Metabolite profiling of human amniotic fluid by hyphenated nuclear magnetic resonance spectroscopy

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study

Background

Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms.

Methods

Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis.

Results

Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants.

Conclusion

By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

LC–NMR for Natural Product Analysis: A Journey from an Academic Curiosity to a Robust Analytical Tool

Liquid chromatography (LC)–nuclear magnetic resonance (NMR) combines the advantage of the outstanding separation power of liquid chromatography (LC) and the superior structural elucidating capability of nuclear magnetic resonance (NMR). NMR has proved that it is a standout detector for LC by providing maximum structural information about plant originated extracts, particularly on the isolating ability of isomeric (same molecular formula) and/or isobaric (same molecular weight) compounds as compared to other detectors. The present review provides an overview of the developmental trends and application of LC–NMR in natural product analysis. The different LC–NMR operational modes are described, and how technical improvements assist in establishing this powerful technique as an important analytical tool in the analysis of complex plant-derived compounds is also highlighted. On-flow, stop-flow and loop-storage modes, as well as the new offline mode LC–solid phase extraction (SPE)–NMR and capillary LC (capLC)–NMR configurations which avoid the ingestion of expensive deuterated solvents throughout the experiment, are mentioned. Utilization of cryogenic probe and microprobe technologies, which are the other important promising approaches for guaranteeing sensitivity, are also described. Concluding remarks and future outlooks are also discussed.

NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.

Metabolomic profiling of aqueous humor from glaucoma patients - The metabolomics in surgical ophthalmological patients (MISO) study

Glaucoma is still a poorly understood disease with a clear need for new biomarkers to help in diagnosis and potentially offer new therapeutic targets. We aimed to determine if the metabolic profile of aqueous humor (AH) as determined by nuclear magnetic resonance (NMR) spectroscopy allows the distinction between primary open-angle glaucoma patients and control subjects, and to distinguish between high-tension (POAG) and normal-tension glaucoma (NTG). We analysed the AH of patients with POAG, NTG and control subjects (n = 30/group). ¹H NMR spectra were acquired using a 400 MHz spectrometer. Principle component analysis (PCA), machine learning algorithms and descriptive statistics were applied to analyse the metabolic variance between groups, identify the spectral regions, and hereby potential metabolites that can act as biomarkers for glaucoma. According to PCA, fourteen regions of the NMR spectra were significant in explaining the metabolic variance between the glaucoma and control groups, with no differences found between POAG and NTG groups. These regions were further used in building a classifier for separating glaucoma from control patients, which achieved an AUC of 0.93. Peak integration was performed on these regions and a statistical analysis, after false discovery rate correction and adjustment for the different perioperative topical drug regimen, revealed that five of them were significantly different between groups. The glaucoma group showed a higher content in regions typical for betaine and taurine, possibly linked to neuroprotective mechanisms, and also a higher content in regions that are typical for glutamate, which can indicate damaged neurons and oxidative stress. These results show how aqueous humor metabolomics based on NMR spectroscopy can distinguish glaucoma patients from controls with a high accuracy. Further studies are needed to validate these results in order to incorporate them in clinical practice.

Metabolomic profiles of mid-trimester amniotic fluid are not associated with subsequent spontaneous preterm delivery or gestational duration at delivery

INTRODUCTION

Spontaneous preterm delivery (<37 gestational weeks) has a multifactorial etiology with still incompletely identified pathways. Amniotic fluid is a biofluid with great potential for insights into the feto-maternal milieu. It is rich in metabolites, and metabolic consequences of inflammation is yet researched only to a limited extent. Metabolomic profiling provides opportunities to identify potential biomarkers of inflammatory conditioned pregnancy complications such as spontaneous preterm delivery.

OBJECTIVE

The aim of this study was to perform metabolomic profiling of amniotic fluid from uncomplicated singleton pregnancies in the mid-trimester to identify potential biomarkers associated with spontaneous preterm delivery and gestational duration at delivery. A secondary aim was to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers of spontaneous preterm delivery in asymptomatic women.

