Day 3 :
- Track 10: Proteomics and Genomics
Track 11: Clinical Metabolomics and Lipidomics
Track 12: Analytical Platforms Employed in Metabolomics
Chair
Sam F. Y. Li
National University of Singapore, Singapore
Co-Chair
P K Ragunath
Sri Ramachandra University, India
Session Introduction
Lars Lofgren
Translational Science, AstraZeneca R&D, Sweden
Title: Total lipid extraction for lipidomics made easy – The BUME method
Time : 09:30-09:50
Biography:
Lars Löfgren completed his PhD in 1993 from Chalmers University of Technology, where he developed sampling devices for - and characterized human exposure to - volatile hydrocarbons in urban air. He has continued his scientific work, published in peer-reviewed journals, in the field of sample preparations for lipids and rapidly metabolized biomarkers in biological samples in his current position as an Associate Principal Scientist in Translational Science, AstraZeneca
Abstract:
An important part of the workflow of lipid analysis is the extraction procedure. While there has been an impressive development of automated and high-throughput oriented, analytical methods, the extraction procedure is still often performed manually with traditional chloroform-based methods since more than 50 years. Now is the time for a change. To overcome the drawbacks of time-consuming, manual, and chloroform-based methods we have developed an automated chloroform-free method for total lipid extraction of biofluids and tissue. The method for biofluids is 100% automated, performed using a standard pipetting 96-well pipetting robot. It is based on an initial one-phase extraction using butanol and methanol (BUME). After addition of heptane/ethyl acetate/1% acetic acid a two phase system is formed without the need for centrifugation, with the organic lipid-containing upper phase easily recovered. To validate the methods, extraction recoveries for hundreds of lipid species from 10 lipid classes were tested and compared to the Folch method. The results showed similar or better extraction yields for all investigated lipids using the BUME method. For biofluids, the method was shown to be compatible with volumes ranging from 10-100 µl. For tissue, the method was validated for 15-150mg tissue. In conclusion, we believe that the development of these two methods is a major breakthrough moving lipid extraction into the high-throughput workflows of a modern lipid laboratory.
P K Ragunath
Sri Ramachandra University, India
Title: Relevance of Machine Learning Approach in Analysing Metabolomic Data to gain insights on Comorbidity issues in COPD & Asthma
Time : 09:50-10:10
Biography:
P K Ragunath graduated in March 1983 from Vivekananda College, Chennai, affiliated to University of Madras. He obtained Post Graduate Degree from Pachaiyappa's College, Chennai and MPhil degree from Madras University. Later he completed PhD in Madras Medical College, Chennai, affiliated to The Tamil Nadu Dr. MGR University Chennai. In addition, he has completed many degree/certificate programs in Management and information Technology. He has published 15 research articles in peer-reviewed journals and has also presented around 15 papers in national and international conferences. He is holding membership in many ‘World ‘renowned organizations’ catering to the Bioinformatics & Medical Informatics.
Abstract:
Background: Comorbidity is usually defined as a disease coexisting with the disease of interest. Chronic obstructive pulmonary disease (COPD) and Asthma are the most frequent causes of respiratory ill health, covering all ages and several cases of comorbidity between the two conditions have been reported. Asthma and COPD are different diseases each with a unique natural history and pathophysiology, but differentiating the underlying cause of their symptoms is difficult and often leads to generalized treatment protocols. Metabolomics involves quantitative measurement of time-related multi-parametric metabolic response of living systems to pathophysiological stimuli or change in gene expression profile. Over the past few years a number of studies especially employing Mass Spectrometry and NMR have emerged to identify the presence of metabolite markers specific to Asthma and COPD. The study aims at identifying a consensus metabolic profile of Asthma and COPD using text mining based machine learning approach and gain understand the underlying mechanism causing comorbidity. Lacunae: There is little information available on metabolite profile specific for COPD and Asthma, such knowledge would be invaluable in gain Insights on comorbidities between the 2 conditions. Methodology: Comprehensive text mining was carried to enlist all eligible studies on metabolomic profiling studies to recognize specific metabolite signatures common for Asthma and COPD and employ Machine learning approaches to gain Insights on comorbidity issues between the 2 conditions. Results and Conclusions: Modeling using Machine Learning like ANN, SVM, GA approaches were used to get quantitative effects of exogenous compounds on pathogenesis of COPD and Asthma. Metabolic profiling through the use of pattern recognition statistics on metabolite signals has the potential to identify specific signatures for COPD and Asthma which can aid in differential diagnosis and also to gain understanding on the comorbidity between the 2 disorders. Such knowledge would help in accurate diagnostics and to devise a novel management technique for COPD and Asthma. The results will be presented and discussed.
