Metabolomics: The Stethoscope for the Twenty-First Century.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

IL-7 Restores T Lymphocyte Immunometabolic Failure in Septic Shock Patients through mTOR Activation.

T lymphocyte alterations are central to sepsis pathophysiology, whereas related mechanisms remain poorly understood. We hypothesized that metabolic alterations could play a role in sepsis-induced T lymphocyte dysfunction. Samples from septic shock patients were obtained at day 3 and compared with those from healthy donors. T cell metabolic status was evaluated in the basal condition and after T cell stimulation. We observed that basal metabolic content measured in lymphocytes by nuclear magnetic resonance spectroscopy was altered in septic patients. Basal ATP concentration, oxidative phosphorylation (OXPHOS), and glycolysis pathways in T cells were decreased as well. After stimulation, T lymphocytes from patients failed to induce glycolysis, OXPHOS, ATP production, GLUT1 expression, glucose entry, and proliferation to similar levels as controls. This was associated with significantly altered mTOR, but not Akt or HIF-1α, activation and only minor AMPKα phosphorylation dysfunction. IL-7 treatment improved mTOR activation, GLUT1 expression, and glucose entry in septic patients' T lymphocytes, leading to their enhanced proliferation. mTOR activation was central to this process, because rapamycin systematically inhibited the beneficial effect of recombinant human IL-7. We demonstrate the central role of immunometabolism and, in particular, mTOR alterations in the pathophysiology of sepsis-induced T cell alterations. Our results support the rationale for targeting metabolism in sepsis with recombinant human IL-7 as a treatment option.

Persistently Altered Metabolic Phenotype following Perinatal Excitotoxic Brain Injury.

Excitotoxicity plays a key role during insults to the developing brain such as neonatal encephalopathy, stroke, and encephalopathy of prematurity. Such insults affect many thousands of infants each year. Excitotoxicity causes frank lesions due to cell death and gliosis and disturbs normal developmental process, leading to deficits in learning, memory, and social integration that persist into adulthood. Understanding the underlying processes of the acute effects of excitotoxicity and its persistence during brain maturation provides an opportunity to identify mechanistic or diagnostic biomarkers, thus enabling and designing possible therapies. We applied mass spectrometry to provide metabolic profiles of brain tissue and plasma over time following an excitotoxic lesion (intracerebral ibotenate) to the neonatal (postnatal day 5) mouse brain. We found no differences between the plasma from the control (PBS-injected) and excitotoxic (ibotenate-injected) groups over time (on postnatal days 8, 9, 10, and 30). In the brain, we found that variations in amino acids (arginine, glutamine, phenylananine, and proline) and glycerophospholipids were sustaining acute and delayed (tertiary) responses to injury. In particular, the effect of the excitotoxic lesion on the normal profile of development was linked to alterations in a fingerprint of glycerophospolipids and amino acids. Specifically, we identified increases in the amino acids glutamine, proline, serine, threonine, tryptophan, valine, and the sphingolipid SM C26:1, and decreases in the glycerophospholipids, i.e., the arachidonic acid-containing phosphatidylcholine (PC aa) C30:2 and the PC aa C32:3. This study demonstrates that metabolic profiling is a useful approach to identify acute and tertiary effects in an excitotoxic lesion model, and generating a short list of targets with future potential in the hunt for identification, stratification, and possibly therapy.

Sample size calculation in metabolic phenotyping studies.

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

Power Analysis and Sample Size Determination in Metabolic Phenotyping.

Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY(2) of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography-mass spectrometry data from humans and the model organism C. elegans.

SRV: an open-source toolbox to accelerate the recovery of metabolic biomarkers and correlations from metabolic phenotyping datasets.

MOTIVATION: Supervised multivariate statistical analyses are often required to analyze the high-density spectral information in metabolic datasets acquired from complex mixtures in metabolic phenotyping studies. Here we present an implementation of the SRV-Statistical Recoupling of Variables-algorithm as an open-source Matlab and GNU Octave toolbox. SRV allows the identification of similarity between consecutive variables resulting from the high-resolution bucketing. Similar variables are gathered to restore the spectral dependency within the datasets and identify metabolic NMR signals. The correlation and significance of these new NMR variables for a given effect under study can then be measured and represented on a loading plot to allow a visual and efficient identification of candidate biomarkers. Further on, correlations between these candidate biomarkers can be visualized on a two-dimensional pseudospectrum, representing a correlation map, helping to understand the modifications of the underlying metabolic network. AVAILABILITY: SRV toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) and in GNU Octave. It is available free of charge at http://www.prabi.fr/redmine/projects/srv/repository with a tutorial. CONTACT: benjamin.blaise@chu-lyon.fr or vincent.navratil@univ-lyon1.fr.

Data-driven sample size determination for metabolic phenotyping studies.