METHOD

A nested case-control study was performed within a larger cohort study of asymptomatic pregnant women undergoing mid-trimester genetic amniocentesis at 14-19 gestational weeks in Gothenburg, Sweden. Medical records were used to obtain clinical data and delivery outcome variables. Amniotic fluid samples from women with a subsequent spontaneous preterm delivery (n = 37) were matched with amniotic fluid samples from women with a subsequent spontaneous delivery at term (n = 37). Amniotic fluid samples underwent untargeted metabolomic analyses using liquid chromatography-mass spectrometry. Multivariate random forest analyses were used for data processing. A secondary targeted analysis was performed, aiming to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers in women with a subsequent spontaneous preterm delivery.

RESULTS

Multivariate analysis did not distinguish the samples from women with a subsequent spontaneous preterm delivery from those with a subsequent term delivery. Neither was the metabolic profile associated with gestational duration at delivery. Potential metabolic biomarker candidates were identified from four publications by two different research groups relating mid-trimester amniotic fluid metabolomes to spontaneous PTD, of which fifteen markers were included in the secondary analysis. None of these were replicated.

CONCLUSIONS

Metabolomic profiles of early mid-trimester amniotic fluid were not associated with spontaneous preterm delivery or gestational duration at delivery in this cohort.

The integration of LC-MS and NMR for the analysis of low molecular weight trace analytes in complex matrices

This review discusses the integration of liquid chromatography (LC), mass spectrometry (MS), and nuclear magnetic resonance (NMR) in the comprehensive analysis of small molecules from complex matrices. We first discuss the steps taken toward making the three technologies compatible, so as to create an efficient analytical platform. The development of online LC-MS-NMR, highlighted by successful applications in the profiling of highly concentrated analytes (LODs 10 μg) is discussed next. This is followed by a detailed overview of the alternative approaches that have been developed to overcome the challenges associated with online LC-MS-NMR that primarily stem from the inherently low sensitivity of NMR. These alternative approaches include the use of stop-flow LC-MS-NMR, loop collection of LC peaks, LC-MS-SPE-NMR, and offline NMR. The potential and limitations of all these approaches is discussed in the context of applications in various fields, including metabolomics and natural product discovery.

LC-NMR for Natural Products Analysis: A Journey from an Academic Curiosity to a Robust Analytical Tool

LC–NMR combines the advantage of the outstanding separation power of liquid chromatography (LC) and the superior structural elucidating capability of nuclear magnetic resonance (NMR). NMR has proved that it is a standout detector for LC by providing maximum structural information about plant originated extracts particularly in its isolating ability of isomeric (same molecular formula) and/or isobaric (same molecular weight) compounds as compared to other detectors. The present review provides an overview of the LC–NMR developmental trends and its application in natural products analysis. The different LC–NMR operational modes are described, as well as how technical improvements assist in establishing this powerful technique as an important analytical tool in the analysis of complex plant-derived compounds. On-flow, stop-flow and loop-storage modes, as well as the new offline mode LC–SPE–NMR and capLC-NMR configurations that avoid the ingestion of expensive deuterated solvents throughout the experiment are mentioned. Utilization of cryogenic probe and microprobe technologies which are the other important promising approaches for guaranteeing the sensitivity issues are also described. Concluding remarks and future outlooks are also discussed.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Metabolomic Profile of Amniotic Fluid and Wheezing in the First Year of Life-A Healthy Birth Cohort Study

OBJECTIVES

To apply metabolomic analysis of amniotic fluid in a discovery cohort to see whether a specific biochemical-metabolic profile at birth is associated with the subsequent onset of wheezing over the first year of life.

STUDY DESIGN

This prospective exploratory study was conducted in a healthy term-born Dutch cohort recruited at 2 hospitals in Utrecht (UMCU, Utrecht, and Diakonessenhuis, Utrecht), The Netherlands. A metabolomic approach based on mass spectrometry was applied to analyze 142 amniotic fluid samples collected at birth. The infants were followed up during their first year of life with recording any respiratory symptoms daily, and they were classified according to the onset of wheezing.

