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Country: Austria


37 Projects, page 1 of 8
  • Funder: EC Project Code: 297524
  • Funder: EC Project Code: 796010
    Overall Budget: 178,157 EURFunder Contribution: 178,157 EUR

    Targeted therapies have been widely used in tumors driven by RAS oncogenes. Unfortunately, no effective RAS inhibitors have been translated to the clinic, and attempts to block other signaling nodes usually fail due to the emergence of drug resistance. Chromatin dependent signal transduction and transcription are a point of confluence of multiple signaling networks elicited by hyperactive RAS. Hence, pharmacologic disruption of gene-regulatory dependencies imposed by mutant RAS represents an attractive therapeutic interface less prone to the emergence of resistances. The urgent clinical need of RAS-related therapies is well exemplified by pancreatic cancer, one of the most aggressive and deadly cancers, which will be the disease background of my studies. Using the innovative approach of targeted protein degradation, I want to characterize and understand the consequences of acute mutant KRAS degradation on chromatin remodeling and transcription. Further engineering the models of acute KRAS degradation will enable to devise cellular reporters of KRAS-dependent chromatin regulation amenable to high-throughput phenotypic drug and genetic screens. Coupled to a facile readout via high-throughput microscopy, these screens will allow me to identify molecules and genetic perturbations that interfere with KRAS-dependent, transcriptionally active chromatin. Lead molecules will be characterized for the underpinning mechanism of action and assessed for therapeutic potential. Building on already existing experimental and computational pipelines in the Winter laboratory at CeMM-Research Center for Molecular Medicine of the Austrian Academy of Sciences, this project will increase the understanding of transcriptional control elicited by oncogenic KRAS and could open new avenues for the treatment of RAS-driven tumors based on chemical modulation of critical chromatin and transcription regulators.

  • Funder: EC Project Code: 293921
  • Funder: EC Project Code: 661491
    Overall Budget: 178,157 EURFunder Contribution: 178,157 EUR

    The transport of nutrients and metabolites across lipid membranes is a critical and essential biological process in both normal and pathological conditions. Specialized integral membrane proteins known as transporters are responsible for the movement of virtually any known class of biologically active molecules across membranes. Perhaps not surprisingly, there is increasing evidence that many drugs in clinical use also employ carried-mediated transport as the predominant entry method into the cell. Understanding the molecular and physiological basis of transporter-mediated drug uptake is therefore a key step toward the development of improved pharmacokinetics and models of toxicity for current and future therapeutically active compounds. We hypothesize that most approved drugs may require specific members of the solute carrier (SLC) family to cross the cell membrane, a phenomenon that would directly affect the compound activity, and that this process is basically underestimated in its importance. To test this hypothesis, the study proposed here aims at identifying SLC transporters involved in the uptake of a well-characterized set of commercialized drugs. The CeMM Library Of Unique Drugs (CLOUD) is a library of 300 compounds selected to be representative of the chemical space covered by all commercially available drugs. Human cell lines carrying deletions on specific SLC proteins will be tested for their ability to resist toxicity or challenges due to CLOUD compounds, allowing us to determine which transporter or family of transporters are involved in the uptake of specific drugs classes. The information derived from these studies will represent an important step forward toward a systematic characterization of the role of carrier-mediated transport in drug uptake.

  • Funder: EC Project Code: 842480
    Overall Budget: 186,167 EURFunder Contribution: 186,167 EUR

    Cellular identity is controlled by cell type specific expression of transcription factors (TFs), and it is reflected in the cell’s epigenetic landscape maintained by epigenetic regulator proteins (ERs). Functional dissection of cellular identity has focused mainly on a small number of lineage-defining master regulators, yet there is increasing evidence that multiple TFs and ERs work together to establish and retain the vast number of different cell types and cell states in the human body. For a more quantitative understanding of cellular identity, and of the complexities of its regulation, I propose to develop a machine-learning approach for in silico prediction of TF/ER cocktails that can transdifferentiate any human cell type into any other cell type, thus defining an operational rulebook of cellular transdifferentiation. To this end, I will train a machine-learning model called generative adversarial networks (GANs) on large-scale CRISPR single-cell sequencing (CROP-seq) datasets generated in the host lab. Exploiting unique features of the deep-learning generative approach, the resulting model will be able to generalize the learned genetic perturbations across cell types in silico. I will experimentally validate several of these predicted TF/ER transdifferentiation cocktails in the context of the human hematopoietic system. Importantly, the proposed approach is hypothesis-free and data-driven, exploiting recent advances in machine learning to infer fundamental aspects of the regulation of cellular identity from high-throughput functional CRISPR single-cell sequencing data.

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