years, which provided the longest longitudinal study of metabolic phenotypes to date. More than 2000 NMR metabolic profiles were analyzed. The majority of subjects have a stable metabotype. Subjects who were exposed to important pathophysiological stressful conditions had a significant metabotype drift. When the stress conditions ceased, the original metabotypes were regained, while an irreversible stressful condition resulted in a permanent metabotype change. These results suggest that each individual occupies a well-defined region in the broad metabolic space, within which a limited degree of allostasis is permitted. The insurgence of significant stressful conditions causes a shift of the metabotype to another distinct region. The spontaneous return to the original metabolic region when the stressful conditions are removed suggests that the original metabotype has some degree of resilience. In this picture, precision medicine should aim at reinforcing the patient's metabolic resilience, that is, his or her ability to revert to his or her specific metabotype rather than to a generic healthy one.

Metabolomics in cardiovascular diseases

Cardiovascular diseases (CVDs) are the main cause of death globally. There is a need for the development of specific diagnostic methods, more effective therapeutic procedures as well as drugs, which can decrease the risk of deaths in the course of CVDs. For this reason, better understanding and explanation of molecular pathomechanisms of CVDs are essential. Metabolomics is focused on analysis of metabolites, small molecules which reflect the state of an organism in a certain point of time. Application of metabolomics approach in the investigation of molecular processes responsible for CVDs development may provide valuable information. In this article we overviewed recent reports employing application of untargeted and targeted metabolomic analyses in particular CVDs. Moreover, we focused on applications of various analytical platforms and metabolomics approaches which may contribute to the explanation of the pathomechanisms of different cardiovascular diseases.

mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight <1 KDa fraction in biological samples, and provides a key opportunity to advance neurosciences. Proton nuclear magnetic resonance and mass spectrometry are the main analytical platforms used for metabolic profiling, enabling detection and quantitation of a wide range of compounds of particular neuro-pharmacological and physiological relevance, including neurotransmitters, secondary messengers, structural lipids, as well as their precursors, intermediates and degradation products. Metabolic profiling is therefore particularly indicated for the study of central nervous system by probing metabolic and neurochemical profiles of the healthy or diseased brain, in preclinical models or in human samples. In this review, we introduce the analytical and statistical requirements for metabolic profiling. Then, we focus on key studies in the field of metabolic profiling applied to the characterization of animal models and human samples of central nervous system disorders. We highlight the potential of metabolic profiling for pharmacological and physiological evaluation, diagnosis and drug therapy monitoring of patients affected by brain disorders. Finally, we discuss the current challenges in the field, including the development of systems biology and pharmacology strategies improving our understanding of metabolic signatures and mechanisms of central nervous system diseases.