RESULTS

Orthogonally constrained projection to latent structures discriminant analysis was used to investigate differences in the metabolic profiles of the infants with (n = 86) and without (n = 56) wheezing. A search of the available databases for amniotic fluid metabolites identified by stability selection, combined with pathway analysis, highlighted the possible metabolic perturbations involved in this condition. The model built using 16 relevant variables with plausible biological significance, showed an area under the curve of 0.82 (P < .001) and an area under the curve calculated by 7-fold full cross-validation of 0.72 (P = .003), with the steroid hormone biosynthesis and the 2-phenylalanine metabolism emerging as probably perturbed pathways.

CONCLUSIONS

Infants who will or will not experience wheezing in their first year of life have distinct amniotic fluid metabolomic profiles at birth. Changes occurring in biochemical-metabolic pathways in late intrauterine life may have a pathogenic role in early-onset wheezing.

1H NMR-based metabolomics reveals the effect of maternal habitual dietary patterns on human amniotic fluid profile

Maternal diet may influence offspring’s health, even within well-nourished populations. Amniotic fluid (AF) provides a rational compartment for studies on fetal metabolism. Evidence in animal models indicates that maternal diet affects AF metabolic profile; however, data from human studies are scarce. Therefore, we have explored whether AF content may be influenced by maternal diet, using a validated food-frequency questionnaire and implementing NMR-based metabolomics. Sixty-five AF specimens, from women undergoing second-trimester amniocentesis for prenatal diagnosis, were analysed. Complementary, maternal serum and urine samples were profiled. Hierarchical cluster analysis identified 2 dietary patterns, cluster 1 (C1, n = 33) and cluster 2 (C2, n = 32). C1 was characterized by significantly higher percentages of energy derived from refined cereals, yellow cheese, red meat, poultry, and “ready-to-eat” foods, while C2 by higher (P < 0.05) whole cereals, vegetables, fruits, legumes, and nuts. ¹H NMR spectra allowed the identification of metabolites associated with these dietary patterns; glucose, alanine, tyrosine, valine, citrate, cis-acotinate, and formate were the key discriminatory metabolites elevated in C1 AF specimens. This is the first evidence to suggest that the composition of AF is influenced by maternal habitual dietary patterns. Our results highlight the need to broaden the knowledge on the importance of maternal nutrition during pregnancy.

Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia

Objective

Bronchopulmonary dysplasia (BPD) is a serious complication associated with preterm birth. A growing body of evidence suggests a role for prenatal factors in its pathogenesis. Metabolomics allows simultaneous characterization of low molecular weight compounds and may provide a picture of such a complex condition. The aim of this study was to evaluate whether an unbiased metabolomic analysis of amniotic fluid (AF) can be used to investigate the risk of spontaneous preterm delivery (PTD) and BPD development in the offspring.

Study design

We conducted an exploratory study on 32 infants born from mothers who had undergone an amniocentesis between 21 and 28 gestational weeks because of spontaneous preterm labor with intact membranes. The AF samples underwent untargeted metabolomic analysis using mass spectrometry combined with ultra-performance liquid chromatography. The data obtained were analyzed using multivariate and univariate statistical data analysis tools.

Results

Orthogonally Constrained Projection to Latent Structures-Discriminant Analysis (oCPLS2-DA) excluded effects on data modelling of crucial clinical variables. oCPLS2-DA was able to find unique differences in select metabolites between term (n = 11) and preterm (n = 13) deliveries (negative ionization data set: R² = 0.47, mean AUC ROC in prediction = 0.65; positive ionization data set: R² = 0.47, mean AUC ROC in prediction = 0.70), and between PTD followed by the development of BPD (n = 10), and PTD without BPD (n = 11) (negative data set: R² = 0.48, mean AUC ROC in prediction = 0.73; positive data set: R² = 0.55, mean AUC ROC in prediction = 0.71).

