1H NMR-Metabolomics approaches in the assessment of the non-alcoholic fatty liver diseases and in the follow-up of the hepatocellular cacinoma curative treatment

Les atteintes hépatiques, asymptomatiques pour la plupart d’entre elles et pouvant évoluer vers des complications sévères telles que le carcinome hépatocellulaire (CHC) sont responsables de plus de 15 000 décès par an en France. Le manque de marqueurs cliniques et biologiques fiables pour déterminer le degré de sévérité de l’hépatopathie ainsi que pour reconnaître les stades précoces du CHC constitue actuellement un obstacle majeur à une prise en charge optimale de la maladie. Grâce aux approches de type métabolomique et aux techniques analytiques telles que la résonance magnétique nucléaire, il est désormais possible d’obtenir une véritable cartographie des métabolites d’un individu. L’objectif de ce travail a été d’explorer, par une approche RMN métabolomique, les changements métaboliques dans le foie et dans le sérum causés par différentes pathologies hépatiques afin de proposer de nouvelles pistes dans l’amélioration du diagnostic et de la prise en charge de ces maladies. Une attention particulière a également été donnée à l’étude de la validité des paramètres de qualité des modèles de discrimination réalisés lors des analyses statistiques des données multivariées.Most liver diseases nowadays remain symptomless and tend to lead to hepatocellular carcinoma responsible for more than 15.000 patient deaths per year in France. Liver diseases are therefore a major concern for public health.Clinicians lack of non-invasive biomarkers allowing them to enhance identification of liver diseases stages in order to efficiently target the first HCC signs and accordingly improve clinical prognosis.Identification of new biomarkers set new challenges in translational research in order torefine the prognosis and adapt therapeutic procedures.Proton nuclear magnetic resonance spectroscopy-based metabolomics enable to identifyand quantify such metabolites by defining individual metabolic fingerprints.First part of this work was to explore the metabolic modifications of liver tissue to further establish diseases stages profiles.Second part was focused on the assessment of metabolic variations in HCC patients, by analyzing sequential serums taking, before and after a radiofrequency ablation curative treatment.Third and last part was centered on the validation of the quality parameters of the discriminant models used in multivariate statistical analysis

Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature

Background: Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming. Methods: HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography–mass spectrometry (LC/MS). Results: Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS–DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS. Conclusions: Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis

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"

Development of a multiblock metabolomics approach to explore metabolite variations of two algae of the genus Asparagopsis linked to interspecies and temporal factors

International audienceMetabolomics, the science that describes a full range of small molecules in a sample at a time point, is a powerful tool to evaluate patterns in metabolite variations affected by environmental factors. We developed a multiblock metabolomics approach using LC-HRMS, HS-SPME-GC–MS and 1H NMR to study the interspecies and temporal metabolites variations of two red algae species from the genus Asparagopsis well-known for their broad range of biological activities. Samples were collected over two years at 5 sites. For each sample, a biphasic extraction was performed to allow distinct analyses of apolar phases by LC-HRMS and of polar phases by 1H NMR. The remaining lyophilized algal powder was analysed using a HS-SPME-GC–MS method. Temporal variation of antibacterial activities of extracts of the two algae was also studied and its potential covariation with algal metabolome was evaluated. On the one hand, the multiblock analysis allowed the interspecies and temporal discrimination of the two species, and putative identification of potential chemotaxonomic markers including highly halogenated molecules. Organosulfur compounds enriched in A. armata samples could be detected with both 1H NMR (taurine and isethionic acid) and LC-HRMS (sulfolipids). On the other hand, the variation in several metabolites intensities could be related to temporal effects, probably linked to environmental factors. It is the case of floridoside, a major carbohydrate, and citrulline (1H NMR) that both can have antioxidant properties, but also of various sulfolipids (LC-HRMS). The antibacterial activity of extracts of both species was constant throughout the year and did not covary with metabolome. This work is also the first to report the study of the metabolome of the two different species of the genus Asparagopsis by 1H NMR and HS-SPME-GC–MS

PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters

Among all the software packages available for discriminant analyses based on projection to latent structures (PLS-DA) or orthogonal projection to latent structures (OPLS-DA), SIMCA (Umetrics, Umea Sweden) is the more widely used in the metabolomics field. SIMCA proposes many parameters or tests to assess the quality of the computed model (the number of significant components, R-2, Q(2), PCV-ANOVA, and the permutation test). Significance thresholds for these parameters are strongly application-dependent. Concerning the Q(2) parameter, a significance threshold of 0.5 is generally admitted. However, during the last few years, many PLS-DA/OPLS-DA models built using SIMCA have been published with Q(2) values lower than 0.5. The purpose of this opinion note is to point out that, in some circumstances frequently encountered in metabolomics, the values of these parameters strongly depend on the individuals that constitute the validation subsets. As a result of the way in which the software selects members of the calibration and validation subsets, a simple permutation of dataset rows can, in several cases, lead to contradictory conclusions about the significance of the models when a K-fold cross-validation is used. We believe that, when Q(2) values lower than 0.5 are obtained, SIMCA users should at least verify that the quality parameters are stable towards permutation of the rows in their dataset

Sequential Serum Metabolomic Profiling after Radiofrequency Ablation of Hepatocellular Carcinoma Reveals Different Response Patterns According to Etiology

Radiofrequency ablation (RFA) is commonly performed as a curative approach in patients with hepatocellular carcinoma (HCC); however, the risk of tumor recurrence is difficult to predict due to a lack of reliable clinical and biological markers, and identification of new biomarkers poses a major challenge for improving prognoses. Metabolomics is a promising technique that may lead to the identification and characterization of new disease fingerprints. The objective of the present study was to explore, preoperatively and at various time points post-RFA, the metabolic profile of serum samples from HCC patients to identify factors associated with treatment response and recurrence. Sequential sera obtained before and after RFA procedures for 120 patients with HCC due to cirrhosis were investigated using nuclear magnetic resonance metabolomics. A multilevel orthogonal projection to latent structure analysis was used to discriminate intraindividual metabolic changes in response to RFA treatment. Recurrence-free survival differed depending on the underlying cause of cirrhosis. The statistical model showed significant differences depending on whether the liver disease had a viral or nonviral etiology before RFA intervention (explained variance of <i>R</i>²<i>Y</i> = 0.89 and predictability of <i>Q</i>²<i>Y</i> = 0.34). These profiles were also associated with specific and distinct metabolic responses after RFA

Multi-omics determination of metabolome diversity in natural coral populations in the Pacific Ocean

Coral reefs are considered one of the most emblematic ecosystems in our oceans, but their existence is increasingly threatened by climate change. In this study, natural populations of two reef-building coral genera, Pocillopora spp. and Porites spp., and one hydrocoral Millepora cf. platyphylla from two different marine provinces in the Pacific Ocean were investigated using a multi-omics approach as part of the Tara Pacific expedition. Here, we propose a standardised method consisting of a biphasic extraction method followed by metabolomics analysis using mass spectrometry for the lipidome and ¹H nuclear magnetic resonance for hydrophilic metabolites. Our study assessed a broad range of the metabolome and is the first to identify and add 24 compounds by NMR and over 200 lipids by MS analyses for corals. Metabolic profiles were distinct among genera but not within genotypes of the cnidarian corals. Although endosymbiotic dinoflagellates of the family Symbiodiniaceae are known to play a central role in the metabolomic signature of the coral holobiont, they did not account for all differences. This suggests that a combined effect by different members of the coral holobiont and an interaction with the environment might be at play. Our study provides foundational knowledge on the coral holobiont metabolome.ISSN:2662-443

Multi-omics determination of metabolome diversity in natural coral populations in the Pacific Ocean

Coral reefs are considered one of the most emblematic ecosystems in our oceans, but their existence is increasingly threatened by climate change. In this study, natural populations of two reef-building coral genera, Pocillopora spp. and Porites spp., and one hydrocoral Millepora cf. platyphylla from two different marine provinces in the Pacific Ocean were investigated using a multi-omics approach as part of the Tara Pacific expedition. Here, we propose a standardised method consisting of a biphasic extraction method followed by metabolomics analysis using mass spectrometry for the lipidome and 1 H nuclear magnetic resonance for hydrophilic metabolites. Our study assessed a broad range of the metabolome and is the first to identify and add 24 compounds by NMR and over 200 lipids by MS analyses for corals. Metabolic profiles were distinct among genera but not within genotypes of the cnidarian corals. Although endosymbiotic dinoflagellates of the family Symbiodiniaceae are known to play a central role in the metabolomic signature of the coral holobiont, they did not account for all differences. This suggests that a combined effect by different members of the coral holobiont and an interaction with the environment might be at play. Our study provides foundational knowledge on the coral holobiont metabolome