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Linking Leptin Production to Fatty Acid Production in Commonly Consumed Fish
John Dellinger, Professor, Concordia University Wisconsin School of Pharmacy
Many fish species undergo significant changes in water temperature during the year, requiring an effective means to alter physiology to adapt to the new conditions. Hormones such as leptins and ghrelins are the most obvious control features of these physiological changes. In this study we have concentrated on development of a leptin transcript assay and paired it with an analysis of fatty acids from fish tissue particularly saturated fatty acids and the omega-3 and omega-6 polyunsaturated fatty acids. DNA was isolated from several common consumable fish species: Siscowet lake trout, halibut, and Atlantic salmon. We have developed common primers for the PCR amplification of the leptin A gene from each species, and demonstrated identity of the clones using DNA sequencing. Using these data, primers for qPCR have been developed which can be used to quantify LepA mRNA in fish tissue samples, specifically from the liver. This study will provide metabolomic methods to correlate leptin gene expression with fatty acid variations throughout seasonal changes in temperature using gas chromatograph-polyunsaturated fatty acid analysis.
Characterising the metabolic profile of ALS: results from the EuroMotor study cohort
Alexandros Siskos, Research Associate, Imperial College London
Amyotrophic Lateral Sclerosis (ALS) a devastating disease affecting some 50,000 individuals at any time in Europe. Currently, there is no cure for ALS. The aim of the FP7 Euro-MOTOR study is to identify novel causes of ALS using a comprehensive systems biology approach. Within Euro-MOTOR, a large-scale, pan-European population-based metabolomic study has been conducted. The EuroMotor cohort comprises 1600 individuals (800 cases and 800 controls), from the Netherlands, Italy and Ireland. The patient cases are matched to controls for sex and age (± 5 years). We have carried out metabolomic analysis on serum samples using the targeted AbsoluteIDQTM p180 platform (Biocrates Life Scieneces AG).
Our initial analyses identify major differences in the overall profile of between patients and controls. In patient serum samples we observed a statistically significant decrease in creatinine levels that can reflect muscle waste. Medium chain carnitines are lower in concentration in patient serum samples indicating a perturbation of ß-oxidation activity. Also, the aminoacids citrulline and histidine were found to be lower in patients. Finally PUFA - phosphocholine lipids, appear to be lower in cases compared to controls.
Investigation of cell culture media derived from cells infected or not with hepatitis C virus by NMR spectroscopy
Gaëlle Diserens, PhD Student, Bern University
The aim of the study was to examine the suitability of NMR spectroscopy to investigate interactions between cells and their culture medium in order to get a better understanding of the cell’s consumption and release of metabolites, by analyzing culture media after cell harvest. To test differences in metabolic pathways induced by hepatitis C virus (HCV), infected and uninfected cells were cultured in conditioned Dulbecco’s Modified Eagle’s Medium complemented with (1) additional glucose and glutamine, (2) void of glutamine or (3) void of glucose. Cells and supernatants were harvested between 3 and 5 days after addition of conditioned cell media. For each medium composition and each time point, supernatants of three HCV positive and three HCV negative cell cultures were harvested. Water-suppressed 1D-NOESY spectra of 54 cell media were acquired on a 400MHz NMR spectrometer. Chemometric analyses of the supernatants revealed separations in PCA and PLS between HCV positive and negative cells for all three conditioned media, with different metabolites responsible for the separation depending on the media composition. Moreover, the different harvest days could also be separated. These results show the possibility of using NMR for a better understanding of cell nutrition and excretion pathways by measuring culture media.
CONTRIBUTION OF METABOLIC SIGNATURES IN THE METABOLIC SYNDROME CHARACTERIZATION: A NESTED CASE-CONTROL STUDY
Blandine Comte, Research Director, Centre de Recherche de Clermont-Ferrand/Theix
The rising worldwide prevalence of Metabolic Syndrome (MetS), a cluster of cardiometabolic risk factors predictive of type 2 diabetes, relates largely to increasing obesity and sedentary but also to early metabolic life events. The objective of the study was to develop accurate and robust markers of MetS as well as to bring new knowledge about this pathological state using a multidisciplinary approach. This case-control study (subjects free of MetS at baseline (n=92 born small vs n=76 born adequate for gestational age (SGA vs AGA)) was nested in the French community-based Haguenau cohort. The control group was randomly matched for age and sex. Serum metabolic signatures were determined and compared at the end of follow-up (30 years of age) to determine markers related to MetS using an untargeted mass spectrometry metabolomic approach.
