Andriani Daskalaki

A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.

High-Throughput miRNA and mRNA Sequencing of Paired Colorectal Normal, Tumor and Metastasis Tissues and Bioinformatic Modeling of miRNA-1 Therapeutic Applications

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options.

Prediction in the face of uncertainty: A Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.

Multi-omic profiles of human non-alcoholic fatty liver disease tissue highlight heterogenic phenotypes

Non-alcoholic fatty liver disease (NAFLD) is a consequence of sedentary life style and high fat diets with an estimated prevalence of about 30% in western countries. It is associated with insulin resistance, obesity, glucose intolerance and drug toxicity. Additionally, polymorphisms within, e.g., APOC3, PNPLA3, NCAN, TM6SF2 and PPP1R3B, correlate with NAFLD. Several studies have already investigated later stages of the disease. This study explores the early steatosis stage of NAFLD with the aim of identifying molecular mechanisms underlying the etiology of NAFLD. We analyzed liver biopsies and serum samples from patients with high- and low-grade steatosis (also pre-disease states) employing transcriptomics, ELISA-based serum protein analyses and metabolomics. Here, we provide a detailed description of the various related datasets produced in the course of this study. These datasets may help other researchers find new clues for the etiology of NAFLD and the mechanisms underlying its progression to more severe disease states.

Handbook of Research on Systems Biology Applications in Medicine

Systems biology is the study of the interactions between the components of a biological system and the way these interactions lead to a specific function and behavior of that system.The Handbook of Research on Systems Biology Applications in Medicine presents concepts of the applications of systems biology in medicine, leading students and advanced researchers to an understanding of current ideas in the complex field of systems biology, analysis of data, and development of models, simulation, and drug development. With over 50 incisive research contributions from international experts, this unsurpassed compendium of critical research is an indispensable resource for medical libraries worldwide.

Quality Assurance in Healthcare Service Delivery, Nursing and personalized Medicine: Technologies and Processes

Athina Lazakidou, Ph.D, currently works at the University of Peloponnese, Department of Nursing in Greece as Lecturer in Health Informatics and at the Hellenic Naval Academy as a Visiting Lecturer in Informatics. She worked as a Visiting Lecturer at the Department of Computer Science at the University of Cyprus (2000-2002) and at the Department of Nursing at the University of Athens (2002-2007). She did her undergraduate studies at the Athens University of Economics and Business (Greece) and received her BSc in Computer Science in 1996. In 2000, she received her Ph.D. in Medical Informatics from the Department of Medical Informatics, University Hospital Benjamin Franklin at the Free University of Berlin, Germany. She is also an internationally known expert in the field of computer applications in health care and biomedicine, with six books, and numerous papers to her credit. She was also Editor of the Handbook of Research on Informatics in Healthcare and Biomedicine and Handbook of Research on Distributed Medical Informatics and E-Health, the best authoritative reference sources for information on the newest trends and breakthroughs in computer applications applied to health care and biomedicine. Her research interests include health informatics, e-Learning in medicine, software engineering, graphical user interfaces, (bio)medical databases, clinical decision support systems, hospital and clinical information systems, electronic medical record systems, telematics, and other web-based applications in health care and biomedicine. Athina Lazakidou (University of Peloponnese, Greece) and Andriani Daskalaki (Max Planck Institute for Molecular Genetics, Germany)

Medical Advancements in Aging and Regenerative Technologies: Clinical Tools and Applications

Andriani Daskalaki presently works in the field of molecular medicine and bioinformatics at the Max Planck Institute for Molecular Genetics in Berlin. She completed her PhD in 2002 on working in the applications of photodynamic therapy in the area of oral medicine from the Free University of Berlin. She received a two-year DAAD scholarship (1996-1998) for her research in the field of PDT. Dr. Daskalaki received a MS in Medical Informatics from TFH Berlin with her work in “Development of a documentation software for robot-assisted intraoral operations” and a MS degree in bioinformatics with her work in “Variance analysis of multifactor models in gene expression experiments with application to the identification of genetic markers for hypertension.” She received a poster prize for her participation in the International Photodynamic Association Meeting in Nantes. She is the editor of the Handbook of Research on Systems Biology Applications in Medicine and has presented many oral presentations at national and international meetings. She is a founding member and committee member of the Greek Dental Laser Association. Her research interest areas include systems biology, PDT, and laser applications in dentistry. Andriani Daskalaki (Max Planck Institute for Molecular Genetics, Germany)

Computational Tools and Resources for Systems Biology Approaches in Cancer

Systems biology focuses on the study of interacting components of biological systems rather than on the analysis of single genes or proteins and offers a new approach to understand complex disease mechanisms by the use of computational models. The analysis of such models has become crucial to understand biological processes and their dysfunctions with respect to human diseases. A systems biology approach would be a key step in improving diagnosis and therapy of complex diseases such as cancer. It offers new perspectives for drug development, for example, in detecting drug side effects and alternative response mechanisms through the analysis of large cellular networks in silico.

Pathway Analysis and Modeling of the Differentiation of Human Embryonic Stem Cells into Hepatocyte‐like Cells

A more detailed understanding of the differentiation of human embryonic and induced pluripotent stem cells into hepatocyte‐like cells can help to improve therapies for liver diseases, like steatohepatitis. In this work we used microarray‐based expression data to analyze the in vitro differentiation of human embryonic stem cells into hepatocytes. Pathway analysis has been carried out on gene expression data of different stages of the differentiation process from embryonic stem cells into hepatocyte‐like cells via definitive endoderm and hepatic endoderm. Based on pathway analysis we identified signaling pathways, like the GPCR signaling pathway as well as FOXA2 regulatory networks. Based on these highly enriched pathways we constructed a model prototype to better understand and study the differentiation of stem cells into hepatocytes.