Search

Identification of Endocrine-Disrupting Chemicals (EDCs) in a regulatory context requires a high level of evidence. However, lines of evidence (e.g. human, in vivo, in vitro or in silico) are heterogeneous and incomplete for quantifying evidence of the adverse effects and mechanisms involved. To date, for the regulatory appraisal of metabolism-disrupting chemicals (MDCs), no harmonised guidance to assess the weight of evidence has been developed at the EU or international level. To explore how to develop this, we applied a formal Expert Knowledge Elicitation (EKE) approach within the European GOLIATH project. EKE captures expert judgment in a quantitative manner and provides an estimate of uncertainty of the final opinion. As a proof of principle, we selected one suspected MDC -triphenyl phosphate (TPP) - based on its related adverse endpoints (obesity/adipogenicity) relevant to metabolic disruption and a putative Molecular Initiating Event (MIE): activation of peroxisome proliferator activated receptor gamma (PPARγ). We conducted a systematic literature review and assessed the quality of the lines of evidence with two independent groups of experts within GOLIATH, with the objective of categorising the metabolic disruption properties of TPP, by applying an EKE approach. Having followed the entire process separately, both groups arrived at the same conclusion, designating TPP as a "suspected MDC" with an overall quantitative agreement exceeding 85%, indicating robust reproducibility. The EKE method provides to be an important way to bring together scientists with diverse expertise and is recommended for future work in this area., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes., Results: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA., Conclusion: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA )., (© 2024. The Author(s).)

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wieder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Cooke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied., Objectives: In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized., Methods & Results: In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results., Conclusion: Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks., (© 2024. The Author(s).)

Obesity is a major contributor to the silent and progressive development of type 2 diabetes (T2D) whose prevention could be improved if individuals at risk were identified earlier. Our aim is to identify early phenotypes that precede T2D in diet-induced obese minipigs. We fed four groups of minipigs (n = 5-10) either normal-fat or high-fat high-sugar diet during 2, 4, or 6 months. Morphometric features were recorded, and metabolomics and clinical parameters were assessed on fasting plasma samples. Multivariate statistical analysis on 46 morphometrical and clinical parameters allowed to differentiate 4 distinct phenotypes: NFC (control group) and three others (HF2M, HF4M, HF6M) corresponding to the different stages of the obesity progression. Compared to NFC, we observed a rapid progression of body weight and fat mass (4-, 7-, and tenfold) in obese phenotypes. Insulin resistance (IR; 2.5-fold increase of HOMA-IR) and mild dyslipidemia (1.2- and twofold increase in total cholesterol and HDL) were already present in the HF2M and remained stable in HF4M and HF6M. Plasma metabolome revealed subtle changes of 23 metabolites among the obese groups, including a progressive switch in energy metabolism from amino acids to lipids, and a transient increase in de novo lipogenesis and TCA-related metabolites in HF2M. Low anti-oxidative capacities and anti-inflammatory response metabolites were found in the HF4M, and a perturbed hexose metabolism was observed in HF6M. Overall, we show that IR and progressively obese minipigs reveal phenotype-specific metabolomic signatures for which some of the identified metabolites could be considered as potential biomarkers of early progression to TD2., (© 2022. The Author(s) under exclusive licence to University of Navarra.)

Perfluorooctanoic acid (PFOA) is a synthetic perfluorinated chemical classified as a persistent organic pollutant. PFOA has been linked to many toxic effects, including liver injury. Many studies report that PFOA exposure alters serum and hepatic lipid metabolism. However, lipidomic pathways altered by PFOA exposure are largely unknown and only a few lipid classes, mostly triacylglycerol (TG), are usually considered in lipid analysis. Here, we performed a global lipidomic analysis on the liver of PFOA-exposed (high-dose and short-duration) and control mice by combining three mass spectrometry (MS) techniques: liquid chromatography with tandem mass spectrometry (LC-MS/MS), matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), and time-of-flight secondary ion mass spectrometry (TOF-SIMS). Among all hepatic lipids identified by LC-MS/MS analysis, more than 350 were statistically impacted (increased or decreased levels) after PFOA exposure, as confirmed by multi-variate data analysis. The levels of many lipid species from different lipid classes, most notably phosphatidylethanolamine (PE), phosphatidylcholine (PC), and TG, were significantly altered. Subsequent lipidomic analysis highlights the pathways significantly impacted by PFOA exposure, with the glycerophospholipid metabolism being the most impacted, and the changes in the lipidome network, which connects all the lipid species together. MALDI-MSI displays the heterogeneous distribution of the affected lipids and PFOA, revealing different areas of lipid expression linked to PFOA localization. TOF-SIMS localizes PFOA at the cellular level, supporting MALDI-MSI results. This multi-modal MS analysis unveils the lipidomic impact of PFOA in the mouse liver after high-dose and short-term exposure and opens new opportunities in toxicology.