Statistical analyses allowed identifying 25 metabolites significantly modified according to the MetS phenotype in the whole population, and 42 among the SGA subjects, revealing a specific signature of the foetal imprinting. Correlation analyses with other data (anthropometric, clinical, and biochemical) contributed to better understand the role of these biomarkers in the pathological processes, and therefore to evaluate their potential clinical value.
Metabolic response of MG-63 osteosarcoma Cells to single and combined drugs: a multi-drug metabolomics study
Ana Gil, Associate Professor, Universidade De Aveiro
Cisplatin (cDDP) is widely used in clinical chemotherapeutic protocols, despite its well recognised significant secondary effects, which justify the search for alternative drugs and therapies. In the first part of this study, the metabolic effects of cDDP and the Pd(II) complex Pd2Spm (with potential as an anti-cancer agent [1]) were measured by HRMAS NMR metabolomics in both MG-63 cells and osteoblasts. cDDP was seen to impact strongly on membrane degradation, de novo lipid synthesis and the antioxidative defense mechanism of MG-63 cells (confirming previous reports [2,3]), whereas a milder effect was observed on osteoblasts. Conversely, Pd2Spm impacted more strongly on osteoblasts metabolism than on tumoural cells. Subsequently, these two drugs were investigated when in combination with doxorubicin (DOX) and methotrexate (MTX) (as often used in the clinic), and compared with the performance of each drug alone. Besides the identification of specific metabolic synergistic effects, results showed that Pd2Spm induces identical metabolic effects as cDDP, when in the combined form. This suggests that, in spite of the different performance of sole administered cDDP and Pd2Spm, the latter may be a potentially interesting cisplatin substitute in combination therapeutic schemes, possibly decreasing deleterious side effects and acquired resistance.
NMR Metabolomics of Human Lung Tumours Reveals Distinct Metabolic Signatures for Adenocarcinoma and Squamous Cell Carcinoma
Iola Duarte, Principal Investigator, University of Aveiro
This work demonstrates the potential of NMR metabolomics of human tissues to improve the current understanding of lung cancer altered metabolism and to discriminate between two major histological types, a critical diagnostic requirement in lung cancer management.
Applied metabolomics approaches to discover food-derived metabolites in human urine
Amanda Lloyd, Research Associate, Aberystwyth University
An understanding of causal relations between diet and health is hindered by the lack of robust biological markers of food exposure. Most dietary biomarkers currently have been identified on the basis of knowledge of food composition by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive OMICS analytical instruments, metabolite databases and bioinformatics has aided in the identification of novel biomarkers for the intake of a range of foods.
Spot urine collections are being investigated with a range of metabolomic techniques, starting with Flow Infusion-High Resolution Fingerprinting (FIE-HRMS) using Orbitrap Mass Spectrometry (MS) coupled with multivariate classification and feature selection. Potential food biomarkers are being elucidated using Ultra High Performance Liquid Chromatography-High Resolution MS (UHPLC-HRMS) and tandem mass spectrometry, without the needed for extensive targeted studies. Pre-processing and multivariate analysis of high mass resolution data (both LC and FIE) is computationally intensive, therefore all metabolomics workflows are fully integrated with a High Performance Computer allowing in-depth modelling, quicker processing times and robust validation. Potential food biomarkers are being validated by quantification (using chemical standards where possible) in biofluid samples obtained from controlled clinical studies and free-living individuals.
Advanced methods for automated analysis of primary metabolites in biotechnological cell extracts and cell culture supernatants
Teresa Mairinger, PhD Student, Austrian Center of Biotechnology
Current approaches in modern metabolic profiling of biotechnological samples are facing two major challenges, i.e. the wide concentration ranges and the complexity of the matrix. Intracellular metabolites present in low abundance (e.g. sugar phosphates or some low molecular weight organic acids) therefore require enrichment prior to analysis. While the analysis of cell culture supernatants for extracellular metabolites or nutrients is hampered by the highly complex matrix which contains different nutrients, salts as well as high molecular weight additives such as antifoaming-agents.
Depending on the scope of analysis, different automated sample preparation strategies aiming at matrix separation and selective enrichment of metabolites were developed for both cell culture supernatants and cell extracts from Pichia pastoris and CHO cultures. Liquid chromatography in combination with accurate mass time of flight mass spectrometry was applied as a sensitive analytical method. Different clean-up as well as pre-concentration procedures, employing solid phase extraction and filtration, as well as selective derivatization targeting the carboxylic group will be presented.
PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems
Jan Stanstrup, PostDoc, Fondazione Edmund Mach
Demands in metabolomics research have been a key motivator for the development of repositories for MS spectra1,2 and automated tools to aid compound identification3–5. But utilizing only fragmentation is ignoring half of the available information in LC-MS. The retention time (RT) is equally important.
For LC systems there are currently no coordinated efforts to share and exploit information regarding RT. The reason RT information has been neglected is that the RT is specific to the chromatographic system (CS) and there exist no agreed upon RT references.
A database of compound RTs in different CSs was therefore developed. For each pair of CSs, the RTs are used to construct a projection model between the RTs in the two CSs. Building these models between all CSs allowed the prediction of RTs for a high number of compounds in CSs where they had not been experimentally determined.
With the current small database it was possible to predict up to 400 RTs with a median error between 0.01 and 0.11 min depending on the CS. The median width of the confidence interval for predictions ranged from 0.1 to 0.8 min.
The free, open source and web-based tool is available at predret.org.
ChEBI - An European Bioinformatics Institute (EMBL-EBI) Database and Ontology for Biologically Active Small Molecules
Namrata Kale, Scientific Database Curator, EMBL-EBI
ChEBI (http://www.ebi.ac.uk/chebi) is a freely available, manually curated database that focuses on the nomenclature, structure, and biological properties of “small” molecules or “metabolites”, involved in a biological process [1]. It is widely used as a reference database for chemicals in the context of metabolomics identification. Partially identified metabolites are annotated as generic entries in ChEBI and can be used to represent datasets with undefined structural information. Metabolomics database repository such as MetaboLights, references the metabolite information in ChEBI to display a metabolite-centric view to the users [2]. The chemical data and ontology relationships from ChEBI can also be fruitfully used in building of metabolic models [3] and pathways (powered by Reactome) [4]. The database can be downloaded in various file formats (e.g. SDF, OWL, OBO, Flat file and Tab-delimited).
Efforts are currently underway to further improve ChEBI within the context of systems biology and metabolic modelling. This includes curation of the known metabolomes across four model species (human, mouse, yeast and E.coli) and to provide the users with a facility for bulk submission of novel compounds which will be automatically classified within ontology. Currently ChEBI contains 44263 fully curated entries.
ChEBI is funded by the BBSRC, grant BB/K019783/1.
Mechanisms leading to nonalcoholic steatohepatitis: subtyping for the development of effective treatments
Cristina Alonso, Products & Services Manager, OWL
Nonalcoholic steatohepatitis (NASH) is a histological definition that groups together defects in diverse biochemical processes causing hepatic fat accumulation, inflammation, necrosis and fibrosis. The identification of the types of mechanisms leading to NASH and the discovery of noninvasive biomarkers of NASH’ subtypes are central for the development of effective treatments.
Hepatic biosynthesis of S-adenosylmethionine (SAMe), or MAT reaction was selected as starting point. Hepatic MAT deletion (Mat1a KO) in mice leads to the spontaneous development of NASH. NASH patients often show reduced expression of MAT1A.
