Even with a lipid content of over 10% in the human body, lipids were not in the central scientific focus during the last decade. However, more and more evidence is provided that non-genetically determined biomolecules such as metabolites and lipids are the key to biomolecular regulation. Today it is obvious that lipids are not only important for energy homeostasis and as an environmental-cellular barrier, but also represent a central part of our signal transduction machinery. Disruptions of the sensitive lipid metabolism are highly correlated with different types of diseases including thrombocytopenia, metabolic syndrome, diabetes, obesity and hyperlipidemia. This is especially true for the latter ones, which are also reaching pandemic levels, causing a larger annual health burden than infectious diseases. Therefore, lipid metabolism is again becoming an emerging scientific field and is a central part of pharmacological research today (statins, cyclooxygenase inhibitors, endurobol in doping). Therefore the mission of the Lipidomics group in Vienna is to understand lipids in context with their proteins (enzymes) and building blocks (metabolites) in a true systems biology way.
Lipidomics encompasses analytical approaches that aim to identify and quantify the complete set of lipids, defined as lipidome in a given cell, tissue or organism as well as their interactions with other molecules. The majority of lipidomics workflows is based on mass spectrometry and has been proven as a powerful tool in system biology in concert with other Omics disciplines. Unfortunately, bioinformatics infrastructures for this relatively young discipline are limited only to some specialists. Search engines, quantification algorithms, visualization tools and databases developed by the ‘Lipidomics Informatics for Life-Science’ (LIFS) partners will be restructured and standardized to provide broad access to these specialized bioinformatics pipelines. There are many medical challenges related to lipid metabolic alterations that will be fostered by capacity building suggested by LIFS. LIFS as member of the ‘German Network for Bioinformatics’ (de.NBI) node for ‘Bioinformatics for Proteomics’ (BioInfra.Prot) and will provide access to the described software as well as to tutorials and consulting services via a unified web-portal.
Metabolic dysfunctions are not only highly correlated with insulin resistance and diabetes, but are also associated with a 10 fold higher risk to develop Alzheimer’s disease. The proposed project joins forces between two Leibniz institutions with different expertise to establish a unique research platform for Translational Neuroscience. Here, we will break ground by introducing lipidomics to the field of synapse biology, by investigating insulin resistance in conjunction with high Abetaload and by studying the effect whether synaptic disease states result in an altered lipid composition that in turn leads to synaptic dysfunction in brain.
Phenotypes at cellular and organism level are a result of a multitude of different molecular species. Thereby, interconnected networks are at the heart of both signaling pathways and complex traits that mediate adaptive plasticity and determine phenotypes. To answer the question how different molecular layers are connected and to gain deeper insights into the underlying mechanisms that determine a certain phenotype, a comprehensive and representative analysis of the molecular species involved is necessary. Historically, each molecule class (e.g. DNA, RNA, proteins, metabolites, and lipids) has been studied separately in large scale omics experiments to look for relationships within biological processes. Using this strategy, we have assembled some of the molecular pieces related to signaling networks, but many interactions between them are still unrevealed or unexplained due to the restrictive single‐data‐type study designs. Therefore, multimolecular approaches on the sample processing as well as on the data analysis side are a prerequisite to obtain an integrated perspective. Read more in our recent publications SIMPLEX a multiomics for systems biology (https://www.ncbi.nlm.nih.gov/pubmed/26814187).
Computational proteomics is a constantly growing field to support end users with powerful and reliable tools for performing several computational steps within an analytics workflow for proteomics experiments. Typically, after capturing with a mass spectrometer, the proteins have to be identified and quantified. After certain follow-up analyses, an optional targeted approach is suitable for validating the results. The de.NBI (German network for bioinformatics infrastructure) service center in Dortmund provides several software applications and platforms as services to meet these demands. In this work, we present our tools and services, which is the combination of SearchGUI and PeptideShaker. SearchGUI is a managing tool for several search engines to find peptide spectra matches for one or more complex MS2 measurements. PeptideShaker combines all matches and creates a consensus list of identified proteins providing statistical confidence measures. In a next step, we are planning to release a web service for protein identification containing both tools. This system will be designed for high scalability and distributed computing using solutions like the Docker container system among others. As an additional service, we offer a web service oriented database providing all necessary high-quality and high-resolution data for starting targeted proteomics analyses. The user can easily select proteins of interest, review the according spectra and download both protein sequences and spectral library. All systems are designed to be intuitively and user-friendly operable.
The study of complex biological systems is best approached by incorporating many perspectives. Thus omics strategies are the perfect toolset to deliver quantitative information from multi molecular layers of an investigated system such as differentiating stem cells. By combining proteomics and lipidomics approaches novel control mechanisms such as network based interconnected feedback influencing cell fate decision will be identified and elucidated. The aim of this project is to develop novel strategies in the field of integrative biology allowing the in deep investigation of dynamic systems. To achieve this goal, still a lot of effort has to be invested too continuously improve and to develop novel analytical approaches in separation, detection and quantification of different lipid classes.
Diffuse gliomas are the most frequent primary human brain tumors with glioblastoma being the most aggressive among them. The mean survival for patients suffering from glioblastoma is restricted to 12 months, despite multimodal therapy regimens. Tumor cells commonly exhibit high levels of endoplasmic reticulum stress, which triggers the unfolded protein response (UPR), a mechanism, which recently has gained a lot of attention in the treatment of malignancies. Despite its broad clinical importance, quantitative models that systematically describe the UPR in cancer cells are missing so far. In order to lever the UPR for therapeutic intervention in glioma, strong needs exist for an integrated vision of how this molecular pathway contributes to tumor growth and infiltration.
The aim of the SUPR-G systems biology approach is to combine interdisciplinary approaches and state of the art methodology – including translatome and proteome analyses, computational modeling, human glioma specimen and in vivo animal model target validation – to gain novel and system-wide insights into the UPR.
These data will serve to establish the first highly integrated quantitative network model of the UPR in glioma to reveal potential therapeutic candidates for subsequent validation using the individual model systems of the consortium. The constructed model will be made publicly available via a web based interface and will be integrated into already existing online tools enabling the scientific community to develop novel targeted therapies interfering with UPR-mediated cell fate decisions in the context of glioma and beyond.