Funding

Wnt signaling pathways play a decisive role in development, cell differentiation, tumorigenesis and tumor progression. Wnt ligands are secreted, lipidated proteins that bind to half a dozen different receptors to activate multiple downstream signaling cascades. Wnt pathways relay signals from divergent ligand-receptor interactions to a network of downstream signaling of conserved cytoplasmic factors. Despite having gained an enormous amount of knowledge about components and mechanisms of Wnt signaling, we are now facing a multitude of new questions about the specificity of signaling input and the control of signaling output during development and in disease.

To advance our understanding of molecular mechanisms governing Wnt signal transduction, the DFG-funded Collaborative Research Center CRC1324 is structured into two major research areas: (A) Wnt secretion, trafficking and receptor-ligand interactions and (B) Wnt coupling to downstream and context-dependent signaling. In the first research area, we want to understand how Wnt ligands are produced, how they are modified and how they are transported in the extracellular space. We also want to unravel the Wnt ligand receptor interactions and understand how they specify the signaling response and induce different signaling cascades. In the second research area, we will focus on the molecular dynamics of the Wnt machinery to understand how different Wnt pathways elicit distinct biological responses and how Wnt signaling is coupled to different downstream factors. We also want to understand the spatio-temporal dynamics of Wnt signaling, i.e., how Wnt signaling regulates oscillations and wave patterns. To address these questions, we will combine a wide range of model systems including hydra, fly, fish, frog, as well as mouse and human cells with cutting-edge technologies including advanced fluorescence microscopy, genetic screens and genome engineering, and proteomics. Our lab is particularly interested in understanding the role of Wnt production and trafficking and the role of Wnt signaling in tumorigenesis.

The evolutionary success of multicellular organisms is based on the division of labor between cells. While some of the molecular determinants for cell fate specification have been identified, a fundamental understanding of which genetic activities are required in each cell of a developing tissue is still outstanding. The DECODE project will develop and apply leading-edge system genetics methods to Arabidopsis and Drosophila, two major model systems from the plant and animal kingdoms to decode context-dependent genetic networks in vivo. To achieve this, DECODE brings together experimental and theoretical groups with complementary expertise in model organism genetics and cellular phenotyping, single-cell genomics, statistics and computational biology. Building on our combined expertise, we will create functional genetic maps using conditional CRISPR/Cas9-based single- and higher order knockout perturbations in vivo combined with single-cell expression profiling and imaging. Coupled with powerful computational analysis, this project will not only define, predict and rigorously test the unique genetic repertoire of each cell, but also unravel how genetic networks adapt their topology and function across cell types and external stimuli. With more than thousand conditional knockouts, characterized by several million single-cell transcriptome profiles and high-resolution imaging this project will create the largest single-cell perturbation map in any model organism and will provide fundamental insights into the genetic architecture of complex tissues. Analyzing two tissues with divergent organization and regulatory repertoire will enable us to uncover general principles in the genetic circuits controlling context-dependent cell behavior. Consequently, we expect that the DECODE project in model organisms will lay the conceptual and methodological foundation for perturbation-based functional atlases in other tissues or species.

Colorectal cancer (CRC) is one of the most frequent cancers with a high burden on society. In Europe alone, more than 340,000 people are diagnosed with colorectal cancer each year. While treatment options of CRC have expanded over the last decades, the survival rates of patients suffering from advanced-stage cancers remain poor. Stemness of neoplastic cells has been proposed as a major factor in treatment resistance and high relapse rates, however, drugs targeting stem cell signaling pathways in cancer are still lacking today. In the ACHILLEUS project we aim at developing and validating a new drug discovery platform based on patient-derived organoids (PDOs) and machine learning-assisted phenotypic target validation and drug discovery. Using this platform, we have previously shown that we can identify a broad spectrum of phenotypes beyond lethality, including drug-induced changes in stemness of PDOs. As part of the EIC Open Transition project, we will validate our next-generation platform for identifying therapeutics to target stem cell signaling pathways in CRC. Based on results by us showing that an interaction of MEK and Wnt signaling shifts the balance between stemness and differentiated cells, we will target Wnt pathways as a demonstrator for the technology. The ACHILLEUS project targets critical vulnerabilities in cancer and is based on technologies and approaches developed during previous ERC Advanced and ERC Proof-of-Concept grant-funded projects.

The genetics underlying phenotype-genotype relationships during development and disease is often complex with many genes contributing to a particular phenotypic outcome. While forward genetic screens have uncovered many mutations that are limiting at a particular stage or tissue, the majority of genes in most genomes remain genetically untouched. Recent studies in model organisms, in particular in yeast, have provided evidence for the existence of pervasive genetic interactions with large effects on many phenotypes. Such genetic factors have been rather difficult to identify in classical loss-of-function screens due to buffering and other compensatory mechanisms, therefore requiring novel methodological approaches.

The creation of a synthetic genetic interaction map provides a ground-breaking and unprecedented resource to study genotype-to-phenotype relationships. Multiparametric imaging will allow us to map the epistasis of processes. We utilize the map to globally dissect the interaction between processes with a focus on signaling networks. Furthermore, we  follow-up on selected interactions by in-depth characterization. Synthetic genetic interactions will be further analyzed in vivo in tissues and in human cells.

As part of the ERC project, we are in the process of creating a systematic synthetic genetic interaction map of a metazoan cell. Based on methods that we pioneered, we quantitatively measure genetic interactions using image-based phenotyping between genes. Phenotyping will be performed by multiparametric imaging and pathway-focused reporters. A particular interest is on context-dependent changes in genetic interaction networks and chemico-genetic interactions.