The metabolic interactions between human gut microbiome and green tea polyphenols (GTPs) were poorly studied. In this study, fecal and urine samples from postmenopausal female subjects taking the GTPs supplement or placebo for 12 months were analyzed by liquid chromatography-mass spectrometry-based metabolomics analysis. The GTPs-responsive metabolites were identified and characterized by structural elucidation and quantitative analysis of the metabolites contributing to the separation of control and treatment samples in the multivariate models. Major GTPs and their sulfate and glucuronide metabolites were not found in significant amounts in feces and urine. In contrast, GTPs-derived phenolic acids were identified as the robust exposure markers of GTPs, suggesting extensive microbial biotransformation of GTPs. Interestingly, GTPs decreased the levels of microbial metabolites of aromatic amino acids, including indoxyl sulfate and phenylacetylglutamine in urine and indole-3-carboxaldehyde in feces, but did not affect the levels of aromatic amino acids in feces. Other microbial metabolites, including short-chain fatty acids and secondary bile acids, were not affected by GTPs. 16S rRNA gene analysis indicated that the fecal microbiome were not changed significantly by chronic GTP. Overall, these results suggest that some enzymes in microbial metabolism of aromatic amino acids in human gut might be competitively inhibited by chronic GTP consumption.

Colon cancer is the second most common cancer to affect women worldwide. While women have a 30-40% lower incidence of colon cancer than men, they have a higher likelihood of cancer presentation on the right-side of the colon. This is of high concern because patients with right-sided colon cancer have poorer clinical outcomes than those with left-sided colon cancers. The reasons for this difference in outcome are not known, however, it has been proposed that female hormones influence colonic metabolism, microbiome and affect tumor growth in this region of the colon. We have initially examined the metabolic differences between 210 colon tumor tissues from men and women with left and right-sided colon cancer using untargeted liquid chromatography mass spectrometry-based metabolomics. We show region- and sex-specific differences in tumor tissue metabolism that may influence tumor aggressiveness and patient outcomes.

Video from the Keynote Speaker Dr. Caroline H. Johnson can be found as below:

Malaria threatens approximately 40% of the world population, causing 429 000 deaths annually, and the malaria parasite, Plasmodium falciparum, has developed resistance to most approved antimalarials. New ozonide antimalarials (OZs) are now in clinical trials and early clinical usage, but their mechanism of action remains poorly defined. Metabolomics technology offers the opportunity to measure the impact of drug action on cellular metabolism at a system-wide level, allowing unbiased assessment of the key pathways involved in the mechanism of action. The aim of this study was to use metabolomics to reveal the mechanisms of action of OZ antimalarials.

P. falciparum parasites were cultured and treated with OZ antimalarials, followed by metabolomics analysis using LC-MS with high resolution accurate mass spectrometry. The untargeted metabolomics analysis of drug-treated parasites revealed depletion of specific small peptides, and the kinetics of peptide depletion corresponded with the onset of action of each compound. A dedicated peptidomics method was developed, which revealed drug-induced perturbation to haemoglobin digestion in agreement with the proposal that OZs are activated in the digestive vacuole of the parasite. Additional pathways involved in lipid and nucleotide synthesis were also perturbed with prolonged OZ exposure, and comparative proteomics analysis confirmed the dysregulation of these pathways. This unbiased multi-omics approach revealed an initial impact of OZ antimalarials on haemoglobin digestion, followed by secondary inhibition of additional pathways that are essential for parasite survival and replication.

Video from the Keynote Speaker Dr. Darren Creek can be found as below:

Background: Accurate assessment of dietary intake is crucial for nutritional and health research. However, the dietary assessment tools currently used, such as dietary records or food frequency questionnaires, are subject to different factors that result in inaccurate information. The use of biomarkers of intake to determine dietary exposure offers more objective and potentially more precise information compared to the currently used dietary assessment tools. The identification of biomarkers of intake for highly consumed foods i.e. banana, may promote further research on their impact on human health. Banana is a widely consumed fruit in different countries. However, it has been widely neglected by the research community. Thus, identifying the biomarkers of intake of this fruit may promote further investigation on its impact on human health.