Conclusions

This study suggests that amniotic fluid metabolic profiling may be promising for identifying spontaneous preterm birth and fetuses at risk for developing BPD. These findings support the hypothesis that some prenatal metabolic dysregulations may play a key role in the pathogenesis of PTD and the development of BPD.

Advancing toxicology research using in vivo high throughput toxicology with small fish models

Small freshwater fish models, especially zebrafish, offer advantages over traditional rodent models, including low maintenance and husbandry costs, high fecundity, genetic diversity, physiology similar to that of traditional biomedical models, and reduced animal welfare concerns. The Collaborative Workshop on Aquatic Models and 21st Century Toxicology was held at North Carolina State University on May 5-6, 2014, in Raleigh, North Carolina, USA. Participants discussed the ways in which small fish are being used as models to screen toxicants and understand mechanisms of toxicity. Workshop participants agreed that the lack of standardized protocols is an impediment to broader acceptance of these models, whereas development of standardized protocols, validation, and subsequent regulatory acceptance would facilitate greater usage. Given the advantages and increasing application of small fish models, there was widespread interest in follow-up workshops to review and discuss developments in their use. In this article, we summarize the recommendations formulated by workshop participants to enhance the utility of small fish species in toxicology studies, as well as many of the advances in the field of toxicology that resulted from using small fish species, including advances in developmental toxicology, cardiovascular toxicology, neurotoxicology, and immunotoxicology. We alsoreview many emerging issues that will benefit from using small fish species, especially zebrafish, and new technologies that will enable using these organisms to yield results unprecedented in their information content to better understand how toxicants affect development and health.

Metabolomics of Human Amniotic Fluid and Maternal Plasma during Normal Pregnancy

Metabolic profiles of amniotic fluid and maternal blood are sources of valuable information about fetus development and can be potentially useful in diagnosis of pregnancy disorders. In this study, we applied 1H NMR-based metabolic profiling to track metabolic changes occurring in amniotic fluid (AF) and plasma (PL) of healthy mothers over the course of pregnancy. AF and PL samples were collected in the 2nd (T2) and 3rd (T3) trimester, prolonged pregnancy (PP) until time of delivery (TD). A multivariate data analysis of both biofluids reviled a metabolic switch-like transition between 2nd and 3rd trimester, which was followed by metabolic stabilization throughout the rest of pregnancy probably reflecting the stabilization of fetal maturation and development. The differences were further tested using univariate statistics at α = 0.001. In plasma the progression from T2 to T3 was related to increasing levels of glycerol, choline and ketone bodies (3-hydroxybutyrate and acetoacetate) while pyruvate concentration was significantly decreased. In amniotic fluid, T2 to T3 transition was associated with decreasing levels of glucose, carnitine, amino acids (valine, leucine, isoleucine, alanine, methionine, tyrosine, and phenylalanine) and increasing levels of creatinine, succinate, pyruvate, choline, N,N-dimethylglycine and urocanate. Lactate to pyruvate ratio was decreased in AF and conversely increased in PL. The results of our study, show that metabolomics profiling can be used to better understand physiological changes of the complex interdependencies of the mother, the placenta and the fetus during pregnancy. In the future, these results might be a useful reference point for analysis of complicated pregnancies.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

High-Resolution (1)H NMR Spectroscopy Discriminates Amniotic Fluid of Fetuses with Congenital Diaphragmatic Hernia from Healthy Controls