Background: Plant proteins (PPs) have been associated with better cardiovascular health than animal proteins (APs) in epidemiological studies. However, the underlying metabolic mechanisms remain mostly unknown., Objectives: Using a combination of cutting-edge isotopic methods, we aimed to better characterize the differences in protein and energy metabolisms induced by dietary protein sources (PP compared with AP) in a prudent or western dietary context., Methods: Male Wistar rats (n = 44, 8 wk old) were fed for 4.5 mo with isoproteic diets differing in their protein isolate sources, either AP (100% milk) or PP (50%:50% pea: wheat) and being normal (NFS) or high (HFS) in sucrose (6% or 15% kcal) and saturated fat (7% or 20% kcal), respectively. We measured body weight and composition, hepatic enzyme activities and lipid content, and plasma metabolites. In the intestine, liver, adipose tissues, and skeletal muscles, we concomitantly assessed the extent of amino acid (AA) trafficking using a 15 N natural abundance method, the rates of macronutrient routing to dispensable AA using a 13 C natural abundance method, and the metabolic fluxes of protein synthesis (PS) and de novo lipogenesis using a 2 H labeling method. Data were analyzed using ANOVA and Mixed models., Results: At the whole-body level, PP limited HFS-induced insulin resistance (-27% in HOMA-IR between HFS groups, P < 0.05). In the liver, PP induced lower lipid content (-17%, P < 0.01) and de novo lipogenesis (-24%, P < 0.05). In the different tissues studied, PP induced higher AA transamination accompanied by higher routings of dietary carbohydrates and lipids toward dispensable AA synthesis by glycolysis and β-oxidation, resulting in similar tissue PS and protein mass., Conclusions: In growing rats, compared with AP, a balanced blend of PP similarly supports protein anabolism while better limiting whole-body and tissue metabolic dysregulations through mechanisms related to their less optimal AA profile for direct channeling to PS., (Copyright © 2022 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background: Endocrine disrupting chemicals (EDCs) contribute to the etiology of metabolic disorders such as obesity, insulin resistance and hepatic dysfunction. Concern is growing about the consequences of perinatal EDC exposure on disease predisposition later in life. Metabolomics are promising approaches for studying long-term consequences of early life EDC exposure. These approaches allow for the identification and characterization of biomarkers of direct or ancestral exposures that could be diagnostic for individual susceptibility to disease and help to understand mechanisms through which EDCs act., Objectives: We sought to identify metabolomic fingerprints in mice ancestrally exposed to the model obesogen tributyltin (TBT), to assess whether metabolomics could discriminate potential trans-generational susceptibility to obesity and recognize metabolic pathways modulated by ancestral TBT exposure., Methods: We used non-targeted 1 H NMR metabolomic analyses of plasma and liver samples collected from male and female mice ancestrally exposed to TBT in two independent transgenerational experiments in which F3 and F4 males became obese when challenged with increased dietary fat., Results: Metabolomics confirmed transgenerational obesogenic effects of environmentally relevant doses of TBT in F3 and F4 males, in two independent studies. Although females never became obese, their specific metabolomic fingerprint evidenced distinct transgenerational effects of TBT in female mice consistent with impaired capacity for liver biotransformation., Discussion: This study is the first application of metabolomics to unveil the transgenerational effects of EDC exposure. Very early, significant changes in the plasma metabolome were observed in animals ancestrally exposed to TBT. These changes preceded the onset of obesogenic effects elicited by increased dietary fat in the TBT groups, and which ultimately resulted in significant changes in the liver metabolome. Development of metabolomic fingerprints could facilitate the identification of individuals carrying the signature of ancestral obesogen exposure that might increase their susceptibility to other risk factor such as increased dietary fat., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics., Competing Interests: The authors have declared that no competing interests exist.

