Clément Pontoizeau

A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer

Breast cancer (BC) displays a high heterogeneity from histology to prognosis, metastatic evolution and treatment responses. We report here a (1)H NMR-based metabolic phenotyping study aiming at identifying coordinated metabolic serum changes associated with advanced metastatic breast cancer (MBC) in comparison to the localized early disease (EBC). A model discriminating EBC and MBC patients is obtained (n=85: 46 EBC and 39 MBC), and validated with an independent cohort (n=112: 61 EBC and 51 MBC; 89.8% sensitivity, 79.3% specificity). We identify 9 statistically significant metabolites involved in this discrimination: histidine, acetoacetate, glycerol, pyruvate, glycoproteins (N-acetyl), mannose, glutamate and phenylalanine. This work illustrates the strong potential of NMR metabolic phenotyping for the diagnosis, prognosis, and management of cancer patients.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort

BackgroundHepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.MethodsTo address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.ResultsA metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis.ConclusionOur results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

Broad-Ranging Natural Metabotype Variation Drives Physiological Plasticity in Healthy Control Inbred Rat Strains

Maintaining homeostasis in higher organisms involves a complex interplay of multiple ubiquitous and organ-specific molecular mechanisms that can be characterized using functional genomics technologies such as transcriptomics, proteomics, and metabonomics and dissected out through genetic investigations in healthy and diseased individuals. We characterized the genomic, metabolic, and physiological divergence of several inbred rat strains--Brown Norway, Lewis, Wistar Kyoto, Fisher (F344)--frequently used as healthy controls in genetic studies of the cardiometabolic syndrome. Hierarchical clustering of (1)H NMR-based metabolic profiles (n = 20 for urine, n = 16 for plasma) identified metabolic phenotype (metabotype) divergence patterns similar to the phylogenetic variability based on single nucleotide polymorphisms. However, the observed urinary metabotype variation exceeded that explainable by genetic polymorphisms. To understand further this natural variation, we used an integrative, knowledge-based network biology metabolic pathway analysis approach, coined Metabolite-Set Enrichment Analysis (MSEA). MSEA reveals that homeostasis and physiological plasticity can be achieved despite widespread divergences in glucose, lipid, amino acid, and energy metabolism in the host, together with different gut microbiota contributions suggestive of strain-specific transgenomic interactions. This work illustrates the concept of natural metabolomic variation, leading to physiologically stable albeit diverse strategies within the range of normality, all of which are highly relevant to animal model physiology, genetical genomics, and patient stratification in personalized healthcare.

Targeted projection NMR spectroscopy for unambiguous metabolic profiling of complex mixtures.

Unambiguous identification of individual metabolites present in complex mixtures such as biofluids constitutes a crucial prerequisite for quantitative metabolomics, toward better understanding of biochemical processes in living systems. Increasing the dimensionality of a given NMR correlation experiment is the natural solution for resolving spectral overlap. However, in the context of metabolites, natural abundance acquisition of 1H and 13C NMR data virtually excludes the use of higher dimensional NMR experiments (3D, 4D, etc.) that would require unrealistically long acquisition times. Here, we introduce projection NMR techniques for studies of complex mixtures, and we show how discrete sets of projection spectra from higher dimensional NMR experiments are obtained in a reasonable time frame, in order to capture essential information necessary to resolve assignment ambiguities caused by signal overlap in conventional 2D NMR spectra. We determine optimal projection angles where given metabolite resonances will have the least overlap, to obtain distinct metabolite assignment in complex mixtures. The method is demonstrated for a model mixture composition made of ornithine, putrescine and arginine for which acquisition of a single 2D projection of a 3D 1H–13C TOCSY‐HSQC spectrum allows to disentangle the metabolite signals and to access to complete profiling of this model mixture in the targeted 2D projection plane. Copyright

Metabolomics analysis uncovers that dietary restriction buffers metabolic changes associated with aging in Caenorhabditis elegans.

Dietary restriction (DR) is one of the most universal means of extending lifespan. Yet, whether and how DR specifically affects the metabolic changes associated with aging is essentially unknown. Here, we present a comprehensive and unbiased picture of the metabolic variations that take place with age at the whole organism level in Caenorhabditis elegans by using 1H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) analysis of intact worms. We investigate metabolic variations potentially important for lifespan regulation by comparing the metabolic fingerprint of two previously described genetic models of DR, the long-lived eat-2(ad465) and slcf-1(tm2258) worms, as single mutants or in combination with a genetic suppressor of their lifespan phenotype. Our analysis shows that significant changes in metabolite profiles precede the major physiological decline that accompanies aging and that DR protects from some of those metabolic changes. More specifically, low phosphocholine (PCho) correlates with high life expectancy. A mutation in the tumor suppressor gene PTEN/DAF-18, which suppresses the beneficial effects of DR in both C. elegans and mammals, increases both PCho level and choline kinase expression. Furthermore, we show that choline kinase function in the intestine can regulate lifespan. This study highlights the relevance of NMR metabolomic approaches for identifying potential biomarkers of aging.

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.

Batch profiling calibration for robust NMR metabonomic data analysis

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

Downregulation of transcription factor E4F1 in hepatocarcinoma cells: HBV-dependent effects on autophagy, proliferation and metabolism

The multifunctional E4F1 protein is a cellular target of the E1A adenoviral oncoprotein. Interaction between E4F1 and the hepatitis B virus (HBV) protein HBx has been demonstrated in vitro. In this study, RNA interference has been used to downregulate E4F1 in the hepatocellular carcinoma (HCC) cell line HepG2 (HBV negative) and its derivative, HBV expressing HepG2/2.2.15. Reduction of E4F1 levels induced hepatocyte vacuolation (formation of large cytoplasmic vesicles), increased autophagy and caused mitochondrial defects and metabolism changes in HepG2/2.2.15, but not in HepG2. Moreover, downregulation of E4F1 reduced DNA synthesis with partial cell cycle arrest in G1 in both cell types and this effect was more marked in HepG2/2.2.15 than in HepG2. These effects were partially prevented by RNA interference directed to either HBx or to p53. Coprecipitation and western blot experiments detected complexes between E4F1 and HBx in several HCC cell lines. Although a review of mutation and gene expression public databases did not support that E4F1 is specifically altered in liver cancer, our results suggest that E4F1 may neutralize the capacity of HBx to activate a p53-dependent, metabolic and growth arrest phenotype in liver cells, thus possibly contributing to the viability and proliferation of HBV-infected cells.