Sample size determination is a key question in the experimental design of medical studies. The number of patients to include in a clinical study is actually critical to evaluate costs and inclusion requirements to achieve a sufficient statistical power of test and the identification of significant variations among the factors under study. Metabolic phenotyping is an expanding field of translational research in medicine, focusing on the identification of metabolism rearrangements due to various pathophysiological conditions. This top-down hypothesis-free approach uses analytical chemistry methods, coupled to statistical analysis, to quantify subtle and coordinated metabolite concentration variations and eventually identify candidate biomarkers. The sample size determination in metabolic phenotyping studies is difficult considering the absence of a priori metabolic target. This technical note introduces a data-driven sample size determination for metabolic phenotyping studies. Starting from nuclear magnetic resonance (NMR) spectra belonging to a small cohort, metabolic NMR variables are identified by the statistical recoupling of variables (SRV) procedure. A larger data set is then generated on the basis of Kernel density estimation of SRV variable distributions. Statistically significant variations of metabolic NMR signals identified by SRV are assessed by the Benjamini-Yekutieli correction for simulated data sets of variable sizes. Simulated model robustness is evaluated by receiver operating characteristic analysis (sensitivity and specificity) on an independent cohort and cross-validation. Sample size determination is obtained by identifying the optimal data set size, depending on the purpose of the study: at least one statistically significant variation (biomarker discovery) or a maximum of statistically significant variations (metabolic exploration).

Metabolic phenotyping of traumatized patients reveals a susceptibility to sepsis.

Sepsis is one of the leading causes of morbidity and mortality in patients admitted in intensive care units (ICU) for trauma. The identification of biochemical mechanisms and prediction of patients at risk of early sepsis remain unsolved. Metabolic phenotyping allows the recovery of coordinated metabolic concentration variations. There are no predictive metabolic phenotyping studies based on noninvasive human samples to identify the later development of sepsis in traumatized patients. The aim of this study was to investigate whether the metabolic phenotype could help in the discrimination of patients according to the later development of sepsis. Plasma samples were taken from severely injured patients in the hours following their admission in the ICU. Nuclear magnetic resonance (NMR) based metabolic phenotyping was performed on this prospective cohort. Statistical analyses were run on NMR spectra to discriminate patients according to the later development of sepsis. Twenty-two patients were included. One was excluded because of aberrant metabolic phenotype. Orthogonal partial least-squares analysis allowed the recovery of a predictive metabolic phenotype identifying patients with a later development of sepsis (1 + 4 component model, R(2) = 0.855, Q(2) = 0.384). A cross-validated receiver operator characteristic curve showed a remarkable prediction capacity (AUC = 0.778). Eight metabolic hotspots were identified. NMR-based metabolic phenotyping allows the prediction of patients at high risk of early sepsis after ICU admission for trauma. A larger cohort is necessary to validate and complete this study, understand biochemical mechanisms promoting sepsis development, and identify patients at risk.

Pupillary reflex dilatation and analgesia nociception index monitoring to assess the effectiveness of regional anesthesia in children anesthetised with sevoflurane.

BACKGROUND: Pupillary diameter (PD) monitoring and Analgesia Nociception Index (ANI) (Metrodoloris, Lille, France), an online wavelet transform-based heart rate variability index, have been used in the assessment of pain. OBJECTIVE: The aim of this study was to evaluate the capacity of pupillary reflex dilatation and ANI to provide early assessment of regional anesthesia (RA) success following skin incision in children anesthetised with sevoflurane. METHODS: A total of 58 children, eligible for RA, were included after sevoflurane induction. The sevoflurane concentration was adjusted to maintain a MAC of 1.3 in oxygen and nitrous oxide, and a RA was performed. Pupillary diameter and ANI were recorded just prior to skin incision and then every 30 s for a period of 2 min. Regional anesthesia failure was defined by an increase in heart rate ≥ 10% occurring during the first 2 mins following incision. RESULTS: Thirty-nine and 19 subjects presented RA success and failure, respectively. In the RA failure group, skin incision induced both changes in PD (P < 0.01) and ANI (P < 0.05) within 1 min of incision. Areas under the receiver-operating curves (95% confidence interval) to identify regional anesthesia failure were 0.747 (0.613-0.881) and 0.671 (0.514-0.827) for the minimal value of ANI and the maximal value of PD recorded during the 2-min period from skin incision, respectively. CONCLUSION: Both PD and ANI rapidly change after skin incision in case of RA failure. These indices may provide a useful tool alone, or in combination with heart rate changes in the assessment of RA efficacy in children anesthetised with sevoflurane.

Continuous cerebral blood flow monitoring: What should we do with these extra numbers?

NeoDoppler is a noninvasive monitoring device that can be attached to a patient's head to provide real-time continuous cerebral Doppler evaluation. A feasibility study shows that it can be used in operating theatres during anaesthesia to potentially guide haemodynamic management. We discuss the impact of this new device and which further research would be necessary to find its role in clinical practice.

