Genetic algorithms for simultaneous variable and sample selection in metabonomics

Metabolomic study for the identification of symptomatic carotid plaque biomarkers

Carotid artery stenosis is mainly produced due to the progressive accumulation of atherosclerotic plaque in the vascular wall. The atherosclerotic plaque is characterized by the accumulation of lipids, low density proteins, expression of chemokines and adhesion molecules, and migration of monocytes and lymphocytes into the plaque. Its rupture can produce stroke, but embolic propensity depends principally on the composition and vulnerability of plaque rather than the severity of stenosis. It is important, then, to ascertain which patients with carotid artery stenosis have a greater risk of developing neurological symptomatology. Here, we present a metabolomic study by using nuclear magnetic resonance (NMR) spectroscopy in atheroma plaque and serum samples from patients with recently symptomatic and asymptomatic carotid stenosis to search for metabolites that could be used as biomarkers associated with plaque vulnerability and subsequent risk of rupture. Thirty-eight atheromatous plaque samples (24 asymptomatic patients and 14 symptomatic) and 70 serum samples (43 asymptomatic and 27 symptomatic) were studied by NMR spectroscopy. The data were analysed using multivariate statistics (PLS-DA) to determine a model to discriminate between symptomatic and asymptomatic samples (atheroma plaques and sera). The calculated PLS-DA models showed a 100 % sensitivity and a 96.6 % specificity for the cross validation to discriminate between symptomatic and asymptomatic plaques, and 88.37 % sensitivity and 77.78 % specificity when serum samples were analysed. According to the results of our multivariate and univariate analysis, the most discriminative metabolites for plaque vulnerability were threonine in serum samples, and glutamate in plaque samples. Also, an analysis of the main metabolic pathways involved in plaque vulnerability revealed that d-glutamine and d-glutamate metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis were the most affected pathways in plaque and serum, respectively.

Decoding chronic rhinosinusitis: A metabolomics-based approach

BACKGROUND

Chronic rhinosinusitis (CRS) is a common and intractable disease in otorhinolaryngology, laying a heavy burden on healthcare systems. The worldwide researchers are making efforts to find solutions to this disease. Metabolomics has recently gained more and more traction, and might become a promising tool to unravel the complexity of CRS. This paper provides an overview of current studies on the metabolomics of various CRS subtypes.

METHODS

We conducted a comprehensive literature search in PubMed, Web of Science, EMBASE, Google Scholar, and Cochrane Library, up to May 25, 2023. Search strategies incorporated key terms such as "chronic rhinosinusitis" and "metabolomics" with relevant synonyms and MeSH terms. Titles and abstracts of 86 screened articles were assessed for relevance to CRS and metabolomics. Methodological robustness, data reliability, and relevance were considered for shortlisted articles.

RESULTS

After the refined process, a total of 26 articles were included in this study and sorted out by research themes, methodology and pivotal discoveries. These included studies identified the metabolic pathways and markers related to the pathophysiology in each subtype of CRS.

CONCLUSIONS

Metabolomics helps to shed light on the complexity of CRS. The mentioned findings highlight the importance of specific metabolic pathways and markers in understanding the pathophysiology of CRS. Despite that, challenges and future directions in metabolomics research for CRS would be worth being further explored.

Microbiome and Metabolomics in Liver Cancer: Scientific Technology

Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Gut microbiome and metabolic response in non-alcoholic fatty liver disease

Fatty liver disease (FLD) is one of the largest burdens to human health worldwide and is associated with gut microbiome and metabolite stability. Engineered liver tissues have shown promise in restoring liver functions in non-alcoholic FLD (NAFLD), hepatitis and cirrhosis. Fatty liver, largely noted in obesity and hepatic cancer, is highly fatal and has led to a global increase in death rates. It is associated with complex metabolic reprogramming too. A standard approach to therapy in the newly diagnosed setting includes surgery or identification of biomarkers/ metabolites for therapeutic purposes, which ultimately focus on improvement of liver health in patients. As such there are no standard procedures for patient care, but depending on the severity, systemic therapy with either genomic, proteomic or metabolomic profiling form potential options. Better comparisons and study of underlying mechanisms in gut microbiome-based metabolic functions in obesity are urgently required. Today, an emerging field, focusing on metabolomic approaches and metabolic phenotyping, involved in high-throughput identification of metabolome in obesity and gut disorders, is involved in biomarker and metabolite identification. There are supporting technologies and approaches in NAFLD that throw light on the metabolites and gut microbiome, and also on the understanding of the risk factors of obesity along with liver cancer metabolic reaction networks. We discuss the current state of NAFLD metabolites, gut micro-environmental changes, and the further challenges in digital metabolomics profiling. Innovative clinical trial designs, with biomarker-enrichment strategies that are required to improve the outcome of NAFLD in patients are also discussed.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications

Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the “-omics” disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas’ role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Discrimination of rosé wines using shotgun metabolomics with a genetic algorithm and MS ion intensity ratios

A rapid Ultra Performance Liquid Chromatography coupled with Quadrupole/Time Of Flight Mass Spectrometry (UPLC-QTOF-MS) method was designed to quickly acquire high-resolution mass spectra metabolomics fingerprints for rosé wines. An original statistical analysis involving ion ratios, discriminant analysis, and genetic algorithm (GA) was then applied to study the discrimination of rosé wines according to their origins. After noise reduction and ion peak alignments on the mass spectra, about 14 000 different signals were detected. The use of an in-house mass spectrometry database allowed us to assign 72 molecules. Then, a genetic algorithm was applied on two series of samples (learning and validation sets), each composed of 30 commercial wines from three different wine producing regions of France. Excellent results were obtained with only four diagnostic peaks and two ion ratios. This new approach could be applied to other aspects of wine production but also to other metabolomics studies.