Ablation of Mat1a in mice led to a substantial reduction in hepatic methylthioadenosine and PUFA-PC, whereas methionine, PUFA-PE and methylneogenic substrates were more abundant. SAMe depletion impairs liver’s capacity to adapt TG synthesis to its rate of export via VLDL, accumulating TG, DG and FA. This is explained by the exhaustion of mitochondrial FA oxidation and increased FA uptake.
Finally, it was identified a serum metabolomic profile that could differentiate between Mat1a and WT mice (M-type fingerprint). To translate the findings to humans, an unsupervised cluster analysis using serum of patients with biopsy-proven NASH (n=134) was carried out. Patients were classified into two clusters, showing 63 patients (47%) the M-type NASH serum metabolic profile.
A new comprehensive two-dimensional gas chromatography coupled to high resolution time-of-flight mass spectrometry (GC×GC-HRTOFMS) approach in metabolomics
Nicolas Di Giovanni, PhD Student, Universite de Liege
For the last few years, comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) appeared to have high potential in metabolomics.
Based on our experience in the field of small molecule separation in complex biological fluids, we developed an innovative GC×GC- high resolution(HR)TOFMS method for the metabolic profiling of biofluids, with a focus on human serum and inflammatory bowel diseases.
State-of-the-art experimental design (ED) and data processing tools have been used for the optimization of this analytical approach. We focused our efforts on the implementation of a strong QA/QC procedure to ensure the integrity of the data produced. Our quality control system is based on the use of internal standards, QC serum aliquots, control charts, and carefully chosen criteria and the validation -accuracy and precision- on the certified reference material (NIST SRM 1950).
We further developed a dedicated data processing strategy based on proper signal alignments, class-to-class comparison, implementation of statistically relevant Fcritical threshold values to enhance our chances to highlight and uniquely identify analytes expressing the highest degree of influence on statistical segregation of our samples classes.
We hope this method development exercise would contribute to offer better analytical tools for the use of metabolomics in clinical research.
From Mouse to Man – Metabolomics in Translational Research
Martina Klocker, Marketing Manager, Biocrates
From Mouse to Man – Metabolomics in Translational Research
Introduction
As metabolic markers are not species-restricted, the concept of metabolic phenotyping is highly applicable for translational research.
Methods
Mass spectrometry based targeted metabolomics platform for multi parameter analyses
Preliminary Results
Overall, the validity of metabolic phenotyping will be demonstrated, despite or even because of species-dependent characteristics. It has the potential to explain why findings in animal models cannot always be directly translated into clinical settings and might, therefore, facilitate the establishment of suitable models of disease. Here, this is demonstrated on basis of the analysis of bile acids in mice and men.
Several species individual and comprehensive bile acids were found in humans and mice. Nevertheless, there are major differences in the bile acid composition in both species. Besides the mouse-specific bile acids (Muricholic acids), we have found differences in conjugation patterns of bile acids. In humans the analysis of the bile acids showed more Glycine-conjugated bile acids whereas in mice Taurine-conjugation was dominant. This finding could be related to species-dependent detoxification pathways. In contrast both species show catalytic activity of CYP3A of the cytochrome P450 superfamily, which is important for studies of the drug metabolism. Another very interesting aspect which was found in our bile acid study was that we could easily separate female and male mice by their bile acid profile.
Bile acids analysis could improve selection of correct models for translational research and reduce numbers of animal tests.
A KNIME Pipeline for the Analysis of GC-MS Data in Metabolomics
Sonia Liggi, Research Assistant, Universita degli Studi di Cagliari
Elucidation of the metabolic changes taking place in pathological conditions can help in the identification of new biomarkers, prediction of response to therapy and better understanding of the pathogenesis [1]. Gas Chromatography coupled with Mass Spectrometry (GC-MS) is one of the leading analytical techniques utilised to deconvolute the metabolic profile of biofluids and tissues. However, the large number of experiments deriving from high-throughput studies along with the complex set of steps required to pre-process and analyse the results obtained from GC-MS measurements represents a bottleneck. Indeed, several programs need to be used to accomplish a number of tasks (namely retention time correction, peak extraction, metabolites deconvolution, blanks removal, normalisation and last but not least statistical analysis), requiring computational competences and resources not always present in an experimental group. In this context, the KNIME Analytics Platform [2] was used to develop a pipeline joining the GC-MS pre-processing R [3] library XCMS [4], in-house Python scripts and KNIME functionalities to perform the aforementioned steps even by users unfamiliar with programming. Here, the pipeline was utilised to obtain a matrix of all the signals found in the chromatograms of samples deriving from patients affected by Inflammatory Bowel Diseases.
