Platelet integrity and function critically depend on lipid composition. However, the lipid inventory in platelets was hitherto not quantified (Peng et al., Blood, 2018). Today our lab examines the lipidome of murine and human platelets using lipid-category tailored protocols on quantitative lipidomics platforms. We commonly can cover the platelet lipidome, which is comprised out of 500 lipids (99.9% of the total lipid mass) over a concentration range of seven orders of magnitude. We conduct systematic comparison of lipidomics network in resting and activated murine platelets, validated in human platelets, where we inter alia revealed that less than 20% of the platelet lipidome is changed upon activation, involving mainly lipids containing arachidonic acid. However, the most interesting work that we currently conducting in close collaboration with our partners is the analysis of different diseases models (Scheller et al.,Haematologica, 2019) which display and thrombotic phenotype. E.g., Sphingomyelin phosphodiesterase-1 (Smpd1) deficiency results in a very specific modulation of the platelet lipidome (Peng et al., Blood, 2018) with an order of magnitude up-regulation of lyso-sphingomyelin (SPC), and subsequent modification of platelet activation and thrombus formation, which sheds light on novel mechanisms important for platelet function, and has therefore the potential to open novel diagnostic and therapeutic opportunities.
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.
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 publically 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.
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