Secondary Metabolite Mapping Identifies Peony Episperm Inhibitors of Human Hepatoma Cells

As the main by-product during the oil production of peony seeds, the episperm is traditionally used as a lead component in folk herbal formulas for the cancer treatment in China. However, the investigation of its phytochemical foundation underlying anticancer effects remains an ongoing challenge. The work therefore determined growth inhibition activities of 8 solvent extracts of peony episperms in the human liver cancer cell line. This activity was then mapped onto the secondary metabolite profile of extracts by principal components analysis (PCA). The top 3 principal components of High Performance Liquid Chromatograph (HPLC)-PCA map discriminated extract activities mainly based on the differential content of 5 stilbene compounds, which were then tested individually. The trans-ε-viniferin, gnetin-H, suffruticosol A, suffruticosol B, and suffruticosol C were thus determined as growth inhibitors and apoptosis inducers of human liver cancer cells with activities comparable to that of the antineoplastic cisplatin. A partial least squares regression-HPLC model was also constructed for the prediction of inhibitory effects of peony episperm extracts. These results expand the fundamental understanding of the peony episperms and support its current medicinal uses in China. Moreover, PCA-mediated secondary metabolite mapping was proved to be an efficient approach to qualify biomedical products required for pharmaceutical and medicinal uses.

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.

The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis

Variable selection and outlier detection are important processes in chemical modeling.

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm(-1) related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.

A metabolomics approach to stratify patients diagnosed with diabetes mellitus into excess or deficiency syndromes

The prevalence of type 2 diabetes continuously increases globally. The traditional Chinese medicine (TCM) can stratify the diabetic patients based on their different TCM syndromes and, thus, allow a personalized treatment. Metabolomics is able to provide metabolite biomarkers for disease subtypes. In this study, we applied a metabolomics approach using an ultraperformance liquid chromatography (UPLC) coupled with quadruple-time-of-flight (QTOF) mass spectrometry system to characterize the metabolic alterations of different TCM syndromes including excess and deficiency in patients diagnosed with diabetes mellitus (DM). We obtained a snapshot of the distinct metabolic changes of DM patients with different TCM syndromes. DM patients with excess syndrome have higher serum 2-indolecarboxylic acid, hypotaurine, pipecolic acid, and progesterone in comparison to those patients with deficiency syndrome. The excess patients have more oxidative stress as demonstrated by unique metabolite signatures than the deficiency subjects. The results provide an improved understanding of the systemic alteration of metabolites in different syndromes of DM. The identified serum metabolites may be of clinical relevance for subtyping of diabetic patients, leading to a personalized DM treatment.

Metabolomic fingerprinting: challenges and opportunities

Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs?

There has been a recent shift in how cancers are defined, where tumors are no longer simply classified by their tissue origin, but also by their molecular characteristics. Furthermore, personalized medicine has become a popular term and it could start to play an important role in future medical care. However, today, a "one size fits all" approach is still the most common form of cancer treatment. In this mini-review paper, we report on the role of nuclear magnetic resonance (NMR) metabolomics in drug development and in personalized medicine. NMR spectroscopy has successfully been used to evaluate current and potential therapies, both single-agents and combination therapies, to analyze toxicology, optimal dose, resistance, sensitivity, and biological mechanisms. It can also provide biological insight on tumor subtypes and their different responses to drugs, and indicate which patients are most likely to experience off-target effects and predict characteristics for treatment efficacy. Identifying pre-treatment metabolic profiles that correlate to these events could significantly improve how we view and treat tumors. We also briefly discuss several targeted cancer drugs that have been studied by metabolomics. We conclude that NMR technology provides a key platform in metabolomics that is well-positioned to play a crucial role in realizing the ultimate goal of better tailored cancer medicine.

Metabonomics-based omics study and atherosclerosis

Atherosclerosis results from dyslipidemia and systemic inflammation, associated with the strong metabolism and interaction between diet and disease. Strategies based on the global profiling of metabolism would be important to define the mechanisms involved in pathological alterations. Metabonomics is the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Metabonomics has been used in combination with proteomics and transcriptomics as the part of a systems biology description to understand the genome interaction with the development of atherosclerosis. The present review describes the application of metabonomics to explore the potential role of metabolic disturbances and inflammation in the initiation and development of atherosclerosis. Metabonomics-based omics study offers a new potential for biomarker discovery by disentangling the impacts of diet, environment and lifestyle.

Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy

Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma samples from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.

Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles

Modern nuclear magnetic resonance (NMR) spectroscopy generates complex and information-rich metabolic profiles. These require robust, accurate, and often sophisticated statistical techniques to yield the maximum meaningful knowledge. In this chapter, we describe methods typically used to analyze such data. We begin by describing seven goals of metabolic profile analysis, ranging from production of a data table to multi-omic integration for systems biology. Methods for preprocessing and pretreatment are then presented, including issues such as instrument-level spectral processing, data reduction and deconvolution, normalization, scaling, and transformations of the data. We then discuss methods for exploratory modeling and exemplify three techniques: principal components analysis, hierarchical clustering, and self-organizing maps. Moving to predictive modeling, we focus our discussion on partial least squares regression, orthogonal partial least squares regression, and genetic algorithm approaches. A typical set of in vitro metabolic profiles is used where possible to compare and contrast the methods. The importance of validating statistical models is highlighted, and standard techniques for doing so, such as training/test set and cross-validation are described. Finally, we discuss the contributions of statistical techniques such as statistical total correlation spectroscopy, and other correlation-based methods have made to the process of structural characterization for unknown metabolites.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH₄+-N concentration>Moisture content>Ash Content>Mean Temperature>Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers

Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15±1 weeks' gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15±1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.