Objective: To discover and validate urinary intake biomarkers of banana by applying an untargeted metabolomics approach using two different platforms, UPLC-QTOF-MS and GC×GC-MS, to analyze urine samples from two different study designs.

Methods: In order to discover new biomarkers of banana intake, n=12 healthy subjects were recruited for a three arm, crossover, randomized, controlled meal study. The dietary interventions consisted of: 1) 240 g of banana, 2) 300 g of tomato and 3) 250 ml of control drink; each intervention phase was separated by a washout period of 3 days minimum. Urine samples obtained from the meal intervention study were analyzed by UPLC-QTOF-MS and GC×GC-MS. Following data-analysis, the identification of the relevant features was performed with MS/MS experiments in an Orbitrap-LTQ-XL MS instrument. To confirm the identity of the compounds in both systems, standards were acquired and conjugated when needed. In addition, banana samples were analyzed to look for compounds recovered in urine profiles. Finally, to validate the candidate biomarkers of banana, n=78 samples from an observational study, The Karlsruhe Metabolomics and Nutrition Study (KarMeN), were selected based on the volunteers’ declared amount of banana consumption using 24 h dietary recalls. Samples were grouped based on recorded intakes ( 1)high consumers of banana, 2) low consumers of banana and 3) non consumers of banana) and analysed on both platforms.

Results: The discriminating compounds identified by both platforms in the meal intervention were cross-validated in the observational study. Among the highly discriminant compounds biogenic amine metabolites, methoxyphenols as well as tryptophan and carbohydrate metabolites were observed. The combination of two metabolites, methoxyeugenol glucuronide and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-b-carboline-sulfate, were validated as a parsimonious biomarker of banana intake with excellent ability to predict the intake of banana, exhibiting a ROC curve AUC (CV) of 0.92 (p<0.001). In addition, from the analysis by the GC×GC-MS system three metabolites (5-hydroxyindole-acetic-acid, dopamine and the putatively identified deoxypentitol) were detected in significantly higher concentrations (p <0.001, p= 0.001, p=0.01 respectively) in the urine samples of the high and low-consumers of banana compared to non-consumers.

Conclusion: This collaborative work led to the identification and validation of new candidate biomarkers for the intake of banana. This information may be useful to further investigate the effect of this fruit in human health.

This work was funded by the EU Joint Programming Initiative (JPI) A Healthy diet for Healthy life.

Kidney cancer is fundamentally a metabolic disease.¹ Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide.^{2, 3} More than 30% of patients, often incidentally diagnosed by imaging procedures, exhibit locally advanced or metastatic RCC at the time of diagnosis.^{4, 5} The disease is inherently resistant to chemotherapy⁶ and radiotherapy.⁷ Clear cell RCC (ccRCC) is the most common (75%) lethal subtype, and is considered a glycolytic and lipogenic tumor.^{8, 9} The present work consists on a lipid profiling study of serum samples from a cohort that included patients with different ccRCC stages (stage I, II, III and, IV; n=112) and healthy individuals (n=52). A discovery-based lipidomics approach using reverse phase ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry was implemented to investigate the potential role of lipids in sample classification. Multivariate statistical analysis was conducted on a 386-feature matrix by means of machine learning algorithms using support vector machines (SVM) coupled with the least absolute shrinkage and selection operator (Lasso) variable selection method. This analysis provided a panel of 18 features that allowed discriminating healthy individuals from ccRCC patients with 96% accuracy, 93% specificity, and 100% sensitivity in a training set under cross-validation, and 79% accuracy, 100% specificity, and 79% sensitivity in an independent test set with an AUC of 0.89. A second multivariate model trained to discriminate early stages (I and II) from late stages (III and IV) ccRCC, yielded a panel of 26 features that allowed sample classification with 84% accuracy in the training set under cross-validation, and 82% accuracy in the classification of stage I ccRCC patients from an independent test set. Preliminary putative identification of discriminant lipids was based on exact mass, isotopic pattern and database search. Significant changes in lipid levels were evaluated after correcting for multiple testing between sample classes. Phosphatidylethanolamine levels were significantly decreased (p<0.001) in serum samples from ccRCC patients relative to controls. Significantly (p<0.02) decreased levels of fatty acids were detected in serum samples from ccRCC patients compared to healthy individuals, and along disease progression from early to late ccRCC stages. Current work involves the identification of the discriminant lipid panels by tandem MS experiments and chemical standards. Serum samples were provided by the Public Oncologic Serum Bank from Instituto de Oncología “Ángel H. Roffo” and Hospital Italiano de Buenos Aires.

