Although several lines of evidence have implicated mitochondrial dysfunction in cancer aetiology, it is still unclear how and to what extent the dysregulation of mitochondrial function contributes to the behaviour of cancer cell. Today I will present some recent results obtained using a cell model with defined levels of mitochondrial DNA mutation, mTUNE, to investigate the direct consequences of mitochondrial dysfunction. We found that impaired utilization of reduced nicotinamide adenine dinucleotide (NADH) by the mitochondrial respiratory chain leads to cytosolic reductive carboxylation of glutamine as a new mechanism for cytosol-confined NADH recycling supported by malate dehydrogenase 1 (MDH1). We also observed that increased glycolysis in cells with mitochondrial dysfunction is associated with increased cell migration in an MDH1-dependent fashion. Our results elucidate a novel link between mitochondrial dysfunction and cancer metabolism, associated with changes in cell behaviour.

Video from the Keynote Speaker Dr. Christian Frezza can be found as below:

Phospholipids play numerous roles in biological systems, including the formation of membrane lipid bilayers and the signaling of multiple biological pathways, so that their dyshomeostasis have been associated with the development of multiple diseases, such as Alzheimer’s disease and cancer. Metabolomics based on mass spectrometry has been largely employed to investigate these disease-related perturbations in the phospholipidome. However, the annotation of discriminant features still remains as a major bottleneck in the metabolomic pipeline. Chemical standards of individual phospholipid species are normally not commercially available due to the large number of isomers, so the knowledge of their characteristic fragmentation patterns upon tandem mass spectrometry is of great utility for their annotation (1). In this work, we provide a simplified guideline for the MS/MS-based identification of the most important phospholipid classes and their fatty acid composition.

(1) R. González-Domínguez. Metabolomic approaches for phospholipid analysis: advances and challenges. Bioanalysis 10 (2018) 1069-1071

Strawberry is composed of numerous primary metabolites (sugars, amino acids, organic acids) and secondary metabolites (anthocyanins, flavan-3-ols, phenolic acids), which play an essential role in fruit quality, organoleptic characteristics and healthy benefits. In this context, metabolomics presents a great potential to get a deep overview of this complex chemical meshwork, which can provide valuable information on the effect of multiple growing factors in the strawberry composition. In this work, we show the utility of different metabolomic approaches to investigate the influence of variety and agronomic conditions in the strawberry metabolome on the basis of data acquired in two published studies conducted in our research group. First, we conducted a GC/MS-based non-targeted metabolomic analysis in strawberries of three varieties with different sensitivity to environmental conditions (Camarosa, Festival and Palomar), which in turn were grown in soilless systems by using various agronomic conditions (electrical conductivity, coverage and substrates) (1). Complementarily, a targeted metabolomic approach based on UHPLC-MS/MS was also applied to identify and quantitate the main polyphenol compounds in these strawberry fruits (2). The most discriminant metabolites were several amino acids, sugars, organic acids, anthocyanins, ellagic acid derivatives, flavan-3-ols, chlorogenic acid and quercetin 3-O-glucuronide, which could be associated with differences in organoleptic characteristics and the biosynthesis of strawberry antioxidants.

(1) I. Akhatou, R. González-Domínguez, A. Fernández-Recamales. Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress. Plant Physiol. Biochem. 101 (2016) 14-22

(2) I. Akhatou, A. Sayago, R. González-Domínguez, Á. Fernández-Recamales. Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry. J. Agric. Food Chem. 65 (2017) 9559-9567

Metabolomics generates large datasets that require the use of advanced and complementary statistical tools in order to extract the maximum amount of useful information. Traditionally, various non-supervised and supervised pattern recognition methods have been employed in food traceability and authentication, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or soft independent model class analogy (SIMCA), among others. Complementarily, the use of new machine learning algorithms is emerging in food metabolomics during the last years due to their excellent performance for the analysis of complex datasets, such as random forest (RF) and support vector machines (SVM). In this work, we show the advantages, limitations and complementarities of these statistical tools in food analysis, on the basis of data acquired in various traceability studies performed in our research group with strawberry and extra virgin olive oil (1-4).

(1) I. Akhatou, R. González-Domínguez, A. Fernández-Recamales. Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress. Plant Physiol. Biochem. 101 (2016) 14-22

(2) I. Akhatou, A. Sayago, R. González-Domínguez, Á. Fernández-Recamales. Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry. J. Agric. Food Chem. 65 (2017) 9559-9567

(3) A. Sayago, R. González-Domínguez, R. Beltrán, Á. Fernández-Recamales. Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition. Food Chem. 261 (2018) 42–50

(4) A. Sayago, R. González-Domínguez, J. Urbano, Á. Fernández-Recamales. Combination of vintage and new-fashioned analytical approaches for varietal and geographical authentication of olive oils. Under preparation

Protein corona formation on nanoparticles (NPs), affect NP physicochemical properties, cellular uptake and toxicity, and has been reported extensively. To date, studies of the occurrence and potential importance of small molecule (metabolite) coronas are limited. We sought to determine such a corona using high-sensitivity metabolomics combined with a well-established model system for freshwater ecotoxicology (Daphnia magna feeding on Chlorella vulgaris) and amino functionalised polystyrene NPs (NH2-pNPs). Initially, we optimised our method using a targeted LC-MS/MS approach for sodium dodecylsulphate (SDS) as an analogue to signalling molecules that are known to occur in our freshwater model system. Following, we performed an untargeted discovery metabolomics study – using high-sensitivity nanoelectrospray direct infusion mass spectrometry (DIMS) for the unbiased assessment of the metabolite corona of NH2-pNPs in the freshwater model system. Untargeted DIMS metabolomics reproducibly detected 100s of small molecule peaks extracted from the NH2-pNPs when exposed to conditioned media from the D. magna-C. vulgaris model system. Attempts to annotate these extracted metabolites, including through the application of van Krevelen and Kendrick Mass Defect plots, indicate a diverse range of metabolites that were not clustered into any particular class. Overall, we demonstrate the existence of an ecologically relevant metabolite corona on the surface of NPs through application of a high-sensitivity, untargeted mass spectrometry metabolomics workflow.