Ningombam Sanjib Meitei
Premier Biosoft, USA
Title: SimMet: Informatics tool for automating LC-MS and MS/MS based large metabolomic data processing and analysis
Time : 10:10-10:30
Biography:
Ningombam Sanjib Meitei has completed his PhD; he is the Chief Scientific Officer at Premier Biosoft, a leading bioinformatics company that provides software solutions for mass spectrometry based proteomics, glycomics, metabolomics, lipidomics and imaging data analysis. He has been leading the development of novel informatics tools namely; SimLipid, a LC-, MALDI- MS/MS data analysis tool for lipid identification by annotating product ions corresponding to lipid head-groups, fatty acyls, Charge Remote Fragment ions etc.; SimGlycan, the only high throughput glycan/glycopeptide identification tool that can also quantitate glycans using reporter ions intensity and MALDI Vision, a comprehensive mass spectrometry imaging data visualization and analysis tool.
Abstract:
Recently, there has been rapid growth in innovations related to liquid chromatography-tandem mass spectrometry (LC-MS/MS) based metabolite profiling studies. However, the lack of high throughput software tools has been one of the bottle necks. A typical metabolomics data analysis pipeline may include multiple software tools for example, using of a data processing tool to generate peak lists, a database search tool for metabolite profiling, other tools to validate metabolites using MS/MS data pattern matching or in silico fragment matching, tools for performing statistical analysis for identifying differential metabolites and pathways analysis for the identified metabolites. We have investigated some of the challenges we commonly face while processing raw data and identifying metabolites accurately. In order to address the challenges, we have developed SimMet. We introduce data filters in the software protocol that can effectively remove significant number of peaks corresponding to noise based on shape of the LC-peaks and data from LC-MS runs of blanks, QCs, technical replicates of the biological samples. The application of these filters prior to subjecting the data into conventional statistical techniques such as ANOVA, t-test, PCA etc. may be desirable since data can be reduced without compromising the actual information. This will also enable speedy analysis of large data that are common in mass spectrometry based metabolomics work flows. The software work flow will be demonstrated based on a food metabolomics experiment with data acquired on an LC-compact Qq-TOF mass spectrometer (Bruker Daltonik) system.
Sam F. Y. Li
National University of Singapore, Singapore
Title: Application of metabolomics for toxicity assessment of solid waste re-utilization
Time : 10:30-10:50
Biography:
Professor Sam Li is a faculty member at the Department of Chemistry, National University of Singapore (NUS). He received his BSc, PhD and DSc degrees from Imperial College, UK. His research interests include environmental science and technology, metabolomics, biosensors and nanotechnology. He has authored/co-authored 325 publications in international peer review journals, more than 100 conference presentations and 10 US patents. He serves/served on editorial advisory boards of several international scientific journals, including Electrophoresis (Germany), Journal of Chromatographic Science (USA), LC-GC (Asia Pacific), and Biomedical Chromatography (UK).
Abstract:
Leachate samples from fly ash and bottom ash obtained by gasification of solid wastes were analyzed by non-targeted screening using liquid chromatography-quadrupole-time of flight-mass spectrometry (LC-QTOF-MS). The results were used to determine which organic compounds could contribute to the toxicity of the leachates of solid waste gasification. Subsequently, the effects of the leachate on mortility and immobility of Daphnia mangna were evaluated as a method for monitoring water quality, and as a screening method for toxicity assessment of solid waste re-utilization.