Prediction and treatment of Gestational Diabetes Mellitus through NMR metabolomics of maternal blood and urine
Joana Pinto, Post Doctoral Student, University Of Aveiro
This work firstly presents the discovery of pre-diagnosis metabolic biomarkers of Gestational Diabetes Mellitus (GDM), using multivariate analysis of variable-selected 1H Nuclear Magnetic Resonance (NMR) spectra of maternal plasma and urine [1,2]. Early metabolic changes comprise, among others, changes in 1) lipid metabolism, 2) glucose levels (high in both plasma and urine) and 3) gut microflora, thus unveiling possible GDM markers complementary to existing diagnostic methods. At the time of diagnosis, many changes previously identified were enhanced and a robust profile of 26 maternal plasma resonances was found descriptive of GDM [1].
Furthermore, metabolomics may be used to follow-up GDM therapy and the impact of insulin and controlled-diet treatments on maternal urine showed that diet-treated subjects were metabolically closer to controls, whereas insulin-treated subjects showed persistent deviations in particular pathways. Finally, marker fingerprints of future treatment requirements (either insulin or controlled diet) were found in the urine of subjects prior to treatment, thus unveiling the possibility of predicting individual GDM treatment requirements, at the time of diagnosis.
High resolution magic angle spinning 1H NMR spectroscopic investigation of listeria brainstem encephalitis in small ruminants: preliminary results
Christina Precht, DVM-PhD Candidate, University of Bern
The purpose of our study was to investigate metabolic changes associated with listeria brainstem encephalitis in small ruminants as a model for an inflammatory CNS disease.1
Bilateral brainstem and thalamus biopsies were obtained from 7 healthy control (6 sheep, 1 goat) and 8 diseased animals (4 sheep, 4 goats) diagnosed by histopathology. 1H HR-MAS NMR (1D Carr-Purcell-Meiboom-Gill (CPMG) sequence) was performed on a Bruker Avance II spectrometer (500.13 MHz) and analysed by Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS).
Histopathologically, all diseased animals showed moderate to severe inflammatory changes in the brainstem. In the thalamus the majority of diseased animals had no or only mild inflammatory infiltrates. Chemometric analysis of the brainstem biopsies achieved near complete separation of the diseased and control group in unsupervised PCA, and a complete separation in PLS. Important discriminators were N-acetylaspartate, choline, phosphocholine, glycerophosphocholine and lactate. In contrast, for the thalamus biopsies no separation between the control and diseased group could be achieved by PCA. However, taking the grade of histopathological changes into account, a clustering could be observed in PLS. This may indicate the high sensitivity of NMR spectroscopy to detect metabolic changes, even before inflammatory infiltrates occur.
Fat, Sugar and Metabolomics – Understanding how diabetes arises at the population level, or when 5000 samples is just the pilot study….
Julian Griffin, Group Leader, MRC Human Nutition Research
The majority of genes thus far identified as associated with T2DM are from rare monozygotic forms, and do not model the complex interactions between genotype and environment. Metabolomics may be used to address this in genome wide association studies (GWAS) as the metabolome is downstream of the genome and interacts with diet, pathophysiology and environment. We are applying GC-MS, LC-MS and DI-MS to profile cohorts to explore these interactions. These studies require robust and high-throughput approaches, and include the largest analysis of fatty acids by GC-MS to examine the interaction between diet and T2DM risk using the European Prospective Investigation into Cancer and Nutrition cohort[1], DI-MS to monitor intact lipid profiles in the Fenland[2] (with Nick Wareham, MRC Epidemiology; 1500 individuals) and Pakistan Risk of Myocardial Infarction Study[3] (with John Danesh, Cambridge and Danish Saleheen, UPENN; 5400 individuals) cohorts and quantify aqueous metabolites and polar lipids using the Biocrates kit in the Fenland cohort (11500 individuals). These studies highlight the role of de novo lipogensis in generating triglycerides associated with an increased risk of T2DM. Finally we will update on a lipidomic study of the INTERVAL[4] cohort, looking at relative risk of cardiovascular disease in 50,000 individuals (with John Danesh).
