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.

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.

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/.

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.

The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols. Test #1 examined urine samples from adult volunteers either spiked or not spiked with 32 metabolite standards. Test #2 involved a low biological contrast situation comparing the plasma of rats fed a diet either supplemented or not with vitamin D. The spectral information from each instrument was assembled into separate statistical blocks. Correlations between blocks (e.g., instruments) were examined (RV coefficients) along with the structure of the common spectral information (common components and specific weights analysis). In addition, in Test #1, an outlier individual was blindly introduced, and its identification by the various platforms was evaluated. Despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information both within (NMR and LCMS) and across methods (NMR vs. LCMS) was highly convergent (from 64 to 91 % on average). No effect of the LCMS instrumentation (TOF, QTOF, LTQ-Orbitrap) was noted. The outlier individual was best detected and characterised by LCMS instruments. In conclusion, untargeted metabolomics analyses report consistent information within and across instruments of various technologies, even without prior standardisation.

Many networks under study in Information Visualization are "small world" networks. These networks first appeared in the study social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics.