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.

Wine is a natural product with a unique production method, being considered an art due to its unique features. Due to the singularity of its components and the high production cost, wine adulteration events happen frequently, aiming to achieve higher profits, compromising its authenticity.

By using analytical techniques, such as nuclear magnetic resonance spectroscopy or mass spectrometry, it is possible to acquire large amounts of metabolomics data related to specific metabolites over distinct samples. A number of multivariate statistical and machine learning methods may be applied, with high discriminative power allowing to achieve information with added-value about important features such as cultivar, age and geographic origin, and also to detect possible adulteration events. Nonetheless, metabolomics data analysis still constitutes a challenge, specially over complex matrices, such as wine.

This work entails a comprehensive survey of research work related to metabolomics-based approaches for wine authentication, with particular emphasis on supervised and unsupervised multivariate data analysis.

To illustrate the main tasks and steps of metabolomics data analysis, but also to highlight existing challenges in wine authentication issues, two case studies were performed, using the metabolomics data analysis R package specmine. These cases encompass one published dataset, which is re-analyzed here, and a new dataset of Portuguese and Brazilian wines. In both cases, exploratory data analysis in conjunction with multivariate statistical analysis, including principal component analysis and clustering, were performed. It was possible to discriminate the wines according to their cultivar and geographical origin (in the first case) and age (in the second) based on NMR profiles and metabolite identification.

Analysing metabolomics data correctly and efficiently is nowadays very important in biological and biomedical research. Therefore, having available an easy-to-use tool freely accessible on the web to perform these tasks is a very important asset. The available web tools do not provide a wide variety of methods and data types for analysis, nor ways to store and share metabolomics data and the results generated.

Thus, we have developed WebSpecmine to overcome these limitations. WebSpecmine is a web-based application, freely available at https://webspecmine.bio.di.uminho.pt/, that makes use of the R package specmine, developed by our research group. It was designed to perform analysis of metabolomics data from NMR, LC/GC-MS, spectral (Infrared, UV-visible, and Raman) or concentration data. It includes methods such as univariate statistical analysis, unsupervised and supervised multivariate statistical analysis (including machine learning), metabolite identification and pathway analysis. Users can create an account to store their own data and results privately, being able to share them with other users or make them public. Currently, available data includes projects and publications by the research group and collaborators, but we expect this repository to grow. Finally, we provide abundant documentation: tutorials and a user guide with a detailed description of the tool’s features, including different case studies.

Microbes and plants produce a gold mine of chemically diverse, high-value molecules like antibiotics. However, chemical structures of many natural products (NPs) remain currently unknown, hampering medicinal applications. A key challenge for natural product discovery is the metabolome complexity in natural extracts, from which mass spectrometry data needs to be coupled to chemical structures. Nevertheless, many NPs share molecular substructures and form structurally related molecular families (MFs), which has inspired metabolome mining tools exploiting these biochemical relationships.

Here, we introduce a workflow that combines two existing metabolome mining tools to discover MFs, subfamilies, and subtle structural differences between family members. Where tandem mass spectral Molecular Networking (1) efficiently groups natural products in molecular families, MS2LDA (2) discovers substructures that aid in further recognition of subfamilies and shared modifications. Furthermore, through the combined use of Network Annotation Propagation (3) and ClassyFire (4), we can automatically perform MF chemical classifications. When unexpected MF classifications are observed, they could represent novel chemical scaffolds, thereby guiding follow-up prioritization efforts towards unknown chemistry. Recognition of the smaller building blocks (substructures) that form the basis of molecular families also accelerates data analysis, especially for cases where hardly any reference MS/MS spectra or candidate structures from structural databases are available.

We demonstrate how our integrative workflow discovers dozens of MFs in large-scale metabolomics studies of plant and bacterial extracts. For example, Rhamnaceae plants contained triterpenoid chemistries in which several distinct phenolic acid modifications (e.g., vanillate, protocatechuate) were readily recognized. Furthermore, a previously not annotated tryptophan-based MF was uncovered in marine Streptomyces extracts. In Photo/Xenorhabdus strains, following leads from peptidic natural products finding software Dereplicator (5), a Xenoamicin-based peptidic MF was deciphered and Mass2Motifs for both the peptidic ring and tail were easily annotated highlighting ring-related modifications. Our workflow accelerates NP discovery by MF and substructure annotations and classifications on an unprecedented large scale that will aid in future integration with genome mining workflows. Finally, the workflow applications go beyond the natural products field into nutritional, clinical, and exposome metabolomics.

