Nuclear magnetic resonance based metabolomics and liver diseases Recent advances and future clinical applications

International audienceMetabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an "omics" technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical "routine"

Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics

Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear magnetic resonance (NMR) spectrometry with supervised orthogonal projection on latent structure (OPLS) statistical analysis. 1H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y=0.947, Q2Y=0.856 and CV-ANOVA p-value < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p-value < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy.

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis

Metabolomics with Multi-Block Modelling of Mass Spectrometry and Nuclear Magnetic Resonance in order to Discriminate Haplosclerida Marine Sponges

International audienceA comprehensive metabolomic strategy, integrating 1 H NMR and MS-based multi-block modelling in conjunction with multi-informational molecular networking, has been developed to discriminate sponges of the order Haplosclerida, well known for being taxonomically contentious. A in house collection of 33 marine sponge samples belonging to three families (Callyspongiidae, Chalinidae, Petrosiidae) and four different genera (Callyspongia, Haliclona, Petrosia, Xestospongia) was investigated using LC-MS/MS, molecular networking and the annotations processes combined with NMR data and multivariate statistical modelling. The combination of MS and NMR data into supervised multivariate models led to discriminate, out of the four genera, three groups based on the presence of metabolites, not necessarily previously described in the Haplosclerida order. Although these metabolomic methods have already been applied separately, it is the first time that a multi-block untargeted approach using MS and NMR is combined with molecular networking and statistically analyzed, pointing out the pros and cons of this strategy

Découverte de métabolites prédictifs du risque de cancer du sein : approche métabolomique RMN appliquée à l’épidémiologie nutritionnelle

International audienceIntroduction et but de l’étude : La métabolomique est une science émergeante qui étudie l'ensemble des métabolites présents dans une cellule, un organe, un biofluide. Son application au domaine de l’épidémiologie nutritionnelle ouvre des perspectives considérables. A notre connaissance, aucune étude prospective n’avait été menée pour investiguer les liens entre les profils métabolomiques non-ciblés à l’inclusion et le risque de développer un cancer du sein à long terme. Ce projet propose donc pour la 1ère fois d’étudier si des signatures métabolomiques établies à partir d’un simple prélèvement sanguin pourraient contribuer à mieux comprendre et à prédire le risque de cancer du sein dans la décennie suivante. Matériel et méthodes : Une étude cas-témoin nichée a été mise en place dans la cohorte SU.VI.MAX (1994-2007), incluant 206 cas de cancer du sein et 396 témoins appariés. Les profils métabolomiques RMN ont été établis sur des échantillons de plasma prélevés à l’inclusion (donc avant l’apparition des cancers). Des régressions logistiques conditionnelles multivariées ont été utilisées. La performance prédictive des modèles a été évaluée grâce au NRI (Net Reclassification Improvement). Résultats et Analyse statistique : 237 buckets ont été obtenus après division du spectre RMN par « intelligent bucketing », dont 25 étaient significatifs dans les modèles logistiques pour la séquence NOESY (respectivement 228 buckets et 27 significatifs pour la séquence CPMG). Les probabilités critiques correspondantes allaient de 0,00007 (pour le bucket 5,1869ppm, correspondant au groupement méthine du glycéryl, ORT3vs.T1= 0,37 [0,23-0,61]) à 0,04 (pour le bucket 2,429ppm, correspondant à la glutamine, ORT3vs.T1= 1,62 [1,02-2,57]). Des lipoprotéines, des lipides (dont des acides gras insaturés et des glycérides et/ou des phosphoglycérides et dérivés) et des glycoprotéines étaient associés à une diminution du risque de développer un cancer du sein alors que plusieurs acides aminés et dérivés (Valine, Leucine, Glutamine, Créatine, Créatinine et la Thréonine) et le bêta-glucose étaient associés à une augmentation du risque. La plupart de ces métabolites augmentaient significativement la performance prédictive des modèles. Conclusion : Cette étude pionnière suggère que plusieurs métabolites, dont certains appartenant au food metabolome, seraient impliqués dans l’étiologie du cancer du sein. Des analyses similaires en métabolomique par spectrométrie de masse sont en cours dans l’étude, ainsi que l’étude des corrélations entre les profils métabolomiques et les apports nutritionnels

Loadings plot of the principal component analysis (PCA) performed on clinical variables of periodontitis.

<p>Loadings are scaled so that the correlated variables correctly explained by the components are found close together and near the correlation circle. BL: bone loss; BOP: bleeding on probing; CAL: clinical attachment loss, expressed as a mean (CALMEAN) or according to the severity of the loss (CALmild, CALmoderate, and CALMAX); DMF: decay missing filled; MPPD: mean pocket depth; NRT: number of residual teeth; PCR: plaque control record; TOBACCO: smoking habits (for details see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182767#sec002" target="_blank">Materials and methods</a>).</p

Multivariate logistic regression analysis.

<p>Multivariate logistic regression analysis.</p

Main metabolites identified to discriminate periodontitis from controls according to the loadings plot analysis.

<p>Correlation between bucket intensities and discriminated group members are given with the associated p value.</p

Clinical data of patients with periodontitis and controls.

<p>Clinical data of patients with periodontitis and controls.</p

Evaluation of the clinical significance of the grouped lactate-GABA-butyrate considered as one.

<p>Evaluation of the clinical significance of the grouped lactate-GABA-butyrate considered as one.</p