Untargeted metabolome quantitative trait locus mapping associates variation in urine glycerate to mutant glycerate kinase.

With successes of genome-wide association studies, molecular phenotyping systems are developed to identify genetically determined disease-associated biomarkers. Genetic studies of the human metabolome are emerging but exclusively apply targeted approaches, which restricts the analysis to a limited number of well-known metabolites. We have developed novel technical and statistical methods for systematic and automated quantification of untargeted NMR spectral data designed to perform robust and accurate quantitative trait locus (QTL) mapping of known and previously unreported molecular compounds of the metabolome. For each spectral peak, six summary statistics were calculated and independently tested for evidence of genetic linkage in a cohort of F2 (129S6xBALB/c) mice. The most significant evidence of linkages were obtained with NMR signals characterizing the glycerate (LOD10-42) at the mutant glycerate kinase locus, which demonstrate the power of metabolomics in quantitative genetics to identify the biological function of genetic variants. These results provide new insights into the resolution of the complex nature of metabolic regulations and novel analytical techniques that maximize the full utilization of metabolomic spectra in human genetics to discover mappable disease-associated biomarkers.

Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs.

The world faces complex challenges for chemical hazard assessment. Microfluidic bioartificial organs enable the spatial and temporal control of cell growth and biochemistry, critical for organ-specific metabolic functions and particularly relevant to testing the metabolic dose-response signatures associated with both pharmaceutical and environmental toxicity. Here we present an approach combining a microfluidic system with (1)H NMR-based metabolomic footprinting, as a high-throughput small-molecule screening approach. We characterized the toxicity of several molecules: ammonia (NH(3)), an environmental pollutant leading to metabolic acidosis and liver and kidney toxicity; dimethylsulfoxide (DMSO), a free radical-scavenging solvent; and N-acetyl-para-aminophenol (APAP, or paracetamol), a hepatotoxic analgesic drug. We report organ-specific NH(3) dose-dependent metabolic responses in several microfluidic bioartificial organs (liver, kidney, and cocultures), as well as predictive (99% accuracy for NH(3) and 94% for APAP) compound-specific signatures. Our integration of microtechnology, cell culture in microfluidic biochips, and metabolic profiling opens the development of so-called "metabolomics-on-a-chip" assays in pharmaceutical and environmental toxicology.

Safety of general anaesthetics on the developing brain: are we there yet?

Thirty years ago, neurotoxicity induced by general anaesthetics in the developing brain of rodents was observed. In both laboratory-based and clinical studies, many conflicting results have been published over the years, with initial data confirming both histopathological and neurodevelopmental deleterious effects after exposure to general anaesthetics. In more recent years, animal studies using non-human primates and new human cohorts have identified some specific deleterious effects on neurocognition. A clearer pattern of neurotoxicity seems connected to exposure to repeated general anaesthesia. The biochemistry involved in this neurotoxicity has been explored, showing differential effects of anaesthetic drugs between the developing and developed brains. In this narrative review, we start with a comprehensive description of the initial concerning results that led to recommend that any non-essential surgery should be postponed after the age of 3 yr and that research into this subject should be stepped up. We then focus on the neurophysiology of the developing brain under general anaesthesia, explore the biochemistry of the observed neurotoxicity, before summarising the main scientific and clinical reports investigating this issue. We finally discuss the GAS trial, the importance of its results, and some potential limitations that should not undermine their clinical relevance. We finally suggest some key points that could be shared with parents, and a potential research path to investigate the biochemical effects of general anaesthesia, opening up perspectives to understand the neurocognitive effects of repetitive exposures, especially in at-risk children.

Identification of Modulators of the C. elegans Aryl Hydrocarbon Receptor and Characterization of Transcriptomic and Metabolic AhR-1 Profiles.

The Aryl hydrocarbon Receptor (AhR) is a xenobiotic sensor in vertebrates, regulating the metabolism of its own ligands. However, no ligand has been identified to date for any AhR in invertebrates. In C. elegans, the AhR ortholog, AHR-1, displays physiological functions. Therefore, we compared the transcriptomic and metabolic profiles of worms expressing AHR-1 or not and investigated the putative panel of chemical AHR-1 modulators. The metabolomic profiling indicated a role for AHR-1 in amino acids, carbohydrates, and fatty acids metabolism. The transcriptional profiling in neurons expressing AHR-1, identified 95 down-regulated genes and 76 up-regulated genes associated with neuronal and metabolic functions in the nervous system. A gene reporter system allowed us to identify several AHR-1 modulators including bacterial, dietary, or environmental compounds. These results shed new light on the biological functions of AHR-1 in C. elegans and perspectives on the evolution of the AhR functions across species.