The investigation of nucleic acids, proteins, metabolites, and lipids via analytical high-throughput methods has become an integral part of biomedical research, particularly in the field of cardiovascular diseases. However, the current methods are limited to one molecule class only, so that they cannot gain a global overview of all the molecules in one sample. For such an overview, a combining approach is required that captures the data of several molecular levels and creates an integral image that can represent the networks between these levels. Such combining approaches are also referred to as Multiomics techniques, and they consist of at least two Omics methods. At ISAS, several work groups have joined forces to develop a Multiomics approach. Thus, they can gain a comprehensive picture of biological processes such as interactions in and between signal transduction pathways leading to a better understanding of cardiovascular diseases. The Multiomics approach allows them to discover new molecular contexts, generate hypotheses and find approaches to medication and therapy.
The project is divided into several smaller project parts or work packages, in which the scientists develop and optimise basic techniques that make new molecular classes accessible to the Multiomics approach. These techniques are to be used in cardiovascular studies that deal, for example, with heart attacks or cardiac development.
Overview of the Multiomics project parts:
Lipid detection and structural analysis of proteins
The Miniaturisation group, in close collaboration with the Protein Dynamics and Lipidomics groups, develops its plasma-based detection techniques to a combined injection and ionisation technique. This should be able to detect lipids more sensitive and better than current methods, and in addition can be used to gain additional structural information to alternative fragmentation pathways of biomolecules.
SIMPLEX as Multiomics platform
The SIMPLEX platform developed by the Lipidomics group will be further improved, for example to identify additional PTMs and to perform even better quantitative analyses. Furthermore, the project team will develop a bioinformatics approach for integrative data evaluation. The Multiomics workflow will be used for long-term studies on different model systems, whereby the team wants to investigate the development of physiological and pathophysiological cardiac hypertrophy.
Parallel analysis of protein modification
In this work package, the project team is concerned with the development of methods for qualitative and quantitative analysis of structural changes in proteins, especially with post-translational modifications (PTMs). These are combined with the tools developed by the Lipidomics, Synthetic Biomolecules, and Chemical Proteomics groups for the enrichment of lipid- and glycine-modified peptides to analyse at least three different PTMs in parallel and to perform time-resolved studies in the cardiovascular system. With such a method, both lipids, metabolites, and proteins as well as several different PTMs could be detected in a single sample.
New methods for the analysis of protein lipidations
Die Arbeitsgruppen Lipidomics, Chemical Proteomics und Protein Dynamics entwickeln neue Detektionstechniken zum Erfassen von Lipidmodifikationen an Proteinen. Sie wollen zum Beispiel Fettsäuren und Lipide mit Alkylengruppen synthetisieren und so ein neues Markierungsverfahren etablieren. Außerdem wollen die Gruppen weitere unterschiedlich lipidierte Peptide herstellen, die zur Verbesserung von MS-basierten Arbeitsabläufen genutzt werden. Mit den optimierten Verfahren sollen später endogene Lipidierungen auch ohne Markierung analysiert werden können.
The Lipidomics, Chemical Proteomics, and Protein Dynamics groups develop new techniques for detecting lipid modifications on proteins. For example, they aim to synthesise fatty acids and lipids with alkylene groups and thus establish a new labelling method. Furthermore, the groups want to produce other differently lipidated peptides, which will be used to improve MS-based workflows. With these optimised techniques, the team should be able to analyse endogenous lipidations without any labelling.