Bénédicte Elena-Herrmann

Computation and NMR crystallography of terbutaline sulfate

This article addresses, by means of computation and advanced experiments, one of the key challenges of NMR crystallography, namely the assignment of individual resonances to specific sites in a crystal structure. Moreover, it shows how NMR can be used for crystal structure validation. The case examined is form B of terbutaline sulfate. CPMAS 13C and fast MAS 1H spectra have been recorded and the peaks assigned as far as possible. Comparison of 13C chemical shifts computed using the CASTEP program (incorporating the Gauge Including Projector Augmented Wave principle) with those obtained experimentally enable the accuracy of the two distinct single‐crystal evaluations of the structure to be compared and an error in one of these is located. The computations have substantially aided in the assignments of both 13C and 1H resonances, as has a series of two‐dimensional (2D) spectra (HETCOR, DQ‐CRAMPS and proton–proton spin diffusion). The 2D spectra have enabled many of the proton chemical shifts to be pinpointed. The relationships of the NMR shifts to the specific nuclear sites in the crystal structure have therefore been established for most 13C peaks and for some 1H signals. Emphasis is placed on the effects of hydrogen bonding on the proton chemical shifts. Copyright

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.

Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy.

The development of Statistical Total Correlation Spectroscopy (STOCSY), a representation of the autocorrelation matrix of a spectral data set as a 2D pseudospectrum, has allowed more reliable assignment of one- and two-dimensional NMR spectra acquired from the complex mixtures that are usually used in metabolomics/metabonomics studies, thus, improving precise identification of candidate biomarkers contained in metabolic signatures computed by multivariate statistical analysis. However, the correlations obtained cannot always be interpreted in terms of connectivities between metabolites. In this study, we combine statistical recoupling of variables (SRV) and STOCSY to identify perturbed metabolite systems. The resulting Recoupled-STOCSY (R-STOCSY) method provides a 2D correlation landscape based on the SRV clusters representing physical, chemical, and biological entities. This enables the identification of correlations between distant clusters and extends the recoupling scheme of SRV, which was previously limited to the association of neighboring clusters. This allows the recovery of only meaningful correlations between metabolic signals and significantly enhances the interpretation of STOCSY. The method is validated through the measurement of the distances between the metabolites involved in these correlations, within the whole metabolic network, which shows that the average shortest path length is significantly shorter for the correlations detected in this new way compared to metabolite couples randomly selected from within the entire KEGG metabolic network. This enables the identification without any a priori knowledge of the perturbed metabolic network. The R-STOCSY completes the recoupling procedure between distant clusters, further reducing the high dimensionality of metabolomics/metabonomics data set and finally allows the identification of composite biomarkers, highlighting disruption of particular metabolic pathways within a global metabolic network. This allows the perturbed metabolic network to be extracted through NMR based metabolomics/metabonomics in an automated, and statistical manner.

Predictive toxicology using systemic biology and liver microfluidic “on chip” approaches: Application to acetaminophen injury

We have analyzed transcriptomic, proteomic and metabolomic profiles of hepatoma cells cultivated inside a microfluidic biochip with or without acetaminophen (APAP). Without APAP, the results show an adaptive cellular response to the microfluidic environment, leading to the induction of anti-oxidative stress and cytoprotective pathways. In presence of APAP, calcium homeostasis perturbation, lipid peroxidation and cell death are observed. These effects can be attributed to APAP metabolism into its highly reactive metabolite, N-acetyl-p-benzoquinone imine (NAPQI). That toxicity pathway was confirmed by the detection of GSH-APAP, the large production of 2-hydroxybutyrate and 3-hydroxybutyrate, and methionine, cystine, and histidine consumption in the treated biochips. Those metabolites have been reported as specific biomarkers of hepatotoxicity and glutathione depletion in the literature. In addition, the integration of the metabolomic, transcriptomic and proteomic collected profiles allowed a more complete reconstruction of the APAP injury pathways. To our knowledge, this work is the first example of a global integration of microfluidic biochip data in toxicity assessment. Our results demonstrate the potential of that new approach to predictive toxicology.

Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method

The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by 1H NMR analysis on 807 serum samples originating from a nested liver cancer case–control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.

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.

μHigh Resolution-Magic-Angle Spinning NMR Spectroscopy for Metabolic Phenotyping of Caenorhabditis elegans

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to μHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

Ab initio simulation of proton spin diffusion.

The many-body nature of the ubiquitous spin diffusion phenomenon makes it difficult to predict accurately from first principles. We show how the use of reduced Liouville spaces makes it possible to reproduce experimental proton spin diffusion measurements directly from crystalline geometry for powdered solids under magic-angle spinning.

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.