ERC FUNDED PROJECTS

Acute Myeloid Leukemia (AML), the most common leukemia diagnosed in adults, represents the paradigm of resistance to front-line therapies in hematology. Indeed, AML is so genetically complex that only few targeted therapies are currently tested in this disease and chemotherapy remains the only standard treatment for AML since the past four decades. Despite an initial sustained remission achieved by chemotherapeutic agents, almost all patients relapse with a chemoresistant minimal residual disease (MRD). The goal of my proposal is to characterize the still poorly understood biological mechanisms underlying persistence and emergence of MRD. MRD is the consequence of the re-expansion of leukemia-initiating cells that are intrinsically more resistant to chemotherapy. This cell fraction may be stochastically more prone to survive front-line therapy regardless of their mutational status (the stochastic model), or genetically predetermined to resist by virtue of a collection of chemoprotective mutations (the mutational model). I have already generated in mice, by consecutive rounds of chemotherapy, a stochastic MLL-AF9-driven chemoresistance model that I examined by RNA-sequencing. I will pursue the comprehensive cell autonomous and cell non-autonomous characterization of this chemoresistant AML disease using whole-exome and ChIP-sequencing. To establish a mutationally-induced chemoresistant mouse model, I will conduct an innovative in vivo screen using pooled mutant open reading frame and shRNA libraries in order to predict which combinations of mutations, among those already known in AML, actively promote chemoresistance. Finally, by combining genomic profiling and in vivo shRNA screening experiments, I will decipher the molecular mechanisms and identify the functional effectors of these two modes of resistance. Ultimately, I will then be able to firmly establish the fundamental relevance of the stochastic and/or the mutational model of chemoresistance for MRD genesis.

1 500 000 €

Start date: 2018-03-01, End date: 2023-02-28

Angiogenesis research has focused on the sprouting of new capillaries. The mechanisms of vessel maturation are much less well understood. Yet, the maintenance of a mature, quiescent, and organotypically-differentiated layer of endothelial cells (ECs) lining the inside of all blood vessels is vital for human health. The goal of ANGIOMATURE is to identify, validate, and implement novel mechanisms of vascular maturation and organotypic EC differentiation that are active during development, maintenance of vascular stability in adults, and undergo changes in aging. We recently identified previously unrecognized gene expression signatures of vascular maturation in a genome-wide screen of ECs isolated from newborn and adult mice. Epigenetic mechanisms were identified that control the EC transcriptome through gain and loss of DNA methylation as well as EC differentiation and signaling specification. These findings pave the way for groundbreaking novel opportunities to study vascular maturation. By characterizing functionally diverse types of blood vessels, including continuous ECs in lung and brain and sinusoidal ECs in liver and bone marrow, the ANGIOMATURE project will (1) determine up to single cell resolution the transcriptional and epigenetic program(s) of vascular maturation and organotypic differentiation during adolescence, (2) analyze the functional consequences of such program(s) in differentiated ECs and their adaptation to challenge, and (3) study changes of maturation and differentiation program(s) and vascular responses during aging. We will towards this end employ an interdisciplinary matrix of approaches involving omics, systems biology, conditional gene targeting, organoid cell culture, and experimental pathology to create a high-resolution structural and functional organotypic angioarchitectural map. The project will thereby yield transformative mechanistic insights into vital biological processes that are most important for human health and healthy aging.

2 338 918 €

Start date: 2018-08-01, End date: 2023-07-31

The description of high pressure phases or polymorphism of molecular solids represents a significant scientific challenge both for experiment and theory. Theoretical methods that are currently used struggle to describe the tiny energy differences between different phases. It is the aim of this project to develop a scheme that would allow accurate and reliable predictions of the binding energies of molecular solids and of the energy differences between different phases. To reach the required accuracy, we will combine the coupled cluster approach, widely used for reference quality calculations for molecules, with the random phase approximation (RPA) within periodic boundary conditions. As I have recently shown, RPA-based approaches are already some of the most accurate and practically usable methods for the description of extended systems. However, reliability is not only a question of accuracy. Reliable data need to be precise, that is, converged with the numerical parameters so that they are reproducible by other researchers. Reproducibility is already a growing concern in the field. It is likely to become a considerable issue for highly accurate methods as the calculated energies have a stronger dependence on the simulation parameters such as the basis set size. Two main approaches will be explored to assure precision. First, we will develop the so-called asymptotic correction scheme to speed-up the convergence of the correlation energies with the basis set size. Second, we will directly compare the lattice energies from periodic and finite cluster based calculations. Both should yield identical answers, but if and how the agreement can be reached for general system is currently far from being understood for methods such as coupled cluster. Reliable data will allow us to answer some of the open questions regarding the stability of polymorphs and high pressure phases, such as the possibility of existence of high pressure ionic phases of water and ammonia.

924 375 €

Start date: 2018-01-01, End date: 2022-12-31

The goal of ATOMICAR is to achieve the ultimate sensitivity limit in heterogeneous catalysis: Quantitative measurement of chemical turnover on a single catalytic nanoparticle. Most heterogeneous catalysis occurs on metal nanoparticle in the size range of 3 nm - 10 nm. Model studies have established that there is often a strong coupling between nanoparticle size & shape - and catalytic activity. The strong structure-activity coupling renders it probable that “super-active” nanoparticles exist. However, since there is no way to measure catalytic activity of less than ca 1 million nanoparticles at a time, any super-activity will always be hidden by “ensemble smearing” since one million nanoparticles of exactly identical size and shape cannot be made. The state-of-the-art in catalysis benchmarking is microfabricated flow reactors with mass-spectrometric detection, but the sensitivity of this approach cannot be incrementally improved by six orders of magnitude. This calls for a new measurement paradigm where the activity of a single nanoparticle can be benchmarked – the ultimate limit for catalytic measurement. A tiny batch reactor is the solution, but there are three key problems: How to seal it; how to track catalytic turnover inside it; and how to see the nanoparticle inside it? Graphene solves all three problems: A microfabricated cavity with a thin SixNy bottom window, a single catalytic nanoparticle inside, and a graphene seal forms a gas tight batch reactor since graphene has zero gas permeability. Catalysis is then tracked as an internal pressure change via the stress & deflection of the graphene seal. Crucially, the electron-transparency of graphene and SixNy enables subsequent transmission electron microscope access with atomic resolution so that active nanoparticles can be studied in full detail. ATOMICAR will re-define the experimental limits of catalyst benchmarking and lift the field of basic catalysis research into the single-nanoparticle age.

1 496 000 €

Start date: 2018-02-01, End date: 2023-01-31