Background: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis., Methods: A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men., Findings: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…)., Interpretation: These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS., Funding: The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication., Competing Interests: Declaration of Competing Interest All authors declare they have no conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid β-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors., Competing Interests: Disclosures: B. Nicolay reported "other" from Agios Pharmaceuticals outside the submitted work and is an employee and shareholder of Agios Pharmaceuticals. J.R. Marszalek reported a patent to IACS-010759 issued. K. Takahashi reported personal fees from Celgene during the conduct of the study; and personal fees from Symbio Pharmaceuticals, GSK, and Novartis outside the submitted work. C.D. DiNardo reported personal fees from Agios Pharmaceuticals, Celgene, and AbbVie outside the submitted work. M. Konopleva reported "other" from Amgen, Kisoji, and Reata Pharmaceutical; and grants from AbbVie, Genentech, and Stemline Therapeutics, F. Hoffman La-Roche, Forty Seven, Eli Lilly, Cellectis, Calithera, Ablynx, Agios, Ascentage, Astra Zeneca, Rafael Pharmaceutical, and Sanofi outside the submitted work. In addition, M. Konopleva had a patent to Novartis pending (62/993,166), a patent to Eli Lilly issued, and a patent to Reata Pharmaceutical issued (7,795,305 B2 CDDO). C. Récher reported grants from Celgene, Amgen, Novartis, Jazz, AbbVie, Astellas, MaatPharma, Agios, Daiichi-Sankyo, and Roche; personal fees from Incyte, Macrogenics, Otsuka, Janssen, Pfizer, and Takeda; and non-financial support from Sanofi and Gilead outside the submitted work. No other disclosures were reported., (© 2021 Stuani et al.)

The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom., Competing Interests: The authors have declared that no competing interests exist.

Insulin resistance decreases the ability of insulin to inhibit hepatic gluconeogenesis, a key step in the development of metabolic syndrome. Metabolic alterations, fat accumulation, and fibrosis in the liver are closely related and contribute to the progression of comorbidities, such as hypertension, type 2 diabetes, or cancer. Omega 3 ( n -3) polyunsaturated fatty acids, such as eicosapentaenoic acid (EPA), were identified as potent positive regulators of insulin sensitivity in vitro and in animal models. In the current study, we explored the effects of a transgenerational supplementation with EPA in mice exposed to an obesogenic diet on the regulation of microRNAs (miRNAs) and gene expression in the liver using high-throughput techniques. We implemented a comprehensive molecular systems biology approach, combining statistical tools, such as MicroRNA Master Regulator Analysis pipeline and Boolean modeling to integrate these biochemical processes. We demonstrated that EPA mediated molecular adaptations, leading to the inhibition of miR-34a-5p, a negative regulator of Irs2 as a master regulatory event leading to the inhibition of gluconeogenesis by insulin during the fasting-feeding transition. Omics data integration provided greater biological insight and a better understanding of the relationships between biological variables. Such an approach may be useful for deriving innovative data-driven hypotheses and for the discovery of molecular-biochemical mechanistic links.

The functional understanding of metabolic changes requires both a significant investigation into metabolic pathways, as enabled by global metabolomics and lipidomics approaches, and the comprehensive and accurate exploration of specific key pathways. To answer this pivotal challenge, we propose an optimized approach, which combines an efficient sample preparation, aiming to reduce the variability, with a biphasic extraction method, where both the aqueous and organic phases of the same sample are used for mass spectrometry analyses. We demonstrated that this double extraction protocol allows working with one single sample without decreasing the metabolome and lipidome coverage. It enables the targeted analysis of 40 polar metabolites and 82 lipids, together with the absolute quantification of 32 polar metabolites, providing comprehensive coverage and quantitative measurement of the metabolites involved in central carbon energy pathways. With this method, we evidenced modulations of several lipids, amino acids, and energy metabolites in HepaRG cells exposed to fenofibrate, a model hepatic toxicant, and metabolic modulator. This new protocol is particularly relevant for experiments involving limited amounts of biological material and for functional metabolic explorations and is thus of particular interest for studies aiming to decipher the effects and modes of action of metabolic disrupting compounds.