A combined multi-omics and in silico approach to decipher metabolic networks shifts during the differentiation of a human hepatic cell line
Nathalie Poupin, Research Scientist, INRA
We exploited transcriptomic and metabolomic data in the context of the global human genome-scale metabolic network Recon2 to better characterize the metabolic capacities of the hepatic cell line HepaRG, increasingly used in toxicity studies. We aimed at computing the functional metabolic network of these cells at two developmental stages (bipotent progenitor cells versus differentiated hepatocytes), to explore the metabolic shifts occurring during differentiation.
In a first step we used gene expression data and applied the iMAT algorithm developed by Shlomi et al. [1] to identify sub-networks of reactions specifically active in HepaRG cells. Metabolomic data were then used to further select the most relevant sub-networks.
For each stage, we predicted which reactions of the global network were active. About 80% of the predicted active reactions could not directly be inferred from transcriptomic data and were "newly" inferred by taking into account both the topology of the metabolic network and the metabolomic data. Using pathway enrichment analyses, we showed that reactions predicted to be specifically active in differentiated cells (compared to progenitor cells) mostly belong to pathways related to the development of specific hepatic activities (bile acid synthesis, amino acid metabolism…) and to anabolic/catabolic processes including the detoxification function (cytochrome metabolism).
METABOLIC PROFILING A VERSATILE TOOL TO DETECT GROWTH PROMOTOR ABUSE IN ANIMALS
Marco Blokland, Researcher, RIKILT
Just like in human sports doping it is financially beneficial to use hormonal growth promotors to increase muscle mass in farm animals. During the past years metabolic profiling and fingerprinting techniques have been developed to identify animals which are suspected to be treated with growth promotors. From the techniques developed, steroid profiling has shown to be the most powerful tool so far to detect these animals. The fully validated method consists of analyzing samples of urine for almost all free steroids and the corresponding glucuronide and sulphate conjugates which are part of the steroidogenesis. With this method reference populations were characterized and by use of statistics a predictive model was built. This model is capable to determine whether an animal differs from the reference populations and what class of hormones was used for treatment. Since two years this model is in use as part of the Dutch National control programs on the illegal use of hormonal growth promotors in cattle. In this presentation the method, its validation and application on real life samples will be presented and discussed.
Use of complementary metabolomic techniques based on mass spectrometry to identify potential lung cancer biomarkers in bronchoalveolar lavage fluid
Jose Luis Gomez-Ariza, Professor, Universidad de Huelva
Lung cancer (LC) is one of the ten more common causes of death worldwide [1], and the search for biomarkers of early diagnosis is a very challenging task. The bronchoalveolar lavage fluid (BALF) provides information on a million cells (1% of the lung surface) to yield about 1 ml of pulmonary secretions [2]. But under our knowledge, there are not any metabolomic study based on these samples obtained from patients with lung cancer. In this work, two complementary metabolomic techniques, based on direct infusion high resolution mass spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC-MS), have been applied for the first time to compare LC and control (C) BALF samples, using partial least square discriminant analysis (PLS-DA) in order to find potential biomarkers.
The score plots showed a clear classification between LC and C cases, and a total of 43 metabolites showed alterations. Biomarkers specificity and sensitivity were assessed considering the area under the receiver operator characteristic (ROC). The glycerophospholipid pathway was the most altered one, and ROC curve analysis indicated that glycerol and phosphoric acid were potential biomarkers for lung cancer diagnosis and prognosis.
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