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.
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.
Signaling functions of metabolites have been gathering interest in the context of infection, cancer, and metabolic disorders. However, how metabolic communication networks shape the pathology of infection remains poorly understood. Our group has recently reported that chronic infection with lymphocytic choriomeningitis (LCMV) in mice leads to reprogramming of the hepatic urea cycle with a concomitant increase with blood ammonia levels. Despite being typically considered as a waste product with neurotoxic effect, ammonia is involved in relevant pathways for energy production, cell proliferation, and survival. Additionally, ammonia is a small, gaseous molecule that might modulate cellular functions in distant tissues, as described for other gasotransmitters. Therefore, I hypothesize that infection-induced hyperammonemia has poorly recognized signaling functions that might influence immune responses, tissue damage, or sickness behavior. To test this hypothesis, I will combine state-of-the-art metabolic analyses, pharmacological and genetic tools. I expect to establish whether hyperammonemia is broadly associated with viral infections in mice, and/or whether it is a direct consequence of virus-induced liver damage. Additionally, I will analyze tissue-specific and organismal effects of hyperammonemia and determine whether this impacts on sickness behavior or infection outcomes. This interdisciplinary approach combining immunology, metabolism and neuroscience will allow me to characterize mechanisms of host response to viral infections, which pose outstanding challenges to current biology and medicine. Additionally, I expect to unveil inter-organ communication networks that link metabolically active tissues and the brain. These may have strong implications not only for infection but also for a wide range of metabolic disorders.