The postprandial period represents one of the most challenging phenomena in whole-body metabolism, and it can be used as a unique window to evaluate the phenotypic flexibility of an individual in response to a given meal, which can be done by measuring the resilience of the metabolome. However, this exploration of the metabolism has never been applied to the arteriovenous (AV) exploration of organs metabolism. Here, we applied an AV metabolomics strategy to evaluate the postprandial flexibility across the liver and the intestine of mini-pigs subjected to a high fat-high sucrose (HFHS) diet for 2 months. We identified for the first time a postprandial signature associated to the insulin resistance and obesity outcomes, and we showed that the splanchnic postprandial metabolome was considerably affected by the meal and the obesity condition. Most of the changes induced by obesity were observed in the exchanges across the liver, where the metabolism was reorganized to maintain whole body glucose homeostasis by routing glucose formed de novo from a large variety of substrates into glycogen. Furthermore, metabolites related to lipid handling and energy metabolism showed a blunted postprandial response in the obese animals across organs. Finally, some of our results reflect a loss of flexibility in response to the HFHS meal challenge in unsuspected metabolic pathways that must be further explored as potential new events involved in early obesity and the onset of insulin resistance.

Introduction: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids., Objectives: To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver., Methods: Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology., Results: We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation., Conclusion: This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.

Blood circulation mainly aims at distributing the nutrients required for tissue metabolism and collecting safely the by-products of all tissues to be further metabolized or eliminated. The simultaneous study of arterial (A) and venous (V) specific metabolites therefore has appeared to be a more relevant approach to understand and study the metabolism of a given organ. We propose to implement this approach by applying a metabolomics (NMR) strategy on paired AV blood across the intestine and liver on high fat/high sugar (HFHS)-fed minipigs. Our objective was to unravel kinetically and sequentially the metabolic adaptations to early obesity/insulin resistance onset specifically on these two tissues. After two months of HFHS feeding our study of AV ratios of the metabolome highlighted three major features. First, the hepatic metabolism switched from carbohydrate to lipid utilization. Second, the energy demand of the intestine increased, resulting in an enhanced uptake of glutamine, glutamate, and the recruitment of novel energy substrates (choline and creatine). Third, the uptake of methionine and threonine was considered to be driven by an increased intestine turnover to cope with the new high-density diet. Finally, the unique combination of experimental data and modelling predictions suggested that HFHS feeding was associated with changes in tryptophan metabolism and fatty acid β-oxidation, which may play an important role in lipid hepatic accumulation and insulin sensitivity.

Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.

The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance ( 1 H-NMR) based untargeted metabolite profiling, followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation induced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA (10 -6 M, 10 -9 M, and 10 -12 M), vs. the female reference hormone 17β-estradiol (E2, 10 -9 M, 10 -12 M, and 10 -15 M). Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.

Chemical pollutant exposure is a risk factor contributing to the growing epidemic of non-alcoholic fatty liver disease (NAFLD) affecting human populations that consume a western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here, we describe the alterations in gene expression profiles and metabolite levels in the human HepaRG liver cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments.

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.

Little is known about how diet-induced obesity and insulin resistance affect protein and amino acid (AA) metabolism in tissues. The natural relative abundances of the heavy stable isotopes of C (δ 13C) and N (δ 15N) in tissue proteins offer novel and promising biomarkers of AA metabolism. They, respectively, reflect the use of dietary macronutrients for tissue AA synthesis and the relative metabolic use of tissue AA for oxidation v. protein synthesis. In this study, δ 13C and δ 15N were measured in the proteins of various tissues in young adult rats exposed perinatally and/or fed after weaning with a normal- or a high-fat (HF) diet, the aim being to characterise HF-induced tissue-specific changes in AA metabolism. HF feeding was shown to increase the routing of dietary fat to all tissue proteins via non-indispensable AA synthesis, but did not affect AA allocation between catabolic and anabolic processes in most tissues. However, the proportion of AA directed towards oxidation rather than protein synthesis was increased in the small intestine and decreased in the tibialis anterior muscle and adipose tissue. In adipose tissue, the AA reallocation was observed in the case of perinatal or post-weaning exposure to HF, whereas in the small intestine and tibialis anterior muscle the AA reallocation was only observed after HF exposure that covered both the perinatal and post-weaning periods. In conclusion, HF exposure induced an early reorganisation of AA metabolism involving tissue-specific effects, and in particular a decrease in the relative allocation of AA to oxidation in several peripheral tissues.

Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context., Availability and Implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/., (© The Author 2017. Published by Oxford University Press.)

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

Body tissues are generally 15N-enriched over the diet, with a discrimination factor (Δ15N) that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both 15N bioaccumulation and intra- and inter-individual Δ15N variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental Δ15N measurements in rats, we developed a multi-compartmental model that provides the first detailed representation of the complex functioning of the body's Δ15N system, by explicitly linking the sizes and Δ15N values of 21 nitrogen pools to the rates and isotope effects of 49 nitrogen metabolic fluxes. We have shown that (i) besides urea production, several metabolic pathways (e.g., protein synthesis, amino acid intracellular metabolism, urea recycling and intestinal absorption or secretion) are most probably associated with isotope fractionation and together contribute to 15N accumulation in tissues, (ii) the Δ15N of a tissue at steady-state is not affected by variations of its P turnover rate, but can vary according to the relative orientation of tissue free amino acids towards oxidation vs. protein synthesis, (iii) at the whole-body level, Δ15N variations result from variations in the body partitioning of nitrogen fluxes (e.g., urea production, urea recycling and amino acid exchanges), with or without changes in nitrogen balance, (iv) any deviation from the optimal amino acid intake, in terms of both quality and quantity, causes a global rise in tissue Δ15N, and (v) Δ15N variations differ between tissues depending on the metabolic changes involved, which can therefore be identified using simultaneous multi-tissue Δ15N measurements. This work provides proof of concept that Δ15N measurements constitute a new promising tool to investigate how metabolic fluxes are nutritionally or physiopathologically reorganized or altered. The existence of such natural and interpretable isotopic biomarkers promises interesting applications in nutrition and health.

The consumption of cocoa and dark chocolate is associated with a lower risk of CVD, and improvements in endothelial function may mediate this relationship. Less is known about the effects of cocoa/chocolate on the augmentation index (AI), a measure of vascular stiffness and vascular tone in the peripheral arterioles. We enrolled thirty middle-aged, overweight adults in a randomised, placebo-controlled, 4-week, cross-over study. During the active treatment (cocoa) period, the participants consumed 37 g/d of dark chocolate and a sugar-free cocoa beverage (total cocoa = 22 g/d, total flavanols (TF) = 814 mg/d). Colour-matched controls included a low-flavanol chocolate bar and a cocoa-free beverage with no added sugar (TF = 3 mg/d). Treatments were matched for total fat, saturated fat, carbohydrates and protein. The cocoa treatment significantly increased the basal diameter and peak diameter of the brachial artery by 6% (+2 mm) and basal blood flow volume by 22%. Substantial decreases in the AI, a measure of arterial stiffness, were observed in only women. Flow-mediated dilation and the reactive hyperaemia index remained unchanged. The consumption of cocoa had no effect on fasting blood measures, while the control treatment increased fasting insulin concentration and insulin resistance (P= 0·01). Fasting blood pressure (BP) remained unchanged, although the acute consumption of cocoa increased resting BP by 4 mmHg. In summary, the high-flavanol cocoa and dark chocolate treatment was associated with enhanced vasodilation in both conduit and resistance arteries and was accompanied by significant reductions in arterial stiffness in women.

Fractional synthesis rates (FSR) of tissue proteins (P) are usually measured using labeled amino acid (AA) tracer methods over short periods of time under acute, particular conditions. By combining the long-term and non-steady-state (15)N labeling of AA and P tissue fractions with compartmental modeling, we have developed a new isotopic approach to investigate the degree of compartmentation of P turnover in tissues and to estimate long-term FSR values under sustained and averaged nutritional and physiological conditions. We measured the rise-to-plateau kinetics of nitrogen isotopic enrichments (δ(15)N) in the AA and P fractions of various tissues in rats for 2 mo following a slight increase in diet δ(15)N. Using these δ(15)N kinetics and a numerical method based on a two-compartment model, we determined reliable FSR estimates for tissues in which P turnover is adequately represented by such a simple precursor-product model. This was the case for kidney, liver, plasma, and muscle, where FSR estimates were 103, 101, 58, and 11%/day, respectively. Conversely, we identified tissues, namely, skin and small intestine, where P turnover proved to be too complex to be represented by a simple two-compartment model, evidencing the higher level of subcompartmentation of the P and/or AA metabolism in these tissues. The present results support the value of this new approach in gaining cognitive and practical insights into tissue P turnover and propose new and integrated FSR values over all individual precursor AA and all diurnal variations in P kinetics.