References:

1. Wang, M.. et al., “Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking”., Biotech.34(8):828-837, 2016.
2. Van der Hooft, J.J.J. et al., “Topic modeling for untargeted substructure exploration in metabolomics”. N.A.S.113(48):13738-13743, 2016.
3. da Silva, R.R. et al., “Propagating annotations of molecular networks using in silico fragmentation”.PLoS Comp. Biol. 14(4):e1006089, 2018.
4. Djoumbou Feunang, Y. et al.“ClassyFire: automated chemical classification with a comprehensive, computable taxonomy”. Cheminformatics8(1): 61, 2016.
5. Mohimani, H. et al., “Dereplication of peptidic natural products through database search of mass spectra”, Chem. Biol.13(1):30-37, 2017.

The quantitative analysis of urine offers the greatest challenge for quantitative NMR (qNMR) of biofluids - as for any analytical method. It has been proposed that nearly 200 metabolites could be analyzed from a standard 1D ¹H NMR spectrum [1] and it was also concluded that qNMR is the best method for urine profiling. The qNMR analysis is not straightforward and many approaches have been proposed for biofluids, but there is one, qQMSA [2], which is superior over the others, at least we believe so. qNMR analysis is based on the assumption that an NMR spectrum is a sum of the model spectra of its components [2]. In qQMSA the model spectra are prepared by fitting experimental spectra using the Quantum Mechanical (QM) theory, which is able to interpret even the smallest details of the spectra – but removing noise, impurity signals and other artefacts [2]. The QM models are field independent and pack effectively the spectral information. The QM models obtained from spectra measured at any field can be used as model for biofluid spectra measured at any other field. Simulation of our 212 metabolites and 1001 chemical shifts urine model takes < 0.5 sec/spectrum (if parallel simulation) and whole the analysis demands < 60 sec/spectrum – thus the speed is not anymore the bottle-neck in qQMSA. Our presentation describes essential features of qQMSA and the ChemAdder platform, developed specially for qQMSA, and reports the most recent results for the urine qQMSA. We also describe other novel applications, including metabolic flux analysis based on qQMSA of 2D HSQC spectra. See also http://chemadder.com.

[1] Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076, 2013. [2] Tiainen M, Soininen P, Laatikainen R, Quantitative Quantum Mechanical Spectra Analysis (qQMSA) of ¹H NMR Spectra of Complex Mixtures and Biofluids, J.Magn.Reson., 2014, 242, 67-78.

SYM1 is an ortholog of the human MPV17 gene whose mutation causes mitochondrial DNA depletion syndrome. Sym1 protein is located in the inner mitochondrial membrane and its deletion results in impaired mitochondrial bioenergetic functions and morphological features under stress conditions. However, the functions of both Mpv17 and Sym1 have not been clearly characterized. Recently, compartment-specific metabolic alterations to mitochondrial mutations or inhibitors were revealed by analyzing isolated mitochondria. This development opens new doors for uncovering the function of Sym1. In order to find evidence for the molecular function of Sym1, mitochondria and the corresponding cytoplasmic fraction were isolated from wild type and sym1Δ cells through differential centrifugation. The samples were subjected to GC-MS profiling, after derivatization, or analyzed directly by LC-MS profiling, without derivatization. Eighty-nine metabolites were annotated by GC-MS profiling, while forty-five were annotated by LC-MS profiling. TCA cycle intermediates were reduced overall in sym1Δ. This correlates with the results of Dallabona et al. which showed severe OXPHOS defects of sym1Δ under stress conditions. At the same time, reduced glutathione was accumulated in mitochondria but reduced in cytosol, indicating an impaired redox balance in mutant cells. Interestingly, glutamine and aspartate, which can feed the TCA cycle, were up-regulated or maintained in mitochondria of sym1Δ. Furthermore, saccharopine was up-regulated while lysine was down-regulated in sym1Δ, exposing arrested lysine biosynthesis. Overall, GC- and LC-MS profiling in one workflow complement each other in identifying metabolites, which is helpful in understanding the metabolic dysregulations caused by deletion of sym1.