(1) Linehan, W. M.; Srinivasan, R.; Schmidt, L. S., The genetic basis of kidney cancer: a metabolic disease. Nat. Rev. Urol. 2010, 7, 277-85.

(2) Linehan, W. M.; Bratslavsky, G.; Pinto, P. A.; Schmidt, L. S.; Neckers, L.; Bottaro, D. P.; Srinivasan, R., Molecular Diagnosis and Therapy of Kidney Cancer. Annu. Rev. Med. 2010, 61, 329-343.

(3) IARC Globocan 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=prostate

(4) Hu, B.; Lara, P. N., Jr.; Evans, C. P., Defining an Individualized Treatment Strategy for Metastatic Renal Cancer. Urol. Clin. North Am. 2012, 39, 233-249.

(5) Graves, A.; Hessamodini, H.; Wong, G.; Lim, W. H., Metastatic renal cell carcinoma: update on epidemiology, genetics, and therapeutic modalities. Immunotargets Ther. 2013, 2, 73-90.

(6) Diamond, E.; Molina, A. M.; Carbonaro, M.; Akhtar, N. H.; Giannakakou, P.; Tagawa, S. T.; Nanus, D. M., Cytotoxic chemotherapy in the treatment of advanced renal cell carcinoma in the era of targeted therapy. Crit. Rev. Oncol. Hematol. 2015, 96, 518-526.

(7) De Meerleer, G.; Khoo, V.; Escudier, B.; Joniau, S.; Bossi, A.; Ost, P.; Briganti, A.; Fonteyne, V.; Van Vulpen, M.; Lumen, N.; Spahn, M.; Mareel, M., Radiotherapy for renal-cell carcinoma. Lancet Oncol. 2014, 15, e170-e177.

(8) Hsieh, J. J.; Purdue, M. P.; Signoretti, S.; Swanton, C.; Albiges, L.; Schmidinger, M.; Heng, D. Y.; Larkin, J.; Ficarra, V., Renal cell carcinoma. Nature Reviews Disease Primers 2017, 3, 17009.

(9) Hakimi, A. A.; Pham, C. G.; Hsieh, J. J., A clear picture of renal cell carcinoma. Nat. Genet. 2013, 45, 849-850.

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.

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

The latest version of the Human MetabolomeDatabase (v4.0) lists 114,100 individual entries, nearly a threefold increase from version 3. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to fully identify all metabolites detected (which I term the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publicly funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field. In this talk I will identify potential issues with metabolite identification and how how new and discuss some of the emerging technologies which may help solve this problem (thus illuminating the dark metabolome) and advance metabolomics. I will specifically discusses Dynamic Nuclear Polarisation Nuclear Magnetic Resonance Spectroscopy (DNP-NMR), non-proton NMR active nuclei, Two-Dimensional Liquid Chromatography (2DLC) and Raman Spectroscopy (RS) and show how developing new methods for metabolomics with these techniques could lead to advances in metabolomics and better characterisation of biological systems.

Video from the Keynote Speaker Dr. Oliver Jones can be found:

https://www.youtube.com/watch?v=d_0OHA1umoY

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.

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.