ERC FUNDED PROJECTS

At the end of their life, stars spread their inner material into the diffuse interstellar medium. This diffuse medium gets locally denser and form dark clouds (also called dense or molecular clouds) whose innermost part is shielded from the external UV field by the dust, allowing for molecules to grow and get more complex. Gravitational collapse occurs inside these dense clouds, forming protostars and their surrounding disks, and eventually planetary systems like (or unlike) our solar system. The formation and evolution of molecules, minerals, ices and organics from the diffuse medium to planetary bodies, their alteration or preservation throughout this cosmic chemical history set the initial conditions for building planets, atmospheres and possibly the first bricks of life. The current view of interstellar chemistry is based on fragmental works on key steps of the sequence that are observed. The objective of this proposal is to follow the fractionation of the elements between the gas-phase and the interstellar grains, from the most diffuse medium to protoplanetary disks, in order to constrain the chemical composition of the material in which planets are formed. The potential outcome of this project is to get a consistent and more accurate description of the chemical evolution of interstellar matter. To achieve this objective, I will improve our chemical model by adding new processes on grain surfaces relevant under the diffuse medium conditions. This upgraded gas-grain model will be coupled to 3D dynamical models of the formation of dense clouds from diffuse medium and of protoplanetary disks from dense clouds. The computed chemical composition will also be used with 3D radiative transfer codes to study the chemical tracers of the physics of protoplanetary disk formation. The robustness of the model predictions will be studied with sensitivity analyses. Finally, model results will be confronted to observations to address some of the current challenges.

1 166 231 €

Start date: 2013-09-01, End date: 2018-08-31

"How are actions initiated by the human brain when there is no external sensory cue or other immediate imperative? How do subtle ongoing interactions within the brain and between the brain, body, and sensory context influence the spontaneous initiation of action? How should we approach the problem of trying to identify the neural events that cause spontaneous voluntary action? Much is understood about how the brain decides between competing alternatives, leading to different behavioral responses. But far less is known about how the brain decides "when" to perform an action, or "whether" to perform an action in the first place, especially in a context where there is no sensory cue to act such as during foraging. This project seeks to open a new chapter in the study of spontaneous voluntary action building on a novel hypothesis recently introduced by the applicant (Schurger et al, PNAS 2012) concerning the role of ongoing neural activity in action initiation. We introduce brain-behavior forecasting, the converse of movement-locked averaging, as an approach to identifying the neurodynamic states that commit the motor system to performing an action "now", and will apply it in the context of information foraging. Spontaneous action remains a profound mystery in the brain basis of behavior, in humans and other animals, and is also central to the problem of asynchronous intention-detection in brain-computer interfaces (BCIs). A BCI must not only interpret what the user intends, but also must detect "when" the user intends to act, and not respond otherwise. This remains the biggest challenge in the development of high-performance BCIs, whether invasive or non-invasive. This project will take a systematic and collaborative approach to the study of spontaneous self-initiated action, incorporating computational modeling, neuroimaging, and machine learning techniques towards a deeper understanding of voluntary behavior and the robust asynchronous detection of decisions-to-act."

1 338 130 €

Start date: 2015-10-01, End date: 2020-09-30

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

Interferons (IFNs), which are signalling proteins produced by infected cells, are the first line of defence against viral infections. IFNs induce, in infected and neighbouring cells, the expression of hundreds of IFN-stimulated genes (ISGs). The ISGs in turn induce in cells a potent antiviral state, capable of preventing replication of most viruses, including Human Immunodeficiency Virus type 1 (HIV-1) and influenza A virus (FLUAV). Identifying the antiviral ISGs and understanding their mechanisms of action is therefore crucial to progress in the fight against viruses. ISGs playing a role in the antiviral state have been identified, such as human MX1, a well-known antiviral factor able to restrict numerous viruses including FLUAV, and MX2, an HIV-1 inhibitor. Both proteins bind to viral components but their detailed mechanisms of action, as well as the consequences of restriction on the activation of the innate immune system, remain unclear. Moreover, our preliminary work shows that additional anti-HIV-1 and anti-FLUAV ISGs remain to identify. In this context, this proposal seeks an ERC StG funding to explore 3 major aims: 1) unravelling the mechanisms of antiviral action of MX proteins, by taking advantage of their similar structure and engineered chimeric proteins, and by using functional genetic screens to identify their cofactors; 2) investigating the consequences of incoming virus recognition by MX proteins on innate immune signalling, by altering their expression in target cells and measuring the cell response in terms of gene induction and cytokine production; 3) identifying and characterizing new ISGs able to inhibit viral replication with a combination of powerful approaches, including a whole-genome CRISPR/Cas9 knock-out screen. Overall, this proposal will provide a better understanding of the molecular mechanisms involved in the antiviral effect of IFN, and may guide future efforts to identify novel therapeutic targets against major pathogenic viruses.

1 499 794 €

Start date: 2017-12-01, End date: 2022-11-30