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.

In untargeted MS studies involving metabolomics the proportion of unknown or unidentifiable compounds (i.e. features) detected can often be >90%. Given that the proper identification of a true unknown can take many months or years of work, it is little wonder that few investigators are willing to undertake the task of rigorously identifying these unknowns. While experimental techniques such as suspect screening can lead to the occasional “lucky” hit, a more rapid and robust approach is needed for unknown identification. In this presentation I will introduce the concept of in silico metabolomics. This is a computational approach to unknown identification that combines the extensive knowledge of known compounds with the existing knowledge of how compounds are chemically or biologically transformed. In silico metabolomics fundamentally requires a large collection of known structures. Over the past 10 years we have created a number of compound databases that catalogue the known compounds, including human metabolites (HMDB), food constituents (FooDB), drugs (DrugBank), plant products (PhytoBank) and contaminants (ContaminantDB). We have also developed a software package called BioTransformer, that uses expert-knowledge combined with machine learning to accurately predict the biological and chemical transformations that known compounds may undergo in humans and in the environment. This software has been used to create a database called BioTranformerDB consisting of several million “biologically feasible” structures. By exploiting several in-house tools for accurate MS/MS and NMR spectral prediction we have been able to calculate the MS/MS and NMR spectra for all of the compounds in BioTransformerDB. Using these newly developed software tools and resources for in silico metabolomics, I will show how unknown compounds may be identified from untargeted MS studies.

Video from the Keynote Speaker Dr. David S. Wishart can be found:

https://www.youtube.com/watch?v=CAU_cWPtNHQ&feature=youtu.be

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

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.

Introduction: Virus infections of the upper respiratory tract in combination with secondary bacterial infections can lead to severe lung infections. The aim of the current project KoInfekt is to elucidate the host-pathogen interactions establishing the pig as an animal infection model due to high genetic and physiological similarities to human beings.

Material and Methods: Animal experiments were done on the Federal Research Institute for Animal Health (Isle of Riems, Germany). A group of 25 pigs were infected with Influenza A virus (H1N1, Germany) and samples were collected over 31 days. For metabolic analysis tissues samples (lung, spleen), biofluids (blood plasma, BALF) and feces were collected and analyzed by a combination of ¹H-NMR, GC-MS and LC-MS/MS.

Results: The increased amounts of pro-inflammatory eicosanoids like prostaglandins and thromboxane in the spleen were detected during infection. Furthermore, a specific eicosanoid profile was observed in the different sample types. The analysis of metabolites from the feces reveals a high time-and animal-dependent level for the majority of compounds.

Discussion: Perturbations in the eicosanoid profile of Influenza A virus infected pigs were detected. The occurrence of different pro-and anti-inflammatory lipid mediators gives a hint for the immune status of the analyzed organs from the pig. The analysis of the fecal metabolites enables an overview about the gut microbiota, which is linked to the host immune response and the interplay of the host and the bacteria community. This is the first step for the metabolic analysis of bacto-viral co-infections, which play an important role in human and animal health.