Net acid production, which is composed of volatile acids (15,000 mEq/day) and metabolic acids (70-100 mEq/day) is relatively small compared to whole-body H⁺ turnover (150,000 mEq/day). Metabolic acids are ingested from the diet or produced as intermediary or end products of endogenous metabolism. The three commonly reported sources of net acid production are the metabolism of sulphur amino acids, the metabolism or ingestion of organic acids, and the metabolism of phosphate esters or dietary phosphoproteins. Net base production occurs mainly as a result of absorption of organic anions from the diet. To maintain acid-base balance, ingested and endogenously produced acids are neutralized within the body by buffer systems or eliminated from the body through the respiratory (excretion of volatile acid in the form of CO₂) and urinary (excretion of fixed acids and remaining H⁺) pathways. Because of the many reactions involved in the acid-base balance, the direct determination of acid production is complex and is usually estimated through direct or indirect measurements of acid excretion. However, indirect approaches, which assess the acid-forming potential of the ingested diet based on its composition, do not take all the acid-producing reactions into account. Direct measurements therefore seem more reliable. Nevertheless, acid excretion does not truly provide information on the way acidity is dealt with in the plasma and this measurement should be interpreted with caution when assessing acid-base imbalance., (Copyright © 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

High-protein (HP) diets exert a hypercalciuric effect at constant levels of calcium intake, even though the effect may depend on the nature of the dietary protein. Lower urinary pH is also consistently observed for subjects consuming HP diets. The combination of these two effects was suspected to be associated with a dietary environment favorable for demineralization of the skeleton. However, increased calcium excretion due to HP diet does not seem to be linked to impaired calcium balance. In contrast, some data indicate that HP intakes induce an increase of intestinal calcium absorption. Moreover, no clinical data support the hypothesis of a detrimental effect of HP diet on bone health, except in a context of inadequate calcium supply. In addition, HP intake promotes bone growth and retards bone loss and low-protein diet is associated with higher risk of hip fractures. The increase of acid and calcium excretion due to HP diet is also accused of constituting a favorable environment for kidney stones and renal diseases. However, in healthy subjects, no damaging effect of HP diets on kidney has been found in either observational or interventional studies and it seems that HP diets might be deleterious only in patients with preexisting metabolic renal dysfunction. Thus, HP diet does not seem to lead to calcium bone loss, and the role of protein seems to be complex and probably dependent on other dietary factors and the presence of other nutrients in the diet.

Due to the existence of isotope effects on some metabolic pathways of amino acid and protein metabolism, animal tissues are (15)N-enriched relative to their dietary nitrogen sources and this (15)N enrichment varies among different tissues and metabolic pools. The magnitude of the tissue-to-diet discrimination (Δ(15)N) has also been shown to depend on dietary factors. Since dietary protein sources affect amino acid and protein metabolism, we hypothesized that they would impact this discrimination factor, with selective effects at the tissue level. To test this hypothesis, we investigated in rats the influence of a milk or soy protein-based diet on Δ(15)N in various nitrogen fractions (urea, protein and non-protein fractions) of blood and tissues, focusing on visceral tissues. Regardless of the diet, the different protein fractions of blood and tissues were generally (15)N-enriched relative to their non-protein fraction and to the diet (Δ(15)N>0), with large variations in the Δ(15)N between tissue proteins. Δ(15)N values were markedly lower in tissue proteins of rats fed milk proteins compared to those fed soy proteins, in all sampled tissues except in the intestine, and the amplitude of Δ(15)N differences between diets differed between tissues. Both between-tissue and between-diet Δ(15)N differences are probably related to modulations of the relative orientation of dietary and endogenous amino acids in the different metabolic pathways. More specifically, the smaller Δ(15)N values observed in tissue proteins with milk than soy dietary protein may be due to a slightly more direct channeling of dietary amino acids for tissue protein renewal and to a lower recycling of amino acids through fractionating pathways. In conclusion, the present data indicate that natural Δ(15)N of tissue are sensitive markers of the specific subtle regional modifications of the protein and amino acid metabolism induced by the protein dietary source.