Lung hypoplasia in congenital diaphragmatic hernia (CDH) is a life-threatening birth defect. Severe cases can be offered tracheal occlusion to boost prenatal lung development, although defining those to benefit remains challenging. Metabonomics of (1)H NMR spectra collected from amniotic fluid (AF) can identify general changes in diseased versus healthy fetuses. AF embodies lung secretions and hence might contain pulmonary next to general markers of disease in CDH fetuses. AF from 81 healthy and 22 CDH fetuses was collected. NMR spectroscopy was performed at 400 MHz to compare AF from fetuses with CDH against controls. Several advanced feature extraction methods based on statistical tests that explore spectral variability, similarity, and dissimilarity were applied and compared. This resulted in the identification of 30 spectral regions, which accounted for 80% variability between CDH and controls. Combination with automated classification discriminates AF from CDH versus healthy fetuses with up to 92% accuracy. Within the identified spectral regions, isoleucine, leucine, valine, pyruvate, GABA, glutamate, glutamine, citrate, creatine, creatinine, taurine, and glucose were the most concentrated metabolites. As the metabolite pattern of AF changes with fetal development, we have excluded metabolites with a high age-related variability and repeated the analysis with 12 spectral regions, which has resulted in similar classification accuracy. From this analysis, it was possible to distinguish between AF from CDH fetuses versus healthy controls independent of gestational age.

Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes

This paper highlights the metabolomic roles in systems biology towards the elucidation of metabolic mechanisms in obesity and type 2 diabetes.

Use of amniotic fluid for determining pregnancies at risk of preterm birth and for studying diseases of potential environmental etiology

Amniotic fluid (AF) is a biological medium uniquely suited for the study of early exposure of the human fetus to environmental contaminants acquired by the mother before and during pregnancy. Traditional diagnostic applications of AF have focused almost exclusively on the diagnosis of genetic aberrations such as Trisomy-21 and on heritable diseases in high-risk pregnancies. Since more than 50 anthropogenic compounds have been detected in AF, there is considerable potential in utilizing fetal protein biomarkers as indicators of health effects related to prenatal toxic exposure. Here, we focus on preterm birth (PTB) to illustrate opportunities and limitations of using AF as a diagnostic matrix. Representing a pervasive public health challenge worldwide, PTB cannot be managed simply by improving hygiene and broadening access to healthcare. This is illustrated by 15-year increases of PTB in the U.S. from 1989 to 2004. AF is uniquely suited as a matrix for early detection of the association between fetal exposures and PTB due to its fetal origin and the fact that it is sampled from women who are at higher risk of PTB. This critical review shows the occurrence in AF of a number of xenobiotics, including endocrine-disrupting compounds (EDCs), which are known or may reasonably be expected to shorten fetal gestation. It is not yet known whether EDCs, including bisphenol A, phytoestrogens, and polychlorinated biphenyls (PCBs), can affect the expression of proteins considered viable or potential biomarkers for the onset of PTB. As such, the diagnostic value of AF is broad and has not yet been fully explored for prenatal diagnosis of pregnancies at risk from toxic, environmental exposures and for the elucidation of mechanisms underlying important public health challenges including PTB.

A metabolomic perspective on coeliac disease

Metabolomics is an "omic" science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism.

Combining Hydrophilic Interaction Chromatography (HILIC) and Isotope Tagging for Off-Line LC-NMR Applications in Metabolite Analysis

The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, (1)H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using (1)H-(15)N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated.

Metabolomics application in maternal-fetal medicine

Metabolomics in maternal-fetal medicine is still an “embryonic” science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future.

Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry

The choice of sample-preparation method is extremely important in metabolomic studies because it affects both the observed metabolite content and biological interpretation of the data. An ideal sample-preparation method for global metabolomics should (i) be as non-selective as possible to ensure adequate depth of metabolite coverage; (ii) be simple and fast to prevent metabolite loss and/or degradation during the preparation procedure and enable high-throughput; (iii) be reproducible; and (iv) incorporate a metabolism-quenching step to represent true metabolome composition at the time of sampling. Despite its importance, sample preparation is often an overlooked aspect of metabolomics, so the focus of this review is to explore the role, challenges, and trends in sample preparation specifically within the context of global metabolomics by liquid chromatography-mass spectrometry (LC-MS). This review will cover the most common methods including solvent precipitation and extraction, solid-phase extraction and ultrafiltration, and discuss how to improve analytical quality and metabolite coverage in metabolomic studies of biofluids, tissues, and mammalian cells. Recent developments in this field will also be critically examined, including in vivo methods, turbulent-flow chromatography, and dried blood spot sampling.

UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers

We report on the first untargeted UPLC-MS study of 2nd trimester maternal urine and amniotic fluid (AF), to investigate the possible metabolic effects of fetal malformations (FM), gestational diabetes mellitus (GDM) and preterm delivery (PTD). For fetal malformations, considerable metabolite variations were identified in AF and, to a lesser extent, in urine. Using validated PLS-DA models and statistical correlations between UPLC-MS data and previously acquired NMR data, a metabolic picture of fetal hypoxia, enhanced gluconeogenesis, TCA activity and hindered kidney development affecting FM pregnancies was reinforced. Moreover, changes in carnitine, pyroglutamate and polyols were newly noted, respectively, reflecting lipid oxidation, altered placental amino acid transfer and alterations in polyol pathways. Higher excretion of conjugated products in maternal urine was seen suggesting alterations in conjugation reactions. For the pre-diagnostic GDM group, no significant changes were observed, either considering amniotic fluid or maternal urine, whereas, for the pre-PTD group, some newly observed changes were noted, namely, the decrease of particular amino acids and the increase of an hexose (possibly glucose), suggesting alteration in placental amino acid fluxes and a possible tendency for hyperglycemia. This work shows the potential of UPLC-MS for the study of fetal and maternal biofluids, particularly when used in tandem with comparable NMR data. The important roles played by sampling characteristics (e.g. group dimensions) and the specific experimental conditions chosen for MS methods are discussed.

Microbial and bioconversion production of D-xylitol and its detection and application

D-Xylitol is found in low content as a natural constituent of many fruits and vegetables. It is a five-carbon sugar polyol and has been used as a food additive and sweetening agent to replace sucrose, especially for non-insulin dependent diabetics. It has multiple beneficial health effects, such as the prevention of dental caries, and acute otitis media. In industry, it has been produced by chemical reduction of D-xylose mainly from photosynthetic biomass hydrolysates. As an alternative method of chemical reduction, biosynthesis of D-xylitol has been focused on the metabolically engineered Saccharomyces cerevisiae and Candida strains. In order to detect D-xylitol in the production processes, several detection methods have been established, such as gas chromatography (GC)-based methods, high performance liquid chromatography (HPLC)-based methods, LC-MS methods, and capillary electrophoresis methods (CE). The advantages and disadvantages of these methods are compared in this review.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

OBJECTIVE

Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the 'metabolic network' of cells (metabolome) is now possible by using a 'metabolomics' approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI).

STUDY DESIGN

Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second.

RESULTS

(1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide).

CONCLUSION

Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

Identification of 4-deoxythreonic acid present in human urine using HPLC and NMR techniques

The 1H NMR spectrum of urine exhibits a large number of detectable and quantifiable metabolites and hence urine metabolite profiling is potentially useful for the study of systems biology and the discovery of biomarkers for drug development or clinical applications. While a number of metabolites (50-100) are readily detectable in urine by NMR, a much larger number is potentially available if lower concentration species can be detected unambiguously. Lower concentration metabolites are thought to be more specific to certain disease states and thus it is important to detect these metabolites with certainty. We report the identification of 4-deoxythreonic acid, a relatively low concentration endogenous metabolite that has not been previously identified in the 1H NMR spectrum of human urine. The use of HPLC and NMR spectroscopy facilitated the unequivocal and non-invasive identification of the molecule in urine which is complicated by extensive peak overlap and multiple, similar resonances from other metabolites such as 3-hydroxybutanoic acid. High-resolution detection and good sensitivity were achieved by the combination of multiple chromatographic fraction collection, sample pre-concentration, and the use of a cryogenically cooled NMR probe.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.