- Young Researchers Forum
Session Introduction
Inmaculada Martinez-Reyes
Northwestern University, USA
Title: Dissecting the distinct functions of mitochondria
Time : 11:00-11:20
Biography:
Inmaculada Martinez-Reyes received her BA in Pharmacy and completed her PhD in Molecular Biology under the guidance of Dr. Jose Manuel Cuezva at University Autonoma of Madrid, Spain. During her Doctorate studies, she focused on the role of mitochondria in cancer. She was awarded a Postdoctoral Fellowship from Ramon Areces Foundation of Spain to perform her Postdoctoral training in Dr. Navdeep Chandel’s laboratory at Northwestern University. She is currently studying fundamental biology related to mitochondrial metabolism in hopes of finding new therapies for mitochondrial-associated diseases including cancer. She has published papers in outstanding journals and participated in numerous international conferences.
Abstract:
Mitochondria are the major site of energy production in the cell. An interesting and unique feature of the Electron Transport Chain (ETC) is that it is regulated under dual genetic control. Whereas the majority of the proteins that constitute the ETC are encoded by the nuclear DNA, 13 proteins are encoded by the Mitochondrial DNA (mtDNA). It is important to note, that mutations in mtDNA lead to very different phenotypes, suggesting that mitochondrial perturbations must manifest beyond bioenergetics. Other two salient functions of mitochondria are their biosynthetic capacity and their role as signaling organelles. We hypothesize that biological outcomes derived from these functions of mitochondria are also involved in the mechanisms mediating mitochondrial-associated diseases. However, it is not fully understood the involvement of distinct mitochondrial functions in the regulation of key cellular processes such as proliferation. In this study, HEK293 cells stably expressing a dominant negative form of the mtDNA polymerase POLG were used to eliminate mtDNA from cells in culture in a doxycycline-dependent manner as a model to dissect the importance of the different mitochondrial functions. First, the metabolic changes that occur during loss of mtDNA were studied by analyzing the whole cell metabolome profile to identify unique pathways activated during mitochondrial dysfunction. Next, the TCA cycle function and the mitochondrial membrane potential in cells with mtDNA loss were independently restored to elucidate the final consequences derived from the mitochondrial ability to carry out biosynthetic processes and to overall communicate with the rest of the cell.
Christin Zasada
BIMSB, Germany
Title: Quantifying metabolic fluxes in cancer and stem cells
Time : 11:20-11:40
Biography:
Christin Zasada studied Biosystems engineering at the Otto-von-Guericke University Magdeburg, Germany. In 2011 she started her PhD thesis in the lab of Stefan Kempa (Integrative Proteomics and Metabolomics) at the Berlin Institute of Medical Systems Biology at the Max-Delbrueck-Center for Molecular Medicine in Berlin, Germany.
Abstract:
Despite their different origin, cancer and stem cells share the feature of fast growth and unlimited proliferation. It is under debate if their mode of proliferation should require likewise distinct demands of precursors for biosynthesis or energy; regardless both cell types favoring aerobic glycolysis. In our lab we combine MS-based ‘omics’-technologies (LC-MS / GC-MS) to deliver relative and absolute quantitative information about enzyme abundance and metabolite concentrations of the central carbon metabolism (CCM). We have developed pulsed stable isotope resolved metabolomics (pSIRM) to monitor how cells utilize substrates like glucose and glutamine to meet their energetic requirements. Finally, the integration of quantitative and time-resolved isotope incorporation in a mathematical framework enables the calculation of the metabolic fluxes; the only functional readout of a cell. Isotopically non-stationary (INST) metabolic flux analysis uses the collected data (absolute poolsizes, extracellular rates) in combination with a network model (material balances, carbon transitions) to jointly estimate intracellular metabolic fluxes and pool sizes in cell culture experiments. The complete workflow-from the petri dish to the metabolic flux map-has been applied to track the rerouting of carbon usage during pluripotency, reprogramming and differentiation. We applied stable isotope labeled substrates and analyzed their fate in fibroblasts and their pluripotent counter parts (iPSC), in breast cancer MDA-MB231 and in human embryonic stem cells (hESC). The analysis endorsed the switch from a glycolytic to respiratory metabolism after differentiation both in hESC and iPSC-derived fibroblasts. Specifically the comparison of the metabolic profiles of stem cells and cancer cells revealed distinct metabolic features of these cell types.