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.

Thymus capitatus is known in Palestine as ‘‘Za’tar Farsi’’ and is commonly used as tisane and was reported to possess various biological effects including antimicrobial and antioxidant activities. Although there have been many attempts to study the chemical composition (essential oil composition) of Thymus capitatus, however, to our knowledge, yet there is no extensive study available on the phytochemicals and secondary metabolites of Thymus capitatus leaves. The aim of this study was to investigate the phytochemical components in the hydro-methanolic extracts of Thymus capitatus by means of LC-MS/MS. Samples were extracted with aqueous methanol, centrifuged, followed by supernatant gathering, evaporated, and finally recovered with aqueous methanol. The analysis of the phytochemicals from M. fruticosa extract was carried out on an Agilent 1200 series LC equipped with an Agilent Zorbax C18 column. Acetonitrile and acidic water were used as mobile phases. The LC system was coupled to QTOF-MS, equipped with an electrospray ionization source operated in the negative and positive ion modes over the range from m/z 100-1100. In the present work, more than 53 phytochemical metabolites have been identified in Thymus capitatus, highlighting the importance of this plant as an important medicinal herb and as a promising source of bioactive compounds.

Olive oil is an important constituent of the Mediterranean diet with proven health benefits due to its chemical composition, which is rich in unsaturated fatty acids and phenolic compounds that contribute to its organoleptic and nutraceutical properties. According to previous studies, climate change affected the olive tree phenology, increasing growth, anticipating flowering and ripening processes, accompanied by a reduction in yield and fruit quality [3-5]. The present investigation aimed at studying the effect of a thermal increase on the metabolite profile of olive fruits. Changes in the metabolite profile at 4 ºC above ambient temperature was performed on field trees (cv. Picual) by using a UPLC-MS/MS approach. For the above ambient temperature treatment, a temperature-controlled Open-Top-Chamber, equipped with heating and ventilation devices was employed [5]. Twenty fruits per tree (3 trees per treatment) were sampled at 3 ripening stages [6]; green, turning red and purple. Metabolites were extracted from olive pulp using aqueous and organic solvents [7], being the extracts subjected to UPLC-qTOF analysis. Metabolite identification was carried out by MS/MS spectra with different databases (PlantCyc, Plant Metabolic Network, KEGG, MassBank and FDA) using Progenesis QI algorithm. A total of 1162 and 9877 annotated compounds were found in negative and positive mode, respectively. Around 10 % of the total were confidently identified (compounds present on 2/3 replicates but not in blank, with fragmentaion score and present on Quality control or Standar phenolics mixture). Qualitative and quantitative differences have been found between treatments (AT and AT+4ºC); differential metabolites corresponded to the lipid and phenol chemical families, these compounds being responsible of the organoleptic and nutritional properties of the olive oil.

References

[1] Casini L., et al., 2014. Nutrition & Food Science, 44(6):586-600.

[2] Poole S. and Blades M. 2013.The Mediterranean diet – a review of evidence relevant to the food and drink industry. Nutrition & Food Science, 43(1):7-16.

[4] Tupper N.,2012. Spanish olive oil under constant threat from climate change. Olive Oil Times, Oct-26.

[3] Dag A. et al., 2014. Optimizing olive harvest time under hot climatic conditions of Jordan Valley, Israel. Eur. J. Lipid Sci. Technol, 116:169-176.

[5] Benlloch-González M. et al., 2018. An approach to global warming effects on flowering and fruit set of olive trees growing under field conditions. Scientia Horticulturae, 240:405-410.

[6] Roca M. and Minguez-Mosquera M.I. 2001. Changes in chloroplast pigments of olive varieties during fruit ripening. Journal of agricultural and food chemistry, 49(2):832-839.

[7] Valledor, L. et al., 2014. A Universal Protocol for the Combined Isolation of Metabolites, DNA, Long RNAs, Small RNAs, and Proteins from Plants and Microorganisms. Plant Journal 79(1): 173-180.

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