Concept and application of a computational vaccinology workflow

BACKGROUND : The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS : We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION : Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges

Omics profile interpretation on molecular interaction graphs

Zsfassung in dt. SpracheMolekulare Interaktionsetzwerke stellen ein Kernkonzept in Life Sciences - ein Forschungsgebiet dessen Hauptaugenmerk auf der integrativen Informationsanalyse liegt - dar und sind gleichzeitig ein optimales Werkzeug für die Modellierung von zellulären Prozessen. Auf molekularer Ebene stellen solche Abläufe die Ursache für den Phänotyp dar und alle Merkmale eines Organismus lassen sich zu einem oder mehreren solchen Prozessen zurückverfolgen.Eine der größten Herausforderungen der heutigen Forschung ist die Variabilität der Erkrankung auf Prozessebene, dh ähnliche Phänotypen haben oft verschiedene Ursachen. Mit dem Advent der Omics Revolution, ist eine Lawine an relevanten Daten verfügbar geworden, denen jedoch zum Großteil noch eine sinnvolle Interpretation fehlt.In dieser Arbeit werden zwei Ansätze zur Bekämpfung von Heterogenität auf Prozessebene präsentiert, welche auf Omics Datenintegration auf molekularen Interaktionsgraphen basieren. Der erste Ansatz benutzt ein Netzwerk von synthetischen letalen Interaktionen um die Heterogenität in der Krebstherapie in den Griff zu bekommen, während der zweite ein erweitertes Protein-Protein Interaktionsnetzwerk verwendet um biologischer Varianz hinsichtlich Patientenstratifizierung entgegen zu wirken.Die Ergebnisse der ersten Methode zeigen, dass es möglich ist sowohl in Neuroblastom Zelllinien als auch im menschlichen Gewebe synthetisch letale Hubs zu identifizieren, der Knock-down welcher den Tod maligner Zellen herbeiführen würde. Diese Methode wird anschließend für drei weitere Tumorarten verallgemeinert und relevante Hubs und Medikamente werden identifiziert.In der zweiten Methode wird ein neues Interaktionsnetzwerk präsentiert, das einerseits validierte Protein-Protein Interaktionen und andererseits aus hochqualitativen Pathway-, Ontologie- und Domänendaten abgeleitete Kanten enthält. Das Netzwerk wird benutzt um die diabetische Nephropathie aus klinischer Sicht zu durchleuchten. Ein Units-Konzept zur Identifikation von Biomarker Kombinationen zwecks Patientenstratifizierung wird abschließend exemplifiziert.Die Ergebnisse zeigen, dass es durch Informationsintegration möglich ist die biologische Variabilität bei gleichzeitiger Verbesserung der Interpretierbarkeit in den Griff zu bekommen. Wir behaupten, dass die Methoden, die in dieser Arbeit präsentiert wurden, die derzeit verfügbaren Behandlungsansätze erweitern und sich mittelfristig als wertvoller Schritt in Richtung Systemmedizin erweisen werden.Molecular interaction networks are a core concept in Life Sciences - a field of study with the specific focus on integrative information analysis - and an ideal tool for modeling cellular processes. On the molecular level, cellular processes are the direct cause of phenotype, whether healthy or diseased, and all observable properties of a cell can be traced back to one or more processes.One of today's main challenges in research is the variability of disease on the process level, meaning similar phenotypes often have different causes. With the advent of the Omics revolution, an enormous amount of data relevant in this context has become available, much of it, however, still pending meaningful interpretation.Here we demonstrate two approaches to tackle heterogeneity on the process level, based on Omics data integration on molecular interaction graphs. The first one uses a synthetic lethality network to address heterogeneity in cancer, while the second one uses an extended protein-protein interaction network for overcoming variance towards patient stratification.Our first method demonstrates both in neuroblastoma cell-lines and in human tissue how to find synthetic lethal hubs the knock-down of which would lead to the death of malignant cells. We generalize this method for three additional tumor types and identify relevant hubs including drugs for targeting them.In our second method we propose a novel interaction network holding validated protein-protein interactions and edges additionally inferred from high quality pathway, ontology and domain data. We use this network to investigate diabetic nephropathy from a clinical perspective based on literature, drug, clinical trial and patent information. Subsequently, we introduce the concept of units towards identifying multi-biomarker panels for patient stratification.Our results demonstrate that it is possible through information integration to address biological variability issues while at the same improving causative interpretability. We assert that the methods presented in this thesis expand the set of available treatment approaches and will prove in the midterm to be a valuable stepping stone towards Systems Medicine.12

Data models, graph analysis, and information retrieval from biological data

Zsfassung in dt. SpracheThis thesis introduces a possible approach to information extraction from a biological context, by building a molecular dependency network.Our aim is to optimize hypothesis generation for laboratory experiments, with the ultimate goal of disease-associated biomarker and drug target discovery.We present a three-stage method involving data warehousing, scientific information consolidation and analysis. Further, we investigate the method by running tests using specific datasets from B-cell lymphoma and renal transplant ischemia reperfusion injury.8

Synthetic lethal hubs associated with vincristine resistant neuroblastoma.

Chemotherapy of cancer experiences a number of shortcomings including development of drug resistance. This fact also holds true for neuroblastoma utilizing chemotherapeutics as vincristine. We performed a comparative analysis of molecular and cellular mechanisms associated with vincristine resistance utilizing cell line as well as human tissue data. Differential gene expression analysis revealed molecular features, processes and pathways afflicted with drug resistance mechanisms in general, and specifically with vincristine significantly involving actin associated features. However, specific mode of resistance as well as underlying genotype of parental, vincristine sensitive cells apparently exhibited significant heterogeneity. No consensus profile for vincristine resistance could be derived, but resistance-associated changes on the level of individual neuroblastoma cell lines as well as individual patient profiles became clearly evident. Based on these prerequisites we utilized the concept of synthetic lethality aimed at identifying hub proteins which when inhibited promise to induce cell death due to a synthetic lethal interaction with down-regulated, chemoresistance associated features. Our screening procedure identified synthetic lethal hub proteins afflicted with actin associated processes holding synthetic lethal interactions to down-regulated features individually found in all chemoresistant cell lines tested, therefore promising an improved therapeutic window. Verification of such synthetic lethal hub candidates in human neuroblastoma tissue expression profiles indicated the feasibility of this screening approach for addressing vincristine resistance in neuroblastoma