Behaviour emerges from an interplay of various brain regions and billions of neurons forming a network. The brain acquires its organization by a series of developmental events. The correct wiring of the brain is crucially important for all brain functions. Nevertheless, the brain is both stereotyped and variable across individuals. Currently only a subset of genes have been identified that shape specificity in connectivity and it is highly elusive what promotes variability in synaptic connectivity and whether this is behaviourally relevant. To work on these questions I have chosen the Drosophila olfactory system, which is well-defined both on the levels of circuitry and behaviour and as such well-suited to answer these question. Furthermore, from the perspective of circuitry, it is particularly appropriate as it is comprised of both deterministic and non-deterministic synaptic connectivity. In a first step to tackle the question, I will perform a screen to identify genes shaping deterministic and non-deterministic neural connectivity. In parallel, I will investigate olfactory behavioural variability in the fly and correlate this to circuitry variations on the morphological, synaptic and electrophysiological level. After identifying genes that are important for connectivity and correlating circuit to behavioural variability, I aim to modify circuit variability to alter behaviour.

Resistance to anticancer drugs, which often develops from a heterogeneous pool of drug-tolerant cells known as minimal residual disease (MRD), is thought to mainly occur through acquisition of genetic alterations. Emerging evidence indicates that drug resistance may also be acquired in absence of a genetic cause. It remains unclear, however, whether genetic versus non-genetic mechanisms of resistance are selected in a stochastic manner, and what are the epigenomics mechanisms underlying the transition from drug-tolerance to resistance. This project aims at identifying the drug-tolerant subpopulation(s) that drive non-genetic resistance by performing lineage tracing and depletion experiments in pre-clinical models. Taking advantage of up-to-date technologies combining in vivo barcoding and single-cell multi-omics approaches, this project aims to provide a dynamic and integrated view of the evolution of epigenomic profiles -at single-cell resolution- before, during and after acquisition of drug resistance phenotypes in a in vivo clinically-relevant context. A third objective of this proposal is to search for predictive biomarkers of non-genetic resistance and to assess the percentage of melanoma patients that undergo non-genetic resistance through combination of multiplexed staining and targeted DNA sequencing. The success of this project is ensured by my personal background in epigenetics and related data computational analysis, the achievements of the JCM lab in melanoma epigenetic reprogramming and the close collaborations with experts in single-cell multi-omics fields. I anticipate that my involvement in this project will broaden my skills and knowledge and help me become a high-qualified European independent scientist.

The SYSCID consortium aims to develop a systems medicine approach for disease prediction in CID. We will focus on three major CID indications with distinct characteristics, yet a large overlap of their molecular risk map: inflammatory bowel disease, systemic lupus erythematodes and rheumatoid arthritis. We have joined 15 partners from major cohorts and initiatives in Europe (e.g.IHEC, ICGC, TwinsUK and Meta-HIT) to investigate human data sets on three major levels of resolution: whole blood signatures, signatures from purified immune cell types (with a focus on CD14 and CD4/CD8) and selected single cell level analyses. Principle data layers will comprise SNP variome, methylome, transcriptome and gut microbiome. SYSCID employs a dedicated data management infrastructure, strong algorithmic development groups (including an SME for exploitation of innovative software tools for data deconvolution) and will validate results in independent retrospective and prospective clinical cohorts. Using this setup we will focus on three fundamental aims : (i) the identification of shared and unique "core disease signatures” which are associated with the disease state and independent of temporal variation, (ii) the generation of "predictive models of disease outcome"- builds on previous work that pathways/biomarkers for disease outcome are distinct from initial disease risk and may be shared across diseases to guide therapy decisions on an individual patient basis, (iii) "reprogramming disease"- will identify and target temporally stable epigenetic alterations in macrophages and lymphocytes in epigenome editing approaches as biological validation and potential novel therapeutic tool . Thus, SYSCID will foster the development of solid biomarkers and models as stratification in future long-term systems medicine clinical trials but also investigate new causative therapies by editing the epigenome code in specific immune cells, e.g. to alleviate macrophage polarization defects.

Distant metastases, i.e. secondary tumors, are the leading cause of cancer deaths. Many patients (especially with breast cancer) are diagnosed when cancer cells have already disseminated to distant organs. Thus, it is of profound importance to prevent the metastatic outgrowth of these disseminated cancer cells into secondary tumors. The ability to remodel the extracellular matrix (ECM) of the metastatic niche is essential for disseminated breast cancer cells to promote their own metastatic outgrowth. This process is to date believed to be transcriptionally regulated. However, the Fendt laboratory has discovered that the nutrient pyruvate metabolically drives ECM remodeling by breast cancer cells. In my project, I will build on this discovery and explore how to target ECM remodeling in metastatic outgrowth. First, I will determine the interaction between the discovered metabolic and the known transcriptional regulation of ECM remodeling. Secondly, I will translate this novel finding into therapeutic potential by defining how to selectively target it, thereby impairing metastatic outgrowth in the lung environment. Thirdly, I will define how the metastatic site and the cancer cell origin affect the discovered metabolic regulation of ECM remodeling, and thus metastatic outgrowth. To address these aims, I will use metabolomics, 13C tracer analysis, genetic engineering and nanotechnology-based drug-delivery in breast cancer 3D cultures and mouse models. With MetaTarGet, I will deliver (i) a mechanistic understanding of pyruvate metabolism as a regulator of ECM remodeling, and (ii) a novel therapeutic strategy to target metastatic outgrowth. This project will allow me to bridge my nanotechnology-based drug-delivery knowledge with the expertise of the Fendt laboratory in metastasis metabolism. Consequently, I will strengthen my research competences and enhance my personal research profile.