Mass Spectrometry Informatics in Systems Biology

Mass spectrometry (MS) became a standard tool for identifying metabolites in biological tissues, and metabolomics is slowly acknowledged as a legitimate research discipline for characterizing biological conditions. The computational analyses of metabolomics, however, lag behind compared with the rapid advances in analytical aspects for two reasons. First is the lack of standardized data repository for mass spectra: each research institution is flooded with gigabytes of mass-spectral data from its own analytical groups and cannot host a world-class repository for mass spectra. The second reason is the lack of informatics experts that are fully experienced with spectral analyses. The two barriers must be overcome to establish a publicly free data server for MS analysis in metabolomics as does GenBank in genomics and UniProt in proteomics. The workshop brought together bioinformaticians working on mass spectral analyses in Finland and Japan with the goal to establish a consortium to freely exchange and publicize mass spectra of metabolites measured on various platforms computational tools to analyze spectra spectral knowledge that are computationally predicted from standardized data. This book contains the abstracts of the presentations given in the workshop. The programme of the workshop consisted of oral presentations from Japan and Finland, invited lectures from Steffen Neumann (Leibniz Institute of Plant Biochemistry), Matej Oresic (VTT), Merja Penttila (VTT) and Nicola Zamboni (ETH Zurich) as well as free form discussion among the participants. The event was funded by Academy of Finland (grants 139203 and 118653), Japan Society for the Promotion of Science (JSPS Japan-Finland Bilateral Semi- nar Program 2010) and Department of Computer Science University of Helsinki. We would like to thank all the people contributing to the technical pro- gramme and the sponsors for making the workshop possible. Helsinki, October 2010 Masanori Arita, Markus Heinonen and Juho Rousu

Molecular mechanisms of hypertension-induced heart failure : Experimental studies with special emphasis on local renin-angiotensin system, cardiac metabolism and levosimendan

Hypertension is a major risk factor for stroke, ischaemic heart disease, and the development of heart failure. Hypertension-induced heart failure is usually preceded by the development of left ventricular hypertrophy (LVH), which represents an adaptive and compensatory response to the increased cardiac workload. Biomechanical stress and neurohumoral activation are the most important triggers of pathologic hypertrophy and the transition of cardiac hypertrophy to heart failure. Non-clinical and clinical studies have also revealed derangements of energy metabolism in hypertensive heart failure. The goal of this study was to investigate in experimental models the molecular mechanisms and signalling pathways involved in hypertension-induced heart failure with special emphasis on local renin-angiotensin-aldosterone system (RAAS), cardiac metabolism, and calcium sensitizers, a novel class of inotropic agents used currently in the treatment of acute decompensated heart failure. Two different animal models of hypertensive heart failure were used in the present study, i.e. hypertensive and salt-sensitive Dahl/Rapp rats on a high salt diet (a salt-sensitive model of hypertensive heart failure) and double transgenic rats (dTGR) harboring human renin and human angiotensinogen genes (a transgenic model of hypertensive heart failure with increased local RAAS activity). The influence of angiotensin II (Ang II) on cardiac substrate utilization and cardiac metabolomic profile was investigated by using gas chromatography coupled to time-of-flight mass spectrometry to detect 247 intermediary metabolites. It was found that Ang II could alter cardiac metabolomics both in normotensive and hypertensive rats in an Ang II receptor type 1 (AT1)-dependent manner. A distinct substrate use from fatty acid oxidation towards glycolysis was found in dTGR. Altered cardiac substrate utilization in dTGR was associated with mitochondrial dysfunction. Cardiac expression of the redox-sensitive metabolic sensor sirtuin1 (SIRT1) was increased in dTGR. Resveratrol supplementation prevented cardiovascular mortality and ameliorated Ang II-induced cardiac remodeling in dTGR via blood pressure-dependent pathways and mechanisms linked to increased mitochondrial biogenesis. Resveratrol dose-dependently increased SIRT1 activity in vitro. Oral levosimendan treatment was also found to improve survival and systolic function in dTGR via blood pressure-independent mechanisms, and ameliorate Ang II-induced coronary and cardiomyocyte damage. Finally, using Dahl/Rapp rats it was demonstrated that oral levosimendan as well as the AT1 receptor antagonist valsartan improved survival and prevented cardiac remodeling. The beneficial effects of levosimendan were associated with improved diastolic function without significantly improved systolic changes. These positive effects were potentiated when the drug combination was administered. In conclusion, the present study points to an important role for local RAAS in the pathophysiology of hypertension-induced heart failure as well as its involvement as a regulator of cardiac substrate utilization and mitochondrial function. Our findings suggest a therapeutic role for natural polyphenol resveratrol and calcium sensitizer, levosimendan, and the novel drug combination of valsartan and levosimendan, in prevention of hypertension-induced heart failure. The present study also provides a better understanding of the pathophysiology of hypertension-induced heart failure, and may help identify potential targets for novel therapeutic interventions.

A metabolomic approach to studying mechanisms of polymeric gene delivery to retinal pigment epithelial cells

Tiivistelmä ReferatAbstract Metabolomics is a rapidly growing research field that studies the response of biological systems to environmental factors, disease states and genetic modifications. It aims at measuring the complete set of endogenous metabolites, i.e. the metabolome, in a biological sample such as plasma or cells. Because metabolites are the intermediates and end products of biochemical reactions, metabolite compositions and metabolite levels in biological samples can provide a wealth of information on on-going processes in a living system. Due to the complexity of the metabolome, metabolomic analysis poses a challenge to analytical chemistry. Adequate sample preparation is critical to accurate and reproducible analysis, and the analytical techniques must have high resolution and sensitivity to allow detection of as many metabolites as possible. Furthermore, as the information contained in the metabolome is immense, the data set collected from metabolomic studies is very large. In order to extract the relevant information from such large data sets, efficient data processing and multivariate data analysis methods are needed. In the research presented in this thesis, metabolomics was used to study mechanisms of polymeric gene delivery to retinal pigment epithelial (RPE) cells. The aim of the study was to detect differences in metabolomic fingerprints between transfected cells and non-transfected controls, and thereafter to identify metabolites responsible for the discrimination. The plasmid pCMV-β was introduced into RPE cells using the vector polyethyleneimine (PEI). The samples were analyzed using high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) coupled to a triple quadrupole (QqQ) mass spectrometer (MS). The software MZmine was used for raw data processing and principal component analysis (PCA) was used in statistical data analysis. The results revealed differences in metabolomic fingerprints between transfected cells and non-transfected controls. However, reliable fingerprinting data could not be obtained because of low analysis repeatability. Therefore, no attempts were made to identify metabolites responsible for discrimination between sample groups. Repeatability and accuracy of analyses can be influenced by protocol optimization. However, in this study, optimization of analytical methods was hindered by the very small number of samples available for analysis. In conclusion, this study demonstrates that obtaining reliable fingerprinting data is technically demanding, and the protocols need to be thoroughly optimized in order to approach the goals of gaining information on mechanisms of gene delivery.